Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.

Global change drivers alter multiple components of community composition, with cascading impacts on ecosystem stability. However, few studies have examined the complex interplay between global change drivers, synchrony, and diversity, especially over long-term successional dynamics. We analyzed a 22-year time series of grassland community data from Cedar Creek, USA, to examine the joint effects of pulse soil disturbance and press nitrogen addition on community synchrony, diversity, and stability during transient and post-transient periods of succession. Using multiple regression and structural equation modeling, we found that global change drivers decreased both synchrony and stability, thereby decoupling classic theoretical relationships, such as the portfolio effect. While the effect of soil disturbance weakened through time, nitrogen addition induced unexpected dynamics with maintained long-term impacts on composition, synchrony, and stability. Our findings underscore the need for long-term data and a comprehensive approach when managing ecosystems under ongoing global environmental changes.

The effective management of heat transfer processes is critical in various engineering applications, including thermal management systems and advanced cooling technology. Nanofluids, with their distinct thermal properties, present significant opportunities for improving heat transmission. The Arrhenius activation model is used to investigate the thermal behaviour of nanofluids. This study investigates the joint effect of Arrhenius activation on the non-darcy hydromagnetic convective heat and mass transfer of a nanofluid in a vertical channel using both SWCNT and MWCNT. The Runge-Kutta fourth-order shooting technique was used to analyze the coupled nonlinear equations governing flow heat transfer. Graphical research has been conducted to investigate the impact of different activation energies, viscosity levels, heat sources, and chemical reactions on flow characteristics. A numerical analysis is performed to determine the skin friction, the rate of heat transfer, and the mass transfer for various parametric variables.

Solar-driven interfacial evaporation is a promising technology for desalination. The photothermal conversion materials are at the core and play a key role in this field. Design of photothermal conversion materials based on organic dyes for desalination is still a challenge due to lack of efficient guiding strategy. Herein, a new D (donor)-A (acceptor) type conjugated tetraphenylpyrazine (TPP) luminophore (namely TPP-2IND) was prepared as a photothermal conversion molecule. It exhibited a broad absorption spectrum and strong π– π stacking in the solid state, resulting in efficient sunlight harvesting and boosting nonradiative decay. TPP-2IND powder exhibited high photothermal efficiency upon 660 nm laser irradiation (0.9 Wcm -2), and the surface temperature can reach to 200 oC. Then, an interfacial heating system based on TPP-2IND is established successfully. The water evaporation rate and the solar-driven water evaporation efficiency were evaluated up to 1.04 kgm -2h -1 and 65.8% under 1 sunlight, respectively. Thus, this novel solar-driven heating system shows high potential for desalination and stimulates the development of advanced photothermal conversion materials.

This article presents the first ever fully autonomous UAV (Unmanned Aerial Vehicle) mission to perform gas measurements after a real blast in an underground mine. The demonstration mission was deployed around 40 minutes after the blast took place, and as such realistic gas levels were measured. We also present multiple field robotics experiments in different mines detailing the development process. The presented novel autonomy stack, denoted as the Routine Inspection Autonomy (RIA) framework, combines a risk-aware 3D path planning D + ∗ , with 3D LiDAR-based global relocalization on a known map, and it is integrated on a custom hardware and a sensing stack with an onboard gas sensing device. In the presented framework, the autonomous UAV can be deployed in incredibly harsh conditions (dust, significant deformations of the map) shortly after blasting to perform inspections of lingering gases that present a significant safety risk to workers. We also present a change detection framework that can extract and visualize the areas that were changed in the blasting procedure, a critical parameter for planning the extraction of materials, and for updating existing mine maps. As will be demonstrated, the RIA stack can enable robust autonomy in harsh conditions, and provides reliable and safe navigation behavior for autonomous Routine Inspection missions.

