Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimates of possible regional hydrologic regime changes in the 21st century based on global climate models

Numerical results of global climate models are used to infer possible changes in regional hydrologic cycle characteristics, including precipitation and river runoff, in the 21st century. Both coupled atmosphere-ocean general circulation models and a climatic model of intermediate complexity, forced by scenarios of an anthropogenic increase in the atmospheric greenhouse cyas content, are used. The model results show that the precipitation amounts considerably increase in the high latitudes, in particular, over Eurasia, under anthropogenic warming in the 21st century. This is associated primarily with a large increase in the winter precipitation intensity, especially over northeastern Eurasia. The changes in summer precipitation differ widely, with a large neoative trend occurring in the midlatitudes. This trend is associated with corresponding changes in the probability of wet days. Despite a decline in summer precipitation over a large Eurasian region, the precipitation intensity increases. The relative contribution of intense precipitation to the total precipitation amount increases as well. Model estimates are presented for possible changes occurring in the 21st century in precipitation and river runoff over various regions, including the basins of the Volga, the Caspian Sea, the Neva, Lake Ladoga, the Ob, Yenisei, and Lena rivers. On the whole, the precipitation and runoff in these basins, as well as their variability, are found to generally increase in the 21st century against the background of considerable interannual and interdecadal variability. The average runoffs of the Volga, Ob, Yenisei, and Neva rivers somewhat decline in the first half of the 21st century in the general circulation climate models, whereas no such decline is found for the Lena runoff.
Proving Query Equivalence Using Linear Integer Arithmetic

Proving the equivalence between SQL queries is a fundamental problem in database research. Existing solvers model queries using algebraic representations and convert such representations into first-order logic formulas so that query equivalence can be verified by solving a satisfiability problem. The main challenge lies in "unbounded summations", which appear commonly in a query's algebraic representation in order to model common SQL features, such as projection and aggregate functions. Unfortunately, existing solvers handle unbounded summations in an ad-hoc manner based on heuristics or syntax comparison, which severely limits the set of queries that can be supported. This paper develops a new SQL equivalence prover called SQLSolver, which can handle unbounded summations in a principled way. Our key insight is to use the theory of LIA^*, which extends linear integer arithmetic formulas with unbounded sums and provides algorithms to translate a LIA^* formula to a LIA formula that can be decided using existing SMT solvers. We augment the basic LIA^* theory to handle several complex scenarios (such as nested unbounded summations) that arise from modeling real-world queries. We evaluate SQLSolver with 359 equivalent query pairs derived from the SQL rewrite rules in Calcite and Spark SQL. SQLSolver successfully proves 346 pairs of them, which significantly outperforms existing provers.
Correlation Joins over Time Series Data Streams Utilizing Complementary Dimension Reduction and Transformation

A common analysis task over a stream of time series is to find all pairs of windows whose correlation is above a given threshold. For a large number of streams, doing so naively, i.e., checking the Cartesian product, is too expensive. In essence, finding correlated pairs in a non-naive way boils down to a high-dimensional similarity join in a Euclidean space. While there are similarity join algorithms, such as Quickjoin and ε-kdB tree, they are inefficient for high-dimensional data. We propose CorrJoin, short for Correlation Join, that combines a complementary dimension reduction and transformation step with a subsequent double-filtering step. In the first step, we reduce the dimensionality of data stream windows by combining a fast but inaccurate method, Piecewise Aggregate Approximation (PAA), with an accurate and slow one, Singular Value Decomposition (SVD). Not only does SVD compensate for the weaknesses of PAA, it also transforms the data to make the first filter based on bucketing more effective. The second filter, which uses Euclidean distances, reduces the number of false positives before computing exact correlations. Our experiments reveal that in common settings, CorrJoin is an order of magnitude faster than state-of-the-art approaches (up to 20 times faster than Quickjoin).
Exploring Commonality in Liquidity and Volatility Risk Pricing in Indian Stock Markets

Research and market analysts have been following the liquidity and volatility risk microstructures of stock markets with interest. Both types of risks have a significant impact on asset returns and exhibit a high level of persistence in various ways, viz. commonality in liquidity and illiquidity sensitivity to market returns. Returns shocks can be used to anticipate liquidity and volatility shocks across a variety of assets. The study attempts to extract the common factors for selected volatility and liquidity measures of stocks traded on the national stock exchange, NIFTY 50, over a time span of November 2010 to October 2022. Results indicate a high correlation between the selected measures and the uncertainty factor. There is no notable price phenomenon for the unique liquidity risk element in the cross-sections. The findings of our study carry important implications for regulators and policymakers for market controls and risk management mechanisms.
Regularized Generalized Linear Models to Disclose Host-Microbiome Associations in Colorectal Cancer

