Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipid biomarkers recording marine microbial community structure changes through the Frasnian‐Famennian mass extinction event

Studying the response and recovery of marine microbial communities during mass extinction events provides an evolutionary window through which to understand the adaptation and resilience of the marine ecosystem in the face of significant environmental disturbances. The goal of this study is to reconstruct changes in the marine microbial community structure through the Late Devonian Frasnian‐Famennian (F‐F) transition. We performed a multiproxy investigation on a drill core of the Upper Devonian New Albany Shale from the Illinois Basin (western Kentucky, USA). Aryl isoprenoids show green sulfur bacteria expansion and associated photic zone euxinia (PZE) enhancement during the F‐F interval. These changes can be attributed to augmented terrigenous influxes, as recorded collectively by the long‐chain/short‐chain normal alkane ratio, carbon preference index, C30 moretane/C30 hopane, and diahopane index. Hopane/sterane ratios reveal a more pronounced dominance of eukaryotic over prokaryotic production during the mass extinction interval. Sterane distributions indicate that the microalgal community was primarily composed of green algae clades, and their dominance became more pronounced during the F‐F interval and continued to rise in the subsequent periods. The 2α‐methylhopane index values do not show an evident shift during the mass extinction interval, whereas the 3β‐methylhopane index values record a greater abundance of methanotrophic bacteria during the extinction interval, suggesting enhanced methane cycling due to intensified oxygen depletion. Overall, the Illinois Basin during the F‐F extinction experienced heightened algal productivity due to intensified terrigenous influxes, exhibiting similarities to contemporary coastal oceans that are currently undergoing globalized cultural eutrophication. The observed microbial community shifts associated with the F‐F environmental disturbances were largely restricted to the extinction interval, which suggests a relatively stable, resilient marine microbial ecosystem during the Late Devonian.
Botox injection in treatment of sialorrhea in children with cerebral palsy.

BACKGROUND The occurrence of sialorrhea (drooling) in children with cerebral palsy is one of the important complications of this disease, which is associated with the impaired quality of life of patients and also the dissatisfaction of their parents. Botox injection in the salivary glands is one of the treatment methods that has recently received special attention in these patients, but there are still many challenges regarding its effectiveness and safety. We aimed to test the effectiveness and safety of botulinum toxin type A in reducing sialorrhea in children with cerebral palsy. METHODS This semi-experimental before-after study was performed on 12 children who suffering from sialorrhea. The ethics code of this project is IR.MUI. MED.REC. 1400.774 and the clinical trial registry code is IRCT20220516054868N1 (https://www.irct.ir/trial/64393). In each of the parotid and submandibular glands, an amount of 0.5 U/kg of botulinum toxin type A was injected by ultrasound guidance under general anesthesia. Before and 6 months after the intervention, the severity and frequency of drooling were tested by Drooling Frequency and Severity Scale. RESULTS We found a decreasing trend in the severity and frequency scores for drooling within one month; however, after that time, until the end of the 24th week, we saw an increasing trend in the intensity and frequency of this complication. Only two-thirds of parents were satisfied with the therapeutic protocol. Side effects related to botox injection were revealed in 25.0% mostly as dysphagia. CONCLUSION Botox injection in salivary glands is not a definitive and stable treatment in the treatment of sialorrhea in children with cerebral palsy.
Modulating effects of Astragalus polysaccharide on immune disorders via gut microbiota and the TLR4/NF-κB pathway in rats with syndrome of dampness stagnancy due to spleen deficiency

