Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Modeling to Predict N 2 O Production Within the Hyporheic Zone by Coupling Denitrifying Microbial Community Abundance to Geochemical and Hydrological Parameters

vi LIST OF TABLES x LIST OF FIGURES xi STATISTICAL MODELING TO PREDICT N2O PRODUCTION WITHIN THE HYPORHEIC ZONE BY COUPLING DENITRIFYING MICROBIAL COMMUNITY ABUNDANCE TO GEOCHEMICAL AND HYDROLOGICAL PARAMETERS 1 Abstract 2 2 Introduction 3 Methods 8 Flume Construction and Design 8 Water Sample Collection for N2O and Water Chemistry Analysis 9 N2O Analysis 9 Lachat Flow Injection Analysis 10 Microbial Community Analysis 10 Statistical Analysis 11 Results 12 Development of a Stable Redox Gradient in the HZ 12 Effect of Dune Amplitude on Hyporheic Zone Geochemical Conditions . 13
A protocol for systematic review and meta analysis

Background:Diabetic neuropathic pain (DNP) is a common complication of diabetes mellitus, it severely affects the quality of life of Diabetic patients. Acupuncture is proofed to have favorable effects in treating DNP, however, evidence needs to be gathered and interpreted. We will make a comprehensive review of clinical trials concerning acupuncture in treating DNP and do meta-analysis if possible. Method: The following databases will be searched from the inception to September 2020: PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Wan-Fang Database, and Chinese Scientific Journal Database. RCTs that evaluated acupuncture for patients with DNPwill be included. The primary outcomewill be patient-reported pain intensity using validated scales or verbal reporting. The secondary outcomes including the Toronto clinical scoring system, Sensory Nerve Conduction Velocity, Motor Nerve Conduction Velocity, and quality of life. The study selection, data extraction, and study quality evaluation will be performed independently by 2 researchers. A meta-analysis will be performed using RevMan V5.3 statistical software if possible; otherwise, descriptive analysis or subgroup analysis will be conducted. The quality of evidence for outcomes will be assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: This study will evaluate the effect and safety of acupuncture in treating DNP. Conclusions: The evidence we generated from the present study will provide more options for DNP management in clinical practice. Systematic review registration: INPLASY202090043. Abbreviations: CI = confidence interval, CNKI = China National Knowledge Infrastructure, DNP = diabetic neuropathic pain, GRADE = Grading of Recommendations Assessment Development and Evaluation, MNCV = motor nerve conduction velocity, PRISMA-P= Preferred Reporting Items for Systematic Reviews andMeta-Analysis Protocol, RCT= randomized controlled trials, SF36 = 36-Item Short Form Health Survey, SNCV = sensory nerve conduction velocity, TCSS = Toronto clinical scoring system, VAS = visual analogue scales.
Regulatory T cells in skin mediate immune privilege of the hair follicle stem cell niche

Immune tolerance is maintained in lymphoid organs (LOs). Despite the presence of complex immune cell networks in non-LOs, it is unknown whether self-tolerance is maintained in these tissues. We developed a technique to restrict genetic recombination to regulatory T cells (Tregs) only in skin. Selective depletion of skin Tregs resulted in T cell–mediated inflammation of hair follicles (HFs). Suppression did not rely on CTLA-4, but instead on high-affinity interleukin-2 (IL-2) receptor expression by skin Tregs, functioning exclusively in a cell-extrinsic manner. In a novel model of HF stem cell (HFSC)–driven autoimmunity, we reveal that skin Tregs immunologically protect the HFSC niche. Finally, we used spatial transcriptomics to identify aberrant IL-2 signaling at stromal-HF interfaces in a rare form of human alopecia characterized by HFSC destruction and alopecia areata. Collectively, these results reveal the fundamental biology of Tregs in skin uncoupled from the systemic pool and elucidate a mechanism of self-tolerance. Skin-specific manipulation of regulatory T cells uncovers a role in protecting hair follicle stem cells from autoimmune attack. Editor’s summary Regulatory T cells (Tregs) are critical for maintaining peripheral tolerance, but delineation of their tissue-specific functions has been challenging because of limited availability of tools that selectively target tissue Tregs. By developing a technique to induce genetic recombination specifically in murine skin Tregs, Cohen et al. demonstrated that skin Tregs protect the hair follicle from steady-state inflammation. Suppression of hair follicle inflammation required skin Treg expression of the high-affinity interleukin-2 (IL-2) receptor but not CTLA-4. In a mouse model of T cell–mediated autoimmune attack, skin Tregs prevented destruction of hair follicle stem cells. Patients with autoimmune alopecia displayed an enhanced IL-2/STAT5 gene signature around hair follicles, indicating that dysregulation of skin Treg-mediated immunosuppression may contribute to the pathogenesis of autoimmune skin diseases. —Claire Olingy
Hyperspectral analysis for the determination of tensor differential and integral characteristics of the micro profile from the topograms of the strokes printed by intaglio

