Extensive Resources Research Articles

Accurate Prediction of Essential Proteins Using Ensemble Machine Learning

Abstract Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods. While experimental approaches are highly accurate, they often demand extensive time and resources. To address these challenges, we present a computational ensemble learning framework designed to identify essential proteins more efficiently. Our method begins by using node2vec to transform proteins in the Protein-Protein Interaction (PPI) network into continuous, low-dimensional vectors. We also extract a range of features from protein sequences, including graph-theory-based, information-based, compositional, and physiochemical attributes. Additionally, we leverage deep learning techniques to analyze high-dimensional Position-Specific Scoring Matrices (PSSMs) and capture evolutionary information. We then combine these features for classification using various machine learning algorithms. To enhance performance, we integrate the outputs of these algorithms through ensemble methods such as voting, weighted averaging, and stacking. This approach effectively addresses data imbalances and improves both robustness and accuracy. Our ensemble learning framework achieved an AUC of 0.948 and an accuracy of 0.9252, outperforming other computational methods. These results demonstrate the effectiveness of our approach in accurately identifying essential proteins and highlight its superior feature extraction capabilities.

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework

The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently failing to identify optimal material formulations. As an alternative, adaptive experimental design methods based on Bayesian optimization (BO) principles have been recently proposed to streamline material development by iteratively refining experiments based on prior results. However, most implementations are not suited to manage the heteroscedastic noise inherently present in physical experiments. This work introduces a heteroscedastic Gaussian process (HGP) model within the BO framework to account for varying levels of uncertainty in the data, improve the accuracy of the predictions, and increase the overall experimental efficiency. The aim is to find the optimal TPS film composition that maximizes its elongation at break and tensile strength. To demonstrate the effectiveness of this approach, TPS films were prepared by mixing potato starch, distilled water, glycerol as a plasticizer, and acetic acid as a catalyst. After gelation, the mixture was degassed via centrifugation and molded into films, which were dried at room temperature. Tensile tests were conducted according to ASTM D638 standards. After five iterations and 30 experiments, the films containing 4.5 wt% plasticizer and 2.0 wt% starch exhibited the highest elongation at break (M = 96.7%, SD = 5.6%), while the films with 0.5 wt% plasticizer and 7.0 wt% starch demonstrated the highest tensile strength (M = 2.77 MPa, SD = 1.54 MPa). These results demonstrate the potential of the HGP model within a BO framework to improve material development efficiency and performance in TPS film and other potential material formulations.

Knowledge Distillation and Student-Teacher Learning for Weed Detection in Turf

Abstract Machine vision-based herbicide applications relying on object detection or image classification deep convolutional neural network (DCNN) demand high memory and computational resources, resulting in lengthy inference times. To tackle these challenges, this study assessed the effectiveness of three teacher models, each trained on datasets of varying sizes, including D-20k (comprising 10,000 true positive and true negative images) and D-10k (comprising 5,000 true positive and true negative images). Additionally, knowledge distillation was performed on their corresponding student models across a range of temperature settings. After the process of student-teacher learning, the parameters of all student models were reduced. ResNet18 not only achieved higher accuracy (ACC≥0.989) but also maintained higher frames per second (FPS≥742.9) under its optimal temperature condition (T=1). Overall, the results suggest that employing knowledge distillation on the machine vision models enabled accurate and reliable weed detection in turf while reducing the need for extensive computational resources, thereby facilitating real-time weed detection and contributing to the development of smart, machine vision-based sprayers.

Burden of vaso-occlusive crisis, its management and impact on quality of life of Indian sickle cell disease patients.

