Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted Metagenomics of Retting in Flax: The Beginning of the Quest to Harness the Secret Powers of the Microbiota

The mechanical and chemical properties of natural plant fibers are determined by many different factors, both intrinsic and extrinsic to the plant, during growth but also after harvest. A better understanding of how all these factors exert their effect and how they interact is necessary to be able to optimize fiber quality for use in different industries. One important factor is the post-harvest process known as retting, representing the first step in the extraction of bast fibers from the stem of species such as flax and hemp. During this process microorganisms colonize the stem and produce hydrolytic enzymes that target cell wall polymers thereby facilitating the progressive destruction of the stem and fiber bundles. Recent advances in sequencing technology have allowed researchers to implement targeted metagenomics leading to a much better characterization of the microbial communities involved in retting, as well as an improved understanding of microbial dynamics. In this paper we review how our current knowledge of the microbiology of retting has been improved by targeted metagenomics and discuss how related ‘-omics’ approaches might be used to fully characterize the functional capability of the retting microbiome.
“Now It's Just Old Habits and Misery”–Understanding the Impact of the Covid-19 Pandemic on People With Current or Life-Time Eating Disorders: A Qualitative Study

Background: Many aspects of the Covid-19 pandemic may make living with or recovery from an eating disorder (ED) particularly challenging. Understanding the processes which underlie the psychological and behavioral responses of people with EDs during this time are key to ensure tailored support in these unprecedented circumstances. Methods: People with lifetime EDs (n = 32) were recruited through social media from May to June 2020 during a period of strict infection control measures in the United Kingdom (i.e., “lockdown,” “social distancing”). They completed open-ended questions in an online anonymous questionnaire that invited them to reflect on how various aspects of their lives have been affected by the Covid-19 pandemic, including ED symptoms and coping strategies. Responses were analyzed using thematic analysis. Results: Most respondents reported that their ED worsened or resurfaced. Isolation, low mood, anxiety, lack of structure, disruption to routines, and media/social media messages around weight and exercise seemed to contribute to this. There was a clear sense that individuals struggled with which aspects of psychological distress to prioritize, i.e., mood vs. ED cognitions and behaviors, particularly as attempts to cope with one often exacerbated the other. Nonetheless, some participants reported “silver linings” of the pandemic. Conclusions: In this self-selected sample, deterioration or recurrence of ED symptoms were the norm. This has implications for the provision of treatment and care for people with EDs both in the immediate short-term and in potential future waves of the pandemic, with a significant surge of new and re-referrals expected.
Zoology

municate? ). They are grouped into seven chapters each containing seven to 14 questions. The volume begins with the chapter Bat Basics and ends with the chapter Bats and People. It also includes eight pages of high-quality photographs as well as an extensive bibliography. This is the second book about bats using a question-and-answer format. An earlier publication by Don E. Wilson, Bats in Question: The Smithsonian Answer Book (1997. Washington (DC): Smithsonian Institution Press), covers much of the same ground as the present book, has a stronger focus on bat diversity, and is more profusely illustrated. One of the strengths of the current volume is its coverage of new research findings about bats (e.g., magnetic sensory detection, vortex dynamics of bat wings) and the spread of White Nose Syndrome in the northeastern United States. On the negative side, there are some glaring errors: using the name Mangifera indica (mango) for the bat Cynopterus sphinx in two places; an erroneous description of how pollen fertilizes ovules (p. 100); the claim that bats are pollinators of mangoes, guavas, avocados, and date palms—all are insectpollinated plants; and typographical errors in some scientific names of bats (e.g., Macrotis for Macrotus). Despite these problems, this will serve as a useful introduction to bat biology for nonspecialists. Theodore H. Fleming, Biology, University of Miami, Coral Gables, Florida
Security Assessment of Interposer-based Chiplet Integration

With transistor scaling reaching its limits, interposer-based integration of dies (chiplets) is gaining traction. Such an interposer-based integration enables finer and tighter interconnect pitch than traditional system-on-packages and offers two key benefits: 1. It reduces design-to-market time by bypassing the time-consuming process of verification and fabrication. 2. It reduces the design cost by reusing chiplets. While black-boxing of the slow design stages cuts down the design time, it raises significant security concerns. We study the security implications of the emerging interposer-based integration methodology. The black-boxed design stages deploy security measures against hardware Trojans, reverse engineering, and intellectual property piracy in traditional systems-on-chip (SoC) designs and hence are not suitable for interposer-based integration. We propose using functionally diverse chiplets to detect and thwart hardware Trojans and use the inherent logic redundancy to shore up anti-piracy measures. Our proposals do not rely on access to the black-box design stages. We evaluate the security, time and cost benefits of our plan by implementing a MIPS processor, a DCT core, and an AES core using various IPs from the Xilinx CORE GENERATOR IP catalog, on an interposer-based Xilinx FPGA.
Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein’s Unbiased Risk Estimate

Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor? In this work we use Stein’s unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications.
A Parallel Process Growth Mixture Model to Examine Heterogeneity in Joint Development with Nonlinear Trajectories

Researchers are interested in uncovering heterogeneity in change patterns and grouping trajectories into more homogeneous latent classes. A considerable number of theoretical and empirical studies with such aims usually focus on investigating heterogeneity in change patterns of a univariate repeated outcome. However, developmental processes rarely unfold in separation; therefore, empirical researchers often desire to examine two or more outcomes over time, hoping to understand their joint development where these outcomes and their change patterns are correlated. It is also of importance to examine the impacts that covariates have on the heterogeneity of joint development. This study examines the heterogeneity in parallel nonlinear trajectories and identifies baseline characteristics as predictors of latent classes. Our simulation studies show that the proposed model can separate parallel change patterns and provide unbiased point estimates with small standard errors and confidence intervals with satisfactory coverage probabilities for the parameters of interests. We illustrate how to apply the model to investigate the heterogeneity of parallel nonlinear trajectories of joint development of reading and mathematics ability from Grade K to $5$. In this real-world example, we demonstrate how to employ two methods, feature selection and feature reduction, to address covariate space with a large-dimension and highly correlated subsets in the structural equation modeling framework.
Comparison of Structural Subscapularis Integrity After Latarjet Procedure Versus Iliac Crest Bone Graft Transfer

Background: Although clinical outcome scores are comparable after coracoid transfer procedure (Latarjet) and iliac crest bone graft transfer (ICBGT) for anterior shoulder instability with glenoid bone loss, a significant decrease in internal rotation capacity has been reported for the Latarjet procedure. Hypothesis: The subscapularis (SSC) musculotendinous integrity will be less compromised by ICBGT than by the Latarjet procedure. Study Design: Cohort study; Level of evidence, 3. Methods: We retrospectively analyzed pre- and postoperative computed tomography (CT) scans at short-term follow-up of 52 patients (26 Latarjet, 26 ICBGT) previously assessed in a prospective randomized controlled trial. Measurements included the preoperative glenoid defect area and graft area protruding the glenoid rim at follow-up and tendon thickness assessed through SSC and infraspinatus (ISP) ratios. Fatty muscle infiltration was graded according to Goutallier, quantified with muscle attenuation in Hounsfield units, and additionally calculated as percentages. We measured 3 angles to describe rerouting of the SSC musculotendinous unit around the bone grafts. Results: SSC fatty muscle infiltration was 2.0% ± 2.2% in the Latarjet group versus 2.4% ± 2.2% in ICBGT (P = .546) preoperatively and showed significantly higher values in the Latarjet group at follow-up (5.3% ± 4.5% vs 2.3% ± 1.7%; P = .001). In total, 4 patients (15.4%) in the Latarjet group showed a progression from grade 0 to grade 1 at follow-up, whereas no changes in the ICBGT group were noted. The measured rerouting angle of the SSC muscle was significantly increased in the Latarjet group (11.8° ± 2.1°) compared with ICBGT (7.5° ± 1.3°; P < .001) at follow-up, with a significant positive correlation between this angle and fatty muscle infiltration (R = 0.447; P = .008). Ratios of SSC/ISP tendon thickness were 1.03 ± 0.3 in the Latarjet group versus 0.97 ± 0.3 (P = .383) in ICBGT preoperatively and showed significantly lower ratios in the Latarjet group (0.7 ± 0.3 vs 1.0 ± 0.2; P < .001) at follow-up. Conclusion: Although clinical outcome scores after anterior shoulder stabilization with a Latarjet procedure and ICBGT are comparable, this study shows that the described decline in internal rotation capacity after Latarjet procedure has a radiographic structural correlate in terms of marked thinning and rerouting of the SSC tendon as well as slight fatty degeneration of the muscle.
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task

