Machine learning-enabled forward prediction and inverse design of 4D-printed active plates.

Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs. However, efficiently designing the material distribution to achieve desired 3D shape changes is significantly challenging yet greatly needed. Here, we present an approach that combines machine learning (ML) with both gradient-descent (GD) and evolutionary algorithm (EA) to design AC plates with 3D shape changes. A residual network ML model is developed for the forward shape prediction. A global-subdomain design strategy with ML-GD and ML-EA is then used for the inverse material-distribution design. For a variety of numerically generated target shapes, both ML-GD and ML-EA demonstrate high efficiency. By further combining ML-EA with a normal distance-based loss function, optimized designs are achieved for multiple irregular target shapes. Our approach thus provides a highly efficient tool for the design of 4D-printed active composites.

Diamedica Draw-over Vaporiser: bench testing the UK Defence Anaesthesia System in the deployed environment.

INTRODUCTION: The Diamedica Draw-over Vaporiser 2 (DDV2) is the sevoflurane vaporiser used by the UK Defence Medical Services to provide deployed volatile general anaesthesia. The Defence Anaesthesia System employs the DDV2 with a turbine-driven ventilator as a 'push-over' vaporiser, a modification from the manufacturer's design. We investigated sevoflurane delivery at varying minute volumes (MVs), vaporiser settings and temperatures in this configuration. METHODS: A range of DDV2 settings (1%, 2%, 3%, 4% and induction) and MVs (2, 4, 6 and 8 L/min at 12 ventilations per minute) were tested at two ambient temperatures (20 and 30±3°C) over 30 min. A supplemental experiment, simulating anaesthesia during damage control surgery, was also completed, where he DDV2 was set to 2% with a 6 L/min MV for 90 min. RESULTS: In both experiments, two distinct phases of sevoflurane delivery were noted, a 'wash-in phase' followed by a 'maintenance period'. The wash-in phase normally lasted less than 5 min. During the maintenance period at low MVs and vaporiser settings the DDV2 delivered a constant output, while at higher MVs and settings vapour output fell predictably. At 20±3°C, using DDV2 settings likely to be encountered in clinical practice, sevoflurane delivery was within 20% of that set. Higher vaporiser settings, MVs and temperatures resulted in greater variation between vaporiser setting and agent delivery. This variation is explained by the incomplete temperature compensation of the DDV2. CONCLUSIONS: The DDV2 functions predictably at a range of settings, MVs and temperatures. Anaesthetic delivery in the defence anaesthesia configuration is like that previously described in the draw-over configuration. The equipment was found to be reliable and robust. This experimental work supports the continued use of the Defence Anaesthesia System for the delivery of and training in deployed general anaesthesia.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Transgender Women Exhibit a Distinct Stress Echocardiography Profile Compared With Age-Matched Cisgender Counterparts: The Mayo Clinic Women's Heart Clinic Experience.

BACKGROUND: Stress echocardiographic (SE) testing is an important modality in cardiovascular risk stratification and obstructive coronary artery disease assessment. Binary sex-based parameters are classically used for the interpretation of these studies, even among transgender women (TGW). Coronary artery disease is a leading cause of morbidity and mortality in this population. Yet, it remains unclear whether TGW exhibit a distinct stress testing profile from their cisgender counterparts. METHODS: Using a matched case-control study design, the authors compared the echocardiographic stress testing profiles of TGW (n = 43) with those of matched cisgender men (CGM; n = 84) and cisgender women (CGW; n = 86) at a single center. Relevant data, including demographics, comorbidities, and cardiac testing data, were manually extracted from the patients' charts. RESULTS: The prevalence of hypertension and dyslipidemia was similar between TGW and CGW and lower than that of CGM (P = .003 and P = .009, respectively). The majority of comorbidities and laboratory values were similar. On average, TGW had higher heart rates than CGM (P = .002) and had lower blood pressures than CGM and CGW (P < .05). TGW's double product and metabolic equivalents were similar to those among CGW and lower than those of CGM (P = .016 and P = .018, respectively). On echocardiography, left ventricular end-diastolic and end-systolic diameters among TGW were similar to those of CGW but lower than those of CGM (P = .023 and P = .018, respectively). Measures of systolic and diastolic function, except for exercise mitral valve E/e' ratio, which was lower in TGW than CGW (P = .029), were largely similar among the three groups. There was no difference in the wall motion score index, and therefore, no difference in the percentage of positive SE test results. CONCLUSIONS: This study shows, for the first time, that TGW have a SE profile that is distinct from that of their cisgender counterparts. Larger, multicenter, prospective studies are warranted to further characterize the SE profile of TGW.

Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices.

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

Use of Implementation Strategies to Promote the Uptake of Knee Osteoarthritis Practice Guidelines and Improve Patient Outcomes: A Systematic Review.

OBJECTIVE: Translation of knee osteoarthritis (KOA) clinical practice guidelines (CPGs) to practice remains suboptimal. The primary purpose of this systematic review was to describe the use of implementation strategies to promote KOA CPG-recommended care. METHODS: Medline (via PubMed), Embase, CINAHL, and Web of Science were searched from inception to February 23, 2023, and the search was subsequently updated and expanded on January 16, 2024. Implementation strategies were mapped per the Expert Recommendations for Implementing Change taxonomy. Risk of bias (RoB) was assessed using the Cochrane Effective Practice and Organisation of Care criteria. The review was registered prospectively (PROSPERO identifier: CRD42023402383). RESULTS: Nineteen studies were included in the final review. All (100% [n = 4]) studies that included the domains of "provide interactive assistance," "train and educate stakeholders" (89% [n = 16]), "engage consumers" (87% [n = 13]), and "support clinicians" (79% [n = 11]) showed a change to provider adherence. Studies that showed a change to disability included the domains of "train and educate stakeholders," "engage consumers," and "adapt and tailor to context." Studies that used the domains "train and educate stakeholders," "engage consumers," and "support clinicians" showed a change in pain and quality of life. Most studies had a low to moderate RoB. CONCLUSION: Implementation strategies have the potential to impact clinician uptake of CPGs and patient-reported outcomes. The implementation context, using an active learning strategy with a patient partner, restructuring funding models, and integrating taxonomies to tailor multifaceted strategies should be prioritized. Further experimental research is recommended to determine which implementation strategies are most effective.

Structural racism in primary schools and changes in epigenetic age acceleration among Black and White youth.

Structural racism generates racial inequities in U.S. primary education, including segregated schools, inequitable funding and resources, racial disparities in discipline and achievement, and hostile racial climates, which are risk factors for adverse youth health and development. Black youth are disproportionately exposed to adverse school contexts that may become biologically embedded via stress-mediated epigenetic pathways. This study examined whether childhood exposure to adverse school contexts is associated with changes in epigenetic aging during adolescent development. DNA methylation-based epigenetic clocks were calculated from saliva samples at ages 9 and 15 among Black (n = 774) and White (n = 287) youth in the Future of Families and Child Wellbeing Study (2009-2015). We performed latent class analyses to identify race-specific primary school contexts using administrative data on segregation, discipline, achievement, resources, economic disadvantage, and racial harassment. We then estimated change in epigenetic age acceleration from childhood to adolescence across school typologies using GrimAge, PhenoAge, and DunedinPACE epigenetic clocks. Three distinct school contexts were identified for Black youth: segregated and highly-disadvantaged (17.0%), segregated and moderately-disadvantaged (52.1%), and integrated and moderately-disadvantaged (30.8%). Two school contexts emerged for White youth: integrated and unequal (46.5%) and predominantly White & advantaged (53.5%). At age 15, Black youth who attended segregated and highly-disadvantaged primary schools experienced increases in their speed of epigenetic aging with GrimAge and DunedinPACE. Slowed epigenetic aging with GrimAge was observed for Black youth who attended integrated and moderately-disadvantaged schools. School contexts were not associated with changes in epigenetic age acceleration for White youth. Our findings suggest that manifestations of structural racism in primary school contexts are associated with early-life epigenetic age acceleration and may forecast future health inequities.

