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There is a lack of quantitative research examining how the pandemic has affected individuals at different income levels. The Asian American population has the highest level of income inequality and serves as an excellent case study for examining differences in experience between income groups. A non-probability sample of 3,084 Asian American adults living in the US was surveyed in June 2020, examining health-related behaviors and outcomes. Descriptive analyses and chi-squared statistics were conducted to identify differences in income groups (low, medium, high) among Asian Americans across regional subgroups (East, South, Southeast, Multiethnic) and disaggregated ethnicities (Chinese, Asian Indian, Japanese, and Filipino). In bivariable analyses, a significantly (p < .05) greater percentage of high-income individuals during the pandemic reported having enough money to buy the food they needed, a away to get to the store for food, and reported stores where they get food had everything they needed. High-income Chinese, Japanese, and Filipino individual also noted that, since the COVID-19 crisis, they are now working partially or fully from home. In the total sample, multivariable adjusted logistic regressions revealed medium- and low-income individuals to have low odds of working partially or fully from home (AOR:0.55, 95%CI:0.42-0.72), higher odds of not having enough money to buy the food they needed (AOR:3.54, 95%CI:1.43-11.81), and higher odds of eating less (AOR:1.58, 95%CI:1.14-2.22). These results highlight the importance of considering income distribution when characterizing disparities in health behaviors within racial/ethnic minority groups and underscore the need to bolster the infrastructure supporting low-income Asian Americans.

Background Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC. Methods We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses. Results 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort. Conclusion We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.

BACKGROUND As the COVID-19 pandemic evolves, challenges in frontline work continue to impose a significant psychological impact on nurses. However, there is a lack of data on how nurses fared compared to other health care workers in the Asia-Pacific region. OBJECTIVE This study aims to investigate (1) the psychological outcome characteristics of nurses in different Asia-Pacific countries and (2) psychological differences between nurses, doctors, and nonmedical health care workers. METHODS Exploratory data analysis and visualization were conducted on the data collected through surveys. A machine learning modeling approach was adopted to further discern the key psychological characteristics differentiating nurses from other health care workers. Decision tree–based machine learning models (Light Gradient Boosting Machine, GradientBoost, and RandomForest) were built to predict whether a set of psychological distress characteristics (ie, depression, anxiety, stress, intrusion, avoidance, and hyperarousal) belong to a nurse. Shapley Additive Explanation (SHAP) values were extracted to identify the prominent characteristics of each of these models. The common prominent characteristic among these models is akin to the most distinctive psychological characteristic that differentiates nurses from other health care workers. RESULTS Nurses had relatively higher percentages of having normal or unchanged psychological distress symptoms relative to other health care workers (n=233-260 [86.0%-95.9%] vs n=187-199 [74.8%-91.7%]). Among those without psychological symptoms, nurses constituted a higher proportion than doctors and nonmedical health care workers (n=194 [40.2%], n=142 [29.5%], and n=146 [30.3%], respectively). Nurses in Vietnam showed the highest level of depression, stress, intrusion, avoidance, and hyperarousal symptoms compared to those in Singapore, Malaysia, and Indonesia. Nurses in Singapore had the highest level of anxiety. In addition, nurses had the lowest level of stress, which is the most distinctive psychological outcome characteristic derived from machine learning models, compared to other health care workers. Data for India were excluded from the analysis due to the differing psychological response pattern observed in nurses in India. A large number of female nurses emigrating from South India could not have psychologically coped well without the support from family members while living alone in other states. CONCLUSIONS Nurses were least psychologically affected compared to doctors and other health care workers. Different contexts, cultures, and points in the pandemic curve may have contributed to differing patterns of psychological outcomes amongst nurses in various Asia-Pacific countries. It is important that all health care workers practice self-care and render peer support to bolster psychological resilience for effective coping. In addition, this study also demonstrated the potential use of decision tree–based machine learning models and SHAP value plots in identifying contributing factors of sophisticated problems in the health care industry.

BACKGROUND Elective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes. OBJECTIVE The aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers. METHODS A multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties. RESULTS Based on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period. CONCLUSIONS This form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run. CLINICALTRIAL ClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363

The journal RIMA would be the bridge between researchers and industry practitioners. The key theme on smart factories encompasses many topics of interests; for example, digital design, novel control algorithms, digital twins, cobots (collaborative robots) and more. Furthermore, in today’s pandemic world which has significantly transformed the way traditional manufacturing industries operate, there is an even greater drive for a change in the manufacturing paradigm. Scientists and engineers of today should take bold steps in proposing and validating new workspace architecture that is reflective of the future. For instance, the development of digital twins or even virtual collaborative manufacturing are key drivers as we move into a future where both the virtual world and reality become seamless.