Coronary artery aneurysms and ST-segment elevation myocardial infarction (STEMI) are seldom encountered in clinical practice and pose a management challenge. We describe the case of a patient diagnosed with anterior wall myocardial infarction with a complete occluded left main coronary artery (LMCA). After the establishment of partial blood flow within the

A new temperature noise model, including the influence of gate-drain series resistance Rgd on the noise performance for an InP HEMT, is presented in this paper. An equivalent temperature Tgd of Rgd has been taken into account based on pospieszalski’s noise model. The corresponding extraction procedure of noise parameters is given. Good correlation between the simulated and measured noise parameters in the frequency range of 8GHz~50GHz for a wide range of bias points verify the validity of the improved noise model.

Unveiling the Uncommon: Gas-Containing Renal Stones - A Unique Case Study.Running Title: GAS-CONTAINING RENAL STONES

Demands for flexible and seamless system management necessitated the growth of software-defined networks (SDN). Yet, securing these environments with effective measures is critical as SDN continues to evolve into more intricate architectures. Intrusion detection is paramount among these measures; thus far, studies suggest that artificial intelligence (AI) approaches may be helpful in this domain. By systematically examining relevant works addressing AI-based intrusion prevention strategies within hyper-evolved SDN settings, our review aims to present an inclusive evaluation alongside suggesting areas requiring additional scrutiny. This research introduces readers to key concepts related to SDN and how deep learning algorithms, machine learning algorithms, and neural networks can be applied for effective intrusion detection within an SDN environment. Drawing from existing literature on this subject matter, our analysis critically examines the benefits and drawbacks of these AI-based techniques while highlighting gaps in knowledge requiring further research attention. Some areas include real-time protection capabilities, scalability concerns, and seamless integration with different security mechanisms. We then present future research directions in this area. This literature review employs a systematic approach to elucidate the current research on using AI methods to detect intrusions in SDN.

Rubber tree powdery mildew, a major fungal leaf disease of Heveae brasiliensis trees caused by obligate parasitic fungi (Erysiphe quercicola), is among the most important diseases affecting rubber planting areas worldwide. Phyllosphere microbiomes are currently a new research hotspot in molecular ecology, and their biological community is extremely rich. This study used high-throughput sequencing and other molecular techniques to analyze the structure and diversity of dominant fungi and bacteria in the communities of rubber tree leaves (N=16) in four representative rubber-producing regions in Hainan Province, China, after the occurrence of white powder disease. The sampled area had high species richness and stable biodiversity. This study mainly focused on (i) the dominant bacterial and fungal phyla and genera of the sampled regions; (ii) differences in the phyllosphere microbial community structure and relative abundance between healthy leaves and powdery mildew Level 3-diseased leaves within the same region; and (iii) differences in the phyllosphere microbial community structure and relative abundance between healthy leaves and diseased leaves, as well as their correlations with environmental factors, in different regions. This work is innovative in three respects: (i) distinguishing the dominant populations of phyllosphere microbes for rubber trees with powdery mildew via high-throughput detection technology to determine the impact of powdery mildew on these microbes; (ii) exploring the effects of pathogenic bacteria on phyllosphere microbes and hosts from a relatively macro perspective; and (iii) investigating the effects on phyllosphere microbial communities before and after the infection of obligatory parasite leaf pathogenic bacteria. This project is among the first conducted in China’s representative rubber-producing areas to study Erysiphe’s influence on rubber tree leaf microorganisms. These findings provide data support to better understand the biological evolution of powdery mildew diseases and establish new strategies for their ecological control in rubber tree plantations.

