Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Geophysical Research: Earth Surface Remote sensing measurements of thermokarst subsidence using InSAR

Thawing of ice-rich permafrost followed by surface subsidence results in irregular, depressed landforms known as thermokarst. Many remote sensing studies have identiﬁed thermokarst landforms and mapped their changes. However, the intrinsic dynamic thermokarst process of surface subsidence remains a challenge to quantify and is seldom examined using remote sensing methods. In this study we used spaceborne interferometric synthetic aperture radar (InSAR) data to map surface subsidence trends at a thermokarst landform located near Deadhorse on the North Slope of Alaska. A pipeline access road constructed in the 1970s triggered the thawing of the permafrost, causing subsequent expansion of the thermokarst landform. Using Phased Array type L band Synthetic Aperture Radar images acquired by the Advanced Land Observing Satellite-1, our InSAR analysis reveals localized thermokarst subsidence of 2–8 cm/yr between 2006 and 2010, equivalent to an ice volume loss of about 1 . 2 × 10 7 m 3 /yr. Comparisons between InSAR subsidence trends and lidar microtopography suggest a characteristic time of 8 years of thermokarst development. We also quantitatively explain the diﬃculty, uncertainties, and possible biases in separating thermokarst-induced, irreversible subsidence from cyclic seasonal deformation. Our study illustrates that InSAR is an eﬀective tool for mapping and studying active thermokarst processes and quantifying ice loss.
Visualization of Adverse Drug Reactions Based on the Knowledge Graphs

In today's society, a series of new drugs are constantly put into the medical market. Without necessary supervision, adverse drug reactions (ADR) caused by these drugs may threaten public health safety. Therefore, in the process of drug use, it is of great guiding significance for drug users and drug research and development to obtain drug-related adverse reactions earlier and more accurately. This paper obtains the adverse drug reaction data in the network through the data crawler technology, constructs the ADR knowledge map and makes a visual analysis. The specific methods are divided into: (1) use the browser to find the website and find the address of the corresponding URL; (2) Clean and remove the weight of the data crawled by the crawler, so as to obtain valuable data according to the requirements; (3) Store the valuable data in the database; (4) For the stored data, we should find the attribute extraction, adjust the storage of entities and knowledge, and then visually establish the knowledge map. The results show that human's understanding of diseases and clinical drugs changes indefinitely. Combined with clinical drugs, adverse reactions, drug dosage, medical clinical performance data with some connections between different drugs, we can express attributes, entities and relationships by means of graphs, build a complete knowledge map, and query with Neo4j query sentences.
An initial investigation of ChatGPT unit test generation capability

Context: Software testing ensures software quality, but developers often disregard it. The use of automated testing generation is pursued to reduce the consequences of overlooked test cases in a software project. Problem: In the context of Java programs, several tools can completely automate generating unit test sets. Additionally, studies are conducted to offer evidence regarding the quality of the generated test sets. However, it is worth noting that these tools rely on machine learning and other AI algorithms rather than incorporating the latest advancements in Large Language Models (LLMs). Solution: This work aims to evaluate the quality of Java unit tests generated by an OpenAI LLM algorithm, using metrics like code coverage and mutation test score. Method: For this study, 33 programs used by other researchers in the field of automated test generation were selected. This approach was employed to establish a baseline for comparison purposes. For each program, 33 unit test sets were generated automatically, without human interference, by changing Open AI API parameters. After executing each test set, metrics such as code line coverage, mutation score, and success rate of test execution were collected to evaluate the efficiency and effectiveness of each set. Summary of Results: Our findings revealed that the OpenAI LLM test set demonstrated similar performance across all evaluated aspects compared to traditional automated Java test generation tools used in the previous research. These results are particularly remarkable considering the simplicity of the experiment and the fact that the generated test code did not undergo human analysis.
SelectNLTest - Selection and natural language rewriting of test cases generated by the DRL-MOBTEST tool