The presence or absence of sexual consent distinguishes between sexual contact that is sexual assault and sex that is not assault. While temporality is an implicit focus in studies of sexual consent, it has received relatively little attention as an object of analysis. Drawing on in-depth ethnographic research conducted as part of a mixed methods study on sexual health and sexual assault among college students, this article examines the role of time in sexual consent. Specifically, we attend to how socially- and discursively-patterned experiences of time influence college students' capacity to grant or withhold consent. We identify three important temporalities. “Calendar time” refers to how events throughout the year influence the expectation of sexual contact and the negotiation of sexual consent. “Relationship time” refers to how the temporal dimensions of a sexual relationship impact how consent is navigated. Finally, “sexual time” pushes us to think of sex itself as a temporal process that locates consent at different points in time: before, during, and after a single sexual encounter. We conclude by outlining how time-based approaches to sexual consent may contribute to more effective sexual violence prevention initiatives.

Background Gait event detection (GED) is an important aspect in identifying and interpret a user’s gait to assess gait abnormalities and design intelligent assistive devices. Research question There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments. Methods This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user’s gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made. Results Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment. Significance This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions.

Achieving satisfactory fracture fixation in osteoporotic patients with unstable proximal humerus fractures remains a major clinical challenge. Varus collapse is one of the more prominent complications that may lead to screw cutout. This aim of this study was to compare the fixation provided by conventional locking plates with novel design concepts that are only feasible through additive manufacturing (AM) techniques. In addition to reversed engineered implants, two novel implant designs with integrated struts were included in the study to provide medial support to humeral head. The medial strut was either solid or included a porous lattice structure intended to promote bone ingrowth. Biomechanical tests were performed using low density synthetic bones with simulated 3-part comminuted fractures. Nondestructive torsion and compression were performed, followed by increasing cyclic loading. The relative displacements between the bone fragments were determined using a 3D motion capture system. The AM manufactured implants with medial strut showed significant reduction of varus displacement during the increasing cyclic loading when compared to conventional designs. AM reversed-engineered locking plates showed similar mechanical behavior to conventional plates with identical geometry. This study demonstrates the feasibility and potential of employing alternative design via AM for fixation of unstable comminuted proximal humerus fractures to reduce fragment displacement.

Drawing from the work of Jamaica’s Gay Freedom Movement (1977–84), this essay uses the term gaydren to consider the basis for activism around same-sex desire in Jamaica in the 1970s and 1980s. Gaydren is a combination of gay, a North Atlantic reference to subjects of same-sex desire, and bredren, a word initially constructed in Rastafarian lexicon as a masculinist expression of collective solidarity. Examining the construction of gaydren highlights the cultural work of Jamaican activists as they transform North Atlantic political discourses to align with the particular contingencies of sexual politics in Jamaica. As a form of political practice, gaydren challenges normative configurations of bredren and gay that emerge from political contexts that oppose white imperial domination to consider more nuanced approaches to both Jamaican and North Atlantic cultural influences.

Review of Lyndon K. Gill, Erotic Islands: Art and Activism in the Queer Caribbean. Duke UP, 2018. 267 pages.

Gait patterns of Cerebral Palsy (CP) patients have been used for cluster and classification analysis. Diplegia is the paralysis of one or more body parts which may be caused by CP and may come in various forms. Current clinical practice in gait issue diagnosis relies heavily on observation and is prone to human error. Following previous studies, the effectiveness of introducing modern machine learning techniques and processes in improving the classification accuracy on gait video data was investigated. This paper proposes a novel feature engineering approach by transforming the original video into overlapping sub-clips which not only maintains important features but also reduces training time. Multiple machine learning models have been constructed to examine their individual performances before ensembling them to improve overall performance. The ensemble architecture consists of two stages, a probabilistic-based aggregator and normalizer and a performance-weighted ensemble. Finally, the model classification accuracy was able to achieve over 95%, a marked improvement from the results obtained by the models applied on similar dataset from literature. Hence, this highlights the effectiveness of the proposed method in classification of gait patterns and potentially changing current clinical practice in gait-related diagnosis.

