I. IntroductionModels trained on instruction-following datasets, such as InstructGPT and Alpaca, represent an important evolution in the field of artificial intelligence (AI). These models are specifically designed to process and respond to human instructions, bridging the gap between natural language understanding and task execution. Unlike traditional language models that are typically trained on large corpora of unstructured text, instruction-following models are fine-tuned on datasets containing pairs of instructions and corresponding responses. This targeted approach enables the models to perform tasks with greater alignment to user intent, improving their utility across a wide range of applications.As AI continues to integrate more deeply into daily life, the demand for systems capable of interpreting and responding to human instructions with accuracy and contextual awareness has grown. Instruction-following models are pivotal in this shift, offering a level of interactivity that enhances user experience in industries such as virtual assistance, customer support, education, and healthcare. These models provide significant potential for automating complex tasks, solving problems, and offering insights based on natural language commands.In this section, we introduce the concept of instruction-following models, explore their importance in advancing AI applications, and briefly highlight some of the most well-known examples, such as InstructGPT and Alpaca. By understanding their foundations and capabilities, we set the stage for exploring the technologies, challenges, and future potential of instruction-following models in the broader context of AI development.

Conventional pretreatment processes have been in commercial use for over 100 years and are relatively expensive to build and expensive to operate. These processes have evolved from low-volume edible oil refining with meaningfully different product requirements and are not well suited to petro-scale, high volume fuel production. This research explores the feasibility of a high temperature, selective hydrolysis, liquid-liquid extraction process as a more suitable alternative that offers lower capital cost, higher yield, lower variable cost, increased feedstock flexibility and streamlined operations. A series of benchtop batch reactions was performed to explore the design space as defined by reaction conditions such as temperature and residence time. Additionally, feedstock selection, water content, and catalyst were also varied in these benchtop trials. Upon discovering a range of conditions and reaction constituents that provided favorable results, a fully continuous pilot system was constructed to further explore the concept feasibility in a more commercially relevant environment. Starting feedstocks and pretreated samples were analyzed via ICP-OES with the target of achieving less than 2 ppm phosphorus and less than 5 ppm total metals in treated samples. This target was exceeded with a wide variety of feedstocks in the continuous pilot system. This research demonstrates the feasibility of a selective hydrolysis, liquid-liquid extraction process as a successful approach to feedstock pretreatment applicable to renewable fuel production. The process is also well suited for the purification of edible vegetable oils and industrial fats. This approach eliminates the complications and yield loss associated with centrifuges and sorbent powders while providing hardware that is more familiar to the fuel industry and better suited to high-volume oil processing.

In this paper, we will present a few new extensions of the theory of modular forms, a departure from the typical theory-i.e. the theory of modular forms over the matrix group SL2(Z) with inputs in H C .

A document by Aakriti Aggarwal. Click on the document to view its contents.

Natural Language Processing (NLP) is revolutionizing the utilization of Electronic Health Records (EHR) by automating clinical summarization and patient risk stratification, thereby enhancing healthcare delivery. This chapter explores the integration of NLP technologies into EHR systems, highlighting their capacity to transform vast amounts of unstructured clinical data into actionable insights. Clinical summarization, a critical application of NLP, improves communication among healthcare providers and ensures continuity of care by generating concise, relevant summaries from comprehensive patient records. Through both extractive and abstractive summarization techniques, NLP facilitates efficient information retrieval, allowing clinicians to focus on key patient data.

Table of Contents1. Introduction 3 2. Document Control 3 3. Purpose 4 4. Recovery Objectives 4 4.1 Recovery Time Objective (RTO): 4 4.2 Maximum Tolerable Downtime (MTD): 4 4.3 Recovery Point Objective (RPO): 5 5. Roles and Responsibilities 5 6. Incident Response 7 7. Plan Activation 7 8. Activation Steps: 8 8.1 The Disaster Recovery Team Lead authorizes activation. 8 8.2 Submit the Emergency Change Request (ECR) to the IT board. 8 8.3 Assemble teams at the designated recovery location. 8 9. Backup Plan and Schedule 8 10. Contacting Stakeholders to be notified of recovery events: 8 11. Disaster Recovery Steps 9 11.1 LMS (iLearn) System Recovery: 9 11.2 Emergency Change Request (ECR): 9 11.3 Remove Original Server (if compromised): 9 12. Procedures 9 13. Testing Schedule 10 14. Tracking 10 15. Document History 10 16. APPENDIX A – After Action Report 11 17. References 13

