nils-herrmann/clean_scirepeval huggingface dataset:
Facial Expression Recognition Using Local Binary Pattern and Support Vector Machine

Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human computer interaction and data driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP featureswhich are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well organized representation of face and is helpful in determining the resemblance among images.  Face Recognition, Facial Expression Recognition, Local Binary Pattern, Principal Component Analysis, Support Vector Machine.
Facial expression recognition based on adaptively weighted improved local binary pattern
Venue: International Conference on Digital Image Processing

In order to fully describe the texture informations of the image, and to distinguish the sub regions which contain different texture informations, this paper proposes a method of facial expression recognition based on adaptively weighted improved Local Binary Pattern (LBP) Firstly, the whole face region and expression sub regions of eyebrows, eyes, nose and mouth are isolated by preprocessing. Secondly, the features of the sub regions are extracted by improved LBP, the Fisher Linear Discriminant (FLD) is applied to calculated the weights of sub regions, and then the weighted histograms of expression sub regions are fused as the histogram of facial expression feature. Finally, the fused features are classified by Support Vector Machine (SVM) The experiments are performed on JAFFE and Extended Cohn Kanada database(CK+ and the experimental results demonstrate that the proposed method has better recognition performance.
Risky sexual behaviors among sexually active first year students matriculating at a historically Black college: Is a positive self image an instigator?
Venue: Social work in health care

A sample of 498 sexually active first year students matriculating at a historically Black college in North Carolina was used to determine correlates of risky sexual behaviors. In an Ordinary Least Squares regression, the self esteem element "I take a positive attitude toward myself" (B 1.12, p .05) non condom use because of partner issues (B .53, p .05) and being drunk or high (B 1.20, p .001) oral sex (B 1.74, p .001) anal sex (B .61, p .04) and bisexuality (B .85, p .03) all increased the number of these behaviors. Higher scores on the condom usage scale (B .38, p .002) were found to decrease the number of risky sexual behaviors. Illicit drug use was an underpinning of the surprisingly positive relationship between positive self image and risky sexual behaviors. It was concluded that school based social workers, mental health care professionals, and community based prevention providers can play a critical role in the training of peer facilitators, development, and supervision of peer driven risk reduction programs to address the complex interplay among self esteem, sex, and substances.
Fostering the Integration of the Clinical Nurse Leader Role in Japan through a Multidisciplinary Clinical Immersion Program in the United States

Quality Improvement and unit assessment Dana Madon, CNL students Nursing Leadership Horizontal Leadership, interdisciplinary communication collaboration skills. Health care advocacy. Shannon Lizer, PhD, FNP BC Care Environment Management Healthcare systems healthcare policy quality Improvement. Jane Zielinski Carter, DNP, RN, CNL, CCRN, CNRN Healthcare Informatics Dana Dermody, MSN, RN, CNL Amy Kirchhoff, RN, BSN, Informatics Specialist Team coordination, healthcare finance economics. Kristine Dreesen, MBA, MSHA, RN, BSN Ethics, Shannon Lizer, PhD, FNP BC Clinical Outcomes Management Knowledge management EBP Illness and disease management, advanced clinical assessment health prevention and disease prevention management Jessica Schneiderman, MSN, RN, CNSAPN Nursing Leadership Integration of the CNL role, role integrity, lateral integration of care services. CNL curriculum Certification Process AACN End of Program competencies, CNL Certification process, Q A Dana Dermody, MSN,CNL, CNL Presentations of Quality Improvement Projects of CNL Role
An improved algorithm for finding all minimal paths in a network
Venue: Reliab. Eng. Syst. Saf.

Minimal paths (MPs) play an important role in network reliability evaluation. In this paper, we report an efficient recursive algorithm for finding all MPs in two terminal networks, which consist of a source node and a sink node. A linked path structure indexed by nodes is introduced, which accepts both directed and undirected form of networks. The distance between each node and the sink node is defined, and a simple recursive algorithm is presented for labeling the distance for each node. Based on the distance between each node and the sink node, additional conditions for backtracking are incorporated to reduce the number of search branches. With the newly introduced linked node structure, the distances between each node and the sink node, and the additional backtracking conditions, an improved backtracking algorithm for searching for all MPs is developed. In addition, the proposed algorithm can be adapted to search for all minimal paths for each source sink pair in networks consisting of multiple source nodes and/or multiple sink nodes. Through computational experiments, it is demonstrated that the proposed algorithm is more efficient than existing algorithms when the network size is not too small. The proposed algorithm becomes more advantageous as the size of the network grows.
Oral Administration of Reduced Coenzyme Q10 Ameliorates the Endocrine Disrupting Chemical Induced Sperm Toxicity in Rats