The syndrome of dampness stagnancy due to spleen deficiency (DSSD) is relatively common globally. Although the pathogenesis of DSSD remains unclear, evidence has suggested that the gut microbiota might play a significant role. Radix Astragali, used as both medicine and food, exerts the effects of tonifying spleen and qi. Astragalus polysaccharide (APS) comprises a macromolecule substance extracted from the dried root of Radix Astragali, which has many pharmacological functions. However, whether APS mitigates the immune disorders underlying the DSSD syndrome via regulating gut microbiota and the relevant mechanism remains unknown. Here, we used DSSD rats induced by high-fat and low-protein (HFLP) diet plus exhaustive swimming, and found that APS of moderate molecular weight increased the body weight gain and immune organ indexes, decreased the levels of interleukin-1β (IL-1β), IL-6, and endotoxin, and suppressed the Toll-like receptor 4/nuclear factor-κB (TLR4/NF-κB) pathway. Moreover, a total of 27 critical genera were significantly enriched according to the linear discriminant analysis effect size (LEfSe). APS increased the diversity of the gut microbiota and changed its composition, such as reducing the relative abundance of Pseudoflavonifractor and Paraprevotella, and increasing that of Parasutterella, Parabacteroides, Clostridium XIVb, Oscillibacter, Butyricicoccus, and Dorea. APS also elevated the contents of short-chain fatty acids (SCFAs). Furthermore, the correlation analysis indicated that 12 critical bacteria were related to the body weight gain and immune organ indexes. In general, our study demonstrated that APS ameliorated the immune disorders in DSSD rats via modulating their gut microbiota, especially for some bacteria involving immune and inflammatory response and SCFA production, as well as the TLR4/NF-κB pathway. This study provides an insight into the function of APS as a unique potential prebiotic through exerting systemic activities in treating DSSD.
Physiologically relevant fluorescent assay for identification of 17β‐hydroxysteroid dehydrogenase type 10 inhibitors

Mitochondrial enzyme 17β‐hydroxysteroid dehydrogenase type 10 (HSD10) is a potential molecular target for treatment of mitochondrial‐related disorders such as Alzheimer's disease (AD). Its over‐expression in AD brains is one of the critical factors disturbing the homeostasis of neuroprotective steroids and exacerbating amyloid beta (Aβ)‐mediated mitochondrial toxicity and neuronal stress. This study was focused on revalidation of the most potent HSD10 inhibitors derived from benzothiazolyl urea scaffold using fluorescent‐based enzymatic assay with physiologically relevant substrates of 17β‐oestradiol and allopregnanolone. The oestradiol‐based assay led to the identification of two nanomolar inhibitors (IC50 70 and 346 nM) differing from HSD10 hits revealed from the formerly used assay. Both identified inhibitors were found to be effective also in allopregnanolone‐based assay with non‐competitive or uncompetitive mode of action. In addition, both inhibitors were confirmed to penetrate the HEK293 cells and they were able to inhibit the HSD10 enzyme in the cellular environment. Both molecules seem to be potential lead structures for further research and development of HDS10 inhibitors.
Genetic liability for gastrointestinal inflammation disorders and association with gastrointestinal symptoms in children with and without autism

Children with autism spectrum disorder (ASD) have a greater prevalence of gastrointestinal (GI) symptoms than children without ASD. We tested whether polygenic scores for each of three GI disorders (ulcerative colitis, inflammatory bowel disease, and Crohn's disease) were related to GI symptoms in children with and without ASD. Using genotyping data (564 ASD cases and 715 controls) and external genome‐wide association study summary statistics, we computed GI polygenic scores for ulcerative colitis (UC‐PGS), inflammatory bowel disease (IDB‐PGS), and Crohn's disease (CD‐PGS). Multivariable logistic regression models, adjusted for genetic ancestry, were used to estimate associations between each GI‐PGS and (1) ASD case–control status, and (2) specific GI symptoms in neurotypical children and separately in ASD children. In children without ASD, polygenic scores for ulcerative colitis were significantly associated with experiencing any GI symptom (adjusted odds ratio (aOR) = 1.36, 95% confidence interval (CI) = 1.03–1.81, p = 0.03) and diarrhea specifically (aOR = 5.35, 95% CI = 1.77–26.20, p = 0.01). Among children without ASD, IBD‐PGS, and Crohn's PGS were significantly associated with diarrhea (aOR = 3.55, 95% CI = 1.25–12.34, p = 0.02) and loose stools alternating with constipation (aOR = 2.57, 95% CI = 1.13–6.55, p = 0.03), respectively. However, the three PGS were not associated with GI symptoms in the ASD case group. Furthermore, polygenic scores for ulcerative colitis significantly interacted with ASD status on presentation of any GI symptom within a European ancestry subset (aOR = 0.42, 95% CI = 0.19–0.88, p = 0.02). Genetic risk factors for some GI symptoms differ between children with and without ASD. Furthermore, our finding that increased genetic risks for GI inflammatory disorders are associated with GI symptoms in children without ASD informs future work on the early detection of GI disorders.
Analysis and Synthesis of Discrete-Time Interconnected Positive Systems

