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Alpha-linolenic acid (ALA) is plant-based essential omega-3 polyunsaturated fatty acids that must be obtained through the diet. This could explain in part why the severe deficiency in omega-3 intake pointed by numerous epidemiologic studies may increase the brain’s vulnerability representing an important risk factor in the development and/or deterioration of certain cardio- and neuropathologies. The roles of ALA in neurological disorders remain unclear, especially in stroke that is a leading cause of death. We and others have identified ALA as a potential nutraceutical to protect the brain from stroke, characterized by its pleiotropic effects in neuroprotection, vasodilation of brain arteries, and neuroplasticity. This review highlights how chronic administration of ALA protects against rodent models of hypoxic-ischemic injury and exerts an anti-depressant-like activity, effects that likely involve multiple mechanisms in brain, and may be applied in stroke prevention. One major effect may be through an increase in mature brain-derived neurotrophic factor (BDNF), a widely expressed protein in brain that plays critical roles in neuronal maintenance, and learning and memory. Understanding the precise roles of ALA in neurological disorders will provide the underpinnings for the development of new therapies for patients and families who could be devastated by these disorders.

\"Jake Gyllenhaal headlines this sci-fi time-travel thriller directed by Moon's Duncan Jones from a script by Ben Ripley. A bomb explodes on a Chicago train, derailing the locomotive and killing hundreds. In an attempt to identify the bomber and prevent another, larger attack on downtown Chicago, Captain Colter Stevens (Gyllenhaal) agrees to take part in a clandestine government experiment dubbed \"Source Code,\" which allows him to enter the body of a male passenger during the eight minutes before the man is killed. But during his first trip back, Capt. Stevens fails to gather enough clues to prevent the second attack. With time quickly running out, he repeats the mission ad nauseam in a desperate race to head off one of the most deadly terrorist attacks ever to take place on American soil\"--Allmovie.com, viewed August 29, 2017.

MALDI-TOF mass spectrometry (MS) is set to make inroads into clinical chemistry because it offers advantages over other analytical platforms. These advantages include low acquisition and operating costs, ease of use, ruggedness, and high throughput. When coupled with innovative front-end strategies and applied to important clinical problems, it can deliver rapid, sensitive, and cost-effective assays. This review describes the general principles of MALDI-TOF MS, highlights the unique features of the platform, and discusses some practical methods based upon it. There is substantial potential for MALDI-TOF MS to make further inroads into clinical chemistry because of the selectivity of mass detection and its ability to independently quantify proteoforms. MALDI-TOF MS has already transformed the practice of clinical microbiology and this review illustrates how and why it is now set to play an increasingly important role in in vitro diagnostics in particular, and clinical chemistry in general.

Alpha-linolenic acid (ALA) is plant-based essential omega-3 polyunsaturated fatty acids that must be obtained through the diet. This could explain in part why the severe deficiency in omega-3 intake pointed by numerous epidemiologic studies may increase the brain's vulnerability representing an important risk factor in the development and/or deterioration of certain cardio- and neuropathologies. The roles of ALA in neurological disorders remain unclear, especially in stroke that is a leading cause of death. We and others have identified ALA as a potential nutraceutical to protect the brain from stroke, characterized by its pleiotropic effects in neuroprotection, vasodilation of brain arteries, and neuroplasticity. This review highlights how chronic administration of ALA protects against rodent models of hypoxic-ischemic injury and exerts an anti-depressant-like activity, effects that likely involve multiple mechanisms in brain, and may be applied in stroke prevention. One major effect may be through an increase in mature brain-derived neurotrophic factor (BDNF), a widely expressed protein in brain that plays critical roles in neuronal maintenance, and learning and memory. Understanding the precise roles of ALA in neurological disorders will provide the underpinnings for the development of new therapies for patients and families who could be devastated by these disorders.

Recommendations on how best to exploit the strengths of peptide-centric proteomics and avoid its pitfalls.

Multilayer thermoplastic composites offer sustainable alternatives to traditional thermoset and metal materials. However, their design is inherently complex, involving numerous interdependent parameters that render conventional processes both expensive and time‐consuming. While machine learning‐assisted methods provide a potential solution, they typically require large datasets that can be costly to obtain. This study explores a robust neural network, specifically, an Advanced Multilayer Perceptron (AdvMLP) Regressor, to predict the peel strength of multilayer thermoplastic composites. Through architectural enhancements, the AdvMLP is effectively trained on a limited yet authentic manufacturing dataset, yielding robust predictions validated by benchmark metrics and k‐fold cross‐validation. The model captures the intricate interplay between manufacturing processes and composite properties, enabling comprehensive feature importance analysis and dimensionality reduction. Overall, this study establishes a robust and generalizable machine learning‐assisted methodology to guide and accelerate the design and optimization of multilayer thermoplastic composites. This study presents a machine learning‐assisted design of multilayer thermoplastic composites, utilizing robust neural network prediction and feature importance analysis. Advanced Multilayer Perceptron Regressor is developed on limited manufacturing dataset to accurately predict peel strength. The model effectively reduced design problem's complexity from 13 parameters down to 6 critical ones, facilitating efficient and sustainable composite development in real‐world applications.

