Output Formats Research Articles

Microbial Bioindicators for Monitoring the Impact of Emerging Contaminants on Soil Health in the European Framework

Antibiotic resistance (AR) is recognized by the World Health Organization as a major threat to human health, and recent studies highlight the role of microplastics (MPs) in its spread. MPs in the environment may act as vectors for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Bacterial communities on the plastisphere, the surface of MPs, are influenced by plastic properties, allowing ARB to colonize and form biofilms. These biofilms facilitate the transfer of ARGs within microbial communities. This study analyzed data from the LUCAS soil dataset (885 soil samples across EU countries) using the Emu tool to characterize microbial communities at the genus/species level. Functional annotation via PICRUSt2, supported by a custom tool for Emu output formatting, revealed significant correlations between the genera Solirubrobacter, Bradyrhizobium, Nocardioides, and Bacillus with pathways linked to microplastic degradation and antibiotic resistance. These genera were consistently present in various soil types (woodland, grassland, and cropland), suggesting their potential as bioindicators of soil health in relation to MP pollution. The findings underscore MPs as hotspots for ARB and ARGs, offering new insights into the identification of bioindicators for monitoring soil health and the ecological impacts related to MP contamination.

Krait2: a versatile software for microsatellite investigation, visualization and marker development

BackgroundMicrosatellites are highly polymorphic repeat sequences ubiquitously interspersed throughout almost all genomes which are widely used as powerful molecular markers in diverse fields. Microsatellite expansions play pivotal roles in gene expression regulation and are implicated in various neurological diseases and cancers. Although much effort has been devoted to developing efficient tools for microsatellite identification, there is still a lack of a powerful tool for large-scale microsatellite analysis.ResultsWe present Krait2, a user-friendly graphical tool for investigating perfect, imperfect and compound microsatellites from FASTA and FASTQ formatted genomic datasets. Krait2 not only provides features such as primer design, repeat filtering, repeat annotation and statistical analysis, but also offers various output formats to support customized downstream analysis. Moreover, it has capability of analyzing multiple genomes simultaneously and conducting comparative analysis.ConclusionsKrait2 is a versatile and easy-to-use software for both novices and experts to identify and analyze microsatellites. The installer and source code are available at https://github.com/lmdu/krait2.

HapNetworkView: a tool for haplotype network exploration and visualization

BackgroundHaplotype networks are useful for investigating the genealogical relationships among haplotypes and have been extensively used in molecular evolution and population genetic studies. However, existing tools for visualizing haplotype networks lack comprehensive functionalities for data exploration and layout adjustments. Therefore, there is a need for a useful tool capable of constructing and visualizing haplotype networks, facilitating the exploration of data, particularly with datasets with large sample sizes.ResultsWe present HapNetworkView, a user-friendly tool that facilitates the construction and interactive visualization of haplotype networks, enabling users to explore haplotypes and mutation information effectively. HapNetworkView offers both automatic optimization and manual adjustment for haplotype network layouts. Additionally, HapNetworkView supports various input and output formats, as well as color customization.ConclusionsThese comprehensive functionalities collectively satisfy various needs of users in haplotype network analysis, making HapNetworkView a valuable tool.

GoChem: A Composable Library for Multi-Scale Computational Chemistry Data Analysis.

Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data. The library, written in and for the Go programming language, allows for easy integration of different levels of theory, in an easy-to-use API, allowing the development of both one-use and complex analysis programs in Go. We describe the library and detail some selected applications that illustrate the capabilities and potential of this tool. The library is available at http://gochem.org.