Food additives and agricultural chemicals consumed in daily life can be endocrine disrupting chemicals (EDCs) for humans. EDCs have been reported to influence our genital function, learning ability and recognition. We have reported that the production of reactive oxygen species (ROS) precedes the decrease in sperm motility and/or numbers (FRR 2010) suggesting oxidative stress plays a key role. Therefore, we have studied the effects of a strong antioxidant, coenzyme Q 10 (CoQ10) on the rat sperm toxicity induced by diethylstilbestrol (DES) one of EDCs.  Drinking water containing DES (0.1 mg/L) CoQ10 (300 or 600 mg/kg/day; Kaneka Co. were given to Wistar rats (13 weeks old) for 8 weeks. The numbers and the motility of sperm, and lipid peroxidation products in the testicle were measured. Result Reduced CoQ10 was absorbed and transferred to rat blood and liver, but only a little to the testicle. Administration of DES decreased the number of sperm in the cauda epididymidis, and motility functions (curve line speed, and the head amplitude) Supplementation of reduced CoQ10 did not rescue the decline of sperm numbers. However, both the curved line speed and the head amplitude of the sperm which are important functions of fertilization were improved significantly by reduced CoQ10. HNE (4 hydroxy 2 nonenal) modified proteins (28 and 53 kDa) in the testicle were increased significantly by DES administration. Reduced CoQ10 significantly decreased HNE modified proteins.  Although the reduced CoQ10 supplementation to rats did not rescue the decline of total sperm numbers induced by DES administration, it significantly reduced oxidative stress in the testis and improved sperm function. These results suggested that the systemic absorption of reduced CoQ10 might inhibit oxidative stress of the testicle and dysfunction of the sperm.
Effect of Coenzyme Q10 as an Antioxidant Added to Semen Extender During Cryopreservation of Buffalo and Cattle Semen

This study was conducted to assess the influence of supplementing Tris extender with Coenzyme Q10 (COQ10) as an antioxidant on sperm characteristics of Egyptian buffalo and cattle. This study was carried out at Animal Production Research Station, El Gemmezah, belonging to Animal Production Research Institute, Egypt. Five sexually mature buffalo (4 y and 400 450 kg) and Friesian (3 y and 350 400 kg) bulls were used in this study. Semen was collected twice weekly for five successive weeks. Only ejaculates with =70% mass motility were pooled and diluted at 37degC with Tris extender containing 0, 20 and 30 mM of CoQ10 in T1, T2 and T3, respectively. Equilibration period was at 5degC for 4 h and semen was packed in 0.25 ml French straws frozen in liquid nitrogen 196oC. Stored straws were thawed individually at 37degC for 30 s. Semen was evaluated for sperm characteristics including percentages of progressive motility, livability, abnormality, curled tail and acrosomal damage of spermatozoa. Enzymatic activity of AST, ALT and LDH was determined in seminal plasma of post thawed semen. Results showed that there were no species differences in all sperm characteristics studied, except for increasing (P<0.05) percentage of abnormality and damage acrosome spermatozoa in post thawed semen of cattle compared with buffalo bulls. All sperm characteristics were improved (P<0.05) by 30 mM COQ10 supplementation T3 in post diluted, post equilibrated and post thawed semen as compared to control without supplementation T1 in buffalo and cattle semen. Activity of all enzymes reduced (P<0.05) in seminal plasma of both species. In conclusion, CoQ10, as antioxidant, at a level of 30 mM in Tris extender is able to enhance most sperm characteristics including mobility, livability, plasma membrane integrity, with decreasing sperm abnormalities and it has strong protective power against acrosomal damage of buffalo and cattle spermatozoa.
Formal language models for finding groups of experts
Venue: Inf. Process. Manag.

We introduce a new information retrieval task: given a topic, try to find knowledgeable groups that have expertise on the topic.Five probabilistic language models are proposed to tackle the challenge of automatically finding groups of experts in heterogeneous document collections.For evaluation purpose, a data set is created based on a publicly downloadable corpus used in the TREC Enterprise 2005 and 2006 tracks and three types of ground truth are defined.We provide a detailed analysis of the performance of the proposed group finding models. The task of finding groups or teams has recently received increased attention, as a natural and challenging extension of search tasks aimed at retrieving individual entities. We introduce a new group finding task: given a query topic, we try to find knowledgeable groups that have expertise on that topic. We present five general strategies for this group finding task, given a heterogenous document repository. The models are formalized using generative language models. Two of the models aggregate expertise scores of the experts in the same group for the task, one locates documents associated with experts in the group and then determines how closely the documents are associated with the topic, whilst the remaining two models directly estimate the degree to which a group is a knowledgeable group for a given topic. For evaluation purposes we construct a test collection based on the TREC 2005 and 2006 Enterprise collections, and define three types of ground truth for our task. Experimental results show that our five knowledgeable group finding models achieve high absolute scores. We also find significant differences between different ways of estimating the association between a topic and a group.
Informed Active Learning to Aid Domain Experts in Modeling Compliance
Venue: 2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC)

