8] Bioconductor: An Open Source Framework for Bioinformatics and Computational Biology

Biological experiments often require a series of precisely timed operations, and small variations in treatment can result in inconsistent or biased results. To handle multiple samples in parallel with precise temporal resolution, experimentalists may stagger treatments by initiating the workflow of one sample during the wait or incubation time of another. However, as the number of samples processed in parallel and the number of operations increase, it becomes increasingly difficult to identify and execute valid treatment regimens that permit the handling of each sample. To address this, I developed StaggR, an interactive web application that calculates and visualizes compatible staggering intervals for parallelized execution of identical processing workflows. This tool provides a user-friendly interface for defining protocol operations, durations, and wait times. It can automatically calculate the shortest possible conflict-free interval for initiating sample treatments, or allow users to simulate specific intervals to explore potential treatment regimens or bottlenecks. Using StaggR, users of any experience level can rapidly generate complete, color-coded experimental schedules, visualize these workflows in an easy-to-read chart, and execute them using a built-in timer displaying a treatment schedule with live updates. The experimental designs can be saved, shared, and re-imported, ensuring full reproducibility and user control. The application of StaggR is expected to expedite the design and throughput of complex experimental workflows while maximizing reproducibility.

Biological experiments often require a series of precisely timed operations, and small variations in treatment can result in inconsistent or biased results. To handle multiple samples in parallel with precise temporal resolution, experimentalists may stagger treatments by initiating the workflow of one sample during the wait or incubation time of another. However, as the number of samples processed in parallel and the number of operations increase, it becomes increasingly difficult to identify and execute valid treatment regimens that permit the handling of each sample. To address this, I developed StaggR, an interactive web application that calculates and visualizes compatible staggering intervals for complex experimental workflows. This tool provides a user-friendly interface for defining protocol operations, durations, and wait times. It can automatically calculate the shortest possible conflict-free interval for initiating sample treatments, or allow users to simulate specific intervals to explore potential treatment regimens or bottlenecks. Using StaggR, users of any experience level can rapidly generate complete, color-coded experimental workflows, visualize these workflows in an easy-to-read chart, and execute them using a built-in timer displaying a treatment schedule with live updates. The experimental designs can be saved, shared, and re-imported, ensuring full reproducibility and user control. The application of StaggR is expected to expedite the design and throughput of complex experimental workflows while maximizing reproducibility.

The Overview, Design concepts and Details (ODD) protocol for describing Individual-and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design ratio-nale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Proteins mediate and perform various fundamental functions of life. This versatility of protein function is an attribute of its 3D structure. In recent years, our understanding of protein 3D structure has been complemented with advances in computational and mathematical tools for protein modelling and protein design. 3D molecular visualisation is an essential part in every protein design and protein modelling workflow. Over the years, stand-alone and web-based molecular visualisation tools have been used to emulate three-dimensional view on computers. The advent of virtual reality provided the scope for immersive control of molecular visualisation. While these technologies have significantly improved our insights into protein modelling, designing new proteins with a defined function remains a complicated process. Current tools to design proteins lack user-interactivity and demand high computational skills. In this work, we present the Pepblock Builder VR, a gaming-based molecular visualisation tool for bio-edutainment and understanding protein design. Simulating the concepts of protein design and incorporating gaming principles into molecular visualisation promotes effective game-based learning. Unlike traditional sequence-based protein design and fragment-based stitching, the Pepblock Builder VR provides a building block style environment for complex structure building. This provides users a unique visual structure building experience. Furthermore, the inclusion of virtual reality to the Pepblock Builder VR brings immersive learning and provides users with “being there” experience in protein visualisation. The Pepblock Builder VR works both as a stand-alone and VR-based application, and with a gamified user interface, the Pepblock Builder VR aims to expand the horizons of scientific data generation to the masses.

As advancements in language model-powered text-to-image AI platforms accelerate, individuals can increasingly generate high-fidelity visual content more efficiently, regardless of background. These platforms are powerful tools for rapidly iterating and visualizing packaging design concepts. This study assesses whether AI-generated milk package designs, steered by academic packaging research, will produce packaging outcomes that perform equal to or better than human-designed outcomes with minimal designer input. For this study, researchers curated, summarized, and combined leading academic articles on packaging design into textual AI-prompts. The textual prompts were input into the platform RunDiffusion to generate visual milk packaging designs. The images created by the platform were reviewed by 48 human participants and compared to existing, human-made milk packaging designs to determine which designs perform better according to metrics used in a previously published study at E&PDE ‘23. The survey results indicate that the human designs slightly outperformed the AI designs in purchase likelihood and most other categories when including all survey participants. However, when non-milk users (those who rarely or never drink milk) were excluded from the results, the AI designs slightly outperformed the human designs regarding purchase likelihood. This outcome suggests that AI platforms can efficiently produce packaging design outcomes that can compete with human designs. Further, it is important that design educators understand the implications of these results, suggesting that AI platforms will increasingly be used in design workflows and academic training.

