An open-source tool for automated analysis of breathing behaviors in common marmosets and rodents.

The respiratory system maintains homeostatic levels of oxygen (O2) and carbon dioxide (CO2) in the body through rapid and efficient regulation of breathing frequency and depth (tidal volume). The commonly used methods of analyzing breathing data in behaving experimental animals are usually subjective, laborious, and time-consuming. To overcome these hurdles, we optimized an analysis toolkit for the unsupervised study of respiratory activities in animal subjects. Using this tool, we analyzed breathing behaviors of the common marmoset (Callithrix jacchus), a New World non-human primate model. Using whole-body plethysmography in room air as well as acute hypoxic (10% O2) and hypercapnic (6% CO2) conditions, we describe breathing behaviors in awake, freely behaving marmosets. Our data indicate that marmosets' exposure to acute hypoxia decreased metabolic rate and increased sigh rate. However, the hypoxic condition did not augment ventilation. Hypercapnia, on the other hand, increased both the frequency and depth (i.e., tidal volume) of breathing.

Comparação entre processamentos de dados do acelerômetro gt3x+ para atividade física moderada e vigorosa / Comparison between data processing from gt3x+ accelerometer for vigorous and moderate physical activity

RESUMO O uso do acelerômetro para mensurar a atividade física em pesquisas epidemiológicas, apresenta desafios para aumentar a comparabilidade entre os estudos que utilizam esse equipamento. Nesse sentido o objetivo deste trabalho é comparar estimativas de tempo em AFMV para adultos provenientes de diferentes métodos de processamentos de dados, através do acelerômetro Actigraph GT3X+. Trata-se de um estudo transversal, da linha de base do estudo piloto do Estudo Longitudinal dos Determinantes da Atividade Física. Amostra contou com 31 funcionários terceirizados de ambos os sexos, com idade média de 47.05anos (DP=9.35). Os participantes utilizaram acelerômetros do modelo GT3X+ durante sete dias consecutivos. A estimativa de tempo de AFMV foi gerada através de software Actilife e R-package GGIR. Análises estatísticas descritivas, ANOVA e pos-hoc de Bonferroni para comparabilidade foram realizadas no software R. Análise de Bland-Altman foi realizado no SigmaPlot para avaliação de viés e concordância. Houve diferença significativa no tempo médio de AFMV entre os dados baseados em counts e dados brutos (p<0,001). O tempo médio em AFMV foi menor a partir do processamento por dados brutos do que o em counts (-264,81min/dia; p<0,001). Concluindo que os achados sugerem não haver, estatisticamente, equivalência entre os métodos comparados para estimar tempo de AFMV.

ABSTRACT The use of accelerometers to measure physical activity in epidemiological research presents challenges to increase comparability between studies that use this equipment. In this sense, the objective of this work is to compare time estimates in MVPA for adults from different data processing methods, using the Actigraph GT3X+ accelerometer. This is a cross-sectional study, from the baseline of the pilot study of the Longitudinal Study of the Determinants of Physical Activity. Sample had 31 outsourced employees of both genders, with an average age of 47.05 years (SD=9.35). Participants used GT3X+ model accelerometers for seven consecutive days. The MVPA time estimate was generated using Actilife and R-package GGIR software. Descriptive statistical analyses, ANOVA and Bonferroni post-hoc for comparability were performed in the R software. Bland-Altman analysis was performed in SigmaPlot to assess bias and agreement. There was a significant difference in the mean time of MVPA between count-based data and raw data (p<0.001). The average time in MVPA was shorter from processing by raw data than in counts (-264.81 min/day; p<0.001). Concluding that the findings suggest that there is no statistically equivalence between the methods compared to estimate MVPA time.

An Observational Study in Manipur State, India on Preventive Behavior Influenced by Social Media During the COVID-19 Pandemic Mediated by Cyberchondria and Information Overload.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic is a public health emergency posing unprecedented challenges for health authorities. Social media may serve as an effective platform to disseminate health-related information. This study aimed to assess the extent of social media use, its impact on preventive behavior, and negative health effects such as cyberchondria and information overload. METHODS: A cross-sectional observational study was conducted between June 10, 2020 and August 9, 2020 among people visiting the outpatient department of the authors' institution, and participants were also recruited during field visits for an awareness drive. Questions were developed on preventive behavior, and the Short Cyberchondria Scale and instruments dealing with information overload and perceived vulnerability were used. RESULTS: The study recruited 767 participants with a mean age of about 45 years. Most of the participants (>90%) engaged in preventive behaviors, which were influenced by the extent of information received through social media platforms (ß=3.297; p<0.001) and awareness of infection when a family member tested positive (ß=29.082; p<0.001) or a neighbor tested positive (ß=27.964; p<0.001). The majority (63.0%) of individuals often searched for COVID-19 related news on social media platforms. The mean±standard deviation scores for cyberchondria and information overload were 9.09±4.05 and 8.69±2.56, respectively. Significant and moderately strong correlations were found between cyberchondria, information overload, and perceived vulnerability to COVID-19. CONCLUSIONS: This study provides evidence that the use of social media as an information- seeking platform altered preventive behavior. However, excessive and misleading information resulted in cyberchondria and information overload.

