Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning.

Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection.

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2--2.7% and 1--3.8% respectively and Average Recall by 1.3--9.3 and 2.4--7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.

Automated location invariant animal detection in camera trap images using publicly available data sources.

A time-consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain-specific applications.We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image-sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out-of-sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%-88.59%) than that achieved by the models trained only on camera trap datasets (38.5%-66.74%). Infusion further improved mAP by 1.78%-32.08%.Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out-of-the-box software. Models can be further optimized by infusion of 5%-10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

U-Infuse: Democratization of Customizable Deep Learning for Object Detection.

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

Modelling the potential effects of climate factors on Dubas bug (Ommatissus lybicus) presence/absence and its infestation rate: A case study from Oman.

BACKGROUND: Ommatissus lybicus de Bergevin (Hemiptera: Tropiduchidae) (Dubas Bug, DB) is an insect pest attacking date palms. It occurs in Arab countries including Oman. In this paper, the logistic, ordinary least square, and geographical weighted regressions were applied to model the absence/presence and density of DB against climate factors. A method is proposed for modelling spatially correlated prorations annually over the study period, based on annual and seasonal outbreaks. The historical 2006-2015 climate data were obtained from weather stations located in nine governorates in northern Oman, while dataloggers collected the 2017 microclimate data in eight of these nine governorates. RESULTS: Logistic regression model showed the percentages of correctly predicted values using a cut-off point of 0.5 were 90%, 88% and 84%, indicating good classification accuracy. OLS and GWR models showed an overall trend of strong linear correlation between DB infestation levels and short- and long-term climate factors. The three models suggested that precipitation, elevation, temperature, humidity, wind direction and wind speed are important in influencing the spatial distribution and the presence/absence of dense DB populations. CONCLUSION: The results provide an improved understanding of climate factors that impact DB's spread and is considered useful for managing DB infestations in date palm plantations. © 2019 Society of Chemical Industry.

Predicting the potential geographical distribution of parasitic natural enemies of the Dubas bug (Ommatissus lybicus de Bergevin) using geographic information systems.

The Dubas bug (Ommatissus lybicus de Bergevin) is a pest species whose entire life cycle occurs on date palms, Phoenix dactylifera L, causing serious damage and reducing date palm growth and yield. Pseudoligosita babylonica Viggiani, Aprostocetus nr. Beatus, and Bocchus hyalinus Olmi are very important parasitic natural enemies of Ommatissus lybicus in northern Oman. In this study, random farms were selected to (a) model the link between occurrences of the Pseudoligosita babylonica, Aprostocetus nr beatus, and Bocchus hyalinus (dependent variables) with environmental, climatological, and Dubas bug infestation levels (the independent variables), and (b) produce distribution and predictive maps of these natural enemies in northern Oman. The multiple R2 values showed the model explained 63%, 89%, and 94% of the presence of P. babylonica, A. nr beatus, and Bocchus hyalinus, respectively. However, the distribution of each species appears to be influenced by distinct and geographically associated climatological and environmental factors, as well as habitat characteristics. This study reveals that spatial analysis and modeling can be highly useful for studying the distribution, the presence or absence of Dubas bugs, and their natural enemies. It is anticipated to help contribute to the reduction in the extent and costs of aerial and ground insecticidal spraying needed in date palm plantations.

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman.

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investment coming from both the government and from private individuals. However, a global infestation of Dubas bug (Ommatissus lybicus Bergevin) has impacted the Middle East region, and infestations of date palms have been widespread. In this study, spatial analysis and geostatistical techniques were used to model the spatial distribution of Dubas bug infestations to (a) identify correlations between Dubas bug densities and different environmental variables, and (b) predict the locations of future Dubas bug infestations in Oman. Firstly, we considered individual environmental variables and their correlations with infestation locations. Then, we applied more complex predictive models and regression analysis techniques to investigate the combinations of environmental factors most conducive to the survival and spread of the Dubas bug. Environmental variables including elevation, geology, and distance to drainage pathways were found to significantly affect Dubas bug infestations. In contrast, aspect and hillshade did not significantly impact on Dubas bug infestations. Understanding their distribution and therefore applying targeted controls on their spread is important for effective mapping, control and management (e.g., resource allocation) of Dubas bug infestations.

