Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Resource Allocation in a Cowpea (Vigna unguiculata L. Walp.) Landrace Through Whole-Plant Field Phenotyping and Non-stop Selection to Sustain Increased Genetic Gain Across a Decade

Cowpea is a warm-season legume, often characterized as an orphan or underutilized crop, with great future potential, particularly under the global change. A traditional cowpea landrace in Cyprus is highly valued for fresh pod consumption in the local cuisine. In order to improve the yield potential of the landrace, the long-term response to direct selection for fresh pod yield and the associated changes in fodder and root biomass were investigated in a variety of fertility regimes under real field conditions. The non-stop selection process employed comprehensive pod, fodder, and root phenotyping at the level of the individual plant and resulted in the creation of a range of highly improved sibling lines with differential adaptation to micro-environments and with an improved ratio of pod to shoot and root biomass. The average rate of increase per year for fresh pod yield is at the level of 180 g per plant despite the relatively narrow genetic base of a single landrace and it is seemingly inexhaustible testifying to the great plasticity of the cowpea genome and the potential of the methodology to capture it. The corresponding high genetic gain was also confirmed under dense stands where the difference in pod yield between the best selection and the control amounted to 31.37%. Thus, the new focus apart from the simple variety maintenance should also include the continuous improvement and exploitation of micro-adaptation processes specific for individual fields that allow quick responses to environmental and climatic changes. This work presents also a novel approach to the multiple challenges encountered in root phenotyping and a method to meaningfully associate it with whole-plant performance in field conditions.
Epigenome-Wide Comparative Study Reveals Key Differences Between Mixed Connective Tissue Disease and Related Systemic Autoimmune Diseases

Mixed Connective Tissue Disease (MCTD) is a rare complex systemic autoimmune disease (SAD) characterized by the presence of increased levels of anti-U1 ribonucleoprotein autoantibodies and signs and symptoms that resemble other SADs such as systemic sclerosis (SSc), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). Due to its low prevalence, this disease has been very poorly studied at the molecular level. We performed for the first time an epigenome-wide association study interrogating DNA methylation data obtained with the Infinium MethylationEPIC array from whole blood samples in 31 patients diagnosed with MCTD and 255 healthy subjects. We observed a pervasive hypomethylation involving 170 genes enriched for immune-related function such as those involved in type I interferon signaling pathways or in negative regulation of viral genome replication. We mostly identified epigenetic signals at genes previously implicated in other SADs, for example MX1, PARP9, DDX60, or IFI44L, for which we also observed that MCTD patients exhibit higher DNA methylation variability compared with controls, suggesting that these sites might be involved in plastic immune responses that are relevant to the disease. Through methylation quantitative trait locus (meQTL) analysis we identified widespread local genetic effects influencing DNA methylation variability at MCTD-associated sites. Interestingly, for IRF7, IFI44 genes, and the HLA region we have evidence that they could be exerting a genetic risk on MCTD mediated through DNA methylation changes. Comparison of MCTD-associated epigenome with patients diagnosed with SLE, or Sjögren's Syndrome, reveals a common interferon-related epigenetic signature, however we find substantial epigenetic differences when compared with patients diagnosed with rheumatoid arthritis and systemic sclerosis. Furthermore, we show that MCTD-associated CpGs are potential epigenetic biomarkers with high diagnostic value. Our study serves to reveal new genes and pathways involved in MCTD, to illustrate the important role of epigenetic modifications in MCTD pathology, in mediating the interaction between different genetic and environmental MCTD risk factors, and as potential biomarkers of SADs.
Postural Instability in Subjects With Usher Syndrome

