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BACKGROUND: Cognitive impairment is common in Parkinson's disease (PD) and often leads to dementia, with no effective treatment. Aging studies suggest that physical activity (PA) intensity has a positive impact on cognition and enhanced functional connectivity may underlie these benefits. However, less is known in PD. This cross-sectional study examined the relationship between PA intensity, cognitive performance, and resting state functional connectivity in PD and whether PA intensity influences the relationship between functional connectivity and cognitive performance. METHODS: 96 individuals with mild-moderate PD completed a comprehensive neuropsychological battery. Intensity of PA was objectively captured over a seven-day period using a wearable device (ActiGraph). Time spent in light and moderate intensity PA was determined based on standardized actigraphy cut points. Resting-state fMRI was assessed in a subset of 50 individuals to examine brain-wide functional connectivity. RESULTS: Moderate intensityPA (MIPA), but not light PA, was associated with better global cognition, visuospatial function, memory, and executive function. Individuals who met the WHO recommendation of ≥150 min/week of MIPA demonstrated better global cognition, executive function, and visuospatial function. Resting-state functional connectivity associated with MIPA included a combination of brainstem, hippocampus, and regions in the frontal, cingulate, and parietal cortices, which showed higher connectivity across the brain in those achieving the WHO MIPA recommendation. Meeting this recommendation positively moderated the associations between identified functional connectivity and global cognition, visuospatial function, and language. CONCLUSION: Encouraging MIPA, particularly the WHO recommendation of ≥150 min of MIPA/week, may represent an important prescription for PD cognition.

Description: Increasingly, pharmacy services are provided using telehealth-based modalities. This paper describes a pharmacy skills course that utilised telehealth principles to train students on the technical and communications skills necessary for the ambulatory care setting. Zoom breakout rooms, electronic health records, YouTube video vignettes, and teaching assistants portraying patients/physicians simulated a telehealth-based ambulatory care setting. Evaluation: Five quizzes and six written assignments were utilised to measure student’s knowledge and skills. At the end of the course, students were evaluated through a three-station objective structured clinical exam (OSCE). Students also completed a pre/post attitudes survey. Result: Overall, students performed well on various assessments including quizzes and written assignments. The majority of the students performed well on the OSCE. Significant improvement was noted on all items in the attitudes survey. Conclusion: This study suggests that a telehealth training model can be effective in teaching pharmacy students both the technical and communication skills necessary for practice in the ambulatory care setting.

Stirling motor is a type of outside ignition heat motor that can utilize various fuel sources from customary structures (coal, oil, kindling, rice husk, and so forth) to sustainable power sources (sun-oriented energy), climate, squander heat usage, and so forth). The article centers around introducing the fundamental highlights of the improvement history, activity qualities, and plan techniques for certain sorts of Stirling motors, in this way offering useful appropriateness as well as a college preparing for understudies. The understudy studying Thermal Engineering in our nation today.

Higher volume fraction of perivascular space (PVS) has recently been reported in Parkinson's disease (PD) and related disorders. Both elevated PVS and altered levels of neurometabolites, assayed by proton magnetic resonance spectroscopy (MRS), are suspected indicators of neuroinflammation, but no published reports have concurrently examined PVS and MRS neurometabolites.In an exploratory pilot study, we acquired multivoxel 3-T MRS using a semi-Localization by Adiabatic SElective Refocusing (sLASER) pulse-sequence (repetition time/echo time = 2810/60 ms, voxels 10 × 10 × 10 mm3) from a 2D slab sampling bilateral frontal white matter (FWM) and anterior middle cingulate cortex (aMCC). PVS maps obtained from high-resolution (0.8 × 0.8 × 0.8 mm3) T1-weighted MRI were co-registered with MRS. In each MRS voxel, PVS volume and neurometabolite levels were measured.Linear regression accounting for age, sex, and BMI found greater PVS volume for higher levels of choline-containing compounds (Cho; P = 0.047) in FWM and lower PVS volume for higher levels of N-acetyl compounds (NAA; P = 0.012) in aMCC. Since (putatively) higher Cho is associated with inflammation while NAA has anti-inflammatory properties, these observations add to evidence that higher PVS load is a sign of inflammation. Additionally, lower Montreal Cognitive Assessment scores were associated with lower NAA in aMCC (P = 0.002), suggesting that local neuronal dysfunction and inflammation contribute to cognitive impairment in PD.These exploratory findings indicate that co-analysis of PVS and MRS is feasible and may help elucidate the cellular and metabolic substrates of glymphatic and inflammatory processes in PD.