The software testing process is important to ensure quality, especially in mobile applications that have characteristics such as platform diversity, hardware limitations, portability, frequent updates, among others. Software companies need to deliver quickly with increasingly complex functionalities; therefore, the testing process must be efficient and avoid bottlenecks, such as the creation of test cases. Among the solutions found in the literature, the state-of-the-art tool DRL-MOBTEST aims to assist in the automatic generation of test cases for mobile applications using deep reinforcement learning. The experiments show promising results; however, the tool has some limitations, such as generating duplicate and less readable tests. In this article, we present SelectNLTest, a module developed to identify and remove similar test scripts and transcribe the test cases generated by the tool using Natural Language Processing techniques. This allows for the removal of similar tests and improves the readability and understanding of the generated test cases for professionals in the field. The results of the experiments showed that, in 10 Android applications used in the comparative analysis, the proposed module reduced the number of test cases by 58.3% while maintaining code coverage and application functionality.
Self-Care and Quality of Life: Associated Factors Among MSW Students

ABSTRACT There is an increasing concern for self-care and quality of life among Master of Social Work (MSW) students as they prepare to serve vulnerable individuals, families, and communities. Evidence suggests numerous benefits to practicing self-care; however, the majority of these studies focus on practitioners in the helping professions as opposed to graduate students working toward their degrees, suggesting a need for research. Accordingly, this study investigates the association between self-care actions, distress tolerance, mindfulness and quality of life among MSW students. A cross-sectional survey design, was used to examine these associations among 148 MSW students. Three multiple regression analyses were conducted to determine the relationship among the key variables. Results suggest that emotional self-care was significantly associated with quality of life, and that the level of engagement in self-care actions differed across students’ socioeconomic class. Students’ mindfulness and emotional self-care also were positively associated with distress tolerance. Findings suggest that promoting engagement in emotional self-care and mindfulness self-care may promote higher quality of life among MSW students. Implications for MSW programs and the potential to support students’ health and sustainable learning are discussed.
Accelerated brain change in healthy adults is associated with genetic risk for Alzheimer’s disease and uncovers adult lifespan memory decline

Across healthy adult life our brains undergo gradual structural change in a pattern of atrophy that resembles accelerated brain changes in Alzheimer’s disease (AD). Here, using four polygenic risk scores for AD (PRS-AD) in a longitudinal adult lifespan sample aged 30 to 89 years (2-7 timepoints), we show that healthy individuals who lose brain volume faster than expected for their age, have a higher genetic AD risk. We first demonstrate PRS-AD associations with change in early Braak regions, namely hippocampus, entorhinal cortex, and amygdala, and find evidence these extend beyond that predicted by APOE genotype. Next, following the hypothesis that brain changes in ageing and AD are largely shared, we performed machine learning classification on brain change trajectories conditional on age in longitudinal AD patient-control data, to obtain a list of AD-accelerated features and model change in these in adult lifespan data. We found PRS-AD was associated with a multivariate marker of accelerated change in many of these features in healthy adults, and that most individuals above ∼50 years of age are on an accelerated change trajectory in AD-accelerated brain regions. Finally, high PRS-AD individuals also high on a multivariate marker of change showed more adult lifespan memory decline, compared to high PRS-AD individuals with less brain change. Our results support a dimensional account linking normal brain ageing with AD, suggesting AD risk genes speed up the shared pattern of ageing- and AD-related neurodegeneration that starts early, occurs along a continuum, and tracks memory change in healthy adults.
Using the pan-genomic framework for the discovery of genomic islands in the haloarchaeon Halorubrum ezzemoulense

In this study, we use pan-genomics to characterize the organized variability from the widely dispersed halophilic archaeal species Halorubrum ezzemoulense. We include a multi-regional sampling of newly sequenced, high-quality draft genomes. Using the pan-genome graph of the species, we discover 50 genomic islands which represent rare accessory genetic capabilities available to members of the species. 19 of these islands are likely the remnant of mobile genetic elements and are enriched for genomic dark matter. 10 islands encode for niche adapting solute transporters, with a cosmopolitan but limited distribution throughout the strains. We also observe rearrangements which have led to the insertion/recombination/replacement of mutually exclusive genomic islands in equivalent genome positions (“homeocassettes”). These conflicting islands encode for similar functions, but homologs from islands located between the same core genes exhibit extreme divergence on the amino acid level. Homeocasettes provide variations for a homologous function, which may confer a greater range of adaptability to the species group. We observe some islands that appear geographically restricted; however, we also observe the coexistence of genomes, in a single geographic region, with and without certain genomic islands, demonstrating the retention and spread of rare genes in the pan-genome. Importance Understanding the evolution of genome content is a key puzzle in evolutionary biology. Despite its importance, this area hasn’t received thorough investigation. This is especially true of Archaeal organisms, which constitute a large fraction of Earth’s diversity, but are often referred to as the “forgotten” or “third” domain of life. This study dives into those questions by finding rare genes amongst a group of closely related Archaeal species, and describes how their transfer, utilization and persistence may contribute to the speciation and specialization of the group.
Panera: A novel framework for surmounting uncertainty in microbial community modelling using Pan-genera metabolic models