Babek Habibi Norouzlou

† These authors contributed equally to this work.*Corresponding authors’ emails: michael.dolan@takeda.com (Michael E. Dolan), sheldon.oppenheim@takeda.com (Sheldon F. Oppenheim), and z.zhou@northeastern.edu (Zhaohui Sunny Zhou)

Understanding the intricate ecological implications of species coexistence through trophic network analysis is crucial for biodiversity studies and for deciphering the environmental drivers of ecosystem dynamics. This study examines, in detail, the complexity, structure, and potential responses of the Strait of Magellan’s trophic network to both environmental and anthropogenic disturbances. Based on an extensive dataset of prey-predator interactions, we characterized the network’s topology and its theoretical resilience using a complex network methodology, analyzing the system at both the network-wide (holistic) and species-specific (reductionist) levels. Our analysis of 140 trophic species and 438 interactions reveals a network with low connectivity (0.022) and an asymmetrical distribution of links, where a few species disproportionately perform most interactions. The network exhibits a ”small-world” architecture, with high clustering and short path lengths, suggesting a rapid propagation of local disturbances. A significant proportion (over 50%) of species are omnivorous, a trait likely contributing to ecosystem stability amidst fluctuating prey availability. Key taxa – polychaetes, Fuegian sprat (Sprattus fuegensis) “sardina”, squat lobster (Grimothea gregaria) “langostino”, and Patagonian blenny (Eleginops maclovinus) “róbalo” – emerge as central conduits for matter and energy flow, effectively linking benthic and pelagic primary productivity to higher trophic levels and significantly underpinning ecosystem function. While the network shows potential robustness to general fluctuations, its concentrated interaction structure makes it vulnerable to the loss or migration of these pivotal species. These findings highlight the importance of understanding trophic relationships to inform effective conservation and ecosystem management strategies in this sensitive Sub-Antarctic marine region. This study provides a foundational understanding of the Strait of Magellan’s marine trophic architecture.

Deep learning has emerged as a transformative tool in medical diagnostics, particularly in the fields of radiology and histopathology. This study presents a comparative analysis of three prominent deep learning architectures: Convolutional Neural Networks (CNNs), Transformers, and hybrid models that combine the strengths of both. We explore the computational methods employed in each architecture, emphasizing their technical novelties, such as the use of attention mechanisms in Transformers and the integration of feature extraction and contextual learning in hybrid models.

AbstractHuman activities alter connectivity via resource flows, ecosystem (patch) size, and disturbance regimes. While resource flows can interact with ecosystem size to affect ecosystem function, we lack evidence on whether and how these two variables further interact with disturbance. Here, we conducted a highly replicated microcosm experiment with two-patch autotrophic-heterotrophic meta-ecosystems, manipulating resource flows (connected/unconnected), patch size, and disturbance (fixed magnitude or relative to patch size) to study their effects on biomass density across patches as a meta-ecosystem function. Our results showed an interaction between resource flows, patch size, and disturbance. In intermediate-small patches only, resource flows increased meta-ecosystem function when the disturbance was fixed, while they had no effect when the disturbance was relative to patch size. Our results suggest that to understand the interactive effects of ecosystem size and resource flows on ecosystem function, we might have to consider how disturbance covaries with ecosystem size.

not-yet-known not-yet-known not-yet-known unknown A This case report describes the use of the Scope VOR&GDI® videovaginoscope for hysteroscopy in a mare following uterine prolapse treatment. A 6-year-old mare presented with uterine prolapse after miscarriage was manually treated, sutured, and administered antibiotics and anti-inflammatories. Five days post-treatment, hysteroscopy was performed to assess endometrial health. The videovaginoscope provided high-quality images, allowing thorough evaluation of the uterine lumen without discomfort to the mare or operator. The examination confirmed a preserved endometrium, free of hemorrhage or necrosis, demonstrating the treatment’s effectiveness. The Scope VOR&GDI® proved valuable for gynecological assessment, offering diagnostic accuracy and comfort in evaluating reproductive tract conditions.

This paper describes a fast, robust and efficient method to calculate the scattered field due to multiple PEC cylinders located at random positions excited by a plane wave. The cylinder collocation follows stochastic geometry principles while including multiple electromagnetic interactions. By using this method, we study the statistics of scattered field due to Poisson point process distributed multiple cylinder samples.