Recent studies have shown that gut microbiome is associated with colorectal cancer (CRC) progression and anti-cancer therapy efficacy. This study aims to optimize the ridge, elastic net, and lasso regularized generalized linear models (GLM), widely used for supervised machine learning, for multiclass classification tasks (healthy/adenoma/carcinoma). The models are applied to a benchmark gut microbiome dataset using raw and transformed data. A cross-validation procedure is used to select an optimal value for the shrinkage parameter, λ. The results show a higher accuracy of the ridge and elastic net models compared to the lasso model. We confirm known associations of several microbiome genera with CRC and adenoma. These findings are expected to contribute to the definition of CRC-microbiome signatures to be further validated in microbiome-related therapy studies.
DEWP: Deep Expansion Learning for Wind Power Forecasting

Wind is one kind of high-efficient, environmentally-friendly, and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, and so on. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose DEWP, a novel Deep Expansion learning for Wind Power forecasting framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a variable expansion block that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a time expansion block that applies Fourier series and backcast/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an inference block corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Accurate wind power forecasting (WPF) is then better achieved through fine-grained outputs by continuously removing stack residues and accumulating useful stack forecasts. Finally, we present extensive experiments in the real-world WPF application on two datasets from two different turbines, in order to demonstrate the effectiveness of our approach.
A Deep Learning Based Semi-Supervised Network Intrusion Detection System Robust to Adversarial Attacks

Network intrusion detection systems (NIDS) are used to detect abnormal behavior in network traffic, which is vital for secure communication. Recently, deep learning based solutions have been adopted for NIDS which suffer from two main problems. Most of them are based on supervised learning and cannot utilize the information that can be obtained from unlabeled data. Also, deep learning based methods are shown to be vulnerable to adversarial attacks. In this paper, we propose a novel semi-supervised and adversarially robust deep learning based approach which can utilize both labeled and unlabeled training samples. Our IDS first performs K-Means clustering to soft label part of the unlabeled data and then obtain a decision tree based on labeled and soft labeled samples. It then pretrains an autoencoder based multi-layer perceptron and later learns separate multi-layer perceptrons on each individual leaf of the decision tree. Our results show that the performance of our system is comparable to state-of-the art supervised learning approaches and outperforms existing state-of-the-art semi-supervised NIDS. Furthermore, we have extensively tested the adversarial robustness of our method using the popular blackbox Fast Gradient Sign Method (FGSM) and Generative Adversarial Network based IDSGAN approaches. Comparisons with other state-of-the-art NIDS baselines show that our proposed mechanism provides significantly higher adversarial detection rates, proving the robustness of our system to adversarial attacks.
The Keiper-Li Criterion for the Riemann Hypothesis and Generalized Lambert Functions

Keiper [1] and Li [2] published independent investigations of the connection between the Riemann hypothesis and the properties of sums over powers of zeros of the Riemann zeta function. Here we consider a reframing of the criterion, to work with higher-order derivatives ξr of the symmetrized function ξ(s) at s = 1/2, with all ξr known to be positive. The reframed criterion requires knowledge of the asymptotic properties of two terms, one being an infinite sum over the ξr. This is studied using known asymptotic expansions for the ξr, which give the location of the summand as a relationship between two parameters. This relationship needs to be inverted, which we show can be done exactly using a generalized Lambert function. The result enables an accurate asymptotic expression for the value of the infinite sum.
A Pan-respiratory Antiviral Chemotype Targeting a Transient Host Multiprotein Complex

A Pan-respiratory Antiviral Chemotype Targeting a Transient Host Multiprotein Complex 1 2 Authors: Andreas Müller-Schiffmann, Maya Michon, Anuradha F. Lingappa, Shao Feng Yu, Li Du, 3 Fred Deiter, Sean Broce, Suguna Mallesh, Jackelyn Crabtree, Usha F. Lingappa, Amanda Macieik, 4 Lisa Müller, Philipp Niklas Ostermann, Marcel Andrée, Ortwin Adams, Heiner Schaal, Robert J. 5 Hogan, Ralph A. Tripp, Umesh Appaiah, Sanjeev K. Anand, Thomas W. Campi, Michael J. Ford, 6 Jonathan C. Reed, Jim Lin, Olayemi Akintunde, Kiel Copeland, Christine Nichols, Emma Petrouski, 7 A. Raquel Moreira, I-ting Jiang, Nicholas DeYarman, Ian Brown, Sharon Lau, Ilana Segal, Danielle 8 Goldsmith, Shi Hong, Vinod Asundi, Erica M. Briggs, Ngwe Sin Phyo, Markus Froehlich, Bruce 9 Onisko, Kent Matlack, Debendranath Dey, Jaisri R. Lingappa, M. Dharma Prasad, Anatoliy 10 Kitaygorodskyy, Dennis Solas, Homer Boushey, John Greenland, Satish Pillai, Michael K. Lo, 11 Joel M. Montgomery, Christina F. Spiropoulou, Carsten Korth, Suganya Selvarajah, Kumar 12 Paulvannan, and Vishwanath R. Lingappa* 13 14
Treatment with sodium butyrate has therapeutic benefits for Machado-Joseph disease through the induction of autophagy

