The shortage of food and freshwater sources threatens human health and environmental sustainability. Spirulina grown in seawater-based media as a healthy food is promising and environmentally friendly. This study used three machine learning techniques to identify important cultivation parameters and their hidden interrelationships and optimize the biomass yield of Spirulina grown in seawater-based media. Through optimization of hyperparameters and features, eXtreme Gradient Boosting, along with the recursive feature elimination (RFE) model demonstrated optimal performance and identified 28 important features. Among them, illumination intensity and initial pH value were critical determinants of biomass, which impacted other features. Specifically, high initial pH values (>9.0) mainly increased biomass but also increased nutrient sedimentation and ammonia (NH3) losses. Both batch and continuous additions could decrease nutrient losses by increasing their availability in the seawater-based media. When illumination intensity exceeded 200 μmol photons/m2/s, it amplified the growth of Spirulina by mitigating the light attenuation caused by a high initial inoculum level and counteracted the negative effect of low temperature (<25 °C). In large-scale cultivation, production efficiency would be reduced if illumination was not maintained at a high level. High salinity and sodium bicarbonate (NaHCO3) addition promoted carbohydrate accumulation, but suitable dilution could keep the required protein content in Spirulina with relatively low media and production costs. These findings reveal the interactive influence of cultivation parameters on biomass yield and help us determine the optimal cultivation conditions for large-scale cultivation of Spirulina-based seawater system based on a developed graphical user interface website.

Sustainable intensification (SI) of agriculture can produce more food to meet the demand of a growing population while considering ecosystem health. The current SI estimation framework ignores the complex coupling between input and output intensity of arable land. A method for coupled analysis of arable land input intensity and output intensity based on sliding windows is proposed. By calculating the correlation coefficient and partial correlation coefficient between input intensity and output intensity in different value ranges as the order parameter, the phase transition and the influence process of input intensity on output intensity can be explained. Meanwhile, a python-based framework is developed. An application of the method was made to reveal the interaction process between annual provincial input intensity and output intensity in mainland China. Researchers in many fields may benefit from the method by obtaining a fast way to analysis the coupling relationship between driving and dependent variables in complex systems.•New method for SI estimation is presented.•The order parameter of the coupling relationship between input and output intensity is calculated based on sliding windows.•Analysis of coupling relationships between driving and dependent variables in complex systems.

An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with a user-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.

UNASSIGNED: The segmentation of the carotid vessel wall using black-blood magnetic resonance images was a crucial step in the diagnosis of atherosclerosis. The objective was to accurately isolate the region between the artery lumen and outer wall. Although supervised learning methods achieved remarkable accuracy in vessel segmentation, their effectiveness remained limited due to their reliance on extensive labeled data and human intervention. Furthermore, when confronted with three-dimensional datasets featuring insufficient and discontinuous label data, these learning-based approaches could lose their efficacy. In this paper, we proposed a novel Joint 2D-3D Cross-Pseudo Supervision (JCPS) method for accurate carotid vessel wall segmentation.
UNASSIGNED: In this study, a vascular center-of-gravity positioning module was developed to automatically estimate the region of blood vessels. To achieve accurate segmentation, we proposed a joint 2D-3D semi-supervised network to model the three-dimensional continuity of vascular structure. In addition, a novel loss function tailored for vessel segmentation was introduced, consisting of four components: supervision loss, cross-pseudo supervision loss, pseudo label supervision loss, and continuous supervision loss, all aimed at ensuring the accuracy and continuity of the vessel structure. In what followed, we also built up a user-friendly Graphical User Interface based on our JCPS method for end-users.
UNASSIGNED: Our proposed JCPS method was evaluated using the Carotid Artery Vessel Wall Segmentation Challenge dataset to assess its performance. The experimental results clearly indicated that our approach surpassed the top 10 methods on the leaderboard, resulting in a significant enhancement in segmentation accuracy. Specifically, we achieved an average Dice similarity coefficient increase from 0.775 to 0.806 and an average quantitative score improvement from 0.837 to 0.850, demonstrating the effectiveness of our proposed JCPS method for carotid artery vessel wall segmentation.
UNASSIGNED: The experimental results suggested that the JCPS method had a high level of generalization performance by producing pseudo labels that were comparable with software annotations for data-imbalanced segmentation tasks.