This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German↔Upper Sorbian. Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on German) and fine-tuning it on both German and Upper Sorbian, before initializing a UNMT model, which is trained with online backtranslation. Pseudo-parallel data obtained from an unsupervised statistical machine translation (USMT) system is used to fine-tune the UNMT model. We also apply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more robust system. We additionally experiment with residual adapters and find them useful in the Upper Sorbian→German direction. We explore sampling during backtranslation and curriculum learning to use SMT translations in a more principled way. Finally, we ensemble our best-performing systems and reach a BLEU score of 32.4 on German→Upper Sorbian and 35.2 on Upper Sorbian→German.
Attention Is All You Need In Speech Separation

Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parallelization of their computations. Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism.In this paper, we propose the SepFormer, a novel RNN-free Transformer-based neural network for speech separation. The Sep-Former learns short and long-term dependencies with a multi-scale approach that employs transformers. The proposed model achieves state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets. It reaches an SI-SNRi of 22.3 dB on WSJ0-2mix and an SI-SNRi of 19.5 dB on WSJ0-3mix. The SepFormer inherits the parallelization advantages of Transformers and achieves a competitive performance even when downsampling the encoded representation by a factor of 8. It is thus significantly faster and it is less memory-demanding than the latest speech separation systems with comparable performance.
Exploring Host Genetic Polymorphisms Involved in SARS-CoV Infection Outcomes: Implications for Personalized Medicine in COVID-19

Objective To systematically explore genetic polymorphisms associated with the clinical outcomes in SARS-CoV infection in humans. Methods This comprehensive literature search comprised available English papers published in PubMed/Medline and SCOPUS databases following the PRISMA-P guidelines and PICO/AXIS criteria. Results Twenty-nine polymorphisms located in 21 genes were identified as associated with SARS-CoV susceptibility/resistance, disease severity, and clinical outcomes predominantly in Asian populations. Thus, genes implicated in key pathophysiological processes such as the mechanisms related to the entry of the virus into the cell and the antiviral immune/inflammatory responses were identified. Conclusions Although caution must be taken, the results of this systematic review suggest that multiple genetic polymorphisms are associated with SARS-CoV infection features by affecting virus pathogenesis and host immune response, which could have important applications for the study and understanding of genetics in SARS-CoV-2/COVID-19 and for personalized translational clinical practice depending on the population studied and associated environments.
ChoiRbot: A ROS 2 Toolbox for Cooperative Robotics

In this letter, we introduce ChoiRbot, a toolbox for distributed cooperative robotics based on the novel Robot Operating System (ROS) 2. ChoiRbot provides a fully-functional toolset to execute complex distributed multi-robot tasks, either in simulation or experimentally, with a particular focus on networks of heterogeneous robots without a central coordinator. Thanks to its modular structure, ChoiRbot allows for a highly straight implementation of optimization-based distributed control schemes, such as distributed optimal control, model predictive control, task assignment, in which local computation and communication with neighboring robots are alternated. To this end, the toolbox provides functionalities for the solution of distributed optimization problems. The package can be also used to implement distributed feedback laws that do not need optimization features but do require the exchange of information among robots. The potential of the toolbox is illustrated with simulations and experiments on distributed robotics scenarios with mobile ground robots. The ChoiRbot toolbox is available at https://github.com/OPT4SMART/choirbot.
Development of Trait Emotional Intelligence in Response to Childbirth: A Longitudinal Couple Perspective

The aim of the current paper was to investigate the influence of childbirth on parents' trait emotional intelligence (EI). A three-wave longitudinal research program (during the second trimester of pregnancy, at 6 months postpartum, and at 1 year postpartum) using the Actor-Partner Interdependence Model with a hierarchical linear modeling was conducted on 204 parental couples with parental group (i.e., primiparous and multiparous parents) as a time-invariant predictor and the partner's EI development as a time-varying covariate. Results showed that parents' EI was stable, except for Self-Control that increases after childbirth. Moreover, there was a significant negative association between the actor's and the partner's development around childbirth. Childbirth pushes parents to function in dyad rather than individually. Compensatory effects may be observed between both parents in terms of emotional management of parenting: When one partner cannot cope emotionally with parenting, the other partner would compensate and better manage the emotional aspects of parenting. The discussion underlined the importance of the dyadic perspective in understanding the childbirth experience, specifically the parents' receptivity to variation in their partners' emotional levels.
Genetic Dissection and Validation of Chromosomal Regions for Transmission Ratio Distortion in Intersubspecific Crosses of Rice