Nanoparticles within the realm of plant biotechnology, have garnered considerable interest in recent years, as a result of their notable and unique chemical and physical criteria, which can affect the growth, productivity, and biochemical synthesis of plants secondary metabolites, and help to enhance them. The focus of this review is to outline the recent developments in plant Nano biotechnology and different Nanomaterials with focusing on bio compatible ones to summarize their effects on plant secondary metabolites. Also, we have discussed the physico-chemical properties of Nanoparticles which makes them an attractive and suitable tool for plant biotechnology and a general view of their mechanisms in modulate the biosynthetic pathways of phytochemical metabolites. Different types of Nanomaterials used in biotechnology, like metal and non-metal Nanomaterials and carbon based Nanoparticles were provided in this review. Moreover, we have highlighted potential utilizations of Nanomaterials based approaches in plant biotechnology in the production of phytochemicals with agricultural, industrial, and pharmaceutical applications.

G-protein coupled receptors (GPCRs) are the most abundant and varied family of transmembrane proteins. Beyond their basic biological functions, they are of relevance to pharmacology, as they are involved in a variety of human pathologies. Molecular dynamics (MD) are a reliable and effective simulation technique that allows us to investigate and examine the structure and activity that shape biomolecular energy systems. In this article, Dimensionality Reduction (DR) methods are used to explore protein trajectories by generating different data abstractions with the objective of finding the best simplified representation of such proteins. For that, these methods are applied to publicly available data from a GPCR MD database. The protein abstractions generated by several DR methods applied to the inactive, intermediate, and active conformational states of the GPRC MD simulations are compared through their entropy quantification and visual representation. In addition, experiments involve three types of MD data representation, namely the 3D position of amino acids, the distances between the helices of the molecules, and the dihedral angles representing the torsion angles of Psi and Phi of each amino acid. The reported results show how some of the DR methods capture the smooth temporal evolution of the GPCR representations and their state transitions.

Plant responses to multifactorial stress combinations are seldom studied, but combinations of abiotic and biotic stresses may cause drastic yield reductions in crops. While an abiotic stress, the salinization of agricultural soil, affects crop production, added pressures of insect herbivores could lead to further losses. In this paper, we investigate the effects of salinity on an insect herbivore, the corn earworm caterpillar ( Helicoverpa zea), feeding on tomato ( Solanum lycopersicum cv. Better Boy) plants. We show that salt-stressed tomato plants are poor hosts for H. zea, impacting caterpillar growth rates, caterpillar feeding preference, and moth oviposition. We further show that these observations are best explained by reductions in both relative leaf water content and leaf total protein content, along with ionic toxicity and imbalance. We observe that salt stress does not influence anti-insect herbivory defense protein (PPO and TPI) levels. Finally, we observe that salt treatment leads to differences in specific volatiles, with lower emissions of 2-carene, α-phellandrene, β-phellandrene, α-humulene, and β-caryophyllene in salt-treated plants. We demonstrate that salt exposure changes tomato plant quality and chemical composition, which in turn negatively affects insect herbivores feeding on these plants.

Introduction The aims of this study were to update our experience with biodegradable polydioxanone (PDO) airway stents in children focusing on effectiveness and safety, and to analyse the factors involved in the different outcomes observed. Materials and methods Retrospective study of patients managed with PDO stents from 2012 to 2023. Variables collected: demographics, comorbidities, indication , clinical baseline, stent size, location, complications, clinical outcome, and time of follow-up. Statistical analyses were performed in order to detect the eventual contribution of variables in the different outcomes observed. Results 54 PDO stents were placed in 26 patients (median age, 4 m). All showed severe symptoms of central airway obstruction due to: tracheomalacia 9 patients, bronchomalacia 5, tracheobronchomalacia 10, and tracheal stenosis 2. Stent placement was uneventful in every case: 29 stents in the trachea and 25 in main bronchi. 53,8% of patients needed successive stenting and all exhibited comorbidities. Complete clinical resolution was observed in 8 cases (30.7%), partial improvement in 13 (50%), unchanged in 4 (15.3%), and worsen in one. Age had a significant positive impact in outcome (6 months vs 3 m.; p=0.024). Additionally, smaller stents were associated with a better outcome (20 vs 26 mm; p=0.044). Granulation tissue was the most frequent complication (34.6%). Five patients (19.2%) died due to severe comorbidities, follow-up was complete in survivors (median, 58 m). Conclusions PDO stents are safe and effective when dealing with severe tracheobronchial obstruction. Stent-related granulation tissue continues to be a relevant matter of concern. This issue together with increased degradation times deserve further research.