Background: Elective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes. Objective: The aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers. Methods: A multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties. Results: Based on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period. Conclusions: This form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run. Trial Registration: ClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363

Hand pose estimation has recently attracted increasing research interest, especially with the advance of deep learning. Albeit many successes, the current state of research does present some opportunities for improvement of estimation accuracy. This paper presents several image enhancement techniques to embed with existing deep learning architectures to improve the performance of hand pose estimation. In particular, we propose a preprocessing approach for image data using a low light illuminance model or a dehazing algorithm before passing the image data to a hand pose estimation model. Both the preprocessing methods are evaluated on a rendered hand-pose dataset using different evaluation metrics. The experimental results shows success in boosting the performance of hand pose estimation for both 2D and 3D image data.

Rehabilitation of the hands is crucial for stroke survivors to regain their ability to perform activities of daily living. Various technologies were explored and found unmatured, expensive and uncomfortable. Existing devices to assist rehabilitation are typically costly, bulky and difficult to set up. Our proposed solution aims to provide an end-to-end hand rehabilitation system that can be produced at low cost with greater ease of use. It incorporates gamification to motivate stroke survivors to perform physical rehabilitation through an infra-red depth camera and computer system. MediaPipe was employed for hand detection and hand landmark extraction. A single-shot neural network model is proposed for hand gesture detection with an accuracy rate of 98%. Lastly, a visually interactive game was developed to promote engagement of the user during the performance of rehabilitation.

Parkinson’s disease (PD) is a progressive neurological disorder that affects movement of millions of people worldwide. Many methods have been developed to identify and diagnose PD in patients. However, most of these approaches require extensive setup and involve costly equipment such as using depth cameras or devices worn on the body. In this study, we investigate the use of vertical ground reaction force (VGRF) sensor readings to classify PD subjects from non-PD subjects. This presents a low-cost and straightforward approach to identify PD through the gait characteristics associated with PD. By fusing together data points from individual sensors to create an ensemble and combining it with a deep short network capable of accurately identifying the gait characteristics of PD from the sensory input, we present a novel approach to classify gait characteristic of PD which is feasible in a clinical setting. We tested our model on a public dataset from PhysioNet which consists of VGRF sensor readings of PD and non-PD patients. Preprocessing was done by extracting out several meaningful features from the raw data when was then split and normalized. Classification was done using a multilayer feed-forward artificial neural network. Experimental results conclude that this model achieved 84.78% accuracy on the PhysioNet dataset, which is a significant improvement over various state-of-the-art models.

The increasing importance of human activity recognition in Ambient Assisted Living systems (AAL) has brought a tremendous growth in research efforts in the field. Amongst these, the identification of activities of daily living using sensors available in mobile devices has emerged as one of the most interesting goals for AAL systems. With the development of deep learning algorithms in recent years, there has been increasing interest in hybrid models, which are able to perform as well as traditional machine learning models and yet entail the self-learning capabilities of deep learning at the same time. Using the time series data obtained from the MotionSense dataset, this paper introduces the concept of converting time series signals to images. The paper then proposes a novel approach for activity recognition by fusing a variety of traditional machine learning models with a deep convolutional neural network via a majority voting ensemble. The proposed method produced better results than the traditional machine learning models.

Prostate MP-MRI scan is a non-invasive method of detecting early stage prostate cancer which is increasing in popularity. However, this imaging modality requires highly skilled radiologists to interpret the images which incurs significant time and cost. Convolutional neural networks may alleviate the workload of radiologists by discriminating between prostate tumor positive scans and negative ones, allowing radiologists to focus their attention on a subset of scans that are neither clearly positive nor negative. The major challenges of such a system are speed and accuracy. In order to address these two challenges, a new approach using ensemble learning of convolutional neural networks (CNNs) was proposed in this paper, which leverages different imaging modalities including T2 weight, B-value, ADC and Ktrans in a multi-parametric MRI clinical dataset with 330 samples of 204 patients for training and evaluation. The results of prostate tumor identification will display benign or malignant based on extracted features by the individual CNN models in seconds. The ensemble of the four individual CNN models for different image types improves the prediction accuracy to 92% with sensitivity at 94.28% and specificity at 86.67% among given 50 test samples. The proposed framework potentially provides rapid classification in high-volume quantitative prostate tumor samples.