Qualitative analysis of peer reviews of a large introductory programming course

ABSTRACT Background and Context Getting timely feedback is important for learning. However, providing individual feedback is a problem in large courses. Peer code review can address this issue and has shown to offer various advantages such as enhanced collaboration among students, improved coding skills, and seeing and criticizing different solution strategies. Objectives The goal is to gain a comprehensive understanding of the contents of voluntary peer reviews and students’ review skills in a first-semester university-based introductory programming course with about 900 enrolled students and no special support or training for conducting reviews. Method A qualitative analysis was conducted on 215 randomly sampled peer reviews as well as the associated code submissions. Qualitative factors such as frequencies of corrections, coding tips, questions, “empty” submissions/feedback, and the sentiment are analyzed. Furthermore, errors in the submissions and mentioned errors in the reviews are investigated exploratively. Findings The analysis results indicate that, in general, students are better in detecting incorrect solutions as incorrect than correct submissions as correct. The feedback is neutral to positive contains a lot of praise, but is rather short and uncertainty is expressed quite often. Students seem to be very nice to each other. Submissions without intensive solution attempts and “empty” reviews are quite rare. Students can correct errors and provide coding tips, however, often do not see subtle errors such as partly incorrect algorithms, typos in method names, or output formatting errors. Implications The results help to train students to write better reviews and to inform educators on how to provide better instruction or support for peer review.

Systematic Inference of Multi-scale Chromatin Sub-compartments Using Calder2.

The compartmentalization of chromatin reflects its underlying biological activities. Inferring chromatin sub-compartments using Hi-C data is challenged by data resolution constraints. Consequently, comprehensive characterizations of sub-compartments have been limited to a select number of Hi-C experiments, with systematic comparisons across a wide range of tissues and conditions still lacking. Our original Calder algorithm marked a significant advancement in this field, enabling the identification of multi-scale sub-compartments at various data resolutions and facilitating the inference and comparison of chromatin architecture in over 100 datasets. Building on this foundation, we introduce Calder2, an updated version of Calder that brings notable improvements. These include expanded support for a wider array of genomes and organisms, an optimized bin size selection approach for more accurate chromatin compartment detection, and extended support for input and output formats. Calder2 thus stands as a refined analysis tool, significantly advancing genome-wide studies of 3D chromatin architecture and its functional implications.

BenchING: A Benchmark for Evaluating Large Language Models in Following Structured Output Format Instruction in Text-Based Narrative Game Tasks

BenchING: A Benchmark for Evaluating Large Language Models in Following Structured Output Format Instruction in Text-Based Narrative Game Tasks

Event co-occurrences for prompt-based generative event argument extraction

Recent works have introduced prompt learning for Event Argument Extraction (EAE) since prompt-based approaches transform downstream tasks into a more consistent format with the training task of Pre-trained Language Model (PLM). This helps bridge the gap between downstream tasks and model training. However, these previous works overlooked the complex number of events and their relationships within sentences. In order to address this issue, we propose Event Co-occurrences Prefix Event Argument Extraction (ECPEAE). ECPEAE utilizes the co-occurrences events prefixes module to incorporate template information corresponding to all events present in the current input as prefixes. These co-occurring event knowledge assist the model in handling complex event relationships. Additionally, to emphasize the template corresponding to the current event being extracted and enhance its constraint on the output format, we employ the present event bias module to integrate the template information into the calculation of attention at each layer of the model. Furthermore, we introduce an adjustable copy mechanism to overcome potential noise introduced by the additional information in the attention calculation at each layer. We validate our model using two widely used EAE datasets, ACE2005-EN and ERE-EN. Experimental results demonstrate that our ECPEAE model achieves state-of-the-art performance on both the ACE2005-EN dataset and the ERE dataset. Additionally, according to the results, our model also can be adapted to the low resource environment of different training sizes effectively.

