Preliminary data used to assess the accuracy of estimating female white-tailed deer diel birthing-season home ranges using only daytime locations

USGS Publications Warehouse

Barber-Meyer, Shannon M.; Mech, L. David

2014-01-01

Because many white-tailed deer (Odocoileus virginianus) home-range and habitat-use studies rely only on daytime radio-tracking data, we were interested in whether diurnal data sufficiently represented diel home ranges. We analyzed home-range and core-use size and overlap of 8 adult-female Global-Positioning-System-collared deer during May and June 2001 and 2002 in the Superior National Forest, Minnesota, USA. We used 2 traditional means of analysis: minimum-convex polygons (MCP) and fixed kernels (95% FK, home range and 50% FK, core use) and two methods to partition day and night location data: (1) daytime = 0800-2000 h versus nighttime = 2000-0800 h and (2) sunup versus sundown. We found no statistical difference in size of home-range and core-use areas across day and night comparisons; however, in terms of spatial overlap, approximately 30% of night-range areas on average were not accounted for using daytime locations, with even greater differences between core-use areas (on average approximately 50%). We conclude that diurnal data do not adequately describe diel adult-female-deer, May-June home-ranges due to differences in spatial overlap (location). We suggest research to determine (1) if our findings hold in other circumstances (e.g., exclusive of the parturition period, other age classes, etc.), (2) if our conclusions generalize under other conditions (e.g., across deer range, varying seasons, etc.), (3) if habitat-use conclusions are affected by the incomplete overlap between diurnal and diel data, (4) how many nocturnal locations must be included to generate sufficient overlap, and (5) the influence of using other kernel sizes (e.g., 75%, 90%).

Social structure and space use of Amur tigers (Panthera tigris altaica) in Southern Russian Far East based on GPS telemetry data.

PubMed

Hernandez-Blanco, Jose A; Naidenko, Sergei V; Chistopolova, Maria D; Lukarevskiy, Victor S; Kostyrya, Alexey; Rybin, Alexandr; Sorokin, Pavel A; Litvinov, Mikhail N; Kotlyar, Andrey K; Miquelle, Dale G; Rozhnov, Viatcheslav V

2015-07-01

To better understand the spatial structure of Amur tigers (Panthera tigris altaica) at the southern edge of their range we fitted 14 tigers (6♀♀ and 8♂♂) with 15 GPS-Argos collars between 2008 and 2011 in 2 study sites: the Ussuriskii Reserve of southern Sikhote-Alin and the Land of the Leopard National Park in southwest Primorye, Russian Far East. Fixed kernel estimates of male home ranges were larger than those of female home ranges (P < 0.05 [mean 95% fixed kernel(♀) = 401 ± 205 km(2) ; mean 95% fixed kernel(♂) = 778 ± 267 km(2)]). The home range size of females varied greatly, but on average was similar to estimates derived from earlier work further north. Low overlap of adjacent home ranges suggested that females retained exclusive territories. Real core areas of females overlapped only slightly, and remained stable over multiple years. The home ranges of adult males were smaller than those of males to the north, and in contrast to previous studies, high overlap among males indicated the absence of territoriality. Nonetheless, real core areas of males did not overlap, suggesting some spatial separation. In comparison to other tiger populations and other areas of the Russian Far East, the sex ratio in our 2 study areas was highly skewed towards males. We believe this skewed sex ratio resulted in the dissolution of territoriality of males due to an inability to defend individual females, with males resorting to scramble competition for mates. Continued monitoring of these sites to determine whether shifts in the sex ratio might result in a return to male territoriality would provide confirmation of our tentative hypothesis. © 2015 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and Wiley Publishing Asia Pty Ltd.

Home range and residency status of Northern Goshawks breeding in Minnesota

USGS Publications Warehouse

Boal, C.W.; Andersen, D.E.; Kennedy, P.L.

2003-01-01

We used radio-telemetry to estimate breeding season home-range size of 17 male and 11 female Northern Goshawks (Accipiter gentilis) and combined home ranges of 10 pairs of breeding goshawks in Minnesota. Home-range sizes for male and female goshawks were 2593 and 2494 ha, respectively, using the minimum convex polygon, and 3927 and 5344 ha, respectively, using the 95% fixed kernel. Home ranges of male and female members of 10 goshawk pairs were smaller than combined home-range size of those pairs (mean difference = 3527 ha; 95% CI = 891 to 6164 ha). Throughout the nonbreeding season, the maximum distance from the nest recorded for all but one goshawk was 12.4 km. Goshawks breeding in Minnesota have home ranges similar to or larger than those reported in most other areas. Home-range overlap between members of breeding pairs was typically ???50%, and both members of breeding pairs were associated with breeding home ranges year round. Goshawk management plans based on estimated home-range size of individual hawks may substantially underestimate the area actually used by a nesting pair.

LoCoH: Non-parameteric kernel methods for constructing home ranges and utilization distributions

USGS Publications Warehouse

Getz, Wayne M.; Fortmann-Roe, Scott; Cross, Paul C.; Lyons, Andrew J.; Ryan, Sadie J.; Wilmers, Christopher C.

2007-01-01

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: ‘‘fixed sphere-of-influence,’’ or r -LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an ‘‘adaptive sphere-of-influence,’’ or a -LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a ), and compare them to the original ‘‘fixed-number-of-points,’’ or k -LoCoH (all kernels constructed from k -1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a -LoCoH is generally superior to k - and r -LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).

Space use of wintering waterbirds in India: Influence of trophic ecology on home-range size

USGS Publications Warehouse

Namgail, Tsewang; Takekawa, John Y.; Balachandran, Sivananinthaperumal; Sathiyaselvam, Ponnusamy; Mundkur, Taej; Newman, Scott H.

2014-01-01

Relationship between species' home range and their other biological traits remains poorly understood, especially in migratory birds due to the difficulty associated with tracking them. Advances in satellite telemetry and remote sensing techniques have proved instrumental in overcoming such challenges. We studied the space use of migratory ducks through satellite telemetry with an objective of understanding the influence of body mass and feeding habits on their home-range sizes. We marked 26 individuals, representing five species of migratory ducks, with satellite transmitters during two consecutive winters in three Indian states. We used kernel methods to estimate home ranges and core use areas of these waterfowl, and assessed the influence of body mass and feeding habits on home-range size. Feeding habits influenced the home-range size of the migratory ducks. Carnivorous ducks had the largest home ranges, herbivorous ducks the smallest, while omnivorous species had intermediate home-ranges. Body mass did not explain variation in home-range size. To our knowledge, this is the first study of its kind on migratory ducks, and it has important implications for their conservation and management.

Movements and home ranges of mountain plovers raising broods in three Colorado landscapes

USGS Publications Warehouse

Dreitz, V.J.; Wunder, Michael B.; Knopf, F.L.

2005-01-01

We report movements and home-range sizes of adult Mountain Plovers (Charadrius montanus) with broods on rangeland, agricultural fields, and prairie dog habitats in eastern Colorado. Estimates of home range size (95% fixed kernel) were similar across the three habitats: rangeland (146.1 ha ± 101.5), agricultural fields (131.6 ha ± 74.4), and prairie dog towns (243.3 ha ± 366.3). Our minimum convex polygon estimates of home-range size were comparable to those on rangeland reported by Knopf and Rupert (1996). In addition, movements—defined as the distance between consecutive locations of adults with broods—were equivalent across habitats. However, our findings on prairie dog habitat suggest that home-range size for brood rearing may be related to whether the prairie dog habitat is in a complex of towns or in an isolated town.

Documenting Presence and Movements of Piscivorous Birds along the Illinois River and within the Chicago Area Waterway System

DTIC Science & Technology

2017-07-31

promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product...15 Figure 7. Breeding home range polygons of Double-Crested Cormorant movements from Baker’s Lake (blue outline) and the TNC Emiquon Preserve (red...48 ERDC/EL TR-17-12 v Figure B1. 95% kernel home range polygons for 14 breeding cormorants from Baker’s Lake, May 26 - July 12

Modelling ranging behaviour of female orang-utans: a case study in Tuanan, Central Kalimantan, Indonesia.

PubMed

Wartmann, Flurina M; Purves, Ross S; van Schaik, Carel P

2010-04-01

Quantification of the spatial needs of individuals and populations is vitally important for management and conservation. Geographic information systems (GIS) have recently become important analytical tools in wildlife biology, improving our ability to understand animal movement patterns, especially when very large data sets are collected. This study aims at combining the field of GIS with primatology to model and analyse space-use patterns of wild orang-utans. Home ranges of female orang-utans in the Tuanan Mawas forest reserve in Central Kalimantan, Indonesia were modelled with kernel density estimation methods. Kernel results were compared with minimum convex polygon estimates, and were found to perform better, because they were less sensitive to sample size and produced more reliable estimates. Furthermore, daily travel paths were calculated from 970 complete follow days. Annual ranges for the resident females were approximately 200 ha and remained stable over several years; total home range size was estimated to be 275 ha. On average, each female shared a third of her home range with each neighbouring female. Orang-utan females in Tuanan built their night nest on average 414 m away from the morning nest, whereas average daily travel path length was 777 m. A significant effect of fruit availability on day path length was found. Sexually active females covered longer distances per day and may also temporarily expand their ranges.

Habitat use and home range of the Laysan Teal on Laysan Island, Hawaii

USGS Publications Warehouse

Reynolds, M.H.

2004-01-01

The 24-hour habitat use and home range of the Laysan Teal (Anas laysanensis), an endemic dabbling duck in Hawaii, was studied using radio telemetry during 1998-2000. Radios were retained for a mean of 40 days (0-123 d; 73 adult birds radio-tagged). Comparisons of daily habitat use were made for birds in the morning, day, evening, and night. Most birds showed strong evidence of selective habitat use. Adults preferred the terrestrial vegetation (88%), and avoided the lake and wetlands during the day. At night, 63% of the birds selected the lake and wetlands. Nocturnal habitat use differed significantly between the non-breeding and breeding seasons, while the lake and wetland habitats were used more frequently during the non-breeding season. Most individuals showed strong site fidelity during the study, but habitat selection varied between individuals. Mean home range size was 9.78 ha (SE ?? 2.6) using the fixed kernel estimator (95% kernel; 15 birds, each with >25 locations). The average minimum convex polygon size was 24 ha (SE ?? 5.6). The mean distance traveled between tracking locations was 178 m (SE ?? 30-5), with travel distances between points ranging up to 1,649 m. Tracking duration varied from 31-121 days per bird (mean tracking duration 75 days).

Home range characteristics of Mexican Spotted Owls in the canyonlands of Utah

USGS Publications Warehouse

Willey, D.W.; van Riper, Charles

2007-01-01

We studied home-range characteristics of adult Mexican Spotted Owls (Strix occidentalis lucida) in southern Utah. Twenty-eight adult owls were radio-tracked using a ground-based telemetry system during 1991-95. Five males and eight females molted tail feathers and dropped transmitters within 4 wk. We estimated cumulative home ranges for 15 Spotted Owls (12 males, 3 females). The mean estimate of cumulative home-range size was not statistically different between the minimum convex polygon and adaptive kernel (AK) 95% isopleth. Both estimators yielded relatively high SD, and male and female range sizes varied widely. For 12 owls tracked during both the breeding and nonbreeding seasons, the mean size of the AK 95% nonbreeding home range was 49% larger than the breeding home-range size. The median AK 75% bome-range isopleth (272 ha) we observed was similar in size to Protected Activity Centers (PACs) recommended by a recovery team. Our results lend support to the PAC concept and we support continued use of PACs to conserve Spotted Owl habitat in Utah. ?? 2007 The Raptor Research Foundation, Inc.

Habitat use, movements and home range of wintering Lesser Scaup in Florida

USGS Publications Warehouse

Herring, G.; Collazo, J.A.

2005-01-01

Radio telemetry and diurnal time activity budgets were used to show that wintering Lesser Scaup (Aythya affinis) used different habitats for comfort and feeding activities at Merritt Island National Wildlife Refuge (Merritt Island), Florida and adjacent estuarine areas. Management should take this spatial consideration into account. The same data were used to determine if habitat use differed between sexes. Data on movements and home range were used to evaluate habitat quality and potential effects of human disturbance. Scaup foraged more in impounded wetlands and rested more in open estuarine regions. Mean distance between diurnal and nocturnal sites was 2.7 km (SE ?? 0.3), and was similar between sexes and from mid to late winter. Male and female fixed kernel home ranges and core use areas did not differ. Mean fixed kernel 95% home range and 50% core use areas were 15.1 km2 (SE ?? 2.0) and 2.7 km2 (SE ?? 0.5) respectively, representing 3% and 0.5% of surveyed habitats. Males and females used habitats similarly and short distances traveled between diurnal and nocturnal sites suggested that habitat conditions were similar across the impounded wetlands and shallow portions of both the Indian River and Banana River. Sedentary or short movements suggested that disturbance was probably negligible at the principal areas used by Lesser Scaup. Habitat management strategies for scaup should not be restricted to Merritt Island. Adjustments should be made to take into account that maintenance activities occur in adjacent estuarine areas as well.

Home range and use of habitat of western yellow-billed cuckoos on the middle Rio Grande, New Mexico

USGS Publications Warehouse

Sechrist, Juddson; Ahlers, Darrell; Potak Zehfuss, Katherine; Doster, Robert; Paxton, Eben H.; Ryan, Vicky M.

2013-01-01

The western yellow-billed cuckoo (Coccyzus americanus occidentalis) is a Distinct Population Segment that has been proposed for listing under the Endangered Species Act, yet very little is known about its spatial use on the breeding grounds. We implemented a study, using radio telemetry, of home range and use of habitat for breeding cuckoos along the Middle Rio Grande in central New Mexico in 2007 and 2008. Nine of 13 cuckoos were tracked for sufficient time to generate estimates of home range. Overall size of home ranges for the 2 years was 91 ha for a minimum-convex-polygon estimate and 62 ha for a 95%-kernel-home-range estimate. Home ranges varied considerably among individuals, highlighting variability in spatial use by cuckoos. Additionally, use of habitat differed between core areas and overall home ranges, but the differences were nonsignificant. Home ranges calculated for western yellow-billed cuckoos on the Middle Rio Grande are larger than those in other southwestern riparian areas. Based on calculated home ranges and availability of riparian habitat in the study area, we estimate that the study area is capable of supporting 82-99 nonoverlapping home ranges of cuckoos. Spatial data from this study should contribute to the understanding of the requirements of area and habitat of this species for management of resources and help facilitate recovery if a listing occurs.

Food resources, distribution and seasonal variations in ranging in lion-tailed macaques, Macaca silenus in the Western Ghats, India.

PubMed

Erinjery, Joseph J; Kavana, T S; Singh, Mewa

2015-01-01

The distribution and availability of food was examined to see how it influenced ranging patterns and sleeping site selection in a group of lion-tailed macaques. The home range and core area were 130.48 ha (95% kernel) and 26.68 ha (50% kernel) respectively. The lion-tailed macaques had a longer day range, had a greater number of sleeping sites and used more core areas in the summer as compared to the monsoon and the post-monsoon seasons. The ranging patterns and sleeping site use were influenced by the major food resources used in a particular season. The ranging was mainly influenced by Artocarpus heterophyllus in monsoon, Cullenia exarillata and Toona ciliata in post- monsoon, and Artocarpus heterophyllus and Ficus amplissima in summer. The distribution of these four plant species is, therefore, critical to ranging, and thus to conservation of the lion-tailed macaque.

Comparative habitat use of sympatric Mexican spotted and great horned owls

Treesearch

Joseph L. Ganey; William M. Block; Jeffrey S. Jenness; Randolph A. Wilson

1997-01-01

To provide information on comparative habitat use, we studied radiotagged Mexican spotted owls (Strix occidentalis lucida: n = 13) and great horned owls (Bubo virginianus: n = 4) in northern Arizona. Home-range size (95% adaptive kernel estimate) did not differ significantly between species during either the breeding or nonbreeding...

Home range size of Tengmalm's owl during breeding in Central Europe is determined by prey abundance.

PubMed

Kouba, Marek; Bartoš, Luděk; Tomášek, Václav; Popelková, Alena; Šťastný, Karel; Zárybnická, Markéta

2017-01-01

Animal home ranges typically characterized by their size, shape and a given time interval can be affected by many different biotic and abiotic factors. However, despite the fact that many studies have addressed home ranges, our knowledge of the factors influencing the size of area occupied by different animals is, in many cases, still quite poor, especially among raptors. Using radio-telemetry (VHF; 2.1 g tail-mounted tags) we studied movements of 20 Tengmalm's owl (Aegolius funereus) males during the breeding season in a mountain area of Central Europe (the Czech Republic, the Ore Mountains: 50° 40' N, 13° 35' E) between years 2006-2010, determined their average hunting home range size and explored what factors affected the size of home range utilised. The mean breeding home range size calculated according to 95% fixed kernel density estimator was 190.7 ± 65.7 ha (± SD) with a median value of 187.1 ha. Home range size was affected by prey abundance, presence or absence of polygyny, the number of fledglings, and weather conditions. Home range size increased with decreasing prey abundance. Polygynously mated males had overall larger home range than those mated monogamously, and individuals with more fledged young possessed larger home range compared to those with fewer raised fledglings. Finally, we found that home ranges recorded during harsh weather (nights with strong wind speed and/or heavy rain) were smaller in size than those registered during better weather. Overall, the results provide novel insights into what factors may influence home range size and emphasize the prey abundance as a key factor for breeding dynamics in Tengmalm's owl.

Home range characteristics of Mexican Spotted Owls in the Rincon Mountains, Arizona

USGS Publications Warehouse

Willey, David W.; van Riper, Charles

2014-01-01

We studied a small isolated population of Mexican Spotted Owls (Strix occidentalis lucida) from 1996–1997 in the Rincon Mountains of Saguaro National Park, southeastern Arizona, USA. All mixed-conifer and pine-oak forest patches in the park were surveyed for Spotted Owls, and we located, captured, and radio-tagged 10 adult birds representing five mated pairs. Using radio-telemetry, we examined owl home range characteristics, roost habitat, and monitored reproduction within these five territories. Breeding season (Mar–Sep) home range size for 10 adult owls (95% adaptive kernel isopleths) averaged 267 ha (±207 SD), and varied widely among owls (range 34–652 ha). Mean home range size for owl pairs was 478 ha (±417 ha SD), and ranged from 70–1,160 ha. Owls that produced young used smaller home ranges than owls that had no young. Six habitat variables differed significantly between roost and random sites, including: percent canopy cover, number of trees, number of vegetation layers, average height of trees, average diameter of trees, and tree basal area. Radio-marked owls remained in their territories following small prescribed management fires within those territories, exhibiting no proximate effects to the presence of prescribed fire.

Patterns of space and habitat use by northern bobwhites in South Florida, USA

USGS Publications Warehouse

Singh, A.; Hines, T.C.; Hostetler, J.A.; Percival, H.F.; Oli, M.K.

2011-01-01

The manner by which animals use space and select resources can have important management consequences. We studied patterns of habitat selection by northern bobwhites (Colinus virginianus) on Babcock-Webb Wildlife Management Area, Charlotte County, Florida and evaluated factors influencing the sizes of their home ranges. A total of 1,245 radio-tagged bobwhites were monitored for 19,467 radio days during 2002-2007. The mean (?? 1 SE) annual home range size, estimated using the Kernel density method, was 88. 43 (?? 6. 16) ha and did not differ between genders. Winter home ranges of bobwhites (69. 27 ?? 4. 92 ha) were generally larger than summer home ranges (53. 90 ?? 4. 93 ha). Annual and winter home ranges were smaller for bobwhites whose ranges contained food plots compared to those that did not; however, the presence of food plots did not influence summer home ranges. We used distance-based methods to investigate habitat selection by bobwhites at two scales: selection of home ranges within the study site (second-order selection) and selection of habitats within home ranges (third-order selection). Across both scales, bobwhites generally preferred food plots and dry prairie habitat and avoided wet prairies and roads. This pattern was generally consistent between genders and across years. Our data indicate that management practices aimed at increasing and maintaining a matrix of food plots and dry prairie habitat would provide the most favorable environment for bobwhites. ?? 2010 Springer-Verlag.

Attributes of seasonal home range influence choice of migratory strategy in white-tailed deer

USGS Publications Warehouse

Henderson, Charles R.; Mitchell, Michael S.; Myers, Woodrow L.; Lukacs, Paul M.; Nelson, Gerald P.

2018-01-01

Partial migration is a common life-history strategy among ungulates living in seasonal environments. The decision to migrate or remain on a seasonal range may be influenced strongly by access to high-quality habitat. We evaluated the influence of access to winter habitat of high quality on the probability of a female white-tailed deer (Odocoileus virginianus) migrating to a separate summer range and the effects of this decision on survival. We hypothesized that deer with home ranges of low quality in winter would have a high probability of migrating, and that survival of an individual in winter would be influenced by the quality of their home range in winter. We radiocollared 67 female white-tailed deer in 2012 and 2013 in eastern Washington, United States. We estimated home range size in winter using a kernel density estimator; we assumed the size of the home range was inversely proportional to its quality and the proportion of crop land within the home range was proportional to its quality. Odds of migrating from winter ranges increased by 3.1 per unit increase in home range size and decreased by 0.29 per unit increase in the proportion of crop land within a home range. Annual survival rate for migrants was 0.85 (SD = 0.05) and 0.84 (SD = 0.09) for residents. Our finding that an individual with a low-quality home range in winter is likely to migrate to a separate summer range accords with the hypothesis that competition for a limited amount of home ranges of high quality should result in residents having home ranges of higher quality than migrants in populations experiencing density dependence. We hypothesize that density-dependent competition for high-quality home ranges in winter may play a leading role in the selection of migration strategy by female white-tailed deer.

Home ranges and habitat use of sloth bears Melursus ursinus inornatus in Wasgomuwa National Park, Sri Lanka

USGS Publications Warehouse

Ratnayeke, S.; Van Manen, F.T.; Padmalal, U.K.G.K.

2007-01-01

We studied home ranges and habitat selection of 10 adult sloth bears Melursus ursinus inornatus at Wasgomuwa National Park, Sri Lanka during 2002-2003. Very little is known about the ecology and behaviour of M. u. inornatus, which is a subspecies found in Sri Lanka. Our study was undertaken to assess space and habitat requirements typical of a viable population of M. u. inornatus to facilitate future conservation efforts. We captured and radio-collared 10 adult sloth bears and used the telemetry data to assess home-range size and habitat use. Mean 95% fixed kernel home ranges were 2.2 km2 (SE = 0.61) and 3.8 km2 (SE = 1.01) for adult females and males, respectively. Although areas outside the national park were accessible to bears, home ranges were almost exclusively situated within the national park boundaries. Within the home ranges, high forests were used more and abandoned agricultural fields (chenas) were used less than expected based on availability. Our estimates of home-range size are among the smallest reported for any species of bear. Thus, despite its relatively small size, Wasgomuwa National Park may support a sizeable population of sloth bears. The restriction of human activity within protected areas may be necessary for long-term viability of sloth bear populations in Sri Lanka as is maintenance of forest or scrub cover in areas with existing sloth bear populations and along potential travel corridors. ?? Wildlife Biology 2007.

Home range, habitat selection, and movements of California Black Rails at tidal marshes at San Francisco Bay, California

USGS Publications Warehouse

Tsao, Danika C.; Takekawa, John Y.; Woo, Isa; Yee, Julie L.; Evens, Jules G.

2009-01-01

Little is known about the movements and habitat selection of California Black Rails (Laterallus jamaicensis coturniculus) in coastal California. We captured 130 Black Rails, of which we radio-marked 48, in tidal marshes in San Francisco Bay during 2005 and 2006. Our objective was to examine their home ranges, movements, and habitat selection to improve the species' conservation. The mean fixed-kernel home range was 0.59 ha, the mean core area was 0.14 ha. Home ranges and core areas did not differ by year or site. Males had significantly larger home ranges and core areas than did females. All sites combined, Black Rails used areas with ≥94% total vegetative cover, with perennial pickleweed (Sarcocornia pacifica) the dominant plant. The rails' habitat selection varied by year and site but not by sex. A multivariate analysis of variance indicated that Black Rails selected areas with pickleweed taller and denser than average, greater cover and height of alkali bulrush (Bolboschoenus maritimus) and common saltgrass (Distichlis spicata), more stems between 20 and 30 cm above the ground, maximum vegetation height, and shorter distance to refugia. On average, Black Rails moved 27.6 ±1.8 (SE) m daily and 38.4 ± 5.5 m during extreme high tides. Understanding the California Black Rail's movements, home range, and habitat use is critical for management to benefit the species.

Home range and diet of feral cats in Hawaii forests

USGS Publications Warehouse

Smucker, T.D.; Lindsey, G.D.; Mosher, S.M.

2000-01-01

Feral cat Felis catus home range in a Hawaiian montane wet forest and their diet in three habitats - montane wet forest, subalpine dry forest, and lowland dry forest - were determined to provide baseline ecological data and to assess potential impacts to native terrestrial fauna. Seven cats (three males and four females) were captured in 624 trap nights. Mean weight of adult cats was 2.85 ?? 0.27 (SE) Kg for males and 1.87 ?? 0.03 kg for females. Mean diumal home range using the adaptive kernel method was 5.74 ?? 2.73 km2 for three males and 2.23 ?? 0.44 km2 for two females. Daytime locations were always within the montane wet forest with the borders on one or more sides of the home ranges of all cats defined by open grassland pastures. Rodents comprised the majority of the cat diets in all three habitats, with the frequencies of occurence between 0.88 and 0.91. Bird remains were a regular component of the diet of cats, with montane wet forest having the highest frequency of occurence (0.68), followed by subalpine dry forest (0.53), and lowland dry forest (0.21).

Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation

USGS Publications Warehouse

Tracey, Jeff A.; Sheppard, James; Zhu, Jun; Wei, Fu-Wen; Swaisgood, Ronald R.; Fisher, Robert N.

2014-01-01

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.

Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation

PubMed Central

Tracey, Jeff A.; Sheppard, James; Zhu, Jun; Wei, Fuwen; Swaisgood, Ronald R.; Fisher, Robert N.

2014-01-01

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research. PMID:24988114

Spatiotemporal requirements of the Hainan gibbon: Does home range constrain recovery of the world's rarest ape?

PubMed

Bryant, Jessica V; Zeng, Xingyuan; Hong, Xiaojiang; Chatterjee, Helen J; Turvey, Samuel T

2017-03-01

Conservation management requires an evidence-based approach, as uninformed decisions can signify the difference between species recovery and loss. The Hainan gibbon, the world's rarest ape, reportedly exploits the largest home range of any gibbon species, with these apparently large spatial requirements potentially limiting population recovery. However, previous home range assessments rarely reported survey methods, effort, or analytical approaches, hindering critical evaluation of estimate reliability. For extremely rare species where data collection is challenging, it also is unclear what impact such limitations have on estimating home range requirements. We re-evaluated Hainan gibbon spatial ecology using 75 hr of observations from 35 contact days over 93 field-days across dry (November 2010-February 2011) and wet (June 2011-September 2011) seasons. We calculated home range area for three social groups (N = 21 individuals) across the sampling period, seasonal estimates for one group (based on 24 days of observation; 12 days per season), and between-group home range overlap using multiple approaches (Minimum Convex Polygon, Kernel Density Estimation, Local Convex Hull, Brownian Bridge Movement Model), and assessed estimate reliability and representativeness using three approaches (Incremental Area Analysis, spatial concordance, and exclusion of expected holes). We estimated a yearly home range of 1-2 km 2 , with 1.49 km 2 closest to the median of all estimates. Although Hainan gibbon spatial requirements are relatively large for gibbons, our new estimates are smaller than previous estimates used to explain the species' limited recovery, suggesting that habitat availability may be less important in limiting population growth. We argue that other ecological, genetic, and/or anthropogenic factors are more likely to constrain Hainan gibbon recovery, and conservation attention should focus on elucidating and managing these factors. Re-evaluation reveals Hainan gibbon home range as c. 1-2 km 2 . Hainan gibbon home range is, therefore, similar to other Nomascus gibbons. Limited data for extremely rare species does not necessarily prevent derivation of robust home range estimates. © 2016 Wiley Periodicals, Inc.

Quantifying home range habitat requirements for bobcats (Lynx rufus) in Vermont, USA

USGS Publications Warehouse

Donovan, Therese; Freeman, Mark; Abouelezz, Hanem; Royar, Kim; Howard, Alan D.; Mickey, R.

2011-01-01

We demonstrate how home range and habitat use analysis can inform landscape-scale conservation planning for the bobcat, Lynx rufus, in Vermont USA. From 2005 to 2008, we outfitted fourteen bobcats with GPS collars that collected spatially explicit locations from individuals every 4 h for 3–4 months. Kernel home range techniques were used to estimate home range size and boundaries, and to quantify the utilization distribution (UD), which is a spatially explicit, topographic mapping of how different areas within the home range are used. We then used GIS methods to quantify both biotic (e.g. habitat types, stream density) and abiotic (e.g. slope) resources within each bobcat’s home range. Across bobcats, upper 20th UD percentiles (core areas) had 18% less agriculture, 42% less development, 26% more bobcat habitat (shrub, deciduous, coniferous forest, and wetland cover types), and 33% lower road density than lower UD percentiles (UD valleys). For each bobcat, we used Akaike’s Information Criterion (AIC) to evaluate and compare 24 alternative Resource Utilization Functions (hypotheses) that could explain the topology of the individual’s UD. A model-averaged population-level Resource Utilization Function suggested positive responses to shrub, deciduous, coniferous forest, and wetland cover types within 1 km of a location, and negative responses to roads and mixed forest cover types within 1 km of a location. Applying this model-averaged function to each pixel in the study area revealed habitat suitability for bobcats across the entire study area, with suitability scores ranging between −1.69 and 1.44, where higher values were assumed to represent higher quality habitat. The southern Champlain Valley, which contained ample wetland and shrub habitat, was a concentrated area of highly suitable habitat, while areas at higher elevation areas were less suitable. Female bobcat home ranges, on average, had an average habitat suitability score of near 0, indicating that home ranges consisted of both beneficial and detrimental habitat types. We discuss the application of habitat suitability mapping and home range requirements for bobcat conservation and landscape scale management.

Quantifying home range habitat requirements for bobcats (Lynx rufus) in Vermont, USA

USGS Publications Warehouse

Donovan, T.M.; Freeman, M.; Abouelezz, H.; Royar, Kim; Howard, A.; Mickey, R.

2011-01-01

We demonstrate how home range and habitat use analysis can inform landscape-scale conservation planning for the bobcat, Lynx rufus, in Vermont USA. From 2005 to 2008, we outfitted fourteen bobcats with GPS collars that collected spatially explicit locations from individuals every 4. h for 3-4. months. Kernel home range techniques were used to estimate home range size and boundaries, and to quantify the utilization distribution (UD), which is a spatially explicit, topographic mapping of how different areas within the home range are used. We then used GIS methods to quantify both biotic (e.g. habitat types, stream density) and abiotic (e.g. slope) resources within each bobcat's home range. Across bobcats, upper 20th UD percentiles (core areas) had 18% less agriculture, 42% less development, 26% more bobcat habitat (shrub, deciduous, coniferous forest, and wetland cover types), and 33% lower road density than lower UD percentiles (UD valleys). For each bobcat, we used Akaike's Information Criterion (AIC) to evaluate and compare 24 alternative Resource Utilization Functions (hypotheses) that could explain the topology of the individual's UD. A model-averaged population-level Resource Utilization Function suggested positive responses to shrub, deciduous, coniferous forest, and wetland cover types within 1. km of a location, and negative responses to roads and mixed forest cover types within 1. km of a location. Applying this model-averaged function to each pixel in the study area revealed habitat suitability for bobcats across the entire study area, with suitability scores ranging between -1.69 and 1.44, where higher values were assumed to represent higher quality habitat. The southern Champlain Valley, which contained ample wetland and shrub habitat, was a concentrated area of highly suitable habitat, while areas at higher elevation areas were less suitable. Female bobcat home ranges, on average, had an average habitat suitability score of near 0, indicating that home ranges consisted of both beneficial and detrimental habitat types. We discuss the application of habitat suitability mapping and home range requirements for bobcat conservation and landscape scale management. ?? 2011.

Seasonal movements and Home-range use by female pronghorns in sagebrush-steppe communities of western South Dakota

USGS Publications Warehouse

Jacques, C.N.; Jenks, J.A.; Klaver, R.W.

2009-01-01

Knowledge of seasonal movements by pronghorns (Antilocapra americana) within the easternmost extension of sagebrush-steppe communities is limited. Current hypotheses regarding movement patterns suggest that pronghorns initiate seasonal movements in response to severe winter weather, snowfall patterns, spatial and temporal variation in forage abundance, and availability of water. From January 2002 to August 2005, we monitored movements of 76 adult (≥1.5 years) female pronghorns on 2 study areas (Harding and Fall River counties) in western South Dakota. We collected 8,750 visual locations, calculated 204 home ranges, and documented 17 seasonal movements. Eighty-four percent (n = 55) of pronghorns were nonmigratory and 10% (n = 6) were conditional migrators. Mean distance between summer and winter range was 23.1 km (SE = 2.8 km, n = 13). Five adult pronghorns (8%) dispersed a mean distance of 37.6 km (SE = 12.4 km); of which 1 female moved a straight-line distance of 75.0 km. Winter and summer home-range size varied (P < 0.0001) between study sites. Mean 95% adaptive kernel winter and summer home-range size of pronghorns was 55.5 and 19.7 km2, respectively, in Harding County and 127.2 and 65.9 km2, respectively, in Fall River County. Nonmigratory behavior exhibited by pronghorns was likely associated with minimal snow cover and moderate temperatures during winter 2002–2004. Variation in size of adult seasonal home ranges between sites was likely associated with differences in forage distribution and availability between regions.

Fine scale movements and habitat use of black brant during the flightless Wing Molt in Arctic Alaska

USGS Publications Warehouse

Lewis, Tyler L.; Flint, Paul L.; Derksen, Dirk V.; Schmutz, Joel A.

2011-01-01

Thousands of Black Brant (Branta bernicla nigricans) migrate annually to the Teshekpuk Lake Special Area (TLSA), Alaska, to undergo the flightless wing molt on tundra lakes and wetlands. GPS transmitters were attached to Brant over two summers (2007–2008) to examine patterns of movement and habitat use of molting Brant, including variation by habitat type, year and body mass. Molting Brant were located an average of 31 ± 1 m (SE) from shore and this distance did not vary across any of the explanatory variables. Brant moved an average of 123 ± 3 m hr-1 while flightless. Movement rates varied by year, averaging 22 ± 12 m hr-1 faster in 2008, and across habitat types, averaging 22 ± 13 m hr-1 faster in inland versus coastal and estuarine habitats. Two kernel home ranges were estimated: entire home range, which encompassed the complete 95% probability contour, and shoreline home range, which included only shoreline areas used by molting Brant. Entire home range (x bar = 15.1 ± 2.2 km2) was negatively correlated with body mass, suggesting that heavier individuals have more body reserves to contribute to feather growth and thereby require less food and smaller home ranges. Conversely, shoreline home range (x bar = 4.3 ± 0.6 km2) did not vary by body mass, but rather by habitat type, being larger in estuarine habitats. The complex shorelines and numerous deltaic islands of estuarine habitats offer more shoreline per area than either coastal or inland habitats. Brant appear to have limited ability to adjust their home range size or forage further from shore in response to variable food resources across years or habitats, instead altering their movement rate. Given this apparent lack of behavioral flexibility, Brant may be sensitive to development-related disturbances or habitat losses at molt sites in the TLSA.

Home range dynamics, habitat selection, and survival of Greater Roadrunners

USGS Publications Warehouse

Kelley, S.W.; Ransom, D.; Butcher, J.A.; Schulz, G.G.; Surber, B.W.; Pinchak, W.E.; Santamaria, C.A.; Hurtado, L.A.

2011-01-01

Greater Roadrunners (Geococcyx californianus) are common, poorly studied birds of arid and semi-arid ecosystems in the southwestern United States. Conservation of this avian predator requires a detailed understanding of their movements and spatial requirements that is currently lacking. From 2006 to 2009, we quantified home-range and core area sizes and overlap, habitat selection, and survival of roadrunners (N= 14 males and 20 females) in north-central Texas using radio-telemetry and fixed kernel estimators. Median home-range and core-area sizes were 90.4 ha and 19.2 ha for males and 80.1 ha and 16.7 ha for females, respectively. The size of home range and core areas did not differ significantly by either sex or season. Our home range estimates were twice as large (x??= 108.9 ha) as earlier published estimates based on visual observations (x??= 28-50 ha). Mean percent overlap was 38.4% for home ranges and 13.7% for core areas. Male roadrunners preferred mesquite woodland and mesquite savanna cover types, and avoided the grass-forb cover type. Female roadrunners preferred mesquite savanna and riparian woodland cover types, and avoided grass-forb habitat. Kaplan-Meier annual survival probabilities for females (0.452 ?? 0.118[SE]) were twice that estimated for males (0.210 ?? 0.108), but this difference was not significant. Mortality rates of male roadrunners were higher than those of females during the spring when males call from elevated perches, court females, and chase competing males. Current land use practices that target woody-shrub removal to enhance livestock forage production could be detrimental to roadrunner populations by reducing availability of mesquite woodland and mesquite savanna habitat required for nesting and roosting and increasing the amount of grass-forb habitat that roadrunners avoid. ??2011 The Authors. Journal of Field Ornithology ??2011 Association of Field Ornithologists.

7 CFR 51.2113 - Size requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... of range in count of whole almond kernels per ounce or in terms of minimum, or minimum and maximum diameter. When a range in count is specified, the whole kernels shall be fairly uniform in size, and the average count per ounce shall be within the range specified. Doubles and broken kernels shall not be used...

Does Plan B work? Home range estimations from stored on board and transmitted data sets produced by GPS-telemetry in the Colombian Amazon.

PubMed

Cabrera, Jaime A; Molina, Eduardo; González, Tania; Armenteras, Dolors

2016-12-01

Telemetry based on Global Positioning Systems (GPS) makes possible to gather large quantities of information in a very fine scale and work with species that were impossible to study in the past. When working with GPS telemetry, the option of storing data on board could be more desirable than the sole satellite transmitted data, due to the increase in the amount of locations available for analysis. Nonetheless, the uncertainty in the retrieving of the collar unit makes satellite-transmitted technologies something to take into account. Therefore, differences between store-on-board (SoB) and satellite-transmitted (IT) data sets need to be considered. Differences between SoB and IT data collected from two lowland tapirs (Tapirus terrestris), were explored by means of the calculation of home range areas by three different methods: the Minimum Convex Polygon (MCP), the Fixed Kernel Density Estimator (KDE) and the Brownian Bridges (BB). Results showed that SoB and IT data sets for the same individual were similar, with fix ranging from 63 % to 85 % respectively, and 16 m to 17 m horizontal errors. Depending on the total number of locations available for each individual, the home ranges estimated showed differences between 2.7 % and 79.3 %, for the 50 % probability contour and between 9.9 % and 61.8 % for the 95 % probability contour. These differences imply variations in the spatial coincidence of the estimated home ranges. We concluded that the use of IT data is not a good option for the estimation of home range areas if the collar settings have not been designed specifically for this use. Nonetheless, geographical representations of the IT based estimators could be of great help to identify areas of use, besides its assistance to locate the collar for its retrieval at the end of the field season and as a proximate backup when collars disappear.

Travel-time sensitivity kernels in long-range propagation.

PubMed

Skarsoulis, E K; Cornuelle, B D; Dzieciuch, M A

2009-11-01

Wave-theoretic travel-time sensitivity kernels (TSKs) are calculated in two-dimensional (2D) and three-dimensional (3D) environments and their behavior with increasing propagation range is studied and compared to that of ray-theoretic TSKs and corresponding Fresnel-volumes. The differences between the 2D and 3D TSKs average out when horizontal or cross-range marginals are considered, which indicates that they are not important in the case of range-independent sound-speed perturbations or perturbations of large scale compared to the lateral TSK extent. With increasing range, the wave-theoretic TSKs expand in the horizontal cross-range direction, their cross-range extent being comparable to that of the corresponding free-space Fresnel zone, whereas they remain bounded in the vertical. Vertical travel-time sensitivity kernels (VTSKs)-one-dimensional kernels describing the effect of horizontally uniform sound-speed changes on travel-times-are calculated analytically using a perturbation approach, and also numerically, as horizontal marginals of the corresponding TSKs. Good agreement between analytical and numerical VTSKs, as well as between 2D and 3D VTSKs, is found. As an alternative method to obtain wave-theoretic sensitivity kernels, the parabolic approximation is used; the resulting TSKs and VTSKs are in good agreement with normal-mode results. With increasing range, the wave-theoretic VTSKs approach the corresponding ray-theoretic sensitivity kernels.

Optimized data fusion for K-means Laplacian clustering

PubMed Central

Yu, Shi; Liu, Xinhai; Tranchevent, Léon-Charles; Glänzel, Wolfgang; Suykens, Johan A. K.; De Moor, Bart; Moreau, Yves

2011-01-01

Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically. Results: Three variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix. Availability: The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html. Contact: shiyu@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20980271

Comparison of radio-telemetric home range analysis and acoustic detection for Little Brown Bat habitat evaluation

USGS Publications Warehouse

Coleman, Laci S.; Ford, W. Mark; Dobony, Christopher A.; Britzke, Eric R.

2014-01-01

With dramatic declines of bat populations due to mortality caused by Pseudogymnoascus destructans (White-nose Syndrome), assessing habitat preferences of bats in the northeastern US is now critical to guide the development of regional conservation efforts. In the summer of 2012, we conducted fixed-station simultaneous telemetry to determine nocturnal spatial use and fixed-kernel home-range estimates of available habitat of a Myotis lucifugus (Le Conte) (Little Brown Bat) maternity colony in an artificial bat house. In summers of 2011 and 2012, we also deployed a 52-ha grid of 4 × 4 Anabat acoustic detectors over five 6–8-day sampling periods in various riparian and non-riparian environments in close proximity to the same bat house. The mean telemetry home range of 143 ha for bats (n = 7) completely overlapped the acoustic grid. Rankings of habitats from telemetry data for these 7 bats and 5 additional bats not included in home-range calculations but added for habitat-use measures (n = 13) revealed a higher proportional use of forested riparian habitats than other types at the landscape scale. Pair-wise comparisons of habitats indicated that bats were found significantly closer to forested riparian habitats and forests than to open water, developed areas, fields, shrublands, or wetland habitats at the landscape scale. Acoustic sampling showed that naïve occupancy was 0.8 and 0.6 and mean nightly detection probabilities were 0.23 and 0.08 at riparian and non-riparian sites, respectively. Our findings suggest that Little Brown Bats select forested riparian and forested habitats for foraging at the landscape scale but may be most easily detected acoustically at riparian sites when a simple occupancy determination for an area is required.

Late winter and early spring home range and habitat use of the endangered Carolina northern flying squirrel in western North Carolina

USGS Publications Warehouse

Ford, W. Mark; Kelly, Christine A.; Rodrigue, Jane L.; Odom, Richard H.; Newcomb, Douglas; Gilley, L. Michelle; Diggins, Corinne A.

2014-01-01

The Carolina northern flying squirrel Glaucomys sabrinus coloratus is an endangered subspecies that is restricted to high elevation forests in the southern Appalachian Mountains. Owing to rugged terrain and nocturnal habits, the subspecies’ natural history, home range characteristics and habitat preferences are poorly known. We radio-tracked 3 female and 2 male Carolina northern flying squirrels during late winter through spring 2012 in the Pisgah National Forest, North Carolina, USA. Tracked squirrels used 13 yellow birch Betula alleghaniensis and 9 red spruce Picea rubens as diurnal dens. Ten of the yellow birch dens were in cavities, whereas the remainders were dreys. Conversely, 8 of the red spruce dens were dreys and one was in a cavity. Mean (±SE) female 95 and 50% adaptive kernel home ranges were 6.50 ± 2.19 and 0.93 ± 0.33 ha, respectively, whereas the corresponding values for males were 12.6 ± 0.9 and 1.45 ± 0.1 ha, respectively. Squirrels used red spruce stands with canopies >20 m more than expected based on availability at the landscape and home range scales. Results should be interpreted cautiously because of small sample sizes and seasonal observations; however, they provide evidence that although northern hardwoods such as yellow birch are an important den habitat component, mature red spruce-dominated habitats with complex structure provide foraging habitats and are also den habitat. Our findings support efforts to improve the structural condition of extant red spruce forests and/or increase red spruce acreage to potentially benefit Carolina northern flying squirrels.

Crossing Latitudes—Long-Distance Tracking of an Apex Predator

PubMed Central

Ferreira, Luciana C.; Thums, Michele; Meeuwig, Jessica J.; Vianna, Gabriel M. S.; Stevens, John; McAuley, Rory; Meekan, Mark G.

2015-01-01

Tiger sharks (Galeocerdo cuvier) are apex predators occurring in most tropical and warm temperate marine ecosystems, but we know relatively little of their patterns of residency and movement over large spatial and temporal scales. We deployed satellite tags on eleven tiger sharks off the north-western coast of Western Australia and used the Brownian Bridge kernel method to calculate home ranges and analyse movement behaviour. One individual recorded one of the largest geographical ranges of movement ever reported for the species, travelling over 4000 km during 517 days of monitoring. Tags on the remainder of the sharks reported for shorter periods (7-191 days). Most of these sharks had restricted movements and long-term (30-188 days) residency in coastal waters in the vicinity of the area where they were tagged. Core home range areas of sharks varied greatly from 1166.9 to 634,944 km2. Tiger sharks spent most of their time in water temperatures between 23°-26°C but experienced temperatures ranging from 6°C to 33°C. One shark displayed seasonal movements among three distinct home range cores spread along most of the coast of Western Australia and generalized linear models showed that this individual had different patterns of temperature and depth occupancy in each region of the coast, with the highest probability of residency occurring in the shallowest areas of the coast with water temperatures above 23°C. These results suggest that tiger sharks can migrate over very large distances and across latitudes ranging from tropical to the cool temperate waters. Such extensive long-term movements may be a key element influencing the connectivity of populations within and among ocean basins. PMID:25671609

Spatial Ecology of Blanding’s Turtles (Emydoidea blandingii) in Southcentral New Hampshire with Implications to Road Mortality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walston, Leroy J.; Najjar, Stephen J.; LaGory, Kirk E.

