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BACKGROUND Tourette syndrome is a neurological disorder that is characterized by repeated unintentional physical movement and vocal sounds, better known as tics. Cases of mild Tourette can have tics numerous times throughout the day, while severe cases may have tics every 5 to 10 seconds. At certain times, typically during high levels of stress, tics become chained in an incessant, continuous fashion—this is known as a tic attack. Tic attacks incapacitate the patient, rendering it difficult for them to move, perform daily actions, and even communicate with others. Caretakers—usually guardians, family members, or nurses—can help reduce the time tic attacks last with their presence and by providing emotional support to the patient. OBJECTIVE We describe TSBand, a wearable wristband that uses machine learning algorithms and a variety of sensors to monitor for tic attacks and notify caretakers when an attack occurs. METHODS We conducted a research survey with 70 Tourette patients to determine the usability and functionality of TSBand; internal review board approval was not required. RESULTS This study has resulted in a smart wristband prototype that costs US $62.74; it uses movement, heart rate, sweat, and body temperature to detect tic attacks using a hybrid local outlier factoring and regression algorithm. An audio tic attack detection mechanism is also included, using recurrent neural networks, and a manually activated backup button and backup audio mechanism are fitted to alert caretakers on the personalized companion app. CONCLUSIONS TSBand enables the caretaker to provide support faster and prevent excessive self-harm or injury during the attack. It is an affordable and effective solution, solving a problem that many Tourette patients, often children, face. This study has not had the opportunity to test TSBand with any Tourette patients, and we aim to perform rigorous testing and analysis after grant funding is secured.

Depreciative park visitor behaviors in protected areas can alter the relationship between humans, wildlife, and ecosystems and provide new opportunities for pathogens to cause illness in people. We...

Introduction Traditional risk factors for obesity and type 2 diabetes, such as poor diet and low physical activity, cannot fully explain the increased incidence of these diseases. Insufficient sleep and circadian misalignment due to shift work may contribute, in part, to this epidemic through their growing prevalence and associated impaired glucose regulation and eating out of phase. Methods Female nightshift healthcare workers (n=5) worked ≥2 shifts (~7pm-7am) during the 7-day study. Actigraphy was measured and participants wore continuous glucose monitors and completed three 24-hour dietary recalls. Dim light melatonin onset was predicted (pDLMO) for each worker from an actigraphy derived algorithm validated for shift workers. The timepoints two hours after (pDLMO+2) and 10 hours after (pDLMO+10) predicted DLMO are presented as a range in which sleep typically occurs, on average, relative to DLMO. Results Mean participant age was 30.7 years and body mass index 22.8 kg/m2. Dietary recalls report the average total energy consumed on workdays to be 1,653 kcal ± 444 (mean ± SD) compared to 1,517 kcal ± 421 on free days (p=0.64). Average eating windows were 13.88 ± 2.69 hours on nightshifts compared to 9.88 ± 1.82 hours on workfree days (p=0.051, n=4 one individual did not report eating while on shift). Further, eating occasions between 10pm–7:30am were only reported on worknights. Average pDLMO was 23.26 ± 1.27 hours, corresponding to the middle of shift. The average interstitial glucose level between pDLMO+2 and pDLMO+10 on shift was 88.4 mg/dL ± 9.0 and the average during the same interval on workfree nights was 81.1 mg/dL ± 7.0 (p = 0.024). Additionally, an increase in 24-hour average glucose was observed on shift 90.9 ± 4.9 compared to free days 86.1 ± 6.2 (p=0.021). Conclusion Early results from this ongoing study show that an extended eating window, eating overnight, and/or working overnight contribute to increased glucose levels while night workers are on shift compared to their days off. Increased overnight glucose levels, especially at a time the body may be accustomed to sleeping, represent a time of impaired glucose tolerance and potentially reduced insulin sensitivity. Support (if any) Anschutz CCTSI Pilot Grant Award, CTSA Grant UL1 TR002535

Background Tourette syndrome is a neurological disorder that is characterized by repeated unintentional physical movement and vocal sounds, better known as tics. Cases of mild Tourette can have tics numerous times throughout the day, while severe cases may have tics every 5 to 10 seconds. At certain times, typically during high levels of stress, tics become chained in an incessant, continuous fashion—this is known as a tic attack. Tic attacks incapacitate the patient, rendering it difficult for them to move, perform daily actions, and even communicate with others. Caretakers—usually guardians, family members, or nurses—can help reduce the time tic attacks last with their presence and by providing emotional support to the patient. Objective We describe TSBand, a wearable wristband that uses machine learning algorithms and a variety of sensors to monitor for tic attacks and notify caretakers when an attack occurs. Methods We conducted a research survey with 70 Tourette patients to determine the usability and functionality of TSBand; internal review board approval was not required. Results This study has resulted in a smart wristband prototype that costs US $62.74; it uses movement, heart rate, sweat, and body temperature to detect tic attacks using a hybrid local outlier factoring and regression algorithm. An audio tic attack detection mechanism is also included, using recurrent neural networks, and a manually activated backup button and backup audio mechanism are fitted to alert caretakers on the personalized companion app. Conclusions TSBand enables the caretaker to provide support faster and prevent excessive self-harm or injury during the attack. It is an affordable and effective solution, solving a problem that many Tourette patients, often children, face. This study has not had the opportunity to test TSBand with any Tourette patients, and we aim to perform rigorous testing and analysis after grant funding is secured.