An LLM‐Based Method for Quality Information Extraction From Web Text for Crowed‐Sensing Spatiotemporal Data

ABSTRACTKnowledge‐driven GIS increasingly requires multi‐source, multi‐type, and multi‐model crowd‐sensing spatiotemporal data, whose data quality is difficult to guarantee and determine. Hence, extracting quality indicator information, widely present in various unstructured web texts, is crucial to providing supplementary quality information for crowd‐sensing spatiotemporal data. Recent advances in large language models show potential in extracting quality indicator information. However, it is still hard to get accurate results from large language models that use different quality indicators for crowd‐sensing spatiotemporal data. Therefore, we have designed a large language model that is fine‐tuned for the extraction of spatiotemporal quality information from quality description text (LLMFT‐STQIE). Firstly, we establish a quality indicator vocabulary to determine whether the text includes quality indicator information from the spatiotemporal data. Then, we create a two‐stage prompt model with QILE and QIVE prompts that include input text, task type, instructions, the quality indicator vocabulary, output format, and a reference case. This model is based on the fine‐tuning technology of large language models. The results show that our LLMFT‐STQIE achieves an accuracy of 91% and a recall rate of 80%, respectively, representing improvements of 23% and 38% compared to untuned large language models. These results further show that the suggested method easily and accurately extracts quality indicator information from web texts for crowd‐sensing spatiotemporal data. The study helps investigate strategies for optimizing huge language models for specific scenarios or task specifications.

LDWLE: self-supervised driven low-light object detection framework

Low-light object detection involves identifying and locating objects in images captured under poor lighting conditions. It plays a significant role in surveillance and security, night pedestrian recognition, and autonomous driving, showcasing broad application prospects. Most existing object detection algorithms and datasets are designed for normal lighting conditions, leading to a significant drop in detection performance when applied to low-light environments. To address this issue, we propose a Low-Light Detection with Low-Light Enhancement (LDWLE) framework. LDWLE is an encoder-decoder architecture where the encoder transforms the raw input data into a compact, abstract representation (encoding), and the decoder gradually generates the target output format from the representation produced by the encoder. Specifically, during training, low-light images are input into the encoder, which produces feature representations that are decoded by two separate decoders: an object detection decoder and a low-light image enhancement decoder. Both decoders share the same encoder and are trained jointly. Throughout the training process, the two decoders optimize each other, guiding the low-light image enhancement towards improvements that benefit object detection. If the input image is normally lit, it first passes through a low-light image conversion module to be transformed into a low-light image before being fed into the encoder. If the input image is already a low-light image, it is directly input into the encoder. During the testing phase, the model can be evaluated in the same way as a standard object detection algorithm. Compared to existing object detection algorithms, LDWLE can train a low-light robust object detection model using standard, normally lit object detection datasets. Additionally, LDWLE is a versatile training framework that can be implemented on most one-stage object detection algorithms. These algorithms typically consist of three components: the backbone, neck, and head. In this framework, the backbone functions as the encoder, while the neck and head form the object detection decoder. Extensive experiments on the COCO, VOC, and ExDark datasets have demonstrated the effectiveness of LDWLE in low-light object detection. In quantitative measurements, it achieves an AP of 25.5 and 38.4 on the synthetic datasets COCO-d and VOC-d, respectively, and achieves the best AP of 30.5 on the real-world dataset ExDark. In qualitative measurements, LDWLE can accurately detect most objects on both public real-world low-light datasets and self-collected ones, demonstrating strong adaptability to varying lighting conditions and multi-scale objects.

Audio Extraction from a Video

This Python script leverages the MoviePy library to facilitate the extraction of audio from video files. Featuring a user-friendly GUI built with Tkinter, the script allows users to select a video file and specify their preferred audio output format. Once a video is selected, it is saved to an 'input' directory, and the user can choose an audio format, such as MP3 or WAV. The script extracts the audio using MoviePy and saves it in the 'output' directory with the same filename as the original video but in the chosen audio format. Upon successful extraction, a confirmation message is displayed, while any issues encountered during the process are communicated to the user through Tkinter’s messagebox. This ensures a seamless experience by integrating error handling to address potential problems. Designed for efficiency and ease of use, this script provides a reliable solution for separating audio from video, making it ideal for multimedia processing tasks. Key Words: Python- moviepy, tkinter

Comparative Analysis of Prompt Strategies for Large Language Models: Single-Task vs. Multitask Prompts