BACKGROUND Physical activity (PA) is known to improve quality of life (QoL) as well as reduce mortality and disease progression in individuals with chronic neurological disorders. However, Latina women are less likely to participate in recommended levels of PA due to common socioeconomic barriers, including limited resources and access to exercise programs. Therefore, we developed a community-based intervention with activity monitoring and behavioral coaching to target these barriers and facilitate sustained participation in an exercise program promoting PA. OBJECTIVE The aim of this study was to determine the feasibility and efficacy of a community-based intervention to promote PA through self-monitoring via a Fitbit and behavioral coaching among Latina participants with chronic neurological disorders. METHODS We conducted a proof-of-concept study among 21 Spanish-speaking Latina participants recruited from the Los Angeles County and University of Southern California (LAC+USC) neurology clinic; participants enrolled in the 16-week intervention at The Wellness Center at The Historic General Hospital in Los Angeles. Demographic data were assessed at baseline. Feasibility was defined by participant attrition and Fitbit adherence. PA promotion was determined by examining change in time spent performing moderate-to-vigorous PA (MVPA) over the 16-week period. The effect of behavioral coaching was assessed by quantifying the difference in MVPA on days when coaching occurred versus on days without coaching. Change in psychometric measures (baseline vs postintervention) and medical center visits (16 weeks preintervention vs during the intervention) were also examined. RESULTS Participants were of low socioeconomic status and acculturation. A total of 19 out of 21 (90%) participants completed the study (attrition 10%), with high Fitbit wear adherence (mean 90.31%, SD 10.12%). Time performing MVPA gradually increased by a mean of 0.16 (SD 0.23) minutes per day (PP=.02) and in person (45.5 min/day, P=.01) relative to days without coaching (24 min/day). Participants improved their illness perception (effect size g=0.30) and self-rated QoL (effect size g=0.32). Additionally, a reduction in the number of medical center visits was observed (effect size r=0.44), and this reduction was associated with a positive change in step count during the study period (P.=04). CONCLUSIONS Self-monitoring with behavioral coaching is a feasible community-based intervention for PA promotion among Latina women of low socioeconomic status with chronic neurological conditions. PA is known to be important for brain health in neurological conditions but remains relatively unexplored in minority populations. CLINICALTRIAL ClinicalTrials.gov NCT04820153; https://clinicaltrials.gov/ct2/show/NCT04820153

Background Physical activity (PA) is known to improve quality of life (QoL) as well as reduce mortality and disease progression in individuals with chronic neurological disorders. However, Latina women are less likely to participate in recommended levels of PA due to common socioeconomic barriers, including limited resources and access to exercise programs. Therefore, we developed a community-based intervention with activity monitoring and behavioral coaching to target these barriers and facilitate sustained participation in an exercise program promoting PA. Objective The aim of this study was to determine the feasibility and efficacy of a community-based intervention to promote PA through self-monitoring via a Fitbit and behavioral coaching among Latina participants with chronic neurological disorders. Methods We conducted a proof-of-concept study among 21 Spanish-speaking Latina participants recruited from the Los Angeles County and University of Southern California (LAC+USC) neurology clinic; participants enrolled in the 16-week intervention at The Wellness Center at The Historic General Hospital in Los Angeles. Demographic data were assessed at baseline. Feasibility was defined by participant attrition and Fitbit adherence. PA promotion was determined by examining change in time spent performing moderate-to-vigorous PA (MVPA) over the 16-week period. The effect of behavioral coaching was assessed by quantifying the difference in MVPA on days when coaching occurred versus on days without coaching. Change in psychometric measures (baseline vs postintervention) and medical center visits (16 weeks preintervention vs during the intervention) were also examined. Results Participants were of low socioeconomic status and acculturation. A total of 19 out of 21 (90%) participants completed the study (attrition 10%), with high Fitbit wear adherence (mean 90.31%, SD 10.12%). Time performing MVPA gradually increased by a mean of 0.16 (SD 0.23) minutes per day (P Conclusions Self-monitoring with behavioral coaching is a feasible community-based intervention for PA promotion among Latina women of low socioeconomic status with chronic neurological conditions. PA is known to be important for brain health in neurological conditions but remains relatively unexplored in minority populations. Trial Registration ClinicalTrials.gov NCT04820153; https://clinicaltrials.gov/ct2/show/NCT04820153