OBJECTIVES To make direct comparisons of the biomechanical properties of a control (CTL) group and implants that were augmented with far cortical locking (FCL), bone substitute material (BSM), and a combination of both (ALL) to determine which fixation is most effective in reducing implant failure. METHODS The constructs were tested with osteopenic cadaveric specimens in a two-part fracture model. Specimens were subjected to a battery of nondestructive torsion and axial compression tests, followed by a cyclic test. Construct stiffness and cycles to failure were documented, pre- and post-test fluoroscopy was performed, and implant and bone kinematics were quantified. RESULTS During nondestructive testing, the BSM group exhibited significantly increased torsional and axial stiffness compared with the FCL (P = 0.006, P < 0.001) group and ALL group (P < 0.001, P = 0.006). There were no significant differences in resistance to cyclic loading between groups. Fluoroscopic analysis indicated significant differences in the motions of nonlocked cannulated screws (used in BSM and ALL) versus locked screws (used in CTL and FCL). CONCLUSIONS Patients with poor bone quality and proximal humerus fracture may necessitate added compliance or rigidity to achieve fixation. Both have exhibited favorable biomechanical characteristics in this cadaveric 2-part proximal humerus fracture model.

Hand gesture recognition is a hot topic and a central key for different types of application. As applications of computers and intelligent systems are growing in our daily life, facilitating natural human computer interaction becomes more important. In this paper, we focus on video-based approach on hand gesture recognition integrated with 3-D hand skeletal features to construct the raw video sequences, retaining the key video frames, extracting spatial temporal data and feeding them into a Support Vector Machine model for 2-D hand sign classification. Our novel method integrates hand skeletal descriptor into video sequence to retain the spatial temporal information which will be extracted as vectors for classification task. As oppose to conventional method of requiring a well placed pair of cameras or depth detection hardware, our method only require only one camera. The proposed approach outperforms state-of-the-art static hand gesture recognition methods, achieving almost 100% accuracy among 24 classes.

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. DR is the leading cause of blindness among working aged adults around the world and estimated it may affect more than 93 million people. Progression to vision impairment can be slowed or controlled if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment.Currently, detecting DR is a time-consuming and manual process, which requires an ophthalmologist or trained clinician to examine and evaluate digital color fundus photographs of the retina, to identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease.The automated method of DR screening will speed up the detection and decision-making process, which will help to control or manage DR progression. This paper presents an automated classification system, in which it analyzes fundus images with varying illumination and fields of view and generates a severity grade for diabetic retinopathy (DR) using machine learning models such as CNN, VGG-16 and VGG-19.This system achieves 80% sensitivity, 82% accuracy, 82% specificity, and 0.904 AUC for classifying images into 5 categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR.

We present our approach in achieving the Quadratic Weighted Kappa (QWK) score of 0.90 on the retinal image dataset from the APTOS 2019 Blindness Detection Kaggle challenge. We analysed various image preprocessing techniques then classified the images with a modified EfficientNet deep learning model. Our image preprocessing techniques helped to bring out the cell loss to the retina, highlight blood vessels, and centering the retina. We found that subtracting the average local color using a Gaussian mask was the most effective preprocessing technique, improving the QWK score by 0.03. We modified the EfficientNet-B5 network with the Batch Normalization layers replaced with Group Normalization and trained the network using the Rectified Adam (RAdam) optimizer. Group Normalization was found to do better for the batch size of 4 and RAdam trained the network better than Adam. This led to an increment of the QWK score by 0.02.

Despite its emphasis on social justice, social work in the United States has not always attended to issues of diversity in doctoral education. This article examines the state of the discipline’s re...

Skill training in the medical device manufacturing industry is essential to optimize and expedite the efficiency level of new workers. This process, however, gives rise to many underlying issues such as contamination and safety risks, long training period, high skill and experience requirements of operators, and greater training costs. In this paper, we proposed and evaluated a novel virtual reality (VR) training system for the assembly of hybrid medical devices. The proposed system, which is an integration of Artificial Intelligence (AI), VR and gaming concepts, is self-adaptive and autonomous. This enables the training to take place in a virtual workcell environment without the supervision of a physical trainer. In this system, a sequential framework is proposed and utilized to enhance the training through its various “game” levels of familiarity-building processes. A type of hybrid medical device: carbon nanotubes-polydimethylsiloxane (CNT-PDMS) based artificial trachea prosthesis is used as a case study in this paper to demonstrate the effectiveness of the proposed system. Evaluation results with quantitative and qualitative comparisons demonstrated that our proposed training method has significant advantages over common VR training and conventional training methods. The proposed system has addressed the underlying training issues for hybrid medical device assembly by providing trainees with effective, efficient, risk-free and low cost training.