Understanding the spatial ecology and habitat requirements of rare turtle species and the factors that threaten their populations is important for the success of long-term conservation programs. We present results on an eight-year field study in which we used radiotelemetry to monitor the activity and habitat use of 23 adult (male, n = 7; female, n = 16) Blanding’s turtles in southcentral New Hampshire. We found that females occupied home ranges (as defined by minimum convex polygons) that were approximately two times larger than the home ranges of males. Despite the sex difference in home range size, we found nomore » sex difference in core area size (defined as the 50% kernel density estimate). We found that activity patterns varied by season, with increased activity each month after hibernation, and peak activity coinciding with the late spring-early summer nesting season. We observed sex-based and seasonal differences in wetland use. Males appeared to prefer emergent and scrub-shrub wetlands in each season, whereas females preferred scrub-shrub wetlands in spring and ponds in summer and fall. We identified road mortality risk as a potentially important threat for this population because females crossed roads ten times more frequently than males (based on proportion of observations). The preservation of wetland networks, as well as the implementation of measures to minimize road mortality, are important considerations for the long-term persistence of this population.« less

Foraging distance and home range of Cassin's Auklets nesting at two colonies in the California Channel Islands

USGS Publications Warehouse

Adams, Josh; Takekawa, John Y.; Carter, Harry R.

2004-01-01

We radio-marked 99 Cassin's Auklets (Ptychoramphus aleuticus) nesting at two colonies, Prince Island and Scorpion Rock, separated by 90 km in the California Channel Islands to quantify foraging distance, individual home-range area, and colony-based foraging areas during three consecutive breeding seasons. Auklets generally foraged < 30 km from each colony in all years. Core foraging areas (50% fixed kernel) from Prince Island in 1999-2001 were north to northeast of the colony over the insular shelf near the shelfbreak. Core foraging areas from Scorpion Rock in 2000-2001 occurred in two focal areas: the Anacapa Passage, a narrow interisland passage adjacent to the colony, and over the southeastern Santa Barbara Channel. During 2000, intercolony foraging areas overlapped by 10%; however, auklets from each colony used the overlapping area at different times. Equivalent-sample-size resampling indicated Prince Island foraging area (1216 ?? 654 km2) was twice that of Scorpion Rock (598 ?? 204 km2). At Prince Island, mean individual distances, home-range areas, and colony-based activity areas were greater for females than males, especially during 2001. At Prince Island, core foraging areas of females and males, pooled separately, overlapped by 63% in 1999 and 2000, and by 35% in 2001. Postbreeding auklets from both colonies dispersed northward and moved to active upwelling centers off central California, coincident with decreased upwelling and sea-surface warming throughout the Santa Barbara Channel.

Habitat use and spatial structure of a barking frog (Eleutherodactylus augusti) population in southeastern Arizona

USGS Publications Warehouse

Goldberg, C.S.; Schwalbe, C.R.

2004-01-01

Barking Frogs (Eleutherodactylus augusti) are the northernmost ranging member of the large tropical family Leptodactylidae. We investigated the ecology of this saxicolous species at the northern edge of its range in a canyon in southern Arizona. We captured 54 frogs on discontinuous rock outcrops; eight of nine females and 39 of 45 males were on limestone outcrops. The remaining frogs were closer to limestone outcrops by more than 200 m than would be expected if they were distributed randomly with respect to limestone formations. Seven of 10 frogs radio-tracked had core home ranges (50% fixed kernel) from 94 to 100% on limestone; the other three frogs did not have any part of their home range on limestone outcrops. During five years of mark-recapture efforts, no frogs were found on a different outcrop from the one where they were originally captured; no radio-tracked frogs moved between outcrops during the breeding season. We estimated that four to 20 Barking Frogs occupied each outcrop; these groups probably are connected primarily by juvenile dispersal. As an organism living at the edge of its range, Barking Frogs in Arizona may rely heavily on extensive underground areas such as those found in limestone to protect them from a physiologically challenging environment. To manage for the persistence of Barking Frogs in southern Arizona, we must identify and protect habitat patches and movement pathways among them.

Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting.

PubMed

Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele

2016-01-19

The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted) from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B₁ and B₂ were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB₁ + AFB₂, whereas AFG₁ and AFG₂ were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%-19.9% of total peeled kernels) removed 97.3%-99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%-99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB₁ + AFB₂ measured in rejected fractions (15%-18% of total kernels) ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01-0.05 µg/kg) was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB₁ and from 0.06 to 1.79 μg/kg for total aflatoxins.

Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

PubMed

Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab; Zhao, Ni; Shen, Judong; Li, Yun; Wu, Michael C

Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

Improved modeling of clinical data with kernel methods.

PubMed

Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

2012-02-01

Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems. Copyright © 2011 Elsevier B.V. All rights reserved.

Insights into the spurious long-range nature of local rs-dependent non-local exchange-correlation kernels

DOE PAGES

Lu, Deyu

2016-08-05

A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, r s(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace r s(r) by a global, average r s in the kernel.more » Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average r s significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

Wigner functions defined with Laplace transform kernels.

PubMed

Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

2011-10-24

We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

Vis- and NIR-based instruments for detection of black-tip damaged wheat kernels: A comparative study

USDA-ARS?s Scientific Manuscript database

Black-tip (BT) present in wheat kernels is a non-mycotoxic fungus that attacks the kernels wherein any of a number of molds forms a dark brown or black sooty mold at the tip of the wheat kernel. Three spectrometers covering the spectral ranges 950-1636nm (Spec1), 600-1045nm (Spec2), and 380-780nm (S...

Movement patterns of Antillean manatees in Chetumal Bay (Mexico) and coastal Belize: A challenge for regional conservation

USGS Publications Warehouse

Castelblanco-Martínez, Delma Nataly; Padilla-Saldivar, J.; Hernández-Arana, Héctor Abuid; Slone, D.H.; Reid, J.P.; Morales-Vela, B.

2013-01-01

Information from 15 satellite-tracked Antillean manatees (Trichechus manatus manatus) was analyzed in order to assess individual movements, home ranges, and high-use areas for conservation decisions. Manatees were captured in Chetumal Bay, Mexico, and tagged with Argos-monitored satellite transmitters. Location of the manatees and physical characteristics were assessed to describe habitat properties. Most manatees traveled to freshwater sources. The Maximum Area Size (MAS) for each manatee was determined using the observation-area method. Additional kernel densities of 95% home range and 50% Center of Activity (COA) were also calculated, with manatees having 1–3 COAs. Manatees exhibited two different movement patterns: remaining in Chetumal Bay, and long-distance (up to 240 km in 89 d). The residence time in Chetumal Bay was higher for females (89.6% of time) than for males (72.0%), but the daily travel rate (0.4–0.5 km/d) was similar for both sexes. Most of the COAs fell within Natural Protected Areas (NPA). However, manatees also travel for long distances into unprotected areas, where they face uncontrolled boat traffic, fishing activities, and habitat loss. Conservation of movement corridors may promote long-distance movements and facilitate genetic exchange.

Reduction of Aflatoxins in Apricot Kernels by Electronic and Manual Color Sorting

PubMed Central

Zivoli, Rosanna; Gambacorta, Lucia; Piemontese, Luca; Solfrizzo, Michele

2016-01-01

The efficacy of color sorting on reducing aflatoxin levels in shelled apricot kernels was assessed. Naturally-contaminated kernels were submitted to an electronic optical sorter or blanched, peeled, and manually sorted to visually identify and sort discolored kernels (dark and spotted) from healthy ones. The samples obtained from the two sorting approaches were ground, homogenized, and analysed by HPLC-FLD for their aflatoxin content. A mass balance approach was used to measure the distribution of aflatoxins in the collected fractions. Aflatoxin B1 and B2 were identified and quantitated in all collected fractions at levels ranging from 1.7 to 22,451.5 µg/kg of AFB1 + AFB2, whereas AFG1 and AFG2 were not detected. Excellent results were obtained by manual sorting of peeled kernels since the removal of discolored kernels (2.6%–19.9% of total peeled kernels) removed 97.3%–99.5% of total aflatoxins. The combination of peeling and visual/manual separation of discolored kernels is a feasible strategy to remove 97%–99% of aflatoxins accumulated in naturally-contaminated samples. Electronic optical sorter gave highly variable results since the amount of AFB1 + AFB2 measured in rejected fractions (15%–18% of total kernels) ranged from 13% to 59% of total aflatoxins. An improved immunoaffinity-based HPLC-FLD method having low limits of detection for the four aflatoxins (0.01–0.05 µg/kg) was developed and used to monitor the occurrence of aflatoxins in 47 commercial products containing apricot kernels and/or almonds commercialized in Italy. Low aflatoxin levels were found in 38% of the tested samples and ranged from 0.06 to 1.50 μg/kg for AFB1 and from 0.06 to 1.79 μg/kg for total aflatoxins. PMID:26797635

Avoidance behavior by prairie grouse: implications for development of wind energy.

PubMed

Pruett, Christin L; Patten, Michael A; Wolfe, Donald H

2009-10-01

New wind-energy facilities and their associated power transmission lines and roads are being constructed at a rapid pace in the Great Plains of North America. Nevertheless, little is known about the possible negative effects these anthropogenic features might have on prairie birds, one of the most threatened groups in North America. We examined radiotelemetry tracking locations of Lesser Prairie-Chickens (Tympanuchus pallidicinctus) and Greater Prairie-Chickens (T. cupido) in two locations in Oklahoma to determine whether these birds avoided or changed movement behavior near power lines and paved highways. We tracked 463 Lesser Prairie-Chickens (15,071 tracking locations) and 216 Greater Prairie-Chickens (5,750 locations) for 7 and 3 years, respectively. Individuals of both species avoided power lines by at least 100 m and Lesser Prairie-Chickens avoided one of the two highways by 100 m. Prairie-chickens crossed power lines less often than expected if birds moved randomly (p < 0.05) but did not appear to perceive highways as a movement barrier (p > 0.05). In addition, home ranges of Lesser Prairie-Chickens overlapped the power line less often than would be expected by chance placement of home ranges; this result was supported by kernel-density estimation of home ranges. It is likely that new power lines (and other tall structures such as wind turbines) will lead to avoidance of previously suitable habitat and will serve as barriers to movement. These two factors will likely increase fragmentation in an already fragmented landscape if wind energy development continues in prairie habitats.

L2-norm multiple kernel learning and its application to biomedical data fusion

PubMed Central

2010-01-01

Background This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L∞, L1, and L2 MKL. In particular, L2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L∞ MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources. Results We provide a theoretical analysis of the relationship between the L2 optimization of kernels in the dual problem with the L2 coefficient regularization in the primal problem. Understanding the dual L2 problem grants a unified view on MKL and enables us to extend the L2 method to a wide range of machine learning problems. We implement L2 MKL for ranking and classification problems and compare its performance with the sparse L∞ and the averaging L1 MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. L2 MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel L2 MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing. Conclusions This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in L∞ MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing L2 kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL. Availability The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html. PMID:20529363

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Antioxidant capacity and phenolics content of apricot (Prunus armeniaca L.) kernel as a function of genotype.

PubMed

Korekar, Girish; Stobdan, Tsering; Arora, Richa; Yadav, Ashish; Singh, Shashi Bala

2011-11-01

Fourteen apricot genotypes grown under similar cultural practices in Trans-Himalayan Ladakh region were studied to find out the influence of genotype on antioxidant capacity and total phenolic content (TPC) of apricot kernel. The kernels were found to be rich in TPC ranging from 92.2 to 162.1 mg gallic acid equivalent/100 g. The free radical-scavenging activity in terms of inhibitory concentration (IC(50)) ranged from 43.8 to 123.4 mg/ml and ferric reducing antioxidant potential (FRAP) from 154.1 to 243.6 FeSO(4).7H(2)O μg/ml. A variation of 1-1.7 fold in total phenolic content, 1-2.8 fold in IC(50) by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and 1-1.6 fold in ferric reducing antioxidant potential among the examined kernels underlines the important role played by genetic background for determining the phenolic content and antioxidant potential of apricot kernel. A positive significant correlation between TPC and FRAP (r=0.671) was found. No significant correlation was found between TPC and IC(50); FRAP and IC(50); TPC and physical properties of kernel. Principal component analysis demonstrated that genotypic effect is more pronounced towards TPC and total antioxidant capacity (TAC) content in apricot kernel while the contribution of seed and kernel physical properties are not highly significant.

Increasing accuracy of dispersal kernels in grid-based population models

USGS Publications Warehouse

Slone, D.H.

2011-01-01

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, ...

Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement

PubMed Central

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes. PMID:21765890

Should I stay or should I go? A habitat-dependent dispersal kernel improves prediction of movement.

PubMed

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes.

Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Ranging, Activity and Habitat Use by Tigers in the Mangrove Forests of the Sundarban.

PubMed

Naha, Dipanjan; Jhala, Yadvendradev V; Qureshi, Qamar; Roy, Manjari; Sankar, Kalyansundaram; Gopal, Rajesh

2016-01-01

The Sundarban of India and Bangladesh (about 6000 km²) are the only mangrove forests inhabited by a sizeable population of tigers. The adjoining area also supports one of the highest human densities and experiences severe human-tiger conflicts. We used GPS-Satellite and VHF radio-collars on 6 (3 males and 3 female) tigers to study their ranging patterns and habitat preference. The average home range (95% Fixed Kernel) for resident females was 56.4 (SE 5.69) and for males it was 110 (SE 49) km². Tigers crossed an average of 5 water channels > 30 meters per day with a mean width of 54 meters, whereas channels larger than 400 meters were rarely crossed. Tigers spent over 58% of their time within Phoenix habitat but compositional analysis showed a habitat preference of the order Avicennia-Sonneratia > Phoenix > Ceriops > Barren > Water. Average daily distance moved was 4.6 km (range 0.1-23). Activity of tigers peaked between 05:00 hours and 10:00 hours showing some overlap with human activity. Territory boundaries were demarcated by large channels which tigers intensively patrolled. Extra caution should be taken while fishing or honey collection during early morning in Avicennia-Sonneratia and Phoenix habitat types along wide channels to reduce human-tiger conflict. Considering home-range core areas as exclusive, tiger density was estimated at 4.6 (SE range 3.6 to 6.7) tigers/100 km2 giving a total population of 76 (SE range 59-110) tigers in the Indian Sundarban. Reluctance of tigers to cross wide water channels combined with increasing commercial boat traffic and sea level rise due to climate change pose a real threat of fragmenting the Sundarban tiger population.

Ranging, Activity and Habitat Use by Tigers in the Mangrove Forests of the Sundarban

PubMed Central

Naha, Dipanjan; Jhala, Yadvendradev V.; Qureshi, Qamar; Roy, Manjari; Sankar, Kalyansundaram; Gopal, Rajesh

2016-01-01

The Sundarban of India and Bangladesh (about 6000 km²) are the only mangrove forests inhabited by a sizeable population of tigers. The adjoining area also supports one of the highest human densities and experiences severe human-tiger conflicts. We used GPS-Satellite and VHF radio-collars on 6 (3 males and 3 female) tigers to study their ranging patterns and habitat preference. The average home range (95% Fixed Kernel) for resident females was 56.4 (SE 5.69) and for males it was 110 (SE 49) km². Tigers crossed an average of 5 water channels > 30 meters per day with a mean width of 54 meters, whereas channels larger than 400 meters were rarely crossed. Tigers spent over 58% of their time within Phoenix habitat but compositional analysis showed a habitat preference of the order Avicennia-Sonneratia > Phoenix > Ceriops > Barren > Water. Average daily distance moved was 4.6 km (range 0.1–23). Activity of tigers peaked between 05:00 hours and 10:00 hours showing some overlap with human activity. Territory boundaries were demarcated by large channels which tigers intensively patrolled. Extra caution should be taken while fishing or honey collection during early morning in Avicennia-Sonneratia and Phoenix habitat types along wide channels to reduce human-tiger conflict. Considering home-range core areas as exclusive, tiger density was estimated at 4.6 (SE range 3.6 to 6.7) tigers/100 km2 giving a total population of 76 (SE range 59–110) tigers in the Indian Sundarban. Reluctance of tigers to cross wide water channels combined with increasing commercial boat traffic and sea level rise due to climate change pose a real threat of fragmenting the Sundarban tiger population. PMID:27049644

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

PubMed Central

Bandeira e Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-01-01

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. PMID:28455415

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

PubMed

Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-06-07

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Deyu

A systematic route to go beyond the exact exchange plus random phase approximation (RPA) is to include a physical exchange-correlation kernel in the adiabatic-connection fluctuation-dissipation theorem. Previously, [D. Lu, J. Chem. Phys. 140, 18A520 (2014)], we found that non-local kernels with a screening length depending on the local Wigner-Seitz radius, r s(r), suffer an error associated with a spurious long-range repulsion in van der Waals bounded systems, which deteriorates the binding energy curve as compared to RPA. Here, we analyze the source of the error and propose to replace r s(r) by a global, average r s in the kernel.more » Exemplary studies with the Corradini, del Sole, Onida, and Palummo kernel show that while this change does not affect the already outstanding performance in crystalline solids, using an average r s significantly reduces the spurious long-range tail in the exchange-correlation kernel in van der Waals bounded systems. Finally, when this method is combined with further corrections using local dielectric response theory, the binding energy of the Kr dimer is improved three times as compared to RPA.« less

The Dispersal and Persistence of Invasive Marine Species

NASA Astrophysics Data System (ADS)

Glick, E. R.; Pringle, J.

2007-12-01

The spread of invasive marine species is a continuing problem throughout the world, though not entirely understood. Why do some species invade more easily than the rest? How are the range limits of these species set? Recent research (Byers & Pringle 2006, Pringle & Wares 2007) has produced retention criteria that determine whether a coastal species with a benthic adult stage and planktonic larvae can be retained within its range and invade in the direction opposite that of the mean current experienced by the larvae (i.e. upstream). These results however, are only accurate for Gaussian dispersal kernels. For kernels whose kurtosis differs from a Gaussian's, the retention criteria becomes increasingly inaccurate as the mean current increases. Using recent results of Lutscher (2006), we find an improved retention criterion which is much more accurate for non- Gaussian dispersal kernels. The importance of considering non-Gaussian kernels is illustrated for a number of commonly used dispersal kernels, and the relevance of these calculations is illustrated by considering the northward limit of invasion of Hemigrapsus sanguineus, an important invader in the Gulf of Maine.

Oil point and mechanical behaviour of oil palm kernels in linear compression

NASA Astrophysics Data System (ADS)

Kabutey, Abraham; Herak, David; Choteborsky, Rostislav; Mizera, Čestmír; Sigalingging, Riswanti; Akangbe, Olaosebikan Layi

2017-07-01

The study described the oil point and mechanical properties of roasted and unroasted bulk oil palm kernels under compression loading. The literature information available is very limited. A universal compression testing machine and vessel diameter of 60 mm with a plunger were used by applying maximum force of 100 kN and speed ranging from 5 to 25 mm min-1. The initial pressing height of the bulk kernels was measured at 40 mm. The oil point was determined by a litmus test for each deformation level of 5, 10, 15, 20, and 25 mm at a minimum speed of 5 mmmin-1. The measured parameters were the deformation, deformation energy, oil yield, oil point strain and oil point pressure. Clearly, the roasted bulk kernels required less deformation energy compared to the unroasted kernels for recovering the kernel oil. However, both kernels were not permanently deformed. The average oil point strain was determined at 0.57. The study is an essential contribution to pursuing innovative methods for processing palm kernel oil in rural areas of developing countries.

Influence of wheat kernel physical properties on the pulverizing process.

PubMed

Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

2014-10-01

The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

Space and habitat use by black bears in the Elwha valley prior to dam removal

USGS Publications Warehouse

Sager-Fradkin, K.A.; Jenkins, K.J.; Happe, P.J.; Beecham, J.J.; Wright, R.G.; Hoffman, R.A.

2008-01-01

Dam removal and subsequent restoration of salmon to the Elwha River is expected to cause a shift in nutrient dynamics within the watershed. To document how this influx of nutrients and energy may affect black bear (Ursus americanus) ecology, we used radio-telemetry to record movements of 11 male and two female black bears in the Elwha Valley from 2002-06. Our objective was to collect baseline data on bear movements prior to dam removal. We calculated annual home ranges, described seasonal timing of den entry and emergence, and described seasonal patterns of distribution and habitat use. Adaptive kernel home ranges were larger formales (mean = 151.1 km2, SE = 21.4) than females (mean = 38.8 km2, SE = 13.0). Males ranged widely and frequently left the watershed during late summer. Further, they exhibited predictable and synchronous patterns of elevation change throughout each year. Bears entered their winter dens between 8 October and 15 December and emerged from dens between 10 March and 9 May. Male bears used low-elevation conifer and hardwood forests along the Elwha floodplain during spring, mid- to high-elevation forests and meadows during early summer, high-elevation forests, meadows and shrubs during late summer, and mid-elevation forests, shrubs and meadows during fall. Data acquired during this study provide important baseline information for comparison after dam removal, when bears may alter their late summer and fall movement and denning patterns to take advantage of energy-rich spawning salmon.

The effect of microwave roasting on bioactive compounds, antioxidant activity and fatty acid composition of apricot kernel and oils.

PubMed

Al Juhaimi, Fahad; Musa Özcan, Mehmet; Ghafoor, Kashif; Babiker, Elfadıl E

2018-03-15

In this study, the effect of microwave (360W, 540W and 720W) oven roasting on oil yields, phenolic compounds, antioxidant activity, and fatty acid composition of some apricot kernel and oils was investigated. While total phenol contents of control group of apricot kernels change between 54.41mgGAE/100g (Soğancıoğlu) and 59.61mgGAE/100g (Hasanbey), total phenol contents of kernel samples roasted in 720W were determined between 27.41mgGAE/100g (Çataloğlu) and 34.52mgGAE/100g (Soğancıoğlu). Roasting process in microwave at 720W caused the reduction of some phenolic compounds of apricot kernels. The gallic acid contents of control apricot kernels ranged between 7.23mg/100g (Kabaaşı) and 11.23mg/100g (Çataloğlu) whereas the gallic acid contents of kernels roasted in 540W changed between 15.35mg/100g (Soğancıoğlu) and 21.17mg/100g (Çataloğlu). In addition, oleic acid contents of control group oils vary between 65.98% (Soğancıoğlu) and 71.86% (Hasanbey), the same fatty acid ranged from 63.48% (Soğancıoğlu) to 70.36% (Hasanbey). Copyright © 2017 Elsevier Ltd. All rights reserved.

Kernel reconstruction methods for Doppler broadening - Temperature interpolation by linear combination of reference cross sections at optimally chosen temperatures

NASA Astrophysics Data System (ADS)

Ducru, Pablo; Josey, Colin; Dibert, Karia; Sobes, Vladimir; Forget, Benoit; Smith, Kord

2017-04-01

This article establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (Tj). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T0 to a higher temperature T - namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernel of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (Tj). The choice of the L2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (Tj) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [Tmin ,Tmax ]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope 238U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of 238U total cross section over the temperature range [ 300 K , 3000 K ] with only 9 reference temperatures.

Development of low-frequency kernel-function aerodynamics for comparison with time-dependent finite-difference methods

NASA Technical Reports Server (NTRS)

Bland, S. R.

1982-01-01

Finite difference methods for unsteady transonic flow frequency use simplified equations in which certain of the time dependent terms are omitted from the governing equations. Kernel functions are derived for two dimensional subsonic flow, and provide accurate solutions of the linearized potential equation with the same time dependent terms omitted. These solutions make possible a direct evaluation of the finite difference codes for the linear problem. Calculations with two of these low frequency kernel functions verify the accuracy of the LTRAN2 and HYTRAN2 finite difference codes. Comparisons of the low frequency kernel function results with the Possio kernel function solution of the complete linear equations indicate the adequacy of the HYTRAN approximation for frequencies in the range of interest for flutter calculations.

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

PubMed

Makanza, R; Zaman-Allah, M; Cairns, J E; Eyre, J; Burgueño, J; Pacheco, Ángela; Diepenbrock, C; Magorokosho, C; Tarekegne, A; Olsen, M; Prasanna, B M

2018-01-01

Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure. A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

A Kernel-based Lagrangian method for imperfectly-mixed chemical reactions

NASA Astrophysics Data System (ADS)

Schmidt, Michael J.; Pankavich, Stephen; Benson, David A.

2017-05-01

Current Lagrangian (particle-tracking) algorithms used to simulate diffusion-reaction equations must employ a certain number of particles to properly emulate the system dynamics-particularly for imperfectly-mixed systems. The number of particles is tied to the statistics of the initial concentration fields of the system at hand. Systems with shorter-range correlation and/or smaller concentration variance require more particles, potentially limiting the computational feasibility of the method. For the well-known problem of bimolecular reaction, we show that using kernel-based, rather than Dirac delta, particles can significantly reduce the required number of particles. We derive the fixed width of a Gaussian kernel for a given reduced number of particles that analytically eliminates the error between kernel and Dirac solutions at any specified time. We also show how to solve for the fixed kernel size by minimizing the squared differences between solutions over any given time interval. Numerical results show that the width of the kernel should be kept below about 12% of the domain size, and that the analytic equations used to derive kernel width suffer significantly from the neglect of higher-order moments. The simulations with a kernel width given by least squares minimization perform better than those made to match at one specific time. A heuristic time-variable kernel size, based on the previous results, performs on par with the least squares fixed kernel size.

Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

PubMed

Cid, Jaime A; von Davier, Alina A

2015-05-01

Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

Post-release monitoring of Antillean manatees: an assessment of the Brazilian rehabilitation and release programme

USGS Publications Warehouse

Normande, Iran C.; Malhado, Ana C. M.; Reid, James P.; Viana Junior, P.C.; Savaget, P. V. S.; Correia, R. A.; Luna, F. O.; R. J. Ladle,

2016-01-01

Mammalian reintroduction programmes frequently aim to reconnect isolated sub-populations and restore population viability. However, these long-term objectives are rarely evaluated due to the inadequacy of post-release monitoring. Here, we report the results of a unique long term telemetry-based monitoring programme for rehabilitated Antillean manatees (Trichechus manatus manatus) reintroduced into selected sites in northeast Brazil with the aim of reconnecting isolated relict populations. Twenty-one satellite-tagged rehabilitated manatees, 13 males and 8 females, were released into the wild from two sites between November 2008 and June 2013. Individual accumulation curves were plotted and home ranges were calculated through the fixed kernel method using 95% of the utilization distribution. The number and size of the Centres of Activity (COAs) were calculated using 50% of the utilization distribution. Manatees displayed a dichotomous pattern of movement, with individuals either characterized by sedentary habits or by much more extensive movements. Moreover, home range size was not significantly influenced by gender, age at release or release site. COAs were strongly associated with sheltered conditions within reefs and estuaries, and also by the presence of freshwater and feeding sites. Our data confirm that manatee reintroductions in Brazil have the potential to reconnect distant sub-populations. However, pre-release identification of potential long-distance migrants is currently unfeasible, and further analysis would be required to confirm genetic mixing of distant sub-populations.

SU-F-SPS-09: Parallel MC Kernel Calculations for VMAT Plan Improvement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chamberlain, S; Roswell Park Cancer Institute, Buffalo, NY; French, S

Purpose: Adding kernels (small perturbations in leaf positions) to the existing apertures of VMAT control points may improve plan quality. We investigate the calculation of kernel doses using a parallelized Monte Carlo (MC) method. Methods: A clinical prostate VMAT DICOM plan was exported from Eclipse. An arbitrary control point and leaf were chosen, and a modified MLC file was created, corresponding to the leaf position offset by 0.5cm. The additional dose produced by this 0.5 cm × 0.5 cm kernel was calculated using the DOSXYZnrc component module of BEAMnrc. A range of particle history counts were run (varying from 3more » × 10{sup 6} to 3 × 10{sup 7}); each job was split among 1, 10, or 100 parallel processes. A particle count of 3 × 10{sup 6} was established as the lower range because it provided the minimal accuracy level. Results: As expected, an increase in particle counts linearly increases run time. For the lowest particle count, the time varied from 30 hours for the single-processor run, to 0.30 hours for the 100-processor run. Conclusion: Parallel processing of MC calculations in the EGS framework significantly decreases time necessary for each kernel dose calculation. Particle counts lower than 1 × 10{sup 6} have too large of an error to output accurate dose for a Monte Carlo kernel calculation. Future work will investigate increasing the number of parallel processes and optimizing run times for multiple kernel calculations.« less

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

PubMed

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.

PubMed

Jankowska, Marta M; Natarajan, Loki; Godbole, Suneeta; Meseck, Kristin; Sears, Dorothy D; Patterson, Ruth E; Kerr, Jacqueline

2017-07-01

Background: Environmental factors may influence breast cancer; however, most studies have measured environmental exposure in neighborhoods around home residences (static exposure). We hypothesize that tracking environmental exposures over time and space (dynamic exposure) is key to assessing total exposure. This study compares breast cancer survivors' exposure to walkable and recreation-promoting environments using dynamic Global Positioning System (GPS) and static home-based measures of exposure in relation to insulin resistance. Methods: GPS data from 249 breast cancer survivors living in San Diego County were collected for one week along with fasting blood draw. Exposure to recreation spaces and walkability was measured for each woman's home address within an 800 m buffer (static), and using a kernel density weight of GPS tracks (dynamic). Participants' exposure estimates were related to insulin resistance (using the homeostatic model assessment of insulin resistance, HOMA-IR) controlled by age and body mass index (BMI) in linear regression models. Results: The dynamic measurement method resulted in greater variability in built environment exposure values than did the static method. Regression results showed no association between HOMA-IR and home-based, static measures of walkability and recreation area exposure. GPS-based dynamic measures of both walkability and recreation area were significantly associated with lower HOMA-IR ( P < 0.05). Conclusions: Dynamic exposure measurements may provide important evidence for community- and individual-level interventions that can address cancer risk inequities arising from environments wherein breast cancer survivors live and engage. Impact: This is the first study to compare associations of dynamic versus static built environment exposure measures with insulin outcomes in breast cancer survivors. Cancer Epidemiol Biomarkers Prev; 26(7); 1078-84. ©2017 AACR . ©2017 American Association for Cancer Research.

Relationship of source and sink in determining kernel composition of maize

PubMed Central

Seebauer, Juliann R.; Singletary, George W.; Krumpelman, Paulette M.; Ruffo, Matías L.; Below, Frederick E.

2010-01-01

The relative role of the maternal source and the filial sink in controlling the composition of maize (Zea mays L.) kernels is unclear and may be influenced by the genotype and the N supply. The objective of this study was to determine the influence of assimilate supply from the vegetative source and utilization of assimilates by the grain sink on the final composition of maize kernels. Intermated B73×Mo17 recombinant inbred lines (IBM RILs) which displayed contrasting concentrations of endosperm starch were grown in the field with deficient or sufficient N, and the source supply altered by ear truncation (45% reduction) at 15 d after pollination (DAP). The assimilate supply into the kernels was determined at 19 DAP using the agar trap technique, and the final kernel composition was measured. The influence of N supply and kernel ear position on final kernel composition was also determined for a commercial hybrid. Concentrations of kernel protein and starch could be altered by genotype or the N supply, but remained fairly constant along the length of the ear. Ear truncation also produced a range of variation in endosperm starch and protein concentrations. The C/N ratio of the assimilate supply at 19 DAP was directly related to the final kernel composition, with an inverse relationship between the concentrations of starch and protein in the mature endosperm. The accumulation of kernel starch and protein in maize is uniform along the ear, yet adaptable within genotypic limits, suggesting that kernel composition is source limited in maize. PMID:19917600

Problematic projection to the in-sample subspace for a kernelized anomaly detector

DOE PAGES

Theiler, James; Grosklos, Guen

2016-03-07

We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors, we can trace the problem to a projection in feature space, which arises when a pseudoinverse is used on the covariance matrix in that feature space. Here, we show that a regularized variant of KRX overcomes this difficulty and achieves superior performance over a widemore » range of bandwidths.« less

Abiotic stress growth conditions induce different responses in kernel iron concentration across genotypically distinct maize inbred varieties

PubMed Central

Kandianis, Catherine B.; Michenfelder, Abigail S.; Simmons, Susan J.; Grusak, Michael A.; Stapleton, Ann E.

2013-01-01

The improvement of grain nutrient profiles for essential minerals and vitamins through breeding strategies is a target important for agricultural regions where nutrient poor crops like maize contribute a large proportion of the daily caloric intake. Kernel iron concentration in maize exhibits a broad range. However, the magnitude of genotype by environment (GxE) effects on this trait reduces the efficacy and predictability of selection programs, particularly when challenged with abiotic stress such as water and nitrogen limitations. Selection has also been limited by an inverse correlation between kernel iron concentration and the yield component of kernel size in target environments. Using 25 maize inbred lines for which extensive genome sequence data is publicly available, we evaluated the response of kernel iron density and kernel mass to water and nitrogen limitation in a managed field stress experiment using a factorial design. To further understand GxE interactions we used partition analysis to characterize response of kernel iron and weight to abiotic stressors among all genotypes, and observed two patterns: one characterized by higher kernel iron concentrations in control over stress conditions, and another with higher kernel iron concentration under drought and combined stress conditions. Breeding efforts for this nutritional trait could exploit these complementary responses through combinations of favorable allelic variation from these already well-characterized genetic stocks. PMID:24363659

A multispectral sorting device for isolating single wheat kernels with high protein content

USDA-ARS?s Scientific Manuscript database

Automated sorting of single wheat kernels according to protein content was demonstrated using two novel multispectral sorting devices with different spectral ranges; 470-1070 nm (silicone based detector) and 910nm-1550 nm (InGaAs based detector). The multispectral data were acquired by rapidly (~12...

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations.

PubMed

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D; Flint-Garcia, Sherry A

2016-08-09

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. Copyright © 2016 Liu et al.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

PubMed Central

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D.; Flint-Garcia, Sherry A.

2016-01-01

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. PMID:27317774

Dielectric properties of almond kernels associated with radio frequency and microwave pasteurization

NASA Astrophysics Data System (ADS)

Li, Rui; Zhang, Shuang; Kou, Xiaoxi; Ling, Bo; Wang, Shaojin

2017-02-01

To develop advanced pasteurization treatments based on radio frequency (RF) or microwave (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-line probe and impedance analyzer at frequencies between 10 and 3000 MHz, moisture contents between 4.2% to 19.6% w.b. and temperatures between 20 and 90 °C. The results showed that both dielectric constant and loss factor of the almond kernels decreased sharply with increasing frequency over the RF range (10-300 MHz), but gradually over the measured MW range (300-3000 MHz). Both dielectric constant and loss factor of almond kernels increased with increasing temperature and moisture content, and largely enhanced at higher temperature and moisture levels. Quadratic polynomial equations were developed to best fit the relationship between dielectric constant or loss factor at 27, 40, 915 or 2450 MHz and sample temperature/moisture content with R2 greater than 0.967. Penetration depth of electromagnetic wave into samples decreased with increasing frequency (27-2450 MHz), moisture content (4.2-19.6% w.b.) and temperature (20-90 °C). The temperature profiles of RF heated almond kernels under three moisture levels were made using experiment and computer simulation based on measured dielectric properties. Based on the result of this study, RF treatment has potential to be practically used for pasteurization of almond kernels with acceptable heating uniformity.

Dielectric properties of almond kernels associated with radio frequency and microwave pasteurization.

PubMed

Li, Rui; Zhang, Shuang; Kou, Xiaoxi; Ling, Bo; Wang, Shaojin

2017-02-10

To develop advanced pasteurization treatments based on radio frequency (RF) or microwave (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-line probe and impedance analyzer at frequencies between 10 and 3000 MHz, moisture contents between 4.2% to 19.6% w.b. and temperatures between 20 and 90 °C. The results showed that both dielectric constant and loss factor of the almond kernels decreased sharply with increasing frequency over the RF range (10-300 MHz), but gradually over the measured MW range (300-3000 MHz). Both dielectric constant and loss factor of almond kernels increased with increasing temperature and moisture content, and largely enhanced at higher temperature and moisture levels. Quadratic polynomial equations were developed to best fit the relationship between dielectric constant or loss factor at 27, 40, 915 or 2450 MHz and sample temperature/moisture content with R 2 greater than 0.967. Penetration depth of electromagnetic wave into samples decreased with increasing frequency (27-2450 MHz), moisture content (4.2-19.6% w.b.) and temperature (20-90 °C). The temperature profiles of RF heated almond kernels under three moisture levels were made using experiment and computer simulation based on measured dielectric properties. Based on the result of this study, RF treatment has potential to be practically used for pasteurization of almond kernels with acceptable heating uniformity.

Seasonal movement, residency, and migratory patterns of Wilson's Snipe (Gallinago delicata)

USGS Publications Warehouse

Cline, Brittany B.; Haig, Susan M.

2011-01-01

Cross-seasonal studies of avian movement establish links between geographically distinct wintering, breeding, and migratory stopover locations, or assess site fidelity and movement between distinct phases of the annual cycle. Far fewer studies have investigated individual movement patterns within and among seasons over an annual cycle. Within western Oregon's Willamette Valley throughout 2007, we quantified intra- and interseasonal movement patterns, fidelity (regional and local), and migratory patterns of 37 radiomarked Wilson's Snipe (Gallinago delicata) to elucidate residency in a region of breeding- and wintering-range overlap. Telemetry revealed complex regional population structure, including winter residents (74%), winter transients (14%), summer residents (9%), and one year-round resident breeder (3%). Results indicated a lack of connectivity between winter and summer capture populations, some evidence of partial migration, and between-season fidelity to the region (winter-resident return; subsequent fall). Across seasons, the extent of movements and use of multiple wetland sites suggested that Wilson's Snipe were capable of exploratory movements but more regularly perceived local and fine-scale segments of the landscape as connected. Movements differed significantly by season and residency; individuals exhibited contracted movements during late winter and more expansive movements during precipitation-limited periods (late spring, summer, fall). Mean home-range size was 3.5 ± 0.93 km2 (100% minimum convex polygon [MCP]) and 1.6 ± 0.42 km2 (95% fixed kernel) and did not vary by sex; however, home range varied markedly by season (range of 100% MCPs: 1.04–7.56 km2). The results highlight the need to consider seasonal and interspecific differences in shorebird life histories and space-use requirements when developing regional wetland conservation plans.

Roaming behaviour and home range estimation of domestic dogs in Aboriginal and Torres Strait Islander communities in northern Australia using four different methods.

PubMed

Dürr, Salome; Ward, Michael P

2014-11-15

Disease transmission parameters are the core of epidemic models, but are difficult to estimate, especially in the absence of outbreak data. Investigation of the roaming behaviour, home range (HR) and utilization distribution (UD) can provide the foundation for such parameter estimation in free-ranging animals. The objectives of this study were to estimate HR and UD of 69 domestic dogs in six Aboriginal and Torres Strait Islander communities in northern Australia and to compare four different methods (the minimum convex polygon, MCP; the location-based kernel density estimation, LKDE; the biased random bridge, BRB; and Time Local Convex Hull, T-LoCoH) for investigation of UD and estimating HR sizes. Global positioning system (GPS) collars were attached to community dogs for a period of 1-3 days and positions (fixes) were recorded every minute. Median core HRs (50% isopleth) of the 69 dogs were estimated to range from 0.2 to 0.4 ha and the more extended HR (95% isopleth) to range from 2.5 to 5.3 ha, depending on the method used. The HR and UD shapes were found to be generally circular around the dog owner's house. However, some individuals were found to roam much more with a HR size of 40-104 ha and cover large areas of their community or occasionally beyond. These far roaming dogs are of particular interest for infectious disease transmission. Occasionally, dogs were taken between communities and out of communities for hunting, which enables the contact of dogs between communities and with wildlife (such as dingoes). The BRB and T-LoCoH are the only two methods applied here which integrate the consecutiveness of GPS locations into the analysis, a substantial advantage. The recently developed BRB method produced significantly larger HR estimates than the other two methods; however, the variability of HR sizes was lower compared to the other methods. Advantages of the BRB method include a more realistic analytical approach (kernel density estimation based on movements rather than on locations), possibilities to deal with irregular time periods between consecutive GPS fixes and parameter specification which respects the characteristics of the GPS unit used to collect the data. The BRB method was therefore the most suitable method for UD estimation in this dataset. The results of this study can further be used to contact rates between the dogs within and between communities, a foundation for estimating transmission parameters for canine infectious disease models, such as a rabies spread model in Australia. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Size and spatial distribution of stray dog population in the University of São Paulo campus, Brazil.

PubMed

Dias, Ricardo Augusto; Guilloux, Aline Gil Alves; Borba, Mauro Riegert; Guarnieri, Maria Cristina de Lourdes; Prist, Ricardo; Ferreira, Fernando; Amaku, Marcos; Neto, José Soares Ferreira; Stevenson, Mark

2013-06-01

A longitudinal study was carried out to describe the size and spatial distribution of the stray dog population in the University of São Paulo campus, Brazil from November 2010 to November 2011. The campus is located within the urban area of São Paulo, the largest city of Brazil, with a population over 11 million. The 4.2 km(2) that comprise the university grounds are walled, with 10 access gates, allowing stray dogs to move in and out freely. Over 100,000 people and 50,000 vehicles circulate in the campus daily. Five observations were made during the study period, using a mark-resight method. The same route was performed in all observations, being traveled twice on each observation day. Observed animals were photographed and the sight coordinates were obtained using a GPS device. The estimated size of the stray dog population varied from 32 (CI 95% 23-56) to 56 (CI 95% 45-77) individuals. Differences between in- and outward dog movements influenced dog population estimates. Overlapping home ranges of docile dogs were observed in areas where most people circulate. An elusive group was observed close to a protected rain forest area and the estimated home range for this group did not overlap with the home ranges for other dogs within the campus. A kernel density map showed that higher densities of stray dog sighting is associated with large organic matter generators, such as university restaurants. We conclude that the preferred source of food of the stray dogs on the University of São Paulo campus was leftover food deliberately offered by restaurant users. The population was stable during the study period and the constant source of food was the main reason to retain this population within the campus. Copyright © 2012 Elsevier B.V. All rights reserved.

Evaluation of Argos Telemetry Accuracy in the High-Arctic and Implications for the Estimation of Home-Range Size

PubMed Central

Christin, Sylvain; St-Laurent, Martin-Hugues; Berteaux, Dominique

2015-01-01

Animal tracking through Argos satellite telemetry has enormous potential to test hypotheses in animal behavior, evolutionary ecology, or conservation biology. Yet the applicability of this technique cannot be fully assessed because no clear picture exists as to the conditions influencing the accuracy of Argos locations. Latitude, type of environment, and transmitter movement are among the main candidate factors affecting accuracy. A posteriori data filtering can remove “bad” locations, but again testing is still needed to refine filters. First, we evaluate experimentally the accuracy of Argos locations in a polar terrestrial environment (Nunavut, Canada), with both static and mobile transmitters transported by humans and coupled to GPS transmitters. We report static errors among the lowest published. However, the 68th error percentiles of mobile transmitters were 1.7 to 3.8 times greater than those of static transmitters. Second, we test how different filtering methods influence the quality of Argos location datasets. Accuracy of location datasets was best improved when filtering in locations of the best classes (LC3 and 2), while the Douglas Argos filter and a homemade speed filter yielded similar performance while retaining more locations. All filters effectively reduced the 68th error percentiles. Finally, we assess how location error impacted, at six spatial scales, two common estimators of home-range size (a proxy of animal space use behavior synthetizing movements), the minimum convex polygon and the fixed kernel estimator. Location error led to a sometimes dramatic overestimation of home-range size, especially at very local scales. We conclude that Argos telemetry is appropriate to study medium-size terrestrial animals in polar environments, but recommend that location errors are always measured and evaluated against research hypotheses, and that data are always filtered before analysis. How movement speed of transmitters affects location error needs additional research. PMID:26545245

Tocochromanols composition in kernels recovered from different apricot varieties: RP-HPLC/FLD and RP-UPLC-ESI/MS(n) study.

PubMed

Górnaś, Paweł; Mišina, Inga; Grāvīte, Ilze; Soliven, Arianne; Kaufmane, Edīte; Segliņa, Dalija

2015-01-01

Composition of tocochromanols in kernels recovered from 16 different apricot varieties (Prunus armeniaca L.) was studied. Three tocopherol (T) homologues, namely α, γ and δ, were quantified in all tested samples by an RP-HPLC/FLD method. The γ-T was the main tocopherol homologue identified in apricot kernels and constituted approximately 93% of total detected tocopherols. The RP-UPLC-ESI/MS(n) method detected trace amounts of two tocotrienol homologues α and γ in the apricot kernels. The concentration of individual tocopherol homologues in kernels of different apricots varieties, expressed in mg/100 g dwb, was in the following range: 1.38-4.41 (α-T), 42.48-73.27 (γ-T) and 0.77-2.09 (δ-T). Moreover, the ratio between individual tocopherol homologues α:γ:δ was nearly constant in all varieties and amounted to approximately 2:39:1.

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

PubMed

Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D

2010-05-01

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to 0.91 when a threshold of either 20 or 100 ng g(-1) was used. Overall, the results indicate that fluorescence hyperspectral imaging may be applicable in estimating aflatoxin content in individual corn kernels.

Jdpd: an open java simulation kernel for molecular fragment dissipative particle dynamics.

PubMed

van den Broek, Karina; Kuhn, Hubert; Zielesny, Achim

2018-05-21

Jdpd is an open Java simulation kernel for Molecular Fragment Dissipative Particle Dynamics with parallelizable force calculation, efficient caching options and fast property calculations. It is characterized by an interface and factory-pattern driven design for simple code changes and may help to avoid problems of polyglot programming. Detailed input/output communication, parallelization and process control as well as internal logging capabilities for debugging purposes are supported. The new kernel may be utilized in different simulation environments ranging from flexible scripting solutions up to fully integrated "all-in-one" simulation systems.