Machine-Guided Discovery of Acrylate Photopolymer Compositions.

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

Musculoskeletal injuries during trail sports: Sex- and age-specific analyses over 20 years from a national injury database.

INTRODUCTION: Musculoskeletal (MSK) injuries in US trail sports are understudied as trail sport popularity grows. This study describes MSK injury patterns among hikers, trail runners, and mountain bikers from 2002 through 2021 and investigates MSK injury trends acquired during mountain sports. METHODS: The National Electronic Injury Surveillance System (NEISS) was used to identify US emergency department (ED) patients from 2002-2021 (inclusive) who endured MSK injuries during hiking, trail running, or mountain biking. Injury rates and national estimates were calculated across demographics. RESULTS: 9835 injuries were included (48.4% male, 51.6% female). Injuries increased over time, with 1213 from 2002-2005 versus 2417 from 2018-2021. No sex differences existed before 2010, after which female injury rates exceeded those of males. The following findings were statistically significant, with P<0.05: females endured more fractures and strains/sprains; males endured more lacerations; concussions and head injuries were higher among those <18 y; dislocations and strains/sprains were higher for 18 to 65 y; fractures were higher for >65 y; <18 y had high mountain-biking and low running rates; 18 to 65 y had high running rates; and >65 y had low biking and running rates. Although all diagnoses increased in number over time, no significant differences existed in the proportion of any given diagnosis relative to total injuries. CONCLUSIONS: MSK injuries during trail sports have increased since 2002. Males endured more injuries until 2009, after which females endured more. Significant sex and age differences were found regarding injury diagnosis and body parts. Further studies are needed to confirm these trends and their causes.

Environmental enrichment promotes adaptive responding during tests of behavioral regulation in male heterogeneous stock rats.

Organisms must regulate their behavior flexibly in the face of environmental challenges. Failure can lead to a host of maladaptive behavioral traits associated with a range of neuropsychiatric disorders, including attention deficit hyperactivity disorder, autism, and substance use disorders. This maladaptive dysregulation of behavior is influenced by genetic and environmental factors. For example, environmental enrichment produces beneficial neurobehavioral effects in animal models of such disorders. The present study determined the effects of environmental enrichment on a range of measures related to behavioral regulation using a large cohort of male, outbred heterogeneous stock (HS) rats as subjects. Subjects were reared from late adolescence onwards either in pairs in standard housing with minimal enrichment (n = 200) or in groups of 16 in a highly enriched environment consisting of a large multi-level cage filled with toys, running wheels, and shelters (n = 64). Rats were subjected to a battery of tests, including: (i) locomotor response to novelty, (ii) light reinforcement, (iii) social reinforcement, (iv) reaction time, (v) a patch-depletion foraging test, (vi) Pavlovian conditioned approach, (vii) conditioned reinforcement, and (viii) cocaine conditioned cue preference. Results indicated that rats housed in the enriched environment were able to filter out irrelevant stimuli more effectively and thereby regulate their behavior more efficiently than standard-housing rats. The dramatic impact of environmental enrichment suggests that behavioral studies using standard housing conditions may not generalize to more complex environments that may be more ethologically relevant.

Oystershell scale (Hemiptera: Diaspididae) population growth, spread, and phenology on aspen in Arizona, USA.

