The Metaproteomics Initiative: Five Years of Community-Driven Progress

Global CO2 emissions reduction requires industries to manage and understand their CO2 emission levels in real-time. This paper examines the Industrial Internet of Things (IIoT) for real-time monitoring, measurement, and visualization of reducing CO2 emissions in industrial and environmental domains. Methodology: The methodology consists of a literature review based on peer-reviewed publications and use cases to explore the current state and practical implications. Furthermore, a technical analysis of IIoT systems, CO2 sensors, and data processing techniques is also identified. Results: IIoT systems can support CO2 emission monitoring and accuracy optimization in industrial domains by combining CO2 sensors, wireless communication, and data fusion techniques. In addition, machine learning and artificial intelligence can be used to reduce anomalies in CO2 sensor readings and predictive maintenance of systems. Challenges: Challenges include interoperability, data security and system scalability. To resolve these issues standardized communication protocols, data security methods and implementation barriers should be improved. Future Directions: To enhance data processing and security features, future work should focus on integrating edge computing, artificial intelligence, machine learning, and blockchain techniques. In addition, data visualizations and cost-effective solutions should also be in focus, to provide more adoptable IIoT systems in industrial domains. Conclusion: As IIoT systems and CO2 sensor technologies evolve, IIoT systems can contribute significantly to increasing global air quality and CO2 emission control in industry, agricultural, and urban areas.

The incidence of psychiatric disorders, such as post-traumatic stress disorder (PTSD), anxiety, and depression, has been steadily increasing, while current treatment approaches remain limited in efficacy. As a result, there is an urgent need to explore more effective therapeutic interventions. In recent years, MDMA (3,4- methylenedioxymethamphetamine)-assisted therapy (MDMA-AT) has emerged as a promising and innovative approach, demonstrating favorable clinical potential in the treatment of these disorders. Although preliminary studies have confirmed its therapeutic efficacy, a comprehensive and systematic analysis of the research trends and current limitations of MDMA-AT remains lacking. This study employed a bibliometric approach to systematically retrieve and analyze research literature published between 1994 and 2023 on the application of MDMA in the treatment of PTSD, anxiety, and depression. Relevant data were obtained from three prominent databases: Web of Science Core Collection, PubMed, and Scopus. VOSviewer and Microsoft Excel were used to perform visual and quantitative analyses, focusing on publication trends, research hotspots, prolific authors, leading institutions, and international collaboration networks. The findings indicated a substantial increase in MDMA-related research over the past decade. The United States has led the field in publication output, with the Multidisciplinary Association for Psychedelic Studies (MAPS) identified as the most productive institution. Key figures, such as Rick Doblin, have demonstrated high influence and centrality within the global research network. The research focus has gradually shifted from investigations of the neurotoxic properties of MDMA to explorations of its therapeutic mechanisms, safety profiles, and clinical applications. This study provides a comprehensive synthesis of the past thirty years of research on MDMA-AT in the treatment of PTSD, anxiety, and depression, identifying major research trajectories and critical challenges in the field. While current findings highlight the therapeutic promise of MDMA and its translational potential, further research is needed to improve trial design, enhance sample diversity, and evaluate long-term effects in order to support the standardization and evidence-based integration of MDMA- assisted therapy into clinical practice.

Blockchain technologies have recently come to the forefront of the research and industrial communities as they bring potential benefits for many industries. This is due to their practical capabilities in solving many issues currently inhibiting further advances in various industrial domains. Securely recording and sharing transactional data, establishing automated and efficient supply chain processes, and enhancing transparency across the whole value chain are some examples of these issues. Blockchain offers an effective way to tackle these issues using distributed, shared, secure, and permissioned transactional ledgers. The employment of blockchain technologies and the possibility of applying them in different situations enables many industrial applications through increased efficiency and security; enhanced traceability and transparency; and reduced costs. In this paper, different industrial application domains where the use of blockchain technologies has been proposed are reviewed. This paper explores the opportunities, benefits, and challenges of incorporating blockchain in different industrial applications. Furthermore, the paper attempts to identify the requirements that support the implementation of blockchain for different industrial applications. The review reveals that several opportunities are available for utilizing blockchain in various industrial sectors; however, there are still some challenges to be addressed to achieve better utilization of this technology.