Facial expression classification aims to recognize human emotion via face images. The major challenge of facial expression classification is how to extract discriminative features from the human face images to differentiate various emotions. To tackle this challenge, a new feature extraction approach is proposed in this paper. The proposed approach defines a new set of salient patterns at the facial keypoint locations. This is in contrast to conventional approaches that either represent the whole face image as a regular grid or use local image patches centered at all facial key point locations. Driven by the proposed salient patterns, both the geometric and textual features are extracted and then concatenated and further incorporated into a machine learning framework to perform facial expression classification. The proposed approach is evaluated in the well-known CK + benchmark dataset to demonstrate its superior performance.

While many scholars have demonstrated the effectiveness of theatre-based knowledge mobilization, fewer studies have examined its psychological dimensions. This article examines some psychological t...

Human immune response is compromised and bacteria can become more antibiotic resistant in space microgravity (MG). We report that under low-shear modeled microgravity (LSMMG), stationary-phase uropathogenic Escherichia coli (UPEC) become more resistant to gentamicin (Gm), and that this increase is dependent on the presence of σs (a transcription regulator encoded by the rpoS gene). UPEC causes urinary tract infections (UTIs), reported to afflict astronauts; Gm is a standard treatment, so these findings could impact astronaut health. Because LSMMG findings can differ from MG, we report preparations to examine UPEC's Gm sensitivity during spaceflight using the E. coli Anti-Microbial Satellite (EcAMSat) as a free-flying "nanosatellite" in low Earth orbit. Within EcAMSat's payload, a 48-microwell fluidic card contains and supports study of bacterial cultures at constant temperature; optical absorbance changes in cell suspensions are made at three wavelengths for each microwell and a fluid-delivery system provides growth medium and predefined Gm concentrations. Performance characterization is reported here for spaceflight prototypes of this payload system. Using conventional microtiter plates, we show that Alamar Blue (AB) absorbance changes can assess the Gm effect on E. coli viability, permitting telemetric transfer of the spaceflight data to Earth. Laboratory results using payload prototypes are consistent with wellplate and flask findings of differential sensitivity of UPEC and its ∆rpoS strain to Gm. if σs plays the same role in space MG as in LSMMG and Earth gravity, countermeasures discovered in recent Earth studies (aimed at weakening the UPEC antioxidant defense) to control UPEC infections would prove useful also in space flights. Further, EcAMSat results should clarify inconsistencies from previous space experiments on bacterial antibiotic sensitivity and other issues.

Introduction: Dengue fever has been one of the most concerning endemic diseases of recent times. Every year, 50-100 million people get infected by the dengue virus across the world. Historically, it has been most prevalent in Southeast Asia and the Pacific Islands. In recent years, frequent dengue epidemics have started occurring in Latin America as well. This study focused on assessing the impact of different short and long-term lagged climatic predictors on dengue cases. Additionally, it assessed the impact of building an ensemble model using multiple time series and regression models, in improving prediction accuracy. Materials and Methods: Experimental data were based on two Latin American cities, viz. San Juan (Puerto Rico) and Iquitos (Peru). Due to weather and geographic differences, San Juan recorded higher dengue incidences than Iquitos. Using lagged cross-correlations, this study confirmed the impact of temperature and vegetation on the number of dengue cases for both cities, though in varied degrees and time lags. An ensemble of multiple predictive models using an elaborate set of derived predictors was built and validated. Results: The proposed ensemble prediction achieved a mean absolute error of 21.55, 4.26 points lower than the 25.81 obtained by a standard negative binomial model. Changes in climatic conditions and urbanization were found to be strong predictors as established empirically in other researches. Some of the predictors were new and informative, which have not been explored in any other relevant studies yet. Discussion and Conclusions: Two original contributions were made in this research. Firstly, a focused and extensive feature engineering aligned with the mosquito lifecycle. Secondly, a novel covariate pattern-matching based prediction approach using past time series trend of the predictor variables. Increased accuracy of the proposed model over the benchmark model proved the appropriateness of the analytical approach for similar epidemic prediction research.

Drawing on 2 years of ethnographic fieldwork, this article examines the accessibility organizing efforts of queer and transgender of color community initiatives in Toronto, Canada. I argue that these efforts constitute a kind of counterpublic making in which queer and trans of color organizers discursively construct the marginalized populations that they seek to include. In contrast to approaches to accessibility that prioritize conventional service institutions as the locus of social transformation, this article illustrates the significance of social workers supporting the work of existing community initiatives in their drive toward an intersectional politics of inclusion.