This study investigated postural performances and vestibular impairment in Usher patients. The three groups studied were: 11 patients with Usher type I (with visual and vestibular impairment), 14 patients with Usher type II (with only visual impairment), and 14 healthy control subjects. Postural stability was measured with a Framiral Multitest Equilibre platform with three visual conditions: eyes open (EO), eyes closed (EC), and vision disturbed by optokinetic stimulation (OPT), and two different postural conditions: stable or unstable platform. The surface and mean velocity of the center of pressure displacement (CoP) were measured and a postural instability index (PII) was calculated. Usher type I and II patients were more unstable than control subjects, but only for the unstable platform. Patients with Usher type I (with severe vestibular impairment) were also significantly more unstable than patients with Usher type II (with normal vestibular function) on the unstable platform. The severity of the vestibular impairment was correlated with the surface of the CoP displacement. We suggest that poor postural control of Usher patients is due to the abnormalities in their visual and, when defective, vestibular inputs. Measurements of postural stability on an unstable platform can distinguish type I from type II Usher patients. We emphasize the importance of multisensory evaluation in these patients to guide development of personalized visuo-vestibular rehabilitation techniques to improve their postural stability and improve their quality of life.
Community Composition and Diversity of Intestinal Microbiota in Captive and Reintroduced Przewalski’s Horse (Equus ferus przewalskii)

Large and complex intestinal microbiota communities in hosts have profound effects on digestion and metabolism. To better understand the community structure of intestinal microbiota in Przewalski’s horse (Equus ferus przewalskii) under different feeding regimes, we compared bacterial diversity and composition between captive and reintroduced Przewalski’s horses, using high-throughput 16S-rRNA gene sequencing for identification. Reintroduced Przewalski’s horses were sampled in two Chinese nature reserves, i.e., Dunhuang Xihu Nature Reserve (DXNR; n = 8) in Gansu Province and Kalamaili Nature Reserve (KNR; n = 12) in Xinjiang Province, and compared to a captive population at the Przewalski’s Horse Breeding Center in Xinjiang (PHBC; n = 11). The composition of intestinal microbiota in Przewalski’s horses was significantly different at the three study sites. Observed species was lowest in DXNR, but highest in KNR. Lowest Shannon diversity was observed in DXNR, while in KNR and PHBC had a moderately high diversity; Simpson diversity showed an opposite trend compared with the Shannon index. Linear Discriminant Analysis effect size was used to determine differentially distributed bacterial taxa at each study site. The most dominant phyla of intestinal microbiota were similar in all feeding regimes, including mainly Firmicutes, Bacteroidetes, Verrucomicrobia, and Spirochaetes. Differing abundances of intestinal microbiota in Przewalski’s horses may be related to different food types at each study site, differences in diversity may be attributed to low quality food in DXNR. Results indicated that diet is one of the important factors that can influence the structure of intestinal microbiota communities in Przewalski’s horse. These findings combined with a detailed knowledge of the available and consumed food plant species could provide guidelines for the selection of potential future reintroduction sites.
Iturinic Lipopeptide Diversity in the Bacillus subtilis Species Group – Important Antifungals for Plant Disease Biocontrol Applications

Iturins and closely related lipopeptides constitute a family of antifungal compounds known as iturinic lipopeptides that are produced by species in the Bacillus subtilis group. The compounds that comprise the family are: iturin, bacillomycin D, bacillomycin F, bacillomycin L, mycosubtilin, and mojavensin. These lipopeptides are prominent in many Bacillus strains that have been commercialized as biological control agents against fungal plant pathogens and as plant growth promoters. The compounds are cyclic heptapeptides with a variable length alkyl sidechain, which confers surface activity properties resulting in an affinity for fungal membranes. Above a certain concentration, enough molecules enter the fungal cell membrane to create a pore in the cell wall, which leads to loss of cell contents and cell death. This study identified 330 iturinic lipopeptide clusters in publicly available genomes from the B. subtilis species group. The clusters were subsequently assigned into distinguishable types on the basis of their unique amino acid sequences and then verified by HPLC MS/MS analysis. The results show some lipopeptides are only produced by one species, whereas certain others can produce up to three. In addition, four species previously not known to produce iturinic lipopeptides were identified. The distribution of these compounds among the B. subtilis group species suggests that they play an important role in their speciation and evolution.
First molecular approach to the octopus fauna from the southern Caribbean