The possibility of nucleotide abiogenic synthesis in conditions of “KOSMOS-2044” satellite space flight

Metagenomic analysis, the study of microbial communities found in environmental samples, presents considerable challenges in quantity of data and computational cost. We present a novel metagenomic analysis pipeline that leverages emerging large address space compute nodes with NVRAM to hold a searchable, memory-mapped database of all known genomes and their taxonomic lineage. We describe challenges to creating the many hundred gigabytes-sized databases and describe database organization optimizations that enable our Livermore Metagenomic Analysis Toolkit (LMAT) software to effectively query the k-mer key-value store, which resides in high performance flash storage, as if fully in memory. To make database creation tractable, we have designed, implemented, and evaluated an optimized ingest pipeline. To optimize query performance for the database, we present a twolevel index scheme that yields speedups of 8.4 ? -74? over a conventional hash table index. LMAT, including the ingest pipeline, is available as open source at SourceForge.

Both developed and developing countries have contributed to the world economy. The level of welfare of a country can be achieved by dynamic economic growth, which is a condition that describes an increase in the GDP of the people of a country. GDP is the value of goods and services in a country produced by the production factors belonging to that country's citizens and foreign countries. GDP growth from year to year is affected by various factors with their respective portions. This study aims to determine the factors that affecting GDP in Indonesia and Korea which were analyzed using multiple linear regression analysis using R Studio software. The test methods we used include the coefficient of determination (R2), F statistical test (simultaneous test) and t statistical test (partial test). There are three independent variables, Total Unemployment, Inflation, and Export Value Index, and the dependent variable is GDP. And the results show that Unemployment variable and Export Value Index have a partially significant effect on GDP and Inflation variable has no partial effect on GDP in Indonesia. Meanwhile in Korea, Unemployment and Inflation have no partial effect on GDP and the Export Value Index has a partially significant effect on GDP.

This research was conducted with the aim of determining the influence of job characteristics and work stress on counterproductive work behavior PT Telkom Datel Mamuju. This research uses quantitative methods, the research sample uses a total sampling technique with a total of 42 respondents. Data collection was carried out by observation, interviews, document analysis, and distributing questionnaires directly to respondents. Testing the quality of research instruments uses validity and reliability tests. The analytical tool used is multiple linear regression analysis, then testing the hypothesis using a partial test (t statistical test), simultaneous test (F statistical test), and to determine the contribution of the independent variable to the dependent variable using the coefficient of determination.The research results obtained: (1) Job characteristics do not have a significant effect on counterproductive work behavior; (2) Job stress has a significant effect on counterproductive work behavior; (3) Job characteristics and work stress simultaneously have a significant effect on counterproductive work behavior. Meanwhile, the contribution of job characteristics and work stress to employee counterproductive work behavior is classified as moderate with a percentage of 48.1%, which means there are still 51.9% other factors that can influence counterproductive work behavior PT Telkom Datel Mamuju

PT BFI Finance is one of the companies engaged in financing. This company is an independent company that has branches in various regions, one of which is in Mamuju which was established on October 11, 2010. This research was conducted with the aim of knowing the effect of work professionalism, work stress and employee psychology on employee performance at PT BFI Finance Mamuju Branch. This research uses quantitative methods, with sample withdrawal using probability sampling techniques with 30 respondents. Data collection was carried out by observation, interviews, and distributing questionnaires directly to respondents. Testing the quality of research instruments using validity tests, and reliability tests. The analytical tool used is multiple linear regression analysis, then hypothesis testing using partial test (t statistical test), and simultaneous test (F statistical test). The research results obtained (1) work professionalism has a significant partial effect on employee performance. (2) job stress has a partial and significant effect on employee performance. (3) employee psychology has a partial and significant effect on employee performance. (4) Work professionalism, work stress and employee psychology simultaneously have a significant and significant effect on employee performance.

This research aims to find out how much influence promotion and product diversity partially or simultaneously have on purchasing decisions. The type of research used in this research is quantitative research using a Likert scale and questionnaire data which is tested using statistical tests with SPSS version 18. The tests used in this research are validity, reliability, classical assumption test, correlation test, coefficient of determination test, regression , Hypothesis test. The object of this research is consumers with primary data and simple random sampling techniques, the sample used was 171 respondents. The results of the simultaneous regression test obtained the equation Y = 6.809 + 0.249 X1 + 0.723 X2. A constant of 6.809 means that there is a significant relationship between promotion and product diversity on purchasing decisions. The results of the simultaneous correlation test are 0.835, meaning that the promotion and product diversity variables on purchasing decisions are in the interval 0.80 – 0.100, there is a very strong relationship between these three variables. The results of the simultaneous coefficient of determination test were 0.698, meaning that promotion and product diversity contributed 69.8% to purchasing decisions, while 30.2% was influenced by other variables. The results of the hypothesis test for promotional variables and product diversity on purchasing decisions simultaneously through the Fcount test are 193.809 Ftable 3.05 with a significance of 0.000, so Ho is rejected and Ha is accepted so it can be concluded that there is a significant influence.