Substandard and falsified antimalarials contribute to the global malaria burden by increasing the risk of treatment failures, adverse events, unnecessary health expenditures, and avertable deaths. Yet no study has examined this impact in Western Francophone Africa to date. In Benin, where malaria remains endemic and is the leading cause of mortality among children under age five, there is a lack of robust data to combat the issue effectively and inform policy decisions. We adapted the Substandard and Falsified Antimalarial Research Impact (SAFARI) model to assess the health and economic impact of poor-quality antimalarials in this population. The model simulated population characteristics, malaria infection, care-seeking behavior, disease progression, treatment outcomes, and associated costs of malaria. We estimated approximately 1.8 million cases of malaria in Benin among children under age five, which cost $177 million (95% CI: $176 – $178 million) in treatment costs and productivity losses annually. Substandard and falsified antimalarials were responsible for 2.8% (n=165) of deaths and nearly $4.9 million in annual costs. Moreover, we found that replacing all antimalarials with quality-assured artemisinin combination therapies (ACTs) could result in $13.6 million in cost-savings, and increasing pediatric malaria care-seeking by 20% could bring $19.4 million in cost-savings. These results highlight the value of improving access to quality-assured ACTs for malaria treatment and increasing care-seeking in Benin. Policymakers and key stakeholders should use these findings to advocate for increased access to quality-assured antimalarials, inform policies and interventions to improve healthcare access and quality, and reduce the burden of malaria.

Smartwatch with health tracking is essential for everyone in their daily workouts in which the information step count is helpful for developing services to smartwatch users, e.g. distance, pace. In addition, people who track their daily steps may not only be more active, they may also be less likely to develop health problems that lead to events like heart attacks or broken bones. Built-in MEMS motion sensors, the smartwatch continuously senses the movements of the arm on a 3-axis accelerometer. Cyclic nature of human walking results in cyclic signals. The concept of correlation is to measure how similar two time-series cycles are. Misinterpreting the correlation value results in undercounting or overcounting the number of steps. In this paper, a mean correlation is constructed from all three accelerometer data and its features are analyzed comprehensively. High precision of stride classification is achieved in practical scenarios in terms of true positive rates and true negative rates. The algorithm accuracy has been found 99.03% that is stable with arbitrary orientation of the smartwatch.

Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application.

Multi-atlas based segmentation is an effective technique that transforms a representative set of atlas images and labels into a target image for structural segmentation. However, a significant limitation of this approach relates to the fact that the atlas and the target images need to be similar in volume orientation, coverage, or acquisition protocols in order to prevent image misregistration and avoid segmentation fault. In this study, we aim to evaluate the impact of using a heterogeneous Computed Tomography Angiography (CTA) dataset on the performance of a multi-atlas cardiac structure segmentation framework. We propose a generalized technique based upon using the Simple Linear Iterative Clustering (SLIC) supervoxel method to detect a bounding box region enclosing the heart before subsequent cardiac structure segmentation. This technique facilitates our framework to process CTA datasets acquired from distinct imaging protocols and to improve its segmentation accuracy and speed. In a four-way cross comparison based on 60 CTA studies from our institution and 60 CTA datasets from the Multi-Modality Whole Heart Segmentation MICCAI challenge, we show that the proposed framework performs well in segmenting seven different cardiac structures based upon interchangeable atlas and target datasets acquired from different imaging settings. For the overall results, our automated segmentation framework attains a median Dice, mean distance, and Hausdorff distance of 0.88, 1.5 mm, and 9.69 mm over the entire datasets. The average processing time was 1.55 min for both datasets. Furthermore, this study shows that it is feasible to exploit heterogenous datasets from different imaging protocols and institutions for accurate multi-atlas cardiac structure segmentation.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Fluorescence microscopy (FM) is costly, time consuming, and requires considerable sample preparation. Here we present a virtual fluorescence staining method based on DL to transform fluorescence images of molecular labels into other molecular fluorescence labels.