Understanding the factors driving phenotypic and genomic differentiation of insular populations is of major interest to gain insight into the speciation process. Comparing patterns across different insular taxa subjected to similar selective pressures upon colonizing oceanic islands provides the opportunity to study parallel evolution and identify shared patterns in their genomic landscapes of differentiation. We selected four species of passerine birds (common chaffinch Fringilla coelebs/canariensis, red-billed chough Pyrrhocorax pyrrhocorax, house finch Haemorhous mexicanus and dark-eyed/island junco Junco hyemalis/insularis) that have both mainland and insular populations. For each species, we sequenced whole genomes from mainland and insular individuals to infer their demographic history, characterize their genomic differentiation, and identify the factors shaping them. We estimated the relative (FST) and absolute (dxy) differentiation, nucleotide diversity (π), Tajima’s D, gene density and recombination rate. We also searched for selective sweeps and chromosomal inversions along the genome. Changes in body size between island and mainland were consistent with the island rule. All species shared a marked reduction in effective population size (Ne) upon island colonization. We found highly differentiated genomic regions in all four species, suggesting the role of selection in island-mainland differentiation, yet the lack of congruence in the location of these regions indicates that each species adapted to insular environments differently. Our results suggest that the genomic mechanisms involved, which include selective sweeps, chromosomal inversions, and historical factors like recurrent selection, differ in each species despite the highly conserved structure of avian genomes and the similar selective factors involved.

Signals of natural selection can be quickly eroded in high gene-flow systems, severely challenging efforts to understand how and when genetic adaptation occurs in the ocean. This long-standing, unresolved topic in ecology has renewed importance because rapidly changing environmental conditions are driving range expansions that, in many cases, necessitate rapid evolutionary responses. To test for genetic adaptation in a coastal marine species with high dispersal potential, we performed a series of crosses on Kellet’s whelk (Kelletia kelletii) collected from its historical and recently colonized range, and conducted RNA-Seq on offspring that we reared in a common garden environment. We identified 2,770 differentially expressed genes between 54 samples with historical-range and expanded-range ancestry. Using SNPs called directly from the differentially expressed genes, we revealed parental population structure that enabled us to assign “unknown” samples back to their range of origin with unprecedented accuracy for a marine species (92.6 to 94.5%). The SNP with the highest predictive importance occurred on triosephosphate isomerase (TPI), an essential enzyme for glycolysis and glucogenesis, which also plays a role in cold stress response. TPI is both highly upregulated and contains a non-synonymous mutation in the expanded range, where ocean temperatures are colder than in the historical range. Our findings pave the way for accurately identifying patterns of dispersal, gene flow, and population connectivity in the ocean by demonstrating that rapid genetic adaptation can occur even in high gene flow species and that experimental transcriptomics can reveal mechanisms for how marine organisms respond to changing environmental conditions.

An extremely effective and straightforward multi-component reaction strategy for the universally highly selective synthesis of 5-CF3 pyrimidine derivatives has been established. This method employs a distinctive trifluoromethylation-cyclization strategy, introducing an innovative reaction mechanism and avoiding the inherent selectivity challenges in direct pyrimidine trifluoromethylation. The no-table advantages of this work are the exceptional selectivity, extremely short reaction time, and convenience of a one-pot synthetic procedure.

Introduction:Myocardial metastatic cancer is rare, and diagnosis is challenging. Supradiaphragmatic malignancies are the most common to metastasize to the heart. Endocardial involvement is rare, followed by involvement of the pericardium, epicardium, and myocardium. Myocardial metastasis leads to varied clinical presentations ranging from arrhythmias, congestive heart failure, myocardial infarction, and intracavitary mass lesions (1). We are describing a patient with a prior diagnosis of coronary artery disease (CAD) and Coronary artery bypass graft (CABG) presenting as recurrent ST-elevation myocardial infarction (STEMI) and diagnosed with cardiac metastasis from lung squamous cell carcinoma (SCC).