Evaluation of a scattering correction method for high energy tomography

NASA Astrophysics Data System (ADS)

Tisseur, David; Bhatia, Navnina; Estre, Nicolas; Berge, Léonie; Eck, Daniel; Payan, Emmanuel

2018-01-01

One of the main drawbacks of Cone Beam Computed Tomography (CBCT) is the contribution of the scattered photons due to the object and the detector. Scattered photons are deflected from their original path after their interaction with the object. This additional contribution of the scattered photons results in increased measured intensities, since the scattered intensity simply adds to the transmitted intensity. This effect is seen as an overestimation in the measured intensity thus corresponding to an underestimation of absorption. This results in artifacts like cupping, shading, streaks etc. on the reconstructed images. Moreover, the scattered radiation provides a bias for the quantitative tomography reconstruction (for example atomic number and volumic mass measurement with dual-energy technique). The effect can be significant and difficult in the range of MeV energy using large objects due to higher Scatter to Primary Ratio (SPR). Additionally, the incident high energy photons which are scattered by the Compton effect are more forward directed and hence more likely to reach the detector. Moreover, for MeV energy range, the contribution of the photons produced by pair production and Bremsstrahlung process also becomes important. We propose an evaluation of a scattering correction technique based on the method named Scatter Kernel Superposition (SKS). The algorithm uses a continuously thickness-adapted kernels method. The analytical parameterizations of the scatter kernels are derived in terms of material thickness, to form continuously thickness-adapted kernel maps in order to correct the projections. This approach has proved to be efficient in producing better sampling of the kernels with respect to the object thickness. This technique offers applicability over a wide range of imaging conditions and gives users an additional advantage. Moreover, since no extra hardware is required by this approach, it forms a major advantage especially in those cases where experimental complexities must be avoided. This approach has been previously tested successfully in the energy range of 100 keV - 6 MeV. In this paper, the kernels are simulated using MCNP in order to take into account both photons and electronic processes in scattering radiation contribution. We present scatter correction results on a large object scanned with a 9 MeV linear accelerator.

Discontinuous functional for linear-response time-dependent density-functional theory: The exact-exchange kernel and approximate forms

NASA Astrophysics Data System (ADS)

Hellgren, Maria; Gross, E. K. U.

2013-11-01

We present a detailed study of the exact-exchange (EXX) kernel of time-dependent density-functional theory with an emphasis on its discontinuity at integer particle numbers. It was recently found that this exact property leads to sharp peaks and step features in the kernel that diverge in the dissociation limit of diatomic systems [Hellgren and Gross, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.85.022514 85, 022514 (2012)]. To further analyze the discontinuity of the kernel, we here make use of two different approximations to the EXX kernel: the Petersilka Gossmann Gross (PGG) approximation and a common energy denominator approximation (CEDA). It is demonstrated that whereas the PGG approximation neglects the discontinuity, the CEDA includes it explicitly. By studying model molecular systems it is shown that the so-called field-counteracting effect in the density-functional description of molecular chains can be viewed in terms of the discontinuity of the static kernel. The role of the frequency dependence is also investigated, highlighting its importance for long-range charge-transfer excitations as well as inner-shell excitations.

Home range, habitat use, and migrations of hawksbill turtles tracked from Dry Tortugas National Park, Florida, USA

USGS Publications Warehouse

Hart, Kristen M.; Sartain-Iverson, Autumn R.; Fujisaki, Ikuko; Pratt, Harold L.; Morley, Danielle; Feeley, Michael W.

2012-01-01

To determine habitat-use patterns of sub-adult hawksbills Eretmochelys imbricata, we conducted satellite- and acoustic-tracking of 3 turtles captured in August 2008 within Dry Tortugas National Park (DRTO), south Florida, USA, in the Gulf of Mexico; turtles ranged in size from 51.9 to 69.8 cm straight carapace length. After 263, 699, and 655 d of residence in the park, turtles migrated out of the DRTO. Within the park, core-use areas (i.e. 50% kernel density estimates) were 9.2 to 21.5 km2; all 3 turtle core-use areas overlapped in an area 6.1 km2 within a zone of the park with multiple human uses (e.g. fishing, anchoring). Two turtles migrated to Cuba and ceased transmitting after 320 and 687 tracking days; the third turtle migrated toward Key West, Florida, and ceased transmitting after 884 tracking days. The present study highlights previously unknown regional connections for hawksbills, possible turtle-harvest incidents, and fine-scale habitat use of sub-adult hawksbills within a United States National Park.

Kernel reconstruction methods for Doppler broadening — Temperature interpolation by linear combination of reference cross sections at optimally chosen temperatures

DOE PAGES

Ducru, Pablo; Josey, Colin; Dibert, Karia; ...

2017-01-25

This paper establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (T j). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T 0 to a higher temperature T — namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernelmore » of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (T j). The choice of the L 2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (T j) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [T min,T max]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope 238U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of 238U total cross section over the temperature range [300 K,3000 K] with only 9 reference temperatures.« less

Three-Dimensional Sensitivity Kernels of Z/H Amplitude Ratios of Surface and Body Waves

NASA Astrophysics Data System (ADS)

Bao, X.; Shen, Y.

2017-12-01

The ellipticity of Rayleigh wave particle motion, or Z/H amplitude ratio, has received increasing attention in inversion for shallow Earth structures. Previous studies of the Z/H ratio assumed one-dimensional (1D) velocity structures beneath the receiver, ignoring the effects of three-dimensional (3D) heterogeneities on wave amplitudes. This simplification may introduce bias in the resulting models. Here we present 3D sensitivity kernels of the Z/H ratio to Vs, Vp, and density perturbations, based on finite-difference modeling of wave propagation in 3D structures and the scattering-integral method. Our full-wave approach overcomes two main issues in previous studies of Rayleigh wave ellipticity: (1) the finite-frequency effects of wave propagation in 3D Earth structures, and (2) isolation of the fundamental mode Rayleigh waves from Rayleigh wave overtones and converted Love waves. In contrast to the 1D depth sensitivity kernels in previous studies, our 3D sensitivity kernels exhibit patterns that vary with azimuths and distances to the receiver. The laterally-summed 3D sensitivity kernels and 1D depth sensitivity kernels, based on the same homogeneous reference model, are nearly identical with small differences that are attributable to the single period of the 1D kernels and a finite period range of the 3D kernels. We further verify the 3D sensitivity kernels by comparing the predictions from the kernels with the measurements from numerical simulations of wave propagation for models with various small-scale perturbations. We also calculate and verify the amplitude kernels for P waves. This study shows that both Rayleigh and body wave Z/H ratios provide vertical and lateral constraints on the structure near the receiver. With seismic arrays, the 3D kernels afford a powerful tool to use the Z/H ratios to obtain accurate and high-resolution Earth models.

QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize.

PubMed

Huo, Dongao; Ning, Qiang; Shen, Xiaomeng; Liu, Lei; Zhang, Zuxin

2016-01-01

The kernel number is a grain yield component and an important maize breeding goal. Ear length, kernel number per row and ear row number are highly correlated with the kernel number per ear, which eventually determines the ear weight and grain yield. In this study, two sets of F2:3 families developed from two bi-parental crosses sharing one inbred line were used to identify quantitative trait loci (QTL) for four kernel number-related traits: ear length, kernel number per row, ear row number and ear weight. A total of 39 QTLs for the four traits were identified in the two populations. The phenotypic variance explained by a single QTL ranged from 0.4% to 29.5%. Additionally, 14 overlapping QTLs formed 5 QTL clusters on chromosomes 1, 4, 5, 7, and 10. Intriguingly, six QTLs for ear length and kernel number per row overlapped in a region on chromosome 1. This region was designated qEL1.10 and was validated as being simultaneously responsible for ear length, kernel number per row and ear weight in a near isogenic line-derived population, suggesting that qEL1.10 was a pleiotropic QTL with large effects. Furthermore, the performance of hybrids generated by crossing 6 elite inbred lines with two near isogenic lines at qEL1.10 showed the breeding value of qEL1.10 for the improvement of the kernel number and grain yield of maize hybrids. This study provides a basis for further fine mapping, molecular marker-aided breeding and functional studies of kernel number-related traits in maize.

Viscozyme L pretreatment on palm kernels improved the aroma of palm kernel oil after kernel roasting.

PubMed

Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan

2018-05-01

With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Theiler, James; Grosklos, Guen

We examine the properties and performance of kernelized anomaly detectors, with an emphasis on the Mahalanobis-distance-based kernel RX (KRX) algorithm. Although the detector generally performs well for high-bandwidth Gaussian kernels, it exhibits problematic (in some cases, catastrophic) performance for distances that are large compared to the bandwidth. By comparing KRX to two other anomaly detectors, we can trace the problem to a projection in feature space, which arises when a pseudoinverse is used on the covariance matrix in that feature space. Here, we show that a regularized variant of KRX overcomes this difficulty and achieves superior performance over a widemore » range of bandwidths.« less

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Software implementation of the SKIPSM paradigm under PIP

NASA Astrophysics Data System (ADS)

Hack, Ralf; Waltz, Frederick M.; Batchelor, Bruce G.

1997-09-01

SKIPSM (separated-kernel image processing using finite state machines) is a technique for implementing large-kernel binary- morphology operators and many other operations. While earlier papers on SKIPSM concentrated mainly on implementations using pipelined hardware, there is considerable scope for achieving major speed improvements in software systems. Using identical control software, one-pass binary erosion and dilation structuring elements (SEs) ranging from the trivial (3 by 3) to the gigantic (51 by 51, or even larger), are readily available. Processing speed is independent of the size of the SE, making the SKIPSM approach practical for work with very large SEs on ordinary desktop computers. PIP (prolog image processing) is an interactive machine vision prototyping environment developed at the University of Wales Cardiff. It consists of a large number of image processing operators embedded within the standard AI language Prolog. This paper describes the SKIPSM implementation of binary morphology operators within PIP. A large set of binary erosion and dilation operations (circles, squares, diamonds, octagons, etc.) is available to the user through a command-line driven dialogue, via pull-down menus, or incorporated into standard (Prolog) programs. Little has been done thus far to optimize speed on this first software implementation of SKIPSM. Nevertheless, the results are impressive. The paper describes sample applications and presents timing figures. Readers have the opportunity to try out these operations on demonstration software written by the University of Wales, or via their WWW home page at http://bruce.cs.cf.ac.uk/bruce/index.html .

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

NASA Astrophysics Data System (ADS)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

PubMed

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

A new iterative scheme for solving the discrete Smoluchowski equation

NASA Astrophysics Data System (ADS)

Smith, Alastair J.; Wells, Clive G.; Kraft, Markus

2018-01-01

This paper introduces a new iterative scheme for solving the discrete Smoluchowski equation and explores the numerical convergence properties of the method for a range of kernels admitting analytical solutions, in addition to some more physically realistic kernels typically used in kinetics applications. The solver is extended to spatially dependent problems with non-uniform velocities and its performance investigated in detail.

A Non-Local, Energy-Optimized Kernel: Recovering Second-Order Exchange and Beyond in Extended Systems

NASA Astrophysics Data System (ADS)

Bates, Jefferson; Laricchia, Savio; Ruzsinszky, Adrienn

The Random Phase Approximation (RPA) is quickly becoming a standard method beyond semi-local Density Functional Theory that naturally incorporates weak interactions and eliminates self-interaction error. RPA is not perfect, however, and suffers from self-correlation error as well as an incorrect description of short-ranged correlation typically leading to underbinding. To improve upon RPA we introduce a short-ranged, exchange-like kernel that is one-electron self-correlation free for one and two electron systems in the high-density limit. By tuning the one free parameter in our model to recover an exact limit of the homogeneous electron gas correlation energy we obtain a non-local, energy-optimized kernel that reduces the errors of RPA for both homogeneous and inhomogeneous solids. To reduce the computational cost of the standard kernel-corrected RPA, we also implement RPA renormalized perturbation theory for extended systems, and demonstrate its capability to describe the dominant correlation effects with a low-order expansion in both metallic and non-metallic systems. Furthermore we stress that for norm-conserving implementations the accuracy of RPA and beyond RPA structural properties compared to experiment is inherently limited by the choice of pseudopotential. Current affiliation: King's College London.

Assessment of the microbiological safety of edible roasted nut kernels on retail sale in England, with a focus on Salmonella.

PubMed

Little, C L; Jemmott, W; Surman-Lee, S; Hucklesby, L; de Pinnal, E

2009-04-01

There is little published information on the prevalence of Salmonella in edible nut kernels. A study in early 2008 of edible roasted nut kernels on retail sale in England was undertaken to assess the microbiological safety of this product. A total of 727 nut kernel samples of different varieties were examined. Overall, Salmonella and Escherichia coli were detected from 0.2 and 0.4% of edible roasted nut kernels. Of the nut varieties examined, Salmonella Havana was detected from 1 (4.0%) sample of pistachio nuts, indicating a risk to health. The United Kingdom Food Standards Agency was immediately informed, and full investigations were undertaken. Further examination established the contamination to be associated with the pistachio kernels and not the partly opened shells. Salmonella was not detected in other varieties tested (almonds, Brazils, cashews, hazelnuts, macadamia, peanuts, pecans, pine nuts, and walnuts). E. coli was found at low levels (range of 3.6 to 4/g) in walnuts (1.4%), almonds (1.2%), and Brazils (0.5%). The presence of Salmonella is unacceptable in edible nut kernels. Prevention of microbial contamination in these products lies in the application of good agricultural, manufacturing, and storage practices together with a hazard analysis and critical control points system that encompass all stages of production, processing, and distribution.

A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

PubMed

Miller, Nathan D; Haase, Nicholas J; Lee, Jonghyun; Kaeppler, Shawn M; de Leon, Natalia; Spalding, Edgar P

2017-01-01

Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel's major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data. Millimeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a diverse group of inbred maize lines were resolved. This system for measuring maize ear, cob, and kernel attributes is being used by multiple research groups as an automated Web service running on community high-throughput computing and distributed data storage infrastructure. Users may create their own workflow using the source code that is staged for download on a public repository. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Sterols and squalene in apricot (Prunus armeniaca L.) kernel oils: the variety as a key factor.

PubMed

Rudzińska, Magdalena; Górnaś, Paweł; Raczyk, Marianna; Soliven, Arianne

2017-01-01

The profile of sterols and squalene content in oils recovered from the kernels of 15 apricot (Prunus armeniaca L.) varieties were investigated. Nine sterols (campesterol, β-sitosterol, Δ5-avenasterol, 24-methylene-cycloartanol, cholesterol, gramisterol, Δ7-stigmasterol, Δ7-avenasterol and citrostadienol) were identified in apricot kernel oils. The β-sitosterol was the predominant sterol in each cultivar and consisted of 76-86% of the total detected sterols. The content of total sterols and squalene were significantly affected by the variety and ranged between 215.7-973.6 and 12.6-43.9 mg/100 g of oil, respectively.

The effect of relatedness and pack size on territory overlap in African wild dogs.

PubMed

Jackson, Craig R; Groom, Rosemary J; Jordan, Neil R; McNutt, J Weldon

2017-01-01

Spacing patterns mediate competitive interactions between conspecifics, ultimately increasing fitness. The degree of territorial overlap between neighbouring African wild dog ( Lycaon pictus ) packs varies greatly, yet the role of factors potentially affecting the degree of overlap, such as relatedness and pack size, remain unclear. We used movement data from 21 wild dog packs to calculate the extent of territory overlap (20 dyads). On average, unrelated neighbouring packs had low levels of overlap restricted to the peripheral regions of their 95% utilisation kernels. Related neighbours had significantly greater levels of peripheral overlap. Only one unrelated dyad included overlap between 75%-75% kernels, but no 50%-50% kernels overlapped. However, eight of 12 related dyads overlapped between their respective 75% kernels and six between the frequented 50% kernels. Overlap between these more frequented kernels confers a heightened likelihood of encounter, as the mean utilisation intensity per unit area within the 50% kernels was 4.93 times greater than in the 95% kernels, and 2.34 times greater than in the 75% kernels. Related packs spent significantly more time in their 95% kernel overlap zones than did unrelated packs. Pack size appeared to have little effect on overlap between related dyads, yet among unrelated neighbours larger packs tended to overlap more onto smaller packs' territories. However, the true effect is unclear given that the model's confidence intervals overlapped zero. Evidence suggests that costly intraspecific aggression is greatly reduced between related packs. Consequently, the tendency for dispersing individuals to establish territories alongside relatives, where intensively utilised portions of ranges regularly overlap, may extend kin selection and inclusive fitness benefits from the intra-pack to inter-pack level. This natural spacing system can affect survival parameters and the carrying capacity of protected areas, having important management implications for intensively managed populations of this endangered species.

Determination of active ingredients in corn silk, leaf, and kernel by capillary electrophoresis with electrochemicaI detection.

PubMed

Lin, Miao; Chu, Qing-Cui; Tian, Xiu-Hui; Ye, Jian-Nong

2007-01-01

Corn has been known for its accumulation of flavones and phenolic acids. However, many parts of corn, except kernel, have not drawn much attention. In this work, a method based on capillary zone electrophoresis with electrochemical detection has been used for the separation and determination of epicatechin, rutin, ascorbic acid (Vc), kaempferol, chlorogenic acid, and quercetin in corn silk, leaf, and kernel. The distribution comparison of the ingredients among silk, leaf, and kernel is discussed. Several important factors--including running buffer acidity, separation voltage, and working electrode potential--were evaluated to acquire the optimum analysis conditions. Under the optimum conditions, the analytes could be well separated within 19 min in a 40-mmol/L borate buffer (pH 9.2). The response was linear over three orders of magnitude with detection limits (S/N = 3) ranging from 4.97 x 10(-8) to 9.75 x 10(-8) g/mL. The method has been successfully applied for the analysis of corn silk, leaf, and kernel with satisfactory results.

Calculation of plasma dielectric response in inhomogeneous magnetic field near electron cyclotron resonance

NASA Astrophysics Data System (ADS)

Evstatiev, Evstati; Svidzinski, Vladimir; Spencer, Andy; Galkin, Sergei

2014-10-01

Full wave 3-D modeling of RF fields in hot magnetized nonuniform plasma requires calculation of nonlocal conductivity kernel describing the dielectric response of such plasma to the RF field. In many cases, the conductivity kernel is a localized function near the test point which significantly simplifies numerical solution of the full wave 3-D problem. Preliminary results of feasibility analysis of numerical calculation of the conductivity kernel in a 3-D hot nonuniform magnetized plasma in the electron cyclotron frequency range will be reported. This case is relevant to modeling of ECRH in ITER. The kernel is calculated by integrating the linearized Vlasov equation along the unperturbed particle's orbits. Particle's orbits in the nonuniform equilibrium magnetic field are calculated numerically by one of the Runge-Kutta methods. RF electric field is interpolated on a specified grid on which the conductivity kernel is discretized. The resulting integrals in the particle's initial velocity and time are then calculated numerically. Different optimization approaches of the integration are tested in this feasibility analysis. Work is supported by the U.S. DOE SBIR program.

Higher-order kinetic expansion of quantum dissipative dynamics: mapping quantum networks to kinetic networks.

PubMed

Wu, Jianlan; Cao, Jianshu

2013-07-28

We apply a new formalism to derive the higher-order quantum kinetic expansion (QKE) for studying dissipative dynamics in a general quantum network coupled with an arbitrary thermal bath. The dynamics of system population is described by a time-convoluted kinetic equation, where the time-nonlocal rate kernel is systematically expanded of the order of off-diagonal elements of the system Hamiltonian. In the second order, the rate kernel recovers the expression of the noninteracting-blip approximation method. The higher-order corrections in the rate kernel account for the effects of the multi-site quantum coherence and the bath relaxation. In a quantum harmonic bath, the rate kernels of different orders are analytically derived. As demonstrated by four examples, the higher-order QKE can reliably predict quantum dissipative dynamics, comparing well with the hierarchic equation approach. More importantly, the higher-order rate kernels can distinguish and quantify distinct nontrivial quantum coherent effects, such as long-range energy transfer from quantum tunneling and quantum interference arising from the phase accumulation of interactions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Zheming; Yoshii, Kazutomo; Finkel, Hal

Open Computing Language (OpenCL) is a high-level language that enables software programmers to explore Field Programmable Gate Arrays (FPGAs) for application acceleration. The Intel FPGA software development kit (SDK) for OpenCL allows a user to specify applications at a high level and explore the performance of low-level hardware acceleration. In this report, we present the FPGA performance and power consumption results of the single-precision floating-point vector add OpenCL kernel using the Intel FPGA SDK for OpenCL on the Nallatech 385A FPGA board. The board features an Arria 10 FPGA. We evaluate the FPGA implementations using the compute unit duplication andmore » kernel vectorization optimization techniques. On the Nallatech 385A FPGA board, the maximum compute kernel bandwidth we achieve is 25.8 GB/s, approximately 76% of the peak memory bandwidth. The power consumption of the FPGA device when running the kernels ranges from 29W to 42W.« less

Phenolic constituents of shea (Vitellaria paradoxa) kernels.

PubMed

Maranz, Steven; Wiesman, Zeev; Garti, Nissim

2003-10-08

Analysis of the phenolic constituents of shea (Vitellaria paradoxa) kernels by LC-MS revealed eight catechin compounds-gallic acid, catechin, epicatechin, epicatechin gallate, gallocatechin, epigallocatechin, gallocatechin gallate, and epigallocatechin gallate-as well as quercetin and trans-cinnamic acid. The mean kernel content of the eight catechin compounds was 4000 ppm (0.4% of kernel dry weight), with a 2100-9500 ppm range. Comparison of the profiles of the six major catechins from 40 Vitellaria provenances from 10 African countries showed that the relative proportions of these compounds varied from region to region. Gallic acid was the major phenolic compound, comprising an average of 27% of the measured total phenols and exceeding 70% in some populations. Colorimetric analysis (101 samples) of total polyphenols extracted from shea butter into hexane gave an average of 97 ppm, with the values for different provenances varying between 62 and 135 ppm of total polyphenols.

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

PubMed Central

King, Tania L; Bentley, Rebecca J; Thornton, Lukar E; Kavanagh, Anne M

2016-01-01

Objectives Little is known about how the distribution of destinations in the local neighbourhood is related to body mass index (BMI). Kernel density estimation (KDE) is a spatial analysis technique that accounts for the location of features relative to each other. Using KDE, this study investigated whether individuals living near destinations (shops and service facilities) that are more intensely distributed rather than dispersed, have lower BMIs. Study design and setting A cross-sectional study of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Methods Destinations were geocoded, and kernel density estimates of destination intensity were created using kernels of 400, 800 and 1200 m. Using multilevel linear regression, the association between destination intensity (classified in quintiles Q1(least)–Q5(most)) and BMI was estimated in models that adjusted for the following confounders: age, sex, country of birth, education, dominant household occupation, household type, disability/injury and area disadvantage. Separate models included a physical activity variable. Results For kernels of 800 and 1200 m, there was an inverse relationship between BMI and more intensely distributed destinations (compared to areas with least destination intensity). Effects were significant at 1200 m: Q4, β −0.86, 95% CI −1.58 to −0.13, p=0.022; Q5, β −1.03 95% CI −1.65 to −0.41, p=0.001. Inclusion of physical activity in the models attenuated effects, although effects remained marginally significant for Q5 at 1200 m: β −0.77 95% CI −1.52, −0.02, p=0.045. Conclusions This study conducted within urban Melbourne, Australia, found that participants living in areas of greater destination intensity within 1200 m of home had lower BMIs. Effects were partly explained by physical activity. The results suggest that increasing the intensity of destination distribution could reduce BMI levels by encouraging higher levels of physical activity. PMID:26883235

Accuracy of estimating wolf summer territories by daytime locations

USGS Publications Warehouse

Demma, D.J.; Mech, L.D.

2011-01-01

We used locations of 6 wolves (Canis lupus) in Minnesota from Global Positioning System (GPS) collars to compare day-versus-night locations to estimate territory size and location during summer. We employed both minimum convex polygon (MCP) and fixed kernel (FK) methods. We used two methods to partition GPS locations for day-versus-night home-range comparisons: (1) daytime = 0800-2000 Ah; nighttime = 2000-0800 Ah; and (2) sunup versus sundown. Regardless of location-partitioning method, mean area of daytime MCPs did not differ significantly from nighttime MCPs. Similarly, mean area of daytime FKs (95% probability contour) were not significantly different from nightime FKs. FK core use areas (50% probability contour) did not differ between daytime and nighttime nor between sunup and sundown locations. We conclude that in areas similar to our study area day-only locations are adequate for describing the location, extent and core use areas of summer wolf territories by both MCP and FK methods. ?? 2011 American Midland Naturalist.

Accuracy of estimating wolf summer territories by daytime locations

USGS Publications Warehouse

Demma, Dominic J.; Mech, L. David

2011-01-01

We used locations of 6 wolves (Canis lupus) in Minnesota from Global Positioning System (GPS) collars to compare day-versus-night locations to estimate territory size and location during summer. We employed both minimum convex polygon (MCP) and fixed kernel (FK) methods. We used two methods to partition GPS locations for day-versus-night home-range comparisons: (1) daytime = 0800–2000 h; nighttime = 2000–0800 h; and (2) sunup versus sundown. Regardless of location-partitioning method, mean area of daytime MCPs did not differ significantly from nighttime MCPs. Similarly, mean area of daytime FKs (95% probability contour) were not significantly different from nightime FKs. FK core use areas (50% probability contour) did not differ between daytime and nighttime nor between sunup and sundown locations. We conclude that in areas similar to our study area day-only locations are adequate for describing the location, extent and core use areas of summer wolf territories by both MCP and FK methods.

Multi-PSF fusion in image restoration of range-gated systems

NASA Astrophysics Data System (ADS)

Wang, Canjin; Sun, Tao; Wang, Tingfeng; Miao, Xikui; Wang, Rui

2018-07-01

For the task of image restoration, an accurate estimation of degrading PSF/kernel is the premise of recovering a visually superior image. The imaging process of range-gated imaging system in atmosphere associates with lots of factors, such as back scattering, background radiation, diffraction limit and the vibration of the platform. On one hand, due to the difficulty of constructing models for all factors, the kernels from physical-model based methods are not strictly accurate and practical. On the other hand, there are few strong edges in images, which brings significant errors to most of image-feature-based methods. Since different methods focus on different formation factors of the kernel, their results often complement each other. Therefore, we propose an approach which combines physical model with image features. With an fusion strategy using GCRF (Gaussian Conditional Random Fields) framework, we get a final kernel which is closer to the actual one. Aiming at the problem that ground-truth image is difficult to obtain, we then propose a semi data-driven fusion method in which different data sets are used to train fusion parameters. Finally, a semi blind restoration strategy based on EM (Expectation Maximization) and RL (Richardson-Lucy) algorithm is proposed. Our methods not only models how the lasers transfer in the atmosphere and imaging in the ICCD (Intensified CCD) plane, but also quantifies other unknown degraded factors using image-based methods, revealing how multiple kernel elements interact with each other. The experimental results demonstrate that our method achieves better performance than state-of-the-art restoration approaches.

A graphical approach to optimizing variable-kernel smoothing parameters for improved deformable registration of CT and cone beam CT images

NASA Astrophysics Data System (ADS)

Hart, Vern; Burrow, Damon; Li, X. Allen

2017-08-01

A systematic method is presented for determining optimal parameters in variable-kernel deformable image registration of cone beam CT and CT images, in order to improve accuracy and convergence for potential use in online adaptive radiotherapy. Assessed conditions included the noise constant (symmetric force demons), the kernel reduction rate, the kernel reduction percentage, and the kernel adjustment criteria. Four such parameters were tested in conjunction with reductions of 5, 10, 15, 20, 30, and 40%. Noise constants ranged from 1.0 to 1.9 for pelvic images in ten prostate cancer patients. A total of 516 tests were performed and assessed using the structural similarity index. Registration accuracy was plotted as a function of iteration number and a least-squares regression line was calculated, which implied an average improvement of 0.0236% per iteration. This baseline was used to determine if a given set of parameters under- or over-performed. The most accurate parameters within this range were applied to contoured images. The mean Dice similarity coefficient was calculated for bladder, prostate, and rectum with mean values of 98.26%, 97.58%, and 96.73%, respectively; corresponding to improvements of 2.3%, 9.8%, and 1.2% over previously reported values for the same organ contours. This graphical approach to registration analysis could aid in determining optimal parameters for Demons-based algorithms. It also establishes expectation values for convergence rates and could serve as an indicator of non-physical warping, which often occurred in cases  >0.6% from the regression line.

Front propagation and clustering in the stochastic nonlocal Fisher equation

NASA Astrophysics Data System (ADS)

Ganan, Yehuda A.; Kessler, David A.

2018-04-01

In this work, we study the problem of front propagation and pattern formation in the stochastic nonlocal Fisher equation. We find a crossover between two regimes: a steadily propagating regime for not too large interaction range and a stochastic punctuated spreading regime for larger ranges. We show that the former regime is well described by the heuristic approximation of the system by a deterministic system where the linear growth term is cut off below some critical density. This deterministic system is seen not only to give the right front velocity, but also predicts the onset of clustering for interaction kernels which give rise to stable uniform states, such as the Gaussian kernel, for sufficiently large cutoff. Above the critical cutoff, distinct clusters emerge behind the front. These same features are present in the stochastic model for sufficiently small carrying capacity. In the latter, punctuated spreading, regime, the population is concentrated on clusters, as in the infinite range case, which divide and separate as a result of the stochastic noise. Due to the finite interaction range, if a fragment at the edge of the population separates sufficiently far, it stabilizes as a new cluster, and the processes begins anew. The deterministic cutoff model does not have this spreading for large interaction ranges, attesting to its purely stochastic origins. We show that this mode of spreading has an exponentially small mean spreading velocity, decaying with the range of the interaction kernel.

Front propagation and clustering in the stochastic nonlocal Fisher equation.

PubMed

Ganan, Yehuda A; Kessler, David A

2018-04-01

In this work, we study the problem of front propagation and pattern formation in the stochastic nonlocal Fisher equation. We find a crossover between two regimes: a steadily propagating regime for not too large interaction range and a stochastic punctuated spreading regime for larger ranges. We show that the former regime is well described by the heuristic approximation of the system by a deterministic system where the linear growth term is cut off below some critical density. This deterministic system is seen not only to give the right front velocity, but also predicts the onset of clustering for interaction kernels which give rise to stable uniform states, such as the Gaussian kernel, for sufficiently large cutoff. Above the critical cutoff, distinct clusters emerge behind the front. These same features are present in the stochastic model for sufficiently small carrying capacity. In the latter, punctuated spreading, regime, the population is concentrated on clusters, as in the infinite range case, which divide and separate as a result of the stochastic noise. Due to the finite interaction range, if a fragment at the edge of the population separates sufficiently far, it stabilizes as a new cluster, and the processes begins anew. The deterministic cutoff model does not have this spreading for large interaction ranges, attesting to its purely stochastic origins. We show that this mode of spreading has an exponentially small mean spreading velocity, decaying with the range of the interaction kernel.

The effect of CT technical factors on quantification of lung fissure integrity

NASA Astrophysics Data System (ADS)

Chong, D.; Brown, M. S.; Ochs, R.; Abtin, F.; Brown, M.; Ordookhani, A.; Shaw, G.; Kim, H. J.; Gjertson, D.; Goldin, J. G.

2009-02-01

A new emphysema treatment uses endobronchial valves to perform lobar volume reduction. The degree of fissure completeness may predict treatment efficacy. This study investigated the behavior of a semiautomated algorithm for quantifying lung fissure integrity in CT with respect to reconstruction kernel and dose. Raw CT data was obtained for six asymptomatic patients from a high-risk population for lung cancer. The patients were scanned on either a Siemens Sensation 16 or 64, using a low-dose protocol of 120 kVp, 25 mAs. Images were reconstructed using kernels ranging from smooth to sharp (B10f, B30f, B50f, B70f). Research software was used to simulate an even lower-dose acquisition of 15 mAs, and images were generated at the same kernels resulting in 8 series per patient. The left major fissure was manually contoured axially at regular intervals, yielding 37 contours across all patients. These contours were read into an image analysis and pattern classification system which computed a Fissure Integrity Score (FIS) for each kernel and dose. FIS values were analyzed using a mixed-effects model with kernel and dose as fixed effects and patient as random effect to test for difference due to kernel and dose. Analysis revealed no difference in FIS between the smooth kernels (B10f, B30f) nor between sharp kernels (B50f, B70f), but there was a significant difference between the sharp and smooth groups (p = 0.020). There was no significant difference in FIS between the two low-dose reconstructions (p = 0.882). Using a cutoff of 90%, the number of incomplete fissures increased from 5 to 10 when the imaging protocol changed from B50f to B30f. Reconstruction kernel has a significant effect on quantification of fissure integrity in CT. This has potential implications when selecting patients for endobronchial valve therapy.

Fatty acids and bioactive compounds of the pulps and kernels of Brazilian palm species, guariroba (Syagrus oleraces), jerivá (Syagrus romanzoffiana) and macaúba (Acrocomia aculeata).

PubMed

Coimbra, Michelle C; Jorge, Neuza

2012-02-01

Bioactive compounds are capable of providing health benefits, reducing disease incidence or favoring body functioning. There is a growing search for vegetable oils containing such compounds. This study aimed to characterize the pulp and kernel oils of the Brazilian palm species guariroba (Syagrus oleracea), jerivá (Syagrus romanzoffiana) and macaúba (Acrocomia aculeata), aiming at possible uses in several industries. Fatty acid composition, phenolic and carotenoid contents, tocopherol composition were evaluated. The majority of the fatty acids in pulps were oleic and linoleic; macaúba pulp contained 526 g kg⁻¹ of oleic acid. Lauric acid was detected in the kernels of all three species as the major saturated fatty acid, in amounts ranging from 325.8 to 424.3 g kg⁻¹. The jerivá pulp contained carotenoids and tocopherols on average of 1219 µg g⁻¹ and 323.50 mg kg⁻¹, respectively. The pulps contained more unsaturated fatty acids than the kernels, mainly oleic and linoleic. Moreover, the pulps showed higher carotenoid and tocopherol contents. The kernels showed a predominance of saturated fatty acids, especially lauric acid. The fatty acid profiles of the kernels suggest that these oils may be better suited for the cosmetic and pharmaceutical industries than for use in foods. Copyright © 2011 Society of Chemical Industry.

Kernel-based whole-genome prediction of complex traits: a review.

PubMed

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, Rodney Dale; Johnson, Jared A.; Collins, Jack Lee

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC 2), which is UC 1.86, with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UCmore » 2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90–92% of TD with full conversion of UC to UC 2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC 2. Lastly, the selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.« less

Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

PubMed

Poon, Art F Y

2015-09-01

The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

NASA Astrophysics Data System (ADS)

Hunt, R. D.; Johnson, J. A.; Collins, J. L.; McMurray, J. W.; Reif, T. J.; Brown, D. R.

2018-01-01

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC2), which is UC1.86, with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UC2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90-92% of TD with full conversion of UC to UC2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC2. The selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

DOE PAGES

Hunt, Rodney Dale; Johnson, Jared A.; Collins, Jack Lee; ...

2017-10-12

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC 2), which is UC 1.86, with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UCmore » 2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90–92% of TD with full conversion of UC to UC 2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC 2. Lastly, the selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.« less

Error and Complexity Analysis for a Collocation-Grid-Projection Plus Precorrected-FFT Algorithm for Solving Potential Integral Equations with LaPlace or Helmholtz Kernels

NASA Technical Reports Server (NTRS)

Phillips, J. R.

1996-01-01

In this paper we derive error bounds for a collocation-grid-projection scheme tuned for use in multilevel methods for solving boundary-element discretizations of potential integral equations. The grid-projection scheme is then combined with a precorrected FFT style multilevel method for solving potential integral equations with 1/r and e(sup ikr)/r kernels. A complexity analysis of this combined method is given to show that for homogeneous problems, the method is order n natural log n nearly independent of the kernel. In addition, it is shown analytically and experimentally that for an inhomogeneity generated by a very finely discretized surface, the combined method slows to order n(sup 4/3). Finally, examples are given to show that the collocation-based grid-projection plus precorrected-FFT scheme is competitive with fast-multipole algorithms when considering realistic problems and 1/r kernels, but can be used over a range of spatial frequencies with only a small performance penalty.

Evolution of phenotypic clusters through competition and local adaptation along an environmental gradient.

PubMed

Leimar, Olof; Doebeli, Michael; Dieckmann, Ulf

2008-04-01

We have analyzed the evolution of a quantitative trait in populations that are spatially extended along an environmental gradient, with gene flow between nearby locations. In the absence of competition, there is stabilizing selection toward a locally best-adapted trait that changes gradually along the gradient. According to traditional ideas, gradual spatial variation in environmental conditions is expected to lead to gradual variation in the evolved trait. A contrasting possibility is that the trait distribution instead breaks up into discrete clusters. Doebeli and Dieckmann (2003) argued that competition acting locally in trait space and geographical space can promote such clustering. We have investigated this possibility using deterministic population dynamics for asexual populations, analyzing our model numerically and through an analytical approximation. We examined how the evolution of clusters is affected by the shape of competition kernels, by the presence of Allee effects, and by the strength of gene flow along the gradient. For certain parameter ranges clustering was a robust outcome, and for other ranges there was no clustering. Our analysis shows that the shape of competition kernels is important for clustering: the sign structure of the Fourier transform of a competition kernel determines whether the kernel promotes clustering. Also, we found that Allee effects promote clustering, whereas gene flow can have a counteracting influence. In line with earlier findings, we could demonstrate that phenotypic clustering was favored by gradients of intermediate slope.

Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals.

PubMed

Fox, Glen; Manley, Marena

2014-01-30

Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain. © 2013 Society of Chemical Industry.

Conservation of reef manta rays (Manta alfredi) in a UNESCO World Heritage Site: Large-scale island development or sustainable tourism?

PubMed Central

Elamin, Nasreldin Alhasan; Yurkowski, David James; Chekchak, Tarik; Walter, Ryan Patrick; Klaus, Rebecca; Hill, Graham; Hussey, Nigel Edward

2017-01-01

A large reef manta ray (Manta alfredi) aggregation has been observed off the north Sudanese Red Sea coast since the 1950s. Sightings have been predominantly within the boundaries of a marine protected area (MPA), which was designated a UNESCO World Heritage Site in July 2016. Contrasting economic development trajectories have been proposed for the area (small-scale ecotourism and large-scale island development). To examine space-use, Wildlife Computers® SPOT 5 tags were secured to three manta rays. A two-state switching Bayesian state space model (BSSM), that allowed movement parameters to switch between resident and travelling, was fit to the recorded locations, and 50% and 95% kernel utilization distributions (KUD) home ranges calculated. A total of 682 BSSM locations were recorded between 30 October 2012 and 6 November 2013. Of these, 98.5% fell within the MPA boundaries; 99.5% for manta 1, 91.5% for manta 2, and 100% for manta 3. The BSSM identified that all three mantas were resident during 99% of transmissions, with 50% and 95% KUD home ranges falling mainly within the MPA boundaries. For all three mantas combined (88.4%), and all individuals (manta 1–92.4%, manta 2–64.9%, manta 3–91.9%), the majority of locations occurred within 15 km of the proposed large-scale island development. Results indicated that the MPA boundaries are spatially appropriate for manta rays in the region, however, a close association to the proposed large-scale development highlights the potential threat of disruption. Conversely, the focused nature of spatial use highlights the potential for reliable ecotourism opportunities. PMID:29069079

Conservation of reef manta rays (Manta alfredi) in a UNESCO World Heritage Site: Large-scale island development or sustainable tourism?

PubMed

Kessel, Steven Thomas; Elamin, Nasreldin Alhasan; Yurkowski, David James; Chekchak, Tarik; Walter, Ryan Patrick; Klaus, Rebecca; Hill, Graham; Hussey, Nigel Edward

2017-01-01

A large reef manta ray (Manta alfredi) aggregation has been observed off the north Sudanese Red Sea coast since the 1950s. Sightings have been predominantly within the boundaries of a marine protected area (MPA), which was designated a UNESCO World Heritage Site in July 2016. Contrasting economic development trajectories have been proposed for the area (small-scale ecotourism and large-scale island development). To examine space-use, Wildlife Computers® SPOT 5 tags were secured to three manta rays. A two-state switching Bayesian state space model (BSSM), that allowed movement parameters to switch between resident and travelling, was fit to the recorded locations, and 50% and 95% kernel utilization distributions (KUD) home ranges calculated. A total of 682 BSSM locations were recorded between 30 October 2012 and 6 November 2013. Of these, 98.5% fell within the MPA boundaries; 99.5% for manta 1, 91.5% for manta 2, and 100% for manta 3. The BSSM identified that all three mantas were resident during 99% of transmissions, with 50% and 95% KUD home ranges falling mainly within the MPA boundaries. For all three mantas combined (88.4%), and all individuals (manta 1-92.4%, manta 2-64.9%, manta 3-91.9%), the majority of locations occurred within 15 km of the proposed large-scale island development. Results indicated that the MPA boundaries are spatially appropriate for manta rays in the region, however, a close association to the proposed large-scale development highlights the potential threat of disruption. Conversely, the focused nature of spatial use highlights the potential for reliable ecotourism opportunities.

Wintering ecology of sympatric subspecies of Sandhill Crane: Correlations between body size, site fidelity, and movement patterns

USGS Publications Warehouse

Ivey, Gary L.; Dugger, Bruce D.; Herziger, Caroline P.; Casazza, Michael L.; Fleskes, Joseph P.

2015-01-01

Body size is known to correlate with many aspects of life history in birds, and this knowledge can be used to manage and conserve bird species. However, few studies have compared the wintering ecology of sympatric subspecies that vary significantly in body size. We used radiotelemetry to examine the relationship between body size and site fidelity, movements, and home range in 2 subspecies of Sandhill Crane (Grus canadensis) wintering in the Sacramento–San Joaquin Delta of California, USA. Both subspecies showed high interannual return rates to the Delta study area, but Greater Sandhill Cranes (G. c. tabida) showed stronger within-winter fidelity to landscapes in our study region and to roost complexes within landscapes than did Lesser Sandhill Cranes (G. c. canadensis). Foraging flights from roost sites were shorter for G. c. tabida than for G. c. canadensis (1.9 ± 0.01 km vs. 4.5 ± 0.01 km, respectively) and, consequently, the mean size of 95% fixed-kernel winter home ranges was an order of magnitude smaller for G. c. tabida than for G. c. canadensis (1.9 ± 0.4 km2 vs. 21.9 ± 1.9 km2, respectively). Strong site fidelity indicates that conservation planning to manage for adequate food resources around traditional roost sites can be effective for meeting the habitat needs of these cranes, but the scale of conservation efforts should differ by subspecies. Analysis of movement patterns suggests that conservation planners and managers should consider all habitats within 5 km of a known G. c. tabida roost and within 10 km of a G. c. canadensis roost when planning for habitat management, mitigation, acquisition, and easements.

Quantifying the sensitivity of post-glacial sea level change to laterally varying viscosity

NASA Astrophysics Data System (ADS)

Crawford, Ophelia; Al-Attar, David; Tromp, Jeroen; Mitrovica, Jerry X.; Austermann, Jacqueline; Lau, Harriet C. P.

2018-05-01

We present a method for calculating the derivatives of measurements of glacial isostatic adjustment (GIA) with respect to the viscosity structure of the Earth and the ice sheet history. These derivatives, or kernels, quantify the linearised sensitivity of measurements to the underlying model parameters. The adjoint method is used to enable efficient calculation of theoretically exact sensitivity kernels within laterally heterogeneous earth models that can have a range of linear or non-linear viscoelastic rheologies. We first present a new approach to calculate GIA in the time domain, which, in contrast to the more usual formulation in the Laplace domain, is well suited to continuously varying earth models and to the use of the adjoint method. Benchmarking results show excellent agreement between our formulation and previous methods. We illustrate the potential applications of the kernels calculated in this way through a range of numerical calculations relative to a spherically symmetric background model. The complex spatial patterns of the sensitivities are not intuitive, and this is the first time that such effects are quantified in an efficient and accurate manner.

Dietary fiber, organic acids and minerals in selected wild edible fruits of Mozambique.

PubMed

Magaia, Telma; Uamusse, Amália; Sjöholm, Ingegerd; Skog, Kerstin

2013-12-01

The harvesting, utilization and marketing of indigenous fruits and nuts have been central to the livelihoods of the majority of rural communities in African countries. In this study we report on the content of dietary fiber, minerals and selected organic acids in the pulps and kernels of the wild fruits most commonly consumed in southern Mozambique. The content of soluble fiber in the pulps ranged from 4.3 to 65.6 g/100 g and insoluble fiber from 2.6 to 45.8 g/100 g. In the kernels the content of soluble fiber ranged from 8.4 to 42.6 g/100 g and insoluble fiber from 14.7 to 20.9 g/100 g. Citric acid was found in all fruits up to 25.7 g/kg. The kernels of Adansonia digitata and Sclerocarya birrea were shown to be rich in calcium, iron, magnesium and zinc. The data may be useful in selecting wild fruit species appropriate for incorporation into diets.

TURBULENCE-INDUCED RELATIVE VELOCITY OF DUST PARTICLES. IV. THE COLLISION KERNEL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan, Liubin; Padoan, Paolo, E-mail: lpan@cfa.harvard.edu, E-mail: ppadoan@icc.ub.edu

Motivated by its importance for modeling dust particle growth in protoplanetary disks, we study turbulence-induced collision statistics of inertial particles as a function of the particle friction time, τ{sub p}. We show that turbulent clustering significantly enhances the collision rate for particles of similar sizes with τ{sub p} corresponding to the inertial range of the flow. If the friction time, τ{sub p,} {sub h}, of the larger particle is in the inertial range, the collision kernel per unit cross section increases with increasing friction time, τ{sub p,} {sub l}, of the smaller particle and reaches the maximum at τ{sub p,}more » {sub l} = τ{sub p,} {sub h}, where the clustering effect peaks. This feature is not captured by the commonly used kernel formula, which neglects the effect of clustering. We argue that turbulent clustering helps alleviate the bouncing barrier problem for planetesimal formation. We also investigate the collision velocity statistics using a collision-rate weighting factor to account for higher collision frequency for particle pairs with larger relative velocity. For τ{sub p,} {sub h} in the inertial range, the rms relative velocity with collision-rate weighting is found to be invariant with τ{sub p,} {sub l} and scales with τ{sub p,} {sub h} roughly as ∝ τ{sub p,h}{sup 1/2}. The weighting factor favors collisions with larger relative velocity, and including it leads to more destructive and less sticking collisions. We compare two collision kernel formulations based on spherical and cylindrical geometries. The two formulations give consistent results for the collision rate and the collision-rate weighted statistics, except that the spherical formulation predicts more head-on collisions than the cylindrical formulation.« less

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Fast Unitary Transforms - Benefits and Restrictions.