Impacts of human-related practices on Ommatissus lybicus infestations of date palm in Oman.

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investments coming from both the government and private individuals. However, a widespread Dubas bug (DB) (Ommatissus lybicus Bergevin) infestation has impacted regions including the Middle East, North Africa, Southeast Russia, and Spain, resulting in widespread damages to date palms. In this study, techniques in spatial statistics including ordinary least squares (OLS), geographically weighted regression (GRW), and exploratory regression (ER) were applied to (a) model the correlation between DB infestations and human-related practices that include irrigation methods, row spacing, palm tree density, and management of undercover and intercropped vegetation, and (b) predict the locations of future DB infestations in northern Oman. Firstly, we extracted row spacing and palm tree density information from remote sensed satellite images. Secondly, we collected data on irrigation practices and management by using a simple questionnaire, augmented with spatial data. Thirdly, we conducted our statistical analyses using all possible combinations of values over a given set of candidate variables using the chosen predictive modelling and regression techniques. Lastly, we identified the combination of human-related practices that are most conducive to the survival and spread of DB. Our results show that there was a strong correlation between DB infestations and several human-related practices parameters (R2 = 0.70). Variables including palm tree density, spacing between trees (less than 5 x 5 m), insecticide application, date palm and farm service (pruning, dethroning, remove weeds, and thinning), irrigation systems, offshoots removal, fertilisation and labour (non-educated) issues, were all found to significantly influence the degree of DB infestations. This study is expected to help reduce the extent and cost of aerial and ground sprayings, while facilitating the allocation of date palm plantations. An integrated pest management (IPM) system monitoring DB infestations, driven by GIS and remote sensed data collections and spatial statistical models, will allow for an effective DB management program in Oman. This will in turn ensure the competitiveness of Oman in the global date fruits market and help preserve national yields.

Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep.

BACKGROUND: Knowledge of the genetic structure and overall diversity of livestock species is important to maximise the potential of genome-wide association studies and genomic prediction. Commonly used measures such as linkage disequilibrium (LD), effective population size (N e ), heterozygosity, fixation index (F ST) and runs of homozygosity (ROH) are widely used and help to improve our knowledge about genetic diversity in animal populations. The development of high-density single nucleotide polymorphism (SNP) arrays and the subsequent genotyping of large numbers of animals have greatly increased the accuracy of these population-based estimates. METHODS: In this study, we used the Illumina OvineSNP50 BeadChip array to estimate and compare LD (measured by r (2) and D'), N e , heterozygosity, F ST and ROH in five Australian sheep populations: three pure breeds, i.e., Merino (MER), Border Leicester (BL), Poll Dorset (PD) and two crossbred populations i.e. F1 crosses of Merino and Border Leicester (MxB) and MxB crossed to Poll Dorset (MxBxP). RESULTS: Compared to other livestock species, the sheep populations that were analysed in this study had low levels of LD and high levels of genetic diversity. The rate of LD decay was greater in Merino than in the other pure breeds. Over short distances (<10 kb), the levels of LD were higher in BL and PD than in MER. Similarly, BL and PD had comparatively smaller N e than MER. Observed heterozygosity in the pure breeds ranged from 0.3 in BL to 0.38 in MER. Genetic distances between breeds were modest compared to other livestock species (highest F ST = 0.063) but the genetic diversity within breeds was high. Based on ROH, two chromosomal regions showed evidence of strong recent selection. CONCLUSIONS: This study shows that there is a large range of genome diversity in Australian sheep breeds, especially in Merino sheep. The observed range of diversity will influence the design of genome-wide association studies and the results that can be obtained from them. This knowledge will also be useful to design reference populations for genomic prediction of breeding values in sheep.

Genome-wide association study of body weight in Australian Merino sheep reveals an orthologous region on OAR6 to human and bovine genomic regions affecting height and weight.