Nurses' Compliance With Bar-code Medication Administration Technology: Results of Direct Observation of Jordanian Nurses' Practice.

Medication administration using bar-code medication administration technology enhances the verification of medication administration rights. Nurses' compliance with bar-code medication administration procedure is essential to maximize the benefits. This study evaluated the current rate of nurses' compliance with bar-code medication administration use through direct observation. A descriptive design was used and 134 RNs were recruited from two public hospitals located in the middle region of Jordan. Compliance with bar-code medication administration was evaluated using an evidence-based checklist of 17 items. Participants' compliance with the bar-code medication administration was 55%, which had a significant positive correlation with their level of comfort using bar-code medication administration, usefulness, and ease of use, perceived job productivity, and overall rating of bar-code medication administration. Stakeholders can benefit from assessing end-user acceptance and perceptions regarding the bar-code medication administration technology to promote acceptance and compliance.

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks.

The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime.

Proof of Concept for a Universal Identification System for Medical Devices.

Medical devices need a unified way of accessing information that uniquely identifies them. This can provide traceability to specifications, lot numbers, recalls, and the like. Such a system would have applications for devices both in and out of the body. Common barcodes, such as a UPC code, can only be read in plain sight, when nothing comes between the scanner and the code. UPC coding is not suitable for all medical devices because some are implanted in the body or are otherwise inaccessible without invasive techniques. This article demonstrates a proof of concept for XRF coding on devices. Material codes were made and read externally by an XRF reader. The reading showed trace amounts of the chemicals that compose the medical device in the background signal. The energy levels of the chemicals were assigned values to build a readable code correlated with information about the medical device it is attached to. Attachment can be made during material synthesis, part or product manufacture, or even after final assembly. The technique demonstrated here is a promising concept for the future of medical device detection.

Electronic Patient-Reported Outcome Measures in Radiation Oncology: Initial Experience After Workflow Implementation.

BACKGROUND: Mobile health (mHealth) technologies are increasingly used in various medical fields. However, the potential of mHealth to improve patient care in radiotherapy by acquiring electronic patient reported outcome measures (ePROMs) during treatment has been poorly studied so far. OBJECTIVE: The aim of this study was to develop and implement a novel Web app (PROMetheus) for patients undergoing radiotherapy. Herein, we have reported our experience with a focus on feasibility, patient acceptance, and a correlation of ePROMs with the clinical course of the patients. METHODS: In the period between January and June 2018, 21 patients used PROMetheus to score side effects, symptoms, and quality of life-related parameters during and after their treatment. Items of the Patient Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) were chosen based on the primary site of disease, 27 items for head and neck tumors, 21 items for thoracic tumors, and 24 items for pelvic tumors. RESULTS: In total, 17 out of the 21 patients (81%) regularly submitted ePROMs and more than 2500 data points were acquired. An average of 5.2, 3.5, and 3.3 min was required to complete the head and neck, thorax, and pelvis questionnaires, respectively. ePROMS were able to detect the occurrence of both expected and unexpected side effects during the treatment. In addition, a gradual increase in the severity of side effects over the course the treatment and their remission afterward could be observed with ePROMs. In total, 9 out of the 17 patients (53%), mostly those with head and neck and thoracic cancers, reported PRO-CTCAE grade III or IV fatigue with severe impairments of activities of daily life. CONCLUSIONS: This study shows the successful implementation of an ePROM system and a high patient acceptance. ePROMs have a great potential to improve patient care in radiotherapy by providing a comprehensive documentation of symptoms and side effects, especially of ones that are otherwise underreported.

Toward a New Approach in Wearable Devices in Safety Monitoring: Miniaturization and 3D Space Utilization.

This commentary is focused on the requirements and general strategy of designing a multiparameter monitoring wrist-worn prototype. The solution is based on an innovative hardware approach to ensure the safety of working conditions through environmental parameter measurement. In some cases, exposure to environmental parameters for a long time can endanger an individual's health (e.g., exposure to toxic gases or sound level beyond a certain threshold). Therefore, measuring the environmental elements can protect individuals' health as well. In this work, we emphasize that a new approach and strategy in wearable devices, multiparameter monitoring, miniaturization, sensor integration, data fusion, and system adoption within the Internet of Things (IoT) is necessary according to today's demands.

