The data archive for “Distinguishing Thermal Fluctuations from Polaron Formation in Halide Perovskites” contains the complete numerical output behind every figure in the article and its Supporting Information. Each plot is paired with a .dat file that lists the raw values—for example, k-point coordinates and corresponding eigen-energies for the band-structure graphs. All AIMD snapshots discussed in the manuscript are supplied as compressed POSCAR files.

The data archive for “Distinguishing Thermal Fluctuations from Polaron Formation in Halide Perovskites” contains the complete numerical output behind every figure in the article and its Supporting Information. Each plot is paired with a .dat file that lists the raw values—for example, k-point coordinates and corresponding eigen-energies for the band-structure graphs. All AIMD snapshots discussed in the manuscript are supplied as compressed POSCAR files.

Rice (Oryza sativa) is one of the most important staple food crops worldwide, and its wild relatives serve as an important gene pool in its breeding. Compared with cultivated rice species, African wild rice (Oryza longistaminata) has several advantageous traits, such as resistance to increased biomass production, clonal propagation via rhizomes, and biotic stresses. However, previous O. longistaminata genome assemblies have been hampered by gaps and incompleteness, restricting detailed investigations into their genomes. To streamline breeding endeavors and facilitate functional genomics studies, we generated a 331 Mb telomere-to-telomere (T2T) genome assembly for this species, covering all telomeres and centromeres across the 12 chromosomes. This newly assembled genome has markedly improved over previous versions. Comparative analysis revealed a high degree of synteny with previously published genomes. A large number of structural variations were identified between O. longistaminata and O. sativa. A total of 2,466 segmentally duplicated genes were identified and enriched in cellular amino acid metabolic processes. We detected a slight expansion of some subfamilies of resistance genes and transcription factors. This newly assembled T2T genome of O. longistaminata provides a valuable resource for the exploration and exploitation of beneficial alleles present in wild relative species of cultivated rice.

Current microbial sequencing relies on short-read platforms like Illumina and DNBSEQ, favored for their low cost and high accuracy. However, these methods often produce fragmented draft genomes, hindering comprehensive bacterial function analysis. CycloneSEQ, a novel long-read sequencing platform developed by BGI-Research, its sequencing performance and assembly improvements has been evaluated.
Using CycloneSEQ long-read sequencing, the type strain produced long reads with an average length of 11.6 kbp and an average quality score of 14.4. After hybrid assembly with short reads data, the assembled genome exhibited an error rate of only 0.04 mismatches and 0.08 indels per 100 kbp compared to the reference genome. This method was validated across 9 diverse species, successfully assembling complete circular genomes. Hybrid assembly significantly enhances genome completeness by using long reads to fill gaps and accurately assemble multi-copy rRNA genes, which unable be achieved by short reads solely. Through data subsampling, we found that over 500 Mbp of short-read data combined with 100 Mbp of long-read data can result in a high-quality circular assembly. Additionally, using CycloneSEQ long reads effectively improves the assembly of circular complete genomes from mixed microbial communities.
CycloneSEQs read length is sufficient for circular bacterial genomes, but its base quality needs improvement. Integrating DNBSEQ short reads improved accuracy, resulting in complete and accurate assemblies. This efficient approach can be widely applied in microbial sequencing.

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

The increasing adoption of collaborative robots to support job execution in manufacturing has catalyzed companies' attention to safety and well-being issues. Sharing the human-centric perspective and harmonious human-machine collaboration concepts emphasized by Industry 5.0, the design phase of a collaborative workstation must integrate both psychological and physical risk evaluations to provide a safe and inclusive work environment suitable for a diversified workforce. Accelerating the pre-deployment phase to quickly reconfigure workstation design and assess its impact on workload balancing and task sequencing during the deployment of assembly lines still represents a challenging task considering the available software tools. This research proposes a new mathematical model to accelerate the design of ergonomic human-robot collaborative workstations based on task alternatives and the combined consideration of postural assessment and fatigue analyses for each of them to design an ergo-friendly collaborative environment. Surface electromyography analysis is jointly adopted with postural risk assessment measured with inertial measurement units and developed by a digital ergonomic platform to determine the optimal workplace configuration for tools, equipment, and resources to promote physical well-being while considering station productivity. Experimental tests are performed to investigate arm muscles and postural risk assessment for different configurations of workstation design and collaborative human-robot job progression. Experimental results demonstrate the feasibility, and the advantages of the proposed approach compared to existing simulation software to quickly generate and assess alternative scenarios and find a trade-off between ergo-quality levels and system performance. The final discussion offers valuable information for decision-makers and practitioners to facilitate the integration of human factors throughout the early stages of ergo-friendly workspace design, while effectively managing the complexity generated by resource allocation and collaborative robots.

This repository contains the data and code needed to recreate figures in the main and supplementary text of the paper "Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs" by the same authors. Instructions for running the code are provided in the README.md file. See also https://github.com/lskfs/ascidian-endostyle.

This repository contains the data and code needed to recreate figures in the main and supplementary text of the paper "Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs" by the same authors. Instructions for running the code are provided in the README.md file. See also https://github.com/lskfs/ascidian-endostyle.

As genomic sequencing technology continues to advance, it becomes increasingly important to perform multiple dataset joint analysis of transcriptomics to understand complex biological systems. However, batch effect presents challenges for dataset integration, such as sequencing measured by different platforms and datasets collected at different times. Here, we develop a BatchEval Pipeline, which is used to evaluate batch effect of dataset integration and output a comprehensive report. This report consists of a series of HTML pages for the assessment findings, including a main page, a raw dataset evaluation page and several built-in methods evaluation pages. The main page exhibits basic information of integrated datasets, comprehensive score of batch effect and the most recommended method for batch effect removal to current datasets. The residual pages exhibit the evaluation details of raw dataset and evaluation results of many built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, BatchEval Pipeline represents a significant advancement in batch effect evaluation and is a valuable tool to improve the accuracy and reliability of the experimental results.