This paper presents analysis and synthesis of interconnected systems where the interconnected system of interest consists of discrete-time positive subsystems and an interconnection matrix. The paper gives sufficient conditions for the discrete-time single-input single-output (SISO) subsystems and the interconnection matrix so that the interconnected system has the property of persistence. The fundamental differences for the persistence between the conditions of the discrete-time setting and those of a continuous-time setting are also discussed. The obtained analysis result can apply to formation control for multi-agent systems, which is a synthesis part of the paper. Numerical examples including formation control of mobile robots are shown to illustrate the proposed formation control design method.
Interval cancer after two rounds of a Swedish population-based screening program using gender-specific cut-off levels in fecal immunochemical test

Objective To evaluate interval cancer (IC) after two screening rounds of the Swedish population-based screening program of Stockholm–Gotland applying gender-specific cut-off levels in the fecal immunochemical test (FIT). Methods All 60- to 69-year-olds invited to screening 2015–2019 were included. The cut-off level for a positive test was 40 µg/g in women and 80 µg/g in men. Screening-detected colorectal cancers (SD CRCs) and ICs were verified in the Swedish Colorectal Cancer Register, and the follow-up time was two years from invitation. The test sensitivity, the IC rate (ICs per 10,000 screening negatives) and the IC incidence (ICs per 100,000 person-years) relative to the background CRC incidence were assessed by gender and age. The FIT levels were compared in men and women for CRCs diagnosed within one year of the sample. Results In the second screening round, 229,187 were invited, and SD CRCs and ICs were diagnosed in 193 and 144, respectively. The IC rate was 8.9 (7.4–10.3) and test sensitivity 0.61 (0.55–0.66), and was similar in men and women. For two screening rounds, the IC rate was significantly higher in men than in women, but the IC incidence/ background CRC incidence was similar in both genders. The FIT levels in female participants with CRC were significantly lower overall, and in early-staged CRCs as compared to men, and proximal localization was more common in women. In multivariable analysis, FIT levels were significantly lower in proximal CRCs. Conclusion Over two rounds, the IC incidence relative to the background CRC incidence was similar in men and women supporting a gender-specific screening strategy. The results could be explained by lower FIT levels in women due to proximal CRC localization.
Comparison of BTP, NGAL, KIM-1, & ADMA biomarkers in CKD and non-CKD subjects.

INTRODUCTION New biomarkers, such as neutrophil gelatinase-associated lipocalin (NGAL), a member of the lipocalin family, and kidney injury molecule-1 (KIM-1) have been utilised in recent years to identify the development of chronic kidney disease (CKD). However, attempts to use them as broad markers to check for renal injury in patients and to pinpoint the kidney injury site have been unsuccessful. Therefore the search for an ideal panel of biomarkers predicting progression of CKD is still ongoing. The present study is designed to evaluate the biomarkers for CKD from NGAL, KIM-1, Beta trace protein (BTP) and Asymmetric dimethylarginine (ADMA). MATERIALS AND METHODS For this case-control study, 100 participants were selected on the basis of inclusion and exclusion criteria at the Index Medical College Hospital and Research Centre, Madhya Pradesh, there were 67 male subjects and 33 female CKD subjects and 100 non-CKD subjects (60 male and 40 female) matched for age and sex were taken from the hospital. RESULTS Between the CKD and non-CKD participants, the levels of BTP, plasma NGAL, KIM-1, and ADMA were compared and found to be substantially higher than those in the controls. CONCLUSION Abnormal renal function is linked to disturbed blood levels of BTP, NGAL, KIM-1, and ADMA. Strong correlations exist between increased blood levels of BTP, NGAL, KIM-1, and ADMA and reduced kidney function. They might thus be used to estimate the decline of renal function and the progression of CKD.
Slicing conditions for axisymmetric gravitational collapse of Brill waves