DTIC Science & Technology

1980-04-01

transformation kernel, and u assumes values in the range 0, 1, ... , N-i. Similarly, the inverse transform is given by the relation N-1 f(x) E T(u)h(x...function to obtain T(u,v). Similar comments hold for the inverse transform if h(x,y,u,v) is separable. If the kernel g(xy,u,v) is separable and symmetric...the forward transform can be used directly to obtain the inverse transform simply by multiplying the result of the algorithm by N. 12 The forward and

Moisture Sorption Isotherms and Properties of Sorbed Water of Neem ( Azadirichta indica A. Juss) Kernels

NASA Astrophysics Data System (ADS)

Ngono Mbarga, M. C.; Bup Nde, D.; Mohagir, A.; Kapseu, C.; Elambo Nkeng, G.

2017-01-01

A neem tree growing abundantly in India as well as in some regions of Asia and Africa gives fruits whose kernels have about 40-50% oil. This oil has high therapeutic and cosmetic qualities and is recently projected to be an important raw material for the production of biodiesel. Its seed is harvested at high moisture contents, which leads tohigh post-harvest losses. In the paper, the sorption isotherms are determined by the static gravimetric method at 40, 50, and 60°C to establish a database useful in defining drying and storage conditions of neem kernels. Five different equations are validated for modeling the sorption isotherms of neem kernels. The properties of sorbed water, such as the monolayer moisture content, surface area of adsorbent, number of adsorbed monolayers, and the percent of bound water are also defined. The critical moisture content necessary for the safe storage of dried neem kernels is shown to range from 5 to 10% dry basis, which can be obtained at a relative humidity less than 65%. The isosteric heats of sorption at 5% moisture content are 7.40 and 22.5 kJ/kg for the adsorption and desorption processes, respectively. This work is the first, to the best of our knowledge, to give the important parameters necessary for drying and storage of neem kernels, a potential raw material for the production of oil to be used in pharmaceutics, cosmetics, and biodiesel manufacturing.

Scuba: scalable kernel-based gene prioritization.

PubMed

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Steady/unsteady aerodynamic analysis of wings at subsonic, sonic and supersonic Mach numbers using a 3D panel method

NASA Astrophysics Data System (ADS)

Cho, Jeonghyun; Han, Cheolheui; Cho, Leesang; Cho, Jinsoo

2003-08-01

This paper treats the kernel function of an integral equation that relates a known or prescribed upwash distribution to an unknown lift distribution for a finite wing. The pressure kernel functions of the singular integral equation are summarized for all speed range in the Laplace transform domain. The sonic kernel function has been reduced to a form, which can be conveniently evaluated as a finite limit from both the subsonic and supersonic sides when the Mach number tends to one. Several examples are solved including rectangular wings, swept wings, a supersonic transport wing and a harmonically oscillating wing. Present results are given with other numerical data, showing continuous results through the unit Mach number. Computed results are in good agreement with other numerical results.

Characterization and competitive ability of non-aflatoxigenic Aspergillus flavus isolated from the maize agro-ecosystem in Argentina as potential aflatoxin biocontrol agents.

PubMed

Alaniz Zanon, María Silvina; Clemente, María Paz; Chulze, Sofía Noemí

2018-07-20

Aspergillus flavus is an opportunistic pathogen and may produce aflatoxins in maize, one of the most important crops in Argentina. A promising strategy to reduce aflatoxin accumulation is the biological control based on competitive exclusion. In order to select potential biocontrol agents among isolates from the maize growing region in Argentina, a total of 512 A. flavus strains were isolated from maize kernels and soil samples. Thirty-six per cent of the isolates from maize kernels did not produce detectable levels of aflatoxins, while 73% of the isolates from soil were characterized as non-aflatoxin producers. Forty percent and 49% of the isolates from maize kernels and soil samples, respectively, were not producers of cyclopiazonic acid (CPA). Sclerotia morphology was evaluated using Czapek Dox media. Eighty-six per cent of the isolates from maize kernels and 85% of the isolates from soil samples were L sclerotia morphotype (average diameter > 400 μm). The remaining isolates did not produce sclerotia. All isolates had MAT 1-1 idiomorph. The competitive ability of 9 non aflatoxigenic strains, 4 CPA(+) and 5 CPA(-), was evaluated in co-inoculations of maize kernels with an aflatoxigenic strain. All evaluated strains significantly (p < 0.05) reduced aflatoxin contamination in maize kernels. The aflatoxin B 1 (AFB 1 ) reduction ranged from 6 to 60%. The strain A. flavus ARG5/30 isolated from maize kernels would be a good candidate as a potential biocontrol agent to be used in maize, since it was characterized as neither aflatoxin nor CPA producer, morphotype L, MAT 1-1 idiomorph, and reduced AFB 1 content in maize kernels by 59%. This study showed the competitive ability of potential aflatoxin biocontrol agents to be evaluated under field trials in a maize agro-ecosystem in Argentina. Copyright © 2018 Elsevier B.V. All rights reserved.

Unsupervised multiple kernel learning for heterogeneous data integration.

PubMed

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

TH-C-BRD-04: Beam Modeling and Validation with Triple and Double Gaussian Dose Kernel for Spot Scanning Proton Beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hirayama, S; Takayanagi, T; Fujii, Y

2014-06-15

Purpose: To present the validity of our beam modeling with double and triple Gaussian dose kernels for spot scanning proton beams in Nagoya Proton Therapy Center. This study investigates the conformance between the measurements and calculation results in absolute dose with two types of beam kernel. Methods: A dose kernel is one of the important input data required for the treatment planning software. The dose kernel is the 3D dose distribution of an infinitesimal pencil beam of protons in water and consists of integral depth doses and lateral distributions. We have adopted double and triple Gaussian model as lateral distributionmore » in order to take account of the large angle scattering due to nuclear reaction by fitting simulated inwater lateral dose profile for needle proton beam at various depths. The fitted parameters were interpolated as a function of depth in water and were stored as a separate look-up table for the each beam energy. The process of beam modeling is based on the method of MDACC [X.R.Zhu 2013]. Results: From the comparison results between the absolute doses calculated by double Gaussian model and those measured at the center of SOBP, the difference is increased up to 3.5% in the high-energy region because the large angle scattering due to nuclear reaction is not sufficiently considered at intermediate depths in the double Gaussian model. In case of employing triple Gaussian dose kernels, the measured absolute dose at the center of SOBP agrees with calculation within ±1% regardless of the SOBP width and maximum range. Conclusion: We have demonstrated the beam modeling results of dose distribution employing double and triple Gaussian dose kernel. Treatment planning system with the triple Gaussian dose kernel has been successfully verified and applied to the patient treatment with a spot scanning technique in Nagoya Proton Therapy Center.« less

The use of satellite tags to redefine movement patterns of spiny dogfish (Squalus acanthias) along the U.S. east coast: implications for fisheries management.

PubMed

Carlson, Amy E; Hoffmayer, Eric R; Tribuzio, Cindy A; Sulikowski, James A

2014-01-01

Spiny dogfish (Squalus acanthias) are assumed to be a highly migratory species, making habitual north-south migrations throughout their northwestern Atlantic United States (U.S.) range. Also assumed to be a benthic species, spiny dogfish stock structure is estimated through Northeast Fisheries Science Center (NEFSC) bottom-trawl surveys. Recent anomalies in population trends, including a recent four-fold increase in estimated spawning stock biomass, suggest alternative movement patterns could exist for this shark species. To obtain a better understanding of the horizontal and vertical movement dynamics of this species, Microwave Telemetry pop-up satellite archival X-Tags were attached to forty adult spiny dogfish at the northern (Gulf of Maine) and southern (North Carolina) extents of their core U.S. geographic range. Reconstructed geolocation tracks ranging in lengths from two to 12 months suggest that the seasonal migration patterns appear to be local in nature to each respective northern and southern deployment site, differing from previously published migration paradigms. Differences in distance and direction traveled between seasonal geolocations possibly indicate separate migratory patterns between groups. Kernel utilization distribution models also suggest strong separate core home ranges. Significant differences in seasonal temperature and depths between the two regions further substantiate the possibility of separate regional movement patterns between the two groups. Vertical utilization also suggests distinct diel patterns and that this species may not utilize the benthos as previously thought, potentially decreasing availability to benthic gear.

The Use of Satellite Tags to Redefine Movement Patterns of Spiny Dogfish (Squalus acanthias) along the U.S. East Coast: Implications for Fisheries Management

PubMed Central

Carlson, Amy E.; Hoffmayer, Eric R.; Tribuzio, Cindy A.; Sulikowski, James A.

2014-01-01

Spiny dogfish (Squalus acanthias) are assumed to be a highly migratory species, making habitual north-south migrations throughout their northwestern Atlantic United States (U.S.) range. Also assumed to be a benthic species, spiny dogfish stock structure is estimated through Northeast Fisheries Science Center (NEFSC) bottom-trawl surveys. Recent anomalies in population trends, including a recent four-fold increase in estimated spawning stock biomass, suggest alternative movement patterns could exist for this shark species. To obtain a better understanding of the horizontal and vertical movement dynamics of this species, Microwave Telemetry pop-up satellite archival X-Tags were attached to forty adult spiny dogfish at the northern (Gulf of Maine) and southern (North Carolina) extents of their core U.S. geographic range. Reconstructed geolocation tracks ranging in lengths from two to 12 months suggest that the seasonal migration patterns appear to be local in nature to each respective northern and southern deployment site, differing from previously published migration paradigms. Differences in distance and direction traveled between seasonal geolocations possibly indicate separate migratory patterns between groups. Kernel utilization distribution models also suggest strong separate core home ranges. Significant differences in seasonal temperature and depths between the two regions further substantiate the possibility of separate regional movement patterns between the two groups. Vertical utilization also suggests distinct diel patterns and that this species may not utilize the benthos as previously thought, potentially decreasing availability to benthic gear. PMID:25068584

Indetermination of particle sizing by laser diffraction in the anomalous size ranges

NASA Astrophysics Data System (ADS)

Pan, Linchao; Ge, Baozhen; Zhang, Fugen

2017-09-01

The laser diffraction method is widely used to measure particle size distributions. It is generally accepted that the scattering angle becomes smaller and the angles to the location of the main peak of scattered energy distributions in laser diffraction instruments shift to smaller values with increasing particle size. This specific principle forms the foundation of the laser diffraction method. However, this principle is not entirely correct for non-absorbing particles in certain size ranges and these particle size ranges are called anomalous size ranges. Here, we derive the analytical formulae for the bounds of the anomalous size ranges and discuss the influence of the width of the size segments on the signature of the Mie scattering kernel. This anomalous signature of the Mie scattering kernel will result in an indetermination of the particle size distribution when measured by laser diffraction instruments in the anomalous size ranges. By using the singular-value decomposition method we interpret the mechanism of occurrence of this indetermination in detail and then validate its existence by using inversion simulations.

Fidelity and persistence of Ring-billed (Larus delawarensis) and Herring (Larus argentatus) gulls to wintering sites

USGS Publications Warehouse

Clark, Daniel E.; Koenen, Kiana K. G.; Whitney, Jillian J.; MacKenzie, Kenneth G.; DeStefano, Stephen

2016-01-01

While the breeding ecology of gulls (Laridae) has been well studied, their movements and spatial organization during the non-breeding season is poorly understood. The seasonal movements, winter-site fidelity, and site persistence of Ring-billed (Larus delawarensis) and Herring (L. argentatus) gulls to wintering areas were studied from 2008–2012. Satellite transmitters were deployed on Ring-billed Gulls (n = 21) and Herring Gulls (n = 14). Ten Ring-billed and six Herring gulls were tracked over multiple winters and > 300 wing-tagged Ring-billed Gulls were followed to determine winter-site fidelity and persistence. Home range overlap for individuals between years ranged between 0–1.0 (95% minimum convex polygon) and 0.31–0.79 (kernel utilization distributions). Ringbilled and Herring gulls remained at local wintering sites during the non-breeding season from 20–167 days and 74–161 days, respectively. The probability of a tagged Ring-billed Gull returning to the same site in subsequent winters was high; conversely, there was a low probability of a Ring-billed Gull returning to a different site. Ring-billed and Herring gulls exhibited high winter-site fidelity, but exhibited variable site persistence during the winter season, leading to a high probability of encountering the same individuals in subsequent winters.

Toward lattice fractional vector calculus

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2014-09-01

An analog of fractional vector calculus for physical lattice models is suggested. We use an approach based on the models of three-dimensional lattices with long-range inter-particle interactions. The lattice analogs of fractional partial derivatives are represented by kernels of lattice long-range interactions, where the Fourier series transformations of these kernels have a power-law form with respect to wave vector components. In the continuum limit, these lattice partial derivatives give derivatives of non-integer order with respect to coordinates. In the three-dimensional description of the non-local continuum, the fractional differential operators have the form of fractional partial derivatives of the Riesz type. As examples of the applications of the suggested lattice fractional vector calculus, we give lattice models with long-range interactions for the fractional Maxwell equations of non-local continuous media and for the fractional generalization of the Mindlin and Aifantis continuum models of gradient elasticity.

Characteristics of uranium carbonitride microparticles synthesized using different reaction conditions

NASA Astrophysics Data System (ADS)

Silva, Chinthaka M.; Lindemer, Terrence B.; Voit, Stewart R.; Hunt, Rodney D.; Besmann, Theodore M.; Terrani, Kurt A.; Snead, Lance L.

2014-11-01

Three sets of experimental conditions were tested to synthesize uranium carbonitride (UC1-xNx) kernels from gel-derived urania-carbon microspheres. Primarily, three sequences of gases were used, N2 to N2-4%H2 to Ar, Ar to N2 to Ar, and Ar-4%H2 to N2-4%H2 to Ar-4%H2. Physical and chemical characteristics such as geometrical density, phase purity, and chemical compositions of the synthesized UC1-xNx were measured. Single-phase kernels were commonly obtained with densities generally ranging from 85% to 93% TD and values of x as high as 0.99. In-depth analysis of the microstrutures of UC1-xNx has been carried out and is discussed with the objective of large batch fabrication of high density UC1-xNx kernels.

Estimated home ranges can misrepresent habitat relationships on patchy landscapes

USGS Publications Warehouse

Mitchell, M.S.; Powell, R.A.

2008-01-01

Home ranges of animals are generally structured by the selective use of resource-bearing patches that comprise habitat. Based on this concept, home ranges of animals estimated from location data are commonly used to infer habitat relationships. Because home ranges estimated from animal locations are largely continuous in space, the resource-bearing patches selected by an animal from a fragmented distribution of patches would be difficult to discern; unselected patches included in the home range estimate would bias an understanding of important habitat relationships. To evaluate potential for this bias, we generated simulated home ranges based on optimal selection of resource-bearing patches across a series of simulated resource distributions that varied in the spatial continuity of resources. For simulated home ranges where selected patches were spatially disjunct, we included interstitial, unselected cells most likely to be traveled by an animal moving among selected patches. We compared characteristics of the simulated home ranges with and without interstitial patches to evaluate how insights derived from field estimates can differ from actual characteristics of home ranges, depending on patchiness of landscapes. Our results showed that contiguous home range estimates could lead to misleading insights on the quality, size, resource content, and efficiency of home ranges, proportional to the spatial discontinuity of resource-bearing patches. We conclude the potential bias of including unselected, largely irrelevant patches in the field estimates of home ranges of animals can be high, particularly for home range estimators that assume uniform use of space within home range boundaries. Thus, inferences about the habitat relationships that ultimately define an animal's home range can be misleading where animals occupy landscapes with patchily distributed resources.

Spectrofluorimetric determination of melatonin in kernels of four different Pistacia varieties after ultrasound-assisted solid-liquid extraction.

PubMed

Oladi, Elham; Mohamadi, Maryam; Shamspur, Tayebeh; Mostafavi, Ali

2014-11-11

Melatonin is normally consumed to regulate the body's biological cycle. However it also has therapeutic properties, such as anti-tumor, anti-aging and protects the immune system. There are some reports on the presence of melatonin in edible kernels such as walnuts, but the extraction of melatonin from pistachio kernels is reported here for the first time. For this, the methanolic extract of pistachio kernels was exposed to gas chromatography/mass spectrometry analysis which confirmed the presence of melatonin. A fluorescence-based method was applied for the determination of melatonin in different extracts. When excited at λ=275 nm, the fluorescence emission intensity of melatonin was measured at λ=366 nm. Ultrasound-assisted solid-liquid extraction was used for the extraction of melatonin from pistachio kernels prior to fluorimetric determination. To achieve the highest extraction recovery, the main parameters affecting the extraction efficiency such as extracting solvent type and volume, temperature, sonication time and pH were evaluated. Under the optimized conditions, a linear dependence of fluorescence intensity on melatonin concentration was observed in the range of 0.0040-0.160 μg mL(-1), with a detection limit of 0.0036 μg mL(-1). This method was applied successfully for measuring and comparing the melatonin content in the kernels of four different varieties of Pistacia including Ahmad Aghaei, Akbari, Kalle Qouchi and Fandoghi. In addition, the results obtained were compared with those obtained using GC/MS. A good agreement was observed indicating the reliability of the proposed method. Copyright © 2014 Elsevier B.V. All rights reserved.

Spectrofluorimetric determination of melatonin in kernels of four different Pistacia varieties after ultrasound-assisted solid-liquid extraction

NASA Astrophysics Data System (ADS)

Oladi, Elham; Mohamadi, Maryam; Shamspur, Tayebeh; Mostafavi, Ali

2014-11-01

Melatonin is normally consumed to regulate the body's biological cycle. However it also has therapeutic properties, such as anti-tumor, anti-aging and protects the immune system. There are some reports on the presence of melatonin in edible kernels such as walnuts, but the extraction of melatonin from pistachio kernels is reported here for the first time. For this, the methanolic extract of pistachio kernels was exposed to gas chromatography/mass spectrometry analysis which confirmed the presence of melatonin. A fluorescence-based method was applied for the determination of melatonin in different extracts. When excited at λ = 275 nm, the fluorescence emission intensity of melatonin was measured at λ = 366 nm. Ultrasound-assisted solid-liquid extraction was used for the extraction of melatonin from pistachio kernels prior to fluorimetric determination. To achieve the highest extraction recovery, the main parameters affecting the extraction efficiency such as extracting solvent type and volume, temperature, sonication time and pH were evaluated. Under the optimized conditions, a linear dependence of fluorescence intensity on melatonin concentration was observed in the range of 0.0040-0.160 μg mL-1, with a detection limit of 0.0036 μg mL-1. This method was applied successfully for measuring and comparing the melatonin content in the kernels of four different varieties of Pistacia including Ahmad Aghaei, Akbari, Kalle Qouchi and Fandoghi. In addition, the results obtained were compared with those obtained using GC/MS. A good agreement was observed indicating the reliability of the proposed method.

Classification and quantification analysis of peach kernel from different origins with near-infrared diffuse reflection spectroscopy

PubMed Central

Liu, Wei; Wang, Zhen-Zhong; Qing, Jian-Ping; Li, Hong-Juan; Xiao, Wei

2014-01-01

Background: Peach kernels which contain kinds of fatty acids play an important role in the regulation of a variety of physiological and biological functions. Objective: To establish an innovative and rapid diffuse reflectance near-infrared spectroscopy (DR-NIR) analysis method along with chemometric techniques for the qualitative and quantitative determination of a peach kernel. Materials and Methods: Peach kernel samples from nine different origins were analyzed with high-performance liquid chromatography (HPLC) as a reference method. DR-NIR is in the spectral range 1100-2300 nm. Principal component analysis (PCA) and partial least squares regression (PLSR) algorithm were applied to obtain prediction models, The Savitzky-Golay derivative and first derivative were adopted for the spectral pre-processing, PCA was applied to classify the varieties of those samples. For the quantitative calibration, the models of linoleic and oleinic acids were established with the PLSR algorithm and the optimal principal component (PC) numbers were selected with leave-one-out (LOO) cross-validation. The established models were evaluated with the root mean square error of deviation (RMSED) and corresponding correlation coefficients (R2). Results: The PCA results of DR-NIR spectra yield clear classification of the two varieties of peach kernel. PLSR had a better predictive ability. The correlation coefficients of the two calibration models were above 0.99, and the RMSED of linoleic and oleinic acids were 1.266% and 1.412%, respectively. Conclusion: The DR-NIR combined with PCA and PLSR algorithm could be used efficiently to identify and quantify peach kernels and also help to solve variety problem. PMID:25422544

A locally adaptive kernel regression method for facies delineation

NASA Astrophysics Data System (ADS)

Fernàndez-Garcia, D.; Barahona-Palomo, M.; Henri, C. V.; Sanchez-Vila, X.

2015-12-01

Facies delineation is defined as the separation of geological units with distinct intrinsic characteristics (grain size, hydraulic conductivity, mineralogical composition). A major challenge in this area stems from the fact that only a few scattered pieces of hydrogeological information are available to delineate geological facies. Several methods to delineate facies are available in the literature, ranging from those based only on existing hard data, to those including secondary data or external knowledge about sedimentological patterns. This paper describes a methodology to use kernel regression methods as an effective tool for facies delineation. The method uses both the spatial and the actual sampled values to produce, for each individual hard data point, a locally adaptive steering kernel function, self-adjusting the principal directions of the local anisotropic kernels to the direction of highest local spatial correlation. The method is shown to outperform the nearest neighbor classification method in a number of synthetic aquifers whenever the available number of hard data is small and randomly distributed in space. In the case of exhaustive sampling, the steering kernel regression method converges to the true solution. Simulations ran in a suite of synthetic examples are used to explore the selection of kernel parameters in typical field settings. It is shown that, in practice, a rule of thumb can be used to obtain suboptimal results. The performance of the method is demonstrated to significantly improve when external information regarding facies proportions is incorporated. Remarkably, the method allows for a reasonable reconstruction of the facies connectivity patterns, shown in terms of breakthrough curves performance.

Fractionation of Gallotannins from mango (Mangifera indica L.) kernels by high-speed counter-current chromatography and determination of their antibacterial activity.

PubMed

Engels, Christina; Gänzle, Michael G; Schieber, Andreas

2010-01-27

High-speed counter-current chromatography was applied to the separation of gallotannins from mango (Mangifera indica L.) kernels. The kernels were defatted and subsequently extracted with aqueous acetone [80% (v/v)]. The crude extract was purified by being partitioned against ethyl acetate. A hexane/ethyl acetate/methanol/water solvent system [0.5:5:1:5 (v/v/v/v)] was used in the head-to-tail mode to elute tannins according to their degree of galloylation (tetra-O-galloylglucose to deca-O-galloylglucose). The compounds were characterized using liquid chromatography and mass spectrometry in the negative ionization mode. Purities ranged from 72% (tetra-O-galloylglucose) to 100% (octa-O-galloylglucose). The iron binding capacity of gallotannins was dependent on the number of galloyl groups in the molecule, with a larger capacity at lower degrees of galloylation. The minimum inhibitory concentration against Bacillus subtilis did not change among the different gallotannins tested and was in the range of 0.05-0.1 g/L in Luria-Bertani broth but up to 20 times higher in media containing more iron and divalent cations.

Biosynthesis and characterization of poly(3-hydroxybutyrate-co-3- hydroxyhexanoate) from palm oil products in a Wautersia eutropha mutant.

PubMed

Loo, Ching-Yee; Lee, Wing-Hin; Tsuge, Takeharu; Doi, Yoshiharu; Sudesh, Kumar

2005-09-01

Palm kernel oil, palm olein, crude palm oil and palm acid oil were used for the synthesis of poly (3-hydroxybutyrate-co-3-hydroxyhexanoate) [P(3HB-co-3HHx)] by a mutant strain of Wautersia eutropha (formerly Ralstonia eutropha) harboring the Aeromonas caviae polyhydroxyalkanoate (PHA) synthase gene. Palm kernel oil was an excellent carbon source for the production of cell biomass and P(3HB-co-3HHx). About 87% (w/w) of the cell dry weight as P(3HB-co-3HHx) was obtained using 5 g palm kernel oil/l. Gravimetric and microscopic analyses further confirmed the high PHA content in the recombinant cells. The molar fraction of 3HHx remained constant at 5 mol % regardless of the type and concentration of palm oil products used. The small amount of 3HHx units was confirmed by 13C NMR analysis. The number average molecular weight (M(n)) of the PHA copolymer produced from the various palm oil products ranged from 27 0000 to 46 0000 Da. The polydispersity was in the range of 2.6-3.9.

Mycobiota and mycotoxin contamination of maize flours and popcorn kernels for human consumption commercialized in Spain.

PubMed

Alborch, L; Bragulat, M R; Castellá, G; Abarca, M L; Cabañes, F J

2012-10-01

Mycobiota and co-occurrence of aflatoxins, citrinin, ochratoxin A and zearalenone in 30 samples of maize flours and 30 of popcorn kernels purchased in Spain for human consumption were determined. The mycotoxin-producing ability of Aspergillus, Fusarium and Penicillium spp. was also studied. Total fungal counts of maize flours ranged from <10 to 8.4 × 10(4) CFU/g and predominant mycobiota belonged to Aspergillus spp. and Penicillium spp. In popcorn kernels samples the most frequent species were Aspergillus spp., Mucorales, Fusarium spp. and Penicillium spp. Aflatoxins were produced by Aspergillus flavus and Aspergillus parasiticus, citrinin by Penicillium citrinum and Penicillium verrucosum, ochratoxin A by Aspergillus niger and patulin by Aspergillus clavatus and Penicillium griseofulvum. Identification of all the mycotoxin-producing strains as well as some Aspergillus spp. difficult to identify using phenotypic characters only was also performed by molecular methods. Aflatoxins were detected in 14 maize flours and 2 popcorn kernels samples, while ochratoxin A was detected in 4 maize flours and 10 popcorn samples. Co-occurrence of aflatoxins and ochratoxin A was found in the 4 ochratoxin-positive maize flour samples. Citrinin and zearalenone were not detected. This is the first report of aflatoxins and ochratoxin A contamination in maize flours and popcorn kernels commercialized in Spain. Copyright © 2012 Elsevier Ltd. All rights reserved.

Computational investigation of intense short-wavelength laser interaction with rare gas clusters

NASA Astrophysics Data System (ADS)

Bigaouette, Nicolas

Current Very High Temperature Reactor designs incorporate TRi-structural ISOtropic (TRISO) particle fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel by dropping a cold precursor solution into a column of hot trichloroethylene (TCE). The temperature difference drives the liquid precursor solution to precipitate the metal solution into gel spheres before reaching the bottom of a production column. Over time, gelation byproducts inhibit complete gelation and the TCE must be purified or discarded. The resulting mixed-waste stream is expensive to dispose of or recycle, and changing the forming fluid to a non-hazardous alternative could greatly improve the economics of kernel production. Selection criteria for a replacement forming fluid narrowed a list of ~10,800 chemicals to yield ten potential replacements. The physical properties of the alternatives were measured as a function of temperature between 25 °C and 80 °C. Calculated terminal velocities and heat transfer rates provided an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane were selected for further testing, and surrogate yttria-stabilized zirconia (YSZ) kernels were produced using these selected fluids. The kernels were characterized for density, geometry, composition, and crystallinity and compared to a control group of kernels produced in silicone oil. Production in 1-bromotetradecane showed positive results, producing dense (93.8 %TD) and spherical (1.03 aspect ratio) kernels, but proper gelation did not occur in the other alternative forming fluids. With many of the YSZ kernels not properly gelling within the length of the column, this project further investigated the heat transfer properties of the forming fluids and precursor solution. A sensitivity study revealed that the heat transfer properties of the precursor solution have the strongest impact on gelation time. A COMSOL heat transfer model estimated an effective thermal diffusivity range for the YSZ precursor solution as 1.13x10 -8 m2/s to 3.35x10-8 m 2/s, which is an order of magnitude smaller than the value used in previous studies. 1-bromotetradecane is recommended for further investigation with the production of uranium-based kernels.

New adaptive statistical iterative reconstruction ASiR-V: Assessment of noise performance in comparison to ASiR.

PubMed

De Marco, Paolo; Origgi, Daniela

2018-03-01

To assess the noise characteristics of the new adaptive statistical iterative reconstruction (ASiR-V) in comparison to ASiR. A water phantom was acquired with common clinical scanning parameters, at five different levels of CTDI vol . Images were reconstructed with different kernels (STD, SOFT, and BONE), different IR levels (40%, 60%, and 100%) and different slice thickness (ST) (0.625 and 2.5 mm), both for ASiR-V and ASiR. Noise properties were investigated and noise power spectrum (NPS) was evaluated. ASiR-V significantly reduced noise relative to FBP: noise reduction was in the range 23%-60% for a 0.625 mm ST and 12%-64% for the 2.5 mm ST. Above 2 mGy, noise reduction for ASiR-V had no dependence on dose. Noise reduction for ASIR-V has dependence on ST, being greater for STD and SOFT kernels at 2.5 mm. For the STD kernel ASiR-V has greater noise reduction for both ST, if compared to ASiR. For the SOFT kernel, results varies according to dose and ST, while for BONE kernel ASIR-V shows less noise reduction. NPS for CT Revolution has dose dependent behavior at lower doses. NPS for ASIR-V and ASiR is similar, showing a shift toward lower frequencies as the IR level increases for STD and SOFT kernels. The NPS is different between ASiR-V and ASIR with BONE kernel. NPS for ASiR-V appears to be ST dependent, having a shift toward lower frequencies for 2.5 mm ST. ASiR-V showed greater noise reduction than ASiR for STD and SOFT kernels, while keeping the same NPS. For the BONE kernel, ASiR-V presents a completely different behavior, with less noise reduction and modified NPS. Noise properties of the ASiR-V are dependent on reconstruction slice thickness. The noise properties of ASiR-V suggest the need for further measurements and efforts to establish new CT protocols to optimize clinical imaging. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Scanning Apollo Flight Films and Reconstructing CSM Trajectories

NASA Astrophysics Data System (ADS)

Speyerer, E.; Robinson, M. S.; Grunsfeld, J. M.; Locke, S. D.; White, M.

2006-12-01

Over thirty years ago, the astronauts of the Apollo program made the journey from the Earth to the Moon and back. To record their historic voyages and collect scientific observations many thousands of photographs were acquired with handheld and automated cameras. After returning to Earth, these films were developed and stored at the film archive at Johnson Space Center (JSC), where they still reside. Due to the historical significance of the original flight films typically only duplicate (2nd or 3rd generation) film products are studied and used to make prints. To allow full access to the original flight films for both researchers and the general public, JSC and Arizona State University are scanning and creating an online digital archive. A Leica photogrammetric scanner is being used to insure geometric and radiometric fidelity. Scanning resolution will preserve the grain of the film. Color frames are being scanned and archived as 48 bit pixels to insure capture of the full dynamic range of the film (16 bit for BW). The raw scans will consist of 70 Terabytes of data (10,000 BW Hasselblad, 10,000 color Hasselblad, 10,000 Metric frames, 4500 Pan frames, 620 35mm frames counts; are estimates). All the scanned films will be made available for download through a searchable database. Special tools are being developed to locate images based on various search parameters. To geolocate metric and panoramic frames acquired during Apollos 15\\-17, prototype SPICE kernels are being generated from existing photographic support data by entering state vectors and timestamps from multiple points throughout each orbit into the NAIF toolkit to create a type 9 Spacecraft and Planet Ephemeris Kernel (SPK), a nadir pointing C\\- matrix Kernel (CK), and a Spacecraft Clock Kernel (SCLK). These SPICE kernels, in addition to the Instrument Kernel (IK) and Frames Kernel (FK) that also under development, will be archived along with the scanned images. From the generated kernels, several IDL programs have been designed to display orbital tracks, produce footprint plots, and create image projections. Using the output from these SPICE based programs enables accurate geolocating of SIM bay photography as well as providing potential data from lunar gravitational studies.

Hydroxocobalamin treatment of acute cyanide poisoning from apricot kernels.

PubMed

Cigolini, Davide; Ricci, Giogio; Zannoni, Massimo; Codogni, Rosalia; De Luca, Manuela; Perfetti, Paola; Rocca, Giampaolo

2011-05-24

Clinical experience with hydroxocobalamin in acute cyanide poisoning via ingestion remains limited. This case concerns a 35-year-old mentally ill woman who consumed more than 20 apricot kernels. Published literature suggests each kernel would have contained cyanide concentrations ranging from 0.122 to 4.09 mg/g (average 2.92 mg/g). On arrival, the woman appeared asymptomatic with a raised pulse rate and slight metabolic acidosis. Forty minutes after admission (approximately 70 min postingestion), the patient experienced headache, nausea and dyspnoea, and was hypotensive, hypoxic and tachypnoeic. Following treatment with amyl nitrite and sodium thiosulphate, her methaemoglobin level was 10%. This prompted the administration of oxygen, which evoked a slight improvement in her vital signs. Hydroxocobalamin was then administered. After 24 h, she was completely asymptomatic with normalised blood pressure and other haemodynamic parameters. This case reinforces the safety and effectiveness of hydroxocobalamin in acute cyanide poisoning by ingestion.

Hydroxocobalamin treatment of acute cyanide poisoning from apricot kernels.

PubMed

Cigolini, Davide; Ricci, Giogio; Zannoni, Massimo; Codogni, Rosalia; De Luca, Manuela; Perfetti, Paola; Rocca, Giampaolo

2011-09-01

Clinical experience with hydroxocobalamin in acute cyanide poisoning via ingestion remains limited. This case concerns a 35-year-old mentally ill woman who consumed more than 20 apricot kernels. Published literature suggests each kernel would have contained cyanide concentrations ranging from 0.122 to 4.09 mg/g (average 2.92 mg/g). On arrival, the woman appeared asymptomatic with a raised pulse rate and slight metabolic acidosis. Forty minutes after admission (approximately 70 min postingestion), the patient experienced headache, nausea and dyspnoea, and was hypotensive, hypoxic and tachypnoeic. Following treatment with amyl nitrite and sodium thiosulphate, her methaemoglobin level was 10%. This prompted the administration of oxygen, which evoked a slight improvement in her vital signs. Hydroxocobalamin was then administered. After 24 h, she was completely asymptomatic with normalised blood pressure and other haemodynamic parameters. This case reinforces the safety and effectiveness of hydroxocobalamin in acute cyanide poisoning by ingestion.

21 CFR 145.170 - Canned peaches.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 227 grams (8 ounces) of finished canned peaches. (v) Peach kernels, except in the cases of peeled... pale yellow to rich red orange. (b) White—the varietal types in which the predominant color ranges from white to yellow-white. (c) Red—the varietal types in which the predominant color ranges from pale yellow...

21 CFR 145.170 - Canned peaches.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 227 grams (8 ounces) of finished canned peaches. (v) Peach kernels, except in the cases of peeled... pale yellow to rich red orange. (b) White—the varietal types in which the predominant color ranges from white to yellow-white. (c) Red—the varietal types in which the predominant color ranges from pale yellow...

21 CFR 145.170 - Canned peaches.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 227 grams (8 ounces) of finished canned peaches. (v) Peach kernels, except in the cases of peeled... pale yellow to rich red orange. (b) White—the varietal types in which the predominant color ranges from white to yellow-white. (c) Red—the varietal types in which the predominant color ranges from pale yellow...

21 CFR 145.170 - Canned peaches.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 227 grams (8 ounces) of finished canned peaches. (v) Peach kernels, except in the cases of peeled... pale yellow to rich red orange. (b) White—the varietal types in which the predominant color ranges from white to yellow-white. (c) Red—the varietal types in which the predominant color ranges from pale yellow...

Variation in home range size of red foxes Vulpes vulpes along a gradient of productivity and human landscape alteration

PubMed Central

2017-01-01

Home range size is a fundamental concept for understanding animal dispersion and ecological needs, and it is one of the most commonly reported ecological attributes of free-ranging mammals. Previous studies indicate that red foxes Vulpes vulpes display great variability in home range size. Yet, there has been little consensus regarding the reasons why home range sizes of red foxes vary so extensively. In this study, we examine possible causes of variation in red fox home range sizes using data from 52 GPS collared red foxes from four study areas representing a gradient of landscape productivity and human landscape alteration in Norway and Sweden. Using 90% Local Convex Hull home range estimates, we examined how red fox home range size varied in relation to latitude, elevation, vegetation zone, proportion of agricultural land and human settlement within a home range, and sex and age. We found considerable variation in red fox home range sizes, ranging between 0.95 km2 to 44 km2 (LoCoH 90%) and 2.4 km2 to 358 km2 (MCP 100%). Elevation, proportion of agricultural land and sex accounted for 50% of the variation in home range size found amongst foxes, with elevation having the strongest effect. Red foxes residing in more productive landscapes (those in more southern vegetation zones), had home ranges approximately four times smaller than the home ranges of foxes in the northern boreal vegetation zone. Our results indicate that home range size was influenced by a productivity gradient at both the landscape (latitude) and the local (elevation) scale. The influence of the proportion of agriculture land on home range size of foxes illustrates how human landscape alteration can affect the space use and distribution of red foxes. Further, the variation in home range size found in this study demonstrates the plasticity of red foxes to respond to changing human landscape alteration as well as changes in landscape productivity, which may be contributing to red fox population increases and northern range expansions. PMID:28384313

White noise analysis of Phycomyces light growth response system. I. Normal intensity range.

PubMed Central

Lipson, E D

1975-01-01

The Wiener-Lee-Schetzen method for the identification of a nonlinear system through white gaussian noise stimulation was applied to the transient light growth response of the sporangiophore of Phycomyces. In order to cover a moderate dynamic range of light intensity I, the imput variable was defined to be log I. The experiments were performed in the normal range of light intensity, centered about I0 = 10(-6) W/cm2. The kernels of the Wierner functionals were computed up to second order. Within the range of a few decades the system is reasonably linear with log I. The main nonlinear feature of the second-order kernel corresponds to the property of rectification. Power spectral analysis reveals that the slow dynamics of the system are of at least fifth order. The system can be represented approximately by a linear transfer function, including a first-order high-pass (adaptation) filter with a 4 min time constant and an underdamped fourth-order low-pass filter. Accordingly a linear electronic circuit was constructed to simulate the small scale response characteristics. In terms of the adaptation model of Delbrück and Reichardt (1956, in Cellular Mechanisms in Differentiation and Growth, Princeton University Press), kernels were deduced for the dynamic dependence of the growth velocity (output) on the "subjective intensity", a presumed internal variable. Finally the linear electronic simulator above was generalized to accommodate the large scale nonlinearity of the adaptation model and to serve as a tool for deeper test of the model. PMID:1203444

Finite-frequency sensitivity kernels for head waves

NASA Astrophysics Data System (ADS)

Zhang, Zhigang; Shen, Yang; Zhao, Li

2007-11-01

Head waves are extremely important in determining the structure of the predominantly layered Earth. While several recent studies have shown the diffractive nature and the 3-D Fréchet kernels of finite-frequency turning waves, analogues of head waves in a continuous velocity structure, the finite-frequency effects and sensitivity kernels of head waves are yet to be carefully examined. We present the results of a numerical study focusing on the finite-frequency effects of head waves. Our model has a low-velocity layer over a high-velocity half-space and a cylindrical-shaped velocity perturbation placed beneath the interface at different locations. A 3-D finite-difference method is used to calculate synthetic waveforms. Traveltime and amplitude anomalies are measured by the cross-correlation of synthetic seismograms from models with and without the velocity perturbation and are compared to the 3-D sensitivity kernels constructed from full waveform simulations. The results show that the head wave arrival-time and amplitude are influenced by the velocity structure surrounding the ray path in a pattern that is consistent with the Fresnel zones. Unlike the `banana-doughnut' traveltime sensitivity kernels of turning waves, the traveltime sensitivity of the head wave along the ray path below the interface is weak, but non-zero. Below the ray path, the traveltime sensitivity reaches the maximum (absolute value) at a depth that depends on the wavelength and propagation distance. The sensitivity kernels vary with the vertical velocity gradient in the lower layer, but the variation is relatively small at short propagation distances when the vertical velocity gradient is within the range of the commonly accepted values. Finally, the depression or shoaling of the interface results in increased or decreased sensitivities, respectively, beneath the interface topography.

Fatty acid, triacylglycerol, phytosterol, and tocopherol variations in kernel oil of Malatya apricots from Turkey.

PubMed

Turan, Semra; Topcu, Ali; Karabulut, Ihsan; Vural, Halil; Hayaloglu, Ali Adnan

2007-12-26

The fatty acid, sn-2 fatty acid, triacyglycerol (TAG), tocopherol, and phytosterol compositions of kernel oils obtained from nine apricot varieties grown in the Malatya region of Turkey were determined ( P<0.05). The names of the apricot varieties were Alyanak (ALY), Cataloglu (CAT), Cöloglu (COL), Hacihaliloglu (HAC), Hacikiz (HKI), Hasanbey (HSB), Kabaasi (KAB), Soganci (SOG), and Tokaloglu (TOK). The total oil contents of apricot kernels ranged from 40.23 to 53.19%. Oleic acid contributed 70.83% to the total fatty acids, followed by linoleic (21.96%), palmitic (4.92%), and stearic (1.21%) acids. The s n-2 position is mainly occupied with oleic acid (63.54%), linoleic acid (35.0%), and palmitic acid (0.96%). Eight TAG species were identified: LLL, OLL, PLL, OOL+POL, OOO+POO, and SOO (where P, palmitoyl; S, stearoyl; O, oleoyl; and L, linoleoyl), among which mainly OOO+POO contributed to 48.64% of the total, followed by OOL+POL at 32.63% and OLL at 14.33%. Four tocopherol and six phytosterol isomers were identified and quantified; among these, gamma-tocopherol (475.11 mg/kg of oil) and beta-sitosterol (273.67 mg/100 g of oil) were predominant. Principal component analysis (PCA) was applied to the data from lipid components of apricot kernel oil in order to explore the distribution of the apricot variety according to their kernel's lipid components. PCA separated some varieties including ALY, COL, KAB, CAT, SOG, and HSB in one group and varieties TOK, HAC, and HKI in another group based on their lipid components of apricot kernel oil. So, in the present study, PCA was found to be a powerful tool for classification of the samples.

Genome-Wide Association Study Identifies Candidate Genes for Starch Content Regulation in Maize Kernels

PubMed Central

Liu, Na; Xue, Yadong; Guo, Zhanyong; Li, Weihua; Tang, Jihua

2016-01-01

Kernel starch content is an important trait in maize (Zea mays L.) as it accounts for 65–75% of the dry kernel weight and positively correlates with seed yield. A number of starch synthesis-related genes have been identified in maize in recent years. However, many loci underlying variation in starch content among maize inbred lines still remain to be identified. The current study is a genome-wide association study that used a set of 263 maize inbred lines. In this panel, the average kernel starch content was 66.99%, ranging from 60.60 to 71.58% over the three study years. These inbred lines were genotyped with the SNP50 BeadChip maize array, which is comprised of 56,110 evenly spaced, random SNPs. Population structure was controlled by a mixed linear model (MLM) as implemented in the software package TASSEL. After the statistical analyses, four SNPs were identified as significantly associated with starch content (P ≤ 0.0001), among which one each are located on chromosomes 1 and 5 and two are on chromosome 2. Furthermore, 77 candidate genes associated with starch synthesis were found within the 100-kb intervals containing these four QTLs, and four highly associated genes were within 20-kb intervals of the associated SNPs. Among the four genes, Glucose-1-phosphate adenylyltransferase (APS1; Gene ID GRMZM2G163437) is known as an important regulator of kernel starch content. The identified SNPs, QTLs, and candidate genes may not only be readily used for germplasm improvement by marker-assisted selection in breeding, but can also elucidate the genetic basis of starch content. Further studies on these identified candidate genes may help determine the molecular mechanisms regulating kernel starch content in maize and other important cereal crops. PMID:27512395

Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

PubMed Central

Chen, Tianle; Zeng, Donglin

2015-01-01

Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

Fission Product Release and Survivability of UN-Kernel LWR TRISO Fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Besmann, Theodore M; Ferber, Mattison K; Lin, Hua-Tay

2014-01-01

A thermomechanical assessment of the LWR application of TRISO fuel with UN kernels was performed. Fission product release under operational and transient temperature conditions was determined by extrapolation from range calculations and limited data from irradiated UN pellets. Both fission recoil and diffusive release were considered and internal particle pressures computed for both 650 and 800 m diameter kernels as a function of buffer layer thickness. These pressures were used in conjunction with a finite element program to compute the radial and tangential stresses generated with a TRISO particle as a function of fluence. Creep and swelling of the innermore » and outer pyrolytic carbon layers were included in the analyses. A measure of reliability of the TRISO particle was obtained by measuring the probability of survival of the SiC barrier layer and the maximum tensile stress generated in the pyrolytic carbon layers as a function of fluence. These reliability estimates were obtained as functions of the kernel diameter, buffer layer thickness, and pyrolytic carbon layer thickness. The value of the probability of survival at the end of irradiation was inversely proportional to the maximum pressure.« less

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

PubMed

Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

Maize kernel antioxidants and their potential involvement in Fusarium ear rot resistance.

PubMed

Picot, Adeline; Atanasova-Pénichon, Vessela; Pons, Sebastien; Marchegay, Gisèle; Barreau, Christian; Pinson-Gadais, Laëtitia; Roucolle, Joël; Daveau, Florie; Caron, Daniel; Richard-Forget, Florence

2013-04-10

The potential involvement of antioxidants (α-tocopherol, lutein, zeaxanthin, β-carotene, and ferulic acid) in the resistance of maize varieties to Fusarium ear rot was the focus of this study. These antioxidants were present in all maize kernel stages, indicating that the fumonisin-producing fungi (mainly Fusarium verticillioides and Fusarium proliferatum ) are likely to face them during ear colonization. The effect of these compounds on fumonisin biosynthesis was studied in F. verticillioides liquid cultures. In carotenoid-treated cultures, no inhibitory effect of fumonisin accumulation was observed while a potent inhibitory activity was obtained for sublethal doses of α-tocopherol (0.1 mM) and ferulic acid (1 mM). Using a set of genotypes with moderate to high susceptibility to Fusarium ear rot, ferulic acid was significantly lower in immature kernels of the very susceptible group. Such a relation was nonexistent for tocopherols and carotenoids. Also, ferulic acid in immature kernels ranged from 3 to 8.5 mg/g, i.e., at levels consistent with the in vitro inhibitory concentration. Overall, our data support the fact that ferulic acid may contribute to resistance to Fusarium ear rot and/or fumonisin accumulation.

The Swift-Hohenberg equation with a nonlocal nonlinearity

NASA Astrophysics Data System (ADS)

Morgan, David; Dawes, Jonathan H. P.

