2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study.

BACKGROUND: One-third of the US population experiences sleep loss, with the potential to impair physical and cognitive performance, reduce productivity, and imperil safety during work and daily activities. Computer-based fatigue-management systems with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. OBJECTIVE: In this study, we aim to enhance the capability of the 2B-Alert Web tool by providing the means for it to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times under any sleep-loss condition. METHODS: We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web tool, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm's recommendations with those based on the US Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. RESULTS: For the 4 simulated conditions, the 2B-Alert Web-proposed interventions increased mean alertness by 36% to 94% and decreased peak alertness impairment by 31% to 71% while using equivalent or smaller doses of caffeine as the corresponding US Army guidelines. CONCLUSIONS: The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work and rest schedules.

AI algorithm for personalized resource allocation and treatment of hemorrhage casualties.

A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs versus a static population-based method that only uses currently available vital-sign information. The theoretical benefits of this approach included up to 46% additional casualties restored to healthy vital signs and a 119% increase in fluid-utilization efficiency. Although the study is not immune from limitations associated with synthetic data under specific assumptions, the work demonstrated the potential for incorporating neural network-based AI technologies in hemorrhage detection and treatment. The simulated injury and treatment scenarios used delineated possible benefits and opportunities available for using AI in pre-hospital trauma care. The greatest benefit of this technology lies in its ability to provide personalized interventions that optimize clinical outcomes under resource-limited conditions, such as in civilian or military mass-casualty events, involving moderate and severe hemorrhage.

Stationary phase and nutrient levels trigger transcription of a genomic locus containing a novel peptide (TM1316) in the hyperthermophilic bacterium Thermotoga maritima.

The genome of the hyperthermophilic bacterium Thermotoga maritima encodes numerous putative peptides/proteins of 100 amino acids or less. While most of these open reading frames (ORFs) are transcribed during growth, their corresponding physiological roles are largely unknown. The onset of stationary phase in T. maritima was accompanied by significant morphological changes and upregulation of several ORFs located in the TM1298-TM1336 genome locus. This region contains putative HicAB toxin-antitoxin pairs, hypothetical proteins, radical S-adenosylmethionine (SAM) enzymes, and ABC transporters. Of particular note was the TM1315-TM1319 operon, which includes a putative 31-amino-acid peptide (TM1316) that was the most highly transcribed gene in the transcriptome during stationary phase. Antibodies directed against a synthetic version of TM1316 were used to track its production, which correlated closely with transcriptomic data. Immunofluorescence microscopy revealed that TM1316 was localized to the cell envelope and prominent in cell aggregates formed during stationary phase. The only functionally characterized locus with an organization similar to that of TM1315-TM1319 is in Bacillus subtilis, which contains subtilosin A, a cyclic peptide with Cys-to-α-carbon linkages that functions as an antilisterial bacteriocin. While the organization of TM1316 resembled that of the Bacillus peptide (e.g., in its number of amino acids and spacing of Cys residues), preparations containing high levels of TM1316 affected the growth of neither Thermotoga species nor Pyrococcus furiosus, a hyperthermophilic archaeon isolated from the same locale as T. maritima. Several other putative Cys-rich peptides could be identified in the TM1298-TM1336 locus, and while their roles are also unclear, they merit examination as potential antimicrobial agents in hyperthermophilic biotopes.

APPRAISE-HRI: AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR TRIAGE OF HEMORRHAGE CASUALTIES.

ABSTRACT: Background: Hemorrhage remains the leading cause of death on the battlefield. This study aims to assess the ability of an artificial intelligence triage algorithm to automatically analyze vital-sign data and stratify hemorrhage risk in trauma patients. Methods: Here, we developed the APPRAISE-Hemorrhage Risk Index (HRI) algorithm, which uses three routinely measured vital signs (heart rate and diastolic and systolic blood pressures) to identify trauma patients at greatest risk of hemorrhage. The algorithm preprocesses the vital signs to discard unreliable data, analyzes reliable data using an artificial intelligence-based linear regression model, and stratifies hemorrhage risk into low (HRI:I), average (HRI:II), and high (HRI:III). Results: To train and test the algorithm, we used 540 h of continuous vital-sign data collected from 1,659 trauma patients in prehospital and hospital (i.e., emergency department) settings. We defined hemorrhage cases (n = 198) as those patients who received ≥1 unit of packed red blood cells within 24 h of hospital admission and had documented hemorrhagic injuries. The APPRAISE-HRI stratification yielded a hemorrhage likelihood ratio (95% confidence interval) of 0.28 (0.13-0.43) for HRI:I, 1.00 (0.85-1.15) for HRI:II, and 5.75 (3.57-7.93) for HRI:III, suggesting that patients categorized in the low-risk (high-risk) category were at least 3-fold less (more) likely to have hemorrhage than those in the average trauma population. We obtained similar results in a cross-validation analysis. Conclusions: The APPRAISE-HRI algorithm provides a new capability to evaluate routine vital signs and alert medics to specific casualties who have the highest risk of hemorrhage, to optimize decision-making for triage, treatment, and evacuation.