Sickle cell disease (SCD) with vaso-occlusive pain crisis (VOC) significantly impacts patient well-being and often results in extensive healthcare resource utilization. This study assessed the VOC burden, its management and its impact on patients' quality of life (QoL). A cross-sectional observational study was conducted between November 2021 and June 2022, including 1000 SCD patients from high-prevalence states in India. Data on demographics, clinical characteristics, VOC severity, management and QoL were collected. The study revealed that 33.5% of patients reported at least one VOC episode during the study period. In the year prior to their enrolment, 836 (83.60%) patients reported at least one VOC episode, with an equal proportion of 407/487 (83.6%) adults and 429/513 (83.6%) paediatric patients, reducing their QoL across all domains compared to patients without VOC. Of these, 469/1000 patients (46.9%) experienced ≥3 VOC episodes. Additionally, 764/1000 (76.40%) patients managed their VOCs at healthcare facilities, with 501/1000 (50.1%) requiring inpatient admissions. Further, 71.80% of patients received Hydroxyurea (HU) therapy. The study depicts the severity of the Arab-Indian haplotype in Indian SCD patients visiting healthcare settings based on high VOC burden. This highlights the urgent need for better management strategies and resource allocation for these patients.

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment.

The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laboratory resources. The swift identification of MDR-TB poses a particularly challenging endeavor. To identify the biomarkers indicative of multi-drug resistance, we conducted a screening of the GSE147689 dataset for differentially expressed genes (DEGs) and subsequently conducted a gene enrichment analysis. Our analysis identified a total of 117 DEGs, concentrated in pathways related to the immune response. Three machine learning methods, namely random forest, decision tree, and support vector machine recursive feature elimination (SVM-RFE), were implemented to identify the top 10 genes according to their feature importance scores. A4GALT and S1PR1, which were identified as common genes among the three methods, were selected as potential molecular markers for distinguishing between MDR-TB and drug-susceptible tuberculosis (DS-TB). These markers were subsequently validated using the GSE147690 dataset. The findings suggested that A4GALT exhibited area under the curve (AUC) values of 0.8571 and 0.7121 in the training and test datasets, respectively, for distinguishing between MDR-TB and DS-TB. S1PR1 demonstrated AUC values of 0.8163 and 0.5404 in the training and test datasets, respectively. When A4GALT and S1PR1 were combined, the AUC values in the training and test datasets were 0.881 and 0.7551, respectively. The relationship between hub genes and 28 immune cells infiltrating MDR-TB was investigated using single sample gene enrichment analysis (ssGSEA). The findings indicated that MDR-TB samples exhibited a higher proportion of type 1T helper cells and a lower proportion of activated dendritic cells in contrast to DS-TB samples. A negative correlation was observed between A4GALT and type 1T helper cells, whereas a positive correlation was found with activated dendritic cells. S1PR1 exhibited a positive correlation with type 1T helper cells and a negative correlation with activated dendritic cells. Furthermore, our study utilized connectivity map analysis to identify nine potential medications, including verapamil, for treating MDR-TB. In conclusion, our research identified two molecular indicators for the differentiation between MDR-TB and DS-TB and identified a total of nine potential medications for MDR-TB.

A deep-learning-based histopathology classifier for focal cortical dysplasia (FCD) unravels a complex scenario of comorbid FCD subtypes.

Recently, we developed a first artificial intelligence (AI)-based digital pathology classifier for focal cortical dysplasia (FCD) as defined by the ILAE classification. Herein, we tested the usefulness of the classifier in a retrospective histopathology workup scenario. Eighty-six new cases with histopathologically confirmed FCD ILAE type Ia (FCDIa), FCDIIa, FCDIIb, mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE), or mild malformations of cortical development were selected, 20 of which had confirmed gene mosaicism. The classifier always recognized the correct histopathology diagnosis in four or more 1000 × 1000-μm digital tiles in all cases. Furthermore, the final diagnosis overlapped with the largest batch of tiles assigned by the algorithm to one diagnostic entity in 80.2% of all cases. However, 86.2% of all cases revealed more than one diagnostic category. As an example, FCDIIb was identified in all of the 23 patients with histopathologically assigned FCDIIb, whereas the classifier correctly recognized FCDIIa tiles in 19 of these cases (83%), that is, dysmorphic neurons but no balloon cells. In contrast, the classifier misdiagnosed FCDIIb tiles in seven of 23 cases histopathologically assigned to FCDIIa (33%). This mandates a second look by the signing histopathologist to either confirm balloon cells or differentiate from reactive astrocytes. The algorithm also recognized coexisting architectural dysplasia, for example, vertically oriented microcolumns as in FCDIa, in 22% of cases classified as FCDII and in 62% of cases with MOGHE. Microscopic review confirmed microcolumns in the majority of tiles, suggesting that vertically oriented architectural abnormalities are more common than previously anticipated. An AI-based diagnostic classifier will become a helpful tool in our future histopathology laboratory, in particular when large anatomical resections from epilepsy surgery require extensive resources. We also provide an open access web application allowing the histopathologist to virtually review digital tiles obtained from epilepsy surgery to corroborate their final diagnosis.