This study investigates the effectiveness of prompt engineering strategies for Large Language Models (LLMs), comparing single-task and multitasking prompts. Specifically, we analyze whether a single prompt handling multiple tasks—such as named entity recognition (NER), sentiment analysis, and JSON output formatting—can achieve performance comparable to dedicated single-task prompts. To substantiate our findings, we employ statistical analyses, including paired Wilcoxon tests, McNemar tests, and Friedman tests, to validate claims of performance similarity or superiority. Experiments were conducted using five open-weight LLMs: LLama3.1 8B, Qwen2 7B, Mistral 7B, Phi3 Medium, and Gemma2 9B. The results indicate that there is no definitive rule favoring single-task prompts over multitask prompts; rather, their relative performance is highly contingent on the specific model’s data and architecture. This study highlights the nuanced interplay between prompt strategies and LLM characteristics, offering insights into optimizing their use for specific NLP tasks. Limitations and future directions, such as expanding task types, are also discussed.

Coupling Time-Resolved Mass Spectrometry Techniques to Unravel Cobalt Degradation Mechanisms in Acidic Media for Oxygen and Hydrogen Electrocatalysis

Cobalt plays a significant role within sustainable energy technologies, including but not limited to hydrogen fuel cells for transportation, lithium-ion batteries for electric vehicles and portable devices, and electrolyzers for renewable hydrogen production. For devices that contain a proton exchange membrane (PEM), such as proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolyzers (PEMWEs), cobalt-based materials are commonly used to perform the oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER), respectively. However, the PEM in these devices induces an acidic environment (pH = 1) that cobalt is thermodynamically unstable in under ORR and HER relevant potentials. This instability results in cobalt dissolution and as a consequence, substantial decreases in device performance and lifetime. To mitigate such dissolution of cobalt-based materials in acidic environments, a holistic understanding of cobalt’s degradation mechanism is required. To gain such an understanding, we have developed an experimental platform with comparable in-situ and operando techniques, including on-line inductively coupled plasma mass spectrometry (ICP-MS) and electrochemical mass spectrometry (EC-MS), to concurrently monitor relationships between electrochemical output (current/voltage), time, cobalt dissolution, and product formation (i.e, H2 and H2O).Under HER relevant potentials in acidic media (0.1 M HClO4), it was observed that cobalt is active for HER and undergoes little to no dissolution (< 10 ppb Co2+) while evolving H2, per comparable EC-MS and ICP-MS experiments. These observations suggest that cobalt stability in acidic media strongly coincides with hydrogen evolution. Additionally, these data showcase a surprising window of stability for cobalt that is different than would have been predicted with classical chemical thermodynamics (i.e., Pourbaix diagrams). Along with cobalt being HER active, we have also uncovered its activity for ORR in acidic media (O2 saturated 0.1 M HClO4). Furthermore, the rate of cobalt dissolution was different in O2 versus N2 saturated electrolyte, where it was observed that cobalt dissolution initially occurs faster (~3x more dissolution) in the presence of O2. Altogether, this work showcases how time-resolved techniques such as ICP-MS and EC-MS can be combined to unravel material degradation mechanisms, with cobalt as a case study. This experimental platform has high potential to accelerate current understandings of material degradation, paving the way for new avenues to design and develop more robust and affordable materials and devices.

Performance of generative pre-trained transformer-4 on the certification test for mental health management: A factorial design