Oystershell scale (OSS; Lepidosaphes ulmi L.) is an invasive insect that threatens sustainability of aspen (Populus tremuloides Michx.) in the southwestern United States. OSS invasions have created challenges for land managers tasked with maintaining healthy aspen ecosystems for the ecological, economic, and aesthetic benefits they provide. Active management is required to suppress OSS populations and mitigate damage to aspen ecosystems, but before management strategies can be implemented, critical knowledge gaps about OSS biology and ecology must be filled. This study sought to fill these gaps by addressing 3 questions: (i) What is the short-term rate of aspen mortality in OSS-infested stands in northern Arizona, USA? (ii) What are the short-term rates of OSS population growth on trees and OSS spread among trees in aspen stands? (iii) What is the phenology of OSS on aspen and does climate influence phenology? We observed high levels of aspen mortality (annual mortality rate = 10.4%) and found that OSS spread rapidly within stands (annual spread rate = 10-12.3%). We found first, second, and young third instars throughout the year and observed 2 waves of first instars (i.e., crawlers), one throughout the summer and a second in mid-winter. The first wave appeared to be driven by warming seasonal temperatures, but the cause of the second wave is unknown and might represent a second generation. We provide recommendations for future OSS research, including suggestions for more precise quantification of OSS phenology, and discuss how our results can inform management of OSS and invaded aspen ecosystems.

Relationships of brain cholesterol and cholesterol biosynthetic enzymes to Alzheimer's pathology and dementia in the CFAS population-derived neuropathology cohort.

Altered cholesterol metabolism is implicated in brain ageing and Alzheimer's disease. We examined whether key genes regulating cholesterol metabolism and levels of brain cholesterol are altered in dementia and Alzheimer's disease neuropathological change (ADNC). Temporal cortex (n = 99) was obtained from the Cognitive Function and Ageing Study. Expression of the cholesterol biosynthesis rate-limiting enzyme HMG-CoA reductase (HMGCR) and its regulator, SREBP2, were detected using immunohistochemistry. Expression of HMGCR, SREBP2, CYP46A1 and ABCA1 were quantified by qPCR in samples enriched for astrocyte and neuronal RNA following laser-capture microdissection. Total cortical cholesterol was measured using the Amplex Red assay. HMGCR and SREBP2 proteins were predominantly expressed in pyramidal neurones, and in glia. Neuronal HMGCR did not vary with ADNC, oxidative stress, neuroinflammation or dementia status. Expression of HMGCR neuronal mRNA decreased with ADNC (p = 0.022) and increased with neuronal DNA damage (p = 0.049), whilst SREBP2 increased with ADNC (p = 0.005). High or moderate tertiles for cholesterol levels were associated with increased dementia risk (OR 1.44, 1.58). APOE ε4 allele was not associated with cortical cholesterol levels. ADNC is associated with gene expression changes that may impair cholesterol biosynthesis in neurones but not astrocytes, whilst levels of cortical cholesterol show a weak relationship to dementia status.

Genetic Ablation of Dentate Hilar Somatostatin-Positive GABAergic Interneurons is Sufficient to Induce Cognitive Impairment.

Aging is often associated with a decline in cognitive function. A reduction in the number of somatostatin-positive (SOM+) interneurons in the dentate gyrus (DG) has been described in cognitively impaired but not in unimpaired aged rodents. However, it remains unclear whether the reduction in SOM + interneurons in the DG hilus is causal for age-related cognitive dysfunction. We hypothesized that hilar SOM+ interneurons play an essential role in maintaining cognitive function and that a reduction in the number of hilar SOM + interneurons might be sufficient to induce cognitive dysfunction. Hilar SOM+ interneurons were ablated by expressing a diphtheria toxin transgene specifically in these interneurons, which resulted in a reduction in the number of SOM+ /GAD-67+ neurons and dendritic spine density in the DG. C-fos and Iba-1 immunostainings were increased in DG and CA3, but not CA1, and BDNF protein expression in the hippocampus was decreased. Behavioral testing showed a reduced recognition index in the novel object recognition test, decreased alternations in the Y maze test, and longer latencies and path lengths in the learning and reversal learning phases of the Morris water maze. Our results show that partial genetic ablation of SOM+ hilar interneurons is sufficient to increase activity in DG and CA3, as has been described to occur with aging and to induce an impairment of learning and memory functions. Thus, partial ablation of hilar SOM + interneurons may be a significant contributing factor to age-related cognitive dysfunction. These mice may also be useful as a cellularly defined model of hippocampal aging.

