Miracle-dz/UniFAR-FTUs huggingface dataset:
Plant Diversity Surpasses Plant Functional Groups and Plant Productivity as Driver of Soil Biota in the Long Term

Background One of the most significant consequences of contemporary global change is the rapid decline of biodiversity in many ecosystems. Knowledge of the consequences of biodiversity loss in terrestrial ecosystems is largely restricted to single ecosystem functions. Impacts of key plant functional groups on soil biota are considered to be more important than those of plant diversity; however, current knowledge mainly relies on short-term experiments. Methodology/Principal Findings We studied changes in the impacts of plant diversity and presence of key functional groups on soil biota by investigating the performance of soil microorganisms and soil fauna two, four and six years after the establishment of model grasslands. The results indicate that temporal changes of plant community effects depend on the trophic affiliation of soil animals: plant diversity effects on decomposers only occurred after six years, changed little in herbivores, but occurred in predators after two years. The results suggest that plant diversity, in terms of species and functional group richness, is the most important plant community property affecting soil biota, exceeding the relevance of plant above- and belowground productivity and the presence of key plant functional groups, i.e. grasses and legumes, with the relevance of the latter decreasing in time. Conclusions/Significance Plant diversity effects on biota are not only due to the presence of key plant functional groups or plant productivity highlighting the importance of diverse and high-quality plant derived resources, and supporting the validity of the singular hypothesis for soil biota. Our results demonstrate that in the long term plant diversity essentially drives the performance of soil biota questioning the paradigm that belowground communities are not affected by plant diversity and reinforcing the importance of biodiversity for ecosystem functioning.
Polydrug epidemiology: Benzodiazepine prescribing and the drug overdose epidemic in the United States

Although polydrug incidents comprise a substantial proportion of overdose deaths, scholarly and popular focus has centered on prescription opiates. This study examines the role of benzodiazepine and opioid prescriptions on overdose—both individually and synergistically—using data from Medicare Part D, a source of prescription drug claims for about 35 million Americans.
Confirmation detection in human-agent interaction using non-lexical speech cues

Even if only the acoustic channel is considered, human communication is highly multi-modal. Non-lexical cues provide a variety of information such as emotion or agreement. The ability to process such cues is highly relevant for spoken dialog systems, especially in assistance systems. In this paper we focus on the recognition of non-lexical confirmations such as "mhm", as they enhance the system's ability to accurately interpret human intent in natural communication. The architecture uses a Support Vector Machine to detect confirmations based on acoustic features. In a systematic comparison, several feature sets were evaluated for their performance on a corpus of human-agent interaction in a setting with naive users including elderly and cognitively impaired people. Our results show that using stacked formants as features yield an accuracy of 84% outperforming regular formants and MFCC or pitch based features for online classification.
An Image Classification Algorithm Based on Bag of Visual Words and Multi-kernel Learning

In this article, we propose an image classification algorithm based on Bag of Visual Words model and multi-kernel learning. First of all, we extract the D-SIFT (Dense Scale-invariant Feature Transform) features from images in the training set. And then construct visual vocabulary via K-means clustering. The local features of original images are mapped to vectors of fixed length through visual vocabulary and spatial pyramid model. At last, the final classification results are given by generalized multiple kernel proposed by this paper. The experiments are performed on Caltech-101 image dataset and the results show the accuracy and effectiveness of the algorithm
Dictyostelium discoideum Nucleoside Diphosphate Kinase C Plays a Negative Regulatory Role in Phagocytosis, Macropinocytosis and Exocytosis

Nucleoside diphosphate kinases (NDPKs) are ubiquitous phosphotransfer enzymes responsible for producing most of the nucleoside triphosphates except for ATP. This role is important for the synthesis of nucleic acids and proteins and the metabolism of sugars and lipids. Apart from this housekeeping role NDPKs have been shown to have many regulatory functions in diverse cellular processes including proliferation and endocytosis. Although the protein has been shown to have a positive regulatory role in clathrin- and dynamin-mediated micropinocytosis, its roles in macropinocytosis and phagocytosis have not been studied. The additional non-housekeeping roles of NDPK are often independent of enzyme activity but dependent on the expression level of the protein. In this study we altered the expression level of NDPK in the model eukaryotic organism Dictyostelium discoideum through antisense inhibition and overexpression. We demonstrate that NDPK levels affect growth, endocytosis and exocytosis. In particular we find that Dictyostelium NDPK negatively regulates endocytosis in contrast to the positive regulatory role identified in higher eukaryotes. This can be explained by the differences in types of endocytosis that have been studied in the different systems - phagocytosis and macropinocytosis in Dictyostelium compared with micropinocytosis in mammalian cells. This is the first report of a role for NDPK in regulating macropinocytosis and phagocytosis, the former being the major fluid phase uptake mechanism for macrophages, dendritic cells and other (non dendritic) cells exposed to growth factors.
Modulation of Brain β-Endorphin Concentration by the Specific Part of the Y Chromosome in Mice