Over the last decade, microbiome research has witnessed exponential growth, largely driven by the widespread availability of metagenomic data. Despite this influx of data, 16S ‘targeted amplicon’ sequencing, which offers relatively lower resolution, still dominates the landscape over whole-genome shotgun sequencing. Existing algorithms for constructing metabolic models of microbial communities primarily rely on whole-genome sequences and do not fully harness the potential of 16S datasets. In this study, we report ‘Panera’, a novel framework designed to model microbial communities under uncertainty and yet perform inferences by building pan-genus metabolic models. We tested the performance of the models from our approach by analysing their ability to capture the functionality of the entire genus and individual species within a genus. We further exercise the model to explore the comprehensive metabolic abilities of a genus, shedding light on metabolic commonalities between microbial groups. Furthermore, we showcase its application in characterising microbial community models using 16S data. Our hybrid community models, which combine both GSMM and pan-genus metabolic models, exhibit a 10% reduction in prediction error, with error rates diminishing as community size increases. Overall, the Panera framework represents a potent and effective approach for metabolic modelling, enabling robust predictions of the metabolic phenotypes of microbial communities, even when working with limited 16S data. This advancement has the potential to greatly impact the field of microbiome research, offering new insights into the metabolic dynamics of diverse microbial ecosystems.
Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
Biodiversity research in the Neotropics: From conflict to collaboration

Abstract Tropical biodiversity researchers are working in ecological, social, and economic contexts that are completely different from those of 150 years ago. In the 1800s, scientists from developed nations studied tropical biodiversity as part of expeditionary work. The social and economic benefits from these studies went to citizens of the developed nations that funded the research. Benefits accrued to foreigners rather than the native inhabitants of tropical nations explored by natural historians. Today, the governments and citizens of tropical nations view biodiversity components as natural resources that can contribute to national cultural, social, and economic development. Modern biodiversity researchers therefore find themselves working under socio-economic conditions that are radically different from those experienced by Darwin, Wallace, Humboldt, Bates and their contemporaries. In the last quarter of a century, biodiversity researchers from developed nations have struggled to meet the changing focus for their research and have substantially modified research processes to meet some of the needs and directions of developing nations. In this essay, we discuss how developed nation biodiversity researchers are changing their approach to working in tropical countries. We illustrate this change by examining how researchers from developed country work in developing countries. We examine potential reasons for conflict between developed nation biodiversity researchers and citizens of developing nations, and suggest potential solutions to these conflicts. In particular, we suggest that developed nation biodiversity researchers continue to place a strong emphasis on outputs that benefit the countries in which they work. We also suggest that biodiversity researchers should seek input from representatives of developing nations in planning projects so that research efforts can meet the needs of the host-country. We use experiences in Guyana, South America, to illustrate collaborative approaches to biodiversity research.
Sexually dimorphic effects of pexidartinib on nerve injury-induced neuropathic pain in mice