This study aimed to investigate the performance of generative pre-trained transformer-4 (GPT-4) on the Certification Test for Mental Health Management and whether tuned prompts could improve its performance. This study used a 3 × 2 factorial design to examine the performance according to test difficulty (courses) and prompt conditions. We prepared 200 multiple-choice questions (600 questions overall) for each course using the Certification Test for Mental Health Management (levels I-III) and essay questions from the level I test for the previous four examinations. Two conditions were used: a simple prompt condition using the questions as prompts and tuned prompt condition using techniques to obtain better answers. GPT-4 (gpt-4-0613) was adopted and implemented using the OpenAI API. The simple prompt condition scores were 74.5, 71.5, and 64.0 for levels III, II, and I, respectively. The tuned and simple prompt condition scores had no significant differences (Odds ratio = 1.03, 95% Confidence interval; 0.65-1.62, p = 0.908). Incorrect answers were observed in the simple prompt condition because of the inability to make choices, whereas no incorrect answers were observed in the tuned prompt condition. The average score for the essay questions under the simple prompt condition was 22.5 out of 50 points (45.0%). GPT-4 had a sufficient knowledge network for occupational mental health, surpassing the criteria for levels II and III tests. For the level I test, which required the ability to describe more advanced knowledge accurately, GPT-4 did not meet the criteria. External information may be needed when using GPT-4 at this level. Although the tuned prompts did not significantly improve the performance, they were promising in avoiding unintended outputs and organizing output formats. UMIN trial registration: UMIN-CTR ID = UMIN000053582.

Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.

Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.

Results from a multi-laboratory ocean metaproteomic intercomparison: effects of LC-MS acquisition and data analysis procedures

Abstract. Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values averaged 0.62±0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research, including integration into global-scale ocean surveys and ocean biogeochemical models.

Vcfexpress: flexible, rapid user-expressions to filter and format VCFs.

Variant Call Format (VCF) files are the standard output format for various software tools that identify genetic variation from DNA sequencing experiments. Downstream analyses require the ability to query, filter, and modify them simply and efficiently. Several tools are available to perform these operations from the command line, including BCFTools, vembrane, slivar, and others. Here, we introduce vcfexpress, a new, high-performance toolset for the analysis of VCF files, written in the Rust programming language. It is nearly as fast as BCFTools, but adds functionality to execute user expressions in the lua programming language for precise filtering and reporting of variants from a VCF or BCF file. We demonstrate performance and flexibility by comparing vcfexpress to other tools using the vembrane benchmark.

Update (3.0) to LyapXool: Quadratic programming

This is an update to PII: S2352711020303290, LyapXool is a C＋＋ program to compute complete Lyapunov functions and their orbital derivatives for any dynamical system expressed by an autonomous ordinary differential equation. New methods and improvements made in the third version are discussed. In particular, the computation of complete Lyapunov functions within quadratic programming methods are now computed by the software. These new approaches can be used to better analyse the behaviour of dynamical systems expressed as systems of autonomous equations. The structure of the program is object-oriented rather than procedural and the output formatting has been restructured to make it easier to understand. The program maintains its user friendliness. This paper describes how the code is organised, how it can be used to compute complete Lyapunov functions and their subsequent properties for dynamical systems, and it provides an interesting example of its application.

FastProtein-an automated software for in silico proteomic analysis.

Although various tools provide proteomic information, each tool has limitations related to execution platforms, libraries, versions, and data output format. Integrating data generated from different software is a laborious process that can prolong analysis time. Here, we present FastProtein, a protein analysis pipeline that is user-friendly, easily installable, and outputs important information about subcellular location, transmembrane domains, signal peptide, molecular weight, isoelectric point, hydropathy, aromaticity, gene ontology, endoplasmic reticulum retention domains, and N-glycosylation domains. It also helps determine the presence of glycosylphosphatidylinositol and obtain functional information from InterProScan, PANTHER, Pfam, and alignment-based annotation searches. FastProtein provides the scientific community with an easy-to-use computational tool for proteomic data analysis. It is applicable to both small datasets and proteome-wide studies. It can be used through the command line interface mode or a web interface installed on a local server. FastProtein significantly enhances proteomics analysis workflows by producing multiple results in a single-step process, thereby streamlining and accelerating the overall analysis. The software is open-source and freely available. Installation and execution instructions, as well as the source code and test files generated for tool validation, are available at https://github.com/bioinformatics-ufsc/FastProtein.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.