Background Several studies in animal models suggest a possible effect of the specific part of the Y-chromosome (YNPAR) on brain opioid, and more specifically on brain β-endorphin (BE). In humans, male prevalence is found in autistic disorder in which observation of abnormal peripheral or central BE levels are also reported. This suggests gender differences in BE associated with genetic factors and more precisely with YNPAR. Methodology/Principal Findings Brain BE levels and plasma testosterone concentrations were measured in two highly inbred strains of mice, NZB/BlNJ (N) and CBA/HGnc (H), and their consomic strains for the YNPAR. An indirect effect of the YNPAR on brain BE level via plasma testosterone was also tested by studying the correlation between brain BE concentration and plasma testosterone concentration in eleven highly inbred strains. There was a significant and major effect (P<0.0001) of the YNPAR in interaction with the genetic background on brain BE levels. Effect size calculated using Cohen's procedure was large (56% of the total variance). The variations of BE levels were not correlated with plasma testosterone which was also dependent of the YNPAR. Conclusions/Significance The contribution of YNPAR on brain BE concentration in interaction with the genetic background is the first demonstration of Y-chromosome mediated control of brain opioid. Given that none of the genes encompassed by the YNPAR encodes for BE or its precursor, our results suggest a contribution of the sex-determining region (Sry, carried by YNPAR) to brain BE concentration. Indeed, the transcription of the Melanocortin 2 receptor gene (Mc2R gene, identified as the proopiomelanocortin receptor gene) depends on the presence of Sry and BE is derived directly from proopiomelanocortin. The results shed light on the sex dependent differences in brain functioning and the role of Sry in the BE system might be related to the higher frequency of autistic disorder in males.
Genetic Structure, Linkage Disequilibrium and Signature of Selection in Sorghum: Lessons from Physically Anchored DArT Markers

Population structure, extent of linkage disequilibrium (LD) as well as signatures of selection were investigated in sorghum using a core sample representative of worldwide diversity. A total of 177 accessions were genotyped with 1122 informative physically anchored DArT markers. The properties of DArTs to describe sorghum genetic structure were compared to those of SSRs and of previously published RFLP markers. Model-based (STRUCTURE software) and Neighbor-Joining diversity analyses led to the identification of 6 groups and confirmed previous evolutionary hypotheses. Results were globally consistent between the different marker systems. However, DArTs appeared more robust in terms of data resolution and bayesian group assignment. Whole genome linkage disequilibrium as measured by mean r2 decreased from 0.18 (between 0 to 10 kb) to 0.03 (between 100 kb to 1 Mb), stabilizing at 0.03 after 1 Mb. Effects on LD estimations of sample size and genetic structure were tested using i. random sampling, ii. the Maximum Length SubTree algorithm (MLST), and iii. structure groups. Optimizing population composition by the MLST reduced the biases in small samples and seemed to be an efficient way of selecting samples to make the best use of LD as a genome mapping approach in structured populations. These results also suggested that more than 100,000 markers may be required to perform genome-wide association studies in collections covering worldwide sorghum diversity. Analysis of DArT markers differentiation between the identified genetic groups pointed out outlier loci potentially linked to genes controlling traits of interest, including disease resistance genes for which evidence of selection had already been reported. In addition, evidence of selection near a homologous locus of FAR1 concurred with sorghum phenotypic diversity for sensitivity to photoperiod.
Revenue Maximization with an Uncertainty-Averse Buyer

Most work in mechanism design assumes that buyers are risk neutral; some considers risk aversion arising due to a non-linear utility for money. Yet behavioral studies have established that real agents exhibit risk attitudes which cannot be captured by any expected utility model. We initiate the study of revenue-optimal mechanisms under behavioral models beyond expected utility theory. We adopt a model from prospect theory which arose to explain these discrepancies and incorporates agents under-weighting uncertain outcomes. In our model, an event occurring with probability x We present three main results. First, we characterize optimal mechanisms as menus of two-outcome lotteries. Second, we show that under a reasonable bounded-risk-aversion assumption, posted pricing obtains a constant approximation to the optimal revenue. Notably, this result is "risk-robust" in that it does not depend on the details of the buyer's risk attitude. Third, we consider dynamic settings in which the buyer's uncertainty about his future value may allow the seller to extract more revenue. In contrast to the positive result above, here we show it is not possible to achieve any constant-factor approximation to revenue using deterministic mechanisms in a risk-robust manner.
Topological and Functional Characterization of an Insect Gustatory Receptor

