Miracle-dz/UniFAR-FTUs huggingface dataset:
Biological determinants of species diversity

We consider four categories of biological mechanisms of deter- minants which cause and maintain species diversity: niche relations, habitat diver- sity, mass effects and ecological equivalency. Two of these determinants are origi- nal to this paper: mass effect, the establishment of species in sites where they cannot be self-maintaining; and ecological equivalency, the coexistence of species with effectively identical niche and habitat requirements. The mode of action and ecological implications of each biological determinant are discussed using a schematic method for measuring alpha (community), beta (differentiation), and gamma (regional) diversities. The importance of mass effects and ecological equivalency to species richness is documented with several types of field data from Israel and California, U.S.A. Floristic richness and, in particular, the richness of floristic transitions, are discussed and interpreted by use of the biological determinants of diversity. Contact transitions between distinct floras are rich predominantly because of mass effects. Transitions induced by marked environmental changes are rich because of the combined influences of habitat diversity and mass effects. The rate at which species richness increases with sample area is related to the combined effects of all four biological determinants. This complexity explains the failures of simple species-area models. The relative intensity of each determinant is related to area: niche relations are most important at within-community scales, habitat diversity most important at both within-community and land- scape scales, and ecological equivalency most important at regional scales. We suggest that understanding patterns of species diversity will be enhanced by the partitioning of total species richness into the richness caused by each of the four ecologically distinct determinants of diversity.
Fine-grained Financial Opinion Mining: A Survey and Research Agenda

Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. In this position paper, we first define the financial opinions from both coarse-grained and fine-grained points of views, and then provide an overview on the issues already tackled. In addition to listing research issues of the existing topics, we further propose a road map of fine-grained financial opinion mining for future researches, and point out several challenges yet to explore. Moreover, we provide possible directions to deal with the proposed research issues.
Phenotypic effects of introgressing French winter germplasm into hybrid spring canola

Introgression of winter germplasm into spring oilseed Brassica napus L. represents a novel approach to broaden its genetic base and potentially boost seed yields of hybrid cultivars. To determine the potential usefulness of this approach, two large populations of segregating doubled haploid (DH) lines were developed by crossing germplasm derived from two French winter cultivars with a male parent of a hybrid combination. These lines were evaluated per se (2 yr in Wisconsin, USA) and in testcrosses with a spring canola line used in commercial canola hybrids (2 yr each in Wisconsin, USA, and Saskatchewan, Canada). Thirty percent of the hybrids significantly (P < 0.05) out-yielded the mean of commercial hybrids included in each trial. In addition, many hybrids significantly out-yielded the starting hybrid combination, especially in the Canadian environments. Earlier flowering DH lines and hybrids tended to have higher yields, as indicated by the highly significant (P < 0.01) negative correlations between seed yield and days to flowering in most trials. The results of this study illustrate that introgression of these winter germplasms can improve seed yield in spring canola hybrids. These populations will be useful for identifying genes that affect seed yield of canola hybrids.
Determining how atmospheric carbon dioxide concentrations have changed during the history of the Earth

The reconstruction of ancient atmospheric carbon dioxide concentrations is essential to understanding the history of the Earth and life. It is also an important guide to identifying the sensitivity of the Earth system to this greenhouse gas and, therefore, constraining its future impact on climate. However, determining the concentration of CO2 in ancient atmospheres is a challenging endeavour requiring the application of state-of-the-art analytical chemistry to geological materials, underpinned by an understanding of photosynthesis and biochemistry. It is truly an interdisciplinary challenge.
Distribution and Molecular identification of some causative agents of streptococcosis isolated from farmed rainbow trout (Oncorhynchus mykiss, Walbaum) in Iran

Over the past few years, the syndrome of streptococcosis has been associated with outbreaks in rainbow trout (Oncorhynchus mykiss, Walbaum) and caused significant economic losses in the aquaculture industry in Iran. The main purpose of this work was molecular identification of some causative agents of streptococcosis in rainbow trout. A total of 520 samples were collected from the head kidney of diseased fish (weight, 50 _ 200g) in 72 farms of 8 provinces in Iran, during 2008 to 2009. Bacterial isolates representing morphology and biochemical profiles of Streptococcus spp. were further confirmed by polymerase chain reaction (PCR). DNA extraction was carried out from a single colony by using the extraction promega kit following the conditions described by the supplier. The PCR assay was developed based on the 16S rRNA and glucose kinase genes of Streptococcus spp. for the rapid and specific detection and identification of this pathogen from different sources. Approximately 40% of specimens were infected to Streptococcus spp. Consequently, five pathogenic species have been identified, including S. iniae in Fars province, S. faecium in Mazandaran province, S. agalactiae in Gilan and Mazandaran provinces, S. dysgalatiae in Lorestan, Kohgiluyeh and Boyerahmad, Gilan and Kermanshah provinces and S. uberis, which was common in all provinces (except Mazandaran and Lorestan). The dominant species (based on important species index) were S. uberis, S. dysgalactiae and S. agalactiae, respectively.
Prospects for utilising plant‐adaptive mechanisms to improve wheat and other crops in drought‐ and salinity‐prone environments

