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Background Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking. Results We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components. Conclusion pcaExplorer is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/pcaExplorer/ ), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.

This study examines the effects of China’s cross-border e-commerce (CBEC) on its goods and services exports to ‘Belt and Road’ (B&R) countries for the period 2000–2018 using a gravity model. We find that CBEC has a greater positive impact on trade in services than on trade in goods, especially after the implementation of the B&R initiative. Furthermore, as the level of CBEC rises, distance tends to have a lower (higher) impact on services (goods) trade, whereas the impact on services (goods) trade increased (decreased) annually. Hence, promoting the sustainable development of CBEC can lead to increased export volumes.

Traditionally, the one-to-one interaction between heterogeneous software has become the most commonly used method for multi-disciplinary collaboration in building projects, resulting in numerous data interfaces, different data formats, and inefficient collaboration. As the prevalence of Building Information Modeling (BIM) increases in building projects, it is expected that the exchange of Industry Foundation Classes (IFC)-based data can smoothly take place between heterogeneous BIM software. However, interoperability issues frequently occur during bidirectional data exchanges using IFC. Hence, a data interoperability experiment, including architectural, structural and MEP models from a practical project, was conducted to analyze these issues in the process of data import and re-export between heterogeneous software. According to the results, the fundamental causes of interoperability issues can be concluded as follows: (a) software tools cannot well interpret several objects belonging to other disciplines due to the difference in domain knowledge; (b) software tools have diverse methods to represent the same geometry, properties and relations, leading to inconsistent model data. Furthermore, this paper presents a suggested method for improving the existing bidirectional data sharing and exchange: BIM software tools export models using IFC format, and these IFC models are imported into a common IFC-based BIM platform for data interoperability.

How can we make global trade inclusive for smaller sellers and firms? I present causal evidence that a major e-commerce platform increases on-site exports from small sellers through integrating an existing administrative and logistic service. The export increase comes exclusively from small sellers and exclusively along the extensive margin—that is, from new sellers or new destinations. Furthermore, the export increase is larger for more distant countries and differentiated products. I provide strong evidence that the distribution of export increase is driven by a reduction in export entry cost. I discuss the importance of reducing export entry cost for facilitating inclusive global trade. This paper was accepted by Chris Forman, information systems.

Active participation in the global value chains (GVC) has been recognized as an important factor in curbing the growth of carbon emissions. However, how GVC embedment affects carbon emissions in economies and what are the pathways of its impact need to be further studied. This paper analyzes the mechanism of GVC embedment affecting carbon emissions embodied in exports (CEEE) and selects 17 manufacturing industries in 36 economies around the world for empirical testing. It is found that GVC embedment significantly reduces the CEEE. Specifically, GVC embedment has a suppressive effect on the CEEE of both developed and developing countries, and the former has a greater suppressive effect than the latter; the effect on the CEEE of low-tech industries is significantly negative but not conducive to carbon emissions reduction in high-tech industries; complex and forward embedment have higher emissions reduction effects compared with simple and backward embedment. More importantly, GVC embedment reduces the CEEE through energy conservation effect, structure effect and transfer effect, and all of them show significant inverted U-shaped mediation effect. The findings of this paper have important implications for the sustainable economic development around the world under the GVC division of labor system.

In genomics, optical mapping technology provides long-range contiguity information to improve genome sequence assemblies and detect structural variation. Originally a laborious manual process, Bionano Genomics platforms now offer high-throughput, automated optical mapping based on chips packed with nanochannels through which unwound DNA is guided and the fluorescent DNA backbone and specific restriction sites are recorded. Although the raw image data obtained is of high quality, the processing and assembly software accompanying the platforms is closed source and does not seem to make full use of data, labeling approximately half of the measured signals as unusable. Here we introduce two new software tools, independent of Bionano Genomics software, to extract and process molecules from raw images (OptiScan) and to perform molecule-to-molecule and molecule-to-reference alignments using a novel signal-based approach (OptiMap). We demonstrate that the molecules detected by OptiScan can yield better assemblies, and that the approach taken by OptiMap results in higher use of molecules from the raw data. These tools lay the foundation for a suite of open-source methods to process and analyze high-throughput optical mapping data. The Python implementations of the OptiTools are publicly available through http://www.bif.wur.nl/ .