Data fusion methods for statistical process monitoring and quality characterization in metal additive manufacturing

BackgroundHigh-throughput, parallel gene expression analysis by means of microarray technology has become a widely used technique in recent years. There are currently two main dye-labelling strategies for microarray studies based on custom-spotted cDNA or oligonucleotides arrays: (I) Dye-labelling of a single target sample with a particular dye, followed by subsequent hybridisation to a single microarray slide, (II) Dye-labelling of two different target samples with two different dyes, followed by subsequent co-hybridisation to a single microarray slide. The two dyes most frequently used for either method are Cy3 and Cy5. We propose and evaluate a novel experiment set-up utilising three differently labelled targets co-hybridised to one microarray slide. In addition to Cy3 and Cy5, this incorporates Alexa 594 as a third dye-label. We evaluate this approach in line with current data processing and analysis techniques for microarrays, and run separate analyses on Alexa 594 used in single-target, dual-target and the intended triple-target experiment set-ups (a total of 18 microarray slides). We follow this by pointing out practical applications and suitable analysis methods, and conclude that triple-target microarray experiments can add value to microarray research by reducing material costs for arrays and related processes, and by increasing the number of options for pragmatic experiment design.ResultsThe addition of Alexa 594 as a dye-label for an additional – third – target sample works within the framework of more commonplace Cy5/Cy3 labelled target sample combinations. Standard normalisation methods are still applicable, and the resulting data can be expected to allow identification of expression differences in a biological experiment, given sufficient levels of biological replication (as is necessary for most microarray experiments).ConclusionThe use of three dye-labelled target samples can be a valuable addition to the standard repertoire of microarray experiment designs. The method enables direct comparison between two experimental populations as well as measuring these two populations in relation to a third reference sample, allowing comparisons within the slide and across slides. These benefits are only offset by the added level of consideration required in the experimental design and data processing of a triple-target study design. Common methods for data processing and analysis are still applicable, but there is scope for the development of custom models for triple-target data. In summary, we do not consider the triple-target approach to be a new standard, but a valuable addition to the existing microarray study toolkit.

The culinary business is currently one of the most promising business opportunities. However, behind the success of the business there are certainly factors that influence consumers, such as strategic location and lifestyle that is appropriate at this time. This study aims to describe and analyze the influence or impact obtained from location and lifestyle on purchasing decisions at Jiwa Raga Kitchenette Padang Restaurant. This type of research is quantitative with an associative method in the form of a causal relationship. The study population consisted of consumers who visited Jiwa Raga Kitchenette Restaurant with a research sample of 160 samples and the sample was collected by applying purposive sampling technique. The data collection technique used was the distribution of questionnaires to consumers of Jiwa Raga Kitchenette Restaurant. The questionnaire was prepared using a Likert scale and had been tested for validity with the help of SPSS 23.00. The research data were analyzed using multiple linear regression analysis methods, using prerequisite analysis tests, namely normality test, multicollinearity test and heteroscedasticity test. In testing this hypothesis, partial statistical test (t test) and simultaneous statistical test (F test) were used with significance level α = 0.05 (5%). The results of the research data analysis indicate that the independent variables, namely location (X1) and lifestyle (X2), have a positive and significant influence on the dependent variable, namely the purchase decision (Y). The results of the hypothesis test indicate that the location variable has a significantly positive impact on purchasing decisions, as well as the lifestyle variable on purchasing decisions. Overall, the simultaneous test results indicate that both variables, location (X1) and lifestyle (X2), have a positive and significant effect on the purchasing decision variable (Y).

This study aims to analyze the influence of job insecurity and career development on employee loyalty at Informa PTC Palembang. The research employs a quantitative approach with a saturated sampling technique, where all 95 employees in the population were included as respondents. Data were collected through questionnaires and analyzed using statistical tests, including partial test (t-test), simultaneous test (F-test), and coefficient of determination (R²). The results indicate that job insecurity and career development significantly influence employee loyalty both partially and simultaneously. The coefficient of determination shows that 27.9% of the variation in employee loyalty is explained by these variables, while the remaining 72.1% is influenced by other factors outside the research model. The findings suggest that the company should focus on managing job insecurity and strengthening career development programs to enhance employee loyalty Keywords : Job Insecurity, Career Development, Employee Loyalty