Miracle-dz/UniFAR-FTUs huggingface dataset:
Semiparametric Efficiency Gains From Parametric Restrictions on Propensity Scores

We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is very high-dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we reveal that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.
The ALPINE-ALMA [C II] survey: Characterisation of Spatial Offsets in Main-Sequence Galaxies at $z \sim$ 4-6

Galaxy morphology is shaped by stellar activity, feedback, gas and dust properties, and interactions with surroundings, and can therefore provide insight into these processes. In this paper, we study the spatial offsets between stellar and interstellar medium emission in a sample of 54 main-sequence star-forming galaxies at $z\sim4-6$ observed with the Atacama Large Millimeter/submillimeter Array (ALMA) and drawn from the ALMA Large Program to INvestigate C$^+$ at Early times (ALPINE). We find no significant spatial offset for the majority ($\sim$ 70 percent) of galaxies in the sample among any combination of [C II], far-infrared continuum, optical, and ultraviolet emission. However, a fraction of the sample ($\sim$ 30 percent) shows offsets larger than the median by more than 3$\sigma$ significance (compared to the uncertainty on the offsets), especially between [C II] and ultraviolet emission. We find that these significant offsets are of the order of $\sim$0.5-0.7 arcsec, corresponding to $\sim$3.5-4.5 kiloparsecs. The offsets could be caused by a complex dust geometry, strong feedback from stars and active galactic nuclei, large-scale gas inflow and outflow, or a combination of these phenomena. However, our current analysis does not definitively constrain the origin. Future, higher resolution ALMA and JWST observations may help resolve the ambiguity. Regardless, since there exist at least some galaxies that display such large offsets, galaxy models and spectral energy distribution fitting codes cannot assume co-spatial emission in all main-sequence galaxies, and must take into account that the observed emission across wavelengths may be spatially segregated.
Impaired ovarian development in a zebrafish fmr1 knockout model

Fragile X syndrome (FXS) is an inherited neurodevelopmental disorder and the leading genetic cause of autism spectrum disorders. FXS is caused by loss of function mutations in Fragile X mental retardation protein (FMRP), an RNA binding protein that is known to regulate translation of its target mRNAs, predominantly in the brain and gonads. The molecular mechanisms connecting FMRP function to neurodevelopmental phenotypes are well understood. However, neither the full extent of reproductive phenotypes, nor the underlying molecular mechanisms have been as yet determined. Here, we developed new fmr1 knockout zebrafish lines and show that they mimic key aspects of FXS neuronal phenotypes across both larval and adult stages. Results from the fmr1 knockout females also showed that altered gene expression in the brain, via the neuroendocrine pathway contribute to distinct abnormal phenotypes during ovarian development and oocyte maturation. We identified at least three mechanisms underpinning these defects, including altered neuroendocrine signaling in sexually mature females resulting in accelerated ovarian development, altered expression of germ cell and meiosis promoting genes at various stages during oocyte maturation, and finally a strong mitochondrial impairment in late stage oocytes from knockout females. Our findings have implications beyond FXS in the study of reproductive function and female infertility. Dissection of the translation control pathways during ovarian development using models like the knockout lines reported here may reveal novel approaches and targets for fertility treatments. Graphical Abstract
ASSESSING THE ECONOMIC IMPACT OF CLIMATE CHANGE (MAXIMUM AND MINIMUM TEMPERATURE) ON PRODUCTIVITY OF SORGHUM CROP IN GADARIF STATE, SUDAN

This paper was prepared to estimate the current and projected relationship between climate change (temperature) and variability and the productinity of sorghum under rainfed mechanized farming system in Gadarif State. The study ultilized secondary data covering the period 1970-2014. The data analyzed using descriptive analysis, Bias Correction and Spatial Downscaling (BCSD) and Representative Concentration Pathways (RCPs). The most important results revealed that an increasing trend of temperature indicating the evidence of existance of climate change in the State.The results also showed a drop in crop yield associated with an increase in maximum and minimum level of temperatures. As with respect to projection of future temperature and yield, two scenarios based on greenhouse gas concentration in the atmosphere for the period 2020-2100 were used. The best scenario assumed an RCP of 2.6 and the worst one assumed an RCP of 8.5. The results of best scenario predicted an expected increase yearly in maximum and minimum temperatures at rate of 0.015°C and 0.010°C respectively; and consequent decrease in crop yield at rate of 1.121 kg/fed/year in case of maximum temperature; and at rate of 0.618 kg/fed/year in case of minimum temperature. The result of the worst scenario predicted an expected yearly increase in maximum and minimum temperatures at 0.074°C and 0.081°C respectively, with consequent decline in crop yield at a rate of 5.235 kg/fed/year associated with maximum temperature and at rate of 4.844 kg/fed/year associated with minimum temperature. The effect of increase greenhouse gas concentration in future leads to increase maximum and minimum temperatures and decrease yield of sorghum in Gadarif State.
Macrolactin A Is an Inhibitor of Protein Biosynthesis in Bacteria