2014-03-01

It is well known that aspects of the formation of localised states in a one-dimensional Swift-Hohenberg equation can be described by Ginzburg-Landau-type envelope equations. This paper extends these multiple scales analyses to cases where an additional nonlinear integral term, in the form of a convolution, is present. The presence of a kernel function introduces a new lengthscale into the problem, and this results in additional complexity in both the derivation of envelope equations and in the bifurcation structure. When the kernel is short-range, weakly nonlinear analysis results in envelope equations of standard type but whose coefficients are modified in complicated ways by the nonlinear nonlocal term. Nevertheless, these computations can be formulated quite generally in terms of properties of the Fourier transform of the kernel function. When the lengthscale associated with the kernel is longer, our method leads naturally to the derivation of two different, novel, envelope equations that describe aspects of the dynamics in these new regimes. The first of these contains additional bifurcations, and unexpected loops in the bifurcation diagram. The second of these captures the stretched-out nature of the homoclinic snaking curves that arises due to the nonlocal term.

Age-related change in fast adaptation mechanisms measured with the scotopic full-field ERG.

PubMed

Tillman, Megan A; Panorgias, Athanasios; Werner, John S

2016-06-01

To quantify the response dynamics of fast adaptation mechanisms of the scotopic ERG in younger and older adults using full-field m-sequence flash stimulation. Scotopic ERGs were measured for a series of flashes separated by 65 ms over a range of 260 ms in 16 younger (20-26, 22.2 ± 2.1; range mean ±1 SD) and 16 older (65-85, 71.2 ± 7) observers without retinal pathology. A short-wavelength (λ peak = 442 nm) LED was used for scotopic stimulation, and the flashes ranged from 0.0001 to 0.01 cd s m(-2). The complete binary kernel series was derived from the responses to the m-sequence flash stimulation, and the first- and second-order kernel responses were analyzed. The first-order kernel represented the response to a single, isolated flash, while the second-order kernels reflected the adapted flash responses that followed a single flash by one or more base intervals. B-wave amplitudes of the adapted flash responses were measured and plotted as a function of interstimulus interval to describe the recovery of the scotopic ERG. A linear function was fitted to the linear portion of the recovery curve, and the slope of the line was used to estimate the rate of fast adaptation recovery. The amplitudes of the isolated flash responses and rates of scotopic fast adaptation recovery were compared between the younger and older participants using a two-way ANOVA. The isolated flash responses and rates of recovery were found to be significantly lower in the older adults. However, there was no difference between the two age groups in response amplitude or recovery rate after correcting for age-related changes in the density of the ocular media. These results demonstrated that the rate of scotopic fast adaptation recovery of normal younger and older adults is similar when stimuli are equated for retinal illuminance.

[Home range of Aspidoscelis cozumela (Squamata: Teiidae): a parthenogenetic lizard microendemic to Cozumel Island, México].

PubMed

Hernández-Gallegos, Oswaldo; López-Moreno, Ana Esthela; Méndez-Sánchez, José Fernando; Rheubert, Justin Lloyd; Méndez-de la Cruz, Fausto Roberto

2015-09-01

Home range is defined as the area within which an individual moves to acquire resources necessary to increase their fitness and may vary inter and intra-specifically with biotic and abiotic factors. This study details the home range of the parthenogenic lizard, Aspidoscelis cozumela, an active forager microendemic to Cozumel Island, México, with high preference for open sand beaches. The home range of A. cozumela was compared with other species of Aspidoscelis (gonochoric and parthenogenetic) and other lizards that occupy coastal habitats. Furthermore, the biotic and abiotic factors that may influence home range were analyzed. This study was conducted in the beach located on the East side of the island (area of 4,000 M2) that is composed primarily of halophyte vegetation with high levels of sunlight. From 1999 to 2001, nine samples were taken which included the dry, rainy, "nortes", and breeding seasons. During each sampling, capture-mark-recapture techniques were conducted and the date, time of day, and snout-vent length (SVL) were recorded to the nearest millimeter. Individuals were located in the study area using a bi-coordinate reference using 10 x 10 m subdivisions of the habitat. Home range and home range overlap were calculated using the convex polygon method in McPaal and home range/SVL correlation was tested using Pearson's correlation. To calculate females home range, three or more recaptures were considered. A total of 20 home ranges that averaged 45.1 ± 14.0 m2 were obtained and no correlation between SVL and home range size was detected (p = 0.9229, n = 20). However, removing individuals with outlier home ranges (females with home ranges > 100 m2, n = 2) resulted in a positive correlation with SVL (r = 0.61, p = 0.0072, n = 18). A 22.9 ± 5.7% overlap in home range was also detected. The small home range of A. cozumela represents the smallest home range within the Aspidoscelis genus recorded to date (including both parthenogenetic and gonochoric species) and contrasts the theoretical predictions of broad home ranges for widely foraging species. Thermoregulatory benefits and a high population density may explain the small home range of A. cozumela. Although this species is highly adapted to the environmental conditions present on the open sand beaches, anthropogenic effects on these habitats by the development of tourism infrastructure may jeopardize their existence on Cozumel Island.

Kernel abortion in maize : I. Carbohydrate concentration patterns and Acid invertase activity of maize kernels induced to abort in vitro.

PubMed

Hanft, J M; Jones, R J

1986-06-01

Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.

7 CFR 810.602 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of flaxseed kernels that are badly ground-damaged, badly weather... instructions. Also, underdeveloped, shriveled, and small pieces of flaxseed kernels removed in properly... recleaning. (c) Heat-damaged kernels. Kernels and pieces of flaxseed kernels that are materially discolored...

Home range and travels

USGS Publications Warehouse

Stickel, L.F.; King, John A.

1968-01-01

The concept of home range was expressed by Seton (1909) in the term 'home region,' which Burr (1940, 1943) clarified with a definition of home range and exemplified in a definitive study of Peromyscus in the field. Burt pointed out the ever-changing characteristics of home-range area and the consequent absence of boundaries in the usual sense--a finding verified by investigators thereafter. In the studies summarized in this paper, sizes of home ranges of Peromyscus varied within two magnitudes, approximately from 0.1 acre to ten acres, in 34 studies conducted in a variety of habitats from the seaside dunes of Florida to the Alaskan forests. Variation in sizes of home ranges was correlated with both environmental and physiological factors; with habitat it was conspicuous, both in the same and different regions. Food supply also was related to size of home range, both seasonally and in relation to habitat. Home ranges generally were smallest in winter and largest in spring, at the onset of the breeding season. Activity and size also were affected by changes in weather. Activity was least when temperatures were low and nights were bright. Effects of rainfall were variable. Sizes varied according to sex and age; young mice remained in the parents' range until they approached maturity, when they began to travel more widely. Adult males commonly had larger home ranges than females, although there were a number of exceptions. An inverse relationship between population density and size of home range was shown in several studies and probably is the usual relationship. A basic need for activity and exploration also appeared to influence size of home range. Behavior within the home range was discussed in terms of travel patterns, travels in relation to home sites and refuges, territory, and stability of size of home range. Travels within the home range consisted of repeated use of well-worn trails to sites of food, shelter, and refuge, plus more random exploratory travels. Peromyscus generally used and maintained several or many different home sites and refuges in various parts of their home ranges, and frequently shifted about so that their principal activities centered on different sets of holes at different times. Once established, many Peromyscus remained in the same general area for a long time, perhaps for the duration of their lives. Extent of their travels in different directions and intensity of use of different portions of their home ranges varied within a general area in response to habitat changes, loss of neighbors, or other factors. Various authors have obtained both direct and indirect evidence of territoriality, in some degree, among certain species of Peromyscus. Young mice dispersed from their birth sites to establish home ranges of their own. Adults also sometimes left their home areas; some re-established elsewhere; others returned after exploratory travels. Most populations contained a certain proportion of transients; these may have been wanderers or individuals exploring out from established home ranges or seeking new ones. When areas were depopulated by removal trapping, other Peromyscus invaded. Invasion rates generally followed seasonal trends of reproduction and population density. Peromyscus removed from their home areas and released elsewhere returned home from various distances, but fewer returned from greater distances than from nearby; speed of return increased with successive trials. The consensus from present evidence is that ho-ming is made possible by a combination of random wandering and familiarity with a larger area than the day-to-day range. Records of juvenile wanderings during the dispersal phase and of adult explorations very nearly encompassed the distances over which any substantial amount of successful homing occurred. Methods of measuring sizes of home ranges and the limitations of these measurements were discussed in brief synopsis. It was co

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

Kernels cultured in vitro were induced to abort by high temperature (35°C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35°C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth. PMID:16664846

Out-of-Sample Extensions for Non-Parametric Kernel Methods.

PubMed

Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

2017-02-01

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

Optimal use of resources structures home ranges and spatial distribution of black bears

USGS Publications Warehouse

Mitchell, M.S.; Powell, R.A.

2007-01-01

Research has shown that territories of animals are economical. Home ranges should be similarly efficient with respect to spatially distributed resources and this should structure their distribution on a landscape, although neither has been demonstrated empirically. To test these hypotheses, we used home range models that optimize resource use according to resource-maximizing and area-minimizing strategies to evaluate the home ranges of female black bears, Ursus americanus, living in the southern Appalachian Mountains. We tested general predictions of our models using 104 home ranges of adult female bears studied in the Pisgah Bear Sanctuary, North Carolina, U.S.A., from 1981 to 2001. We also used our models to estimate home ranges for each real home range under a variety of strategies and constraints and compared similarity of simulated to real home ranges. We found that home ranges of female bears were efficient with respect to the spatial distribution of resources and were best explained by an area-minimizing strategy with moderate resource thresholds and low levels of resource depression. Although resource depression probably influenced the spatial distribution of home ranges on the landscape, levels of resource depression were too low to quantify accurately. Home ranges of lactating females had higher resource thresholds and were more susceptible to resource depression than those of breeding females. We conclude that home ranges of animals, like territories, are economical with respect to resources, and that resource depression may be the mechanism behind ideal free or ideal preemptive distributions on complex, heterogeneous landscapes. ?? 2007 The Association for the Study of Animal Behaviour.

7 CFR 810.1202 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

... kernels. Kernels, pieces of rye kernels, and other grains that are badly ground-damaged, badly weather.... Also, underdeveloped, shriveled, and small pieces of rye kernels removed in properly separating the...-damaged kernels. Kernels, pieces of rye kernels, and other grains that are materially discolored and...

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize.

PubMed

Chen, Jiafa; Zhang, Luyan; Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

PubMed Central

Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed. PMID:27070143

Survival and home-range size of Northern Spotted Owls in southwestern Oregon

USGS Publications Warehouse

Schilling, Jason W.; Dugger, Katie M.; Anthony, Robert G.

2013-01-01

In the Klamath province of southwestern Oregon, Northern Spotted Owls (Strix occidentalis caurina) occur in complex, productive forests that historically supported frequent fires of variable severity. However, little is known about the relationships between Spotted Owl survival and home-range size and the characteristics of fire-prone, mixed-conifer forests of the Klamath province. Thus, the objectives of this study were to estimate monthly survival rates and home-range size in relation to habitat characteristics for Northern Spotted Owls in southwestern Oregon. Home-range size and survival of 15 Northern Spotted Owls was monitored using radiotelemetry in the Ashland Ranger District of the Rogue River–Siskiyou National Forest from September 2006 to October 2008. Habitat classes within Spotted Owl home ranges were characterized using a remote-sensed vegetation map of the study area. Estimates of monthly survival ranged from 0.89 to 1.0 and were positively correlated with the number of late-seral habitat patches and the amount of edge, and negatively correlated with the mean nearest neighbor distance between late-seral habitats. Annual home-range size varied from to 189 to 894 ha ( x =  576; SE  =  75), with little difference between breeding and nonbreeding home ranges. Breeding-season home-range size increased with the amount of hard edge, and the amount of old and mature forest combined. Core area, annual and nonbreeding season home-range sizes all increased with increased amounts of hard edge, suggesting that increased fragmentation is associated with larger core and home-range sizes. Although no effect of the amount of late-seral stage forest on either survival or home-range size was detected, these results are the first to concurrently demonstrate increased forest fragmentation with decreased survival and increased home-range size of Northern Spotted Owls.

Influence of moisture content on physical properties of minor millets.

PubMed

Balasubramanian, S; Viswanathan, R

2010-06-01

Physical properties including 1000 kernel weight, bulk density, true density, porosity, angle of repose, coefficient of static friction, coefficient of internal friction and grain hardness were determined for foxtail millet, little millet, kodo millet, common millet, barnyard millet and finger millet in the moisture content range of 11.1 to 25% db. Thousand kernel weight increased from 2.3 to 6.1 g and angle of repose increased from 25.0 to 38.2°. Bulk density decreased from 868.1 to 477.1 kg/m(3) and true density from 1988.7 to 884.4 kg/m(3) for all minor millets when observed in the moisture range of 11.1 to 25%. Porosity decreased from 63.7 to 32.5%. Coefficient of static friction of minor millets against mild steel surface increased from 0.253 to 0.728 and coefficient of internal friction was in the range of 1.217 and 1.964 in the moisture range studied. Grain hardness decreased from 30.7 to 12.4 for all minor millets when moisture content was increased from 11.1 to 25% db.

7 CFR 810.802 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of grain kernels for which standards have been established under the.... (d) Heat-damaged kernels. Kernels and pieces of grain kernels for which standards have been...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2011 CFR

2011-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2012 CFR

2012-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2013 CFR

2013-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

Technical Note: Dose gradients and prescription isodose in orthovoltage stereotactic radiosurgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fagerstrom, Jessica M., E-mail: fagerstrom@wisc.edu; Bender, Edward T.; Culberson, Wesley S.

Purpose: The purpose of this work is to examine the trade-off between prescription isodose and dose gradients in orthovoltage stereotactic radiosurgery. Methods: Point energy deposition kernels (EDKs) describing photon and electron transport were calculated using Monte Carlo methods. EDKs were generated from 10  to 250 keV, in 10 keV increments. The EDKs were converted to pencil beam kernels and used to calculate dose profiles through isocenter from a 4π isotropic delivery from all angles of circularly collimated beams. Monoenergetic beams and an orthovoltage polyenergetic spectrum were analyzed. The dose gradient index (DGI) is the ratio of the 50% prescription isodosemore » volume to the 100% prescription isodose volume and represents a metric by which dose gradients in stereotactic radiosurgery (SRS) may be evaluated. Results: Using the 4π dose profiles calculated using pencil beam kernels, the relationship between DGI and prescription isodose was examined for circular cones ranging from 4 to 18 mm in diameter and monoenergetic photon beams with energies ranging from 20 to 250 keV. Values were found to exist for prescription isodose that optimize DGI. Conclusions: The relationship between DGI and prescription isodose was found to be dependent on both field size and energy. Examining this trade-off is an important consideration for designing optimal SRS systems.« less

Lack of Host Specialization in Aspergillus flavus

PubMed Central

St. Leger, Raymond J.; Screen, Steven E.; Shams-Pirzadeh, Bijan

2000-01-01

Aspergillus spp. cause disease in a broad range of organisms, but it is unknown if strains are specialized for particular hosts. We evaluated isolates of Aspergillus flavus, Aspergillus fumigatus, and Aspergillus nidulans for their ability to infect bean leaves, corn kernels, and insects (Galleria mellonella). Strains of A. flavus did not affect nonwounded bean leaves, corn kernels, or insects at 22°C, but they killed insects following hemocoelic challenge and caused symptoms ranging from moderate to severe in corn kernels and bean leaves injured during inoculation. The pectinase P2c, implicated in aggressive colonization of cotton bolls, is produced by most A. flavus isolates, but its absence did not prevent colonization of bean leaves. Proteases have been implicated in colonization of animal hosts. All A. flavus strains produced very similar patterns of protease isozymes when cultured on horse lung polymers. Quantitative differences in protease levels did not correlate with the ability to colonize insects. In contrast to A. flavus, strains of A. nidulans and A. fumigatus could not invade living insect or plant tissues or resist digestion by insect hemocytes. Our results indicate that A. flavus has parasitic attributes that are lacking in A. fumigatus and A. nidulans but that individual strains of A. flavus are not specialized to particular hosts. PMID:10618242

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Classification With Truncated Distance Kernel.

PubMed

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

Simulation analyses of space use: Home range estimates, variability, and sample size

USGS Publications Warehouse

Bekoff, Marc; Mech, L. David

1984-01-01

Simulations of space use by animals were run to determine the relationship among home range area estimates, variability, and sample size (number of locations). As sample size increased, home range size increased asymptotically, whereas variability decreased among mean home range area estimates generated by multiple simulations for the same sample size. Our results suggest that field workers should ascertain between 100 and 200 locations in order to estimate reliably home range area. In some cases, this suggested guideline is higher than values found in the few published studies in which the relationship between home range area and number of locations is addressed. Sampling differences for small species occupying relatively small home ranges indicate that fewer locations may be sufficient to allow for a reliable estimate of home range. Intraspecific variability in social status (group member, loner, resident, transient), age, sex, reproductive condition, and food resources also have to be considered, as do season, habitat, and differences in sampling and analytical methods. Comparative data still are needed.

Fish movement and habitat use depends on water body size and shape

USGS Publications Warehouse

Woolnough, D.A.; Downing, J.A.; Newton, T.J.

2009-01-01

Home ranges are central to understanding habitat diversity, effects of fragmentation and conservation. The distance that an organism moves yields information on life history, genetics and interactions with other organisms. Present theory suggests that home range is set by body size of individuals. Here, we analyse estimates of home ranges in lakes and rivers to show that body size of fish and water body size and shape influence home range size. Using 71 studies including 66 fish species on five continents, we show that home range estimates increased with increasing water body size across water body shapes. This contrasts with past studies concluding that body size sets home range. We show that water body size was a consistently significant predictor of home range. In conjunction, body size and water body size can provide improved estimates of home range than just body size alone. As habitat patches are decreasing in size worldwide, our findings have implications for ecology, conservation and genetics of populations in fragmented ecosystems. ?? 2008 Blackwell Munksgaard.

Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm.

PubMed

Ma, Xiaoling; Sajjad, Muhammad; Wang, Jing; Yang, Wenlong; Sun, Jiazhu; Li, Xin; Zhang, Aimin; Liu, Dongcheng

2017-09-20

Kernel hardness, which has great influence on the end-use properties of common wheat, is mainly controlled by Puroindoline genes, Pina and Pinb. Using EcoTILLING platform, we herein investigated the allelic variations of Pina and Pinb genes and their association with the Single Kernel Characterization System (SKCS) hardness index in a diverse panel of wheat germplasm. The kernel hardness varied from 1.4 to 102.7, displaying a wide range of hardness index. In total, six Pina and nine Pinb alleles resulting in 15 genotypes were detected in 1787 accessions. The most common alleles are the wild type Pina-D1a (90.4%) and Pina-D1b (7.4%) for Pina, and Pinb-D1b (43.6%), Pinb-D1a (41.1%) and Pinb-D1p (12.8%) for Pinb. All the genotypes have hard type kernel hardness of SKCS index (>60.0), except the wild types of Pina and Pinb combination (Pina-D1a/Pinb-D1a). The most frequent genotypes in Chinese and foreign cultivars was Pina-D1a/Pinb-D1b (46.3 and 39.0%, respectively) and in Chinese landraces was Pina-D1a/Pinb-D1a (54.2%). The frequencies of hard type accessions are increasing from 35.5% in the region IV, to 40.6 and 61.4% in the regions III and II, and then to 77.0% in the region I, while those of soft type are accordingly decreasing along with the increase of latitude. Varieties released after 2000 in Beijing, Hebei, Shandong and Henan have higher average kernel hardness index than that released before 2000. The kernel hardness in a diverse panel of Chinese wheat germplasm revealed an increasing of kernel hardness generally along with the latitude across China. The wild type Pina-D1a and Pinb-D1a, and one Pinb mutant (Pinb-D1b) are the most common alleles of six Pina and nine Pinb alleles, and a new double null genotype (Pina-D1x/Pinb-D1ah) possessed relatively high SKCS hardness index. More hard type varieties were released in recent years with different prevalence of Pin-D1 combinations in different regions. This work would benefit the understanding of the selection and molecular processes of kernel hardness across China and different breeding stages, and provide useful information for the improvement of wheat quality in China.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Spotted owl home range and habitat use in the southern Oregon Coast Range.

Treesearch

A.B. Carey; J.A. Reid; S.P. Horton

1991-01-01

We radiotracked 9 adult spotted owls (Strix occidentalis) in the southern Oregon Coast Ranges for 6-12 months. Owls selected home ranges that emphasized old growth within the landscape. Minimum convex polygon home ranges of 4 pairs were 1,153-3,945 ha and contained 726-1,062 ha of old growth. The percentages of. the home ranges in old growth were...

Roads influence movement and home ranges of a fragmentation-sensitive carnivore, the bobcat, in an urban landscape

USGS Publications Warehouse

Poessel, Sharon A; Boydston, Erin E.; Lyren, Lisa M.; Fisher, Robert N.; Burdett, Christopher L.; Alonso, Robert S.; Crooks, Kevin R.

2014-01-01

Roads in urbanized areas can impact carnivore populations by constraining their movements and increasing mortality. Bobcats (Lynx rufus) are felids capable of living in urban environments, but are sensitive to habitat fragmentation and, thus, useful indicators of landscape connectivity; in particular, bobcat habitat selection, movement, and mortality may be affected by roads. We analyzed movement patterns of 52 bobcats in southern California in three study sites and investigated: (1) how bobcats responded to two types of roads within their home ranges; (2) how they placed their home ranges with respect to roads within the study area; and (3) whether male and female bobcats differed in their behavioral responses to roads. Within home ranges, primary and secondary roads did not influence movements, but bobcats more frequently crossed secondary roads when road densities were higher within their home ranges, thus increasing mortality risk. However, road densities within each study site were several times higher than road densities within home ranges, suggesting bobcats selected against roaded areas in home-range placement. Male home ranges bordering roads were smaller than home ranges for other males, but male home ranges containing roads were larger than those without roads. Male bobcats also were more likely to cross roads than females, potentially reflecting larger male home range sizes. Our results suggest roads have important impacts on urban bobcats, with stronger effects on males than females, and continued efforts to mitigate the effects of roads on carnivores and other fragmentation-sensitive species would help promote connectivity conservation in urban systems.

Foraging optimally for home ranges

USGS Publications Warehouse

Mitchell, Michael S.; Powell, Roger A.

2012-01-01

Economic models predict behavior of animals based on the presumption that natural selection has shaped behaviors important to an animal's fitness to maximize benefits over costs. Economic analyses have shown that territories of animals are structured by trade-offs between benefits gained from resources and costs of defending them. Intuitively, home ranges should be similarly structured, but trade-offs are difficult to assess because there are no costs of defense, thus economic models of home-range behavior are rare. We present economic models that predict how home ranges can be efficient with respect to spatially distributed resources, discounted for travel costs, under 2 strategies of optimization, resource maximization and area minimization. We show how constraints such as competitors can influence structure of homes ranges through resource depression, ultimately structuring density of animals within a population and their distribution on a landscape. We present simulations based on these models to show how they can be generally predictive of home-range behavior and the mechanisms that structure the spatial distribution of animals. We also show how contiguous home ranges estimated statistically from location data can be misleading for animals that optimize home ranges on landscapes with patchily distributed resources. We conclude with a summary of how we applied our models to nonterritorial black bears (Ursus americanus) living in the mountains of North Carolina, where we found their home ranges were best predicted by an area-minimization strategy constrained by intraspecific competition within a social hierarchy. Economic models can provide strong inference about home-range behavior and the resources that structure home ranges by offering falsifiable, a priori hypotheses that can be tested with field observations.

Effect of Local TOF Kernel Miscalibrations on Contrast-Noise in TOF PET

NASA Astrophysics Data System (ADS)

Clementel, Enrico; Mollet, Pieter; Vandenberghe, Stefaan

2013-06-01

TOF PET imaging requires specific calibrations: accurate characterization of the system timing resolution and timing offset is required to achieve the full potential image quality. Current system models used in image reconstruction assume a spatially uniform timing resolution kernel. Furthermore, although the timing offset errors are often pre-corrected, this correction becomes less accurate with the time since, especially in older scanners, the timing offsets are often calibrated only during the installation, as the procedure is time-consuming. In this study, we investigate and compare the effects of local mismatch of timing resolution when a uniform kernel is applied to systems with local variations in timing resolution and the effects of uncorrected time offset errors on image quality. A ring-like phantom was acquired on a Philips Gemini TF scanner and timing histograms were obtained from coincidence events to measure timing resolution along all sets of LORs crossing the scanner center. In addition, multiple acquisitions of a cylindrical phantom, 20 cm in diameter with spherical inserts, and a point source were simulated. A location-dependent timing resolution was simulated, with a median value of 500 ps and increasingly large local variations, and timing offset errors ranging from 0 to 350 ps were also simulated. Images were reconstructed with TOF MLEM with a uniform kernel corresponding to the effective timing resolution of the data, as well as with purposefully mismatched kernels. To CRC vs noise curves were measured over the simulated cylinder realizations, while the simulated point source was processed to generate timing histograms of the data. Results show that timing resolution is not uniform over the FOV of the considered scanner. The simulated phantom data indicate that CRC is moderately reduced in data sets with locally varying timing resolution reconstructed with a uniform kernel, while still performing better than non-TOF reconstruction. On the other hand, uncorrected offset errors in our setup have a larger potential for decreasing image quality and can lead to a reduction of CRC of up to 15% and an increase in the measured timing resolution kernel up to 40%. However, in realistic conditions in frequently calibrated systems, using a larger effective timing kernel in image reconstruction can compensate uncorrected offset errors.

Home range location of white-tailed deer.

Treesearch

Michael E. Nelson

1979-01-01

Deer migrations and home range traditions indicated that home range location is determined more by early social experience, learning, and tradition than by an innate ability to select the best habitat. Different deer preferred the same or similar habitat but such selection was a secondary influence on home range location.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

7 CFR 981.7 - Edible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Kernel K-Means Sampling for Nyström Approximation.

PubMed

He, Li; Zhang, Hong

2018-05-01

A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error. We first propose a unified kernel matrix approximation framework, which is able to describe most existing Nyström approximations under many popular kernels, including Gaussian kernel and polynomial kernel. We then show that, the matrix approximation error upper bound, in terms of the Frobenius norm, is equal to the -means error of data points in kernel space plus a constant. Thus, the -means centers of data in kernel space, or the kernel -means centers, are the optimal representative points with respect to the Frobenius norm error upper bound. Experimental results, with both Gaussian kernel and polynomial kernel, on real-world data sets and image segmentation tasks show the superiority of the proposed method over the state-of-the-art methods.

Modeling RF Fields in Hot Plasmas with Parallel Full Wave Code

NASA Astrophysics Data System (ADS)

Spencer, Andrew; Svidzinski, Vladimir; Zhao, Liangji; Galkin, Sergei; Kim, Jin-Soo

2016-10-01

FAR-TECH, Inc. is developing a suite of full wave RF plasma codes. It is based on a meshless formulation in configuration space with adapted cloud of computational points (CCP) capability and using the hot plasma conductivity kernel to model the nonlocal plasma dielectric response. The conductivity kernel is calculated by numerically integrating the linearized Vlasov equation along unperturbed particle trajectories. Work has been done on the following calculations: 1) the conductivity kernel in hot plasmas, 2) a monitor function based on analytic solutions of the cold-plasma dispersion relation, 3) an adaptive CCP based on the monitor function, 4) stencils to approximate the wave equations on the CCP, 5) the solution to the full wave equations in the cold-plasma model in tokamak geometry for ECRH and ICRH range of frequencies, and 6) the solution to the wave equations using the calculated hot plasma conductivity kernel. We will present results on using a meshless formulation on adaptive CCP to solve the wave equations and on implementing the non-local hot plasma dielectric response to the wave equations. The presentation will include numerical results of wave propagation and absorption in the cold and hot tokamak plasma RF models, using DIII-D geometry and plasma parameters. Work is supported by the U.S. DOE SBIR program.

Investigating the Impact of Aerosol Deposition on Snow Melt over the Greenland Ice Sheet Using a New Kernel

NASA Astrophysics Data System (ADS)

Li, Y.; Flanner, M.

2017-12-01

Accelerating surface melt on the Greenland Ice Sheet (GrIS) has led to a doubling of Greenland's contribution to global sea level rise during recent decades. The darkening effect due to black carbon (BC), dust, and other light absorbing impurities (LAI) enhances snow melt by boosting its absorption of solar energy. It is therefore important for coupled aerosol-climate and ice sheet models to include snow darkening effects from LAI, and yet most do not. In this study, we develop an aerosol deposition—snow melt kernel based on the Community Earth System Model (CESM) to investigate changes in melt flux due to variations in the amount and timing of aerosol deposition on the GrIS. The Community Land Model (CLM) component of CESM is driven with a large range of aerosol deposition fluxes to determine non-linear relationships between melt perturbation and deposition amount occurring in different months and location (thereby capturing variations in base state associated with elevation and latitude). The kernel product will include climatological-mean effects and standard deviations associated with interannual variability. Finally, the kernel will allow aerosol deposition fluxes from any global or regional aerosol model to be translated into surface melt perturbations of the GrIS, thus extending the utility of state-of-the-art aerosol models.

Speeding Up the Bilateral Filter: A Joint Acceleration Way.

PubMed

Dai, Longquan; Yuan, Mengke; Zhang, Xiaopeng

2016-06-01

Computational complexity of the brute-force implementation of the bilateral filter (BF) depends on its filter kernel size. To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property. Although each of the above techniques suffers from accuracy and efficiency problems, previous algorithm designers were used to take only one of them to assemble fast implementations due to the hardness of combining them together. Hence, no joint exploitation of these techniques has been proposed to construct a new cutting edge implementation that solves these problems. Jointly employing five techniques: kernel truncation, best N-term approximation as well as previous 2D box filtering, dimension promotion, and shiftability property, we propose a unified framework to transform BF with arbitrary spatial and range kernels into a set of 3D box filters that can be computed in linear time. To the best of our knowledge, our algorithm is the first method that can integrate all these acceleration techniques and, therefore, can draw upon one another's strong point to overcome deficiencies. The strength of our method has been corroborated by several carefully designed experiments. In particular, the filtering accuracy is significantly improved without sacrificing the efficiency at running time.

Tebuconazole and Azoxystrobin Residue Behaviors and Distribution in Field and Cooked Peanut.

PubMed

Hou, Fan; Teng, Peipei; Liu, Fengmao; Wang, Wenzhuo

2017-06-07

Residue behaviors of tebuconazole and azoxystrobin in field condition and the variation of their residue levels during the boiling process were evaluated. The terminal residues of peanut kernels were determined by using a modified QuEChERS method (quick, easy, cheap, effective, rugged, and safe) by means of the optimization of the novel purification procedure with multiwalled carbon nanotubes (MWCNTs) and Fe 3 O 4 -magnetic nanoparticle (Fe 3 O 4 -MNP) in the presence of an external magnetic field, and the terminal residues were all at trace level at harvest time. The residues in shells were detected as well to investigate the distribution in peanuts. Tebuconazole and azoxystrobin residue levels varied before/after boiling in kernels and shells to different degrees due to various factors, such as the modes of action and physicochemical properties of pesticides. The residues have been transferred from peanut into the infusion during boiling with the higher percentage of azoxystrobin as its lower logK ow . The processing factors (PFs) for tebuconazole and azoxystrobin after processing were <1, indicating that home cooking in this study could reduce the residue levels in peanut. Risk assessment showed there was no health risk for consumers.

Handling Density Conversion in TPS.

PubMed

Isobe, Tomonori; Mori, Yutaro; Takei, Hideyuki; Sato, Eisuke; Tadano, Kiichi; Kobayashi, Daisuke; Tomita, Tetsuya; Sakae, Takeji

2016-01-01

Conversion from CT value to density is essential to a radiation treatment planning system. Generally CT value is converted to the electron density in photon therapy. In the energy range of therapeutic photon, interactions between photons and materials are dominated with Compton scattering which the cross-section depends on the electron density. The dose distribution is obtained by calculating TERMA and kernel using electron density where TERMA is the energy transferred from primary photons and kernel is a volume considering spread electrons. Recently, a new method was introduced which uses the physical density. This method is expected to be faster and more accurate than that using the electron density. As for particle therapy, dose can be calculated with CT-to-stopping power conversion since the stopping power depends on the electron density. CT-to-stopping power conversion table is also called as CT-to-water-equivalent range and is an essential concept for the particle therapy.

Optimized Quasi-Interpolators for Image Reconstruction.

PubMed

Sacht, Leonardo; Nehab, Diego

2015-12-01

We propose new quasi-interpolators for the continuous reconstruction of sampled images, combining a narrowly supported piecewise-polynomial kernel and an efficient digital filter. In other words, our quasi-interpolators fit within the generalized sampling framework and are straightforward to use. We go against standard practice and optimize for approximation quality over the entire Nyquist range, rather than focusing exclusively on the asymptotic behavior as the sample spacing goes to zero. In contrast to previous work, we jointly optimize with respect to all degrees of freedom available in both the kernel and the digital filter. We consider linear, quadratic, and cubic schemes, offering different tradeoffs between quality and computational cost. Experiments with compounded rotations and translations over a range of input images confirm that, due to the additional degrees of freedom and the more realistic objective function, our new quasi-interpolators perform better than the state of the art, at a similar computational cost.

Gluten-containing grains skew gluten assessment in oats due to sample grind non-homogeneity.

PubMed

Fritz, Ronald D; Chen, Yumin; Contreras, Veronica

2017-02-01

Oats are easily contaminated with gluten-rich kernels of wheat, rye and barley. These contaminants are like gluten 'pills', shown here to skew gluten analysis results. Using R-Biopharm R5 ELISA, we quantified gluten in gluten-free oatmeal servings from an in-market survey. For samples with a 5-20ppm reading on a first test, replicate analyses provided results ranging <5ppm to >160ppm. This suggests sample grinding may inadequately disperse gluten to allow a single accurate gluten assessment. To ascertain this, and characterize the distribution of 0.25-g gluten test results for kernel contaminated oats, twelve 50g samples of pure oats, each spiked with a wheat kernel, showed that 0.25g test results followed log-normal-like distributions. With this, we estimate probabilities of mis-assessment for a 'single measure/sample' relative to the <20ppm regulatory threshold, and derive an equation relating the probability of mis-assessment to sample average gluten content. Copyright © 2016 Elsevier Ltd. All rights reserved.

Early detection of toxigenic fungi on maize by hyperspectral imaging analysis.

PubMed

Del Fiore, A; Reverberi, M; Ricelli, A; Pinzari, F; Serranti, S; Fabbri, A A; Bonifazi, G; Fanelli, C

2010-11-15

Fungi can grow on many food commodities. Some fungal species, such as Aspergillus flavus, Aspergillus parasiticus, Aspergillus niger and Fusarium spp., can produce, under suitable conditions, mycotoxins, secondary metabolites which are toxic for humans and animals. Toxigenic fungi are a real issue, especially for the cereal industry. The aim of this work is to carry out a non destructive, hyperspectral imaging-based method to detect toxigenic fungi on maize kernels, and to discriminate between healthy and diseased kernels. A desktop spectral scanner equipped with an imaging based spectrometer ImSpector- Specim V10, working in the visible-near infrared spectral range (400-1000 nm) was used. The results show that the hyperspectral imaging is able to rapidly discriminate commercial maize kernels infected with toxigenic fungi from uninfected controls when traditional methods are not yet effective: i.e. from 48 h after inoculation with A. niger or A. flavus. Copyright © 2010 Elsevier B.V. All rights reserved.

Mineral contents and proximate composition of Pistacia vera kernels.

PubMed

Harmankaya, Mustafa; Ozcan, Mehmet Musa; Al Juhaimi, Fahad

2014-07-01

The mineral contents of Pistacia vera kernels were determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). The minimum and maximum values of K, P, Ca, Mg, and S elements ranged from 6,333 to 8,064 mg/kg, 3,630 to 5,228 mg/kg, 1,614 to 3,226 mg/kg, 1,716 to 2,402 mg/kg, and 1,417 to 1,825 mg/kg, respectively. In addition, the mean values of Fe, Zn, Cu, Mn, B, Mo, Cr and Ni elements were determined as 42.48, 20.52, 12.81, 7.48, 11.31, 0.106, 0.511 and 1.67 mg/kg, respectively. Ash levels of kernels were found between 2.28 % (Urfa) and 2.79 % (Halebi). In addition, crude oil and protein contents were determined between 48.8 % (Halebi) to 55.3 % (Siirt) and 23.33 % (Uzun) to 27.16 % (Halebi), respectively.

The Feasibility of Palm Kernel Shell as a Replacement for Coarse Aggregate in Lightweight Concrete

NASA Astrophysics Data System (ADS)

Itam, Zarina; Beddu, Salmia; Liyana Mohd Kamal, Nur; Ashraful Alam, Md; Issa Ayash, Usama

2016-03-01

Implementing sustainable materials into the construction industry is fast becoming a trend nowadays. Palm Kernel Shell is a by-product of Malaysia’s palm oil industry, generating waste as much as 4 million tons per annum. As a means of producing a sustainable, environmental-friendly, and affordable alternative in the lightweight concrete industry, the exploration of the potential of Palm Kernel Shell to be used as an aggregate replacement was conducted which may give a positive impact to the Malaysian construction industry as well as worldwide concrete usage. This research investigates the feasibility of PKS as an aggregate replacement in lightweight concrete in terms of compressive strength, slump test, water absorption, and density. Results indicate that by using PKS for aggregate replacement, it increases the water absorption but decreases the concrete workability and strength. Results however, fall into the range acceptable for lightweight aggregates, hence it can be concluded that there is potential to use PKS as aggregate replacement for lightweight concrete.

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures. We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.

7 CFR 810.2202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernels, foreign material, and shrunken and broken kernels. The sum of these three factors may not exceed... the removal of dockage and shrunken and broken kernels. (g) Heat-damaged kernels. Kernels, pieces of... sample after the removal of dockage and shrunken and broken kernels. (h) Other grains. Barley, corn...

7 CFR 981.8 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

7 CFR 51.1415 - Inedible kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or otherwise...

An Approximate Approach to Automatic Kernel Selection.

PubMed

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

PubMed

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Site Fidelity in Space Use by Spider Monkeys (Ateles geoffroyi) in the Yucatan Peninsula, Mexico

PubMed Central

Ramos-Fernandez, Gabriel; Smith Aguilar, Sandra E.; Schaffner, Colleen M.; Vick, Laura G.; Aureli, Filippo

2013-01-01

Animal home ranges may vary little in their size and location in the short term but nevertheless show more variability in the long term. We evaluated the degree of site fidelity of two groups of spider monkeys (Ateles geoffroyi) over a 10- and 13-year period, respectively, in the northeastern Yucatan peninsula, Mexico. We used the Local Convex Hull method to estimate yearly home ranges and core areas (defined as the 60% probability contour) for the two groups. Home ranges varied from 7.7 to 49.6 ha and core areas varied from 3.1 to 9.2 ha. We evaluated the degree of site fidelity by quantifying the number of years in which different areas were used as either home ranges or core areas. Large tracts were used only as home ranges and only for a few years, whereas small areas were used as either core area or home range for the duration of the study. The sum of the yearly core areas coincided partially with the yearly home ranges, indicating that home ranges contain areas used intermittently. Home ranges, and especially core areas, contained a higher proportion of mature forest than the larger study site as a whole. Across years and only in one group, the size of core areas was positively correlated with the proportion of adult males in the group, while the size of home ranges was positively correlated with both the proportion of males and the number of tree species included in the diet. Our findings suggest that spider monkey home ranges are the result of a combination of long-term site fidelity and year-to-year use variation to enable exploration of new resources. PMID:23675427

Unconventional protein sources: apricot seed kernels.

PubMed

Gabrial, G N; El-Nahry, F I; Awadalla, M Z; Girgis, S M

1981-09-01

Hamawy apricot seed kernels (sweet), Amar apricot seed kernels (bitter) and treated Amar apricot kernels (bitterness removed) were evaluated biochemically. All kernels were found to be high in fat (42.2--50.91%), protein (23.74--25.70%) and fiber (15.08--18.02%). Phosphorus, calcium, and iron were determined in all experimental samples. The three different apricot seed kernels were used for extensive study including the qualitative determination of the amino acid constituents by acid hydrolysis, quantitative determination of some amino acids, and biological evaluation of the kernel proteins in order to use them as new protein sources. Weanling albino rats failed to grow on diets containing the Amar apricot seed kernels due to low food consumption because of its bitterness. There was no loss in weight in that case. The Protein Efficiency Ratio data and blood analysis results showed the Hamawy apricot seed kernels to be higher in biological value than treated apricot seed kernels. The Net Protein Ratio data which accounts for both weight, maintenance and growth showed the treated apricot seed kernels to be higher in biological value than both Hamawy and Amar kernels. The Net Protein Ratio for the last two kernels were nearly equal.

Estimating home-range size: when to include a third dimension?

PubMed Central

Monterroso, Pedro; Sillero, Neftalí; Rosalino, Luís Miguel; Loureiro, Filipa; Alves, Paulo Célio

2013-01-01

Most studies dealing with home ranges consider the study areas as if they were totally flat, working only in two dimensions, when in reality they are irregular surfaces displayed in three dimensions. By disregarding the third dimension (i.e., topography), the size of home ranges underestimates the surface actually occupied by the animal, potentially leading to misinterpretations of the animals' ecological needs. We explored the influence of considering the third dimension in the estimation of home-range size by modeling the variation between the planimetric and topographic estimates at several spatial scales. Our results revealed that planimetric approaches underestimate home-range size estimations, which range from nearly zero up to 22%. The difference between planimetric and topographic estimates of home-ranges sizes produced highly robust models using the average slope as the sole independent factor. Moreover, our models suggest that planimetric estimates in areas with an average slope of 16.3° (±0.4) or more will incur in errors ≥5%. Alternatively, the altitudinal range can be used as an indicator of the need to include topography in home-range estimates. Our results confirmed that home-range estimates could be significantly biased when topography is disregarded. We suggest that study areas where home-range studies will be performed should firstly be scoped for its altitudinal range, which can serve as an indicator for the need for posterior use of average slope values to model the surface area used and/or available for the studied animals. PMID:23919170

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

Design of CT reconstruction kernel specifically for clinical lung imaging

NASA Astrophysics Data System (ADS)

Cody, Dianna D.; Hsieh, Jiang; Gladish, Gregory W.

2005-04-01

In this study we developed a new reconstruction kernel specifically for chest CT imaging. An experimental flat-panel CT scanner was used on large dogs to produce 'ground-truth" reference chest CT images. These dogs were also examined using a clinical 16-slice CT scanner. We concluded from the dog images acquired on the clinical scanner that the loss of subtle lung structures was due mostly to the presence of the background noise texture when using currently available reconstruction kernels. This qualitative evaluation of the dog CT images prompted the design of a new recon kernel. This new kernel consisted of the combination of a low-pass and a high-pass kernel to produce a new reconstruction kernel, called the 'Hybrid" kernel. The performance of this Hybrid kernel fell between the two kernels on which it was based, as expected. This Hybrid kernel was also applied to a set of 50 patient data sets; the analysis of these clinical images is underway. We are hopeful that this Hybrid kernel will produce clinical images with an acceptable tradeoff of lung detail, reliable HU, and image noise.

Quality changes in macadamia kernel between harvest and farm-gate.

PubMed

Walton, David A; Wallace, Helen M

2011-02-01

Macadamia integrifolia, Macadamia tetraphylla and their hybrids are cultivated for their edible kernels. After harvest, nuts-in-shell are partially dried on-farm and sorted to eliminate poor-quality kernels before consignment to a processor. During these operations, kernel quality may be lost. In this study, macadamia nuts-in-shell were sampled at five points of an on-farm postharvest handling chain from dehusking to the final storage silo to assess quality loss prior to consignment. Shoulder damage, weight of pieces and unsound kernel were assessed for raw kernels, and colour, mottled colour and surface damage for roasted kernels. Shoulder damage, weight of pieces and unsound kernel for raw kernels increased significantly between the dehusker and the final silo. Roasted kernels displayed a significant increase in dark colour, mottled colour and surface damage during on-farm handling. Significant loss of macadamia kernel quality occurred on a commercial farm during sorting and storage of nuts-in-shell before nuts were consigned to a processor. Nuts-in-shell should be dried as quickly as possible and on-farm handling minimised to maintain optimum kernel quality. 2010 Society of Chemical Industry.

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

PubMed

Kwak, Nojun

2016-05-20

Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

Incidence of Fusarium spp. and Levels of Fumonisin B1 in Maize in Western Kenya

PubMed Central

Kedera, C. J.; Plattner, R. D.; Desjardins, A. E.

1999-01-01

Maize kernel samples were collected in 1996 from smallholder farm storages in the districts of Bomet, Bungoma, Kakamega, Kericho, Kisii, Nandi, Siaya, Trans Nzoia, and Vihiga in the tropical highlands of western Kenya. Two-thirds of the samples were good-quality maize, and one-third were poor-quality maize with a high incidence of visibly diseased kernels. One hundred fifty-three maize samples were assessed for Fusarium infection by culturing kernels on a selective medium. The isolates obtained were identified to the species level based on morphology and on formation of the sexual stage in Gibberella fujikuroi mating population tests. Fusarium moniliforme (G. fujikuroi mating population A) was isolated most frequently, but F. subglutinans (G. fujikuroi mating population E), F. graminearum, F. oxysporum, F. solani, and other Fusarium species were also isolated. The high incidence of kernel infection with the fumonisin-producing species F. moniliforme indicated a potential for fumonisin contamination of Kenyan maize. However, analysis of 197 maize kernel samples by high-performance liquid chromatography found little fumonisin B1 in most of the samples. Forty-seven percent of the samples contained fumonisin B1 at levels above the detection limit (100 ng/g), but only 5% were above 1,000 ng/g, a proposed level of concern for human consumption. The four most-contaminated samples, with fumonisin B1 levels ranging from 3,600 to 11,600 ng/g, were from poor-quality maize collected in the Kisii district. Many samples with a high incidence of visibly diseased kernels contained little or no fumonisin B1, despite the presence of F. moniliforme. This result may be attributable to the inability of F. moniliforme isolates present in Kenyan maize to produce fumonisins, to the presence of other ear rot fungi, and/or to environmental conditions unfavorable for fumonisin production. PMID:9872757

Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging.