Schizophrenia is a chronic mental disorder with disabling symptoms and variable outcome. Outcome is a multidimensional construct that depends on description of clinical and social domains. Symptomatic remission is one such clinical domain which can determine the outcome of illness. The study aimed to assess functional outcome in symptomatic remitted schizophrenia patients compared to unremitted patients in Indian population. This cross-sectional observational study was conducted at the Institute of Mental Health, Chennai, India. Remitted (symptom free in preceding six months) and unremitted patients were assessed by Positive and Negative Symptom Scale (PANSS), Personal and Social Performance (PSP) scale, World Health Organization-Quality of Life BREF (WHOQOL-BREF) and Global Assessment of Functioning (GAF). There were 30 patients in each group. All statistical analysis was done using SPSS version 20.0 statistical software. Patients in symptomatic remission were found to have better quality of life in personal, environmental and social domains (p<0.01). Their personal and social performance is significantly better in remission group. The overall functioning was assessed by GAF, was better in patients with symptomatic remission (p<0.001). Symptomatic remission may be a good indicator of better clinical status, personal and social functioning and quality of life.

Objective To (i) identify the impact of COVID-19 on provision of UK audiology services across sectors (ii) compare teleaudiology service provision between private and public sectors before and after the introduction of restrictions and (iii) identify barriers to teleaudiology delivery amongst UK hearing care professionals in both sectors. Design A mixed-methods cross-sectional survey study design. Responses to the structured questionnaire were analysed using descriptive and non-parametric statistics. Study Sample UK based hearing care professionals (HCP) (n = 323) completed the survey (218 public sector; 89 private sector). Results Changes in working patterns varied greatly between different sectors, with 61% of national employed and 26% of independent HCPs being furloughed, compared with 1% in the public sector. Use of telehealth was under-utilised across all sectors and groups in UK hearing healthcare, despite 92% of public and 75% of private HCPs reporting feeling comfortable conducting remote consultations. Conclusion This study highlights a variation in teleaudiology adoption and key barriers across sector in the UK. A collaborative approach between hearing device manufacturers, research centres, HCPs and professional bodies is required for the creation of targeted guidance and training materials according to sector, to support clinicians in effective teleaudiology provision.

BackgroundCleaning practices and hand hygiene are important behaviors to prevent and control the spread of infectious disease, especially in congregate settings. This project explored hygiene- and cleaning-related experiences in shelters serving people experiencing homelessness (PEH) during May–June 2020 of the COVID-19 pandemic.MethodsWe conducted qualitative, in-depth interviews by phone with 22 staff from six shelters in Atlanta, Georgia. The interview guide included questions about cleaning routines, cleaning barriers and facilitators, cleaning promotion, hand hygiene promotion, and hand hygiene barriers and facilitators. We analyzed interview transcripts using thematic analysis.ResultsMultiple individuals, such as shelter individuals (clients), volunteers, and staff, played a role in shelter cleaning. Staff reported engaging in frequent hand hygiene and cleaning practices. Barriers to cleaning included staffing shortages and access to cleaning supplies. Staff reported barriers (e.g., differing perceptions of cleanliness) for clients who were often involved in cleaning activities. Barriers to hand hygiene included limited time to wash hands, forgetting, and inconvenient handwashing facilities. Specific guidance about when and how to clean, and what supplies to use, were requested.ConclusionDuring the early months of the COVID-19 pandemic, shelters serving PEH in the Atlanta-metro area needed resources and support to ensure sufficient staffing and supplies for cleaning activities. As part of future pandemic planning and outbreak prevention efforts, shelters serving PEH could benefit from specific guidance and training materials on cleaning and hand hygiene practices.

Sharpening the global focus on ethnicity and race in the time of COVID-19

A key requirement of realizing the connected world featured by IoT is to ensure interoperability among different connected devices. Interoperability is also at the basis of the realization of the novel vision of Industry 4.0; a lot effort is put to make interoperable the interchange of information between industrial applications, also including IoT ecosystems. For this reason, during these last years, several approaches aimed to enhance interoperability between industrial applications and IoT appeared in the literature. In this paper an interoperability proposal is presented. It is based on the idea to realize interworking between the two standards considered among the reference ones in the industrial and IoT domains. They are the OPC UA for the industrial domain and oneM2M for the IoT. Interworking is realized in such a way to allow industrial applications based on OPC UA to acquire information coming from oneM2M-based IoT devices. The proposal allows an OPC UA Server to publish each piece of information produced by oneM2M-based IoT devices, so that this information may be consumed by industrial applications playing the OPC UA Client role.