Background It is well-established that spinal microglia and peripheral macrophages play critical roles in the etiology of neuropathic pain; however, growing evidence suggests sex differences in pain hypersensitivity owing to microglia and macrophages. Therefore, it is crucial to understand sex- and androgen-dependent characteristics of pain-related myeloid cells in mice with nerve injury-induced neuropathic pain. Methods The current study was performed using normal male and female mice, as well as gonadectomized (GDX) male mice. To deplete microglia and macrophages, pexidartinib (PLX3397), an inhibitor of the colony-stimulating factor 1 receptor, was orally administered, and mice were subjected to partial sciatic nerve ligation (PSL). Immunohistochemistry was performed to visualize microglia and macrophages, and PSL-induced mechanical allodynia was evaluated using the von Frey test. Results Following PSL induction, healthy male and female mice and male GDX mice exhibited similar levels of spinal microglial activation, peripheral macrophage accumulation, and mechanical allodynia. Treatment with PLX3397 significantly suppressed mechanical allodynia in normal males; this was not observed in female and GDX male mice. Sex- and androgen-dependent differences in the PLX3397-mediated preventive effects were observed on spinal microglia and dorsal root ganglia (DRG) macrophages, as well as in expression patterns of pain-related inflammatory mediators in these cells. Conversely, no sex- or androgen-dependent differences were detected in sciatic nerve macrophages, and inhibition of peripheral CC-chemokine receptor 5 prevented neuropathic pain in both sexes. Conclusion Collectively, these findings demonstrate the presence of considerable sex- and androgen-dependent differences in the etiology of neuropathic pain in spinal microglia and DRG macrophages but not in sciatic nerve macrophages. Given that the mechanisms of neuropathic pain may differ among experimental models and clinical conditions, accumulating several lines of evidence is crucial to comprehensively clarifying the sex-dependent regulatory mechanisms of pain.
The human microglia responsome: a resource to better understand microglia states in health and disease

Microglia, the immune cells of the brain, are increasingly implicated in neurodegenerative disorders through genetic studies. However, how genetic risk factors for these diseases are related to microglial gene expression, microglial function, and ultimately disease, is still largely unknown. Microglia change rapidly in response to alterations in their cellular environment, which is regulated through changes in transcriptional programs, which are as yet poorly understood. Here, we compared the effects of a set of inflammatory and restorative stimuli (lipopolysaccharide, interferon-gamma, resiquimod, tumor necrosis factor-alpha, adenosine triphosphate, dexamethasone, and interleukin-4) on human microglial cells from 67 different donors (N = 398 samples) at the gene and transcript level. We show that microglia from different anatomical brain regions show distinct responses to inflammatory stimuli. We observed a greater overlap between human stimulated microglia and human monocytes than with mouse microglia. We define specific microglial signatures across conditions which are highly relevant for a wide range of biological functions and complex human diseases. Finally, we used our stimulation signatures to interpret associations from Alzheimer’s disease (AD) genetic studies and microglia by integrating our inflammatory gene expression profiles with common genetic variants to map cis-expression QTLs (eQTLs). Together, we provide the most comprehensive transcriptomic database of the human microglia responsome. Highlights RNA-sequencing of 398 human microglial samples exposed to six different triggers. Microglia from different anatomical regions show distinct stimulation responses. Responses in human microglia show a greater overlap with human monocytes than murine microglia. Mapping of response Quantitative Trait Loci identifies interactions between genotype and effect of stimulation on gene expression. Our atlas provides a reference map for interpreting microglia signatures in health and disease.
Brainstem premotor mechanisms underlying vocal production and vocal-respiratory coordination

Speech generation critically depends on precise controls of laryngeal muscles and coordination with ongoing respiratory activity. However, the neural mechanisms governing these processes remain unknown. Here, we mapped laryngeal premotor circuitry in adult mice and viral-genetically identified excitatory vocal premotor neurons located in the retroambiguus nucleus (RAmVOC) as both necessary and sufficient for driving vocal-cord closure and eliciting mouse ultrasonic vocalizations (USVs). The duration of RAmVOC activation determines the lengths of USV syllables and post-inspiration phases. RAmVOC-neurons receive inhibitory inputs from the preBötzinger complex, and inspiration needs can override RAmVOC-mediated vocal-cord closure. Ablating inhibitory synapses in RAmVOC-neurons compromised this inspiration gating of laryngeal adduction, resulting in de-coupling of vocalization and respiration. Our study revealed the hitherto unknown circuits for vocal pattern generation and vocal-respiratory coupling. One-Sentence Summary Identification of RAmVOC neurons as the critical node for vocal pattern generation and vocal-respiratory coupling.