Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Potential Impact of River Chemistry on Arctic Coastal Production

The Arctic coastal margin receives a disproportionately large fraction of the global river discharge. The bio-geochemistry of the river water as it empties into the marine environment reflects inputs and processes that occur as the water travels from its headwaters. Climate-induced changes to Arctic vegetation and permafrost melt may impact river chemistry. Understanding the impact of river nutrients on coastal marine production, and how this may change in the future, are important for resource managers and community members who monitor and rely on coastal food resources. Using the Energy Exascale Earth System Model we explore the impact of timing and river nutrient concentrations on primary production in each coastal Arctic region and then assess how this influences secondary production and particle fluxes supporting the benthic food web. Our results indicate that while the concentration of Arctic river nitrogen can have a significant impact on annual average nitrogen and primary production in the coastal Arctic, with production increases of up to 20% in the river influenced interior Seas, the timing of the river nutrient inputs into the marine environment appears less important. Bloom timing and partitioning between small and large phytoplankton were minimally impacted by both river nutrient concentration and timing, suggesting that in general, coastal Arctic ecosystem dynamics will continue to be primarily driven by light availability, rather than nutrients. Under a doubling river nutrient scenario, the percentage increase in the POC flux to the benthos on river influenced Arctic coastal shelves was 2-4 times the percentage increase in primary production, suggesting changes to the river nutrient concentration has the potential to modify the Arctic food web structure and dynamics. Generally, the nutrient-induced changes to primary production were smaller than changes previously simulated in response to ice reduction and temperature increase. However, in the Laptev Sea, the production increase resulting from a doubling of river nutrients exceeded the production increase simulated with an atmospheric warming scenario. Dissolved organic carbon is presently poorly represented in the model so its impact on production is hard to simulate. Applying established relationships between modeled DOC, total DOC, and light absorption we illustrate that DOC could play a very important role in modulating production. Our findings highlight the importance of developing more realistic river nutrient and discharge forcing for Earth System Models such that their impact on the critical Arctic coastal domain can be more adequately resolved.
How the Skate Embryo Develops: A Fine Developmental Timeline of Kong Skate Okamejei kenojei (Müller & Henle, 1841)

Cartilaginous fishes, as an important group of jawed vertebrates, hold great promise for improving our understanding of vertebrate evolution. Previous studies have established a good staging scheme for shark embryos. However, to date, little attention has been paid to the development of skate embryos. In this study, we established a fine developmental timeline for the Kong skate. We hatched eggs of the Kong skate (Okamejei kenojei) at fixed temperatures (16.5°C ± 0.5°C) while observing three samples every day and recorded the complete embryonic development. The embryo weight and development time were found to have a quadratic relationship. The weight-at-day curve was fitted by y = 0.0005x2 - 0.0109x + 0.0882 (R² = 0.9406). In addition, complete embryonic development from gastrula to skate larvae took 92 ± 5 days. Here, we divided the entire development process into 39 stages. This shows that the jaw of O. kenojei is developed from the first pair of pharyngeal arches, and its body disc is formed by the fusion of the pectoral fin and head. The eyespots began to form from stage 36 and then became thicker and bigger until the young skate broke out of the egg. Notably, we found that males and females develop differently from stage 31; the end of the male pelvic fins expands inward, which develop into copulatory organ-claspers, whereas in the females, they do not expand. To the best of our knowledge, this is the first study to record the embryonic development of eyes, body discs, eyespots, and other organs of O. kenojei in detail. We hope that our work will provide valuable information for researchers to identify specific time points for developing skate embryos.
The Role of Artificial Intelligence Algorithms in Marine Scientific Research