BACKGROUND: Body weight (BW) is an important trait for meat production in sheep. Although over the past few years, numerous quantitative trait loci (QTL) have been detected for production traits in cattle, few QTL studies have been reported for sheep, with even fewer on meat production traits. Our objective was to perform a genome-wide association study (GWAS) with the medium-density Illumina Ovine SNP50 BeadChip to identify genomic regions and corresponding haplotypes associated with BW in Australian Merino sheep. METHODS: A total of 1781 Australian Merino sheep were genotyped using the medium-density Illumina Ovine SNP50 BeadChip. Among the 53 862 single nucleotide polymorphisms (SNPs) on this array, 48 640 were used to perform a GWAS using a linear mixed model approach. Genotypes were phased with hsphase; to estimate SNP haplotype effects, linkage disequilibrium blocks were identified in the detected QTL region. RESULTS: Thirty-nine SNPs were associated with BW at a Bonferroni-corrected genome-wide significance threshold of 1 %. One region on sheep (Ovis aries) chromosome 6 (OAR6) between 36.15 and 38.56 Mb, included 13 significant SNPs that were associated with BW; the most significant SNP was OAR6_41936490.1 (P = 2.37 × 10(-16)) at 37.69 Mb with an allele substitution effect of 2.12 kg, which corresponds to 0.248 phenotypic standard deviations for BW. The region that surrounds this association signal on OAR6 contains three genes: leucine aminopeptidase 3 (LAP3), which is involved in the processing of the oxytocin precursor; NCAPG non-SMC condensin I complex, subunit G (NCAPG), which is associated with foetal growth and carcass size in cattle; and ligand dependent nuclear receptor corepressor-like (LCORL), which is associated with height in humans and cattle. CONCLUSIONS: The GWAS analysis detected 39 SNPs associated with BW in sheep and a major QTL region was identified on OAR6. In several other mammalian species, regions that are syntenic with this region have been found to be associated with body size traits, which may reflect that the underlying biological mechanisms share a common ancestry. These findings should facilitate the discovery of causative variants for BW and contribute to marker-assisted selection.

An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm.

BACKGROUND: The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. METHODS: In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. RESULTS: Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. CONCLUSIONS: Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.

Long-term vitamin E supplementation reduces atherosclerosis and mortality in Ldlr-/- mice, but not when fed Western style diet.

OBJECTIVES: Epidemiological and experimental evidence have indicated potential health benefits of vitamin E supplementation on coronary heart disease (CHD), but several clinical trials have reported no benefit from vitamin E supplementation on CHD. We hypothesized that supplemental intake of vitamin E from an early age may prevent or retard the development and progression of atherosclerosis and CHD mortality. METHODS: To test this hypothesis, 300 Ldlr(-/-) mice were divided into groups receiving Western style high fat/cholesterol (HFHC), moderate fat/cholesterol (MFMC), or low fat/cholesterol (LFLC) diets all containing 50 IU of vitamin E. These dietary groups were further subdivided into four sub-groups (n = 25) receiving their respective diets with no vitamin E supplementation or additionally supplemented with vitamin E (500 IU/kg diet) starting at the early age of 5 wks, or 6 mo, or 12 mo. All mice remained on their assigned diets until age 18 mo. Body weight, health status and survival rate of mice were monitored and recorded. After 18 mo of dietary treatments, mice were sacrificed. RESULTS: Body weight was the highest in HFHC groups and the lowest in LFLC groups. Plasma concentration of cholesterol and triglycerides was high in all dietary groups, and plasma vitamin E was high in vitamin E supplemented groups. Fifty percent of mice fed Western style HFHC diet and 53% of mice fed MFMC diet survived during the 18 mo, whereas 75% of mice fed LFLC diet survived during the 18 mo dietary treatments. At the age of 18 mo, all the Ldlr(-/-) mice, regardless of dietary treatments, had several advanced atherosclerotic lesions in both aortic root and aortic tree. Within the LFLC groups, those that received vitamin E supplements from age 5 wks up to 18 mo had a significantly higher survival rate of 88% (p = 0.04) and lower mortality (12%) compared to mice that did not receive vitamin E supplements (64%). This lower mortality rate and higher survival rate coincided with significantly (p = 0.03) fewer aortic lesions in the vitamin E supplemented LFLC group (50%) compared to LFLC mice that did not receive vitamin E supplements in their diets (65%). Subjective immunohistochemical evaluation of aortic valves showed that LFLC mice that received vitamin E supplements for 18 mo had less intima media thickness compared to LFLC mice that did not receive vitamin E supplements in their diet. The LFLC mice that were supplemented with vitamin E for 18 mo had the lowest mRNA expression of inflammatory markers such as VCAM-1, MCP-1 and CD36 in samples obtained from lesion and non-lesionareas. CONCLUSION: In conclusion, 500 mg vitamin E/kg diet in Ldlr(-/-) mice is not effective at reducing mortality and atherosclerosis when the diet contained high or medium levels of fat and cholesterol. However, a relatively low dose and long-term vitamin E supplementation started from an early age is effective in reducing mortality and atherosclerotic lesions in genetically prone Ldlr(-/-) mice fed LFLC diet.