Summary Breeding for adaptation to abiotic stress is extremely challenging due to the complexity of the target environments as well as that of the stress-adaptive mechanisms adopted by plants. While many traits have been reported in the literature, these must be considered with respect to the type of environment for which a cultivar is targeted. In theory, stress-adaptive traits can be divided into groups whose genes and/or physiological effects are likely to be relatively independent such that when parents with contrasting traits are crossed, adaptive genes will be pyramided. Currently the following groups of candidate traits are being considered for drought adaptation in wheat: traits relating to: (i) pre-anthesis growth, (ii) water extraction, (iii) water use efficiency, (iv) photo-protection. A number of mechanisms relating to root function have potential to ameliorate drought stress. Hydraulic redistribution (HR) of water by roots of dryland shrubs enables even relatively small amounts of rainwater to be moved down into the soil profile actively by the root system before it evaporates from the soil surface. Another example is the symbiotic relationship of plants with mycorrhizal fungi that produce a glycoprotein that has a positive effect on soil structure and moisture characteristics. From an agronomic point of view, crop water use efficiency can be increased by exploiting the stress-adaptive mechanism whereby leaves reduce transpiration rate in response to a chemical root signal in response to drying soil. While there is limited genetic diversity for adaptation to salinity in wheat, tolerance has been found in the ancestral genomes of polyploid wheat and their relatives associated with sodium exclusion into the xylem. Wide crossing techniques such as production of synthetic hexaploids are being exploited to tap into this source of genetic diversity. Looking further into the future, progress is being made into understanding the regulatory mechanisms that are expressed under abiotic stress to maintain cellular homeostasis, as well as in the ability to genetically transform crop plants with genes from alien species.
Elevating Patient/Staff Safety in State Psychiatric Hospitals

Violence in healthcare settingsa is a major issue in many countries, including the United States. Within the United States violence within the mental health care system is a predominant issue.1 Compared to the patients who they serve, other healthcare workers, and individuals working in high-risk industry occupations (e.g. construction), mental health care workers have a higher likelihood of being injured in the workplace.1,2 The purpose of this literature review is to assess what measures are most appropriate for elevating safety in state psychiatric hospitals for both patients and staff members.
Salinity stress increases the severity of ranavirus epidemics in amphibian populations

The stress-induced susceptibility hypothesis, which predicts chronic stress weakens immune defences, was proposed to explain increasing infectious disease-related mass mortality and population declines. Previous work characterized wetland salinization as a chronic stressor to larval amphibian populations. Thus, we combined field observations with experimental exposures quantifying epidemiological parameters to test the role of salinity stress in the occurrence of ranavirus-associated mass mortality events. Despite ubiquitous pathogen presence (94%), populations exposed to salt runoff had slightly more frequent ranavirus related mass mortality events, more lethal infections, and 117-times greater pathogen environmental DNA. Experimental exposure to chronic elevated salinity (0.8–1.6 g l−1 Cl−) reduced tolerance to infection, causing greater mortality at lower doses. We found a strong negative relationship between splenocyte proliferation and corticosterone in ranavirus-infected larvae at a moderate elevation of salinity, supporting glucocorticoid-medicated immunosuppression, but not at high salinity. Salinity alone reduced proliferation further at similar corticosterone levels and infection intensities. Finally, larvae raised in elevated salinity had 10 times more intense infections and shed five times as much virus with similar viral decay rates, suggesting increased transmission. Our findings illustrate how a small change in habitat quality leads to more lethal infections and potentially greater transmission efficiency, increasing the severity of ranavirus epidemics.
Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
Asymmetrical gene flow in five co-distributed syngnathids explained by ocean currents and rafting propensity