Modern enterprises face an unprecedented regulatory regime. Traditional compliance practices in enterprises rely heavily on domain experts whose judgement determines what compliance means and how to reflect regulations onto the enterprise processes and data to make them compliant. These activities are mostly manual in nature. We present a machine learning approach to modeling compliance. Our key innovations are a) use of active learning a semi supervised system capable of learning interactively from the domain expert to identify regulations and b) informing the feature representation of the active learner based on domain specific entities and relations to effectively build a domain model of regulations. Early results show that our system reduces the burden on domain experts to a large extent, enables latching domain expert's knowledge, and makes further steps in compliance easier by the use of models.
A preposterior analysis to predict identifiability in the experimental calibration of computer models

When using physical experimental data to adjust, or calibrate, computer simulation models, two general sources of uncertainty that must be accounted for are calibration parameter uncertainty and model discrepancy. This is complicated by the well known fact that systems to be calibrated are often subject to identifiability problems, in the sense that it is difficult to precisely estimate the parameters and to distinguish between the effects of parameter uncertainty and model discrepancy. We develop a form of preposterior analysis that can be used, prior to conducting physical experiments but after conducting the computer simulations, to predict the degree of identifiability that will result after conducting the physical experiments for a given experimental design. Specifically, we calculate the preposterior covariance matrix of the calibration parameters and demonstrate that, in the examples that we consider, it provides a reasonable prediction of the actual posterior covariance that is calculated after the experimental data are collected. Consequently, the preposterior covariance can be used as a criterion for designing physical experiments to help achieve better identifiability in calibration problems.
Granular Computing Approach to Two Way Learning Based on Formal Concept Analysis in Fuzzy Datasets
Venue: IEEE Transactions on Cybernetics

The main task of granular computing (GrC) is about representing, constructing, and processing information granules. Information granules are formalized in many different approaches. Different formal approaches emphasize the same fundamental facet in different ways. In this paper, we propose a novel GrC method of machine learning by using formal concept description of information granules. Based on information granules, the model and mechanism of two way learning system is constructed in fuzzy datasets. It is addressed about how to train arbitrary fuzzy information granules to become necessary, sufficient, and necessary and sufficient fuzzy information granules. Moreover, an algorithm of the presented approach is established, and the complexity of the algorithm is analyzed carefully. Finally, to interpret and help understand the theories and algorithm, a real life case study is considered and experimental evaluation is performed by five datasets from the University of California Irvine, which is valuable for applying these theories to deal with practical issues.
Genetic diversity of Enterolobium cyclocarpum in Colombian seasonally dry tropical forest: implications for conservation and restoration
Venue: Biodiversity and Conservation

Enterolobium cyclocarpum is a characteristic legume tree species of seasonally dry tropical forests (SDTFs) of Mesoamerica and northern South America typically used in silvopastoral and agroforestry systems. Remaining populations of E. cyclocarpum in Colombia are severely fragmented owing to the highly degraded state of SDTF in the country, posing threats to both their in situ persistence and their usefulness as seed sources for future planting efforts. We genotyped E. cyclocarpum populations at nine sampling sites across a latitudinal gradient of SDTF in Colombia by means of eight nSSR markers to elucidate the species diversity distribution in the country. Our data suggest that a deep divide seems to have existed between Caribbean and Andean populations of E. cyclocarpum in Colombian SDTF that may date back to the last glacial maximum ~21,000 BP) or longer. However, we only found evidence of genetic differentiation between trees from the southern Cauca River valley and populations at more northern locations. All the latter populations showed signs of admixture which may be the result of human influenced movement of germplasm, particularly after the introduction of cattle by European settlers. Most of the sampled sites showed heterozygosity scores close to Hardy Weinberg expectations. Only the three southern most populations displayed significantly positive values of inbreeding coefficient, potentially affecting their in situ maintenance and their use as seed sources. Based on our findings we identify priority areas for the in situ conservation of remaining E. cyclocarpum populations, and propose a strategy for sourcing of appropriate planting material for use in future tree planting efforts.
The Adult Ventricular Subventricular Zone (V SVZ) and Olfactory Bulb (OB) Neurogenesis.
Venue: Cold Spring Harbor perspectives in biology