Acanthamoeba, an opportunistic pathogen is known to cause an infection of the cornea, central nervous system, and skin. Acanthamoeba feeds different microorganisms, including potentially pathogenic prokaryotes; some of microbes have developed ways of surviving intracellularly and this may mean that Acanthamoeba acts as incubator of important pathogens. A systematic review of the literature was performed in order to capture a comprehensive picture of the variety of microbial species identified within Acanthamoeba following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Forty-three studies met the inclusion criteria, 26 studies (60.5%) examined environmental samples, eight (18.6%) studies examined clinical specimens, and another nine (20.9%) studies analysed both types of samples. Polymerase chain reaction (PCR) followed by gene sequencing was the most common technique used to identify the intracellular microorganisms. Important pathogenic bacteria, such as E. coli, Mycobacterium spp. and P. aeruginosa, were observed in clinical isolates of Acanthamoeba, whereas Legionella, adenovirus, mimivirus, and unidentified bacteria (Candidatus) were often identified in environmental Acanthamoeba. Increasing resistance of Acanthamoeba associated intracellular pathogens to antimicrobials is an increased risk to public health. Molecular-based future studies are needed in order to assess the microbiome residing in Acanthamoeba, as a research on the hypotheses that intracellular microbes can affect the pathogenicity of Acanthamoeba infections.

This research addresses the data overload faced by intelligence searchers in government and defense agencies. The study leverages methods from the Cognitive Systems Engineering (CSE) literature to generate insights into the intelligence search work domain. These insights are applied to a supporting concept and requirements for designing and evaluating a human-AI agent team specifically for intelligence search tasks. Domain analysis reveals the dynamic nature of the ‘value structure’, a term that describes the evolving set of criteria governing the intelligence search process. Additionally, domain insight provides details for search aggregation and conceptual spaces from which the value structure could be efficiently applied for intelligence search. Support system designs that leverage these findings may enable an intelligence searcher to interact with and understand data at more abstract levels to improve task efficiency. Additionally, new system designs can support the searcher by facilitating an ‘Ambient Awareness’ of non-selected objects in a large data field through relevant system cues. Ambient Awareness achieved through the supporting concept and AI teaming has the potential to address the data overload problem while increasing the breadth and depth of search coverage.

Methamphetamine use disorder is a growing public health crisis with limited access to effective treatment. Contingency management (CM) has demonstrated efficacy for stimulant use disorders, but is typically delivered in person. Smartphone-based CM may overcome barriers such as limited access, but its effectiveness and real-world application remain understudied. This study explores patient and clinician experiences with a fully remote, smartphone-based CM intervention for methamphetamine use. This exploratory, descriptive qualitative study analyzes interviews with patients and clinicians involved in a previously published single-arm trial in which smartphone-based CM was offered to individuals using methamphetamine through primary care or specialty addiction treatment clinics within a large health system. The study aims to identify and describe key facilitators, barriers, and perspectives related to engagement of both groups with the intervention, providing actionable insights to inform optimization and implementation of digital CM in health care settings. We conducted a qualitative analysis of semistructured interviews with 14 patients and 14 clinicians from a prior pilot study of a fully remote, smartphone-based CM intervention for methamphetamine use. Interviews were analyzed using grounded theory in a 5-step process: transcript review, codebook development, coding, thematic reduction, and generation of overarching themes. The analysis focused on a priori themes related to facilitators, barriers, and suggestions for improvement. Patients and clinicians identified many benefits, viewing the program as valuable for individuals using methamphetamine. Patients appreciated the flexibility, accessibility, and motivational incentives. Clinicians saw CM as a low-risk, evidence-based strategy that could enhance engagement, especially among patients less responsive to traditional approaches. Common challenges included technological issues such as problems with video-based testing, app navigation, and internet access. Patients had mixed views about educational modules and described difficulty with correct substance test procedures and a lack of human connection. Clinicians expressed concerns for patients with significant psychosocial instability. Differences emerged in the types of concerns raised: patients focused on day-to-day engagement, while clinicians emphasized broader themes of equity, sustainability, and a preference for models rewarding improvement even without full abstinence. Smartphone-based CM shows promise for addressing methamphetamine use disorder, especially in settings lacking traditional treatment access. However, optimizing implementation requires addressing challenges related to technology, accessibility, and equity. Recommendations include integrating CM with clinical infrastructure, expanding rewardable behaviors beyond abstinence, enhancing user experience, and improving technological access. Future research should explore flexible models that incorporate broader recovery goals and strengthen both technical and human support.