Surface topograms obtained by mechanical, laser, optical, and electron-microscopic profilometry are widely used to study the micro profile parameters. Experimentally obtained topograms are widely used to evaluate the quality of metallic and non-metallic surfaces for various purposes, including in printing technologies for quality control of finished printing products, in particular, those produced by the intaglio printing method. Laser topograms describe the micro profile with matrices of ordinates on a rectangular grid of large dimensions. At the same time, the problem of estimating micro profile parameters as a whole arises. Therefore, research aimed at developing integrated methods of micro profile analysis is relevant. One of the ways to solve this problem is to consider the anisotropy of the geometric characteristics of the micro profile in all directions. In this paper, the possibility of determining the integral characteristics of the micro profile based on the topogram of the surface using a mathematical apparatus based on hyperspectral analysis and tensor calculation methods is substantiated. The proposed methods are based on the introduction of a special tensor field, which determines the characteristic directions of the location of peaks and valleys of the micro profile. In particular, a multiple (two-dimensional) Fourier series is used to describe the microprofile. The coefficients are found by integrating the experimentally measured laser topogram of the microprofile smoothed by cubic splines, corrected by multiplication by a special form factor. The coefficients of the series represent the spectral (hyperspectral) characteristic of the micro profile.
Color-based image processing techniques for laser range finder: a comparative study on air and water distance detection

In this paper, we studied comparative distance detection using a Laser Rangefinder equipped with color detection and image processing techniques in air and water environments. The main objective of this study is to evaluate the performance and accuracy of the distance detection methods in these two mediums. Furthermore, color detection is employed in the air environment to identify objects within the captured images, followed by image processing techniques such as edge detection and contour analysis to calculate the distances between the objects and the Laser Range Finder. In the water environment, challenges arise due to the refractive properties of water, which can distort object appearances. To overcome this, we employ an underwater Laser Range Finder system, along with color detection and image processing methods adjusted for the refractive index of water. The findings show that the distance measurement in the air has a small error value of 0.63 %. Meanwhile, measuring distances in water has a high error value of 35.18 %. These results indicate that measurements in the air perform better than measurements in water. Water’s scattering and refractive properties caused significant deviations and higher error values. The light intensity fluctuations had minimal impact on measurements in water but were significant in water.
Disentanglement with Futures, State, and Interaction

Recent work has proposed a memory property for parallel programs, called disentanglement, and showed that it is pervasive in a variety of programs, written in different languages, ranging from C/C++ to Parallel ML, and showed that it can be exploited to improve the performance of parallel functional programs. All existing work on disentanglement, however, considers the "fork/join" model for parallelism and does not apply to "futures", the more powerful approach to parallelism. This is not surprising: fork/join parallel programs exhibit a reasonably strict dependency structure (e.g., series-parallel DAGs), which disentanglement exploits. In contrast, with futures, parallel computations become first-class values of the language, and thus can be created, and passed between functions calls or stored in memory, just like other ordinary values, resulting in complex dependency structures, especially in the presence of mutable state. For example, parallel programs with futures can have deadlocks, which is impossible with fork-join parallelism. In this paper, we are interested in the theoretical question of whether disentanglement may be extended beyond fork/join parallelism, and specifically to futures. We consider a functional language with futures, Input/Output (I/O), and mutable state (references) and show that a broad range of programs written in this language are disentangled. We start by formalizing disentanglement for futures and proving that purely functional programs written in this language are disentangled. We then generalize this result in three directions. First, we consider state (effects) and prove that stateful programs are disentangled if they are race free. Second, we show that race freedom is sufficient but not a necessary condition and non-deterministic programs, e.g. those that use atomic read-modify-operations and some non-deterministic combinators, may also be disentangled. Third, we prove that disentangled task-parallel programs written with futures are free of deadlocks, which arise due to interactions between state and the rich dependencies that can be expressed with futures. Taken together, these results show that disentanglement generalizes to parallel programs with futures and, thus, the benefits of disentanglement may go well beyond fork-join parallelism.
Automatic Parallelism Management