Hyperthermophilic Thermotoga species differ with respect to specific carbohydrate transporters and glycoside hydrolases.

Four hyperthermophilic members of the bacterial genus Thermotoga (T. maritima, T. neapolitana, T. petrophila, and Thermotoga sp. strain RQ2) share a core genome of 1,470 open reading frames (ORFs), or about 75% of their genomes. Nonetheless, each species exhibited certain distinguishing features during growth on simple and complex carbohydrates that correlated with genomic inventories of specific ABC sugar transporters and glycoside hydrolases. These differences were consistent with transcriptomic analysis based on a multispecies cDNA microarray. Growth on a mixture of six pentoses and hexoses showed no significant utilization of galactose or mannose by any of the four species. T. maritima and T. neapolitana exhibited similar monosaccharide utilization profiles, with a strong preference for glucose and xylose over fructose and arabinose. Thermotoga sp. strain RQ2 also used glucose and xylose, but was the only species to utilize fructose to any extent, consistent with a phosphotransferase system (PTS) specific for this sugar encoded in its genome. T. petrophila used glucose to a significantly lesser extent than the other species. In fact, the XylR regulon was triggered by growth on glucose for T. petrophila, which was attributed to the absence of a glucose transporter (XylE2F2K2), otherwise present in the other Thermotoga species. This suggested that T. petrophila acquires glucose through the XylE1F1K1 transporter, which primarily serves to transport xylose in the other three Thermotoga species. The results here show that subtle differences exist among the hyperthermophilic Thermotogales with respect to carbohydrate utilization, which supports their designation as separate species.

A biomechanical-based approach to scale blast-induced molecular changes in the brain.

Animal studies provide valuable insights on how the interaction of blast waves with the head may injure the brain. However, there is no acceptable methodology to scale the findings from animals to humans. Here, we propose an experimental/computational approach to project observed blast-induced molecular changes in the rat brain to the human brain. Using a shock tube, we exposed rats to a range of blast overpressures (BOPs) and used a high-fidelity computational model of a rat head to correlate predicted biomechanical responses with measured changes in glial fibrillary acidic protein (GFAP) in rat brain tissues. Our analyses revealed correlates between model-predicted strain rate and measured GFAP changes in three brain regions. Using these correlates and a high-fidelity computational model of a human head, we determined the equivalent BOPs in rats and in humans that induced similar strain rates across the two species. We used the equivalent BOPs to project the measured GFAP changes in the rat brain to the human. Our results suggest that, relative to the rat, the human requires an exposure to a blast wave of a higher magnitude to elicit similar brain-tissue responses. Our proposed methodology could assist in the development of safety guidelines for blast exposure.

The genus Thermotoga: recent developments.

The genus Thermotoga comprises extremely thermophilic (Topt > or = 70 degrees C) and hyperthermophilic (Topt > or = 80 degrees C) bacteria, which have been extensively studied for insights into the basis for life at elevated temperatures and for biotechnological opportunities (e.g. biohydrogen production, biocatalysis). Over the past decade, genome sequences have become available for a number of Thermotoga species, leading to functional genomics efforts to understand growth physiology as well as genomics-based identification and characterization of novel high-temperature biocatalysts. Discussed here are recent developments along these lines for this group of microorganisms.

Extreme Thermophiles: Moving beyond single-enzyme biocatalysis.

Extremely thermophilic microorganisms have been sources of thermostable and thermoactive enzymes for over 30 years. However, information and insights gained from genome sequences, in conjunction with new tools for molecular genetics, have opened up exciting new possibilities for biotechnological opportunities based on extreme thermophiles that go beyond single-step biotransformations. Although the pace for discovering novel microorganisms has slowed over the past two decades, genome sequence data have provided clues to novel biomolecules and metabolic pathways, which can be mined for a range of new applications. Furthermore, recent advances in molecular genetics for extreme thermophiles have made metabolic engineering for high temperature applications a reality.