The orange carotenoid protein (OCP) is involved in the photoprotective processes in cyanobacteria via nonphotochemical quenching. Triggered by blue-green light absorption, the carotenoid chromophore undergoes translocation, displacing around 12 Å from the C-terminal domain (CTD) to the N-terminal domain (NTD). The detailed molecular rearrangements that occur within the carotenoid and the protein during this process remain largely elusive. By using a combination of molecular dynamics, well-tempered metadynamics, and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations, we were able to mimic the translocation of the carotenoid from the inactive OCPO and obtain metastable red-shifted states in the photoactivation mechanism, replicating the λmax values of reference experimental spectra. In addition, our simulations give insight into the structure of the red-shifted form of the inactive state of OCP. Fil: Pigni, Natalia Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Ciencia y Tecnología de Alimentos Córdoba; Argentina. University of Connecticut; Estados Unidos Fil: Clark, Kevin L.. University of Connecticut; Estados Unidos Fil: Beck, Warren F.. Michigan State University; Estados Unidos Fil: Gascón, José A.. University of Connecticut; Estados Unidos

The species Sclerocarya birrea, also known as Marula, is present in Sub-Saharan Africa. In Burkina Faso, the leaves are sold as a traditional improved medicine with antidiabetic indication. The objective of this study was to develop a simple, rapid and robust high-performance thin-layer chromatographic (HPTLC) method for the reliable quality control of S. birrea leaves in Burkina Faso and other West African countries. Chromatography was performed using HPTLC Si 60 F254 plates (20 × 10 cm) as the stationary phase and ethyl acetate‒formic acid‒water (80:10:10, V/V) as the mobile phase. Derivatization steps using natural product and anisaldehyde reagents were used after the development for the detections of flavonoids and triterpenoids. Detection was carried out prior to and after derivatization under UV 254 nm, 366 nm and white light. The HPTLC method was successfully applied to the analysis of 182 collected samples including different origins, related herbal drugs (different plant parts, confounding species) and 12 traditional improved medicines in different forms (capsule, powdered sample, tea). The method was then adapted for use in quality control at different levels in developing countries.

Background In 2018, Canadian postgraduate emergency medicine (EM) programs began implementing a competency-based medical education (CBME) assessment program. Studies evaluating these programs have focused on broad outcomes using data from national bodies and lack data to support program-specific improvement. Objective We evaluated the implementation of a CBME assessment program within and across programs to identify successes and opportunities for improvement at the local and national levels. Methods Program-level data from the 2018 resident cohort were amalgamated and analyzed. The number of entrustable professional activity (EPA) assessments (overall and for each EPA) and the timing of resident promotion through program stages were compared between programs and to the guidelines provided by the national EM specialty committee. Total EPA observations from each program were correlated with the number of EM and pediatric EM rotations. Results Data from 15 of 17 (88%) programs containing 9842 EPA observations from 68 of 77 (88%) EM residents in the 2018 cohort were analyzed. Average numbers of EPAs observed per resident in each program varied from 92.5 to 229.6, correlating with the number of blocks spent on EM and pediatric EM (r = 0.83, P < .001). Relative to the specialty committee's guidelines, residents were promoted later than expected (eg, one-third of residents had a 2-month delay to promotion from the first to second stage) and with fewer EPA observations than suggested. Conclusions There was demonstrable variation in EPA-based assessment numbers and promotion timelines between programs and with national guidelines.

This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly. The mechanisms for this have been a prime mystery of human language acquisition, while engineering work has mainly proceeded by supervised learning on treebanks of sentences hand labeled for this latent structure. However, we demonstrate that modern deep contextual language models learn major aspects of this structure, without any explicit supervision. We develop methods for identifying linguistic hierarchical structure emergent in artificial neural networks and demonstrate that components in these models focus on syntactic grammatical relationships and anaphoric coreference. Indeed, we show that a linear transformation of learned embeddings in these models captures parse tree distances to a surprising degree, allowing approximate reconstruction of the sentence tree structures normally assumed by linguists. These results help explain why these models have brought such large improvements across many language-understanding tasks.