Ocean circulation driving macro-algal rafting is believed to serve as an important mode of dispersal for many marine organisms, leading to predictions on population-level genetic connectivity and the directionality of effective dispersal. Here, we use genome-wide single nucleotide polymorphism data to investigate whether gene flow directionality in two seahorses (Hippocampus) and three pipefishes (Syngnathus) follows the predominant ocean circulation patterns in the Gulf of Mexico and northwestern Atlantic. In addition, we explore whether gene flow magnitudes are predicted by traits related to active dispersal ability and habitat preference. We inferred demographic histories of these co-distributed syngnathid species, and coalescent model-based estimates indicate that gene flow directionality is in agreement with ocean circulation data that predicts eastward and northward macro-algal transport. However, the magnitude to which ocean currents influence this pattern appears strongly dependent on the species-specific traits related to rafting propensity and habitat preferences. Higher levels of gene flow and stronger directionality are observed in Hippocampus erectus, Syngnathus floridae and Syngnathus louisianae, which closely associated with the pelagic macro-algae Sargassum spp. , compared to Hippocampus zosterae and the Syngnathus scovelli/Syngnathus fuscus sister-species pair, which prefer near shore habitats and are weakly associated with pelagic Sargassum. This study highlights how the combination of population genomic inference together with ocean circulation data can help explain patterns of population structure and diversity in marine ecosystems.
Cuticular reticulation replicates the pattern of epidermal cells in lowermost Cambrian scalidophoran worms

The cuticle of ecdysozoans (Panarthropoda, Scalidophora, Nematoida) is secreted by underlying epidermal cells and renewed via ecdysis. We explore here the relationship between epidermis and external cuticular ornament in stem-group scalidophorans from the early Cambrian of China (Kuanchuanpu Formation; ca 535 Ma) that had two types of microscopic polygonal cuticular networks with either straight or microfolded boundaries. Detailed comparisons with modern scalidophorans (priapulids) indicate that these networks faithfully replicate the cell boundaries of the epidermis. This suggests that the cuticle of early scalidophorans formed through the fusion between patches of extracellular material secreted by epidermal cells, as observed in various groups of present-day ecdysozoans, including arthropods. Key genetic, biochemical and mechanical processes associated with ecdysis and cuticle formation seem to have appeared very early (at least not later than 535 Ma) in the evolution of ecdysozoans. Microfolded reticulation is likely to be a mechanical response to absorbing contraction exerted by underlying muscles. The polygonal reticulation in early and extant ecdysozoans is clearly a by-product of the epidermal cell pavement and interacted with the sedimentary environment.
Selection of Eye-Tracking Stimuli for Prediction by Sparsely Grouped Input Variables for Neural Networks: towards Biomarker Refinement for Autism

Eye tracking has become a powerful tool in the study of autism spectrum disorder (ASD). Current, large-scale efforts aim to identify specific eye-tracking stimuli to be used as biomarkers for ASD, with the intention of informing the diagnostic process, monitoring therapeutic response, predicting outcomes, or identifying subgroups with the spectrum. However, there are hundreds of candidate experimental paradigms, each of which contains dozens or even hundreds of individual stimuli. Each stimuli is associated with an array of potential derived outcome variables, thus the number of variables to consider can be enormous. Standard variable selection techniques are not applicable to this problem, because selection must be done at the level of stimuli and not individual variables. In other words, this is a grouped variable selection problem. In this work, we apply lasso, group lasso, and a new technique, Sparsely Grouped Input Variables for Neural Network (SGIN), to select experimental stimuli for group discrimination and regression with clinical variables. Using a dataset obtained from children with and without ASD who were administered a battery containing 109 different stimuli presentations involving 9647 features, we are able to retain strong group separation even with only 11 out of the 109 stimuli. This work sets the stage for concerted techniques designed around engines to iteratively refine and define next-generation biomarkers using eye tracking for psychiatric conditions. http://github.com/beibinli/SGIN
Analyzing Gaze Behavior Using Object Detection and Unsupervised Clustering

Gaze behavior is important in early development, and atypical gaze behavior is among the first symptoms of autism. Here we describe a system that quantitatively assesses gaze behavior using eye-tracking glasses. Objects in the subject’s field of view are detected using a deep learning model on the video captured by the glasses’ world-view camera, and a stationary frame of reference is estimated using the positions of the detected objects. The gaze positions relative to the new frame of reference are subjected to unsupervised clustering to obtain the time sequence of looks. The clustering method increases the accuracy of look detection on test videos compared against a previous algorithm, and is considerably more robust on videos with poor calibration.
The Role of Engagement in Teleneurorehabilitation: A Systematic Review