PubMed

Manley, Marena; du Toit, Gerida; Geladi, Paul

2011-02-07

The combination of near infrared (NIR) hyperspectral imaging and chemometrics was used to follow the diffusion of conditioning water over time in wheat kernels of different hardnesses. Conditioning was attempted with deionised water (dH(2)O) and deuterium oxide (D(2)O). The images were recorded at different conditioning times (0-36 h) from 1000 to 2498 nm with a line scan imaging system. After multivariate cleaning and spectral pre-processing (either multiplicative scatter correction or standard normal variate and Savitzky-Golay smoothing) six principal components (PCs) were calculated. These were studied visually interactively as score images and score plots. As no clear clusters were present in the score plots, changes in the score plots were investigated by means of classification gradients made within the respective PCs. Classes were selected in the direction of a PC (from positive to negative or negative to positive score values) in almost equal segments. Subsequently loading line plots were used to provide a spectroscopic explanation of the classification gradients. It was shown that the first PC explained kernel curvature. PC3 was shown to be related to a moisture-starch contrast and could explain the progress of water uptake. The positive influence of protein was also observed. The behaviour of soft, hard and very hard kernels was different in this respect, with the uptake of water observed much earlier in the soft kernels than in the harder ones. The harder kernels also showed a stronger influence of protein in the loading line plots. Difference spectra showed interpretable changes over time for water but not for D(2)O which had a too low signal in the wavelength range used. NIR hyperspectral imaging together with exploratory chemometrics, as detailed in this paper, may have wider applications than merely conditioning studies. Copyright © 2010 Elsevier B.V. All rights reserved.

Anthraquinones isolated from the browned Chinese chestnut kernels (Castanea mollissima blume)

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Qi, J. H.; Qin, L.; Wang, F.; Pang, M. X.

2016-08-01

Anthraquinones (AQS) represent a group of secondary metallic products in plants. AQS are often naturally occurring in plants and microorganisms. In a previous study, we found that AQS were produced by enzymatic browning reaction in Chinese chestnut kernels. To find out whether non-enzymatic browning reaction in the kernels could produce AQS too, AQS were extracted from three groups of chestnut kernels: fresh kernels, non-enzymatic browned kernels, and browned kernels, and the contents of AQS were determined. High performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) methods were used to identify two compounds of AQS, rehein(1) and emodin(2). AQS were barely exists in the fresh kernels, while both browned kernel groups sample contained a high amount of AQS. Thus, we comfirmed that AQS could be produced during both enzymatic and non-enzymatic browning process. Rhein and emodin were the main components of AQS in the browned kernels.

Satellite tracking reveals habitat use by juvenile green sea turtles Chelonia mydas in the Everglades, Florida, USA

USGS Publications Warehouse

Hart, Kristen M.; Fujisaki, Ikuko

2010-01-01

We tracked the movements of 6 juvenile green sea turtles captured in coastal areas of southwest Florida within Everglades National Park (ENP) using satellite transmitters for periods of 27 to 62 d in 2007 and 2008 (mean ± SD: 47.7 ± 12.9 d). Turtles ranged in size from 33.4 to 67.5 cm straight carapace length (45.7 ± 12.9 cm) and 4.4 to 40.8 kg in mass (16.0 ± 13.8 kg). These data represent the first satellite tracking data gathered on juveniles of this endangered species at this remote study site, which may represent an important developmental habitat and foraging ground. Satellite tracking results suggested that these immature turtles were resident for several months very close to capture and release sites, in waters from 0 to 10 m in depth. Mean home range for this springtime tracking period as represented by minimum convex polygon (MCP) was 1004.9 ± 618.8 km2 (range 374.1 to 2060.1 km2), with 4 of 6 individuals spending a significant proportion of time within the ENP boundaries in 2008 in areas with dense patches of marine algae. Core use areas determined by 50% kernel density estimates (KDE) ranged from 5.0 to 54.4 km2, with a mean of 22.5 ± 22.1 km2. Overlap of 50% KDE plots for 6 turtles confirmed use of shallow-water nearshore habitats =0.6 m deep within the park boundary. Delineating specific habitats used by juvenile green turtles in this and other remote coastal areas with protected status will help conservation managers to prioritize their efforts and increase efficacy in protecting endangered species.

Fast repurposing of high-resolution stereo video content for mobile use

NASA Astrophysics Data System (ADS)

Karaoglu, Ali; Lee, Bong Ho; Boev, Atanas; Cheong, Won-Sik; Gotchev, Atanas

2012-06-01

3D video content is captured and created mainly in high resolution targeting big cinema or home TV screens. For 3D mobile devices, equipped with small-size auto-stereoscopic displays, such content has to be properly repurposed, preferably in real-time. The repurposing requires not only spatial resizing but also properly maintaining the output stereo disparity, as it should deliver realistic, pleasant and harmless 3D perception. In this paper, we propose an approach to adapt the disparity range of the source video to the comfort disparity zone of the target display. To achieve this, we adapt the scale and the aspect ratio of the source video. We aim at maximizing the disparity range of the retargeted content within the comfort zone, and minimizing the letterboxing of the cropped content. The proposed algorithm consists of five stages. First, we analyse the display profile, which characterises what 3D content can be comfortably observed in the target display. Then, we perform fast disparity analysis of the input stereoscopic content. Instead of returning the dense disparity map, it returns an estimate of the disparity statistics (min, max, meanand variance) per frame. Additionally, we detect scene cuts, where sharp transitions in disparities occur. Based on the estimated input, and desired output disparity ranges, we derive the optimal cropping parameters and scale of the cropping window, which would yield the targeted disparity range and minimize the area of cropped and letterboxed content. Once the rescaling and cropping parameters are known, we perform resampling procedure using spline-based and perceptually optimized resampling (anti-aliasing) kernels, which have also a very efficient computational structure. Perceptual optimization is achieved through adjusting the cut-off frequency of the anti-aliasing filter with the throughput of the target display.

Sensitivity analysis of seismic waveforms to upper-mantle discontinuities using the adjoint method

NASA Astrophysics Data System (ADS)

Koroni, Maria; Bozdağ, Ebru; Paulssen, Hanneke; Trampert, Jeannot

2017-09-01

Using spectral-element simulations of wave propagation, we investigated the sensitivity of seismic waveforms, recorded on transverse components, to upper-mantle discontinuities in 1-D and 3-D background models. These sensitivity kernels, or Fréchet derivatives, illustrate the spatial sensitivity to model parameters, of which those for shear wave speed and the surface topography of internal boundaries are discussed in this paper. We focus on the boundaries at 400 and 670 km depth of the mantle transition zone. SS precursors have frequently been used to infer the topography of upper-mantle discontinuities. These seismic phases are underside reflections off these boundaries and are usually analysed in the distance range of 110°-160°. This distance range is chosen to minimize the interference from other waves. We show sensitivity kernels for consecutive time windows at three characteristic epicentral distances within the 110°-160° range. The sensitivity kernels are computed with the adjoint method using synthetic data. From our simulations we can draw three main conclusions: (i) The exact Fréchet derivatives show that in all time windows, and also in those centred on the SS precursors, there is interference from other waves. This explains the difficulty reported in the literature to correct for 3-D shear wave speed perturbations, even if the 3-D structure is perfectly known. (ii) All studies attempting to map the topography of the 400 and 670 km discontinuities to date assume that the traveltimes of SS precursors can be linearly decomposed into a 3-D elastic structure and a topography part. We recently showed that such a linear decomposition is not possible for SS precursors, and the sensitivity kernels presented in this paper explain why. (iii) In agreement with previous work, we show that other parts of the seismograms have greater sensitivity to upper-mantle discontinuities than SS precursors, especially multiply bouncing S waves exploiting the S-wave triplications due to the mantle transition zone. These phases can potentially improve the inference of global topographic variations of the upper-mantle discontinuities in the context of full waveform inversion in a joint inversion for (an)elastic parameters and topography.

Home ranges of lions in the Kalahari, Botswana exhibit vast sizes and high temporal variability.

PubMed

Zehnder, André; Henley, Stephen; Weibel, Robert

2018-06-01

The central Kalahari region in Botswana is one of the few remaining ecosystems with a stable lion population. Yet, relatively little is known about the ecology of the lions there. As an entry point, home range estimations provide information about the space utilization of the studied animals. The home ranges of eight lions in this region were determined to investigate their spatial overlaps and spatiotemporal variations. We found that, except for MCP, all home range estimators yielded comparable results regarding size and shape. The home ranges of all individuals were located predominantly inside the protected reserves. Their areas were among the largest known for lions with 1131 - 4314km 2 (95%), with no significant differences between males and females. Numerous overlaps between lions of different sexes were detected, although these originate from different groups. A distance chart confirmed that most of these lions directly encountered each other once or several times. Strong temporal variations of the home ranges were observed that did not match a seasonal pattern. The exceptionally large home ranges are likely to be caused by the sparse and dynamic prey populations. Since the ungulates in the study area move in an opportunistic way, too, strong spatiotemporal home range variations emerge. This can lead to misleading home ranges. We therefore recommend clarifying the stability of the home ranges by applying several levels of temporal aggregation. The lack of strict territoriality is likely an adaptation to the variable prey base and the high energetic costs associated with defending a large area. Copyright © 2018 Elsevier GmbH. All rights reserved.

An analysis of monthly home range size in the critically endangered California Condor Gymnogyps californianus

USGS Publications Warehouse

Rivers, James W.; Johnson, Matthew J.; Haig, Susan M.; Schwarz, Carl J.; Burnett, Joseph; Brandt, Joseph; George, Daniel; Grantham, Jesse

2014-01-01

Condors and vultures comprise the only group of terrestrial vertebrates in the world that are obligate scavengers, and these species move widely to locate ephemeral, unpredictable, and patchily-distributed food resources. In this study, we used high-resolution GPS location data to quantify monthly home range size of the critically endangered California Condor Gymnogyps californianus throughout the annual cycle in California. We assessed whether individual-level characteristics (age, sex and breeding status) and factors related to endangered species recovery program efforts (rearing method, release site) were linked to variation in monthly home range size. We found that monthly home range size varied across the annual cycle, with the largest monthly home ranges observed during late summer and early fall (July–October), a pattern that may be linked to seasonal changes in thermals that facilitate movement. Monthly home ranges of adults were significantly larger than those of immatures, but males and females used monthly home ranges of similar size throughout the year and breeding adults did not differ from non-breeding adults in their average monthly home range size. Individuals from each of three release sites differed significantly in the size of their monthly home ranges, and no differences in monthly home range size were detected between condors reared under captive conditions relative to those reared in the wild. Our study provides an important foundation for understanding the movement ecology of the California Condor and it highlights the importance of seasonal variation in space use for effective conservation planning for this critically endangered species.

Anchoring and adjusting amidst humans: Ranging behavior of Persian leopards along the Iran-Turkmenistan borderland

PubMed Central

Johnson, Paul J.; Macdonald, David W.; Hunter, Luke T. B.

2018-01-01

Understanding the space use and movement ecology of apex predators, particularly in mosaic landscapes encompassing different land-uses, is fundamental for formulating effective conservation policy. The top extant big cat in the Middle East and the Caucasus, the Persian leopard Panthera pardus saxicolor, has disappeared from most of its historic range. Its spatial ecology in the areas where it remains is almost unknown. Between September 2014 and May 2017, we collared and monitored six adult leopards (5 males and 1 female) using GPS-satellite Iridium transmitters in Tandoureh National Park (355 km2) along the Iran-Turkmenistan borderland. Using auto-correlated Kernel density estimation based on a continuous-time stochastic process for relocation data, we estimated a mean home range of 103.4 ± SE 51.8 km2 for resident males which is larger than has been observed in other studies of Asian leopards. Most predation events occurred in core areas, averaging 32.4 ± SE 12.7 km2. Although neighboring leopards showed high spatiotemporal overlap, their hunting areas were largely exclusive. Five out of six of leopards spent some time outside the national park, among human communities. Our study suggests that a national park can play an ‘anchoring’ role for individuals of an apex predator that spend some time in the surrounding human-dominated landscapes. Therefore, we envisage that instead of emphasizing either land sharing or land sparing, a combined approach can secure the viability of resilient large carnivores that are able to coexist with humans in the rugged montane landscapes of west and central Asia. PMID:29719005

Anchoring and adjusting amidst humans: Ranging behavior of Persian leopards along the Iran-Turkmenistan borderland.

PubMed

Farhadinia, Mohammad S; Johnson, Paul J; Macdonald, David W; Hunter, Luke T B

2018-01-01

Understanding the space use and movement ecology of apex predators, particularly in mosaic landscapes encompassing different land-uses, is fundamental for formulating effective conservation policy. The top extant big cat in the Middle East and the Caucasus, the Persian leopard Panthera pardus saxicolor, has disappeared from most of its historic range. Its spatial ecology in the areas where it remains is almost unknown. Between September 2014 and May 2017, we collared and monitored six adult leopards (5 males and 1 female) using GPS-satellite Iridium transmitters in Tandoureh National Park (355 km2) along the Iran-Turkmenistan borderland. Using auto-correlated Kernel density estimation based on a continuous-time stochastic process for relocation data, we estimated a mean home range of 103.4 ± SE 51.8 km2 for resident males which is larger than has been observed in other studies of Asian leopards. Most predation events occurred in core areas, averaging 32.4 ± SE 12.7 km2. Although neighboring leopards showed high spatiotemporal overlap, their hunting areas were largely exclusive. Five out of six of leopards spent some time outside the national park, among human communities. Our study suggests that a national park can play an 'anchoring' role for individuals of an apex predator that spend some time in the surrounding human-dominated landscapes. Therefore, we envisage that instead of emphasizing either land sharing or land sparing, a combined approach can secure the viability of resilient large carnivores that are able to coexist with humans in the rugged montane landscapes of west and central Asia.

Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardy, David J., E-mail: dhardy@illinois.edu; Schulten, Klaus; Wolff, Matthew A.

2016-03-21

The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation methodmore » (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle–mesh Ewald method falls short.« less

Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations.

PubMed

Hardy, David J; Wolff, Matthew A; Xia, Jianlin; Schulten, Klaus; Skeel, Robert D

2016-03-21

The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation method (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle-mesh Ewald method falls short.

Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Hardy, David J.; Wolff, Matthew A.; Xia, Jianlin; Schulten, Klaus; Skeel, Robert D.

2016-03-01

The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation method (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle-mesh Ewald method falls short.

Broken rice kernels and the kinetics of rice hydration and texture during cooking.

PubMed

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P < 0.05) but the unbroken kernels became significantly harder. Moisture content and moisture uptake rate were positively correlated, and cooked rice hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

Absorbed dose kernel and self-shielding calculations for a novel radiopaque glass microsphere for transarterial radioembolization.

PubMed

Church, Cody; Mawko, George; Archambault, John Paul; Lewandowski, Robert; Liu, David; Kehoe, Sharon; Boyd, Daniel; Abraham, Robert; Syme, Alasdair

2018-02-01

Radiopaque microspheres may provide intraprocedural and postprocedural feedback during transarterial radioembolization (TARE). Furthermore, the potential to use higher resolution x-ray imaging techniques as opposed to nuclear medicine imaging suggests that significant improvements in the accuracy and precision of radiation dosimetry calculations could be realized for this type of therapy. This study investigates the absorbed dose kernel for novel radiopaque microspheres including contributions of both short and long-lived contaminant radionuclides while concurrently quantifying the self-shielding of the glass network. Monte Carlo simulations using EGSnrc were performed to determine the dose kernels for all monoenergetic electron emissions and all beta spectra for radionuclides reported in a neutron activation study of the microspheres. Simulations were benchmarked against an accepted 90 Y dose point kernel. Self-shielding was quantified for the microspheres by simulating an isotropically emitting, uniformly distributed source, in glass and in water. The ratio of the absorbed doses was scored as a function of distance from a microsphere. The absorbed dose kernel for the microspheres was calculated for (a) two bead formulations following (b) two different durations of neutron activation, at (c) various time points following activation. Self-shielding varies with time postremoval from the reactor. At early time points, it is less pronounced due to the higher energies of the emissions. It is on the order of 0.4-2.8% at a radial distance of 5.43 mm with increased size from 10 to 50 μm in diameter during the time that the microspheres would be administered to a patient. At long time points, self-shielding is more pronounced and can reach values in excess of 20% near the end of the range of the emissions. Absorbed dose kernels for 90 Y, 90m Y, 85m Sr, 85 Sr, 87m Sr, 89 Sr, 70 Ga, 72 Ga, and 31 Si are presented and used to determine an overall kernel for the microspheres based on weighted activities. The shapes of the absorbed dose kernels are dominated at short times postactivation by the contributions of 70 Ga and 72 Ga. Following decay of the short-lived contaminants, the absorbed dose kernel is effectively that of 90 Y. After approximately 1000 h postactivation, the contributions of 85 Sr and 89 Sr become increasingly dominant, though the absorbed dose-rate around the beads drops by roughly four orders of magnitude. The introduction of high atomic number elements for the purpose of increasing radiopacity necessarily leads to the production of radionuclides other than 90 Y in the microspheres. Most of the radionuclides in this study are short-lived and are likely not of any significant concern for this therapeutic agent. The presence of small quantities of longer lived radionuclides will change the shape of the absorbed dose kernel around a microsphere at long time points postadministration when activity levels are significantly reduced. © 2017 American Association of Physicists in Medicine.

Home range and habitat utilization of breeding male merlins, Falco columbarius, in southeastern Montana

Treesearch

Dale M. Becker; Carolyn Hull Sieg

1987-01-01

Home range size and habitat utilization of three breeding male Richardson’s Merlins (Falco columbarius richardsonii) in southeastern Montana were studied using radio telemetry. Home ranges of these birds encompassed 13,23, and 28 km2. Each bird traveled up to 9 km from its nest. Each home range encompassed five habitats;...

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Study on Energy Productivity Ratio (EPR) at palm kernel oil processing factory: case study on PT-X at Sumatera Utara Plantation

NASA Astrophysics Data System (ADS)

Haryanto, B.; Bukit, R. Br; Situmeang, E. M.; Christina, E. P.; Pandiangan, F.

2018-02-01

The purpose of this study was to determine the performance, productivity and feasibility of the operation of palm kernel processing plant based on Energy Productivity Ratio (EPR). EPR is expressed as the ratio of output to input energy and by-product. Palm Kernel plan is process in palm kernel to become palm kernel oil. The procedure started from collecting data needed as energy input such as: palm kernel prices, energy demand and depreciation of the factory. The energy output and its by-product comprise the whole production price such as: palm kernel oil price and the remaining products such as shells and pulp price. Calculation the equality of energy of palm kernel oil is to analyze the value of Energy Productivity Ratio (EPR) bases on processing capacity per year. The investigation has been done in Kernel Oil Processing Plant PT-X at Sumatera Utara plantation. The value of EPR was 1.54 (EPR > 1), which indicated that the processing of palm kernel into palm kernel oil is feasible to be operated based on the energy productivity.

Northern spotted owls: influence of prey base and landscape character.

Treesearch

A.B. Carey; S.P. Horton; B.L. Biswell

1992-01-01

We studied prey populations and the use and composition of home ranges of 47 Northern Spotted Owls (Strix occidentalis caurina) over 12 mo in five landscapes in two forest types in southwestern Oregon. We measured 1-yr home ranges of 23 owl pairs, 2-yr home ranges of 13 pairs, and 3-yr home ranges of 3 pairs. The landscapes differed in the degree...

Linking seasonal home range size with habitat selection and movement in a mountain ungulate.

PubMed

Viana, Duarte S; Granados, José Enrique; Fandos, Paulino; Pérez, Jesús M; Cano-Manuel, Francisco Javier; Burón, Daniel; Fandos, Guillermo; Aguado, María Ángeles Párraga; Figuerola, Jordi; Soriguer, Ramón C

2018-01-01

Space use by animals is determined by the interplay between movement and the environment, and is thus mediated by habitat selection, biotic interactions and intrinsic factors of moving individuals. These processes ultimately determine home range size, but their relative contributions and dynamic nature remain less explored. We investigated the role of habitat selection, movement unrelated to habitat selection and intrinsic factors related to sex in driving space use and home range size in Iberian ibex, Capra pyrenaica . We used GPS collars to track ibex across the year in two different geographical areas of Sierra Nevada, Spain, and measured habitat variables related to forage and roost availability. By using integrated step selection analysis (iSSA), we show that habitat selection was important to explain space use by ibex. As a consequence, movement was constrained by habitat selection, as observed displacement rate was shorter than expected under null selection. Selection-independent movement, selection strength and resource availability were important drivers of seasonal home range size. Both displacement rate and directional persistence had a positive relationship with home range size while accounting for habitat selection, suggesting that individual characteristics and state may also affect home range size. Ibex living at higher altitudes, where resource availability shows stronger altitudinal gradients across the year, had larger home ranges. Home range size was larger in spring and autumn, when ibex ascend and descend back, and smaller in summer and winter, when resources are more stable. Therefore, home range size decreased with resource availability. Finally, males had larger home ranges than females, which might be explained by differences in body size and reproductive behaviour. Movement, selection strength, resource availability and intrinsic factors related to sex determined home range size of Iberian ibex. Our results highlight the need to integrate and account for process dependencies, here the interdependence of movement and habitat selection, to understand how animals use space. This study contributes to understand how movement links environmental and geographical space use and determines home range behaviour in large herbivores.

Home range establishment and utilization by reintroduced lions (Panthera leo) in a small South African wildlife reserve.

PubMed

Yiu, Sze-Wing; Parrini, Francesca; Karczmarski, Leszek; Keith, Mark

2017-07-01

Understanding of animal spatial behavior is essential for informed management decisions. In southern Africa, reintroduction of lions (Panthera leo) to small reserves (<1000 km 2 ) has increased since the early 2000s, however studies on their ranging behavior in these enclosed systems remain lacking. We applied Time Local Convex Hull (T-LoCoH) methods to study the home range establishment and utilization of 11 lions reintroduced to Dinokeng Game Reserve, South Africa, during 2011 through 2014. Lions established home ranges close to their release sites and during the following 3 years their home range sizes continued to increase, but in each individual case the size remained smaller than half of the reserve area (<70 km 2 ). Space use strategies differed between the core and the entire home range, with higher frequency of visits found in core areas. Exceptionally high rates (>60 separate visits) around the largest dam and along rivers suggest the importance of water and its surrounding vegetation in the lions' space utilization pattern. The home range size did not differ with season or sex of the individuals, whereas shifts in locations of home ranges revealed differences in the response of the 2 sexes to territorial conflicts and management interventions. Our study shows a dynamic home range utilization pattern and highlights the importance of both fine-scale space use patterns (frequency and duration of visits) and broad-scale home range changes in understanding the ranging behavior of reintroduced animals. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

7 CFR 981.9 - Kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

Local and Global Gestalt Laws: A Neurally Based Spectral Approach.

PubMed

Favali, Marta; Citti, Giovanna; Sarti, Alessandro

2017-02-01

This letter presents a mathematical model of figure-ground articulation that takes into account both local and global gestalt laws and is compatible with the functional architecture of the primary visual cortex (V1). The local gestalt law of good continuation is described by means of suitable connectivity kernels that are derived from Lie group theory and quantitatively compared with long-range connectivity in V1. Global gestalt constraints are then introduced in terms of spectral analysis of a connectivity matrix derived from these kernels. This analysis performs grouping of local features and individuates perceptual units with the highest salience. Numerical simulations are performed, and results are obtained by applying the technique to a number of stimuli.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hornung, Richard D.; Hones, Holger E.

The RAJA Performance Suite is designed to evaluate performance of the RAJA performance portability library on a wide variety of important high performance computing (HPC) algorithmic lulmels. These kernels assess compiler optimizations and various parallel programming model backends accessible through RAJA, such as OpenMP, CUDA, etc. The Initial version of the suite contains 25 computational kernels, each of which appears in 6 variants: Baseline SequcntiaJ, RAJA SequentiaJ, Baseline OpenMP, RAJA OpenMP, Baseline CUDA, RAJA CUDA. All variants of each kernel perform essentially the same mathematical operations and the loop body code for each kernel is identical across all variants. Theremore » are a few kernels, such as those that contain reduction operations, that require CUDA-specific coding for their CUDA variants. ActuaJ computer instructions executed and how they run in parallel differs depending on the parallel programming model backend used and which optimizations are perfonned by the compiler used to build the Perfonnance Suite executable. The Suite will be used primarily by RAJA developers to perform regular assessments of RAJA performance across a range of hardware platforms and compilers as RAJA features are being developed. It will also be used by LLNL hardware and software vendor panners for new defining requirements for future computing platform procurements and acceptance testing. In particular, the RAJA Performance Suite will be used for compiler acceptance testing of the upcoming CORAUSierra machine {initial LLNL delivery expected in late-2017/early 2018) and the CORAL-2 procurement. The Suite will aJso be used to generate concise source code reproducers of compiler and runtime issues we uncover so that we may provide them to relevant vendors to be fixed.« less

Validation of Born Traveltime Kernels

NASA Astrophysics Data System (ADS)

Baig, A. M.; Dahlen, F. A.; Hung, S.

2001-12-01

Most inversions for Earth structure using seismic traveltimes rely on linear ray theory to translate observed traveltime anomalies into seismic velocity anomalies distributed throughout the mantle. However, ray theory is not an appropriate tool to use when velocity anomalies have scale lengths less than the width of the Fresnel zone. In the presence of these structures, we need to turn to a scattering theory in order to adequately describe all of the features observed in the waveform. By coupling the Born approximation to ray theory, the first order dependence of heterogeneity on the cross-correlated traveltimes (described by the Fréchet derivative or, more colourfully, the banana-doughnut kernel) may be determined. To determine for what range of parameters these banana-doughnut kernels outperform linear ray theory, we generate several random media specified by their statistical properties, namely the RMS slowness perturbation and the scale length of the heterogeneity. Acoustic waves are numerically generated from a point source using a 3-D pseudo-spectral wave propagation code. These waves are then recorded at a variety of propagation distances from the source introducing a third parameter to the problem: the number of wavelengths traversed by the wave. When all of the heterogeneity has scale lengths larger than the width of the Fresnel zone, ray theory does as good a job at predicting the cross-correlated traveltime as the banana-doughnut kernels do. Below this limit, wavefront healing becomes a significant effect and ray theory ceases to be effective even though the kernels remain relatively accurate provided the heterogeneity is weak. The study of wave propagation in random media is of a more general interest and we will also show our measurements of the velocity shift and the variance of traveltime compare to various theoretical predictions in a given regime.

A Kernel for Open Source Drug Discovery in Tropical Diseases

PubMed Central

Ortí, Leticia; Carbajo, Rodrigo J.; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M.; Rai, Arti K.; Taylor, Ginger; Todd, Matthew H.; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A.

2009-01-01

Background Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private–public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues—open source drug discovery—has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such “kernels”. Methodology/Principal Findings Here, we use a computational pipeline for: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. Conclusions/Significance The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases. PMID:19381286

Spectral imaging using consumer-level devices and kernel-based regression.

PubMed

Heikkinen, Ville; Cámara, Clara; Hirvonen, Tapani; Penttinen, Niko

2016-06-01

Hyperspectral reflectance factor image estimations were performed in the 400-700 nm wavelength range using a portable consumer-level laptop display as an adjustable light source for a trichromatic camera. Targets of interest were ColorChecker Classic samples, Munsell Matte samples, geometrically challenging tempera icon paintings from the turn of the 20th century, and human hands. Measurements and simulations were performed using Nikon D80 RGB camera and Dell Vostro 2520 laptop screen as a light source. Estimations were performed without spectral characteristics of the devices and by emphasizing simplicity for training sets and estimation model optimization. Spectral and color error images are shown for the estimations using line-scanned hyperspectral images as the ground truth. Estimations were performed using kernel-based regression models via a first-degree inhomogeneous polynomial kernel and a Matérn kernel, where in the latter case the median heuristic approach for model optimization and link function for bounded estimation were evaluated. Results suggest modest requirements for a training set and show that all estimation models have markedly improved accuracy with respect to the DE00 color distance (up to 99% for paintings and hands) and the Pearson distance (up to 98% for paintings and 99% for hands) from a weak training set (Digital ColorChecker SG) case when small representative training data were used in the estimation.

Time-frequency Features for Impedance Cardiography Signals During Anesthesia Using Different Distribution Kernels.

PubMed

Muñoz, Jesús Escrivá; Gambús, Pedro; Jensen, Erik W; Vallverdú, Montserrat

2018-01-01

This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient's anesthesia state in a statistically significant way. Schattauer GmbH.

7 CFR 51.2295 - Half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

7 CFR 810.206 - Grades and grade requirements for barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... weight per bushel (pounds) Sound barley (percent) Maximum Limits of— Damaged kernels 1 (percent) Heat damaged kernels (percent) Foreign material (percent) Broken kernels (percent) Thin barley (percent) U.S... or otherwise of distinctly low quality. 1 Includes heat-damaged kernels. Injured-by-frost kernels and...

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.2125 - Split or broken kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will not...

7 CFR 51.2296 - Three-fourths half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

The Classification of Diabetes Mellitus Using Kernel k-means

NASA Astrophysics Data System (ADS)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

UNICOS Kernel Internals Application Development

NASA Technical Reports Server (NTRS)

Caredo, Nicholas; Craw, James M. (Technical Monitor)

1995-01-01

Having an understanding of UNICOS Kernel Internals is valuable information. However, having the knowledge is only half the value. The second half comes with knowing how to use this information and apply it to the development of tools. The kernel contains vast amounts of useful information that can be utilized. This paper discusses the intricacies of developing utilities that utilize kernel information. In addition, algorithms, logic, and code will be discussed for accessing kernel information. Code segments will be provided that demonstrate how to locate and read kernel structures. Types of applications that can utilize kernel information will also be discussed.

Detection of maize kernels breakage rate based on K-means clustering

NASA Astrophysics Data System (ADS)

Yang, Liang; Wang, Zhuo; Gao, Lei; Bai, Xiaoping

2017-04-01

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Aflatoxin and nutrient contents of peanut collected from local market and their processed foods

NASA Astrophysics Data System (ADS)

Ginting, E.; Rahmianna, A. A.; Yusnawan, E.

2018-01-01

Peanut is succeptable to aflatoxin contamination and the sources of peanut as well as processing methods considerably affect aflatoxin content of the products. Therefore, the study on aflatoxin and nutrient contents of peanut collected from local market and their processed foods were performed. Good kernels of peanut were prepared into fried peanut, pressed-fried peanut, peanut sauce, peanut press cake, fermented peanut press cake (tempe) and fried tempe, while blended kernels (good and poor kernels) were processed into peanut sauce and tempe and poor kernels were only processed into tempe. The results showed that good and blended kernels which had high number of sound/intact kernels (82,46% and 62,09%), contained 9.8-9.9 ppb of aflatoxin B1, while slightly higher level was seen in poor kernels (12.1 ppb). However, the moisture, ash, protein, and fat contents of the kernels were similar as well as the products. Peanut tempe and fried tempe showed the highest increase in protein content, while decreased fat contents were seen in all products. The increase in aflatoxin B1 of peanut tempe prepared from poor kernels > blended kernels > good kernels. However, it averagely decreased by 61.2% after deep-fried. Excluding peanut tempe and fried tempe, aflatoxin B1 levels in all products derived from good kernels were below the permitted level (15 ppb). This suggests that sorting peanut kernels as ingredients and followed by heat processing would decrease the aflatoxin content in the products.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Home range and territoriality of two Hawaiian honeycreepers, the 'Akohekohe and Maui Parrotbill

USGS Publications Warehouse

Pratt, Thane K.; Simon, John C.; Farm, Brian P.; Berlin, Kim E.; Kowalsky, James R.

2001-01-01

Hawaiian honeycreepers have radiated into a diversity of trophic niches and patterns of space-use. We investigated space-use in two honeycreeper species, the ‘Ākohekohe (Palmeria dolei), an endangered nectarivore, and Maui Parrotbill (Pseudonestor xanthophrys), an endangered wood excavator, by mapping the home ranges and dispersion of color-banded individuals at a study site in relatively undisturbed montane cloud forest on Maui Island, Hawai‘i. With 20% of outlying points excluded, home-range size averaged much smaller for adult male ‘Ākohekohe (0.56 ha) than for male Maui Parrotbill (2.26 ha). In both species, a female's home range mostly overlapped that of her mate. Adult male Maui Parrotbill defended year-round home ranges from which they excluded conspecifics except for their mates and dependent offspring. Although our data suggest that ‘Ākohekohe also maintained all-purpose territories, the evidence is less convincing because these birds were seen feeding in the home ranges of other individuals. By defending all-purpose territories, these two species depart from the more common honeycreeper pattern of sharing large, undefended home ranges.

A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

PubMed Central

Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

2013-01-01

In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

Comparison of spatiotemporal interpolators for 4D image reconstruction from 2D transesophageal ultrasound

NASA Astrophysics Data System (ADS)

Haak, Alexander; van Stralen, Marijn; van Burken, Gerard; Klein, Stefan; Pluim, Josien P. W.; de Jong, Nico; van der Steen, Antonius F. W.; Bosch, Johan G.

2012-03-01

°For electrophysiology intervention monitoring, we intend to reconstruct 4D ultrasound (US) of structures in the beating heart from 2D transesophageal US by scanplane rotation. The image acquisition is continuous but unsynchronized to the heart rate, which results in a sparsely and irregularly sampled dataset and a spatiotemporal interpolation method is desired. Previously, we showed the potential of normalized convolution (NC) for interpolating such datasets. We explored 4D interpolation by 3 different methods: NC, nearest neighbor (NN), and temporal binning followed by linear interpolation (LTB). The test datasets were derived by slicing three 4D echocardiography datasets at random rotation angles (θ, range: 0-180) and random normalized cardiac phase (τ, range: 0-1). Four different distributions of rotated 2D images with 600, 900, 1350, and 1800 2D input images were created from all TEE sets. A 2D Gaussian kernel was used for NC and optimal kernel sizes (σθ and στ) were found by performing an exhaustive search. The RMS gray value error (RMSE) of the reconstructed images was computed for all interpolation methods. The estimated optimal kernels were in the range of σθ = 3.24 - 3.69°/ στ = 0.045 - 0.048, σθ = 2.79°/ στ = 0.031 - 0.038, σθ = 2.34°/ στ = 0.023 - 0.026, and σθ = 1.89°/ στ = 0.021 - 0.023 for 600, 900, 1350, and 1800 input images respectively. We showed that NC outperforms NN and LTB. For a small number of input images the advantage of NC is more pronounced.

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2012 CFR

2012-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2011 CFR

2011-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2013 CFR

2013-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2014 CFR

2014-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 51.1441 - Half-kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2011 CFR

2011-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2012 CFR

2012-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

PubMed

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

PubMed

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Analysis of nonlocal neural fields for both general and gamma-distributed connectivities

NASA Astrophysics Data System (ADS)

Hutt, Axel; Atay, Fatihcan M.

2005-04-01

This work studies the stability of equilibria in spatially extended neuronal ensembles. We first derive the model equation from statistical properties of the neuron population. The obtained integro-differential equation includes synaptic and space-dependent transmission delay for both general and gamma-distributed synaptic connectivities. The latter connectivity type reveals infinite, finite, and vanishing self-connectivities. The work derives conditions for stationary and nonstationary instabilities for both kernel types. In addition, a nonlinear analysis for general kernels yields the order parameter equation of the Turing instability. To compare the results to findings for partial differential equations (PDEs), two typical PDE-types are derived from the examined model equation, namely the general reaction-diffusion equation and the Swift-Hohenberg equation. Hence, the discussed integro-differential equation generalizes these PDEs. In the case of the gamma-distributed kernels, the stability conditions are formulated in terms of the mean excitatory and inhibitory interaction ranges. As a novel finding, we obtain Turing instabilities in fields with local inhibition-lateral excitation, while wave instabilities occur in fields with local excitation and lateral inhibition. Numerical simulations support the analytical results.

A fuzzy pattern matching method based on graph kernel for lithography hotspot detection

NASA Astrophysics Data System (ADS)

Nitta, Izumi; Kanazawa, Yuzi; Ishida, Tsutomu; Banno, Koji

2017-03-01

In advanced technology nodes, lithography hotspot detection has become one of the most significant issues in design for manufacturability. Recently, machine learning based lithography hotspot detection has been widely investigated, but it has trade-off between detection accuracy and false alarm. To apply machine learning based technique to the physical verification phase, designers require minimizing undetected hotspots to avoid yield degradation. They also need a ranking of similar known patterns with a detected hotspot to prioritize layout pattern to be corrected. To achieve high detection accuracy and to prioritize detected hotspots, we propose a novel lithography hotspot detection method using Delaunay triangulation and graph kernel based machine learning. Delaunay triangulation extracts features of hotspot patterns where polygons locate irregularly and closely one another, and graph kernel expresses inner structure of graphs. Additionally, our method provides similarity between two patterns and creates a list of similar training patterns with a detected hotspot. Experiments results on ICCAD 2012 benchmarks show that our method achieves high accuracy with allowable range of false alarm. We also show the ranking of the similar known patterns with a detected hotspot.

Continuity properties of the semi-group and its integral kernel in non-relativistic QED

NASA Astrophysics Data System (ADS)

Matte, Oliver

2016-07-01

Employing recent results on stochastic differential equations associated with the standard model of non-relativistic quantum electrodynamics by B. Güneysu, J. S. Møller, and the present author, we study the continuity of the corresponding semi-group between weighted vector-valued Lp-spaces, continuity properties of elements in the range of the semi-group, and the pointwise continuity of an operator-valued semi-group kernel. We further discuss the continuous dependence of the semi-group and its integral kernel on model parameters. All these results are obtained for Kato decomposable electrostatic potentials and the actual assumptions on the model are general enough to cover the Nelson model as well. As a corollary, we obtain some new pointwise exponential decay and continuity results on elements of low-energetic spectral subspaces of atoms or molecules that also take spin into account. In a simpler situation where spin is neglected, we explain how to verify the joint continuity of positive ground state eigenvectors with respect to spatial coordinates and model parameters. There are no smallness assumptions imposed on any model parameter.

Does money matter in inflation forecasting?

NASA Astrophysics Data System (ADS)

Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

2010-11-01

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Exploring the Brighter-fatter Effect with the Hyper Suprime-Cam

NASA Astrophysics Data System (ADS)

Coulton, William R.; Armstrong, Robert; Smith, Kendrick M.; Lupton, Robert H.; Spergel, David N.

2018-06-01

The brighter-fatter effect has been postulated to arise due to the build up of a transverse electric field, produced as photocharges accumulate in the pixels’ potential wells. We investigate the brighter-fatter effect in the Hyper Suprime-Cam by examining flat fields and moments of stars. We observe deviations from the expected linear relation in the photon transfer curve (PTC), luminosity-dependent correlations between pixels in flat-field images, and a luminosity-dependent point-spread function (PSF) in stellar observations. Under the key assumptions of translation invariance and Maxwell’s equations in the quasi-static limit, we give a first-principles proof that the effect can be parameterized by a translationally invariant scalar kernel. We describe how this kernel can be estimated from flat fields and discuss how this kernel has been used to remove the brighter-fatter distortions in Hyper Suprime-Cam images. We find that our correction restores the expected linear relation in the PTCs and significantly reduces, but does not completely remove, the luminosity dependence of the PSF over a wide range of magnitudes.

Theory and implementation of H-matrix based iterative and direct solvers for Helmholtz and elastodynamic oscillatory kernels

NASA Astrophysics Data System (ADS)

Chaillat, Stéphanie; Desiderio, Luca; Ciarlet, Patrick

2017-12-01

In this work, we study the accuracy and efficiency of hierarchical matrix (H-matrix) based fast methods for solving dense linear systems arising from the discretization of the 3D elastodynamic Green's tensors. It is well known in the literature that standard H-matrix based methods, although very efficient tools for asymptotically smooth kernels, are not optimal for oscillatory kernels. H2-matrix and directional approaches have been proposed to overcome this problem. However the implementation of such methods is much more involved than the standard H-matrix representation. The central questions we address are twofold. (i) What is the frequency-range in which the H-matrix format is an efficient representation for 3D elastodynamic problems? (ii) What can be expected of such an approach to model problems in mechanical engineering? We show that even though the method is not optimal (in the sense that more involved representations can lead to faster algorithms) an efficient solver can be easily developed. The capabilities of the method are illustrated on numerical examples using the Boundary Element Method.

Time reversal seismic imaging using laterally reflected surface waves in southern California

NASA Astrophysics Data System (ADS)

Tape, C.; Liu, Q.; Tromp, J.; Plesch, A.; Shaw, J. H.

2010-12-01

We use observed post-surface-wave seismic waveforms to image shallow (upper 10 km) lateral reflectors in southern California. Our imaging technique employs the 3D crustal model m16 of Tape et al. (2009), which is accurate for most local earthquakes over the period range 2-30 s. Model m16 captures the resonance of the major sedimentary basins in southern California, as well as some lateral surface wave reflections associated with these basins. We apply a 3D Gaussian smoothing function (12 km horizontal, 2 km vertical) to model m16. This smoothing has the effect of suppressing synthetic waveforms within the period range of interest (3-10 s) that are associated with reflections (single and multiple) from these basins. The smoothed 3D model serves as the background model within which we propagate an ``adjoint wavefield'' comprised of time-reversed windows of post-surface-wave coda waveforms that are initiated at the respective station locations. This adjoint wavefield constructively interferes with the regular wavefield in the locations of potential reflectors. The potential reflectors are revealed in an ``event kernel,'' which is the time-integrated volumetric field for each earthquake. By summing (or ``stacking'') the event kernels from 28 well-recorded earthquakes, we identify several reflectors using this imaging procedure. The most prominent lateral reflectors occur in proximity to: the southernmost San Joaquin basin, the Los Angeles basin, the San Pedro basin, the Ventura basin, the Manix basin, the San Clemente--Santa Cruz--Santa Barbara ridge, and isolated segments of the San Jacinto and San Andreas faults. The correspondence between observed coherent coda waveforms and the imaged reflectors provides a solid basis for interpreting the kernel features as material contrasts. The 3D spatial extent and amplitude of the kernel features provide constraints on the geometry and material contrast of the imaged reflectors.

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

PubMed

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Wilton, Donald R.; Champagne, Nathan J.

2008-01-01

Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

PubMed

Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

2014-01-01

The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

Simulation the Effect of Internal Wave on the Acoustic Propagation

NASA Astrophysics Data System (ADS)

Ko, D. S.

2005-05-01

An acoustic radiation transport model with the Monte Carlo solution has been developed and applied to study the effect of internal wave induced random oceanic fluctuations on the deep ocean acoustic propagation. Refraction in the ocean sound channel is performed by means of bi-cubic spline interpolation of discrete deterministic ray paths in the angle(energy)-range-depth coordinates. Scattering by random internal wave fluctuations is accomplished by sampling a power law scattering kernel applying the rejection method. Results from numerical experiments show that the mean positions of acoustic rays are significantly displaced tending toward the sound channel axis due to the asymmetry of the scattering kernel. The spreading of ray depths and angles about the means depends strongly on frequency. The envelope of the ray displacement spreading is found to be proportional to the square root of range which is different from "3/2 law" found in the non-channel case. Suppression of the spreading is due to the anisotropy of fluctuations and especially due to the presence of sound channel itself.

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2013 CFR

2013-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2012 CFR

2012-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

Whitebark pine, population density, and home-range size of grizzly bears in the greater Yellowstone ecosystem

USGS Publications Warehouse

Bjornlie, Daniel D.; van Manen, Frank T.; Ebinger, Michael R.; Haroldson, Mark A.; Thompson, Daniel J.; Costello, Cecily M.

2014-01-01

Changes in life history traits of species can be an important indicator of potential factors influencing populations. For grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem (GYE), recent decline of whitebark pine (WBP; Pinus albicaulis), an important fall food resource, has been paired with a slowing of population growth following two decades of robust population increase. These observations have raised questions whether resource decline or density-dependent processes may be associated with changes in population growth. Distinguishing these effects based on changes in demographic rates can be difficult. However, unlike the parallel demographic responses expected from both decreasing food availability and increasing population density, we hypothesized opposing behavioral responses of grizzly bears with regard to changes in home-range size. We used the dynamic changes in food resources and population density of grizzly bears as a natural experiment to examine hypotheses regarding these potentially competing influences on grizzly bear home-range size. We found that home-range size did not increase during the period of whitebark pine decline and was not related to proportion of whitebark pine in home ranges. However, female home-range size was negatively associated with an index of population density. Our data indicate that home-range size of grizzly bears in the GYE is not associated with availability of WBP, and, for female grizzly bears, increasing population density may constrain home-range size.

Whitebark Pine, Population Density, and Home-Range Size of Grizzly Bears in the Greater Yellowstone Ecosystem

PubMed Central

Bjornlie, Daniel D.; Van Manen, Frank T.; Ebinger, Michael R.; Haroldson, Mark A.; Thompson, Daniel J.; Costello, Cecily M.

2014-01-01

Changes in life history traits of species can be an important indicator of potential factors influencing populations. For grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem (GYE), recent decline of whitebark pine (WBP; Pinus albicaulis), an important fall food resource, has been paired with a slowing of population growth following two decades of robust population increase. These observations have raised questions whether resource decline or density-dependent processes may be associated with changes in population growth. Distinguishing these effects based on changes in demographic rates can be difficult. However, unlike the parallel demographic responses expected from both decreasing food availability and increasing population density, we hypothesized opposing behavioral responses of grizzly bears with regard to changes in home-range size. We used the dynamic changes in food resources and population density of grizzly bears as a natural experiment to examine hypotheses regarding these potentially competing influences on grizzly bear home-range size. We found that home-range size did not increase during the period of whitebark pine decline and was not related to proportion of whitebark pine in home ranges. However, female home-range size was negatively associated with an index of population density. Our data indicate that home-range size of grizzly bears in the GYE is not associated with availability of WBP, and, for female grizzly bears, increasing population density may constrain home-range size. PMID:24520354

Whitebark pine, population density, and home-range size of grizzly bears in the greater yellowstone ecosystem.

PubMed

Bjornlie, Daniel D; Van Manen, Frank T; Ebinger, Michael R; Haroldson, Mark A; Thompson, Daniel J; Costello, Cecily M

2014-01-01

Changes in life history traits of species can be an important indicator of potential factors influencing populations. For grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem (GYE), recent decline of whitebark pine (WBP; Pinus albicaulis), an important fall food resource, has been paired with a slowing of population growth following two decades of robust population increase. These observations have raised questions whether resource decline or density-dependent processes may be associated with changes in population growth. Distinguishing these effects based on changes in demographic rates can be difficult. However, unlike the parallel demographic responses expected from both decreasing food availability and increasing population density, we hypothesized opposing behavioral responses of grizzly bears with regard to changes in home-range size. We used the dynamic changes in food resources and population density of grizzly bears as a natural experiment to examine hypotheses regarding these potentially competing influences on grizzly bear home-range size. We found that home-range size did not increase during the period of whitebark pine decline and was not related to proportion of whitebark pine in home ranges. However, female home-range size was negatively associated with an index of population density. Our data indicate that home-range size of grizzly bears in the GYE is not associated with availability of WBP, and, for female grizzly bears, increasing population density may constrain home-range size.