Comparison of Different Small Clinical Hematology Laboratory Configurations With Focus on Remote Smear Imaging.

CONTEXT.­: Stand-alone clinical sites (eg, infusion centers) are becoming increasingly common. These sites require timely hematology analysis. Here we compare performance and costs of currently available analysis configurations with special focus on a proposed alternative using a minimal hematology analyzer plus a digital imaging device, allowing for remote oversight and interpretation. OBJECTIVES.­: To determine whether low-volume laboratories might realize savings while gaining function by substituting commonly used configurations with a proposed alternative. DESIGN.­: To evaluate the performance of the proposed alternative configuration, blood counts with automated differentials produced by a Sysmex XE5000 (complete blood count reference method) were compared with cell counts from the Sysmex pocH-100i, CellaVision DM96 preclassified differentials, and DM96 reclassified differentials (differential reference method) by using standard regression analyses, 95% CIs, and truth tables. Financial cost modeling used staffing practices, test volumes, and smear production rates observed at remote clinics performing on-site hematology analysis within the University of California at San Diego Health system. RESULTS.­: Differential blood count parameters showed excellent correlation between the XE5000 and preclassification DM96 with R2 > 0.95. For blasts/abnormal cells, immature granulocytes, and nucleated red blood cells, the DM96 showed higher sensitivity and similar specificity to the XE5000. Cost modeling revealed that decreased personnel costs through remote monitoring of results facilitated by the DM96 would lead to lower operational costs relative to more conventional analysis configurations. CONCLUSIONS.­: A digital imaging instrument with an inexpensive hematology analyzer provides similar information to a complex hematology analyzer and allows remote review of the blood smear findings by experts, leading to significant cost savings.

Information Processing Under Reward Versus Under Punishment.

Much is known about the effects of reward and punishment on behavior, yet little research has considered how these incentives influence the information-processing dynamics that underlie decision making. We fitted the linear ballistic accumulator to data from a perceptual-judgment task to examine the impacts of reward- and punishment-based incentives on three distinct components of information processing: the quality of the information processed, the quantity of that information, and the decision threshold. The threat of punishment lowered the average quality and quantity of information processed, compared with the prospect of reward or no performance incentive at all. The threat of punishment also induced less cautious decision making by lowering people's decision thresholds relative to the prospect of reward. These findings suggest that information-processing dynamics are determined not only by objective properties of the decision environment but also by the higher order goals of the system.

Estimating disease burden using Internet data.

Data on disease burden are often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage and social media data, specifically the search volume on Google, page view count on Wikipedia, and disease mentioning frequency on Twitter, correlated with the disease burden, measured by prevalence and treatment cost, for 1633 diseases over an 11-year period. We also applied least absolute shrinkage and selection operator to predict the burden of diseases. We found that Google search volume is relatively strongly correlated with the burdens for 39 of 1633 diseases, including viral hepatitis, diabetes mellitus, multiple sclerosis, and hemorrhoids. Wikipedia and Twitter data strongly correlated with the burdens of 15 and 7 diseases, respectively. However, an accurate analysis must consider each condition's characteristics, including acute/chronic nature, severity, familiarity to the public, and the presence of stigma.

Automated monitoring of mouse feeding and body weight for continuous health assessment.

Routine health assessment of laboratory rodents can be improved using automated home cage monitoring. Continuous, non-stressful, objective assessment of rodents unaware that they are being watched, including during their active dark period, reveals behavioural and physiological changes otherwise invisible to human caretakers. We developed an automated feeder that tracks feed intake, body weight, and physical appearance of individual radio frequency identification-tagged mice in social home cages. Here, we experimentally induce illness via lipopolysaccharide challenge and show that this automated tracking apparatus reveals sickness behaviour (reduced food intake) as early as 2-4 hours after lipopolysaccharide injection, whereas human observers conducting routine health checks fail to detect a significant difference between sick mice and saline-injected controls. Continuous automated monitoring additionally reveals pronounced circadian rhythms in both feed intake and body weight. Automated home cage monitoring is a non-invasive, reliable mode of health surveillance allowing caretakers to more efficiently detect and respond to early signs of illness in laboratory rodent populations.

PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network.

Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and monitoring of its abundance has been conducted manually by experts and is time-consuming, fatiguing and tedious. Automated detection of BPH has been proposed by many studies to overcome human fallibility. However, all studies regarding automated recognition of BPH are investigated based on intact specimen although most of the specimens are imperfect, with missing parts have distorted shapes. The automated recognition of an imperfect insect image is more difficult than recognition of the intact specimen. This study proposes an automated, deep-learning-based detection pipeline, PENYEK, to identify BPH pest in images taken from a readily available sticky pad, constructed by clipping plastic sheets onto steel plates and spraying with glue. This study explores the effectiveness of a convolutional neural network (CNN) architecture, VGG16, in classifying insects as BPH or benign based on grayscale images constructed from Euclidean Distance Maps (EDM). The pipeline identified imperfect images of BPH with an accuracy of 95% using deep-learning's hyperparameters: softmax, a mini-batch of 30 and an initial learning rate of 0.0001.