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS)(NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translationAI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysisCybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network securitySocial good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

Little or no research has been done with Rorschach Scoring Systems to standardize instructional materials and methods which would yield high interscorer agreement by the conclusion of a time-limited course of study in a classroom setting. Clinical applications and research with the Rorschach would benefit from training methods which yield high interscorer reliability. Training materials based on the Perceptanalytic system were standardized on 250 graduate level psychology students over an 11-year span of time, using one class per year. The final training materials were replicated using three classes (n = 58) to test the stability of the system. Data analysis on interscorer agreement involved 20,042 comparisons of pairs of scores and summaries at two levels of difficulty: Intermediate and Advanced. Overall interscorer agreement was consistently high for all replications.

BackgroundNatural language processing (NLP) has become an emerging technology in health care that leverages a large amount of free-text data in electronic health records to improve patient care, support clinical decisions, and facilitate clinical and translational science research. Recently, deep learning has achieved state-of-the-art performance in many clinical NLP tasks. However, training deep learning models often requires large, annotated data sets, which are normally not publicly available and can be time-consuming to build in clinical domains. Working with smaller annotated data sets is typical in clinical NLP; therefore, ensuring that deep learning models perform well is crucial for real-world clinical NLP applications. A widely adopted approach is fine-tuning existing pretrained language models, but these attempts fall short when the training data set contains only a few annotated samples. Few-shot learning (FSL) has recently been investigated to tackle this problem. Siamese neural network (SNN) has been widely used as an FSL approach in computer vision but has not been studied well in NLP. Furthermore, the literature on its applications in clinical domains is scarce.ObjectiveThe aim of our study is to propose and evaluate SNN-based approaches for few-shot clinical NLP tasks.MethodsWe propose 2 SNN-based FSL approaches, including pretrained SNN and SNN with second-order embeddings. We evaluate the proposed approaches on the clinical sentence classification task. We experiment with 3 few-shot settings, including 4-shot, 8-shot, and 16-shot learning. The clinical NLP task is benchmarked using the following 4 pretrained language models: bidirectional encoder representations from transformers (BERT), BERT for biomedical text mining (BioBERT), BioBERT trained on clinical notes (BioClinicalBERT), and generative pretrained transformer 2 (GPT-2). We also present a performance comparison between SNN-based approaches and the prompt-based GPT-2 approach.ResultsIn 4-shot sentence classification tasks, GPT-2 had the highest precision (0.63), but its recall (0.38) and F score (0.42) were lower than those of BioBERT-based pretrained SNN (0.45 and 0.46, respectively). In both 8-shot and 16-shot settings, SNN-based approaches outperformed GPT-2 in all 3 metrics of precision, recall, and F score.ConclusionsThe experimental results verified the effectiveness of the proposed SNN approaches for few-shot clinical NLP tasks.

Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Transformer models have recently achieved state-of-the-art performance in long-range forecasting, they often suffer from interpretability issues and instability in the presence of noise or dynamical uncertainty. We propose , a forecasting framework that combines Transformer-based sequence modeling with Koopman-inspired latent linear dynamics to improve stability, interpretability, and long-horizon robustness. Our model features a modular encoder–propagator–decoder structure, where temporal dynamics are learned via a spectrally constrained, linear Koopman-inspired propagator in a latent space. We impose structural guarantees—i.e., bounded spectral radius, Lyapunov-based energy regularization, and orthogonal parameterization—to ensure stability and interpretability. Comprehensive evaluations are conducted on dynamical systems, climate dataset, financial time series, and electricity generation dataset using the package that is prepared for this purpose. Across all experiments, consistently outperforms standard LSTM and baseline Transformer models in terms of accuracy, robustness to noise, and long-term forecasting stability. These results establish as a flexible, interpretable, and robust framework for forecasting in high-dimensional and dynamical settings.