In numerical relativity, spacetimes involving compact strongly gravitating objects are constructed as numerical solutions of Einstein’s equations. Success of such a process strongly depends on the availability of appropriate coordinates, which are typically constructed dynamically. A very robust coordinate choice is a so-called moving puncture gauge, commonly used for numerical simulations of black hole spacetimes. Nevertheless it is known to fail for evolving near-critical Brill wave data. We construct a new ‘quasi-maximal’ slicing condition and demonstrate that it exhibits better behavior for such data. This condition is based on the 1+log slicing with an additional source term derived from maximal slicing. It is relatively simple to implement in existing moving puncture codes and computationally inexpensive. We also illustrate the properties of constructed spacetimes based on gauge-independent quantities in compactified spacetime diagrams. These invariants are also used to show how created black holes settle down to a Schwarzschild black hole.
Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO

Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully addressed these challenges for parameter estimation. However, the classical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data duration to be useful and are not immune to confounding effects. In this work, we address this disconnect by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. We establish fundamental limits for reliable identification of Granger causal influences using the proposed LASSO-based statistic. We further characterize the false positive error probability and test power of a simple thresholding rule for identifying Granger causal effects and provide two methods to set the threshold in a data-driven fashion. We present simulation studies and application to real data to compare the performance of our proposed method to ordinary least squares and existing LASSO-based methods in detecting Granger causal influences, which corroborate our theoretical results.
Ehrhart quasi-polynomials and parallel translations

Given a rational polytope $P \subset \mathbb R^d$, the numerical function counting lattice points in the integral dilations of $P$ is known to become a quasi-polynomial, called the Ehrhart quasi-polynomial $\mathrm{ehr}_P$ of $P$. In this paper we study the following problem: Given a rational $d$-polytope $P \subset \mathbb R^d$, is there a nice way to know Ehrhart quasi-polynomials of translated polytopes $P+ \mathbf v$ for all $\mathbf v \in \mathbb Q^d$? We provide a way to compute such Ehrhart quasi-polynomials using a certain toric arrangement and lattice point counting functions of translated cones of $P$. This method allows us to visualize how constituent polynomials of $\mathrm{ehr}_{P+\mathbf v}$ change in the torus $\mathbb R^d/\mathbb Z^d$. We also prove that information of $\mathrm{ehr}_{P+\mathbf v}$ for all $\mathbf v \in \mathbb Q^d$ determines the rational $d$-polytope $P \subset \mathbb R^d$ up to translations by integer vectors, and characterize all rational $d$-polytopes $P \subset \mathbb R^d$ such that $\mathrm{ehr}_{P+\mathbf v}$ is symmetric for all $\mathbf v \in \mathbb Q^d$.
Pair then Relation: Pair-Net for Panoptic Scene Graph Generation

Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Thus, current PSG methods have limited performance, which hinders downstream tasks or applications. The goal of this work aims to design a novel and strong baseline for PSG. To achieve that, we first conduct an in-depth analysis to identify the bottleneck of the current PSG models, finding that inter-object pair-wise recall is a crucial factor that was ignored by previous PSG methods. Based on this and the recent query-based frameworks, we present a novel framework: Pair then Relation (Pair-Net), which uses a Pair Proposal Network (PPN) to learn and filter sparse pair-wise relationships between subjects and objects. Moreover, we also observed the sparse nature of object pairs for both Motivated by this, we design a lightweight Matrix Learner within the PPN, which directly learn pair-wised relationships for pair proposal generation. Through extensive ablation and analysis, our approach significantly improves upon leveraging the segmenter solid baseline. Notably, our method achieves new state-of-the-art results on the PSG benchmark, with over 10\% absolute gains compared to PSGFormer. The code of this paper is publicly available at https://github.com/king159/Pair-Net.
A Lightweight Framework for High-Quality Code Generation