Using social media to quantify spatial and temporal dynamics of nature-based recreational activities.

Big data offer a great opportunity for nature-based recreation (NbR) mapping and evaluation. However, it is important to determine when and how it is appropriate to use this resource. We used Scotland as a case study to validate the use of data from Flickr as an indicator of NbR on a national scale and at several regional spatial and temporal resolutions. We compared Flickr photographs to visitor statistics in the Cairngorms National Park (CNP) and determined whether temporal variability in photo counts could be explained by known annual estimates of CNP visitor numbers. We then used a unique recent national survey of nature recreation in Scotland to determine whether the spatial distribution of Flickr photos could be explained by known spatial variability in nature use. Following this validation work, we used Flickr data to identify hotspots of wildlife watching in Scotland and investigated how they changed between 2005 and 2015. We found that spatial and temporal patterns in Flickr count are explained by measures of visitation obtained through surveys and that this relationship is reliable down to a 10 Km scale resolution. Our findings have implications for planning and management of NbR as they suggest that photographs uploaded on Flickr reflect patterns of NbR at spatial and temporal scales that are relevant for ecosystem management.

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Deriving the PRx and CPPopt from 0.2-Hz Data: Establishing Generalizability to Bedmaster Users.

OBJECTIVE: The objective was to explore the validity of industry-parameterized vital signs in the generation of pressure reactivity index (PRx) and optimal cerebral perfusion pressure (CPPopt) values. MATERIALS AND METHODS: Ten patients with intracranial pressure (ICP) monitors from 2008 to 2013 in a tertiary care hospital were included. Arterial blood pressure (ABP) and ICP were sampled at 240 Hz (of waveform data) and 0.2 Hz (of parameterized data produced by heuristic industry proprietary algorithms). 240-Hz ABP were filtered for pulse pressure and diastolic ABP within the limits of 20-150 mmHg. The PRx was calculated as Pearson's correlation coefficient using 10-s averages of ICP and ABP over a 5-min moving window with 80% overlap. For ease of comparison, we used the naming convention of BMx for PRx values derived from 0.2-Hz data. A 5-min median cerebral perfusion pressure (CPP) trend was calculated, PRx or BMx values divided and averaged into CPP bins spanning 5 mmHg. The minimum Y value (PRx or BMx) of the parabolic function fit to the resulting XY plot of 4 h of data was obtained, and updated every 1 min. Pearson's R correlations were calculated for each patient. Linear mixed-effects models were used with a random intercept to assess the overall correlation between the PRx (outcome) and the BMx (fixed effect) or the CPPopt-PRx (outcome) and the CPPopt-BMx (fixed effect). RESULTS: The overall correlation between the PRx and BMx was 0.78 based on the linear mixed effects models (p < 0.0001), and the overall correlation for the CPPopt-PRx and CPPopt-BMx based on the linear mixed effects models was 0.76 (p < 0.0001). One patient had low correlation of CPPopts derived from the PRx vs the BMx; this patient had the least number of hours of CPPopt data to compare. CONCLUSIONS: The BMx shows promise in CPPopt derivation against the validated PRx measure. If further developed, it could expand the capability of centers to derive CPPopt goals for use in clinical trials.

Nanoparticle based bio-bar code technology for trace analysis of aflatoxin B1 in Chinese herbs.

A novel and sensitive assay for aflatoxin B1 (AFB1) detection has been developed by using bio-bar code assay (BCA). The method that relies on polyclonal antibodies encoded with DNA modified gold nanoparticle (NP) and monoclonal antibodies modified magnetic microparticle (MMP), and subsequent detection of amplified target in the form of bio-bar code using a fluorescent quantitative polymerase chain reaction (FQ-PCR) detection method. First, NP probes encoded with DNA that was unique to AFB1, MMP probes with monoclonal antibodies that bind AFB1 specifically were prepared. Then, the MMP-AFB1-NP sandwich compounds were acquired, dehybridization of the oligonucleotides on the nanoparticle surface allows the determination of the presence of AFB1 by identifying the oligonucleotide sequence released from the NP through FQ-PCR detection. The bio-bar code techniques system for detecting AFB1 was established, and the sensitivity limit was about 10-8 ng/mL, comparable ELISA assays for detecting the same target, it showed that we can detect AFB1 at low attomolar levels with the bio-bar-code amplification approach. This is also the first demonstration of a bio-bar code type assay for the detection of AFB1 in Chinese herbs.