On any modern computer architecture today, parallelism comes with a modest cost, born from the creation and management of threads or tasks. Today, programmers battle this cost by manually optimizing/tuning their codes to minimize the cost of parallelism without harming its benefit, performance. This is a difficult battle: programmers must reason about architectural constant factors hidden behind layers of software abstractions, including thread schedulers and memory managers, and their impact on performance, also at scale. In languages that support higher-order functions, the battle hardens: higher order functions can make it difficult, if not impossible, to reason about the cost and benefits of parallelism. Motivated by these challenges and the numerous advantages of high-level languages, we believe that it has become essential to manage parallelism automatically so as to minimize its cost and maximize its benefit. This is a challenging problem, even when considered on a case-by-case, application-specific basis. But if a solution were possible, then it could combine the many correctness benefits of high-level languages with performance by managing parallelism without the programmer effort needed to ensure performance. This paper proposes techniques for such automatic management of parallelism by combining static (compilation) and run-time techniques. Specifically, we consider the Parallel ML language with task parallelism, and describe a compiler pipeline that embeds "potential parallelism" directly into the call-stack and avoids the cost of task creation by default. We then pair this compilation pipeline with a run-time system that dynamically converts potential parallelism into actual parallel tasks. Together, the compiler and run-time system guarantee that the cost of parallelism remains low without losing its benefit. We prove that our techniques have no asymptotic impact on the work and span of parallel programs and thus preserve their asymptotic properties. We implement the proposed techniques by extending the MPL compiler for Parallel ML and show that it can eliminate the burden of manual optimization while delivering good practical performance.
Learning the Optimal Representation Dimension for Restricted Boltzmann Machines

Hyperparameters refer to a set of parameters of a machine learning model that are fixed and not adjusted during training. A fundamental problem in this context is hyperparameter tuning which refers to the problem of identifying the best values for a set of model hyperparameters for a given task. In particular, model performance strongly depends on the choice of hyperparameters, and the right choice often determines the difference between average and stateof- the-art performance. This becomes especially important in models with many hyperparameters, as is common in deep learning models (DL) and automated machine learning (AutoML). However, finding the best set of hyperparameters for a model faced with a given task is very difficult in general, given the large state space and the high computational cost of assessing the quality of a given set of hyperparameters.
Total Type Error Localization and Recovery with Holes

Type systems typically only define the conditions under which an expression is well-typed, leaving ill-typed expressions formally meaningless. This approach is insufficient as the basis for language servers driving modern programming environments, which are expected to recover from simultaneously localized errors and continue to provide a variety of downstream semantic services. This paper addresses this problem, contributing the first comprehensive formal account of total type error localization and recovery: the marked lambda calculus. In particular, we define a gradual type system for expressions with marked errors, which operate as non-empty holes, together with a total procedure for marking arbitrary unmarked expressions. We mechanize the metatheory of the marked lambda calculus in Agda and implement it, scaled up, as the new basis for Hazel, a full-scale live functional programming environment with, uniquely, no meaningless editor states. The marked lambda calculus is bidirectionally typed, so localization decisions are systematically predictable based on a local flow of typing information. Constraint-based type inference can bring more distant information to bear in discovering inconsistencies but this notoriously complicates error localization. We approach this problem by deploying constraint solving as a type-hole-filling layer atop this gradual bidirectionally typed core. Errors arising from inconsistent unification constraints are localized exclusively to type and expression holes, i.e. the system identifies unfillable holes using a system of traced provenances, rather than localized in an ad hoc manner to particular expressions. The user can then interactively shift these errors to particular downstream expressions by selecting from suggested partially consistent type hole fillings, which returns control back to the bidirectional system. We implement this type hole inference system in Hazel.
An End-to-End Benchmarking Tool for Analyzing the Hardware-Software Implications of Multi-modal DNNs

Abstract-Multi-modal deep neural networks (DNNs) have become increasingly pervasive in many machine learning application domains due to their superior accuracy by fusing various modalities together. However, multi-modal DNNs present many unique characteristics such as multi-stage execution, frequent synchronization and high heterogeneity, which are not well understood in the system and architecture community. In this article, we first present and characterize a set of multi-modal DNN workloads of different sizes from five domains and measure metrics like accuracy to ensure the availability of these applications from the algorithm perspective. We then explore their important hardwaresoftware implications from system and architecture aspects by conducting an in-depth analysis on the unique hardware-software characteristics of multimodal DNNs. We hope that our work can help guide future hardware-software design and optimization for efficient inference of multi-modal DNN applications on both cloud and edge computing platforms.
Efficient Matching of Regular Expressions with Lookaround Assertions