The macrolide antibiotic, macrolactin A (McA), has been known for its antimicrobial properties since the late 1980s, but the mechanism of its antibacterial activity is still unknown. In this study, we investigated the microbiological and molecular characteristics of McA antimicrobial activity. McA effect on bacteria was found to be both bacteriostatic and bactericidal, depending on species and strains. Regarding the mechanism of action of McA, the following important results were obtained: 1) using in vivo and in vitro systems, we showed that McA is an inhibitor of protein synthesis in bacteria; 2) the concentration of McA required to inhibit protein synthesis in the E. coli cell-free model was found to be 50 times lower than the concentration required in the S. aureus cell-free model; 3) the toe-printing assay revealed that McA inhibits the first step of elongation stage of protein synthesis; 4) we identified single and multiple nucleotide polymorphisms in the gene encoding the translation elongation factor Tu (EF-Tu) by annotating the genomes of McA-resistant Bacillus pumilus McAR and its parental strain. Molecular modeling showed that the McA molecule can form non-covalent bonds with amino acids at the interface of domains 1 and 2 of EF-Tu, characterized by a relatively high docking score. Overall, our study demonstrated that McA acts as an elfamycin-like antibiotic (targeting EF-Tu), addressing a substantial gap in our understanding of the mechanism of action of macrolactin A, a representative member of macrolides.
Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.
Phospho-KNL-1 recognition by a TPR domain targets the BUB-1–BUB-3 complex to C. elegans kinetochores

During mitosis, the Bub1-Bub3 complex concentrates at kinetochores, the microtubule-coupling interfaces on chromosomes, where it contributes to spindle checkpoint activation, kinetochore-spindle microtubule interactions, and protection of centromeric cohesion. Bub1 has a conserved N-terminal tetratricopeptide (TPR) domain followed by a binding motif for its conserved interactor Bub3. The current model for Bub1-Bub3 localization to kinetochores is that Bub3, along with its bound motif from Bub1, recognizes phosphorylated “MELT” motifs in the kinetochore scaffold protein Knl1. Motivated by the greater phenotypic severity of BUB-1 versus BUB-3 loss in C. elegans, we show that the BUB-1 TPR domain directly recognizes a distinct class of phosphorylated motifs in KNL-1 and that this interaction is essential for BUB-1–BUB-3 localization and function. BUB-3 recognition of phospho-MELT motifs additively contributes to drive super-stoichiometric accumulation of BUB-1–BUB-3 on its KNL-1 scaffold during mitotic entry. Bub1’s TPR domain interacts with Knl1 in other species, suggesting that collaboration of TPR-dependent and Bub3-dependent interfaces in Bub1-Bub3 localization and functions may be conserved.
Exploring the impact of interthalamic adhesion on human cognition: insights from healthy subjects and thalamic stroke patients

Background and Objectives Interthalamic adhesion (IA), also known as the massa intermedia, is a structure that connects the median borders of both thalami across the third ventricle. Given it is difficult to identify on routine neuroimaging, its anatomical variants and function remain poorly studied. The main objective of this study was to clarify the role of IA on cognition. Our main hypothesis was that thalamus stroke patients with an IA would show better performance on neuropsychological tests than individuals without an IA through possible compensatory mechanisms. Methods The study comprised a group of healthy subjects and a cohort of patients with isolated thalamic strokes at the chronic stage. All participants underwent 3T research T1w and FLAIR MRI as well as a neuropsychological assessment. The presence or absence of an IA and type of IA anatomical variant were evaluated by two independent reviewers. Results 42 healthy subjects (mean age= 49) and 40 patients (mean age= 51) were finally included. 76% of participants had an IA, with a higher prevalence among women (92%) than men (61%). The presence or absence of an IA did not effect the neuropsychological performance of healthy subjects nor did the type of IA variant. Across all the tests, patients with an IA (n = 18) showed the lowest BF10 (157) while those without an IA (n = 10) exhibited the highest BF10 (10648) when compared to healthy subjects using a Bayesian rmANOVA. More specifically, patients without an IA performed more poorly in the verbal memory or Stroop task versus healthy subjects than patients with an IA. This effect was not explained by age, laterality of the infarct, volume or, localization of the lesion. Patients with an IA and lesions extending into the IA presented a similar trend to non-IA subjects which could however be explained by a greater volume of lesions. Discussion IA does not appear to have a major role in cognition for healthy subjects but could play a compensatory part in patients with thalamic lesions.
Nanoscale interaction of endonuclease APE-1 with DNA characterized by atomic force microscopy