A novel approach in discovering significant interactions from TCM patient prescription data.

The efficacy of a traditional Chinese medicine medication derives from the complex interactions of herbs or Chinese Materia Medica in a formula. The aim of this paper is to propose a new approach to systematically generate combinations of interacting herbs that might lead to good outcome. Our approach was tested on a data set of prescriptions for diabetic patients to verify the effectiveness of detected combinations of herbs. This approach is able to detect effective higher orders of herb-herb interactions with statistical validation. We present an exploratory analysis of clinical records using a pattern mining approach called Interaction Rules Mining.

Sparse kernel learning with LASSO and Bayesian inference algorithm.

Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In International conference on artificial intelligence and statistics (pp. 580-587). San Juan, Puerto Rico: MIT Press]. This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages.

The impact of perineural invasion on biochemical outcome after permanent prostate iodine-125 brachytherapy.

PURPOSE: Perineural invasion (PNI) in prostate biopsies is associated with increased risk of higher Gleason score and worse pathologic stage. We report the influence of PNI in biochemical no evidence of disease (bNED) survival after (125)I prostate brachytherapy (BT). METHODS AND MATERIALS: Pathology reports of 700 men with localized prostate cancer who underwent (125)I prostate BT in 1999-2008 were reviewed. The presence or absence of PNI in the biopsy was documented in 339 men. Clinical, treatment, and dosimetric parameters, along with PNI status, were evaluated for bNED survival, defined by "nadir+2" definition. RESULTS: Of the 339 patients, 87% had favorable risk and 13% intermediate risk. PNI was present in 89 patients (26%). After a median followup of 32 months, there were five biochemical failures (4: +PNI and 1: -PNI), of which one was local failure (+PNI). Actuarial 5-year bNED survival for the entire group was 97.0% (92.9% for +PNI; 99.2% for -PNI). In univariate analysis age, pretreatment prostate-specific antigen, Gleason score 7, and intermediate risk group predicted for worse biochemical outcome, whereas the presence of PNI showed a trend toward significance (p=0.06). Some of the regression algorithms failed to converge because of low event rates. CONCLUSIONS: We report excellent biochemical control in 339 men treated with (125)I prostate BT. The presence of PNI showed a trend toward significance in predicting 5-year bNED survival but did not impact on local control and should not influence the decision to recommend BT for localized prostate cancer.

Curcumin inhibits adipogenesis in 3T3-L1 adipocytes and angiogenesis and obesity in C57/BL mice.