The octopus fauna from the southern Caribbean is an understudied field. However, recent taxonomic work in the Colombian Caribbean has led to the discovery of several new species in the family Octopodidae. To provide molecular evidence for recent descriptions in the area (i.e., Octopus taganga, O. tayrona and Macrotritopus beatrixi) and contribute to the systematics of the family, we reconstructed the first molecular phylogenies of the family including Colombian Caribbean octopus species. Using cytochrome c oxidase subunit I and rhodopsin sequences from specimens collected in three sites (Santa Marta, Old Providence and San Andrés Islands) we inferred maximum-likelihood trees and delimited species with PTP. Our mitochondrial analysis supported the monophyly of species found in the area (i.e., O. taganga, O. hummelincki and O. briareus). The genetic distinction of the species O. tayrona and O. insularis was not resolved, as these were found in one clade together with Caribbean O. vulgaris and O. aff. tayrona species (O. spB) and delimited as a single species. Additionally, our results suggest a distant relationship of the Type I O. vulgaris group (Caribbean region) from the other forms of the species complex (Old World and Brazil). Lastly, the third newly described species M. beatrixi emerged as an independent lineage and was delimited as a single species. However, its relationship to other species of its genus remains unknown due to the lack of sequences in databases. Altogether, our molecular approach to the octopus fauna from the southern Caribbean adds on information to the relationship of Octopodidae species world-wide by providing sequences from recently described species from an understudied region. Further studies employing higher taxon sampling and more molecular information are needed to fill taxonomic gaps in the area and account for single-locus resolution on the systematics of this group.
Intensified vegetation water use under acid deposition

Acid deposition increases vegetation water use by altering soil calcium availability. Despite the important role vegetation plays in the global water cycle, the exact controls of vegetation water use, especially the role of soil biogeochemistry, remain elusive. In this study, we reveal a new mechanism of soil biogeochemical control of large-scale vegetation water use. Nitrate and sulfate deposition from fossil fuel burning have caused substantial soil acidification, leading to the leaching of soil base cations. Of these, calcium has a unique role in plant cells by regulating stomatal aperture, thus affecting vegetation water use. We hypothesized that the leaching of the soil calcium supply, induced by acid deposition, would increase large-scale vegetation water use. We present evidence from a long-term whole watershed acidification experiment demonstrating that the alteration of the soil calcium supply by acid deposition can significantly intensify vegetation water use (~10% increase in evapotranspiration) and deplete available soil water. These results are critical to understanding future water availability, biogeochemical cycles, and surface energy flux and to help reduce uncertainties in terrestrial biosphere models.
Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls

The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.
Prevalence of Syphilis among Pregnant Women in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

Objective Syphilis is one of the most imperative STIs, caused by the spirochete Treponema pallidum. During pregnancy it is associated with disastrous health outcomes in the newborn. In sub-Saharan Africa, study findings on the prevalence of syphilis among pregnant women are highly dispersed and inconsistent. The aim of the current review is to conduct a systematic review and meta-analysis of syphilis in sub-Saharan Africa among pregnant women. Design Systematic review and meta-analysis. Data Sources Databases including MEDLINE, PubMed, Cochrane Library, Google Scholar, and HINARI and reference lists of previous prevalence studies were systematically searched for relevant literature from January 1999 to November 2018. Results were presented in forest plot, tables, and figures. Random-effects model was used for the meta-analysis. For the purpose of this review, a case of syphilis was defined as positive treponemal or nontreponemal tests among pregnant women. Data Extraction Our search gave a total of 262 citations from all searched databases. Of these, 44 studies fulfilling the inclusion criteria and comprising 175,546 subjects were finally included. Results The pooled prevalence of syphilis among pregnant women in sub-Saharan Africa was 2.9% (95%CI: 2.4%-3.4%). East and Southern African regions had a higher syphilis prevalence among pregnant women (3.2%, 95% CI: 2.3%-4.2% and 3.6%, 95%CI: 2.0%-5.1%, respectively) than the sub-Saharan African pooled prevalence. The prevalence of syphilis among pregnant women in most parts of the region seemed to have decreased over the past 20 years except for the East African region. However, prevalence did not significantly differ by region and time period. Conclusion This review showed a high prevalence of syphilis in sub-Saharan Africa among pregnant women. The evidence suggests strengthening the screening program during pregnancy as part of the care package during antenatal care visits. Programs focusing on primary prevention of syphilis in women should also be strengthened.
Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection

