The Trait Repertoire Enabling Cyanobacteria to Bloom Assessed through Comparative Genomic Complexity and Metatranscriptomics

Water bloom development due to eutrophication constitutes a case of niche specialization among planktonic cyanobacteria, but the genomic repertoire allowing bloom formation in only some species has not been fully characterized. We posited that the habitat relevance of a trait begets its underlying genomic complexity, so that traits within the repertoire would be differentially more complex in species successfully thriving in that habitat than in close species that cannot. To test this for the case of bloom-forming cyanobacteria, we curated 17 potentially relevant query metabolic pathways and five core pathways selected according to existing ecophysiological literature. The available 113 genomes were split into those of blooming (45) or nonblooming (68) strains, and an index of genomic complexity for each strain’s version of each pathway was derived. We show that strain versions of all query pathways were significantly more complex in bloomers, with complexity in fact correlating positively with strain blooming incidence in 14 of those pathways. Five core pathways, relevant everywhere, showed no differential complexity or correlations. Gas vesicle, toxin and fatty acid synthesis, amino acid uptake, and C, N, and S acquisition systems were most strikingly relevant in the blooming repertoire. Further, we validated our findings using metagenomic gene expression analyses of blooming and non- blooming cyanobacteria in natural settings, where pathways in the repertoire were differentially overexpressed according to their relative complexity in bloomers, but not in nonbloomers. We expect that this approach may find applications to other habitats and organismal groups

In search of strategic assets through cross-border merger and acquisitions: evidence from Chinese multinational enterprises in developed economies

Drawing on multiple cases of cross-border merger and acquisitions (CBMAs) by Chinese multinational enterprises (CMNEs), we investigate their search of strategic assets in developed economies (DEs). It is a received view that CMNEs use CBMAs to access strategic assets in DEs so as to address their latecomer disadvantages and competitive weakness. This paper aims to identify the nature of strategic assets that sought after by CMNEs and the post-CBMA integration approach, a partnering approach, adopted in enabling access to these assets. The findings reveal that CMNEs possess firm-specific assets that give them competitive advantages at home and seek for complementary strategic assets in the similar domain, but at a more advanced level. The partnering approach helps securing these strategic assets through no or limited integration, giving autonomy to target firm management team, retaining talents and creating synergy

A comprehensive AI model development framework for consistent Gleason grading

Background: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. Results: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. Conclusions: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Artificial intelligence in breast MRI radiogenomics: towards accurate prediction of neoadjuvant chemotherapy responses

Neoadjuvant Chemotherapy (NAC) in breast cancer patients has considerable prognostic and treatment potential and can be tailored to individual patients as part of precision medicine protocols. This work reviews recent advances in artificial intelligence so as to enable the use of radiogeomics for the accurate NAC analysis and prediction. The work addresses a new problem in radiogenomics mining: How to combine structural radiomics information and non-structural genomics information for accurate NAC prediction. This requires the automated extraction of parameters from structural breast radiomics data, and finding non-structural feature vectors with diagnostic value, which then are combined with genomics data acquired from exocrine bodies in blood samples from a cohort of cancer patients to enable accurate NAC prediction. A self-attention-based deep learning approach along with an effective multi-channel tumour image reconstruction algorithm of high dimensionality is proposed. The aim is to generate non-structural feature vectors for accurate prediction of the NAC responses by combining imaging datasets with exocrine body related genomics analysis

Computer-aided Design for the Route of the Test Waveguides

With the rapid development of China's space industry, digitization and intelligent is the tendency of the future. The applications of the waveguide are gradually widespread. During the thermal test phase, the routes of the test waveguides are similar for each spacecraft. Although the waveguides are highly standardized, so far it needs engineers to design the particular route of the test waveguidess, then map the engineering drawing for every test. In order to efficiently design the route of waveguide, it needs to design an application to help the engineers. With the help of the MFC(Microsoft Foundation Classes) and the pro/toolkit, it is easily to do the modeling and simulation. After automatic design the particular the route of the waveguide, the API of AutoCAD type library is used to help to modify the engineer drawing. Engineers can supervise every step of this application, and easily to modify the key parameters

Additional file 1: of Sleeping Beauty transposon integrates into non-TA dinucleotides

Supplementary tables and figures. (ZIP 3928 kb

Investigation on the Thermal Control Algorithm of the Heat Sink Temperature Regulating System for Thermal Vacuum Tests

The heat sink temperature is often set in a semi-empirical way based on steady-state temperatures . As a result, the entrance parameters of the heat sink working fluid are often too conservative, and the rate of temperature rise is difficult to control. In this paper, transient thermal models for each component of the heat sink temperature regulation system are established. Then they are programmed into a dynamic simulation model by using Matlab/Simulink as the thermal control algorithm.It is shown that the model can accurately reflect the dynamic and steady state characteristics of the heat sink temperature regulation system, and can provide guidance for the selection of control strategies and working fluid parameters in the thermal vacuum test