Apurinic/apyrimidinic endonuclease 1 (APE1) is involved in DNA replication, repair, and transcriptional regulation mechanisms. This multifunctional activity of APE1 should be supported by specific structural properties of APE1 that have not yet been elucidated. Here we applied atomic force microscopy (AFM) to characterize the interactions of APE1 with DNA. Complexes of APE1 with DNA containing G-rich segments were visualized, and analysis of the complexes revealed the affinity of APE1 to G-rich DNA sequences. Furthermore, loops in the DNA-APE1 complexes were visualized, and their yield was as high as 53 %. However, the loops were non-specific, with quantitative analysis revealing the yield of loops bridging two G-rich DNA segments to be 41%. Analysis of protein size in various complexes was performed, and these data showed that loops are formed by APE1 monomer, suggesting that APE1 has two DNA binding sites. The data lead us to a model for the interaction of APE1 with DNA that describes its molecular site search mechanism. The new properties of APE1 in organizing DNA, by bringing two distant sites together, may be important for facilitating the scanning for damage and coordinating repair and transcription.
TL1A/DR3 signaling regulates the generation of pathogenic Th9 cells in experimental inflammatory bowel disease

Objective Death receptor 3 (DR3) and its ligand tumor necrosis factor like ligand 1A (TL1A), are involved in the regulation of the balance between effector and regulatory T cells in IBD. New evidence suggests a role of IL-9-secreting Th9 cells in the pathogenesis of ulcerative colitis (UC), although the molecular pathways through which IL-9 and Th9 cells may mediate intestinal inflammation in Crohn’s disease (CD) are still unclear. Design We investigated the role of DR3 signaling in the differentiation of Th9 cells in mouse models of CD-like ileitis and colitis, including SAMP1/YitFc (SAMP) mice. Results Polarized-Th9 cells with functional DR3 from SAMP WT (Th9WT) harbor a pro-inflammatory signature compared to DR3-deficient Th9 cells that were obtained from DR3-/-×SAMP mice (Th9KO). Conversely, ablation of DR3 signaling generated anti-inflammatory responses, as reflected by higher numbers of IL-10 producing cells in DR3-/-×SAMP mice. Additionally, RNA-seq and phosphoproteomic analyses showed that inflammatory pathways are significantly more activated in Th9WT than in Th9KO cells. Finally, in the T-cell adoptive transfer model, Th9KO cells were less colitogenic than Th9WT, while IL-9 blockade diminished the severity of intestinal inflammation, indicating a crucial role of functional DR3 receptor in Th9 cells pathogenicity. Conclusion We describe herein that a functional DR3 receptor is required for the pathogenicity of Th9 cells, thus, constituting a novel mechanism by which TL1A/DR3 signaling mediates experimental CD-like ileitis. The TL1A/DR3/Th9 pro-inflammatory pathway may offer a novel therapeutic target for patients with CD. What is already known on this topic – TL1A/DR3 system plays a pivotal role in the pathogenesis of Crohn’s disease like ileitis. – Th9 cells are a novel subset of T lymphocytes mainly producing the pro-inflammatory cytokine IL-9 which contributes to intestinal inflammation. – Those finding provided us with a strong rationale to investigate IL-9-producing cells in our SAMP1/YitFc mouse model of CD-like ileitis. What this study adds – DR3 receptor is involved in the regulation and progression of intestinal inflammation by promoting Th9 cell differentiation and pathogenicity. – Using RNA-seq and phosphoproteomic comparative analyses we were able to characterize Th9 cells with and without functional DR3 receptor showing that presence of DR3 confers a pro-inflammatory signature to Th9 cells. – We describe a novel role of DR3 in Th9 cells development that appears to regulate their pro-inflammatory phenotype in models of CD-like ileitis and colitis. How this study might affect research, practice or policy – TLA1/DR3 axis and Th9 cells may be useful as therapeutic targets in CD.
Substrate recognition principles for the PP2A-B55 protein phosphatase