ML is useful for classifying cells based on features derived from quantitative phase images. However, classifying cells with graded phenotype remains challenging. To address this, several ML algorithms were trained and validated on reconstructed phase images.

Objective: Increased left ventricular (LV) mass is an important precursor to heart failure. Inflammation plays an important role in increasing LV mass. However, the contribution of subclinical coronary artery disease (CAD) to the inflammation-LV mass relationship is unknown. In subjects with psoriasis, a chronic inflammatory skin disease, we evaluated if systemic inflammation assessed by plasma glycoprotein A (GlycA) associated with LV mass measured on coronary CT angiography (CCTA). Additionally, we analyzed whether this relationship was mediated by early CAD assessed as noncalcified coronary burden (NCB). Methods: We performed an observational longitudinal study of 213 subjects with psoriasis free of known cardiovascular disease, 189 of whom were followed over one year. All participants had GlycA measurements by nuclear magnetic resonance spectroscopy and LV mass and NCB quantified by CCTA. Results: The cohort had a mean age of 50.3 (±12.9) years and 59% were male. There was moderate psoriasis severity and low cardiovascular risk. LV mass increased by GlycA tertiles [1st tertile:24.6 g/m2.7(3.8), 2nd tertile:25.5 g/m2.7(3.8), 3rd tertile:27.7 g/m2.7(5.5), p

Fluorescence microscopy plays a vital role in understanding the subcellular structures of living cells. However, it requires considerable effort in sample preparation related to chemical fixation, staining, cost, and time. To reduce those factors, we present a virtual fluorescence staining method based on deep neural networks (VirFluoNet) to transform fluorescence images of molecular labels into other molecular fluorescence labels in the same field-of-view. To achieve this goal, we develop and train a conditional generative adversarial network (cGAN) to perform digital fluorescence imaging demonstrated on human osteosarcoma U2OS cell fluorescence images captured under Cell Painting staining protocol. A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case. In addition, we implement a deep learning model to perform autofocusing on another human U2OS fluorescence dataset as a preprocessing step to defocus an out-focus channel in U2OS dataset. A quantitative index of image prediction error is introduced based on signal pixel-wise spatial and intensity differences with ground truth to evaluate the performance of prediction to high-complex and throughput fluorescence. This index provides a rational way to perform image segmentation on error signals and to understand the likelihood of mis-interpreting biology from the predicted image. In total, these findings contribute to the utility of deep learning image regression for fluorescence microscopy datasets of biological cells, balanced against savings of cost, time, and experimental effort. Furthermore, the approach introduced here holds promise for modeling the internal relationships between organelles and biomolecules within living cells., 20 pages, 9 figures

We developed a 3D deep convolutional neural network (3D-DCNN) to perform 3D diffraction optical tomography. We experimentally demonstrate the ability of a 3D-DCNN to reconstruct the 3D index of refraction distribution of a phantom dataset.

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

The demand for efficient data transmission and storage have increased substantially in recent years with the rise of data size and complexity. Video content accounted for 64% of all-world-internet-traffic in 2014, and will reach 80% by 2019. The volume of video content is increasing drastically with the advances in device and communication technology, as well as the infrastructure of internet and social media. Although the size of videos can be reduced significantly by using lossy compression methods if the data is for human eyes, many applications require the videos to be transmitted and stored in their original quality as they are the sole source for future machine analysis. This paper provides a comprehensive performance comparison of ten state-of-art lossless video compression methods in terms of compression ratios and encoding and decoding costs under various color spaces.