This article examines safe space as a site of community practice. I focus specifically on safe space as it is used among lesbian, gay, bisexual, transgender, and queer (LGBTQ) of color community arts organizers. I argue that although safe space operates as a site of refuge against oppression, its implementation can also serve as a barrier for community arts workers who seek to engage with mainstream arts institutions. This limitation encourages community practitioners to recognize safe space as one kind of intervention that is most effectively used in tandem with other tools of community practice.

Based on ethnographic research in Toronto, Canada this article considers how queer and trans of color community arts initiatives come to exist as temporal phenomena. Given the association of racial, sexual, and gender nonnormativity with temporal backwardness, these organizations serve as a useful site to examine how temporal regimes are composed. Drawing on the work of grassroots queer and trans of color community arts initiatives, I show how the short-term, youth-based nature of these efforts is intimately tied to mechanisms of state funding and to the feelings-based relationships that characterize community work. I argue that in their bid to transform their initiatives into sustainable, intergenerational organizations, queer and trans of color organizers must work to change the affective and political economic contexts in which these initiatives exist. By positing the commensurability between “love” and “money” and using the framework of temporality to draw them into the same analytic space, I contribute to existing studies on emotional labor by disrupting Enlightenment logics that separate “spirit” from “matter.” Ultimately, I examine the political ramifications of enacting normative models of temporal development to explore a queer approach to change over time.

By examining the relationship between trans identity, bathroom safety and wellbeing among high school students, this article empirically investigates how educational institutions operate as sites through which gender is negotiated in ways that are consequential for trans youth. We draw cross-sectional survey data, from a multi-school climate survey (n = 1046) conducted in the Midwestern United States, to examine three aspects of high school students' wellbeing: safety at school, self-esteem, and grades. The sample included students in 9th-12th grade who identified as trans (9.2%) and cisgender (41.2% boys, 49.6% girls), as well as LGBQ (21.6%) and heterosexual (78.4%). Most respondents were monoracial white (65.8%), monoracial Black (12.4%), and multiracial (14.1%). Using mediation and moderation linear regression models, we show that feeling safe using school facilities helps to explain widespread inequalities between trans and cisgender students. Based on these results, we suggest that in order to address disparities in educational outcomes between trans and cisgender students, as well as to improve student wellbeing in general, policies and practices need to ensure that all students have the right to safely access bathrooms and school facilities.

Objective To determine the clinical significance of tachycardia in the postoperative period. Patients and Methods Individuals 18 years or older undergoing hip and knee arthroplasty were included in the study. Two data sets were collected from different time periods: development data set from January 1, 2011, through December 31, 2011, and validation data set from December 1, 2012, through September 1, 2014. We used the development data set to identify the optimal definition of tachycardia with the strongest association with the vascular composite outcome (pulmonary embolism and myocardial necrosis and infarction). The predictive value of this definition was assessed in the validation data set for each outcome of interest, pulmonary embolism, myocardial necrosis and infarction, and infection using multiple logistic regression to control for known risk factors. Results In 1755 patients in the development data set, a maximum heart rate (HR) greater than 110 beats/min was found to be the best cutoff as a correlate of the composite vascular outcome. Of the 4621 patients who underwent arthroplasty in the validation data set, 40 (0.9%) had pulmonary embolism. The maximum HR greater than 110 beats/min had an odds ratio (OR) of 9.39 (95% CI, 4.67-18.87; sensitivity, 72.5%; specificity, 78.0%; positive predictive value, 2.8%; negative predictive value, 99.7%) for pulmonary embolism. Ninety-seven patients (2.1%) had myocardial necrosis (elevated troponin). The maximum HR greater than 110 beats/min had an OR of 4.71 (95% CI, 3.06-7.24; sensitivity, 47.4%; specificity, 78.1%; positive predictive value, 4.4%; negative predictive value, 98.6%) for this outcome. Thirteen (.3%) patients had myocardial infarction according to our predetermined definition, and the maximum HR greater than 110 beats/min had an OR of 1.72 (95% CI, 0.47-6.27). Conclusion Postoperative tachycardia within the first 4 days of surgery should not be dismissed as a postoperative variation in HR, but may precede clinically significant adverse outcomes.