The PP2A-B55 phosphatase regulates a plethora of signaling pathways throughout eukaryotes. How PP2A-B55 selects its substrates presents a severe knowledge gap. By integrating AlphaFold modelling with comprehensive high resolution mutational scanning, we show that α-helices in substrates bind B55 through an evolutionary conserved mechanism. Despite a large diversity in sequence and composition, these α-helices share key amino acid determinants that engage discrete hydrophobic and electrostatic patches. Using deep learning protein design, we generate a specific and potent competitive peptide inhibitor of PP2A-B55 substrate interactions. With this inhibitor, we uncover that PP2A-B55 regulates the nuclear exosome targeting complex by binding to an α-helical recruitment module in RBM7. Collectively, our findings provide a framework for the understanding and interrogation of PP2A-B55 in health and disease. One sentence summary α-helices in PP2A-B55 substrates bind a conserved pocket on B55 through a common mechanism that is conserved in eukaryotes.
Input Query NFV & SDN Controllers Analytics Deployer NF Monitors Distributed Queue Result Interface Stream Processors

Application performance monitoring in large data centers relies on either deploying expensive and specialized hardware at fixed locations or heavily customizing applications and collecting logs spread across thousands of servers. Such an endeavor makes performance diagnosis a time-consuming task for cloud providers and a problem beyond the control of cloud customers. We address this problem using emerging software defined paradigms such as Software Defined Networking and Network Function Virtualization as well as big data technologies. In this paper, we propose NetAlytics: a non-intrusive distributed performance monitoring system for cloud data centers. NetAlytics deploys customized monitors in the middle of the network which are transparent to end host applications, and leverages a real-time big data framework to analyze application behavior in a timely manner. NetAlytics can scale to packet rates of 40Gbps using only four monitoring cores and fifteen processing cores. Its placement algorithm can be tuned to minimize network bandwidth cost or server resources, and can reduce monitoring traffic overheads by a factor of 4.5. We present experiments that demonstrates how NetAlytics can be used to troubleshoot performance problems in load balancers, present comprehensive performance analysis, and provide metrics that drive automation tools, all while providing both low overhead monitors and scalable analytics.
Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network

Automatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional layers designed to extract features from the original images effectively. In addition, three fully connected layers are designed to classify the fabric defects into four categories. Using this system, the detection accuracy is improved, and the depth of the model is shortened simultaneously. Optimal detection rates for testing dirty marks, clip marks, broken yams, and defect-free were 88.01%, 90.15%, 98.01%, and 97.73%, respectively. The experimental results show that the proposed method is effective, feasible, and has significant potential for fabric defect detection.
An Iris Localization Algorithm based on Geometric Features of the Circle

An iris localization algorithm with the fast speed is proposed based on geometric features of the circle combing coarse localization with fine localization to avoid the problem of the slow speed of the classical iris localization algorithm. First, the row and column scanning method is employed to find four strings in the pupil. Four couples of tangency points are located by using the perpendicular bisector of strings and the threshold of the pupil boundary. The mean value of the coordinate parameters of the tangency points is regarded as the parameters for inner edge rough localization parameters. Then the calculus method is used for precise localization of the inner edge. The priori knowledge of the close distance between inner and outer edge centers of the circle is used to narrow the search range of the calculus method localizing outside edge, to significantly improving the speed of iris localization. The experimental results show that the proposed algorithm can improve the speed of iris localization with the high localizing accuracy.
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural Network

Punctuation restoration plays an essential role in the post-processing procedure of automatic speech recognition, but model efficiency is a key requirement for this task. To that end, we present EfficientPunct, an ensemble method with a multimodal time-delay neural network that outperforms the current best model by 1.0 F1 points, using less than a tenth of its inference network parameters. We streamline a speech recognizer to efficiently output hidden layer acoustic embeddings for punctuation restoration, as well as BERT to extract meaningful text embeddings. By using forced alignment and temporal convolutions, we eliminate the need for attention-based fusion, greatly increasing computational efficiency and raising performance. EfficientPunct sets a new state of the art with an ensemble that weights BERT's purely language-based predictions slightly more than the multimodal network's predictions. Our code is available at https://github.com/lxy-peter/EfficientPunct.
Mental Illness Aiding Through Machine Learning and Soft-Computing

This research presents an innovative chatbot system aimed at addressing mental health challenges through a fusion of Natural Language Processing (NLP) and Artificial Intelligence (AI). The chatbot stands out by offering personalized therapeutic conversations and tailored activity recommendations, enhancing the well-being of users. By integrating web scraping, the system enriches user experiences with relevant online stories. The study introduces a hybrid approach for response accuracy and includes multilingual support to cater to a diverse user base. The results of this study demonstrate substantial enhancements in both response accuracy and sentiment analysis. Overall, this chatbot system has the potential to make a significant impact on mental health support, showcasing the advancements in technology for the betterment of user well-being in an ever-evolving digital landscape.