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database, the most popular proteomics data repository. Our system utilizes two advanced Large Language Models (LLM), llama2-13b and chatglm2-6b, and includes a web service API (Application Programming Interface), web interface, and sophisticated algorithms. We have developed a novel approach to construct vector-based representations for enabling the LLM responses, featuring a curated version and a comprehensive database of relevant links and paragraphs for each generated response. An important part of the framework is a benchmark component based on an Elo-ranking system, providing a scalable method for evaluating not only the performance of llama2-13b and chatglm2-6b but also, of any other available and future open-source LLMs. Throughout the benchmarking process, the PRIDE documentation for external users was refined to enhance the clarity and efficacy in addressing user queries. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure.

Purpose: Sickle cell disease (SCD) affects all organ systems and is characterized by numerous acute and chronic complications and comorbidities. Standardized codes are needed for complications/comorbidities used in real-world evidence (RWE) studies that rely on administrative and medical coding. This systematic literature review was conducted to produce a comprehensive list of complications/comorbidities associated with SCD, along with their diagnosis codes used in RWE studies. Methods: A search in MEDLINE and Embase identified studies published from 2016-2023. Studies were included if they were conducted in US SCD populations and reported complications/comorbidities and respective International Classification of Diseases, Clinical Modification (ICD-CM) codes. All identified complications/comorbidities and codes were reviewed by a certified medical coding expert and hematologist. Results: Of 1,851 identified studies, 40 were included. The most reported complications/comorbidities were stroke, acute chest syndrome, pulmonary embolism, venous thromboembolism, vaso-occlusive crisis, and priapism. Most of the studies used ICD-9-CM codes (n=21), while some studies used ICD-10-CM codes (n=3) or both (n=16), depending on the study period. Most codes reported in literature were heterogeneous across complications/comorbidities. The medical coding expert recommended modifications for several conditions. Conclusion: While many studies we identified did not report their codes and were excluded from this review, the studies with codes exhibited diverse coding definitions. By providing a standardized set of diagnosis codes that were reported by studies and reviewed by a coding expert and hematologist, our review can serve as a foundation for accurately identifying complications/comorbidities in future research, and may reduce heterogeneity, enhance transparency, and improve reproducibility.

The effect of biomechanical signals induced by a non-toxic and biocompatible gel on Non-Small Cell Lung Cancer cells is investigated in this preliminary study. Gelatin type A, chitosan, and alginate were compared in a quantitative manner by means of rheology, in order to assess the most stable and easily prepared gel in physiological solution conditions for 3D cancer cell culturing. Crucial factors that influence the fabrication of the gel were examined and quantified, including gelation kinetics, reactants' contact time, and temperature, leading to the development of an optimal research protocol. Alginate was found to lead to the most suitable physiological gels and its effect on the cancer cells was examined using flow cytometry. The results implied that alginate gels interfered with the cancerous cells' growth rate, as the live cells' percentage was far lower compared to the reference cell culture. Thus, alginate is a promising material for biomedical engineering applications concerning 3D culturing of cancer cells and its further research and optimization are strongly suggested.