Insect gustatory receptors are predicted to have a seven-transmembrane structure and are distantly related to insect olfactory receptors, which have an inverted topology compared with G-protein coupled receptors, including mammalian olfactory receptors. In contrast, the topology of insect gustatory receptors remains unknown. Except for a few examples from Drosophila, the specificity of individual insect gustatory receptors is also unknown. In this study, the total number of identified gustatory receptors in Bombyx mori was expanded from 65 to 69. BmGr8, a silkmoth gustatory receptor from the sugar receptor subfamily, was expressed in insect cells. Membrane topology studies on BmGr8 indicate that, like insect olfactory receptors, it has an inverted topology relative to G protein-coupled receptors. An orphan GR from the bitter receptor family, BmGr53, yielded similar results. We infer, from the finding that two distantly related BmGrs have an intracellular N-terminus and an odd number of transmembrane spans, that this is likely to be a general topology for all insect gustatory receptors. We also show that BmGr8 functions independently in Sf9 cells and responds in a concentration-dependent manner to the polyalcohols myo-inositol and epi-inositol but not to a range of mono- and di-saccharides. BmGr8 is the first chemoreceptor shown to respond specifically to inositol, an important or essential nutrient for some Lepidoptera. The selectivity of BmGr8 responses is consistent with the known responses of one of the gustatory receptor neurons in the lateral styloconic sensilla of B. mori, which responds to myo-inositol and epi-inositol but not to allo-inositol.
Soft lithography of ceramic microparts using wettability-tunable poly(dimethylsiloxane) (PDMS) molds

Green alumina microparts were fabricated from a high solid content aqueous suspension by microtransfer molding using air plasma-treated poly(dimethylsiloxane) (PDMS) molds. The wettability of the air plasma-treated PDMS molds spontaneously changed between the hydrophilic and hydrophobic states during the process. Initial hydrophilicity of the air plasma-treated PDMS molds significantly improved the flowability of the concentrated suspension. Subsequent hydrophobic recovery of the air plasma-treated PDMS molds enabled a perfect demolding of the green microparts. Consequently, defect-free microchannel parts of 60 μm and a micromixer with an area of several square centimeters were successfully fabricated. In soft lithography, tuning the wetting behavior of PDMS molds has a great effect on the quality of ceramic microparts. Using wettability-tunable PDMS molds has great potential in producing complex-shaped and large-area ceramic microparts and micropatterns.
A Proposed Taxonomy of Anaerobic Fungi (Class Neocallimastigomycetes) Suitable for Large-Scale Sequence-Based Community Structure Analysis

Anaerobic fungi are key players in the breakdown of fibrous plant material in the rumen, but not much is known about the composition and stability of fungal communities in ruminants. We analyzed anaerobic fungi in 53 rumen samples from farmed sheep (4 different flocks), cattle, and deer feeding on a variety of diets. Denaturing gradient gel electrophoresis fingerprinting of the internal transcribed spacer 1 (ITS1) region of the rrn operon revealed a high diversity of anaerobic fungal phylotypes across all samples. Clone libraries of the ITS1 region were constructed from DNA from 11 rumen samples that had distinctly different fungal communities. A total of 417 new sequences were generated to expand the number and diversity of ITS1 sequences available. Major phylogenetic groups of anaerobic fungi in New Zealand ruminants belonged to the genera Piromyces, Neocallimastix, Caecomyces and Orpinomyces. In addition, sequences forming four novel clades were obtained, which may represent so far undetected genera or species of anaerobic fungi. We propose a revised phylogeny and pragmatic taxonomy for anaerobic fungi, which was tested and proved suitable for analysis of datasets stemming from high-throughput next-generation sequencing methods. Comparing our revised taxonomy to the taxonomic assignment of sequences deposited in the GenBank database, we believe that >29% of ITS1 sequences derived from anaerobic fungal isolates or clones are misnamed at the genus level.
An Analysis of the Epidemic of Klebsiella pneumoniae Carbapenemase-Producing K. pneumoniae: Convergence of Two Evolutionary Mechanisms Creates the “Perfect Storm”

Background Carbapenem resistance is a critical healthcare challenge worldwide. Particularly concerning is the widespread dissemination of Klebsiella pneumoniae carbapenemase (KPC). Klebsiella pneumoniae harboring blaKPC (KPC-Kpn) is endemic in many areas including the United States, where the epidemic was primarily mediated by the clonal dissemination of Kpn ST258. We postulated that the spread of blaKPC in other regions occurs by different and more complex mechanisms. To test this, we investigated the evolution and dynamics of spread of KPC-Kpn in Colombia, where KPC became rapidly endemic after emerging in 2005. Methods We sequenced the genomes of 133 clinical isolates recovered from 24 tertiary care hospitals located in 10 cities throughout Colombia, between 2002 (before the emergence of KPC-Kpn) and 2014. Phylogenetic reconstructions and evolutionary mapping were performed to determine temporal and genetic associations between the isolates. Results Our results indicate that the start of the epidemic was driven by horizontal dissemination of mobile genetic elements carrying blaKPC-2, followed by the introduction and subsequent spread of clonal group 258 (CG258) isolates containing blaKPC-3. Conclusions The combination of 2 evolutionary mechanisms of KPC-Kpn within a challenged health system of a developing country created the "perfect storm" for sustained endemicity of these multidrug-resistant organisms in Colombia.
A secure camouflaged logic family using post-manufacturing programming with a 3.6GHz adder prototype in 65nm CMOS at 1V nominal VDD