Spacing behaviour of juvenile corn mice, Calomys musculinus , at the beginning of the breeding period, in absence of adult males

NASA Astrophysics Data System (ADS)

Steinmann, Andrea; Priotto, José; Sommaro, Lucia; Polop, Jaime

2006-05-01

This research was carried out to examine the hypothesis that the absence of fathers promotes a different spacing behaviour in juveniles Calomys musculinus at the beginning of the breeding period. The study was carried out in four 0.25-ha enclosures (two control and two experimental), in a natural pasture, between November 2003 and February 2004. In this study the fathers were removed from the experimental enclosures after juveniles were born. Home-range size depended on sex of juveniles and treatment (father removal). In control and experimental enclosures, female home-range sizes were always smaller than male home-ranges. Male home-ranges were always larger in experimental enclosures than in control enclosures. Treatment and overlap type (intra- and inter-sexual) were not independent. The overlap proportions of male home-ranges were greatest in experimental enclosures than in control enclosures, in both the overlap types (male/male, males/females). The intra- (females/females) and inter-sexual (females/males) overlap proportions of female home-ranges were independent of treatment. In C. musculinus, at the beginning of the breeding period and in absence of adult males, juvenile males increase their home-range size and therefore the degree of inter- and intra-sexual home-range overlap as a mechanism for enlarging the number of receptive females that they encounter.

graphkernels: R and Python packages for graph comparison

PubMed Central

Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-01-01

Abstract Summary Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch Supplementary information Supplementary data are available online at Bioinformatics. PMID:29028902

Aflatoxin variability in pistachios.

PubMed Central

Mahoney, N E; Rodriguez, S B

1996-01-01

Pistachio fruit components, including hulls (mesocarps and epicarps), seed coats (testas), and kernels (seeds), all contribute to variable aflatoxin content in pistachios. Fresh pistachio kernels were individually inoculated with Aspergillus flavus and incubated 7 or 10 days. Hulled, shelled kernels were either left intact or wounded prior to inoculation. Wounded kernels, with or without the seed coat, were readily colonized by A. flavus and after 10 days of incubation contained 37 times more aflatoxin than similarly treated unwounded kernels. The aflatoxin levels in the individual wounded pistachios were highly variable. Neither fungal colonization nor aflatoxin was detected in intact kernels without seed coats. Intact kernels with seed coats had limited fungal colonization and low aflatoxin concentrations compared with their wounded counterparts. Despite substantial fungal colonization of wounded hulls, aflatoxin was not detected in hulls. Aflatoxin levels were significantly lower in wounded kernels with hulls than in kernels of hulled pistachios. Both the seed coat and a water-soluble extract of hulls suppressed aflatoxin production by A. flavus. PMID:8919781

graphkernels: R and Python packages for graph comparison.

PubMed

Sugiyama, Mahito; Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-02-01

Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary data are available online at Bioinformatics. © The Author(s) 2017. Published by Oxford University Press.

Investigation of various energy deposition kernel refinements for the convolution/superposition method

PubMed Central

Huang, Jessie Y.; Eklund, David; Childress, Nathan L.; Howell, Rebecca M.; Mirkovic, Dragan; Followill, David S.; Kry, Stephen F.

2013-01-01

Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm. Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels. Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%–5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity. Conclusions: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C/S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C/S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors. PMID:24320507

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

PubMed

Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Topographic home ranges of white-tailed deer in the central Appalachians

Treesearch

Tyler A. Campbell; Benjamin R. Laseter; W. Mark Ford; Karl V. Miller

2004-01-01

Planimetric comparisons of home range sizes of white-tailed deer (Odocoileus virginianus Zimmermann) from across their range may not be appropriate due to regional differences in topography. We compare seasonal topographic diversity between male and female white-tailed deer home ranges in the central Appalachians using percent increase from...

Comparing Alternative Kernels for the Kernel Method of Test Equating: Gaussian, Logistic, and Uniform Kernels. Research Report. ETS RR-08-12

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; von Davier, Alina A.

2008-01-01

The kernel equating method (von Davier, Holland, & Thayer, 2004) is based on a flexible family of equipercentile-like equating functions that use a Gaussian kernel to continuize the discrete score distributions. While the classical equipercentile, or percentile-rank, equating method carries out the continuization step by linear interpolation,…

7 CFR 810.204 - Grades and grade requirements for Six-rowed Malting barley and Six-rowed Blue Malting barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

...— Damaged kernels 1 (percent) Foreign material (percent) Other grains (percent) Skinned and broken kernels....0 10.0 15.0 1 Injured-by-frost kernels and injured-by-mold kernels are not considered damaged kernels or considered against sound barley. Notes: Malting barley shall not be infested in accordance with...

Effects of population reduction on white-tailed deer home-range dynamics

USGS Publications Warehouse

Crimmins, Shawn M.; Edwards, John W.; Campbell, Tyler A; Ford, W. Mark; Keyser, Patrick D.; Miller, Brad F.; Miller, Karl V.

2015-01-01

Management strategies designed to reduce the negative impacts of overabundant Odocoileus virginianus (White-tailed Deer) populations on forest regeneration may be influenced by changes in both population density and timber harvest. However, there is conflicting evidence as to how such changes in per capita resource availability influence home-range patterns. We compared home-range patterns of 33 female White-tailed Deer from a low-density population at a site with abundant browse to patterns of a sample of >100 females prior to a 75% reduction in population density and a doubling in timber harvest area. Home-range and core-area sizes were approximately 3 times larger than were found prior to population decline and timber harvest increase, consistent with predictions related to intraspecific competition. We also observed greater site fidelity than previously exhibited, although this may be an artifact of increased home-range sizes. Our results support previous research suggesting that White-tailed Deer home-range size is inversely related to population density and is driven, in part, by intraspecific competition for resources. Relationships among population density, resource availability, and home-range patterns among female White-tailed Deer appear to be complex and context specific.

Effects of Clear-Fell Harvest on Bat Home Range

PubMed Central

Borkin, Kerry M.; Parsons, Stuart

2014-01-01

We investigated effects of roost loss due to clear-fell harvest on bat home range. The study took place in plantation forest, inhabited by the New Zealand long-tailed bat (Chalinolobus tuberculatus), in which trees are harvested between the ages 26–32 years. We determined home ranges by radiotracking different bats in areas that had and had not been recently clear-fell harvested. Home ranges were smaller in areas that had been harvested. Adult male bats selected 20–25 year old stands within home ranges before and after harvest. Males selected edges with open unplanted areas when harvest had not occurred but no longer selected these at proportions greater than their availability post harvest, probably because they were then readily available. This is the first radiotracking study to demonstrate a change in home range size and selection concomitant with felling of large areas of plantation forest, and thus quantify negative effects of forestry operations on this speciose group. The use of smaller home ranges post-harvest may reflect smaller colony sizes and lower roost availability, both of which may increase isolation of colonies and vulnerability to local extinction. PMID:24465938

7 CFR 51.1413 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

... well cured; (e) Poorly developed kernels; (f) Kernels which are dark amber in color; (g) Kernel spots when more than one dark spot is present on either half of the kernel, or when any such spot is more...

7 CFR 51.1413 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

... well cured; (e) Poorly developed kernels; (f) Kernels which are dark amber in color; (g) Kernel spots when more than one dark spot is present on either half of the kernel, or when any such spot is more...

7 CFR 810.205 - Grades and grade requirements for Two-rowed Malting barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... (percent) Maximum limits of— Wild oats (percent) Foreign material (percent) Skinned and broken kernels... Injured-by-frost kernels and injured-by-mold kernels are not considered damaged kernels or considered...

Flame Kernel Interactions in a Turbulent Environment

DTIC Science & Technology

2001-08-01

contours ranging from 1 ( fully burned) at the centre to 0 (unburned) on the outer contour. In each case the flames can clearly be seen to propagate outwards...called SENGA. The code solves a fully compressible reacting flow in three dimensions. High accuracy numerical schemes have been employed which are...Finally, results are presented and discussed for simulations with different initial non-dimensional turbulence intensities ranging from 5 to 23. 1

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method

PubMed Central

Zhang, Tingting; Kou, S. C.

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure. PMID:21258615

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

PubMed

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.

Automatic detection of white-light flare kernels in SDO/HMI intensitygrams

NASA Astrophysics Data System (ADS)

Mravcová, Lucia; Švanda, Michal

2017-11-01

Solar flares with a broadband emission in the white-light range of the electromagnetic spectrum belong to most enigmatic phenomena on the Sun. The origin of the white-light emission is not entirely understood. We aim to systematically study the visible-light emission connected to solar flares in SDO/HMI observations. We developed a code for automatic detection of kernels of flares with HMI intensity brightenings and study properties of detected candidates. The code was tuned and tested and with a little effort, it could be applied to any suitable data set. By studying a few flare examples, we found indication that HMI intensity brightening might be an artefact of the simplified procedure used to compute HMI observables.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

Among-Individual Variation in Desert Iguanas (Squamata: Dipsosaurus dorsalis): Endurance Capacity Is Positively Related to Home Range Size.

PubMed

Singleton, Jennifer M; Garland, Theodore

Among species of lizards, endurance capacity measured on a motorized treadmill is positively related to daily movement distance and time spent moving, but few studies have addressed such relationships at the level of individual variation within a sex and age category in a single population. Both endurance capacity and home range size show substantial individual variation in lizards, rendering them suitable for such studies. We predicted that these traits would be positively related because endurance capacity is one of the factors that has the potential to limit home range size. We measured the endurance capacity and home range size of adult male desert iguanas (Dipsosaurus dorsalis). Lizards were field captured for measurements of endurance, and home range data were gathered using visual identification of previously marked individuals. Endurance was significantly repeatable between replicate trials, conducted 1-17 d apart ([Formula: see text] for log-transformed values, [Formula: see text], [Formula: see text]). The log of the higher of two endurance trials was positively but not significantly related to log body mass. The log of home range area was positively but not significantly related to log body mass, the number of sightings, or the time span from first to last sighting. As predicted, log endurance was positively correlated with log home range area ([Formula: see text], [Formula: see text], one-tailed [Formula: see text]; for body-mass residual endurance values: [Formula: see text], one-tailed [Formula: see text]). These results suggest that endurance capacity may have a permissive effect on home range size. Alternatively, individuals with larger home ranges may experience training effects (phenotypic plasticity) that increase their endurance.

Toward a scientific understanding of DDGS

USDA-ARS?s Scientific Manuscript database

The objective of this book is to provide a thorough summary of all aspects of DDGS, ranging from the corn kernel itself all the way through end use. The broad topic of distillers grains intersects a variety of disciplines, including physics, chemistry, microbiology, biology, animal science, as well...

Credit scoring analysis using kernel discriminant

NASA Astrophysics Data System (ADS)

Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

PubMed Central

Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.

2014-01-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435

Classification of Phylogenetic Profiles for Protein Function Prediction: An SVM Approach

NASA Astrophysics Data System (ADS)

Kotaru, Appala Raju; Joshi, Ramesh C.

Predicting the function of an uncharacterized protein is a major challenge in post-genomic era due to problems complexity and scale. Having knowledge of protein function is a crucial link in the development of new drugs, better crops, and even the development of biochemicals such as biofuels. Recently numerous high-throughput experimental procedures have been invented to investigate the mechanisms leading to the accomplishment of a protein’s function and Phylogenetic profile is one of them. Phylogenetic profile is a way of representing a protein which encodes evolutionary history of proteins. In this paper we proposed a method for classification of phylogenetic profiles using supervised machine learning method, support vector machine classification along with radial basis function as kernel for identifying functionally linked proteins. We experimentally evaluated the performance of the classifier with the linear kernel, polynomial kernel and compared the results with the existing tree kernel. In our study we have used proteins of the budding yeast saccharomyces cerevisiae genome. We generated the phylogenetic profiles of 2465 yeast genes and for our study we used the functional annotations that are available in the MIPS database. Our experiments show that the performance of the radial basis kernel is similar to polynomial kernel is some functional classes together are better than linear, tree kernel and over all radial basis kernel outperformed the polynomial kernel, linear kernel and tree kernel. In analyzing these results we show that it will be feasible to make use of SVM classifier with radial basis function as kernel to predict the gene functionality using phylogenetic profiles.

Intraear Compensation of Field Corn, Zea mays, from Simulated and Naturally Occurring Injury by Ear-Feeding Larvae.

PubMed

Steckel, S; Stewart, S D

2015-06-01

Ear-feeding larvae, such as corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), can be important insect pests of field corn, Zea mays L., by feeding on kernels. Recently introduced, stacked Bacillus thuringiensis (Bt) traits provide improved protection from ear-feeding larvae. Thus, our objective was to evaluate how injury to kernels in the ear tip might affect yield when this injury was inflicted at the blister and milk stages. In 2010, simulated corn earworm injury reduced total kernel weight (i.e., yield) at both the blister and milk stage. In 2011, injury to ear tips at the milk stage affected total kernel weight. No differences in total kernel weight were found in 2013, regardless of when or how much injury was inflicted. Our data suggested that kernels within the same ear could compensate for injury to ear tips by increasing in size, but this increase was not always statistically significant or sufficient to overcome high levels of kernel injury. For naturally occurring injury observed on multiple corn hybrids during 2011 and 2012, our analyses showed either no or a minimal relationship between number of kernels injured by ear-feeding larvae and the total number of kernels per ear, total kernel weight, or the size of individual kernels. The results indicate that intraear compensation for kernel injury to ear tips can occur under at least some conditions. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Anti-enteric bacterial activity and phytochemical analysis of the seed kernel extract of Mangifera indica Linnaeus against Shigella dysenteriae (Shiga, corrig.) Castellani and Chalmers.

PubMed

Rajan, S; Thirunalasundari, T; Jeeva, S

2011-04-01

To evaluate the phytochemical and anti-bacterial efficacy of the seed kernel extract of Mangifera indica (M. indica) against the enteropathogen, Shigella dysenteriae (S. dysenteriae), isolated from the diarrhoeal stool specimens. The preliminary phytochemical screening was performed by the standard methods as described by Harborne. Cold extraction method was employed to extract the bioactive compounds from mango seed kernel. Disc diffusion method was adopted to screen antibacterial activity. Minimum inhibitory concentration (MIC) was evaluated by agar dilution method. The crude extracts were partially purified by thin layer chromatography (TLC) and the fractions were analyzed by high performance thin layer chromatography (HPTLC) to identify the bioactive compounds. Phytochemical scrutiny of M. indica indicated the presence of phytochemical constituents such as alkaloids, gums, flavanoids, phenols, saponins, steroids, tannins and xanthoproteins. Antibacterial activity was observed in two crude extracts and various fractions viz. hexane, benzene, chloroform, methanol and water. MIC of methanol fraction was found to be (95±11.8) μg/mL. MIC of other fractions ranged from 130-380 μg/mL. The present study confirmed that each crude extracts and fractions of M. indica have significant antimicrobial activity against the isolated pathogen S. dysenteriae. The antibacterial activity may be due to the phytochemical constituents of the mango seed kernel. The phytochemical tannin could be the reason for its antibacterial activity. Copyright © 2011 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

On The Cloud Processing of Aerosol Particles: An Entraining Air Parcel Model With Two-dimensional Spectral Cloud Microphysics and A New Formulation of The Collection Kernel

NASA Astrophysics Data System (ADS)

Bott, Andreas; Kerkweg, Astrid; Wurzler, Sabine

A study has been made of the modification of aerosol spectra due to cloud pro- cesses and the impact of the modified aerosols on the microphysical structure of future clouds. For this purpose an entraining air parcel model with two-dimensional spectral cloud microphysics has been used. In order to treat collision/coalescence processes in the two-dimensional microphysical module, a new realistic and continuous formu- lation of the collection kernel has been developed. Based on experimental data, the kernel covers the entire investigated size range of aerosols, cloud and rain drops, that is the kernel combines all important coalescence processes such as the collision of cloud drops as well as the impaction scavenging of small aerosols by big raindrops. Since chemical reactions in the gas phase and in cloud drops have an important impact on the physico-chemical properties of aerosol particles, the parcel model has been extended by a chemical module describing gas phase and aqueous phase chemical reactions. However, it will be shown that in the numerical case studies presented in this paper the modification of aerosols by chemical reactions has a minor influence on the microphysical structure of future clouds. The major process yielding in a second cloud event an enhanced formation of rain is the production of large aerosol particles by collision/coalescence processes in the first cloud.

Compressive strength, flexural strength and water absorption of concrete containing palm oil kernel shell

NASA Astrophysics Data System (ADS)

Noor, Nurazuwa Md; Xiang-ONG, Jun; Noh, Hamidun Mohd; Hamid, Noor Azlina Abdul; Kuzaiman, Salsabila; Ali, Adiwijaya

2017-11-01

Effect of inclusion of palm oil kernel shell (PKS) and palm oil fibre (POF) in concrete was investigated on the compressive strength and flexural strength. In addition, investigation of palm oil kernel shell on concrete water absorption was also conducted. Total of 48 concrete cubes and 24 concrete prisms with the size of 100mm × 100mm × 100mm and 100mm × 100mm × 500mm were prepared, respectively. Four (4) series of concrete mix consists of coarse aggregate was replaced by 0%, 25%, 50% and 75% palm kernel shell and each series were divided into two (2) main group. The first group is without POF, while the second group was mixed with the 5cm length of 0.25% of the POF volume fraction. All specimen were tested after 7 and 28 days of water curing for a compression test, and flexural test at 28 days of curing period. Water absorption test was conducted on concrete cube age 28 days. The results showed that the replacement of PKS achieves lower compressive and flexural strength in comparison with conventional concrete. However, the 25% replacement of PKS concrete showed acceptable compressive strength which within the range of requirement for structural concrete. Meanwhile, the POF which should act as matrix reinforcement showed no enhancement in flexural strength due to the balling effect in concrete. As expected, water absorption was increasing with the increasing of PKS in the concrete cause by the porous characteristics of PKS

Optimisation of shape kernel and threshold in image-processing motion analysers.

PubMed

Pedrocchi, A; Baroni, G; Sada, S; Marcon, E; Pedotti, A; Ferrigno, G

2001-09-01

The aim of the work is to optimise the image processing of a motion analyser. This is to improve accuracy, which is crucial for neurophysiological and rehabilitation applications. A new motion analyser, ELITE-S2, for installation on the International Space Station is described, with the focus on image processing. Important improvements are expected in the hardware of ELITE-S2 compared with ELITE and previous versions (ELITE-S and Kinelite). The core algorithm for marker recognition was based on the current ELITE version, using the cross-correlation technique. This technique was based on the matching of the expected marker shape, the so-called kernel, with image features. Optimisation of the kernel parameters was achieved using a genetic algorithm, taking into account noise rejection and accuracy. Optimisation was achieved by performing tests on six highly precise grids (with marker diameters ranging from 1.5 to 4 mm), representing all allowed marker image sizes, and on a noise image. The results of comparing the optimised kernels and the current ELITE version showed a great improvement in marker recognition accuracy, while noise rejection characteristics were preserved. An average increase in marker co-ordinate accuracy of +22% was achieved, corresponding to a mean accuracy of 0.11 pixel in comparison with 0.14 pixel, measured over all grids. An improvement of +37%, corresponding to an improvement from 0.22 pixel to 0.14 pixel, was observed over the grid with the biggest markers.

Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.).

PubMed

Guo, Jie; Shi, Weiping; Zhang, Zheng; Cheng, Jingye; Sun, Daizhen; Yu, Jin; Li, Xinlei; Guo, Pingyi; Hao, Chenyang

2018-02-20

Yield improvement is an ever-important objective of wheat breeding. Studying and understanding the phenotypes and genotypes of yield-related traits has potential for genetic improvement of crops. The genotypes of 215 wheat cultivars including 11 founder parents and 106 derivatives were analyzed by the 9 K wheat SNP iSelect assay. A total of 4138 polymorphic single nucleotide polymorphism (SNP) loci were detected on 21 chromosomes, of which 3792 were mapped to single chromosome locations. All genotypes were phenotyped for six yield-related traits including plant height (PH), spike length (SL), spikelet number per spike (SNPS), kernel number per spike (KNPS), kernel weight per spike (KWPS), and thousand kernel weight (TKW) in six irrigated environments. Genome-wide association analysis detected 117 significant associations of 76 SNPs on 15 chromosomes with phenotypic explanation rates (R 2 ) ranging from 2.03 to 12.76%. In comparing allelic variation between founder parents and their derivatives (106) and other cultivars (98) using the 76 associated SNPs, we found that the region 116.0-133.2 cM on chromosome 5A in founder parents and derivatives carried alleles positively influencing kernel weight per spike (KWPS), rarely found in other cultivars. The identified favorable alleles could mark important chromosome regions in derivatives that were inherited from founder parents. Our results unravel the genetic of yield in founder genotypes, and provide tools for marker-assisted selection for yield improvement.

Evidence-based Kernels: Fundamental Units of Behavioral Influence

PubMed Central

Biglan, Anthony

2008-01-01

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior. PMID:18712600

Integrating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Champagne, Nathan J.; Wilton, Donald R.

2008-01-01

A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

Home in the heat: Dramatic seasonal variation in home range of desert golden eagles informs management for renewable energy development

USGS Publications Warehouse

Braham, Melissa A.; Miller, Tricia A.; Duerr, Adam E.; Lanzone, Michael J.; Fesnock, Amy; LaPre, Larry; Driscoll, Daniel; Katzner, Todd E.

2015-01-01

Renewable energy is expanding quickly with sometimes dramatic impacts to species and ecosystems. To understand the degree to which sensitive species may be impacted by renewable energy projects, it is informative to know how much space individuals use and how that space may overlap with planned development. We used global positioning system–global system for mobile communications (GPS-GSM) telemetry to measure year-round movements of golden eagles (Aquila chrysaetos) from the Mojave Desert of California, USA. We estimated monthly space use with adaptive local convex hulls to identify the temporal and spatial scales at which eagles may encounter renewable energy projects in the Desert Renewable Energy Conservation Plan area. Mean size of home ranges was lowest and least variable from November through January and greatest in February–March and May–August. These monthly home range patterns coincided with seasonal variation in breeding ecology, habitat associations, and temperature. The expanded home ranges in hot summer months included movements to cooler, prey-dense, mountainous areas characterized by forest, grasslands, and scrublands. Breeding-season home ranges (October–May) included more lowland semi-desert and rock vegetation. Overlap of eagle home ranges and focus areas for renewable energy development was greatest when eagle home ranges were smallest, during the breeding season. Golden eagles in the Mojave Desert used more space and a wider range of habitat types than expected and renewable energy projects could affect a larger section of the regional population than was previously thought.

Effects of high density on spacing behaviour and reproduction in Akodon azarae: A fencing experiment

NASA Astrophysics Data System (ADS)

Ávila, Belén; Bonatto, Florencia; Priotto, José; Steinmann, Andrea R.

2016-01-01

We studied the short term spacing behavioural responses of Pampean grassland mouse (Akodon azarae) with regard to population density in four 0.25 ha enclosures (two control and two experimental) in the 2011 breeding season. Based on the hypothesis that A. azarae breeding females exhibit spacing behaviour, and breeding males show a fusion spatial response, we tested the following predictions: (1) home range size and intrasexual overlap degree of females are independent of population density values; (2) at high population density, home range size of males decreases and the intrasexual home range overlap degree increases. To determine if female reproductive success decreases at high population density, we analyzed pregnancy rate, size and weight of litters, and period until fecundation in both low and high enclosure population density. We found that both males and females varied their home range size in relation to population density. Although male home ranges were always bigger than those of females in populations with high density, home range sizes of both sexes decreased. Females kept exclusive home ranges independent of density values meanwhile males decreased home range overlap in high breeding density populations. Although females produced litters of similar size in both treatments, weight of litter, pregnant rate and period until fecundation varied in relation to population density. Our results did not support the hypothesis that at high density females of A. azarae exhibit spacing behaviour neither that males exhibit a fusion spatial response.

Habitat use and movement patterns of Northern Pintails during spring in northern Japan: the importance of agricultural lands

USGS Publications Warehouse

Yamaguchi, Noriyuki M.; Hupp, Jerry W.; Flint, Paul L.; Pearce, John M.; Shigeta, Yusuke; Shimada, Tetsuo; Hiraoka, Emiko N.; Higuchi, Hiroyoshi

2012-01-01

From 2006 to 2009, we marked 198 Northern Pintails (Anas acuta) with satellite transmitters on their wintering areas in Japan to study their migration routes and habitat use in spring staging areas. We hypothesized that the distribution of pintails during spring staging was influenced by patterns of land use and expected that the most frequently used areas would have more agricultural habitat than lesser-used areas. We obtained 3031 daily locations from 163 migrant pintails marked with satellite transmitters and identified 524 stopover sites. Based on a fixed kernel home range analysis of stopover utilization distribution (UD), core staging areas (areas within the 50% UD) were identified in northern Honshu and western Hokkaido, and were used by 71% of marked pintails. Core staging areas had a greater proportion of rice fields than peripheral (51–95% UD) and rarely used (outside the 95% UD) staging areas. Stopover sites also contained more rice fields and other agricultural land than were available at regional scales, indicating that pintails selected rice and other agricultural habitats at regional and local scales. Pintails remained at spring staging areas an average of 51 d. Prolonged staging in agricultural habitats of northern Japan was likely necessary for pintails to prepare for transoceanic migration to Arctic nesting areas in eastern Russia.

Comparison of the ranging behavior of Scotophilus kuhlii (Lesser Asiatic Yellow Bat) in agricultural and urban landscape

NASA Astrophysics Data System (ADS)

Atiqah, Nur; Akbar, Zubaid; Syafrinna, Ubaidah, Nur; Foo, Ng Yong

2015-09-01

Knowledge on home range sizes and movement patterns of animals through the environment is crucial for determining effects of habitat disturbance and fragmentation. To gauge the effects of land-use changes on Scotophilus kuhlii, a telemetric study was conducted between February 2014 and April 2014 in Tasik Chini, Pahang and Universiti Kebangsaan Malaysia (UKM), Bangi Campus. The home range sizes and movement patterns of S. kuhlii inhabiting agricultural landscape (Tasik Chini, Pahang) versus urban landscape (UKM) were compared. A total of ten individuals were successfully radio-tracked. Comparison of home range sizes of both sexes showed male S. kuhlii at Tasik Chini have larger mean home range sizes compared to UKM while female S.kuhlii in UKM have larger mean home range sizes compared to Tasik Chini. All individuals from both localities showed random movement. It is suggested that the home range and activity patterns might be influenced by food availability in the study area, food preferences and diet segregation and breeding behavior. This study provides baseline information on habitat utilization by S. kuhlii in relation to habitat perturbations.

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2011 CFR

2011-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2013 CFR

2013-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2012 CFR

2012-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.

PubMed

Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the kernel fraction was redried at 60°C for 48 h in a forced-air oven and dry sieved to determine GMPS and surface area. Linear relationships between CSPS from WPCS (n=80) and kernel fraction GMPS, surface area, and proportion passing through the 4.75-mm screen were poor. Strong quadratic relationships between proportion of kernel fraction passing through the 4.75-mm screen and kernel fraction GMPS and surface area were observed. These findings suggest that hydrodynamic separation and dry sieving of the kernel fraction may provide a better assessment of kernel breakage in WPCS than CSPS. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

NASA Astrophysics Data System (ADS)

Zhu, Fengle; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert; Bhatnagar, Deepak; Cleveland, Thomas

2015-05-01

Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.

Influence of Kernel Age on Fumonisin B1 Production in Maize by Fusarium moniliforme

PubMed Central

Warfield, Colleen Y.; Gilchrist, David G.

1999-01-01

Production of fumonisins by Fusarium moniliforme on naturally infected maize ears is an important food safety concern due to the toxic nature of this class of mycotoxins. Assessing the potential risk of fumonisin production in developing maize ears prior to harvest requires an understanding of the regulation of toxin biosynthesis during kernel maturation. We investigated the developmental-stage-dependent relationship between maize kernels and fumonisin B1 production by using kernels collected at the blister (R2), milk (R3), dough (R4), and dent (R5) stages following inoculation in culture at their respective field moisture contents with F. moniliforme. Highly significant differences (P ≤ 0.001) in fumonisin B1 production were found among kernels at the different developmental stages. The highest levels of fumonisin B1 were produced on the dent stage kernels, and the lowest levels were produced on the blister stage kernels. The differences in fumonisin B1 production among kernels at the different developmental stages remained significant (P ≤ 0.001) when the moisture contents of the kernels were adjusted to the same level prior to inoculation. We concluded that toxin production is affected by substrate composition as well as by moisture content. Our study also demonstrated that fumonisin B1 biosynthesis on maize kernels is influenced by factors which vary with the developmental age of the tissue. The risk of fumonisin contamination may begin early in maize ear development and increases as the kernels reach physiological maturity. PMID:10388675

7 CFR 51.1447 - Fairly uniform in color.

Code of Federal Regulations, 2011 CFR

2011-01-01

... color. Fairly uniform in color means that 90 percent or more of the kernels in the lot have skin color within the range of one or two color classifications. ... 7 Agriculture 2 2011-01-01 2011-01-01 false Fairly uniform in color. 51.1447 Section 51.1447...

7 CFR 51.1447 - Fairly uniform in color.

Code of Federal Regulations, 2012 CFR

2012-01-01

... color. Fairly uniform in color means that 90 percent or more of the kernels in the lot have skin color within the range of one or two color classifications. ... 7 Agriculture 2 2012-01-01 2012-01-01 false Fairly uniform in color. 51.1447 Section 51.1447...

Behavioral kernels and brief interventions: teaching parents effective behavior management strategies.

PubMed

Shapiro, Cheri J

2013-01-01

Reducing the high prevalence of emotional and behavioral problems among youth requires that parents be given access to high-quality, effective parenting supports and interventions. Recently developed brief parenting interventions can be delivered without stigma in primary health care and other settings by a range of professionals.

7 CFR 51.1447 - Fairly uniform in color.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Fairly uniform in color. 51.1447 Section 51.1447... color. Fairly uniform in color means that 90 percent or more of the kernels in the lot have skin color within the range of one or two color classifications. ...

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize.

PubMed

Janni, James; Weinstock, B André; Hagen, Lisa; Wright, Steve

2008-04-01

A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

Experimental evidence of spatial memory and home range affinity in White-tailed Deer (Odocoileus virginianus)

USGS Publications Warehouse

Nelson, Michael E.

2015-01-01

The role of spatial memory in the movement of animals through landscapes remains elusive. To examine spatial memory and home range affinity of White-tailed Deer (Odocoileus virginianus) in northeastern Minnesota during 1995–2007, I translocated 17 adult does with known home ranges to unfamiliar sites and radio-tracked them after their release. Twelve does wearing transmitting radio-collars returned to their home ranges. Death and collar expiration precluded determination of whether the remaining five does would have returned to home ranges. Three of five does wearing global positioning system collars traveled throughout hundreds of square kilometres, circling, backtracking, and returning to release sites, while two others exhibited directional movement for tens of kilometres. Four does that survived to parturition stopped traveling and moved at hourly rates similar to those of control does during the first three weeks of the typical fawn-rearing period, but continued traveling later. Their aberrant extensive travel before and after interruption by parturition suggests that they recognized they were in unfamiliar areas, demonstrating both their capacity and propensity to search for and occupy the familiar space of their individual home ranges. Their successful return to home ranges provided experimental evidence of spatial memory and further elucidated its pervasive role in White-tailed Deer spatial ecology.

Home range size variation in female arctic grizzly bears relative to reproductive status and resource availability.

PubMed

Edwards, Mark A; Derocher, Andrew E; Nagy, John A

2013-01-01

The area traversed in pursuit of resources defines the size of an animal's home range. For females, the home range is presumed to be a function of forage availability. However, the presence of offspring may also influence home range size due to reduced mobility, increased nutritional need, and behavioral adaptations of mothers to increase offspring survival. Here, we examine the relationship between resource use and variation in home range size for female barren-ground grizzly bears (Ursus arctos) of the Mackenzie Delta region in Arctic Canada. We develop methods to test hypotheses of home range size that address selection of cover where cover heterogeneity is low, using generalized linear mixed-effects models and an information-theoretic approach. We found that the reproductive status of female grizzlies affected home range size but individually-based spatial availability of highly selected cover in spring and early summer was a stronger correlate. If these preferred covers in spring and early summer, a period of low resource availability for grizzly bears following den-emergence, were patchy and highly dispersed, females travelled farther regardless of the presence or absence of offspring. Increased movement to preferred covers, however, may result in greater risk to the individual or family.

Computed tomography coronary stent imaging with iterative reconstruction: a trade-off study between medium kernel and sharp kernel.

PubMed

Zhou, Qijing; Jiang, Biao; Dong, Fei; Huang, Peiyu; Liu, Hongtao; Zhang, Minming

2014-01-01

To evaluate the improvement of iterative reconstruction in image space (IRIS) technique in computed tomographic (CT) coronary stent imaging with sharp kernel, and to make a trade-off analysis. Fifty-six patients with 105 stents were examined by 128-slice dual-source CT coronary angiography (CTCA). Images were reconstructed using standard filtered back projection (FBP) and IRIS with both medium kernel and sharp kernel applied. Image noise and the stent diameter were investigated. Image noise was measured both in background vessel and in-stent lumen as objective image evaluation. Image noise score and stent score were performed as subjective image evaluation. The CTCA images reconstructed with IRIS were associated with significant noise reduction compared to that of CTCA images reconstructed using FBP technique in both of background vessel and in-stent lumen (the background noise decreased by approximately 25.4% ± 8.2% in medium kernel (P

Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population.

PubMed

Cheng, Ruiru; Kong, Zhongxin; Zhang, Liwei; Xie, Quan; Jia, Haiyan; Yu, Dong; Huang, Yulong; Ma, Zhengqiang

2017-07-01

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement. Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419 × Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adaptive kernel function using line transect sampling

NASA Astrophysics Data System (ADS)

Albadareen, Baker; Ismail, Noriszura

2018-04-01

The estimation of f(0) is crucial in the line transect method which is used for estimating population abundance in wildlife survey's. The classical kernel estimator of f(0) has a high negative bias. Our study proposes an adaptation in the kernel function which is shown to be more efficient than the usual kernel estimator. A simulation study is adopted to compare the performance of the proposed estimators with the classical kernel estimators.

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

Pollen source effects on growth of kernel structures and embryo chemical compounds in maize.

PubMed

Tanaka, W; Mantese, A I; Maddonni, G A

2009-08-01

Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P < 0.01) allocation of embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0.98, P < 0.01) and soluble sugars (r = 0.95, P < 0.05) were found. Coincidently, embryos with low oil concentration had an increased (P < 0.05-0.10) scutellum cell area occupied by starch granules and fewer oil bodies. The effects of pollen source on both embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the embryos.

7 CFR 868.254 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall be...

7 CFR 51.2090 - Serious damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

... defect which makes a kernel or piece of kernel unsuitable for human consumption, and includes decay...: Shriveling when the kernel is seriously withered, shrunken, leathery, tough or only partially developed: Provided, that partially developed kernels are not considered seriously damaged if more than one-fourth of...

Anisotropic hydrodynamics with a scalar collisional kernel

NASA Astrophysics Data System (ADS)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

Ideal regularization for learning kernels from labels.

PubMed

Pan, Binbin; Lai, Jianhuang; Shen, Lixin

2014-08-01

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently. Copyright © 2014 Elsevier Ltd. All rights reserved.

Straight-chain halocarbon forming fluids for TRISO fuel kernel production - Tests with yttria-stabilized zirconia microspheres

NASA Astrophysics Data System (ADS)

Baker, M. P.; King, J. C.; Gorman, B. P.; Braley, J. C.

2015-03-01

Current methods of TRISO fuel kernel production in the United States use a sol-gel process with trichloroethylene (TCE) as the forming fluid. After contact with radioactive materials, the spent TCE becomes a mixed hazardous waste, and high costs are associated with its recycling or disposal. Reducing or eliminating this mixed waste stream would not only benefit the environment, but would also enhance the economics of kernel production. Previous research yielded three candidates for testing as alternatives to TCE: 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane. This study considers the production of yttria-stabilized zirconia (YSZ) kernels in silicone oil and the three chosen alternative formation fluids, with subsequent characterization of the produced kernels and used forming fluid. Kernels formed in silicone oil and bromotetradecane were comparable to those produced by previous kernel production efforts, while those produced in chlorooctadecane and iodododecane experienced gelation issues leading to poor kernel formation and geometry.

Numerical study of the ignition behavior of a post-discharge kernel injected into a turbulent stratified cross-flow

NASA Astrophysics Data System (ADS)

Jaravel, Thomas; Labahn, Jeffrey; Ihme, Matthias

2017-11-01

The reliable initiation of flame ignition by high-energy spark kernels is critical for the operability of aviation gas turbines. The evolution of a spark kernel ejected by an igniter into a turbulent stratified environment is investigated using detailed numerical simulations with complex chemistry. At early times post ejection, comparisons of simulation results with high-speed Schlieren data show that the initial trajectory of the kernel is well reproduced, with a significant amount of air entrainment from the surrounding flow that is induced by the kernel ejection. After transiting in a non-flammable mixture, the kernel reaches a second stream of flammable methane-air mixture, where the successful of the kernel ignition was found to depend on the local flow state and operating conditions. By performing parametric studies, the probability of kernel ignition was identified, and compared with experimental observations. The ignition behavior is characterized by analyzing the local chemical structure, and its stochastic variability is also investigated.

The site, size, spatial stability, and energetics of an X-ray flare kernel

NASA Technical Reports Server (NTRS)

Petrasso, R.; Gerassimenko, M.; Nolte, J.

1979-01-01

The site, size evolution, and energetics of an X-ray kernel that dominated a solar flare during its rise and somewhat during its peak are investigated. The position of the kernel remained stationary to within about 3 arc sec over the 30-min interval of observations, despite pulsations in the kernel X-ray brightness in excess of a factor of 10. This suggests a tightly bound, deeply rooted magnetic structure, more plausibly associated with the near chromosphere or low corona rather than with the high corona. The H-alpha flare onset coincided with the appearance of the kernel, again suggesting a close spatial and temporal coupling between the chromospheric H-alpha event and the X-ray kernel. At the first kernel brightness peak its size was no larger than about 2 arc sec, when it accounted for about 40% of the total flare flux. In the second rise phase of the kernel, a source power input of order 2 times 10 to the 24th ergs/sec is minimally required.

The pre-image problem in kernel methods.

PubMed

Kwok, James Tin-yau; Tsang, Ivor Wai-hung

2004-11-01

In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.

Effects of Amygdaline from Apricot Kernel on Transplanted Tumors in Mice.

PubMed

Yamshanov, V A; Kovan'ko, E G; Pustovalov, Yu I

2016-03-01

The effects of amygdaline from apricot kernel added to fodder on the growth of transplanted LYO-1 and Ehrlich carcinoma were studied in mice. Apricot kernels inhibited the growth of both tumors. Apricot kernels, raw and after thermal processing, given 2 days before transplantation produced a pronounced antitumor effect. Heat-processed apricot kernels given in 3 days after transplantation modified the tumor growth and prolonged animal lifespan. Thermal treatment did not considerably reduce the antitumor effect of apricot kernels. It was hypothesized that the antitumor effect of amygdaline on Ehrlich carcinoma and LYO-1 lymphosarcoma was associated with the presence of bacterial genome in the tumor.

Development of a kernel function for clinical data.

PubMed

Daemen, Anneleen; De Moor, Bart

2009-01-01

For most diseases and examinations, clinical data such as age, gender and medical history guides clinical management, despite the rise of high-throughput technologies. To fully exploit such clinical information, appropriate modeling of relevant parameters is required. As the widely used linear kernel function has several disadvantages when applied to clinical data, we propose a new kernel function specifically developed for this data. This "clinical kernel function" more accurately represents similarities between patients. Evidently, three data sets were studied and significantly better performances were obtained with a Least Squares Support Vector Machine when based on the clinical kernel function compared to the linear kernel function.

Manycore Performance-Portability: Kokkos Multidimensional Array Library

DOE PAGES

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; ...

2012-01-01

Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel executionmore » performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].« less

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

PubMed

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

NASA Astrophysics Data System (ADS)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels.

Metabolic network prediction through pairwise rational kernels.

PubMed

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times. The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

Differential metabolome analysis of field-grown maize kernels in response to drought stress

USDA-ARS?s Scientific Manuscript database

Drought stress constrains maize kernel development and can exacerbate aflatoxin contamination. In order to identify drought responsive metabolites and explore pathways involved in kernel responses, a metabolomics analysis was conducted on kernels from a drought tolerant line, Lo964, and a sensitive ...

Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...

7 CFR 868.203 - Basis of determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... FOR CERTAIN AGRICULTURAL COMMODITIES United States Standards for Rough Rice Principles Governing..., heat-damaged kernels, red rice and damaged kernels, chalky kernels, other types, color, and the special grade Parboiled rough rice shall be on the basis of the whole and large broken kernels of milled rice...

7 CFR 868.203 - Basis of determination.

Code of Federal Regulations, 2011 CFR

2011-01-01

... FOR CERTAIN AGRICULTURAL COMMODITIES United States Standards for Rough Rice Principles Governing..., heat-damaged kernels, red rice and damaged kernels, chalky kernels, other types, color, and the special grade Parboiled rough rice shall be on the basis of the whole and large broken kernels of milled rice...

7 CFR 868.304 - Broken kernels determination.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 7 2011-01-01 2011-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the use...

7 CFR 868.304 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.304 Section 868.304 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Application of Standards § 868.304 Broken kernels determination. Broken kernels shall be determined by the use...

Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carter, L.L.; Hendricks, J.S.

1983-01-01

The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays.

Performance Characteristics of a Kernel-Space Packet Capture Module

DTIC Science & Technology

2010-03-01

Defense, or the United States Government . AFIT/GCO/ENG/10-03 PERFORMANCE CHARACTERISTICS OF A KERNEL-SPACE PACKET CAPTURE MODULE THESIS Presented to the...3.1.2.3 Prototype. The proof of concept for this research is the design, development, and comparative performance analysis of a kernel level N2d capture...changes to kernel code 5. Can be used for both user-space and kernel-space capture applications in order to control comparative performance analysis to

Microhabitat selection, demography, and correlates of home range size for the King Rail (Rallus elegans)

USGS Publications Warehouse

Pickens, Bradley A.; King, Sammy L.

2013-01-01

Animal movements and habitat selection within the home range, or microhabitat selection, can provide insights into habitat requirements, such as foraging and area requirements. The King Rail (Rallus elegans) is a wetland bird of high conservation concern in the United States, but little is known about its movements, habitats, or demography. King Rails (n = 34) were captured during the 2010–2011 breeding seasons in the coastal marshes of southwest Louisiana and southeast Texas. Radio telemetry and direct habitat surveys of King Rail locations were conducted to estimate home ranges and microhabitat selection. Within home ranges, King Rails selected for greater plant species richness and comparatively greater coverage of Phragmites australis, Typha spp., and Schoenoplectus robustus. King Rails were found closer to open water compared to random locations placed 50 m from King Rail locations. Home ranges (n = 22) varied from 0.8–32.8 ha and differed greatly among sites. Home range size did not vary by year or sex; however, increased open water, with a maximum of 29% observed in the study, was correlated with smaller home ranges. Breeding season cumulative survivorship was 89% ± 22% in 2010 and 61% ± 43% in 2011, which coincided with a drought. With an equal search effort, King Rail chicks and juveniles observed in May-June decreased from 110 in 2010 to only 16 in the drier year of 2011. The findings show King Rail used marsh with ≤ 29% open water and had smaller home ranges when open water was more abundant.

Optimized Kernel Entropy Components.

PubMed

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Influence of primary prey on home-range size and habitat-use patterns of northern spotted owls (Strix occidentalis caurina)

Treesearch

Cynthia J. Zabel; Kevin S. McKelvey; James P. Ward

1995-01-01

Correlations between the home-range size of northern spotted owls (Strix occidentalis caurina) and proportion of their range in old-growth forest have been reported, but there are few data on the relationship between their home-range size and prey. The primary prey of spotted owls are wood rats and northern flying squirrels (Glaucomys sabrinus). Wood...

Free-ranging farm cats: home range size and predation on a livestock unit in Northwest Georgia.

PubMed

Kitts-Morgan, Susanna E; Caires, Kyle C; Bohannon, Lisa A; Parsons, Elizabeth I; Hilburn, Katharine A

2015-01-01

This study's objective was to determine seasonal and diurnal vs. nocturnal home range size, as well as predation for free-ranging farm cats at a livestock unit in Northwest Georgia. Seven adult cats were tracked with attached GPS units for up to two weeks for one spring and two summer seasons from May 2010 through August 2011. Three and five cats were tracked for up to two weeks during the fall and winter seasons, respectively. Feline scat was collected during this entire period. Cats were fed a commercial cat food daily. There was no seasonal effect (P > 0.05) on overall (95% KDE and 90% KDE) or core home range size (50% KDE). Male cats tended (P = 0.08) to have larger diurnal and nocturnal core home ranges (1.09 ha) compared to female cats (0.64 ha). Reproductively intact cats (n = 2) had larger (P < 0.0001) diurnal and nocturnal home ranges as compared to altered cats. Feline scat processing separated scat into prey parts, and of the 210 feline scats collected during the study, 75.24% contained hair. Of these 158 scat samples, 86 contained non-cat hair and 72 contained only cat hair. Other prey components included fragments of bone in 21.43% of scat and teeth in 12.86% of scat. Teeth were used to identify mammalian prey hunted by these cats, of which the Hispid cotton rat (Sigmodon hispidus) was the primary rodent. Other targeted mammals were Peromyscus sp., Sylvilagus sp. and Microtus sp. Invertebrates and birds were less important as prey, but all mammalian prey identified in this study consisted of native animals. While the free-ranging farm cats in this study did not adjust their home range seasonally, sex and reproductive status did increase diurnal and nocturnal home range size. Ultimately, larger home ranges of free-ranging cats could negatively impact native wildlife.

Free-Ranging Farm Cats: Home Range Size and Predation on a Livestock Unit In Northwest Georgia

PubMed Central

Kitts-Morgan, Susanna E.; Caires, Kyle C.; Bohannon, Lisa A.; Parsons, Elizabeth I.; Hilburn, Katharine A.