The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i.e., app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. To efficiently classify nodes (e.g., apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.
GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction

Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including short-term neighboring and long-term periodic dependencies. What's more, the spatial correlations over different nodes are both local and non-local. To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains a multi-resolution temporal module and a global correlated spatial module in sequence, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed method on both the public transportation network and the road network.
Distributed smoothed tree kernel

In this paper we explore the possibility to merge the world of Compositional Distributional Semantic Models (CDSM) with Tree Kernels (TK). In particular, we will introduce a specific tree kernel (smoothed tree kernel, or STK) and then show that is possibile to approximate such kernel with the dot product of two vectors obtained compositionally from the sentences, creating in such a way a new CDSM.
Improving Nash Social Welfare Approximations

We consider the problem of fairly allocating a set of indivisible goods among n agents. Various fairness notions have been proposed within the rapidly growing field of fair division, but the Nash social welfare (NSW) serves as a focal point. In part, this follows from the ‘unreasonable’ fairness guarantees provided, in the sense that a max NSW allocation meets multiple other fairness metrics simultaneously, all while satisfying a standard economic concept of efficiency, Pareto optimality. However, existing approximation algorithms fail to satisfy all of the remarkable fairness guarantees offered by a max NSW allocation, instead targeting only the specific NSW objective. We address this issue by presenting a 2 max NSW, Prop-1, 1/(2n) MMS, and Pareto optimal allocation in strongly polynomial time. Our techniques are based on a market interpretation of a fractional max NSW allocation. We present novel definitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation in a way that provides most of the fairness properties of an integral max NSW allocation.
GANAK: A Scalable Probabilistic Exact Model Counter

Given a Boolean formula F, the problem of model counting, also referred to as #SAT, seeks to compute the number of solutions of F. Model counting is a fundamental problem with a wide variety of applications ranging from planning, quantified information flow to probabilistic reasoning and the like. The modern #SAT solvers tend to be either based on static decomposition, dynamic decomposition, or a hybrid of the two. Despite dynamic decomposition based #SAT solvers sharing much of their architecture with SAT solvers, the core design and heuristics of dynamic decomposition-based #SAT solvers has remained constant for over a decade. In this paper, we revisit the architecture of the state-of-the-art dynamic decomposition-based #SAT tool, sharpSAT, and demonstrate that by introducing a new notion of probabilistic component caching and the usage of universal hashing for exact model counting along with the development of several new heuristics can lead to significant performance improvement over state-of-the-art model-counters. In particular, we develop GANAK, a new scalable probabilistic exact model counter that outperforms state-of-the-art exact and approximate model counters sharpSAT and ApproxMC3 respectively, both in terms of PAR-2 score and the number of instances solved. Furthermore, in our experiments, the model count returned by GANAK was equal to the exact model count for all the benchmarks. Finally, we observe that recently proposed preprocessing techniques for model counting benefit exact model counters while hurting the performance of approximate model counters.
Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Prediction

Accurate next-day air quality prediction is essential to enable warning and prevention measures for cities and individuals to cope with potential air pollution, such as vehicle restriction, factory shutdown, and limiting outdoor activities. The problem is challenging because air quality is affected by a diverse set of complex factors. There has been prior work on short-term (e.g., next 6 hours) prediction, however, there is limited research on modeling local weather influences or fusing heterogeneous data for next-day air quality prediction. This paper tackles this problem through three key contributions: (1) we leverage multi-source data, especially high-frequency grid-based weather data, to model air pollutant dynamics at station-level; (2) we add convolution operators on grid weather data to capture the impacts of various weather parameters on air pollutant variations; and (3) we automatically group (cross-domain) features based on their correlations, and propose multi-group Encoder-Decoder networks (MGED-Net) to effectively fuse multiple feature groups for next-day air quality prediction. The experiments with real-world data demonstrate the improved prediction performance of MGED-Net over state-of-the-art solutions (4.2% to 9.6% improvement in MAE and 9.2% to 16.4% improvement in RMSE).