Differential responses of woodland caribou to fire and forestry across boreal and montane ecosystems—a literature review

Abstract Ecosystem-based management (EBM) is a landscape-level management and planning process that is common across North America. A primary tenet of EBM is that the area and intensity of anthropogenic disturbance should mimic the historical natural disturbance of the focal ecosystem. Biodiversity should persist, at least at a coarse scale, where anthropogenic disturbance, such as forest harvesting, matches natural disturbance. However, EBM is failing some species, particularly those that are dependent on old forest. Across many areas of Canada, woodland caribou (Rangifer tarandus caribou) are declining because of the direct and indirect effects of habitat loss and fragmentation. This is even though forest management often follows the principles of EBM. We conducted a qualitative comparison of the responses of woodland caribou to wildfire and forest harvesting, considering a broad range of responses, including habitat selection and distribution, forage, movement patterns, and population dynamics. We found that while wildfire and forest harvesting both influence caribou, the negative effects are generally greater following forest harvesting. For example, wildfire and forest harvesting result in the loss of habitat, but caribou are more likely to shift, abandon or contract their range in response to harvest. The literature also suggested a stronger negative population response of caribou to forest harvest when compared to wildfire. This difference could be the result of greater residual forest structure associated with wildfire as well as the extensive resource roads that are necessary for forestry operations. Although there is sound theoretical support for EBM, the practice, as implemented, may not be effective for maintaining the habitat and ultimately populations of woodland caribou.

Optimizing Sustainability Offshore Hybrid Tidal-Wind Energy Storage Systems for an Off-Grid Coastal City in South Africa

South Africa’s extensive marine energy resources present a unique opportunity for advancing sustainable energy solutions. This study focuses on developing a sustainable hybrid power generation system that combines offshore wind and tidal current energy to provide a stable, renewable energy supply for off-grid coastal communities. By addressing the challenges of intermittency and unpredictability in renewable energy sources, the proposed system integrates wind and tidal energy with energy storage and diesel backup to ensure reliability while reducing greenhouse gas emissions and minimizing the environmental footprint. The system is optimized for sustainability, with a configuration of one wind turbine, five tidal turbines, and a diesel generator demonstrated to be the most effective in increasing the renewable energy fraction and lowering the net present cost. Simulations conducted using HOMER Pro version 3.20 software underscore the potential of this hybrid system to support South Africa’s transition to a more sustainable energy future, aligning with national and global sustainability goals. The results emphasize the environmental benefits of combining these renewable energy sources, offering a blueprint for achieving energy security and sustainable development in coastal regions.

Digital Transformation in the Maritime Industry; Opportunities and Challenges for Indonesia