2015-01-01

This study’s objective was to determine seasonal and diurnal vs. nocturnal home range size, as well as predation for free-ranging farm cats at a livestock unit in Northwest Georgia. Seven adult cats were tracked with attached GPS units for up to two weeks for one spring and two summer seasons from May 2010 through August 2011. Three and five cats were tracked for up to two weeks during the fall and winter seasons, respectively. Feline scat was collected during this entire period. Cats were fed a commercial cat food daily. There was no seasonal effect (P > 0.05) on overall (95% KDE and 90% KDE) or core home range size (50% KDE). Male cats tended (P = 0.08) to have larger diurnal and nocturnal core home ranges (1.09 ha) compared to female cats (0.64 ha). Reproductively intact cats (n = 2) had larger (P < 0.0001) diurnal and nocturnal home ranges as compared to altered cats. Feline scat processing separated scat into prey parts, and of the 210 feline scats collected during the study, 75.24% contained hair. Of these 158 scat samples, 86 contained non-cat hair and 72 contained only cat hair. Other prey components included fragments of bone in 21.43% of scat and teeth in 12.86% of scat. Teeth were used to identify mammalian prey hunted by these cats, of which the Hispid cotton rat (Sigmodon hispidus) was the primary rodent. Other targeted mammals were Peromyscus sp., Sylvilagus sp. and Microtus sp. Invertebrates and birds were less important as prey, but all mammalian prey identified in this study consisted of native animals. While the free-ranging farm cats in this study did not adjust their home range seasonally, sex and reproductive status did increase diurnal and nocturnal home range size. Ultimately, larger home ranges of free-ranging cats could negatively impact native wildlife. PMID:25894078

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL

PubMed Central

Wang, Botao; Xiong, Hongkai; Jiang, Xiaoqian; Ling, Fan

2013-01-01

Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called “structure kernel”, which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels. PMID:23666108

Burrower bugs (Heteroptera: Cydnidae) in peanut: seasonal species abundance, tillage effects, grade reduction effects, insecticide efficacy, and management.

PubMed

Chapin, Jay W; Thomas, James S

2003-08-01

Pitfall traps placed in South Carolina peanut, Arachis hypogaea (L.), fields collected three species of burrower bugs (Cydnidae): Cyrtomenus ciliatus (Palisot de Beauvois), Sehirus cinctus cinctus (Palisot de Beauvois), and Pangaeus bilineatus (Say). Cyrtomenus ciliatus was rarely collected. Sehirus cinctus produced a nymphal cohort in peanut during May and June, probably because of abundant henbit seeds, Lamium amplexicaule L., in strip-till production systems. No S. cinctus were present during peanut pod formation. Pangaeus bilineatus was the most abundant species collected and the only species associated with peanut kernel feeding injury. Overwintering P. bilineatus adults were present in a conservation tillage peanut field before planting and two to three subsequent generations were observed. Few nymphs were collected until the R6 (full seed) growth stage. Tillage and choice of cover crop affected P. bilineatus populations. Peanuts strip-tilled into corn or wheat residue had greater P. bilineatus populations and kernel-feeding than conventional tillage or strip-tillage into rye residue. Fall tillage before planting a wheat cover crop also reduced burrower bug feeding on peanut. At-pegging (early July) granular chlorpyrifos treatments were most consistent in suppressing kernel feeding. Kernels fed on by P. bilineatus were on average 10% lighter than unfed on kernels. Pangaeus bilineatus feeding reduced peanut grade by reducing individual kernel weight, and increasing the percentage damaged kernels. Each 10% increase in kernels fed on by P. bilineatus was associated with a 1.7% decrease in total sound mature kernels, and kernel feeding levels above 30% increase the risk of damaged kernel grade penalties.

Imaging and automated detection of Sitophilus oryzae (Coleoptera: Curculionidae) pupae in hard red winter wheat.

PubMed

Toews, Michael D; Pearson, Tom C; Campbell, James F

2006-04-01

Computed tomography, an imaging technique commonly used for diagnosing internal human health ailments, uses multiple x-rays and sophisticated software to recreate a cross-sectional representation of a subject. The use of this technique to image hard red winter wheat, Triticum aestivm L., samples infested with pupae of Sitophilus oryzae (L.) was investigated. A software program was developed to rapidly recognize and quantify the infested kernels. Samples were imaged in a 7.6-cm (o.d.) plastic tube containing 0, 50, or 100 infested kernels per kg of wheat. Interkernel spaces were filled with corn oil so as to increase the contrast between voids inside kernels and voids among kernels. Automated image processing, using a custom C language software program, was conducted separately on each 100 g portion of the prepared samples. The average detection accuracy in the five infested kernels per 100-g samples was 94.4 +/- 7.3% (mean +/- SD, n = 10), whereas the average detection accuracy in the 10 infested kernels per 100-g sample was 87.3 +/- 7.9% (n = 10). Detection accuracy in the 10 infested kernels per 100-g samples was slightly less than the five infested kernels per 100-g samples because of some infested kernels overlapping with each other or air bubbles in the oil. A mean of 1.2 +/- 0.9 (n = 10) bubbles (per tube) was incorrectly classed as infested kernels in replicates containing no infested kernels. In light of these positive results, future studies should be conducted using additional grains, insect species, and life stages.

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

PubMed

Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

2016-11-01

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects. Copyright © 2016 Crop Science Society of America.

Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

PubMed

Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua

2016-02-01

Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

Image quality of mixed convolution kernel in thoracic computed tomography.

PubMed

Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar

2016-11-01

The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P < 0.03). Compared to the hard convolution kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P < 0.004) but a lower image quality for trachea, segmental bronchi, lung parenchyma, and skeleton (P < 0.001).The mixed convolution kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.

Home range size of Black-backed Woodpeckers in burned forests of southwestern Idaho

Treesearch

Jonathan G. Dudley; Victoria A. Saab

2007-01-01

We examined home range size of Black-backed Woodpeckers (Picoides arcticus) in burned ponderosa pine (Pinus ponderosa) / Douglas-fir (Pseudotsuga menziesii) forests of southwestern Idaho during 2000 and 2002 (6 and 8 years following fire). Home range size for 4 adult males during the post-fledging period was 115....

Factors associated with variation in home-range size of Appilachian Ruffed Grouse (Bonasa Umbellus)

Treesearch

Darroch M. Whitaker; Dean F. Stauffer; Gary W. Norman; Patrick K. Devers; John Edwards; William M. Giuliano; Craig Harper; William Igo; Harry Spiker; Brian Tefft

2007-01-01

From 1996 to 2001, researchers at 10 Appalachian study sites collected radio tracking data sufficient to delineate 1,054 seasonal home ranges of Ruffed Grouse (Banasa umbellus; hereafter "grouse"). Using information-theoretic model selection and paired comparison of home ranges from individual grouse, we evaluated individual, local, and...

HOME RANGES AND HABITAT USE OF SUBURBAN RED-SHOULDERED HAWKS

EPA Science Inventory

Radio telemetry was used to determine home range size and habitat use for breeding season and non-breedng season red-shouldered hawks nesting in a surburban area in southwestern Ohio. Home ranges averaged 96.0 ha for males (n=4) and 48.3 for females (n-2) during the breeding sea...

An SVM-based solution for fault detection in wind turbines.

PubMed

Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

2015-03-09

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

Gamma irradiation of peanut kernel to control mold growth and to diminish aflatoxin contamination

NASA Astrophysics Data System (ADS)

Y.-Y. Chiou, R.

1996-09-01

Peanut kernel inoculated with Aspergillus parasiticus conidia were gamma irradiated with 0, 2.5, 5.0 and 10 kGy using Co60. Levels higher than 2.5 kGy were effective in retarding the outgrowth of A. parasiticus and reducing the population of natural mold contaminants. However, complete elimination of these molds was not achieved even at the dose of 10 kGy. After 4 wk incubation of the inoculated kernels in a humidified condition, aflatoxins produced by the surviving A. parasiticus were 69.12, 2.42, 57.36 and 22.28 μ/g, corresponding to the original irradiation levels. Peroxide content of peanut oils prepared from the irradiated peanuts increased with increased irradiation dosage. After storage, at each irradiation level, peroxide content in peanuts stored at -14°C was lower than that in peanuts stored at an ambient temperature. TBA values and CDHP contents of the oil increased with increased irradiation dosage and changed slightly after storage. However, fatty acid contents of the peanut oil varied in a limited range as affected by the irradiation dosage and storage temperature. The SDS-PAGE protein pattern of peanuts revealed no noticeable variation of protein subunits resulting from irradiation and storage.

Body-wave traveltime and amplitude shifts from asymptotic travelling wave coupling

USGS Publications Warehouse

Pollitz, F.

2006-01-01

We explore the sensitivity of finite-frequency body-wave traveltimes and amplitudes to perturbations in 3-D seismic velocity structure relative to a spherically symmetric model. Using the approach of coupled travelling wave theory, we consider the effect of a structural perturbation on an isolated portion of the seismogram. By convolving the spectrum of the differential seismogram with the spectrum of a narrow window taper, and using a Taylor's series expansion for wavenumber as a function of frequency on a mode dispersion branch, we derive semi-analytic expressions for the sensitivity kernels. Far-field effects of wave interactions with the free surface or internal discontinuities are implicitly included, as are wave conversions upon scattering. The kernels may be computed rapidly for the purpose of structural inversions. We give examples of traveltime sensitivity kernels for regional wave propagation at 1 Hz. For the direct SV wave in a simple crustal velocity model, they are generally complicated because of interfering waves generated by interactions with the free surface and the Mohorovic??ic?? discontinuity. A large part of the interference effects may be eliminated by restricting the travelling wave basis set to those waves within a certain range of horizontal phase velocity. ?? Journal compilation ?? 2006 RAS.

Numerical integration of the extended variable generalized Langevin equation with a positive Prony representable memory kernel.

PubMed

Baczewski, Andrew D; Bond, Stephen D

2013-07-28

Generalized Langevin dynamics (GLD) arise in the modeling of a number of systems, ranging from structured fluids that exhibit a viscoelastic mechanical response, to biological systems, and other media that exhibit anomalous diffusive phenomena. Molecular dynamics (MD) simulations that include GLD in conjunction with external and/or pairwise forces require the development of numerical integrators that are efficient, stable, and have known convergence properties. In this article, we derive a family of extended variable integrators for the Generalized Langevin equation with a positive Prony series memory kernel. Using stability and error analysis, we identify a superlative choice of parameters and implement the corresponding numerical algorithm in the LAMMPS MD software package. Salient features of the algorithm include exact conservation of the first and second moments of the equilibrium velocity distribution in some important cases, stable behavior in the limit of conventional Langevin dynamics, and the use of a convolution-free formalism that obviates the need for explicit storage of the time history of particle velocities. Capability is demonstrated with respect to accuracy in numerous canonical examples, stability in certain limits, and an exemplary application in which the effect of a harmonic confining potential is mapped onto a memory kernel.

Sampling strategies for radio-tracking coyotes

USGS Publications Warehouse

Smith, G.J.; Cary, J.R.; Rongstad, O.J.

1981-01-01

Ten coyotes radio-tracked for 24 h periods were most active at night and moved little during daylight hours. Home-range size determined from radio-locations of 3 adult coyotes increased with the number of locations until an asymptote was reached at about 35-40 independent day locations or 3 6 nights of hourly radio-locations. Activity of the coyote did not affect the asymptotic nature of the home-range calculations, but home-range sizes determined from more than 3 nights of hourly locations were considerably larger than home-range sizes determined from daylight locations. Coyote home-range sizes were calculated from daylight locations, full-night tracking periods, and half-night tracking periods. Full- and half-lnight sampling strategies involved obtaining hourly radio-locations during 12 and 6 h periods, respectively. The half-night sampling strategy was the best compromise for our needs, as it adequately indexed the home-range size, reduced time and energy spent, and standardized the area calculation without requiring the researcher to become completely nocturnal. Sight tracking also provided information about coyote activity and sociability.

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing, manufacturing, packing, processing, preparing, treating...

Local Observed-Score Kernel Equating

ERIC Educational Resources Information Center

Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.

2014-01-01

Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…

7 CFR 51.1241 - Damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

... which have been broken to the extent that the kernel within is plainly visible without minute... discoloration beneath, but the peanut shall be judged as it appears with the talc. (c) Kernels which are rancid or decayed. (d) Moldy kernels. (e) Kernels showing sprouts extending more than one-eighth inch from...

7 CFR 981.61 - Redetermination of kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Redetermination of kernel weight. 981.61 Section 981... GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.61 Redetermination of kernel weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds...

7 CFR 981.60 - Determination of kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Determination of kernel weight. 981.60 Section 981.60... Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which settlement...

Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat

USDA-ARS?s Scientific Manuscript database

Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...

7 CFR 999.400 - Regulation governing the importation of filberts.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Definitions. (1) Filberts means filberts or hazelnuts. (2) Inshell filberts means filberts, the kernels or edible portions of which are contained in the shell. (3) Shelled filberts means the kernels of filberts... Filbert kernels or portions of filbert kernels shall meet the following requirements: (1) Well dried and...

7 CFR 51.1404 - Tolerances.

Code of Federal Regulations, 2010 CFR

2010-01-01

.... (2) For kernel defects, by count. (i) 12 percent for pecans with kernels which fail to meet the... kernels which are seriously damaged: Provided, That not more than six-sevenths of this amount, or 6 percent, shall be allowed for kernels which are rancid, moldy, decayed or injured by insects: And provided...

Enhanced gluten properties in soft kernel durum wheat

USDA-ARS?s Scientific Manuscript database

Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...

End-use quality of soft kernel durum wheat

USDA-ARS?s Scientific Manuscript database

Kernel texture is a major determinant of end-use quality of wheat. Durum wheat has very hard kernels. We developed soft kernel durum wheat via Ph1b-mediated homoeologous recombination. The Hardness locus was transferred from Chinese Spring to Svevo durum wheat via back-crossing. ‘Soft Svevo’ had SKC...

7 CFR 51.2560 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... are excessively thin kernels and can have black, brown or gray surface with a dark interior color and the immaturity has adversely affected the flavor of the kernel. (2) Kernel spotting refers to dark brown or dark gray spots aggregating more than one-eighth of the surface of the kernel. (g) Serious...

7 CFR 51.2560 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... are excessively thin kernels and can have black, brown or gray surface with a dark interior color and the immaturity has adversely affected the flavor of the kernel. (2) Kernel spotting refers to dark brown or dark gray spots aggregating more than one-eighth of the surface of the kernel. (g) Serious...

7 CFR 51.1416 - Optional determinations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... throughout the lot. (a) Edible kernel content. A minimum sample of at least 500 grams of in-shell pecans shall be used for determination of edible kernel content. After the sample is weighed and shelled... determine edible kernel content for the lot. (b) Poorly developed kernel content. A minimum sample of at...

Assessing intraspecific variation in effective dispersal along an altitudinal gradient: a test in two Mediterranean high-mountain plants.

PubMed

Lara-Romero, Carlos; Robledo-Arnuncio, Juan J; García-Fernández, Alfredo; Iriondo, Jose M

2014-01-01

Plant recruitment depends among other factors on environmental conditions and their variation at different spatial scales. Characterizing dispersal in contrasting environments may thus be necessary to understand natural intraspecific variation in the processes underlying recruitment. Silene ciliata and Armeria caespitosa are two representative species of cryophilic pastures above the tree line in Mediterranean high mountains. No explicit estimations of dispersal kernels have been made so far for these or other high-mountain plants. Such data could help to predict their dispersal and recruitment patterns in a context of changing environments under ongoing global warming. We used an inverse modelling approach to analyse effective seed dispersal patterns in five populations of both Silene ciliata and Armeria caespitosa along an altitudinal gradient in Sierra de Guadarrama (Madrid, Spain). We considered four commonly employed two-dimensional seedling dispersal kernels exponential-power, 2Dt, WALD and log-normal. No single kernel function provided the best fit across all populations, although estimated mean dispersal distances were short (<1 m) in all cases. S. ciliata did not exhibit significant among-population variation in mean dispersal distance, whereas significant differences in mean dispersal distance were found in A. caespitosa. Both S. ciliata and A. caespitosa exhibited among-population variation in the fecundity parameter and lacked significant variation in kernel shape. This study illustrates the complexity of intraspecific variation in the processes underlying recruitment, showing that effective dispersal kernels can remain relatively invariant across populations within particular species, even if there are strong variations in demographic structure and/or physical environment among populations, while the invariant dispersal assumption may not hold for other species in the same environment. Our results call for a case-by-case analysis in a wider range of plant taxa and environments to assess the prevalence and magnitude of intraspecific dispersal variation.

Production of near-full density uranium nitride microspheres with a hot isostatic press

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMurray, Jacob W.; Kiggans, Jr., Jim O.; Helmreich, Grant W.

Depleted uranium nitride (UN) kernels with diameters ranging from 420 to 858 microns and theoretical densities (TD) between 87 and 91 percent were postprocessed using a hot isostatic press (HIP) in an argon gas media. This treatment was shown to increase the TD up to above 97%. Uranium nitride is highly reactive with oxygen. Therefore, a novel crucible design was implemented to remove impurities in the argon gas via in situ gettering to avoid oxidation of the UN kernels. The density before and after each HIP procedure was calculated from average weight, volume, and ellipticity determined with established characterization techniquesmore » for particle. Furthermore, micrographs confirmed the nearly full densification of the particles using the gettering approach and HIP processing parameters investigated in this work.« less

3DRT-MPASS

NASA Technical Reports Server (NTRS)

Lickly, Ben

2005-01-01

Data from all current JPL missions are stored in files called SPICE kernels. At present, animators who want to use data from these kernels have to either read through the kernels looking for the desired data, or write programs themselves to retrieve information about all the needed objects for their animations. In this project, methods of automating the process of importing the data from the SPICE kernels were researched. In particular, tools were developed for creating basic scenes in Maya, a 3D computer graphics software package, from SPICE kernels.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

PubMed

Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

2016-05-01

The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

Graph wavelet alignment kernels for drug virtual screening.

PubMed

Smalter, Aaron; Huan, Jun; Lushington, Gerald

2009-06-01

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.

Oecophylla longinoda (Hymenoptera: Formicidae) Lead to Increased Cashew Kernel Size and Kernel Quality.

PubMed

Anato, F M; Sinzogan, A A C; Offenberg, J; Adandonon, A; Wargui, R B; Deguenon, J M; Ayelo, P M; Vayssières, J-F; Kossou, D K

2017-06-01

Weaver ants, Oecophylla spp., are known to positively affect cashew, Anacardium occidentale L., raw nut yield, but their effects on the kernels have not been reported. We compared nut size and the proportion of marketable kernels between raw nuts collected from trees with and without ants. Raw nuts collected from trees with weaver ants were 2.9% larger than nuts from control trees (i.e., without weaver ants), leading to 14% higher proportion of marketable kernels. On trees with ants, the kernel: raw nut ratio from nuts damaged by formic acid was 4.8% lower compared with nondamaged nuts from the same trees. Weaver ants provided three benefits to cashew production by increasing yields, yielding larger nuts, and by producing greater proportions of marketable kernel mass. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Kernels, Degrees of Freedom, and Power Properties of Quadratic Distance Goodness-of-Fit Tests

PubMed Central

Lindsay, Bruce G.; Markatou, Marianthi; Ray, Surajit

2014-01-01

In this article, we study the power properties of quadratic-distance-based goodness-of-fit tests. First, we introduce the concept of a root kernel and discuss the considerations that enter the selection of this kernel. We derive an easy to use normal approximation to the power of quadratic distance goodness-of-fit tests and base the construction of a noncentrality index, an analogue of the traditional noncentrality parameter, on it. This leads to a method akin to the Neyman-Pearson lemma for constructing optimal kernels for specific alternatives. We then introduce a midpower analysis as a device for choosing optimal degrees of freedom for a family of alternatives of interest. Finally, we introduce a new diffusion kernel, called the Pearson-normal kernel, and study the extent to which the normal approximation to the power of tests based on this kernel is valid. Supplementary materials for this article are available online. PMID:24764609

The quantitative properties of three soft X-ray flare kernels observed with the AS&E X-ray telescope on Skylab

NASA Technical Reports Server (NTRS)

Kahler, S. W.; Petrasso, R. D.; Kane, S. R.

1976-01-01

The physical parameters for the kernels of three solar X-ray flare events have been deduced using photographic data from the S-054 X-ray telescope on Skylab as the primary data source and 1-8 and 8-20 A fluxes from Solrad 9 as the secondary data source. The kernels had diameters of about 5-7 seconds of arc and in two cases electron densities at least as high as 0.3 trillion per cu cm. The lifetimes of the kernels were 5-10 min. The presence of thermal conduction during the decay phases is used to argue: (1) that kernels are entire, not small portions of, coronal loop structures, and (2) that flare heating must continue during the decay phase. We suggest a simple geometric model to explain the role of kernels in flares in which kernels are identified with emerging flux regions.

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

21 CFR 176.350 - Tamarind seed kernel powder.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2013 CFR

2013-01-01

... generally conforms to the “light” or “light amber” classification, that color classification may be used to... 7 Agriculture 2 2013-01-01 2013-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2014 CFR

2014-01-01

... generally conforms to the “light” or “light amber” classification, that color classification may be used to... 7 Agriculture 2 2014-01-01 2014-01-01 false Kernel color classification. 51.1403 Section 51.1403... Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be...

Nutrition quality of extraction mannan residue from palm kernel cake on brolier chicken

NASA Astrophysics Data System (ADS)

Tafsin, M.; Hanafi, N. D.; Kejora, E.; Yusraini, E.

2018-02-01

This study aims to find out the nutrient residue of palm kernel cake from mannan extraction on broiler chicken by evaluating physical quality (specific gravity, bulk density and compacted bulk density), chemical quality (proximate analysis and Van Soest Test) and biological test (metabolizable energy). Treatment composed of T0 : palm kernel cake extracted aquadest (control), T1 : palm kernel cake extracted acetic acid (CH3COOH) 1%, T2 : palm kernel cake extracted aquadest + mannanase enzyme 100 u/l and T3 : palm kernel cake extracted acetic acid (CH3COOH) 1% + enzyme mannanase 100 u/l. The results showed that mannan extraction had significant effect (P<0.05) in improving the quality of physical and numerically increase the value of crude protein and decrease the value of NDF (Neutral Detergent Fiber). Treatments had highly significant influence (P<0.01) on the metabolizable energy value of palm kernel cake residue in broiler chickens. It can be concluded that extraction with aquadest + enzyme mannanase 100 u/l yields the best nutrient quality of palm kernel cake residue for broiler chicken.

Dynamic Changes in Phenolics and Antioxidant Capacity during Pecan (Carya illinoinensis) Kernel Ripening and Its Phenolics Profiles.

PubMed

Jia, Xiaodong; Luo, Huiting; Xu, Mengyang; Zhai, Min; Guo, Zhongren; Qiao, Yushan; Wang, Liangju

2018-02-16

Pecan ( Carya illinoinensis ) kernels have a high phenolics content and a high antioxidant capacity compared to other nuts-traits that have attracted great interest of late. Changes in the total phenolic content (TPC), condensed tannins (CT), total flavonoid content (TFC), five individual phenolics, and antioxidant capacity of five pecan cultivars were investigated during the process of kernel ripening. Ultra-performance liquid chromatography coupled with quadruple time-of-flight mass (UPLC-Q/TOF-MS) was also used to analyze the phenolics profiles in mixed pecan kernels. TPC, CT, TFC, individual phenolics, and antioxidant capacity were changed in similar patterns, with values highest at the water or milk stages, lowest at milk or dough stages, and slightly varied at kernel stages. Forty phenolics were tentatively identified in pecan kernels, of which two were first reported in the genus Carya , six were first reported in Carya illinoinensis , and one was first reported in its kernel. The findings on these new phenolic compounds provide proof of the high antioxidant capacity of pecan kernels.

Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2016-02-03

A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

Novel characterization method of impedance cardiography signals using time-frequency distributions.

PubMed

Escrivá Muñoz, Jesús; Pan, Y; Ge, S; Jensen, E W; Vallverdú, M

2018-03-16

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

PubMed

Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-06-21

We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Space use and movements of moose in Massachusetts: implications for conservation of large mammals in a fragmented environment

USGS Publications Warehouse

Wattles, David W.; DeStefano, Stephen

2013-01-01

Moose (Alces alces) have recently re-occupied a portion of their range in the temperate deciduous forest of the northeastern United States after a >200 year absence. In southern New England, moose encounter different forest types, more human development, and higher temperatures than in other parts of their geographic range in North America. We analyzed seasonal minimum convex polygon home ranges, utilization distributions, movement rates, and home range composition of GPS-collared moose in Massachusetts. Seasonal home range sizes were not different for males and females and were within the range reported for low latitudes elsewhere in North America. Seasonal movement patterns reflected the seasonal changes in metabolic rate and the influence of the species’ reproductive cycle and weather. Home ranges consisted almost entirely of forested habitat, included large amounts of conservation land, and had lower road densities as compared to the landscape as a whole, indicating that human development may be a limiting factor for moose in the region. The size and configuration of home ranges, seasonal movement patterns, and use relative to human development have implications for conservation of moose and other wide-ranging species in more highly developed portions of their ranges.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, William R.; Lee, John C.; baxter, Alan

Information and measured data from the intial Fort St. Vrain (FSV) high temperature gas reactor core is used to develop a benchmark configuration to validate computational methods for analysis of a full-core, commercial HTR configuration. Large uncertainties in the geometry and composition data for the FSV fuel and core are identified, including: (1) the relative numbers of fuel particles for the four particle types, (2) the distribution of fuel kernel diameters for the four particle types, (3) the Th:U ratio in the initial FSV core, (4) and the buffer thickness for the fissile and fertile particles. Sensitivity studies were performedmore » to assess each of these uncertainties. A number of methods were developed to assist in these studies, including: (1) the automation of MCNP5 input files for FSV using Python scripts, (2) a simple method to verify isotopic loadings in MCNP5 input files, (3) an automated procedure to conduct a coupled MCNP5-RELAP5 analysis for a full-core FSV configuration with thermal-hydraulic feedback, and (4) a methodology for sampling kernel diameters from arbitrary power law and Gaussian PDFs that preserved fuel loading and packing factor constraints. A reference FSV fuel configuration was developed based on having a single diameter kernel for each of the four particle types, preserving known uranium and thorium loadings and packing factor (58%). Three fuel models were developed, based on representing the fuel as a mixture of kernels with two diameters, four diameters, or a continuous range of diameters. The fuel particles were put into a fuel compact using either a lattice-bsed approach or a stochastic packing methodology from RPI, and simulated with MCNP5. The results of the sensitivity studies indicated that the uncertainties in the relative numbers and sizes of fissile and fertile kernels were not important nor were the distributions of kernel diameters within their diameter ranges. The uncertainty in the Th:U ratio in the intial FSV core was found to be important with a crude study. The uncertainty in the TRISO buffer thickness was estimated to be unimportant but the study was not conclusive. FSV fuel compacts and a regular FSV fuel element were analyzed with MCNP5 and compared with predictions using a modified version of HELIOS that is capable of analyzing TRISO fuel configurations. The HELIOS analyses were performed by SSP. The eigenvalue discrepancies between HELIOS and MCNP5 are currently on the order of 1% but these are still being evaluated. Full-core FSV configurations were developed for two initial critical configurations - a cold, clean critical loading and a critical configuration at 70% power. MCNP5 predictions are compared to experimental data and the results are mixed. Analyses were also done for the pulsed neutron experiments that were conducted by GA for the initial FSV core. MCNP5 was used to model these experiments and reasonable agreement with measured results has been observed.« less

Antioxidant and antimicrobial activities of bitter and sweet apricot (Prunus armeniaca L.) kernels.

PubMed

Yiğit, D; Yiğit, N; Mavi, A

2009-04-01

The present study describes the in vitro antimicrobial and antioxidant activity of methanol and water extracts of sweet and bitter apricot (Prunus armeniaca L.) kernels. The antioxidant properties of apricot kernels were evaluated by determining radical scavenging power, lipid peroxidation inhibition activity and total phenol content measured with a DPPH test, the thiocyanate method and the Folin method, respectively. In contrast to extracts of the bitter kernels, both the water and methanol extracts of sweet kernels have antioxidant potential. The highest percent inhibition of lipid peroxidation (69%) and total phenolic content (7.9 +/- 0.2 microg/mL) were detected in the methanol extract of sweet kernels (Hasanbey) and in the water extract of the same cultivar, respectively. The antimicrobial activities of the above extracts were also tested against human pathogenic microorganisms using a disc-diffusion method, and the minimal inhibitory concentration (MIC) values of each active extract were determined. The most effective antibacterial activity was observed in the methanol and water extracts of bitter kernels and in the methanol extract of sweet kernels against the Gram-positive bacteria Staphylococcus aureus. Additionally, the methanol extracts of the bitter kernels were very potent against the Gram-negative bacteria Escherichia coli (0.312 mg/mL MIC value). Significant anti-candida activity was also observed with the methanol extract of bitter apricot kernels against Candida albicans, consisting of a 14 mm in diameter of inhibition zone and a 0.625 mg/mL MIC value.

The Influence of Reconstruction Kernel on Bone Mineral and Strength Estimates Using Quantitative Computed Tomography and Finite Element Analysis.

PubMed

Michalski, Andrew S; Edwards, W Brent; Boyd, Steven K

2017-10-17

Quantitative computed tomography has been posed as an alternative imaging modality to investigate osteoporosis. We examined the influence of computed tomography convolution back-projection reconstruction kernels on the analysis of bone quantity and estimated mechanical properties in the proximal femur. Eighteen computed tomography scans of the proximal femur were reconstructed using both a standard smoothing reconstruction kernel and a bone-sharpening reconstruction kernel. Following phantom-based density calibration, we calculated typical bone quantity outcomes of integral volumetric bone mineral density, bone volume, and bone mineral content. Additionally, we performed finite element analysis in a standard sideways fall on the hip loading configuration. Significant differences for all outcome measures, except integral bone volume, were observed between the 2 reconstruction kernels. Volumetric bone mineral density measured using images reconstructed by the standard kernel was significantly lower (6.7%, p < 0.001) when compared with images reconstructed using the bone-sharpening kernel. Furthermore, the whole-bone stiffness and the failure load measured in images reconstructed by the standard kernel were significantly lower (16.5%, p < 0.001, and 18.2%, p < 0.001, respectively) when compared with the image reconstructed by the bone-sharpening kernel. These data suggest that for future quantitative computed tomography studies, a standardized reconstruction kernel will maximize reproducibility, independent of the use of a quantitative calibration phantom. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

Detecting peanuts inoculated with toxigenic and atoxienic Aspergillus flavus strains with fluorescence hyperspectral imagery

NASA Astrophysics Data System (ADS)

Xing, Fuguo; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Zhu, Fengle; Brown, Robert L.; Bhatnagar, Deepak; Liu, Yang

2017-05-01

Aflatoxin contamination in peanut products has been an important and long-standing problem around the world. Produced mainly by Aspergillus flavus and Aspergillus parasiticus, aflatoxins are the most toxic and carcinogenic compounds among toxins. This study investigated the application of fluorescence visible near-infrared (VNIR) hyperspectral images to assess the spectral difference between peanut kernels inoculated with toxigenic and atoxigenic inocula of A. flavus and healthy kernels. Peanut kernels were inoculated with NRRL3357, a toxigenic strain of A. flavus, and AF36, an atoxigenic strain of A. flavus, respectively. Fluorescence hyperspectral images under ultraviolet (UV) excitation were recorded on peanut kernels with and without skin. Contaminated kernels exhibited different fluorescence features compared with healthy kernels. For the kernels without skin, the inoculated kernels had a fluorescence peaks shifted to longer wavelengths with lower intensity than healthy kernels. In addition, the fluorescence intensity of peanuts without skin was higher than that of peanuts with skin (10 times). The fluorescence spectra of kernels with skin are significantly different from that of the control group (p<0.001). Furthermore, the fluorescence intensity of the toxigenic, AF3357 peanuts with skin was lower than that of the atoxigenic AF36 group. Discriminate analysis showed that the inoculation group can be separated from the controls with 100% accuracy. However, the two inoculation groups (AF3357 vis AF36) can be separated with only ∼80% accuracy. This study demonstrated the potential of fluorescence hyperspectral imaging techniques for screening of peanut kernels contaminated with A. flavus, which could potentially lead to the production of rapid and non-destructive scanning-based detection technology for the peanut industry.

Effect of different ripening stages on walnut kernel quality: antioxidant activities, lipid characterization and antibacterial properties.

PubMed

Amin, Furheen; Masoodi, F A; Baba, Waqas N; Khan, Asma Ashraf; Ganie, Bashir Ahmad

2017-11-01

Packing tissue between and around the kernel halves just turning brown (PTB) is a phenological indicator of kernel ripening at harvest in walnuts. The effect of three ripening stages (Pre-PTB, PTB and Post-PTB) on kernel quality characteristics, mineral composition, lipid characterization, sensory analysis, antioxidant and antibacterial activity were investigated in fresh kernels of indigenous numbered walnut selection of Kashmir valley "SKAU-02". Proximate composition, physical properties and sensory analysis of walnut kernels showed better results for Pre-PTB and PTB while higher mineral content was seen for kernels at Post-PTB stage in comparison to other stages of ripening. Kernels showed significantly higher levels of Omega-3 PUFA (C18:3 n3 ) and low n6/n3 ratio when harvested at Pre-PTB and PTB stages. The highest phenolic content and antioxidant activity was observed at the first stage of ripening and a steady decrease was observed at later stages. TBARS values increased as ripening advanced but did not show any significant difference in malonaldehyde formation during early ripening stages whereas it showed marked increase in walnut kernels at post-PTB stage. Walnut extracts inhibited growth of Gram-positive bacteria ( B. cereus, B. subtilis, and S. aureus ) with respective MICs of 1, 1 and 5 mg/mL and gram negative bacteria ( E. coli, P. and K. pneumonia ) with MIC of 100 mg/mL. Zone of inhibition obtained against all the bacterial strains from walnut kernel extracts increased with increase in the stage of ripening. It is concluded that Pre-PTB harvest stage with higher antioxidant activities, better fatty acid profile and consumer acceptability could be preferred harvesting stage for obtaining functionally superior walnut kernels.

Salt stress reduces kernel number of corn by inhibiting plasma membrane H+-ATPase activity.

PubMed

Jung, Stephan; Hütsch, Birgit W; Schubert, Sven

2017-04-01

Salt stress affects yield formation of corn (Zea mays L.) at various physiological levels resulting in an overall grain yield decrease. In this study we investigated how salt stress affects kernel development of two corn cultivars (cvs. Pioneer 3906 and Fabregas) at and shortly after pollination. In an earlier study, we found an accumulation of hexoses in the kernel tissue. Therefore, it was hypothesized that hexose uptake into developing endosperm and embryo might be inhibited. Hexoses are transported into the developing endosperm by carriers localized in the plasma membrane (PM). The transport is driven by the pH gradient which is built up by the PM H + -ATPase. It was investigated whether the PM H + -ATPase activity in developing corn kernels was inhibited by salt stress, which would cause a lower pH gradient resulting in impaired hexose import and finally in kernel abortion. Corn grown under control and salt stress conditions was harvested 0 and 2 days after pollination (DAP). Under salt stress sucrose and hexose concentrations in kernel tissue were higher 0 and 2 DAP. Kernel PM H + -ATPase activity was not affected at 0 DAP, but it was reduced at 2 DAP. This is in agreement with the finding, that kernel growth and thus kernel setting was not affected in the salt stress treatment at pollination, but it was reduced 2 days later. It is concluded that inhibition of PM H + -ATPase under salt stress impaired the energization of hexose transporters into the cells, resulting in lower kernel growth and finally in kernel abortion. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Isolation, purification and identification of protein associated with corn fiber gum

USDA-ARS?s Scientific Manuscript database

Corn fiber gum (CFG), an alkaline hydrogen peroxide extract of corn kernel milling by-product “corn fiber” is a proteinaceous arabinoxylan with a protein content ranging from ca. 2 to 9% by weight for the CFG samples isolated from different corn milling fiber sources. Several studies have suggested...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2014 CFR

2014-01-01

... halves per pound shall be based upon the weight of half-kernels after all pieces, particles and dust... specified range. (d) Tolerances for pieces, particles, and dust. In order to allow for variations incident..., particles, and dust: Provided, That not more than one-third of this amount, or 5 percent, shall be allowed...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2013 CFR

2013-01-01

... halves per pound shall be based upon the weight of half-kernels after all pieces, particles and dust... specified range. (d) Tolerances for pieces, particles, and dust. In order to allow for variations incident..., particles, and dust: Provided, That not more than one-third of this amount, or 5 percent, shall be allowed...

7 CFR 51.1447 - Fairly uniform in color.

Code of Federal Regulations, 2014 CFR

2014-01-01

... § 51.1447 Fairly uniform in color. Fairly uniform in color means that 90 percent or more of the kernels in the lot have skin color within the range of one or two color classifications. ... 7 Agriculture 2 2014-01-01 2014-01-01 false Fairly uniform in color. 51.1447 Section 51.1447...

7 CFR 51.1447 - Fairly uniform in color.

Code of Federal Regulations, 2013 CFR

2013-01-01

... § 51.1447 Fairly uniform in color. Fairly uniform in color means that 90 percent or more of the kernels in the lot have skin color within the range of one or two color classifications. ... 7 Agriculture 2 2013-01-01 2013-01-01 false Fairly uniform in color. 51.1447 Section 51.1447...

Direct Sum Decomposition of Groups

ERIC Educational Resources Information Center

Thaheem, A. B.

2005-01-01

Direct sum decomposition of Abelian groups appears in almost all textbooks on algebra for undergraduate students. This concept plays an important role in group theory. One simple example of this decomposition is obtained by using the kernel and range of a projection map on an Abelian group. The aim in this pedagogical note is to establish a direct…

Considering causal genes in the genetic dissection of kernel traits in common wheat.

PubMed

Mohler, Volker; Albrecht, Theresa; Castell, Adelheid; Diethelm, Manuela; Schweizer, Günther; Hartl, Lorenz

2016-11-01

Genetic factors controlling thousand-kernel weight (TKW) were characterized for their association with other seed traits, including kernel width, kernel length, ratio of kernel width to kernel length (KW/KL), kernel area, and spike number per m 2 (SN). For this purpose, a genetic map was established utilizing a doubled haploid population derived from a cross between German winter wheat cultivars Pamier and Format. Association studies in a diversity panel of elite cultivars supplemented genetic analysis of kernel traits. In both populations, genomic signatures of 13 candidate genes for TKW and kernel size were analyzed. Major quantitative trait loci (QTL) for TKW were identified on chromosomes 1B, 2A, 2D, and 4D, and their locations coincided with major QTL for kernel size traits, supporting the common belief that TKW is a function of other kernel traits. The QTL on chromosome 2A was associated with TKW candidate gene TaCwi-A1 and the QTL on chromosome 4D was associated with dwarfing gene Rht-D1. A minor QTL for TKW on chromosome 6B coincided with TaGW2-6B. The QTL for kernel dimensions that did not affect TKW were detected on eight chromosomes. A major QTL for KW/KL located at the distal tip of chromosome arm 5AS is being reported for the first time. TaSus1-7A and TaSAP-A1, closely linked to each other on chromosome 7A, could be related to a minor QTL for KW/KL. Genetic analysis of SN confirmed its negative correlation with TKW in this cross. In the diversity panel, TaSus1-7A was associated with TKW. Compared to the Pamier/Format bi-parental population where TaCwi-A1a was associated with higher TKW, the same allele reduced grain yield in the diversity panel, suggesting opposite effects of TaCwi-A1 on these two traits.

Effect of Fungal Colonization of Wheat Grains with Fusarium spp. on Food Choice, Weight Gain and Mortality of Meal Beetle Larvae (Tenebrio molitor)

PubMed Central

Guo, Zhiqing; Döll, Katharina; Dastjerdi, Raana; Karlovsky, Petr; Dehne, Heinz-Wilhelm; Altincicek, Boran

2014-01-01

Species of Fusarium have significant agro-economical and human health-related impact by infecting diverse crop plants and synthesizing diverse mycotoxins. Here, we investigated interactions of grain-feeding Tenebrio molitor larvae with four grain-colonizing Fusarium species on wheat kernels. Since numerous metabolites produced by Fusarium spp. are toxic to insects, we tested the hypothesis that the insect senses and avoids Fusarium-colonized grains. We found that only kernels colonized with F. avenaceum or Beauveria bassiana (an insect-pathogenic fungal control) were avoided by the larvae as expected. Kernels colonized with F. proliferatum, F. poae or F. culmorum attracted T. molitor larvae significantly more than control kernels. The avoidance/preference correlated with larval feeding behaviors and weight gain. Interestingly, larvae that had consumed F. proliferatum- or F. poae-colonized kernels had similar survival rates as control. Larvae fed on F. culmorum-, F. avenaceum- or B. bassiana-colonized kernels had elevated mortality rates. HPLC analyses confirmed the following mycotoxins produced by the fungal strains on the kernels: fumonisins, enniatins and beauvericin by F. proliferatum, enniatins and beauvericin by F. poae, enniatins by F. avenaceum, and deoxynivalenol and zearalenone by F. culmorum. Our results indicate that T. molitor larvae have the ability to sense potential survival threats of kernels colonized with F. avenaceum or B. bassiana, but not with F. culmorum. Volatiles potentially along with gustatory cues produced by these fungi may represent survival threat signals for the larvae resulting in their avoidance. Although F. proliferatum or F. poae produced fumonisins, enniatins and beauvericin during kernel colonization, the larvae were able to use those kernels as diet without exhibiting increased mortality. Consumption of F. avenaceum-colonized kernels, however, increased larval mortality; these kernels had higher enniatin levels than F. proliferatum or F. poae-colonized ones suggesting that T. molitor can tolerate or metabolize those toxins. PMID:24932485

Use of Near-Infrared Spectroscopy and Chemometrics for the Nondestructive Identification of Concealed Damage in Raw Almonds (Prunus dulcis).

PubMed

Rogel-Castillo, Cristian; Boulton, Roger; Opastpongkarn, Arunwong; Huang, Guangwei; Mitchell, Alyson E

2016-07-27

Concealed damage (CD) is defined as a brown discoloration of the kernel interior (nutmeat) that appears only after moderate to high heat treatment (e.g., blanching, drying, roasting, etc.). Raw almonds with CD have no visible defects before heat treatment. Currently, there are no screening methods available for detecting CD in raw almonds. Herein, the feasibility of using near-infrared (NIR) spectroscopy between 1125 and 2153 nm for the detection of CD in almonds is demonstrated. Almond kernels with CD have less NIR absorbance in the region related with oil, protein, and carbohydrates. With the use of partial least squares discriminant analysis (PLS-DA) and selection of specific wavelengths, three classification models were developed. The calibration models have false-positive and false-negative error rates ranging between 12.4 and 16.1% and between 10.6 and 17.2%, respectively. The percent error rates ranged between 8.2 and 9.2%. Second-derivative preprocessing of the selected wavelength resulted in the most robust predictive model.

Coronary Stent Artifact Reduction with an Edge-Enhancing Reconstruction Kernel - A Prospective Cross-Sectional Study with 256-Slice CT.

PubMed

Tan, Stéphanie; Soulez, Gilles; Diez Martinez, Patricia; Larrivée, Sandra; Stevens, Louis-Mathieu; Goussard, Yves; Mansour, Samer; Chartrand-Lefebvre, Carl

2016-01-01

Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel. This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale. Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; p<0.001) or the circumference method (1.13 ± 0.02 versus 1.21 ± 0.02 mm, respectively; p = 0.001). The edge-enhancing kernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; p<0.001) and the circumference (1.06 ± 0.26 versus 1.13 ± 0.31 mm, respectively; p = 0.005) methods. The edge-enhancing kernel was associated with lower SNR and CNR, as well as higher background noise (all p < 0.001), in comparison to the medium-smooth kernel. Stent visual scores were higher with the edge-enhancing kernel (p<0.001). In vivo 256-slice CT assessment of coronary stents shows that the edge-enhancing CT reconstruction kernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel.

Temporal Effects on Internal Fluorescence Emissions Associated with Aflatoxin Contamination from Corn Kernel Cross-Sections Inoculated with Toxigenic and Atoxigenic Aspergillus flavus.

PubMed

Hruska, Zuzana; Yao, Haibo; Kincaid, Russell; Brown, Robert L; Bhatnagar, Deepak; Cleveland, Thomas E

2017-01-01

Non-invasive, easy to use and cost-effective technology offers a valuable alternative for rapid detection of carcinogenic fungal metabolites, namely aflatoxins, in commodities. One relatively recent development in this area is the use of spectral technology. Fluorescence hyperspectral imaging, in particular, offers a potential rapid and non-invasive method for detecting the presence of aflatoxins in maize infected with the toxigenic fungus Aspergillus flavus . Earlier studies have shown that whole maize kernels contaminated with aflatoxins exhibit different spectral signatures from uncontaminated kernels based on the external fluorescence emission of the whole kernels. Here, the effect of time on the internal fluorescence spectral emissions from cross-sections of kernels infected with toxigenic and atoxigenic A. flavus , were examined in order to elucidate the interaction between the fluorescence signals emitted by some aflatoxin contaminated maize kernels and the fungal invasion resulting in the production of aflatoxins. First, the difference in internal fluorescence emissions between cross-sections of kernels incubated in toxigenic and atoxigenic inoculum was assessed. Kernels were inoculated with each strain for 5, 7, and 9 days before cross-sectioning and imaging. There were 270 kernels (540 halves) imaged, including controls. Second, in a different set of kernels (15 kernels/group; 135 total), the germ of each kernel was separated from the endosperm to determine the major areas of aflatoxin accumulation and progression over nine growth days. Kernels were inoculated with toxigenic and atoxigenic fungal strains for 5, 7, and 9 days before the endosperm and germ were separated, followed by fluorescence hyperspectral imaging and chemical aflatoxin determination. A marked difference in fluorescence intensity was shown between the toxigenic and atoxigenic strains on day nine post-inoculation, which may be a useful indicator of the location of aflatoxin contamination. This finding suggests that both, the fluorescence peak shift and intensity as well as timing, may be essential in distinguishing toxigenic and atoxigenic fungi based on spectral features. Results also reveal a possible preferential difference in the internal colonization of maize kernels between the toxigenic and atoxigenic strains of A. flavus suggesting a potential window for differentiating the strains based on fluorescence spectra at specific time points.