In recent years, the use of automated source code generation utilizing transformer-based generative models has expanded, and these models can generate functional code according to the requirements of the developers. However, recent research revealed that these automatically generated source codes can contain vulnerabilities and other quality issues. Despite researchers' and practitioners' attempts to enhance code generation models, retraining and fine-tuning large language models is time-consuming and resource-intensive. Thus, we describe FRANC, a lightweight framework for recommending more secure and high-quality source code derived from transformer-based code generation models. FRANC includes a static filter to make the generated code compilable with heuristics and a quality-aware ranker to sort the code snippets based on a quality score. Moreover, the framework uses prompt engineering to fix persistent quality issues. We evaluated the framework with five Python and Java code generation models and six prompt datasets, including a newly created one in this work (SOEval). The static filter improves 9% to 46% Java suggestions and 10% to 43% Python suggestions regarding compilability. The average improvement over the NDCG@10 score for the ranking system is 0.0763, and the repairing techniques repair the highest 80% of prompts. FRANC takes, on average, 1.98 seconds for Java; for Python, it takes 0.08 seconds.
Explainable AI with counterfactual paths

Explainable AI (XAI) is an increasingly important area of research in machine learning, which in principle aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses counterfactual paths generated by conditional permutations. Our method provides counterfactual explanations by identifying alternative paths that could have led to different outcomes. The proposed method is particularly suitable for generating explanations based on counterfactual paths in knowledge graphs. By examining hypothetical changes to the input data in the knowledge graph, we can systematically validate the behaviour of the model and examine the features or combination of features that are most important to the model’s predictions. Our approach provides a more intuitive and interpretable explanation for the model’s behaviour than traditional feature weighting methods and can help identify and mitigate biases in the model.
Monte Carlo methods in nonlinear filtering theory

This thesis is focused on two basic aspects of the Control Problem: the stochastic modelling of physical systems, and Monte Carlo-based numerical approximation of the nonlinear ltering problem solution. In the rst topic this thesis concerns about clarifying some issues in the mathematical modelling of continuous-time systems with Brownian motion. The hypothesis that physical systems should be modelled in continuous-time approach is defended, once the main results in Physics provide solutions for dynamic systems via continuous-time di erential equations. It was shown, recalling a main result from the 1960's that a physical system is represented by Fisk-Stratonovich stochastic di erential equation, though Ito approach is better to manipulate the mathematical operations. The required conditions for implementing these equations in computers were also studied by using Euler-Maruyama and Milstein schemes of discretization. In the second topic a unied treatment of the available Monte Carlo methods solving the nonlinear ltering problem for continuous and discrete-time modelling is presented with su cient emphasis on basic applications enabling the engineer to use results provided by the theory. This topic is branched in the study of the theory of nonlinear ltering problem in continuous and discrete-time approaches, and in the investigation of the aspects of Monte Carlo-based numerical solutions approximating unnormalized conditional expectations, as those given by the classical Kallianpur-Striebel formula and its derived robust representation. Investigations showed that the estimates obtained via numerical approximations of the robust representation, or pathwise lter, might accumulate errors when the observation makes this lter alternative equation unstable, a limitation of the method. Another result of this thesis refers to the implementation of Monte Carlo lters using Bayesian representation for discretized models. Although Monte Carlo methods are attractive due to their facility of parallelization, their main drawback is the degeneracy phenomenon of the particles. The traditional resampling scheme solves the problem, but it di culties the parallelization of the algorithm. The restoration method was then proposed to move the particles towards higher regions in the likelihood function, given information about the model parameters. This open method, in some sense, might decrease the particles degeneracy.