Digital transformation is reshaping industries worldwide, including the maritime sector, by integrating advanced technologies to improve efficiency, safety, and sustainability. In the context of Indonesia, a country with a vast archipelago and significant maritime activities, digital transformation offers substantial opportunities but also presents distinct challenges. This paper explores the multifaceted impacts of digital transformation on Indonesia's maritime industry, examining both the opportunities and challenges that arise from this technological shift. Opportunities in the maritime sector include enhanced operational efficiency, improved safety, and better environmental management. Technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and blockchain are pivotal in optimizing logistics, predictive maintenance, and real-time monitoring of vessels and cargo. These advancements can lead to reduced operational costs, minimized human errors, and lower environmental footprints. For Indonesia, leveraging these technologies could significantly boost its competitiveness in global shipping, enhance port operations, and support the sustainable management of its extensive marine resources. However, the journey towards digital transformation in Indonesia’s maritime industry is fraught with challenges. Key issues include inadequate digital infrastructure, limited technological expertise, and regulatory hurdles. The uneven distribution of digital infrastructure across Indonesia's archipelago hampers the consistent application of advanced technologies. Additionally, the maritime workforce requires upskilling to handle new digital tools effectively, necessitating substantial investment in education and training. Regulatory frameworks also need to evolve to address data security, privacy concerns, and the integration of international standards, ensuring a seamless transition to digital operations.

SesamumGDB: a comprehensive platform for Sesamum genetics and genomics analysis.

Sesame (Sesamum indicum L., 2n = 26) is a crucial oilseed crop cultivated worldwide. The ancient evolutionary position of the Sesamum genus highlights its value for genomics and molecular genetics research among the angiosperms of other genera. However, Sesamum is considered a small orphan genus with only a few genomic databases for cultivated sesame to date. The urgent need to construct comprehensive, curated genome databases that include genus-specific gene resources for both cultivated and wild Sesamum species is being recognized. In response, we developed Sesamum Genomics Database (SesamumGDB), a user-friendly genomic database that integrates extensive genomic resources from two cultivated sesame varieties (S. indicum) and seven wild Sesamum species, covering all three chromosome groups (2n = 26, 32, and 64). This database showcases a total of 352 471 genes, including 6026 related to lipid metabolism and 17 625 transcription factors within Sesamum. Equipped with an array of bioinformatics tools such as BLAST (basic local alignment search tool) and JBrowse (the Javascript browser), SesamumGDB facilitates data downloading, screening, visualization, and analysis. As the first centralized Sesamum genome database, SesamumGDB offers extensive insights into the genomics and genetics of sesame, potentially enhancing the molecular breeding of sesame and other oilseed crops in the future. Database URL: http://www.sgbdb.com/sgdb/.

Understanding Viral Haemorrhagic Fevers: Virus Diversity, Vector Ecology, and Public Health Strategies.

Viral haemorrhagic fevers encompass a diverse group of severe, often life-threatening illnesses caused by viruses from multiple families, including Arenaviridae, Filoviridae, Flaviviridae, Hantaviridae, Nairoviridae, Peribunyaviridae, and Phenuiviridae. Characterised by fever and haemorrhagic symptoms, these diseases challenge public health systems by overwhelming healthcare facilities, complicating diagnostic processes, and requiring extensive resources for containment and treatment, especially in resource-limited settings. This discussion explores the intricate relationships between VHFs and their transmission vectors-both animal and arthropod-and examines the impact of ecological and geographic factors on disease spread. The primary transmission of VHFs typically occurs through direct contact with infected animals or via bites from haematophagous arthropods, facilitating zoonotic and, at times, human-to-human transmission. With an emphasis on the role of diverse wildlife, domesticated animals, and vectors such as mosquitoes and ticks in the epidemiology of VHFs, there is a recognised need for robust surveillance and strategic public health responses to manage outbreaks. This review discusses the necessity of interdisciplinary approaches that integrate virology, ecology, and public health to enhance diagnostic capabilities, develop vaccines and antivirals, and improve outbreak interventions. Exploring the ecological and biological dynamics of VHFs will help bolster a deeper understanding of these emerging viruses and underpin preparation for future outbreaks. The importance of enhanced global cooperation, continuous research, and collaboration to mitigate the public health threats posed by these complex infections is a central theme, serving as a foundational strategy to reinforce worldwide preparedness and response efforts. Future directions include addressing gaps in vaccine development and tailoring public health strategies to the unique challenges of managing VHFs, such as the rapid mutation rates of viruses, the need for cold chain logistics for vaccine distribution, and socio-economic barriers to healthcare access, in order to ensure readiness for and effective response to emerging threats worldwide.