The effect of elevation on insect herbivory has been a central topic in ecological research for decades. A key question is how insect herbivory varies along elevational gradients and the mechanisms driving these patterns. Recent meta-analyses have examined the relationship of elevation and insect herbivory globally and for tropical, polar and temperate climate zones. However, there is a lack of meta-analysis research focused on smaller-scale climate zones. Mediterranean-type climate (MTC) ecosystems are of particular importance since they constitute biodiversity hotspots and are threatened by anthropogenic forces including climate change which is having a disproportionate effect on these regions. Thus, understanding patterns of insect damage in these systems is important to both basic and applied science. We conducted a meta-analysis to assess the overall relationship of altitude and insect herbivore damage in MTC ecosystems considering the moderators damage type (leaf, seed, and borer); plant habit (woody and non-woody); elevation range (small vs. large); sampling period (< 2010 and 2010 ̵̶ 2025); taxonomic order (Fagales and non-Fagales), and leaf longevity (deciduous, semi-deciduous, evergreen). For overall herbivory and all but one moderator, we found no significant effects of elevation on insect damage in MTC ecosystems. For the 2010 ̵̶ 2025 sampling period moderator, we found a positive relationship of insect herbivory and altitude. A posteriori analyses with the 2010 ̵̶ 2025 sampling period group indicated positive relationships of elevation and herbivory in terms of leaf and seed damage; woody plants; and small elevation range. We point to climate change and the particularly hot and dry conditions in MTC ecosystems to explain results for recent sampling period. Abiotic conditions may be limiting to insects at lower elevations resulting in a range shift to higher elevations. Further research is necessary to validate these findings and to elucidate the underlying mechanisms.

not-yet-known not-yet-known not-yet-known unknown Background Investigate and analyse the relationship between atmospheric patterns and Equine Grass Sickness (EGS) cases to determine the cause of the disease. Objectives 1. Build an atmospheric model that generalises the conditions prior to an EGS case. 2. Use the generalised model and existing knowledge to perform a focused review of prerequisite conditions. 3. Determine the root cause of Equine Grass Sickness, explaining why and how it happens. Study design Retrospective case series. Methods Quantitative and qualitive analysis of environmental data for 180 EGS cases covering a 13-week period (12-week pre and 1-week post-case). Using the principle of superposition, overlapping each cases data to produce a single generalised case. Results Significant correlation between the case dates and global maximums/minimums in atmospheric conditions, with notable periods of low rainfall, high pressure, and high nitrate levels in the days before the case date. Main limitations EGS location data limited to county level. EGS case date inaccuracies with 2–3-day drift. Case data only available from 2017. Unknown if all EGS cases within the analysed time period were reported. Conclusions Combining the superposition model with existing knowledge identified a desiccation resistant, nitrogen-fixing bacteria and elevated levels of iron (Fe), molybdenum (Mo) and vanadium (V), the heavy metals used in nitrogen fixation, as most likely contributors to the disease.

Plant-microbe interactions are critical in shaping forest dynamics, yet molecular functions through which foliar endophytic fungi (FEF) influence seedling survival and coexistence remain unresolved. Combining FEF transcriptomics with seedling dynamics in a subtropical forest, we tested whether FEF molecular functions regulate seedling survival via host fitness differences and niche differentiation under modern coexistence theory. We found FEF transcripts diversity strongly associated with host divergence time and maximum height, and FEF functions significantly enhance seedling survival through fitness-related and niche-related processes. Among these FEF functions, gene ontology (GO) terms with high host-phylogeny dependence enhance seedling survival through fitness differences associated with stimulus responses and multi-organism interactions, and those with high host-trait dependence enhanced seedling survival through fitness differences related to basic life processes. For GOs independent to host-phylogeny and host-trait, they enhance seedling survival via increased niche differentiation. Our findings bridge microbial functional genomics with modern coexistence theory in natural forests.

not-yet-known not-yet-known not-yet-known unknown Introduction Bronchial cyst is a common benign disease, often found near the trachea, bronchus, hilar and pericardium, and 90% of them are located in the middle and upper mediastinum, while thin-walled bronchial cysts located in the posterior mediastinum can be frequently encountered in clinical work, and infected thick-walled cysts are relatively rare, and the cyst wall is not clearly delineated from the esophageal epithelium, which makes the surgery somewhat difficult. Now, we summarize a case of infected thick-walled bronchial cyst in the posterior mediastinum treated by our department in order to analyze its clinical characteristics and discuss the surgical treatment experience.

Nowadays, it’s hard for job seekers to figure out which job postings are real and which ones are fake. To solve this problem, we’ve built a smart tool that uses advanced technology to detect and remove fake job ads. We looked at various methods to study job ads and checked how effectively each one functioned. After doing many tests, we saw that mixing different techniques (ensemble methods) gave the top results in finding fake job postings. Our system, Fake Job Post Prediction, is based on a strong and trustworthy model, the Random Forest Algorithm (RFA), which helps a lot with making accuracy better. This system is known for being both fast and accurate, and it achieves an impressive 98% accuracy rate—way better than older techniques. The goal of this tool is to protect job seekers from scams, like fake job offers or requests for money during the application process. By identifying and removing fake job ads early, it makes the job search process much safer and more trustworthy. This tool is a big step forward—it helps people tell the difference between real and fake job opportunities and creates a safer online space for job hunting. In today’s tricky job market, this tool is a must-have for anyone looking for work.