Machado-Joseph disease (MJD) is a fatal neurodegenerative disease caused by expansion of the trinucleotide repeat region within the ATXN3/MJD gene. Mutation of ATXN3 causes formation of neurotoxic ataxin-3 protein aggregates, neurodegeneration and motor deficits. Here we investigated the therapeutic potential of sodium butyrate (SB), the sodium salt of butyric acid, a metabolite naturally produced by gut microbiota, on cultured SH-SY5Y cells and transgenic zebrafish expressing human ataxin-3 containing 84 glutamine (Q) residues to model MJD. MJD SH-SY5Y cells were found to contain ataxin-3 oligomeric species and protein aggregates. Interestingly, treatment with SB decreased the size of detergentinsoluble ataxin-3 aggregates in vitro. Further investigation revealed that SB treatment increased activity of the autophagy protein quality control pathway in the MJD cells and decreased presence of ataxin-3 oligomers in an autophagy-dependent manner. Treatment with SB was also beneficial in vivo through induction of autophagy and improving swimming performance in transgenic MJD zebrafish. Co-treating the MJD zebrafish with SB and chloroquine, an autophagy inhibitor, prevented the beneficial effects of SB, suggesting that the improved swimming performance was autophagy-dependent . Furthermore, intraperitoneal injection of SB to wild type mice resulted in increased levels of neuronal LC3B levels, indicating induction of autophagy within the brain. Collectively, our findings suggest that SB can induce activity of the autophagy pathway and can produce beneficial effects in vitro and in vivo. We propose that treatment with sodium butyrate warrants further investigation for the treatment of neurodegenerative diseases underpinned by proteinopathy mechanisms, including MJD.
Mechanism of connexin channel inhibition by mefloquine and 2-aminoethoxydiphenyl borate

Gap junction intercellular communication (GJIC) between two adjacent cells involves direct exchange of cytosolic ions and small molecules via connexin gap junction channels (GJCs). Connexin GJCs have emerged as drug targets, with small molecule connexin inhibitors considered a viable therapeutic strategy in a number of diseases. The molecular mechanisms of GJC inhibition by known small molecule connexin inhibitors remain unknown, preventing the development of more potent and connexin-specific therapeutics. Here we show that two GJC inhibitors, mefloquine (MFQ) and 2-aminoethoxydiphenyl borate (2APB) bind to Cx32 and block dye permeation across Cx32 hemichannels (HCs) and GJCs. Cryo-EM analysis shows that 2APB binds to site A, close to the N-terminal gating helix of Cx32 GJC, restricting the entrance to the channel pore. In contrast, MFQ binds to a distinct site M, deeply buried within the pore. MFQ binding to this site modifies the electrostatic properties of Cx32 pore. Mutagenesis of V37, a key residue located in the site M, renders Cx32 HCs and GJCs insensitive to MFQ-mediated inhibition. Moreover, our cryo-EM analysis, mutagenesis and activity assays show that MFQ targets the M site in Cx43 GJC similarly to Cx32. Taken together, our results point to a conserved inhibitor binding site in connexin channels, opening a new route for development of specific drugs targeting connexins. One-Sentence Summary Inhibition of Cx32 and Cx43 channels via functionally relevant drug binding sites
Quantitative Dynamic Analysis of de novo Sphingolipid Biosynthesis in Arabidopsis thaliana

Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed towards fully comprehending the regulatory mechanisms of the sphingolipid pathway. We explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing 15N isotope labeling and quantitative dynamic modeling approach, we developed a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase (LCBK) to be critical for maintaining sphingolipid homeostasis. Disruptions in these enzymes were found to affect cellular viability and increase the potential for programmed cell death (PCD). Thus, this work enhances our understanding of sphingolipid metabolism and demonstrates the utility of dynamic modeling in analyzing complex metabolic pathways.
Multi-parameter phenotyping of platelets and characterisation of the effects of agonists using machine learning