The study of marine science is vital to the survival and development of humanity. On the one hand, the oceans act as a global climate regulator, supplying 70% of oxygen and 87.5% of water vapor to the atmosphere while storing large amounts of heat (Petterson et al., 2021). On the other hand, the oceans act as an important part of the global physical system, in which changes in mass-energy, biological, and geological processes can have a significant impact on marine and terrestrial life (Du et al., 2021). However, lacking knowledge of important areas such as the deep sea and polar regions, humans cannot yet decipher certain specific phenomena and patterns in the oceans. Artificial Intelligence (AI) algorithms are trained on mathematical models with a specific structure using a large amount of statistical data to obtain a fitter that contains the statistical features inherent in the training data. It can be applied to solve optimization problems. As a result, AI algorithms have been very successful in a number of scientific fields, such as autonomous driving (Khan et al., 2021), medical imaging (Hickman et al., 2022), geophysics (Yu and Ma, 2021), and nanoscience (Jiang et al., 2022). As marine scientific research enters a new era of intelligence and constantly improving marine data, AI can effectively tap into the potential information contained in vast amounts of data. As a result, it is also gaining more and more attention from marine researchers (Logares et al., 2021). The integration of AI technology with traditional models to improve marine safety has also been proven (Khayyam et al., 2020). In addition, data processing problems in marine pollution (Agarwala, 2021), wind and wave energy (Gu and Li, 2022) research can be solved using AI algorithms. Therefore, this paper reports on the prospects of applying AI algorithmic methods in marine scientific research, mainly monitoring marine biodiversity, deep-sea resource modelling, and predicting SST, tide level, sea ice, and climate. In addition, the paper discusses the current problems of AI algorithms in processing marine data and building predictive models.
Altered Neuroanatomical Signatures of Patients With Treatment-Resistant Schizophrenia Compared to Patients With Early-Stage Schizophrenia and Healthy Controls

Background The relationship between brain structural changes and cognitive dysfunction in schizophrenia is strong. However, few studies have investigated both neuroanatomical abnormalities and cognitive dysfunction in treatment-resistant schizophrenia (TRS). We examined neuroanatomical markers and cognitive function between patients with TRS or early-stage schizophrenia (ES-S) and healthy controls (HCs). Relationships between neuroanatomical markers and cognitive function in the patient groups were also investigated. Methods A total of 46 and 45 patients with TRS and ES-S and 61 HCs underwent structural magnetic resonance imaging (MRI) brain scanning and comprehensive cognitive tests. MRI scans were analyzed using the FreeSurfer to investigate differences in cortical surface area (CSA), cortical thickness (CT), cortical volume (CV), and subcortical volume (SCV) among the groups. Four cognitive domains (attention, verbal memory, executive function, and language) were assessed. Comparisons of neuroanatomical and cognitive function results among the three groups were performed. Results A widespread reduction in CT was observed in patients with TRS compared to HCs, but differences in cortical thinning between TRS and ES-S patients were mainly limited to the inferior frontal gyrus and insula. Several subcortical structures (accumbens, amygdala, hippocampus, putamen, thalamus and ventricles) were significantly altered in TRS patients compared to both ES-S patients and HCs. Performance in the verbal memory domain was significantly worse in TRS patients compared to ES-S patients. A positive relationship between the thickness of the left middle temporal gyrus and the composite score for language was identified in patients with ES-S. Conclusions Our findings suggest significant cognitive impairment and reductions in CT and SCV in individuals with TRS compared to those with ES-S and HCs. These abnormalities could act as biomarkers for earlier identification of TRS.
OnlineBioinfo: Leveraging the Teaching of Programming Skills to Life Science Students Through Learning Analytics

Online learning has grown in recent years and has become popular with Massive Open Online Courses (MOOCs). The advent of the pandemic has undoubtedly made more teachers and students experience the online learning experience. Distance learning is going to grow even more in the coming years. In this article, we present our computational thinking and programming course focused on life science students. We introduce our approach for analyzing how students interact with didactic resources regarding their probability of completing the course. We discussed several insights this strategy brought us and how we can leverage the teaching of programming skills to life science students through learning analytics. We suggest that machine learning techniques will be increasingly essential for better monitoring and supporting students and for online courses improvements.
Intra-Annual Sea Level Fluctuations and Variability of Mesoscale Processes in the Northern Japan/East Sea From Satellite Altimetry Data