Sociodemographic profiles and organ damage accural in the Black Women's Experience Living with Lupus study.

OBJECTIVE: Black/African American women with systemic lupus erythematosus (SLE) experience greater organ damage and at younger ages than white women. The objective of this study was to advance research on SLE inequities by identifying sociodemographic risk profiles associated with organ damage accrual specifically among Black/African American women. METHODS: Latent profile analysis was conducted among 438 Black/African American women with SLE living in Atlanta, GA and enrolled in the Black Women's Experiences Living with Lupus (BeWELL) Study (May 2015 to April 2017). Proportional hazard and Poisson regression models examined prospective associations between sociodemographic profiles and the timing and degree of organ damage accrual over 2 years. RESULTS: Four profiles emerged: (1) "Younger/Lower SES with Uncontrolled SLE" (44.8%), (2) "Older/Lower SES with Uncontrolled SLE" (23.3%), (3) "Mid-SES with Controlled SLE" (19.6%), and (4) "Higher SES with Controlled SLE" (11.2%). Approximately 42% of participants experienced new organ damage during the follow-up period. Proportional hazard models indicated that "Older/Lower SES with Uncontrolled SLE" participants were at greatest risk of new organ damage (HR = 2.41; 95% CI = 1.39, 4.19), followed by "Younger/Lower SES with Uncontrolled SLE" participants (HR = 1.56; 95% CI = 0.92, 2.67), compared to those in the "Higher SES with Controlled SLE" profile. Poisson regression models revealed that these two groups also exhibited greater organ damage accrual (b = 0.98, SE = 0.24, 95% CI = 0.52, 1.44 and b = 0.72, SE = 0.23, 95% CI = 0.27, 1.17, respectively). CONCLUSIONS: Black/African American women with fewer socioeconomic resources and uncontrolled SLE are at greatest risk for increasing disease severity over time. Social inequities likely contribute to racial inequities in SLE progression.

Nationwide Analysis of Firearm Injury Versus Other Penetrating Trauma: It's Not All the Same Caliber.

INTRODUCTION: Ballistic injuries cause both a temporary and permanent cavitation event, making them far more destructive and complex than other penetrating trauma. We hypothesized that global injury scoring and physiologic parameters would fail to capture the lethality of gunshot wounds (GSW) compared to other penetrating mechanisms. METHODS: The 2019 American College of Surgeons Trauma Quality Programs participant use file was queried for the mortality rate for GSW and other penetrating mechanisms. A binomial logistic regression model ascertained the effects of sex, age, hypotension, tachycardia, mechanism, Glasgow Coma Scale, ISS, and volume of blood transfusion on the likelihood of mortality. Subgroup analyses examined isolated injuries by body regions. RESULTS: Among 95,458 cases (82% male), GSW comprised 46.4% of penetrating traumas. GSW was associated with longer hospital length of stay (4 [2-9] versus 3 [2-5] days), longer intensive care unit length of stay (3 [2-6] versus 2 [2-4] days), and more ventilator days (2 [1-4] versus 2 [1-3]) compared to stab wounds, all P < 0.001. The model determined that GSW was linked to increased odds of mortality compared to stab wounds (odds ratio 4.19, 95% confidence interval 3.55-4.93). GSW was an independent risk factor for acute kidney injury, acute respiratory distress syndrome, venous thromboembolism, sepsis, and surgical site infection. CONCLUSIONS: Injury scoring systems based on anatomical or physiological derangements fail to capture the lethality of GSW compared to other mechanisms of penetrating injury. Adjustments in risk stratification and reporting are necessary to reflect the proportion of GSW seen at each trauma center. Improved classification may help providers develop quality processes of care. This information may also help shape public discourse on this highly lethal mechanism.

Wrist EMG Improves Gesture Classification for Stroke Patients.

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms.