A novel method for the qualification of reduced scale models (RSMs) was illustrated using data from both a 250-ml advanced microscale bioreactor (ambr) and a 5-L bioreactor RSM for a 2,000-L manufacturing scale process using a CHO cell line to produce a recombinant monoclonal antibody. The example study showed how the method was used to identify process performance attributes and product quality attributes that capture important aspects of the RSM qualification process. The method uses two novel statistical approaches: multivariate dimension reduction and data visualization techniques, via partial least squares discriminant analysis (PLS-DA), and Bayesian multivariate linear modeling for inferential analysis. Bayesian multivariate linear modeling allows for individual probability distributions of the differences of the mean of each attribute for each scale, as well as joint probability statements on the differences of the means for multiple attributes. Depending on the results of this inferential procedure, PLS-DA is used to identify the process performance outputs at the different scales which have the greatest negative impact on the multivariate Bayesian joint probabilities. Experience with that particular process can then be leveraged to adjust operating conditions to minimize these differences, and then equivalence can be reassessed using the multivariate linear model.

Little is known of the role of cytotoxic CD4+ T-cells in the control of viral replication. Here, we investigate CD4+ T-cell responses to three dominant SARS-CoV-2 epitopes and evaluate antiviral activity, including cytotoxicity and antiviral cytokine production. Diverse T cell receptor (TCR) usage including public TCRs were identified; surprisingly, cytotoxic CD4+ T-cells were found to have signalling and cytotoxic pathways distinct from classical CD8+ T-cells, with increased expression of chemokines and tissue homing receptors promoting migration. We show the presence of cytolytic CD4+ T-cells during primary infection associates with COVID-19 disease severity. Robust immune memory 6-9 months post-infection or vaccination provides CD4+ T-cells with potent antiviral activity. Our data support a model where CD4+ killer cells drive immunopathogenesis during primary infection and CD4+ memory responses are protective during secondary infection. Our study highlights the unique features of cytotoxic CD4+ T-cells that use distinct functional pathways, providing preventative and therapeutic opportunities.

During the spring of 2020 ChoiceFlows Inc. (Choiceflows) researchers document an approach for small and medium-sized businesses to make informed decisions about equitable and resilient reopening after COVID-19 pandemic shutdowns and restrictions. The client is 501(c)(3) chartered Restart Partners in Washington State and their client, the State of Washington Department of Commerce. After several revisions, an agreement is signed on December 30, 2020 to make the approach real with development and deployment in 2021 using Cares Act Grant money from the U.S. Federal Government. The result is Smart WA – and can be freely accessed online here: https://smartwa.us Smart WA is the result of unique primary research conducted by Choiceflows involving citizens and residents of Washington State, and many days of secondary research finding publicly available data sources and bringing them together in one place to provide both comprehensive and easier to use data to make informed decisions. The research behind Smart WA is the first study to comprehensively examine how specific COVID-19 transmission reduction actions influence planned visits to different types of businesses. Data on Smart WA is organized by Human Health, Economic Health, and Community Experience metrics – and displayed as composite scores. The entire online system is powered by Tanjo.ai machine learning, a Choiceflows business partner, and is updated from all data sources daily.The advent of the COVID pandemic disrupts a wide range of businesses that directly serve the public and causes a dramatic fall in visits to these establishments. Businesses face a wide range of options in how to respond ranging from the pre-pandemic status quo and doing nothing to prevent the spread of the virus to shutting down businesses due to a lack of customers.Recognizing this, Smart WA has a “what-if” game-like section that allows a business to model the choices that they can make for reopening, and what they can expect from customers based on real data from people in the state. The relevant question from a small business perspective is: If we take an action or actions that influence COVID-19 transmission and make it known to our customers, how will that influence those customers to visit? The research supporting this function was conducted in four waves of surveys designed and administered during 2021, making it one of the most comprehensive research programs of its type during COVID with snapshots of customer preferences being collected over several month intervals.We document the development of this COVID Choices research using the Choiceflows pioneered Volumetric Choice Experiment (VCE) method and design and in parallel the online system. This provides a platform for projecting how the insights and methods from this work can be used for other issues facing small and medium size businesses to aid in and speed decision making and choice. This includes and dashboards for policymakers and main street for allocating resources for commerce, including economic development, ongoing community health, and supporting quality of life indicators.

One of the major challenges that autonomous vehicles (AVs) face in an urban setting is communicating with other road users such as pedestrians. In this work, we investigated with what expressive behaviors we can endow AVs such that pedestrians readily recognize the underlying intent of the vehicles’ movements. The purpose of our study was to test the impact of expressive stopping behaviors on pedestrians’ decision to cross a road. We utilized a virtual reality (VR) environment in which participants would have to cross a street in the presence of an oncoming vehicle that may or may not stop. Next, we crafted several expressive AV behaviors conveying its intention to stop for the pedestrian. Then, for each expressive design we recorded how quickly a pedestrian determined that it was safe to cross the street. We also administered repeated surveys of their subjective experiences. Our findings suggest that expressive behaviors such as easing into a full stop or stopping farther away can help pedestrians make quicker decisions to cross the road. Additionally, stopping farther away from the pedestrian also resulted in higher subjective experience for sense of safety, confidence, and intention understanding. We propose further investigation into expressive behaviors such as easing into a stop and stopping farther away to convey yielding intentions to pedestrians in future work. 1, IEEE Robotics and Automation Letters, 7 (2), ISSN:2377-3766