The long transmission distance in wireless power transmission(WPT) system will lead to a sharp decline in transmission efficiency, this paper presents a novel ortho-octagonal double helix metamaterial that can effectively enhance the transmission efficiency of WPT systems, theoretically derived and experimentally verified the enhancement of evanescent wave transmission by this metamaterial dielectric plate. A WPT simulation system operating at 10.78 MHz is constructed by HFSS software, and the effects of adding passive relay coils and different shapes of metamaterials on the transmission efficiency are comparatively investigated. Both simulation and experimental results show that the transmission efficiency of the WPT system can be improved to different degrees after inserting different shapes of metamaterials at the transceiver end of the system. When the transceiver coil spacing of the WPT system is greater than 22 cm, the effect of metamaterials on the system transmission efficiency enhancement shows an increasing and then decreasing trend as the distance increases. The positive octagonal metamaterial improves 12% over the circular metamaterial in the mid-range transmission distance due to its better magnetic coupling effect. When the transceiver coil spacing is 22 cm, the open-circuit voltage of the receiving coil after loading the metamaterial is stabilized to be enhanced by nearly 5 times, and it has the best effect at 30 cm, and the transmission efficiency is enhanced from 30% to 60%, which verifies the effective enhancement of the metamaterial on the transmission efficiency of the WPT system.

Driven by the goal to achieve sustainable mobility, mitigate climate change, and reduce greenhouse gas emissions, electric vehicles (EVs) represent a radical paradigm shift in the automotive industry. Significant progress has been made in electric vehicle (EV) technology in recent times, including enhancements in battery efficiency, range, and charging infrastructure. As the main energy storage option for electric vehicles, lithium-ion battery technology has advanced significantly, increasing energy density and lowering costs. Wider acceptance of EVs has also been encouraged by the development of fast-charging stations and smart grid integration, which have greatly solved issues with convenience and charging time. However, lithium-ion cells used currently have a working temperature range of 15 o C to 35 o C. The temperature above or below can affect the battery performance and range of the vehicle. Thus, implementation of proper Battery Thermal Management System (BTMS) is required to achieve effective cooling performance and maintain the cells within operating range of temperatures. The current study focuses on the use of fly ash nanoparticles dispersed in base fluid water-ethylene glycol to be used as coolant in indirect liquid cooling system. An ANSYS FLUENT model is developed for 14 LFP cylindrical cells having 2S7P arrangement with serpentine cooling channel in between the cells. The current study uses 5 % particle concentration fly ash nanofluid as coolant to understand the rise in temperature at the outlet for different flow velocities. The better cooling performance was observed for fluid flow rate of 0.1 m/sec with an outlet temperature rise of 311.976 K along with rise of 4 % in temperature compared to inlet fluid flow temperature of 300 K. The percentage difference between the rise in temperature of the outlet for the fluid at 0.1 m/sec and 3 m/sec is 3.07 % indicating effective cooling at lower fluid flow velocities. Thus, by the use of Fly ash as nanofluid in battery cooling application will certainly help to reduce the temperature of battery pack and can provide to be a sustainable solution leading to lesser degradation of environment.

The value expectation (reward or punishment) of outcomes based on experience and current information decides whether to do and how much effort to put into executing the current task. Individuals with test anxiety have difficulty inhibiting test-related and test-unrelated threat stimuli. We suggested that the excessive negative value expectation for threat stimuli played an important role. Thus, the current study investigated the value expectation towards test-related and test-unrelated threat stimuli in test anxiety individuals. The ERP results showed that, in the high test anxiety (HTA) group, compared to neutral stimuli, test-related and unrelated threat stimuli induced more negative FRN amplitudes following negative feedback and more positive FRN amplitudes following positive feedback. Moreover, the FRN amplitudes induced by negative feedback following test-related and test-unrelated threat stimuli were significantly more negative in the HTA group than in the LTA group. In the FRN difference wave (d-FRN) results, test-related and unrelated threat stimuli induced more negative d-FRN than neutral stimuli. Additionally, the HTA group showed a more negative d-FRN than the LTA group under the test-related threat condition. No differences were found in the LTA group. In the ERD results of the beta band, the ERD power induced by negative feedback following test-related threat stimuli was significantly stronger than those following test-unrelated threat stimuli in the HTA group. However, no significant differences were found in the average P3 amplitude and ERS results in the theta band. These findings suggested that individuals with HTA had a negative value expectation for threat stimuli.