We present an end-to-end deep learning pipeline for classifying the sentiment of Indian political tweets. We compiled approximately 160 000 tweets from the 2019 general elections using the IndianElection19TwitterData corpus [13] and applied distant-supervision heuristics to label each tweet as ‘pro-BJP’ or ‘pro-Congress,’ discarding neutral or ambiguous cases. This yielded a labeled set of ~75K tweets (≈52% pro-BJP). We trained two neural models: (1) a 1D CNN followed by a unidirectional LSTM, and (2) a bidirectional LSTM with a self-attention layer. Both models used 100-dimensional GloVe embeddings [7]. On held-out validation data, the CNN–LSTM achieved ≈99% accuracy and the BiLSTM–Attention ≈96%. Surprisingly, simply averaging their outputs into an ensemble gave only ≈47.5% accuracy, suggesting the models made correlated errors. Finally, we deployed the trained models in a Streamlit “Votelyzer” web app that offers real-time sentiment predictions on user-input tweets or uploaded CSV files. Our results demonstrate that neural networks can effectively learn political sentiment even from noisy, weakly labeled data, and highlight practical challenges (e.g. label noise and ensembling) in this setting.

Sleep apnea, a prevalent sleep disorder characterized by recurrent episodes of upper airway obstruction during sleep, poses significant health risks, including cardiovascular complications, cognitive impairment, and increased mortality. The timely and accurate detection of sleep apnea is crucial for initiating appropriate treatment and mitigating these adverse outcomes. Traditional methods for sleep apnea diagnosis, such as polysomnography, are resource-intensive, time-consuming, and often inconvenient for patients. Consequently, there is a growing need for automated and accessible sleep apnea detection techniques that can be readily deployed in clinical and home settings Deep learning approaches have emerged as promising tools for analyzing physiological signals and identifying complex patterns indicative of sleep apnea. Leveraging the power of deep learning, researchers are developing innovative solutions to improve the accuracy and efficiency of sleep apnea detection, ultimately leading to better patient care and management. This research explores the application of deep learning techniques, specifically Long Short-Term Memory networks and VGG networks, for the automated detection of sleep apnea using physiological signals. The proposed approach aims to leverage the temporal dependencies captured by LSTM networks and the feature extraction capabilities of VGG networks to develop a robust and accurate sleep apnea detection system. The utilization of wearable sensor data presents a non-invasive and convenient method for monitoring individuals, athletes, and high-risk patients in real-time.

Enterovirus A71 (EV-A71), a member of the genus Enterovirus of the Picornaviridae family, is a major etiological agent of hand, foot, and mouth disease (HFMD) that primarily affects children <5 years of age. While subgenotype C4 has been endemic in mainland China since 1998, surveillance data indicate that the Asia-Pacific region is experiencing persistent fluctuation of subgenotype B5 strains, which demonstrates increasing epidemic potential, delineating the evolutionary landscape of EV-A71 isolates obtained from HFMD cases in the Qingdao metropolitan area during 2023-2024. From 2,083 clinical specimens, we identified 27 EV-A71-positive samples, with successful viral isolation from 19 specimens. Whole-genome phylogenetic analysis revealed the co-circulation of one endemic C4 subgenotype and two phylogenetically distinct B5 clusters. The C4 strains (26.3%, 5/19) formed a monophyletic clade with 96.95% nucleotide identity with their closest relative (Chinese strain MT708805.1, 2019), featuring conserved VP1 proteins but accumulating nonsynonymous substitutions in nonstructural regions (P2: H25Y/A177T; P3: P3L/K98R/A101T/S328N/R545K). The B5 strains (73.7%, 14/19), while retaining conserved VP1 S17 residues, diverged into two lineages on the whole-genome phylogenetic tree: The predominant B5 strains (73.7%, 14/19), while maintaining conserved VP1 S17 residues, segregated into two distinct lineages: Lineage I (n=12) contained ten strains phylogenetically clustered with Vietnam outbreak variants (2023), accompanied by two inter-type recombinant strains containing Coxsackievirus A4-derived sequences in the nonstructural P2 region and P2-P3 junction domains. Lineage II (n=2) demonstrated novel recombination patterns incorporating Coxsackievirus A2 sequences within the nonstructural P3 region. This investigation provides the first laboratory-confirmed evidence of established EV-A71 B5 subgenotype in Qingdao since surveillance initiation in 2007. Concurrent transmission of multiple EV-A71 lineages with distinct evolutionary pathways highlights the importance of implementing real-time genomic surveillance to optimize regional HFMD mitigation strategies and vaccine formulations.