Modern multidimensional medical imaging technology produces very large amount of data especially from the computed tomography modality. These volumetric dataset opens new demands for big data storage and high-speed communication systems which may be alleviated by image compression techniques. Current image compression schemes adopted in the DICOM standard do not exploit the inter-slice correlation within three-dimensional (3D) dataset. Video compression may have a potential to improve the compression ratio by reducing the redundancy in volumetric medical images. In this paper, we compare the performance of five lossless video codecs (h264, H265, Lagarith, MSU, MLC) and three still-image codecs (JPEG, JPEG2000, JPEG-LS) using 3D medical computed tomography datasets. Performance evaluation shows that video codecs improve the compression ratio compared to JPEG and JPEG2000 while being competitive to JPEG-LS.

A bi-telecentric DHM configuration is developed to optically mitigate phase aberrations due to MO and tilt due to reference. A novel automatic background area detection based on deep learning for residual phase aberration compensation is also developed.

The activity and durability of two hybrid bifunctional dimethyl ether (DME) synthesis catalysts: 6CuO–3ZnO–Al2O3/γ-Al2O3 and 6CuO–3ZnO–1Al2O3/HZSM-5, have been evaluated for the conversion of CO2/H2 to DME at 260 °C, 5 MPa, and gas per hourly space velocity (GHSV) of 3000 mL g cat − 1 h − 1 in fixed-bed and slurry reactors. The catalyst 6CuO–3ZnO–Al2O3/γ-Al2O3 mostly promoted reverse-water-gas-shift (RWGS) reaction with very low selectivity to DME, and a low conversion of CO2. The other catalyst, 6CuO–3ZnO–1Al2O3/HZSM-5, showed much higher conversion of CO2 with higher selectivity to DME in both, fixed-bed and slurry reactors.

Al-MCM-41, [SiO2]/[Al2O3] = 30, mesoporous catalysts were synthesized using tetraethylorthosilicate (TEOS) as a source of silica and hexadecyltrimethylammonium bromide (CTAB) as a structure-directing agent (SDA) in the presence of tetrapropylammonium hydroxide (TPAOH) as a co-surfactant additive. The catalytic activities of the calcined samples were evaluated for the dehydration of methanol to dimethyl ether (DME) at 250–450 °C and a gas hourly space velocity (GHSV) of 14,000–80,000 mL g−1cat h−1. Al-MCM-41 samples synthesized in the presence of TPAOH showed higher activity than those synthesized in its absence. The improvement in catalytic activity correlates with the increase in acidity brought about by the enhanced condensation of aluminosilicate framework in the Al-MCM-41 structure. Al-MCM-41 catalysts were also combined with methanol synthesis catalysts to form bifunctional catalysts for the synthesis DME from CO2/H2 at temperature, 260 °C; pressure, 735 psi; GHSV, 2000 mL g−1cat h−1 in a fixed-bed reactor.

Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.

Two different strategies for the preparation of active dimethyl ether catalysts are evaluated using CuO−ZnO−Al2O3 and SiO2−Al2O3 as methanol synthesis and methanol dehydration catalysts components,...

This paper utilizes a synchronized Lorenz chaotic drive/response system, which uses Haar filtering and appropriate thresholding in order to detect a transmitted random binary message. Using the Lorenz chaotic attractor to obscure the message, the transmission is passed through an Additive White Gaussian (AWG) channel to successfully retrieve the original binary random data. The detection mechanism employs the Haar Wavelet Transform in combating the channel noise. A communication technique using Chaotic Parameter Modulation (CPM) is simulated in Matlab and prototyped on a reconfigurable hardware platform from Xilinx.

High bandwidth, fast deployment with relatively low cost implementation are some of the important advantages of free space optical (FSO) communications. However, the atmospheric turbulence has a substantial impact on the quality of a laser beam propagating through the atmosphere. A new method was presented in [1] and [2] to perform bit synchronization and detection of binary Non-Return-to-Zero (NRZ) data from a free-space optical (FSO) communication link. It was shown that, when the data is binary NRZ with no modulation, the Haar wavelet transformation can effectively reduce the scintillation noise. In this paper, we leverage and modify the work presented in [1] in order to provide a real-time streaming hardware prototype. The applicability of these concepts will be demonstrated through providing the hardware prototype using one of the state-of-the-art reconfigurable hardware, namely Field Programmable Gate Arrays, and highly productive high-level design tools such as System Generator for DSP from Xilinx.