Explore the vast range of titles available.

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3–97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.

Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA’s performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future.

Reproducibility of scientific results is a key element of science and credibility. The lack of reproducibility across many scientific fields has emerged as an important concern. In this piece, we assess mathematical model reproducibility and propose a scorecard for improving reproducibility in this field. Graphical Abstract Reproducibility of scientific results is a key element of science and credibility. This piece assesses the reproducibility of mathematical models and presents an 8‐point scorecard for improving model reproducibility.

In this paper, we investigate the performance of non-orthogonal multiple access (NOMA)-based full-duplex Internet-of-Things (IoT) relay systems with simultaneous wireless information and power transfer (SWIPT) over Nakagami-m fading channels to improve the performance of a cell-edge user under perfect and imperfect successive interference cancellation (SIC). Two scenarios, i.e., direct and non-direct links, between the source node and cell-edge user are examined. The exact closed-form analytical and approximate expressions for the outage probability, system throughput, energy efficiency, and ergodic capacities are derived and validated via Monte Carlo simulations to characterize the proposed system performance. To further improve the system performance, we also provide a low-complexity algorithm to maximize the system throughput over-optimizing the time-switching factor. The results show that our proposed NOMA system can achieve superior performance compared to its orthogonal multiple access (OMA) counterpart under perfect SIC and with a low-to-medium signal-to-noise ratio under imperfect SIC, according to the level of residual self-interference and the quality of links.

The considered Tractor Trailer Wheeled Mobile Robot (TTWMR) is type of Mobile Robot including a master robot – Tractor and slave robots – Trailers which moves along Tractor to track a given desired trajectory. The main difficulties of the stabilization and the tracking control of TTWMR are due to nonlinear and underactuated systems subjected to nonholonomic constraints. In order to overcome these problems, firstly, we develop the model of TTWMR and transform the tracking error model to the triangular form to propose a control law and an adaptive law. Secondly, the varying time state feedback controllers are designed to generate actuator torques by using Backstepping technique and Lyapunov direct’s method, in that these are able to guarantee the stability of the whole system including kinematics and dynamics. In addition, the Babarlat’s lemma is used to prove that the proposed tracking errors converge to the origin and the proposed adaptive law is carried on to tackle unknown parameter problem. The simulations are implemented to demonstrate the effective performances of the proposed adaptive law and the proposed control law.

This paper proposes a compact design of dual-sense circularly polarized (CP) antenna for simultaneous transmit (Tx) and receive (Rx) communication systems. The primary radiating aperture of the proposed antenna is a 2 × 2 unit-cell metasurface (MS). The MS is excited by the asymmetric patch in the center, which acts as the CP source of the whole antenna structure. By properly tuning the feeding positions, dual-sense CP with high isolation can be achieved. For verification, an antenna prototype with compact dimensions of 0.36λ × 0.36λ × 0.02λ (λ is the free-space wavelength at the center operating frequency) is fabricated and measured. The measured operating bandwidth is 1.6% (2.45–2.49 GHz), in which the reflection and transmission coefficients are less than—10 dB and the axial ratio is lower than 3 dB. Within this band, the maximum isolation value is 39 dB, and the peak gain is 5.7 dBi.

Ginsenosides are major bioactive compounds present in the Panax species. Ginsenosides exhibit various pharmaceutical properties, including anticancer, anti-inflammatory, antimetastatic, hypertension, and neurodegenerative disorder activities. Although several commercial products have been presented on the market, most of the current chemical processes have an unfriendly environment and a high cost of downstream processing. Compared to plant extraction, microbial production exhibits high efficiency, high selectivity, and saves time for the manufacturing of industrial products. To reach the full potential of the pharmaceutical resource of ginsenoside, a suitable microorganism has been developed as a novel approach. In this review, cell biological mechanisms in anticancer activities and the present state of research on the production of ginsenosides are summarized. Microbial hosts, including native endophytes and engineered microbes, have been used as novel and promising approaches. Furthermore, the present challenges and perspectives of using microbial hosts to produce ginsenosides have been discussed.

Epigenetic dysregulation is a defining feature of tumorigenesis that is implicated in immune escape 1 , 2 . Here, to identify factors that modulate the immune sensitivity of cancer cells, we performed in vivo CRISPR–Cas9 screens targeting 936 chromatin regulators in mouse tumour models treated with immune checkpoint blockade. We identified the H3K9 methyltransferase SETDB1 and other members of the HUSH and KAP1 complexes as mediators of immune escape 3 – 5 . We also found that amplification of SETDB1 (1q21.3) in human tumours is associated with immune exclusion and resistance to immune checkpoint blockade. SETDB1 represses broad domains, primarily within the open genome compartment. These domains are enriched for transposable elements (TEs) and immune clusters associated with segmental duplication events, a central mechanism of genome evolution 6 . SETDB1 loss derepresses latent TE-derived regulatory elements, immunostimulatory genes, and TE-encoded retroviral antigens in these regions, and triggers TE-specific cytotoxic T cell responses in vivo. Our study establishes SETDB1 as an epigenetic checkpoint that suppresses tumour-intrinsic immunogenicity, and thus represents a candidate target for immunotherapy. A CRISPR–Cas9 screen of chromatin regulators in mouse tumour models treated with immune checkpoint blockade identifies SETDB1 as an epigenetic checkpoint protein that suppresses tumour-intrinsic immunogenicity.

Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases 1 – 4 . Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types 5 , 6 . We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome. Combining CRISPRi-FlowFISH to perturb enhancers with an activity-by-contact model to predict complex connections allows systematic mapping of enhancer–gene connections in a given cell type, on the basis of chromatin-state measurements.

Background Quantitative kinetic models of biological regulatory processes play an important role in understanding disease mechanisms. However, their simulation and analysis require specialized domain expertise. Results In this study, we present Talk2Biomodels (T2B), an open-source, user-friendly, large language model-based agentic AI platform designed to facilitate access to computational models of biological systems and promote the FAIRification (Findability, Accessibility, Interoperability, and Reusability) principles in systems biology. T2B allows users to interact with and analyse mathematical models of biological systems through conversations in natural language, thereby lowering the barrier to entry for model interpretation and hypothesis-driven exploration. The platform natively supports models encoded in the Systems Biology Markup Language, a widely adopted standard in the computational biology community. T2B is integrated with the BioModels database ( https://www.ebi.ac.uk/biomodels/ ), enabling retrieval, simulation, and analysis of curated systems biology models. We illustrate the platform’s capabilities through use cases in precision medicine, infectious disease epidemiology, and the study of emergent network-level properties in cellular systems — demonstrating how both computational experts and domain scientists without formal modelling training can derive actionable insights from complex biological models. Talk2Biomodels is available at https://github.com/VirtualPatientEngine/AIAgents4Pharma . Detailed documentation and use cases are available at https://virtualpatientengine.github.io/AIAgents4Pharma/talk2biomodels/intro/ . Conclusions In summary, T2B lowers the barrier for non-experts to engage with and extract insights from computational models of biological systems, while simultaneously providing experts with a streamlined interface for analysing models and overall contributes to the FAIRification of models.