Intra-annual sea level fluctuations and variability of mesoscale processes based on eddy kinetic energy (EKE) were studied in the northern (northward of 41 N) Japan/East Sea (JES) using data from satellite altimetry for 1993–2020. Decomposition to empirical orthogonal functions (EOF) was performed of the high-pass filtered, with the cut-off period of 250 days, sea level anomalies. The leading mode accounting for the major fraction of the variance yielded sea level fluctuations which were simultaneous in the entire sea and occurred in the range from 70 to 250 days without any preferable timescale. EKE in the northern sea was also expanded to EOF and yielded the leading mode capturing mesoscale variability within the Primorye (Liman) Current and the Tsushima Warm Current. The seasonal signal was found in the simultaneous intra-annual sea level fluctuations, which matches that of EKE, and, as found in the earlier studies, of the mean currents. The sea level rises, the mean currents intensify and EKE increases in summer and fall and the opposite changes occur in winter and spring, with the seasonal extremes in October/November and March/April, respectively. This is in line with the EKE generation by instability of the mean currents. The intra-annual sea level fluctuations and EKE manifest rich variability on quasi-biennial, interannual and decadal timescales. However, in contrast with the seasonal signal, the low-frequency variability does not match, implying different kinds of forcing, probably by local wind in the northern JES and by the transport variations in the Korea – Tsushima Strait (KTS) in the southern JES. Intra-annual simultaneous SLA reveal changing relationship with Pacific Decadal Oscillation (PDO): both were in-phase in 1993–1994 and from late 2007 to 2013 and out-of-phase from 1997 to 2002, while there was no specific relationship in other times. However, the relationship of these SLA with the interannual KTS transport variation seems inconclusive.
Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis

Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e −14) and myometrial relaxation and contraction (p = 3e −08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.
High-Efficient and Broadband Acoustic Insulation in a Ventilated Channel With Acoustic Metamaterials

Acoustic insulation in ventilated structures is an important problem in acoustic engineering with many potential practical applications, such as the noise control for ventilating ducts of buildings, vehicles, or air conditioners. Acoustic metamaterial is a good candidate for the design of acoustic insulation for ventilated channel (AIVC) because the structural design with hard boundary has longer lifetime than conventional sound-absorbing cotton. In this paper, an AIVC with an open region and narrow channels of different lengths is proposed. We numerically and experimentally demonstrate its acoustic insulation larger than 20 dB (T < 0.01) within approximately 500–1,200 Hz with a subwavelength channel length of λ/6. The parameter dependence and air flow effect are numerically studied. Our findings show an alternative design of AIVC that may have applications in noise control and architectural acoustics.
Lessons From Transcriptome Analysis of Autoimmune Diseases

Various immune cell types, including monocytes, macrophages, and adaptive immune T and B cells, play major roles in inflammation in systemic autoimmune diseases. However, the precise contribution of these cells to autoimmunity remains elusive. Transcriptome analysis has added a new dimension to biology and medicine. It enables us to observe the dynamics of gene expression in different cell types in patients with diverse diseases as well as in healthy individuals, which cannot be achieved with genomic information alone. In this review, we summarize how transcriptome analysis has improved our understanding of the pathological roles of immune cells in autoimmune diseases with a focus on the ImmuNexUT database we reported. We will also discuss the common experimental and analytical design of transcriptome analyses. Recently, single-cell RNA-seq analysis has provided atlases of infiltrating immune cells, such as pro-inflammatory monocytes and macrophages, peripheral helper T cells, and age or autoimmune-associated B cells in various autoimmune disease lesions. With the integration of genomic data, expression quantitative trait locus (eQTL) analysis can help identify candidate causal genes and immune cells. Finally, we also mention how the information obtained from these analyses can be used practically to predict patient prognosis.
Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

The purpose of this research is to provide power grid energy efficiency solutions. In this paper, a comprehensive review and its optimal solution is proposed considering the various challenges of smart grid demand-side management. The main technique is based on a novel idea in the Smart Grid—demand response optimization which enables autonomous energy management on the demand side for a wide variety of customers. The first section of this research examines the smart grid issue and evaluates the state-of-the-art load management techniques in terms of the work’s scope. The demand-side load management architecture consists of three primary levels, two of them in line planning and low-cost scheduling, while the third layer, demand response which is a significant expansion of this domain. The implementation of the proposed architecture in MATLAB/Simulink, with test results. demonstrating the significance of the proposed solution
Facilitating the Decentralisation of Software Development Projects from a Project Management Perspective: A Literature Review