The rise of global trade and economic development is not only apparently grown in terms of volume but with value as well. Such spurt has evoked the risk management that is associated with export business, and can allow high-tech items fall into the hands of foreign military programs or terrorist organizations. The internal control compliance for export has set the in-house procedures for firms to adopt, facilitate, and abide with defined national export control requirements. This is crucial for firms to prevent potential violations on export rules and regulations. Introducing internal control program for export compliance can mitigate terrorist activities. This present study has taken Taiwan on board to determine the acquaintance of export risk and the implementation of internal control program through a team effort. Hence, the purpose of this study is to investigate the factors affecting the performance of internal control task team within a firm. The findings show that team-based incentives have a positive impact on team cohesion while knowledge sharing and knowledge integration have a significant impact on team performance. Furthermore, it is found that higher social-related risks and technical-related risks may increase team management risk, and the reduction in team management risk is beneficial to improve team performance.

Pepper production is a critical component of the global agricultural economy, with exports reaching a remarkable $6.9B in 2023. This underscores the crop’s importance as a major economic driver of export revenue for producing nations. Botrytis cinerea, the causative agent of gray mold, significantly impacts crops like fruits and vegetables, including peppers. Early detection of this pathogen is crucial for a reduction in fungicide reliance and economic loss prevention. Traditionally, visual inspection has been a primary method for detection. However, symptoms often appear after the pathogen has begun to spread. This study employs the Deep Learning algorithm YOLO for single-class segmentation on plant images to extract spatial details of pepper leaves. The dataset included hyperspectral images at discrete wavelengths (460 nm, 540 nm, 640 nm, 775 nm, and 875 nm) from derived vegetation indices (CVI, GNDVI, NDVI, NPCI, and PSRI) and from RGB. At an Intersection over Union with a 0.5 threshold, the Mean Average Precision (mAP50) achieved by the leaf-segmentation solution YOLOv11-Small was 86.4%. The extracted leaf segments were processed by multiple Transformer models, each yielding a descriptor. These descriptors were combined in ensemble and classified into three distinct classes using a K-nearest neighbor, a Long Short-Term Memory (LSTM), and a ResNet solution. The Transformer models that comprised the best ensemble classifier were as follows: the Swin-L (P:4 × 4–W:12 × 12), the ViT-L (P:16 × 16), the VOLO (D:5), and the XCIT-L (L:24–P:16 × 16), with the LSTM-based classification solution on the RGB, CVI, GNDVI, NDVI, and PSRI image sets. The classifier achieved an overall accuracy of 87.42% with an F1-Score of 81.13%. The per-class F1-Scores for the three classes were 85.25%, 66.67%, and 78.26%, respectively. Moreover, for B. cinerea detection during the initial as well as quiescent stages of infection prior to symptom development, qPCR-based methods (RT-qPCR) were used for quantification of in planta fungal biomass and integrated with the findings from the AI approach to offer a comprehensive strategy. The study demonstrates early and accurate detection of B. cinerea on pepper plants by combining segmentation techniques with Transformer model descriptors, ensembled for classification. This approach marks a significant step forward in the detection and management of crop diseases, highlighting the potential to integrate such methods into in situ systems like mobile apps or robots.

Time needed to report surveillance data within the public health service delays public health actions. The amendment to the infection protection act (IfSG) from 29 March 2013 requires local and state public health agencies to report surveillance data within one working day instead of one week. We analysed factors associated with reporting time and evaluated the IfSG amendment. Local reporting time is the time between date of notification and date of export to the state public health agency and state reporting time is time between date of arrival at the state public health agency and the date of export. We selected cases reported between 28 March 2012 and 28 March 2014. We calculated the median local and state reporting time, stratified by potentially influential factors, computed a negative binominal regression model and assessed quality and workload parameters. Before the IfSG amendment the median local reporting time was 4 days and 1 day afterwards. The state reporting time was 0 days before and after. Influential factors are the individual local public health agency, the notified disease, the notification software and the day of the week. Data quality and workload parameters did not change. The IfSG amendment has decreased local reporting time, no relevant loss of data quality or identifiable workload-increase could be detected. State reporting time is negligible. We recommend efforts to harmonise practices of local public health agencies including the exclusive use of software with fully compatible interfaces.