Background Platelets are crucial for thrombosis and haemostasis, with their function driven by the expression of specialised surface markers. The concept of distinct circulating sub-populations of platelets has emerged in recent years, but their exact nature remains debatable. We reasoned that a more comprehensive characterisation of surface marker changes at rest and upon activation would be valuable in determining this. Objective To use a full spectrum flow cytometry-based panel, together with parameters of physical properties, to describe surface marker changes in healthy platelets at rest and on activation, and to observe how these responses differ according to platelet age. Methods A 14-marker flow cytometry panel was developed and applied to vehicle- or agonist-stimulated platelet-rich plasma samples obtained from healthy volunteers, or to platelets sorted according to SYTO-13 staining intensity as an indicator of platelet age. Data were analysed using both user-led and independent approaches incorporating novel machine learning-based algorithms. Results The assay detected changes in marker expression in healthy platelets, at rest and on agonist activation, that are consistent with the literature. Machine learning identified stimulated populations of platelets with high accuracy (>80%). Similarly, differentiation between young and old platelet populations achieved 76% accuracy, primarily weighted by FSC-A, CD41, SSC-A, GPVI, CD61, and CD42b expression patterns. Conclusions Our findings provide a novel assay to phenotype platelets coupled with a robust bioinformatics and machine learning workflow for deep analysis of the data. This could be valuable in characterising platelets in disease. (240 words) Essentials Platelet function is directed by the expression of specialised surface markers Circulating platelet sub-populations are incompletely characterised Multi-parameter spectral flow cytometry allows robust and comprehensive phenotyping of platelets Coupling multi-parameter spectral flow cytometry with machine learning offers a powerful method to determine platelet sub-populations
Peribacillus frigoritolerans T7-IITJ, a potential biofertilizer, induces plant growth-promoting genes of Arabidopsis thaliana

This study aimed to isolate plant growth and drought tolerance-promoting bacteria from the nutrient- poor rhizosphere soil of several plant species from the Thar desert and unravel their molecular mechanisms of plant growth promotion, to develop effective biofertilizers for arid agriculture. Among our isolates of Thar desert rhizobacteria, Enterobacter cloacae C1P-IITJ, Kalamiella piersonii J4-IITJ, and Peribacillus frigoritolerans T7-IITJ, significantly enhanced root and shoot growth in the model plant Arabidopsis thaliana under PEG-induced drought stress in the lab. Whole genome sequencing and biochemical analyses of the non-pathogenic bacterium T7-IITJ revealed its plant growth-promoting traits, viz., solubilization of phosphate, iron, and nitrate and production of exopolysaccharides and auxin. Transcriptome analysis of Arabidopsis thaliana inoculated with T7-IITJ and exposure to drought revealed the induction of plant genes for photosynthesis, auxin and jasmonate signaling, nutrient mining and sequestration, redox homeostasis, and secondary metabolite biosynthesis pathways related to beneficial bacteria-plant interaction, but repression of many stress-responsive genes. Biochemical analyses indicated enhanced proline, chlorophyll, iron, phosphorous, and nitrogen content and reduced reactive oxygen species in plant tissues due to T7-IITJ inoculation. This bacterium could also improve the germination and seedling growth of Tephrosia purpurea, Triticum aestivum, and Setaria italica under drought. Additionally, T7-IITJ inhibited the growth of two plant pathogenic fungi, Rhizoctonia solani, and Fusarium oxysporum. These results suggest P. frigoritolerans T7-IITJ is a potent biofertilizer which can regulate plant genes promoting growth and drought tolerance.
The gut commensal Blautia maintains colonic mucus function under low fiber consumption through short-chain fatty acid-mediated activation of Ffar2

Beneficial gut bacteria are indispensable for developing colonic mucus and fully establishing its protective function against intestinal microorganisms. Low-fiber diet consumption alters the gut bacterial configuration and disturbs this microbe-mucus interaction, but the specific bacteria and microbial metabolites responsible for maintaining mucus function remain poorly understood. By using human-to-mouse microbiota transplantation and ex vivo analysis of colonic mucus function, our proof-of-concept study demonstrates that individuals who increase their daily dietary fiber intake can improve the capacity of their gut microbiota to prevent diet-mediated mucus defects. Mucus growth, a critical feature of intact colonic mucus, correlated with the abundance of the gut commensal Blautia, and supplementation of Blautia coccoides to mice confirmed its mucus-stimulating capacity. Mechanistically, B. coccoides stimulated mucus growth through the production of the short-chain fatty acids propionate and acetate via activation of the short-chain fatty acid receptor Ffar2, which could serve as a new target to restore mucus growth during mucus-associated lifestyle diseases.