Transmission ratio distortion (TRD) refers to a widespread phenomenon in which one allele is transmitted by heterozygotes more frequently to the progeny than the opposite allele. TRD is considered as a mark suggesting the presence of a reproductive barrier. However, the genetic and molecular mechanisms underlying TRD in rice remain largely unknown. In the present study, a population of backcross inbred lines (BILs) derived from the cross of a japonica cultivar Nipponbare (NIP) and an indica variety 9311 was utilized to study the genetic base of TRD. A total of 18 genomic regions were identified for TRD in the BILs. Among them, 12 and 6 regions showed indica (9311) and japonica (NIP) alleles with preferential transmission, respectively. A series of F2 populations were used to confirm the TRD effects, including six genomic regions that were confirmed by chromosome segment substitution line (CSSL)-derived F2 populations from intersubspecific allelic combinations. However, none of the regions was confirmed by the CSSL-derived populations from intrasubspecific allelic combination. Furthermore, significant epistatic interaction was found between TRD1.3 and TRD8.1 suggesting that TRD could positively contribute to breaking intersubspecific reproductive barriers. Our results have laid the foundation for identifying the TRD genes and provide an effective strategy to breakdown TRD for breeding wide-compatible lines, which will be further utilized in the intersubspecific hybrid breeding programs.
Automated provenance graphs for models@run.time

Software systems are increasingly making decisions autonomously by incorporating AI and machine learning capabilities. These systems are known as self-adaptive and autonomous systems (SAS). Some of these decisions can have a life-changing impact on the people involved and therefore, they need to be appropriately tracked and justified: the system should not be taken as a black box. It is required to be able to have knowledge about past events and records of history of the decision making. However, tracking everything that was going on in the system at the time a decision was made may be unfeasible, due to resource constraints and complexity. In this paper, we propose an approach that combines the abstraction and reasoning support offered by models used at runtime with provenance graphs that capture the key decisions made by a system through its execution. Provenance graphs relate the entities, actors and activities that take place in the system over time, allowing for tracing the reasons why the system reached its current state. We introduce activity scopes, which highlight the high-level activities taking place for each decision, and reduce the cost of instrumenting a system to automatically produce provenance graphs of these decisions. We demonstrate a proof of concept implementation of our proposal across two case studies, and present a roadmap towards a reusable provenance layer based on the experiments.
A Corpus for Argumentative Writing Support in German

In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative Glasso and Projection

Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph signal compression and denoising. Previous graph learning algorithms either i) make assumptions on graph connectivity (e.g., graph sparsity), or ii) make edge weight assumptions such as positive edges only. In this paper, given an empirical covariance matrix ${\mathbf{\bar C}}$ computed from data as input, we consider an eigen-structural assumption on the graph Laplacian matrix L: the first K eigenvectors of L are pre-selected, e.g., based on domain-specific criteria, and the remaining eigenvectors are then learned from data. One example use case is image coding, where the first eigenvector is pre-chosen to be constant, regardless of available observed data. We first prove that the subspace $\mathcal{H}_{\mathbf{u}}^ + $ of symmetric positive semi-definite (PSD) matrices with the first K eigenvectors being {uk} in a defined Hilbert space is a convex cone. We then construct an operator to project a given positive definite (PD) matrix L to $\mathcal{H}_{\mathbf{u}}^ + $, inspired by the Gram-Schmidt procedure. Finally, we design an efficient hybrid graphical lasso / projection algorithm to compute the most suitable graph Laplacian matrix ${{\mathbf{L}}^ * } \in \mathcal{H}_{\mathbf{u}}^ + $ given ${\mathbf{\bar C}}$. Experimental results show that given the first K eigenvectors as a prior, our algorithm outperforms competing graph learning schemes using a variety of graph comparison metrics.
EEGsig machine learning-based toolbox for End-to-End EEG signal processing

In the quest to realize comprehensive EEG signal processing toolbox, in this paper, we demonstrate the first toolbox contain three states of EEG signal processing (preprocessing, feature extraction, classification) together. Our goal is to provide a comprehensive toolbox for EEG signal processing. Using MATLAB software, we have developed an open-source toolbox for end-to-end processing of the EEG signal. As we know, in many research work in the field of neuroscience and EEG signal processing, we first clear the signal and remove noise, artifact, etc. Which we know as preprocessing, and then extract the feature from the relevant signal, and finally Machine learning classifiers used to classification of signal. We have tried to provide all the above steps in the form of EEGsig as a graphical user interface(GUI) so that there is no need for programming for all the above steps and reduce the time to complete these projects to a desirable level.