A large population of neural stem/precursor cells (NSCs) persists in the ventricular subventricular zone (V SVZ) located in the walls of the lateral brain ventricles. V SVZ NSCs produce large numbers of neuroblasts that migrate a long distance into the olfactory bulb (OB) where they differentiate into local circuit interneurons. Here, we review a broad range of discoveries that have emerged from studies of postnatal V SVZ neurogenesis: the identification of NSCs as a subpopulation of astroglial cells, the neurogenic lineage, new mechanisms of neuronal migration, and molecular regulators of precursor cell proliferation and migration. It has also become evident that V SVZ NSCs are regionally heterogeneous, with NSCs located in different regions of the ventricle wall generating distinct OB interneuron subtypes. Insights into the developmental origins and molecular mechanisms that underlie the regional specification of V SVZ NSCs have also begun to emerge. Other recent studies have revealed new cell intrinsic molecular mechanisms that enable lifelong neurogenesis in the V SVZ. Finally, we discuss intriguing differences between the rodent V SVZ and the corresponding human brain region. The rapidly expanding cellular and molecular knowledge of V SVZ NSC biology provides key insights into postnatal neural development, the origin of brain tumors, and may inform the development regenerative therapies from cultured and endogenous human neural precursors.
Dense EM based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb
Venue: Nature Neuroscience

The dense reconstruction of neuronal circuits from volumetric electron microscopy (EM) data has the potential to uncover fundamental structure function relationships in the brain. To address bottlenecks in the workflow of this emerging methodology, we developed a procedure for conductive sample embedding and a pipeline for neuron reconstruction. We reconstructed ~98% of all neurons >1,000) in the olfactory bulb of a zebrafish larva with high accuracy and annotated all synapses on subsets of neurons representing different types. The organization of the larval olfactory bulb showed marked differences from that of the adult but similarities to that of the insect antennal lobe. Interneurons comprised multiple types but granule cells were rare. Interglomerular projections of interneurons were complex and bidirectional. Projections were not random but biased toward glomerular groups receiving input from common types of sensory neurons. Hence, the interneuron network in the olfactory bulb exhibits a specific topological organization that is governed by glomerular identity.
Adult Olfactory Bulb Neurogenesis.
Venue: Cold Spring Harbor perspectives in biology

Most organisms use their olfactory system to detect and analyze chemical cues from the external world to guide essential behaviors. From worms to vertebrates, chemicals are detected by odorant receptors expressed by olfactory sensory neurons, which in vertebrates send an axon to the primary processing center called the olfactory bulb (OB) Within the OB, sensory neurons form excitatory synapses with projection neurons and with inhibitory interneurons. Thus, because of complex synaptic interactions, the output of a given projection neuron is determined not only by the sensory input, but also by the activity of local inhibitory interneurons that are regenerated throughout life in the process of adult neurogenesis. Herein, we discuss how it is optimized and why.
Marine Microbes as a Potential Source of Cellulolytic Enzymes.
Venue: Advances in food and nutrition research

Marine environment hosts the wide range of habitats with remarkably high and diverse microbial populations. The ability of marine microorganisms to survive in extreme temperature, salinity, and pressure depends on the function of multivarious enzyme systems that in turn provide vast potential for biotechnological exploration studies. Therefore, the enzymes from marine microorganism represent novel bio catalytic potential with stable and reliable properties. Microbial cellulases constitute a major group of industrial enzymes that find applications in various industries. Majority of cellulases are of terrestrial origin, and very limited research has been carried out to explore marine microbes as a source of cellulases. This chapter presents an overview about the types of marine polysaccharases, classification and potential applications of cellulases, different sources of marine cellulases, and their future perspectives.
Enzyme System from Aspergillus in Current Industrial Uses and Future Applications in the Production of Second Generation Ethanol

Multiple fungal glycoside hydrolases can be used in biorefinery processes in the conversion of biomass into biofuels. The main polymers of plant biomass are cellulose and hemicellulose, which, together with lignin, constitute the most abundant organic compounds present in nature. Cellulose and hemicellulose are hydrolyzed by cellulolytic and hemicellulolytic enzymatic systems to monomers as glucose and xylose, respectively, which may be fermented by yeasts into second generation ethanol. Aspergillus is a distinguished fungal genus, important in the production of enzymes that are able to degrade plant cell wall, which is a key step in the bioconversion of sugarcane biomass. This chapter will describe several species of Aspergillus as excellent producers of fibrolytic enzymes as well as current advances in the understanding of glycoside hydrolases, auxiliary activities, and important properties for the conversion of biomass into second generation ethanol.