IntroductionTraditionally, physical therapy has adopted a tertiary approach to preventative care. However, recent trends in fall-related injuries and deaths among older individuals suggest a dire need for earlier intervention. The Home-based Older Persons Upstreaming Prevention Physical Therapy (HOP-UP-PT) program has been developed to improve the health and overall function of community-dwelling older adults at risk of functional decline. As demand continually rises for HOP-UP-PT services, online training modules have been developed to safely and efficiently provide HOP-UP-PT competency to physical therapists. The purpose of this study was to examine self-reported experiences and perceptions of physical therapists after completing an asynchronous training program to deliver HOP-UP-PT.MethodsAfter securing Oakland University IRB approval, a qualitative study using a sample of convenience used two structured focus group interviews. Inclusion criteria required participants to be licensed physical therapists (PTs) in the state of Michigan providing at least 20 hours of direct patient care per week. Participants completed eight 30-minute training modules, each with a corresponding quiz. Upon completion, PTs attended one of two video conference focus groups. Data was analyzed using the constant comparative method to develop themes and concepts based on responses about the training modules and the overall HOP-UP-PT program.ResultsTwelve PTs with a median age of 31-40 years participated. Analysis of two focus group sessions identified three concepts (Novel Approach to Physical Therapy Care, Integration of a Preventative Approach into Clinical Practice, and Knowledge Translation) and ten themes (Addressing an Unmet Need, Establishing a Working Relationship with Community Centers, Applicability to Various Settings, Shifting the Mindset to a Prevention-focused Paradigm, Applicability to Physical Therapists that Care for Older Adults, Patient Engagement and Prevention, Value for the Professional, Importance of Availability of Options in a Learning Platform, Ongoing Availability of Program Resources and Tools, and Clinical Application Practice).ConclusionPTs identified the HOP-UP-PT program as a novel, clinically applicable, and adding value to the profession. Furthermore, its upstream focus aligns with the growing role of preventative care by PTs; however, as HOP-UP-PT is not a traditional approach, additional training and clinical support materials may facilitate adoption and clinical application. HOP-UP-PT uses a preventative approach to clinical practice, but efforts to translate knowledge to PT are an important consideration. Additionally, the study identified a need for refinement and modifications to the existing HOP-UP-PT training modules.

Nutritional diagnosis involves identifying a nutritional problem, its cause, and the signs that indicate it to guide appropriate treatment. Few studies report on the most prevalent nutritional diagnoses in people living with type 2 diabetes (T2D). To define nutritional diagnoses across different domains and their association with metabolic, anthropometric, and dietary parameters in individuals with T2D. A personalized nutritional intervention was conducted using the Nutrition Care Process (NCP) model, which encompasses assessment, diagnosis, intervention, and evaluation, utilizing standardized terminology from the Nutrition Care Process Terminology (NCPT). Two dietitians, trained and standardized in applying the NCP and NCPT, performed patient assessments and established the diagnoses. Patients over 18 years old with a diagnosis of T2D for less than 5 years were included. Data from 2,050 patients were analyzed, of whom 55.3% were women, and 44.7% were men, with a median age of 57 and 54 years, respectively. The most prevalent nutritional diagnosis was excessive energy and carbohydrate intake. Diagnoses were distributed across domains: Intake (55.9%), Behavioral/Environmental (32.7%), Clinical (10.2%), and 1.2% without nutritional diagnosis. Significant intergroup differences were observed in anthropometric variables such as BMI, waist circumference, and body fat percentage (p < 0.05). HbA1c and glucose levels were significantly higher in the Intake and Behavioral groups (p < 0.001). Albumin/creatinine ratio (ACR) was higher in the ingestion group (p = 0.007). Caloric and carbohydrate intake were higher in the ingestion group, while protein and fat intake were higher in the clinical and behavioral groups (p < 0.001). Nutritional diagnoses in the intake domain, followed by behavioral/ environmental and clinical domains, are highly prevalent in people with T2D and are associated with worse metabolic control, higher BMI, and increased energy and carbohydrate intake. Timely identification of issues in these domains can support targeted nutritional therapy to improve disease management and promote a healthy lifestyle. https://clinicaltrials.gov/study/NCT02836808?term=caipadi&rank=2, Identifier (NCT02836808).

Fungi play essential roles in human health, agriculture, and ecosystems, yet their diversity has long been underestimated due to reliance on morphology and culture. Molecular innovations—DNA barcoding, multilocus and whole-genome sequencing, NGS, and rapid nucleic acid diagnostics (qPCR, LAMP, CRISPR/Cas)—have revolutionized fungal taxonomy and detection. The ITS region now serves as a universal barcode, often complemented by other loci or genomic data. High-throughput and portable tools enable near real-time identification, supported by AI-driven bioinformatics for species recognition and resistance prediction. Despite progress, challenges remain in database accuracy, primer design, and standardization. Integrating molecular taxonomy, AI, and global collaboration promises scalable, reliable frameworks for fungal surveillance and control across clinical, agricultural, and environmental domains.