Development of Science Diplomacy in the MENA Region: Priorities and Prospects for Cooperation with Russia on the Example of Saudi Arabia, Egypt and the United Arab Emirates

Introduction. The development of science diplomacy is aimed at promotion of a state’s interests in the international arena. Russia is striving to strengthen cooperation in the field of scientific and technological development, humanitarian exchange with such MENA countries as Saudi Arabia, Egypt and the United Arab Emirates. These countries are of significant interest to Russia due to the long-standing forms of cooperation with them. The main purpose of this study is to identify science diplomacy actors, visually present them taking into consideration the process of science development in these countries and determine prospects of cooperation with Russia. Materials and Methods. In their study, the authors used data from open sources, Russian Federal State Statistics Service reports, UNESCO Science reports, World Bank database. The analysis of development of international science and technology cooperation in the three MENA countries was carried out. The authors used the descriptive method to present the main actors of science diplomacy and visually provide the science diplomacy systems. The developed models of science diplomacy of Saudi Arabia, Egypt and the United Arab Emirates enable Russia to identify the priorities in the field of science diplomacy in the context of geopolitical challenges. Results. The study identified the potential partners of Russia in Saudi Arabia, the United Arab Emirates and Egypt to strengthen international science and technology cooperation. The prospects for cooperation with these countries are fixed in the mutual agreements with Russia. The authors studied the policy of these countries in the field of international scientific and technical cooperation, considered strategic directions for the development of science diplomacy. The created models of science diplomacy reflect the main actors of science diplomacy, as well as the current interrelationship between the three MENA countries and Russia in the field of science and technology. The value of this research lies in the preparation of an extensive analytical base that helps to identify potential partners for Russia among the MENA countries, as well as identify key strategies and directions to stimulate the growth of cooperation and knowledge exchange. Discussion and Conclusion. The research has shown that in the studied Arab countries, there is an active development of scientific diplomacy. The governments of these countries are increasingly turning to science to solve political problems. The MENA region has extensive resources and scientific potential, which makes it attractive for scientific cooperation. The analysis of cooperation with these countries made it possible to identify key strategies and priorities to stimulate the growth of cooperation and knowledge exchange between the MENA countries and Russia.

Area–Time-Efficient High-Radix Modular Inversion Algorithm and Hardware Implementation for ECC over Prime Fields

Elliptic curve cryptography (ECC) is widely used for secure communications, because it can provide the same level of security as RSA with a much smaller key size. In constrained environments, it is important to consider efficiency, in terms of execution time and hardware costs. Modular inversion is a key time-consuming calculation used in ECC. Its hardware implementation requires extensive hardware resources, such as lookup tables and registers. We investigate the state-of-the-art modular inversion algorithms, and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at reducing the execution time and hardware costs. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance to other implementations. Our implementation on the Altera Cyclone V FPGA chip used 1227 ALMs (adaptive logic modules) and 1037 registers. The modular inversion calculation took 3.67 ms. The AT (area–time) factor was 8.30, outperforming the other implementations. We also present an implementation of ECC using the proposed radix-8 modular inversion algorithm. The implementation results also showed that our modular inversion algorithm was more efficient in area–time than the other algorithms.

Exploring prompt pattern for generative artificial intelligence in automatic question generation

ABSTRACT The construction of questions is an essential component in educational assessment and student learning processes. However, manually constructing questions is a complex task that requires not only professional training, substantial experience, and extensive resources from teachers but is also time-consuming. This article introduces an Automatic Question Generation (AQG) technology based on a prompt pattern to alleviate this burden and address the ongoing need for new questions in education. The essence of this method lies in constructing a prompt pattern grounded on a collective knowledge base derived from teachers, thereby enhancing the quality of the questions produced. Practical applications and expert evaluations demonstrate that integrating a prompt pattern with a collective knowledge base into Large Language Models (LLMs) results in high-quality questions with statistically significant results. These questions not only meet educational standards but also approach the quality of manually constructed questions by teachers in certain aspects. Our research further emphasizes the feasibility of AI-teacher collaboration in education.