With the continued globalization of the IC manufacturing supply chain, securing that supply chain is becoming increasingly difficult and this opens the door to a myriad of security threats such as unauthorized production, counterfeiting, IP theft, and hardware Trojan Horses. A parallel and related threat is posed by advanced reverse engineering capabilities, such that even chips manufactured at the most advanced technology nodes can be de-layered, imaged, and analyzed [1]. While various manufacturing methodologies and camouflaged gates have been proposed, none fully address these threats, especially in combination. To address these concerns, we use post-manufacturing programmable camouflaged logic topology to simultaneously obscure the design IP from the manufacturer as well as combat reverse engineering. The basis of the design is a threshold-voltage-defined (TVD) logic gate topology that solely uses different threshold voltage implants to determine the logic gate function [2]. Every gate has an identical physical layout and is post-manufacturing programmed with different threshold voltages for different Boolean functions using intentional directed hot-carrier injection (HCI). Similar intentional HCI techniques have previously been used to enhance SRAM margins, boost PUF reliability, and build TRNGs [3][4]. The design is fully compatible with standard CMOS logic processes, requiring no special layers, structures, or process steps.
Limited differentiation in microsatellite DNA variation among northern populations of the yellow warbler: evidence for male‐biased gene flow?

Comparisons of the patterns of differentiation among genetic markers with different modes of inheritance can provide insights into patterns of sex‐biased dispersal and gene flow. Here, we compare the patterns of differentiation in six microsatellite loci among eight northern breeding populations of the yellow warbler (Dendroica petechia) with results obtained with mitochondrial DNA. Significant but low levels of differentiation (overall FST = 0.014; overall RST = 0.015) were present across all populations. The level of differentiation is substantially less than that observed in the same samples based on mitochondrial DNA control region variation. The presence of low population imbalance index values and significant isolation‐by‐distance relationships for both FST and RST suggests that these populations are at evolutionary equilibrium and that the high degree of similarity between populations may be due to high levels of male‐biased gene flow. This suggests that there may be significant but previously unappreciated differences in the long‐distance and/or episodic dispersal behaviour of males and females in these birds.
Active Site Conformational Dynamics in Human Uridine Phosphorylase 1

Uridine phosphorylase (UPP) is a central enzyme in the pyrimidine salvage pathway, catalyzing the reversible phosphorolysis of uridine to uracil and ribose-1-phosphate. Human UPP activity has been a focus of cancer research due to its role in activating fluoropyrimidine nucleoside chemotherapeutic agents such as 5-fluorouracil (5-FU) and capecitabine. Additionally, specific molecular inhibitors of this enzyme have been found to raise endogenous uridine concentrations, which can produce a cytoprotective effect on normal tissues exposed to these drugs. Here we report the structure of hUPP1 bound to 5-FU at 2.3 Å resolution. Analysis of this structure reveals new insights as to the conformational motions the enzyme undergoes in the course of substrate binding and catalysis. The dimeric enzyme is capable of a large hinge motion between its two domains, facilitating ligand exchange and explaining observed cooperativity between the two active sites in binding phosphate-bearing substrates. Further, a loop toward the back end of the uracil binding pocket is shown to flexibly adjust to the varying chemistry of different compounds through an “induced-fit” association mechanism that was not observed in earlier hUPP1 structures. The details surrounding these dynamic aspects of hUPP1 structure and function provide unexplored avenues to develop novel inhibitors of this protein with improved specificity and increased affinity. Given the recent emergence of new roles for uridine as a neuron protective compound in ischemia and degenerative diseases, such as Alzheimer's and Parkinson's, inhibitors of hUPP1 with greater efficacy, which are able to boost cellular uridine levels without adverse side-effects, may have a wide range of therapeutic applications.
Walk-modularity and community structure in networks

Abstract Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on some equivalent random graph model. We explore a natural generalization of modularity based on the difference between the actual and expected number of walks within clusters, which we refer to as walk-modularity. Walk-modularity can be expressed in matrix form, and community detection can be performed by finding the leading eigenvector of the walk-modularity matrix. We demonstrate community detection on both synthetic and real-world networks and find that walk-modularity maximization returns significantly improved results compared to traditional modularity maximization.