“Exercise as medicine” is an important component of lifestyle medicine and has been garnering increasing attention. It is seen as an important adjunct to the treatment of a whole range of chronic diseases, from cancer to cardiac rehabilitation to interventions for Mild Cognitive Impairment.“Exercise as longevity medicine” is a far newer version that seeks not only the alleviation of chronic illnesses but the primordial prevention of these diseases where the greatest risk factor is aging. It also seeks to address not just life extension, but also the prolongation of healthspan, in other words, the compression of morbidity.This sets the next stage to implement exercise as personalized and as precision medicine and thisfocus has been the subject of recent biomedical literature. However, work on the relationshipbetween exercise for longevity and the interactions with supplements is even more nascent. Research on sports supplements have a much longer history, but the focus here is on performance rather than its effects on healthspan and lifespan.Based on a brief survey of extant literature, this paper presents 5 key questions and considerations to be investigated if supplements are to be useful for augmenting the beneficial effects of exercise and physical activities.Examples of supplements commonly used in each category and supported by the literature aregiven in parentheses. With regard to these specific supplements, where there is also research toanswer the questions posed, it will also be cited.1. Could supplements for enhancing athletic performance be re-purposed for enhancing theeffects of exercise as longevity medicine? (Creatine, Hydroxymethylbutyrate)2. Could the use of ergogenic aids be useful as a motivational tool for exercise adherence?(Coffee, not decaffeinated)3. Are there instances when supplements could ameliorate risks of more intense exercise?(Aspirin, Taurine, Niacinamide, Nettle leaf)4. Are there senolytics that interfere with the benefits of exercise? (Metformin, Nicotinamideriboside, Alpha-ketoglutarate, Curcumin)5. What insights can we obtain from medical anthropology studies of hunter gatherer societies,and research relating to longevity hotspots and masters athletes? (Lithium orotate, Fucoidan)Just as drug interactions are of importance in polypharmacy, so too the interactions betweensupplements and exercise, as both become the standard of care for smart aging.This is of especial importance as proven exercise mimetics are yet to be discovered. Until then supplements enhancing the benefits of exercise are an essential adjunct for a large segment of older adults who are unable to engage fully in physical activities to the recommended levels.

not-yet-known not-yet-known not-yet-known unknown Key Clinical Message This case highlights an atypical presentation of Community-acquired Methicillin-resistant Staphylococcus aureus (CA-MRSA) cavitary pneumonia in an immunocompetent host, complicated by the ”skip phenomenon”: a pattern of intermittent negative blood cultures that may obscure persistent bacteremia. Its PVL-mediated severity and diagnostic challenges underscore the need for heightened clinical suspicion, repeated cultures, and early, targeted intervention.

not-yet-known not-yet-known not-yet-known unknown Objectives: This review aims to investigate whether good medication adherence in adults with chronic conditions is associated with a lower mortality risk compared to poor adherence within published literature, and the extent to which this relationship is represented within policy and legislation. Methods: Systematic search of three electronic databases: PubMed, MEDLINE (Ovid), and Scopus, from February to March 2024. South African health legislation and professional guidelines were sourced using search terms aligned with the systematic review strategy and systematically analysed. Results: Twenty-six articles were included in the systematic review. Only 1 of the 17 effect measures reported for good adherence was >1. All 44 effect measures reported for intermediate, poor and non-adherence categories were >1. Pooled estimates for poor adherence and non-adherence had the highest HRs (1.63 and 2.77, respectively). The document review showed a dominance of mortality-related terms (1.323 and 2.98 matches per 1000 words for “mortality” and “death”, respectively) compared to adherence-related terms (0.053 – 2.98 matches per 1000 words). Co-occurrence between medication adherence-related search terms (MARS, adherence, medication adherence, adhere, non-adherence and medication compliance) and mortality-related search terms (death, mortality and survival) was low within all documents analysed. Conclusion: The systematic review demonstrates a clear relationship between good adherence to chronic medication and a lower mortality risk. However, the review of legislative and policy documents suggests that government efforts are focused primarily on surveillance, rather than strategy or preventative measures. This strong evidence should motivate incorporating adherence-based risk assessments into clinical and legal frameworks.