Angiogenesis is necessary for the growth of adipose tissue. Dietary polyphenols may suppress growth of adipose tissue through their antiangiogenic activity and by modulating adipocyte metabolism. We investigated the effect of curcumin, the major polyphenol in turmeric spice, on angiogenesis, adipogenesis, differentiation, apoptosis, and gene expression involved in lipid and energy metabolism in 3T3-L1 adipocyte in cell culture systems and on body weight gain and adiposity in mice fed a high-fat diet (22%) supplemented with 500 mg curcumin/kg diet for 12 wk. Curcumin (5-20 micromol/L) suppressed 3T3-L1 differentiation, caused apoptosis, and inhibited adipokine-induced angiogenesis of human umbilical vein endothelial cells. Supplementing the high-fat diet of mice with curcumin did not affect food intake but reduced body weight gain, adiposity, and microvessel density in adipose tissue, which coincided with reduced expression of vascular endothelial growth factor (VEGF) and its receptor VEGFR-2. Curcumin increased 5'AMP-activated protein kinase phosphorylation, reduced glycerol-3-phosphate acyl transferase-1, and increased carnitine palmitoyltransferase-1 expression, which led to increased oxidation and decreased fatty acid esterification. The in vivo effect of curcumin on the expression of these enzymes was also confirmed by real-time RT-PCR in subcutaneous adipose tissue. In addition, curcumin significantly lowered serum cholesterol and expression of PPARgamma and CCAAT/enhancer binding protein alpha, 2 key transcription factors in adipogenesis and lipogenesis. The curcumin suppression of angiogenesis in adipose tissue together with its effect on lipid metabolism in adipocytes may contribute to lower body fat and body weight gain. Our findings suggest that dietary curcumin may have a potential benefit in preventing obesity.

Twin kernel embedding.

In most existing dimensionality reduction algorithms, the main objective is to preserve relational structure among objects of the input space in a low dimensional embedding space. This is achieved by minimizing the inconsistency between two similarity/dissimilarity measures, one for the input data and the other for the embedded data, via a separate matching objective function. Based on this idea, a new dimensionality reduction method called Twin Kernel Embedding (TKE) is proposed. TKE addresses the problem of visualizing non-vectorial data that is difficult for conventional methods in practice due to the lack of efficient vectorial representation. TKE solves this problem by minimizing the inconsistency between the similarity measures captured respectively by their kernel Gram matrices in the two spaces. In the implementation, by optimizing a nonlinear objective function using the gradient descent algorithm, a local minimum can be reached. The results obtained include both the optimal similarity preserving embedding and the appropriate values for the hyperparameters of the kernel. Experimental evaluation on real non-vectorial datasets confirmed the effectiveness of TKE. TKE can be applied to other types of data beyond those mentioned in this paper whenever suitable measures of similarity/dissimilarity can be defined on the input data.

Vitamin E supplementation suppresses macrophage accumulation and endothelial cell expression of adhesion molecules in the aorta of hypercholesterolemic rabbits.

Suppression of cell adhesion molecule expression and macrophage accumulation by the endothelium is believed to play an important role in preventing the development of atherosclerosis. Earlier, we have shown that in vitro supplementation of human aortic endothelial cells with Vitamin E dose-dependently reduced expression of adhesion molecules and monocyte adhesion. Here, we report the in vivo down-regulation of endothelial cell adhesion molecules expression and macrophage accumulation in the aortas of hypercholesterolemic rabbits supplemented with Vitamin E. To this end, New Zealand White rabbits were fed a semi-purified diet containing 30 (control) or 1000 IU/kg Vitamin E. After 4 weeks, both groups' diets were switched to an atherogenic diet (0.3% cholesterol, 9% hydrogenated coconut oil, and 1% corn oil) containing the respective levels of Vitamin E and fed for 2, 4, and 6 weeks. Vitamin E supplemented rabbits had significantly higher levels of Vitamin E in their plasma and aortas. Frozen aorta sections were fixed and stained by an avidin-biotin complex method using Rb2/3 and Rb1/9 monoclonal antibodies against rabbit ICAM-1 and VCAM-1, respectively, and with RAM-11 for macrophage and von Willebrand factor for endothelial cells, followed by staining with secondary antibodies and counterstaining and evaluation under the microscope. At 6 weeks on atherogenic diet treatment, a trend (P = 0.08) toward a lower score of ICAM-1 expression by endothelial cells was observed in the aorta of Vitamin E treated rabbits compared to the control. However, a decrease in the score of VCAM-1 expression by endothelial cells in Vitamin E treated rabbits did not reach to a statistical significance. At 4 and 6 weeks on atherogenic diet, Vitamin E supplementation also significantly (P = 0.003) inhibited the accumulation of macrophages in the aorta. These results support the concept that down-regulation of adhesion molecule expression and suppression of monocyte/macrophage activation by Vitamin E in vivo is one of the potential mechanisms by which Vitamin E may suppress the development of aortic lesions in a rabbit model of atherosclerosis.