Electromyographic (EMG) control relies on supervised-learning algorithms that correlate EMG to motor intent. The quality of the training dataset is critical to the runtime performance of the algorithm, but labeling motor intent is imprecise and imperfect. Traditional EMG training data is collected while participants mimic predetermined movements of a virtual hand with their own hand. This assumes participants are perfectly synchronized with the predetermined movements, which is unlikely due to reaction time and signal-processing delays. Prior work has used cross-correlation to globally shift and re-align kinematic data and EMG. Here, we quantify the impact of this global re-alignment on both classification algorithms and regression algorithms with and without a human in the loop. We also introduce a novel trial-by-trial re-alignment method to re-align EMG with kinematics on a per-movement basis. We show that EMG and kinematic data are inherently misaligned, and that reaction time is inconsistent throughout data collection. Both global and trial-by-trial re-alignment significantly improved offline performance for classification and regression. Our trial-by-trial re-alignment further improved offline classification performance relative to global realignment. However, online performance, with a human actively in the loop, was no different with or without re-alignment. This work highlights inaccuracies in labeled EMG data and has broad implications for EMG-control applications.

Automated Quantifiable Assessments of Sensorimotor Function Using an Instrumented Fragile Object.

Accurate assessment of hand dexterity plays a critical role in informing rehabilitation and care of upper-limb hemiparetic stroke patients. Common upper-limb assessments, such as the Box and Blocks Test and Nine Hole Peg Test, primarily evaluate gross motor function in terms of speed. These assessments neglect an individual's ability to finely regulate grip force, which is critical in activities of daily living, such as manipulating fragile objects. Here we present the Electronic Grip Gauge (EGG), an instrumented fragile object that assesses both gross and fine motor function. Embedded with a load cell, accelerometer, and Hall-effect sensor, the EGG measures grip force, acceleration, and relative position (via magnetic fields) in real time. The EGG can emit an audible "break" sound when the applied grip force exceeds a threshold. The number of breaks, transfer duration, and applied forces are automatically logged in real-time. Using the EGG, we evaluated sensorimotor function in implicit grasping and gentle grasping for the non-paretic and paretic hands of 3 hemiparetic stroke patients. For all participants, the paretic hand took longer to transfer the EGG during implicit grasping. For 2 of 3 participants, grip forces were significantly greater for the paretic hand during gentle grasping. Differences in implicit grasping forces were unique to each participant. This work constitutes an important step towards more widespread and quantitative measures of sensorimotor function, which may ultimately lead to improved personalized rehabilitation and better patient outcomes.

Relating Antimicrobial Resistance and Virulence in Surface-Water E. coli.

The role of the environment in the emergence and spread of antimicrobial resistance (AMR) is being increasingly recognized, raising questions about the public health risks associated with environmental AMR. Yet, little is known about pathogenicity among resistant bacteria in environmental systems. Existing studies on the association between AMR and virulence are contradictory, as fitness costs and genetic co-occurrence can be opposing influences. Using Escherichia coli isolated from surface waters in eastern North Carolina, we compared virulence gene prevalence between isolates resistant and susceptible to antibiotics. We also compared the prevalence of isolates from sub-watersheds with or without commercial hog operations (CHOs). Isolates that had previously been evaluated for phenotypic AMR were paired by matching isolates resistant to any tested antibiotic with fully susceptible isolates from the same sample date and site, forming 87 pairs. These 174 isolates were evaluated by conventional PCR for seven virulence genes (bfp, fimH, cnf-1, STa (estA), EAST-1 (astA), eae, and hlyA). One gene, fimH, was found in 93.1% of isolates. Excluding fimH, at least one virulence gene was detected in 24.7% of isolates. Significant negative associations were found between resistance to at least one antibiotic and presence of at least one virulence gene, tetracycline resistance and presence of a virulence gene, resistance and STa presence, and tetracycline resistance and STa presence. No significant associations were found between CHO presence and virulence, though some sub-significant associations merit further study. This work builds our understanding of factors controlling AMR dissemination through the environment and potential health risks.

Global mosquito observations dashboard (GMOD): creating a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes.

BACKGROUND: Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide. METHODS: GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection. RESULTS: Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs. CONCLUSIONS: GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.