Regular expressions have been extended with lookaround assertions, which are subdivided into lookahead and lookbehind assertions. These constructs are used to refine when a match for a pattern occurs in the input text based on the surrounding context. Current implementation techniques for lookaround involve backtracking search, which can give rise to running time that is super-linear in the length of input text. In this paper, we first consider a formal mathematical semantics for lookaround, which complements the commonly used operational understanding of lookaround in terms of a backtracking implementation. Our formal semantics allows us to establish several equational properties for simplifying lookaround assertions. Additionally, we propose a new algorithm for matching regular expressions with lookaround that has time complexity O(m · n), where m is the size of the regular expression and n is the length of the input text. The algorithm works by evaluating lookaround assertions in a bottom-up manner. Our algorithm makes use of a new notion of nondeterministic finite automata (NFAs), which we call oracle-NFAs. These automata are augmented with epsilon-transitions that are guarded by oracle queries that provide the truth values of lookaround assertions at every position in the text. We provide an implementation of our algorithm that incorporates three performance optimizations for reducing the work performed and memory used. We present an experimental comparison against PCRE and Java’s regex library, which are state-of-the-art regex engines that support lookaround assertions. Our experimental results show that, in contrast to PCRE and Java, our implementation does not suffer from super-linear running time and is several times faster.
Human cytomegalovirus deploys molecular mimicry to recruit VPS4A to sites of virus assembly

The AAA-type ATPase VPS4 is recruited by proteins of the endosomal sorting complex required for transport III (ESCRT-III) to catalyse membrane constriction and membrane fission. VPS4A accumulates at the cytoplasmic viral assembly complex (cVAC) of cells infected with human cytomegalovirus (HCMV), the site where nascent virus particles obtain their membrane envelope. Here we show that VPS4A is recruited to the cVAC via interaction with pUL71. Sequence analysis, deep-learning structure prediction, molecular dynamics and mutagenic analysis identify a short peptide motif in the C-terminal region of pUL71 that is necessary and sufficient for the interaction with VPS4A. This motif is predicted to bind the same groove of the N-terminal VPS4A Microtubule-Interacting and Trafficking (MIT) domain as the Type 2 MIT-Interacting Motif (MIM2) of cellular ESCRT-III components, and this viral MIM2-like motif (vMIM2) is conserved across β-herpesvirus pUL71 homologues. However, recruitment of VPS4A by pUL71 is dispensable for HCMV morphogenesis or replication and the function of the conserved vMIM2 during infection remains enigmatic. VPS4-recruitment via a vMIM2 represents a previously unknown mechanism of molecular mimicry in viruses, extending previous observations that herpesviruses encode proteins with structural and functional homology to cellular ESCRT-III components.
Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials

Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving mean and P-Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within trial spatial variability primarily comes from soil feature gradients, such as nutrients, but study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row and preliminary yield trial data of a public soybean breeding program across three years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), moving means adjustment, P-spline adjustment, and a machine learning based method called XGBoost. XGBoost modeled soil features at (a) local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both progeny row and preliminary yield trial stages of field testing, and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. These results empower breeders to further refine selection criteria to make more accurate selections, and furthermore include soil variables to select for macro– and micro-nutrients stress tolerance.
Optimal trade-off between boosted tolerance and growth fitness during adaptive evolution of yeast to ethanol shocks

The design of selection protocols to obtain bioethanol yeasts with higher alcohol tolerance poses the challenge of improving industrial strains that are already robust to high ethanol levels. Furthermore, yeasts subjected to mutagenesis and selection, or laboratory evolution, often present adaptation trade-offs wherein higher stress tolerance is attained at the expense of growth and fermentation performance. We conducted an adaptive laboratory evolution by challenging four populations (P1–P4) of the Brazilian bioethanol yeast, Saccharomyces cerevisiae PE-2_H4, through 68–82 cycles of 2-h ethanol shocks (19%–30% v/v) and outgrowths. Colonies isolated from the final populations (P1c–P4c) were subjected to whole-genome sequencing, revealing mutations in genes enriched for the cAMP/PKA and trehalose degradation pathways. Fitness analyses of the isolated clones P1c–P3c and reverse-engineered strains demonstrated that mutations were primarily selected for cell viability under ethanol stress, at the cost of decreased growth rates in cultures with or without ethanol. Under this selection regime for stress survival, the population P4 evolved a protective snowflake phenotype resulting from BUD3 disruption. Despite marked adaptation trade-offs, the combination of reverse-engineered mutations cyr1A1474T/usv1Δ conferred 5.46% higher fitness than the parental PE-2_H4 for propagation in 8% (v/v) ethanol, with only a 1.07% fitness cost in a culture medium without alcohol. The cyr1A1474T/usv1Δ strain and evolved P1c displayed robust fermentations of sugarcane molasses using cell recycling and sulfuric acid treatments, mimicking Brazilian bioethanol production. These results demonstrate that some alleles selected for acute stress survival may further confer stress tolerance and optimal performance under industrial conditions.