The growing understanding of the importance of involving patients with neurological diseases in their healthcare routine either for at-home management of their chronic conditions or after the hospitalization period has opened the research for new rehabilitation strategies to enhance patient engagement in neurorehabilitation. In addition, the use of new digital technologies in the neurorehabilitation field enables the implementation of telerehabilitation systems such as virtual reality interventions, video games, web-based interventions, mobile applications, web-based or telephonic telecoach programs, in order to facilitate the relationship between clinicians and patients, and to motivate and activate patients to continue with the rehabilitation process at home. Here we present a systematic review that aims at reviewing the effectiveness of different engagement strategies and the different engagement assessments while using telerehabilitation systems in patients with neurological disorders. We used PICO's format to define the question of the review, and the systematic review protocol was designed following the Preferred Reported Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Bibliographical data was collected by using the following bibliographic databases: PubMed, EMBASE, Scopus, and Web of Science. Eighteen studies were included in this systematic review for full-text analyses. Overall, the reviewed studies using engagement strategies through telerehabilitation systems in patients with neurological disorders were mainly focused on patient self-management and self-awareness, patient motivation, and patient adherence subcomponents of engagement, that are involved in by the behavioral, cognitive, and emotional dimensions of engagement. Conclusion: The studies commented throughout this systematic review pave the way for the design of new telerehabilitation protocols, not only focusing on measuring quantitative or qualitative measures but measuring both of them through a mixed model intervention design (1). The future clinical studies with a mixed model design will provide more abundant data regarding the role of engagement in telerehabilitation, leading to a possibly greater understanding of its underlying components.
Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39–75.66%) for dermatological and 73.80% (95% CI: 73.10–74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12–65.94%, p < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66–65.83%, p < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem.
Writing and Reading Histone H3 Lysine 9 Methylation in Arabidopsis

In eukaryotes, histone H3 lysine 9 methylation (H3K9me) mediates the silencing of invasive and repetitive sequences by preventing the expression of aberrant gene products and the activation of transposition. In Arabidopsis, while it is well known that dimethylation of histone H3 at lysine 9 (H3K9me2) is maintained through a feedback loop between H3K9me2 and DNA methylation, the details of the H3K9me2-dependent silencing pathway have not been fully elucidated. Recently, the regulation and the function of H3K9 methylation have been extensively characterized. In this review, we summarize work from the recent studies regarding the regulation of H3K9me2, emphasizing the process of deposition and reading and the biological significance of H3K9me2 in Arabidopsis.
ARCAgRP/NPY Neuron Activity Is Required for Acute Exercise-Induced Food Intake in Un-Trained Mice

While much is known about the role of agouti-regulated peptide/neuropeptide Y (AgRP/NPY) and pro-opiomelanocortin (POMC) neurons to regulate energy homeostasis, little is known about how forced energy expenditure, such as exercise, modulates these neurons and if these neurons are involved in post-exercise feeding behaviors. We utilized multiple mouse models to investigate the effects of acute, moderate-intensity exercise on food intake and neuronal activity in the arcuate nucleus (ARC) of the hypothalamus. NPY-GFP reporter mice were utilized for immunohistochemistry and patch-clamp electrophysiology experiments investigating neuronal activation immediately after acute treadmill exercise. Additionally, ARCAgRP/NPY neuron inhibition was performed using the Designer Receptors Exclusively Activated by Designer Drugs (DREADD) system in AgRP-Cre transgenic mice to investigate the importance of AgRP/NPY neurons in post-exercise feeding behaviors. Our experiments revealed that acute moderate-intensity exercise significantly increased food intake, ARCAgRP/NPY neuron activation, and PVNSim1 neuron activation, while having no effect on ARCPOMC neurons. Strikingly, this exercise-induced refeeding was completely abolished when ARCAgRP/NPY neuron activity was inhibited. While acute exercise also increased PVNSim1 neuron activity, inhibition of ARCAgRP/NPY neurons had no effect on PVNSim1 neuronal activation. Overall, our results reveal that ARCAgRP/NPY activation is required for acute exercise induced food intake in mice, thus providing insight into the critical role of ARCAgRP/NPY neurons in maintaining energy homeostasis in cases of exercise-mediated energy deficit.