: With the increasing relevance of decentralisation for the software development process, being aware of the possible challenges and corresponding solutions has become more relevant than ever. The scientific body of knowledge is currently containing many publications about specific aspects of decentralisation, but is lacking in collections that cover more than one area. In this work, the challenges of different forms of decentralisation are examined by means of a literature review. Subsequently, the findings are evaluated and summarised into guidelines that can be applied by project managers and development teams to increase the success of decentralisation of the software development process.
Characterizing Technical Debt in Evolving Open-source Software

: Technical debt represents deﬁciencies in software design or implementation often caused by prioritizing feature development over ﬁxing existing issues. Like its ﬁnancial counterpart, technical debt comprises a principal and an interest. Not addressing it in time leads to development crises, where focus and resources must be shifted to address existing issues. Existing software tools allow measuring the level of debt and pinpointing its sources, which can help practitioners control it. In the present paper we aim to investigate the prevalence, characteristics, and evolution of technical debt in several open-source applications. We used SonarQube to study 112 application versions that covered more than 15 years of development for each application. We studied the way debt characteristics and source code distribution evolved over the target applications’ lifecycles. We addressed concerns regarding the accuracy of the analysis and illustrated some of the limitations of existing tools. We observed that a small number of issue types were responsible for most of the debt. We found that each application had its own technical debt particularities. As future work, we aim to expand our selection of analysis tools, leverage open data sets, and extend our investigation to other systems and types of software.
Augmenting Library Development by Mining Usage Data from Downstream Dependencies

: Software and its dependencies build up a graph where edges connect packages according to their dependencies. In this graph, downstream dependencies are all the nodes that depend on a package of interest. Although gathering and mining such downstream dependencies allow for informed decision-making for a package developer, there is much room for improvement, such as automation and integration of this approach into their development process. This paper makes two contributions: (i) We propose an approach for efficiently gathering downstream dependencies of a single package and extracting usage samples from them using a static type analyzer. (ii) We present a tool that allows npm package developers to survey the aggregated usage data directly in their IDE in an interactive and context-sensitive way. We evaluate the approach and the tool on a selection of open source projects and specific development-related questions with respect to found downstream dependencies, gathering speed, and required storage. Our methods return over 8000 downstream dependencies for popular packages and process about 12 dependencies per minute. The usage sample extraction offers high precision for basic use cases. The main limitations are the exclusion of unpopular and closed-source downstream dependencies as well as failing analysis when encountering complex build configurations or metaprogramming patterns. To summarize, we show that the tool supports package developers in gathering usage data and surveying the use of their packages and APIs within their downstream dependencies, allowing for informed decision-making and improving the resdesign of APIs.
Mining Developer Expertise from Bug Tracking Systems using the Author-topic Model

: During the software development process, software defects, so-called bugs, are captured in a semi-structured manner in a bug tracking system using textual components and categorical features. It is the task of the triage owner to assign open bugs to developers with the required skills and expertise. This task, known as bug triaging , requires in-depth knowledge about a developer’s skills. Various machine learning techniques have been proposed to automate this task, most of these approaches apply topic models, especially Latent Dirichlet Allocation, for mining the textual components of bug reports. However, none of the proposed approaches explicitly models a developer’s expertise. In most cases, these algorithms are treated as a black box, as they allow no explanation about their recommendation. In this work, we show how the Author-Topic Model, a variant of Latent Dirichlet Allocation, can be used to capture a developer’s expertise in the latent topics of a corpus of bug reports from the model itself. Furthermore, we present three novel bug triaging techniques based on the Author-Topic Model. We compare our approach against a baseline model, that is based on Latent Dirichlet Allocation, on a dataset of 18269 bug reports from the Mozilla Firefox project collected between July 1999 to June 2016. The results show that the Author-Topic Model can outperform the baseline approach in terms of the Mean Reciprocal Rank.