High-precision and lightweight small-target detection algorithm for low-cost edge intelligence

The proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50–95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.

Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study.

This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis. The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations. In this study, ChatGPT-4 was embedded in a specialized subenvironment, "AI Family Medicine Board Exam Taker," designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment. In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions. The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.

Environmental sustainability in intensive care: the path forward. An ESICM Green Paper.

The European Society of Intensive Care Medicine (ESICM) Green Paper aims to address the challenge of environmental sustainability in intensive care and proposes actionable strategies for integrating sustainability into intensive care unit (ICU) stakeholder actions. The ESICM Executive Committee appointed a task force of topic experts and ESICM committee representatives to develop the ESICM Green Paper. The task force convened biweekly from January to June 2024, identifying key domains for environmental sustainability and prioritizing actions. Drafts were iteratively refined and approved by the ESICM Executive Committee. Climate change will impact activities in intensive care in many ways, but also the impact of ICU activities on the environment is considerable; drivers for this include extensive resource use and waste generation in ICUs from energy consumption, use of disposable items, and advanced therapies for critically ill patients. The ESICM Green Paper outlines a structured approach for ICUs to reduce their environmental impact, emphasizing energy efficiency, waste reduction, and sustainable procurement. Furthermore, it endorses the need for awareness and education among healthcare professionals, integration of sustainability into research, and sustainable policies within scientific societies. The ESICM Green Paper reviewed the relevance of climate change to intensive care and provided suggestions for clinical practice, research, education, and ESICM organizational domains. It underscores that reducing intensive care's ecological footprint can coexist with high-quality patient care. Promoting a resilient, responsible healthcare system is a joint responsibility of all ICU stakeholders.

A Hybrid Approach for Robust Object Detection: Integrating Template Matching and Faster R-CNN

Object detection is a critical task in computer vision, with applications ranging from autonomous vehicles to medical imaging. Traditional methods like template matching offer precise localization but struggle with variations in object appearance, while deep learning approaches such as Faster R-CNN excel in handling diverse and complex datasets but often require extensive computational resources and large amounts of labeled data. This paper proposes a hybrid approach that integrates template matching with Faster R-CNN to leverage the strengths of both techniques. By combining the accuracy of template matching with the robustness and generalization capabilities of Faster R-CNN, our method achieves superior performance in challenging scenarios, including objects with occlusions, varying scales, and complex backgrounds. Extensive experiments demonstrate that the hybrid model not only enhances detection accuracy but also reduces computational load, making it a practical solution for real-world applications.

An atlas of small non-coding RNAs in human preimplantation development

Understanding the molecular circuitries that govern early embryogenesis is important, yet our knowledge of these in human preimplantation development remains limited. Small non-coding RNAs (sncRNAs) can regulate gene expression and thus impact blastocyst formation, however, the expression of specific biotypes and their dynamics during preimplantation development remains unknown. Here we identify the abundance of and kinetics of piRNA, rRNA, snoRNA, tRNA, and miRNA from embryonic day (E)3-7 and isolate specific miRNAs and snoRNAs of particular importance in blastocyst formation and pluripotency. These sncRNAs correspond to specific genomic hotspots: an enrichment of the chromosome 19 miRNA cluster (C19MC) in the trophectoderm (TE), and the chromosome 14 miRNA cluster (C14MC) and MEG8-related snoRNAs in the inner cell mass (ICM), which may serve as ‘master regulators’ of potency and lineage. Additionally, we observe a developmental transition with 21 isomiRs and in tRNA fragment (tRF) codon usage and identify two novel miRNAs. Our analysis provides a comprehensive measure of sncRNA biotypes and their corresponding dynamics throughout human preimplantation development, providing an extensive resource. Better understanding the sncRNA regulatory programmes in human embryogenesis will inform strategies to improve embryo development and outcomes of assisted reproductive technologies. We anticipate broad usage of our data as a resource for studies aimed at understanding embryogenesis, optimising stem cell-based models, assisted reproductive technology, and stem cell biology.

Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review.

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.