Heatmaps are preferred visualization modes for biologists to display high-dimensional information from high-throughput omics data. Many software including website services and R packages are available to generate various types of customized heatmaps. Here, we describe oppHeatmap (omics pilot platform of heatmap), a new tool constructed to render different kinds of heatmaps through MATLAB. oppHeatmap is available for plotting ordinary heatmaps, hierarchical clustering, TreeMaps, microplates graph, sample correlation (full heatmap and upper and lower triangle parts), gene correlation (between columns or tables), and polar heatmaps. oppHeatmap can support the modification of borders, fonts, and colors to customize the final plots. oppHeatmap can not only read data from Microsoft Excel to generate specific heatmaps but also make Excel heatmaps by coloring each cell in Excel. The graphs can be stored in SVG (supported vector graph) format and modified by other SVG recognition software. oppHeatmap is designed by MATLAB AppDesigner with GUI (graphical user interface) operation. The program for oppHeatmap was available at https://github.com/HangZhouSheep/oppHeatmap .are diagramed by.

Helmet mounted display systems (HMDs) are high-performance display devices for modern aircraft. We propose a novel method combining event-related potentials (ERPs) and BubbleView to measure cognitive load under different HMD interfaces. The distribution of the subjects\' attention resources is reflected by analyzing the BubbleView, and the input of the subjects\' attention resources on the interface is reflected by analyzing the ERP\'s P3b and P2 components. The results showed that the HMD interface with more symmetry and a simple layout had less cognitive load, and subjects paid more attention to the upper portion of the interface. Combining the experimental data of ERP and BubbleView, we can obtain a more comprehensive, objective, and reliable HMD interface evaluation result. This approach has significant implications for the design of digital interfaces and can be utilized for the iterative evaluation of HMD interfaces.

Light scattering and absorption by small particles are widely used in fields such as biomedicine, information technology, and energy technology. However, their theoretical study requires not only a high level of knowledge in electromagnetism but also a high level of computer programming skills. To solve this problem, a software called PyMieLab (https://gitlab.com/Climb12/pymielab.git) for calculating the light scattering and absorption of spherical particles has been developed based on Mie theory. This software is interactive, versatile, visual, flexible, and scalable. It has a friendly graphical user interface and can be used as a numerical simulation platform for scientific research, as well as provides a rich database of particle refractive indices. Moreover, it offers a reliable research platform for discovering new optical properties of specific materials and exploring materials with better optical properties in related fields. This paper describes in detail the theoretical basis, the graphical user interface, the calculation functions, the operational and procedural processes, the features, and the numerical verification of the software. It illustrates the application value of the software with two simulation examples.

Biologists often use systems of ontologies to classify gene lists obtained by high-throughput gene or protein-sequencing instruments, and then enrichment scores were used to rank the ontology system. Therefore, the important molecular functional categories related to the phenotype can be conveniently viewed in the ontology system. Since the birth of GO (Gene Ontology) organization, various types of ontology software have been developed to calculate enrichment scores for the target gene list in the GO system. Herein, we provide an enrichment calculation application oppOntology (Omics Pilot Platform for Ontology) developed by MATLAB. oppOntology supports simultaneous calculation of multiple samples with manifold enrichment scores (GeneCount, GeneRatio, EnrichFactor, HypergeometricTest, and FisherExactTest). oppOntology can not only calculate enrichment scores for generic functional databases, such as GO, KEGG, HPO, and MsigDB, but also for self-defined functional category databases and customized GO Slim. Moreover, oppOntology supports online mapping of KEGG pathway diagrams in a batch way. The GUI (Graphical User Interface) of oppOntology is developed on the architecture of AppDesigner in MATLAB, and all input and output files are Microsoft Excel. oppOntology is an independent, easy-to-use enrichment calculation software, that can be available at https://github.com/HangZhouSheep/oppOntology .

Modern education attaches great importance to interdisciplinary skills, among which computational thinking is a core element, and heralds a new era. IT application has shaped education in the 21st century. Computational thinking has provided further impetus for building an all-encompassing social network and fostering a DIY culture enabled by digital technologies. One empirical study used four apps to test children\'s development in computational thinking and fluency. The article will help students overcome their fears of coding. Peer reviews provide students with an opportunity to learn from each other and become more motivated. These reviews also serve as feedback for teachers to evaluate students\' performance. Experimental design is used in this study, and a peer review system is implemented. Freshmen attending a programming class in a university are used as samples. At the class, students write computer programs with f-Chart, which provides a graphical user interface for students to learn programming logic and design. Zuvio, a cloud-based interactive response system, is used to conduct the peer reviews. The data of this study are analyzed through R. The results show not only an improvement in students\' learning performance but also a gap between students\' peer review scores and teachers\' evaluation scores. Learning feedback and evaluation is crucial to transform education between students and teachers into a sustainable cycle system.

Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3-25.9%) and biochar application rate (0.5-10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.