Adherence and potential factors of adherence to a resistance, coordination and endurance training in older retirement home residents over 6 months.

INTRODUCTION: High exercise adherence is a key factor for effective exercise programmes. However, little is known about predictors of exercise adherence to a multimodal machine-based training in older retirement home residents. AIMS: To assess exercise adherence and potential predictors of adherence. Furthermore, to evaluate user acceptance of the multimodal training and the change in exercise self-efficacy. METHODS: In this sub-analysis of the bestform-F study, a total of 77 retirement home residents ≥65 years (mean age: 85.6 ± 6.6 years, 77.9% female) participated in a 6-month machine-based resistance, coordination and endurance training. Attendance to the training was documented for each training session. To identify potential predictors a multiple linear regression model was fitted to the data. Analyzed predictors included age, sex, body mass index (BMI), physical function, exercise self-efficacy, and physical activity history. Different domains of user acceptance (e.g. safety aspects, infrastructure) and exercise self-efficacy were assessed by a questionnaire and the exercise self-efficacy scale (ESES), respectively. RESULTS: Mean exercise adherence was 67.2% (median: 74.4%). The regression model (R2 = 0.225, p = 0.033) revealed that the 6-minute walk test (6-MWT) at baseline significantly predicted exercise adherence (ß: 0.074, 95% confidence interval (CI): 0.006-0.142, p = 0.033). Different user domains were rated at least as good by 83.9%-96.9% of participants, reflecting high acceptance. No statistically significant change was found for exercise self-efficacy over 6 months (mean change: 0.47 ± 3.08 points, p = 0.156). CONCLUSION: Retirement home residents attended more than two thirds of offered training sessions and physical function at baseline was the key factor for predicting adherence. User acceptance of the training devices was highly rated. These findings indicate good potential for implementation of the exercise programme.

Novel Approach to Intradialytic Progressive Resistance Exercise Training.

Physical activity levels are typically undesirably low in chronic kidney disease patients, especially in those undergoing haemodialysis, and particularly on dialysis days. Intradialytic exercise programmes could be a solution to this issue and have been reported to be safe and relatively easily implemented in dialysis clinics. Nevertheless, such implementation has been failing in part due to barriers such as the lack of funding, qualified personnel, equipment, and patient motivation. Intradialytic aerobic exercise has been the most used type of intervention in dialysis clinics. However, resistance exercise may be superior in eliciting potential benefits on indicators of muscle strength and mass. Yet, few intradialytic exercise programmes have focused on this type of intervention, and the ones which have report inconsistent benefits, diverging on prescribed exercise intensity, absent or subjective load progression, equipment availability, or exercise supervision. Commonly, intradialytic resistance exercise interventions use free weights, ankle cuffs, or elastic bands which hinder load progression and exercise intensity monitoring. Here, we introduce a recently developed intradialytic resistance exercise device and propose an accompanying innovative resistance exercise training protocol which aims to improve the quality of resistance exercise interventions within dialysis treatment sessions.

"Just the Facts Ma'am": Moral and Ethical Considerations for Artificial Intelligence in Medicine and its Potential to Impact Patient Autonomy and Hope.

Applying machine-based learning and synthetic cognition, commonly referred to as artificial intelligence (AI), to medicine intimates prescient knowledge. The ability of these algorithms to potentially unlock secrets held within vast data sets makes them invaluable to healthcare. Complex computer algorithms are routinely used to enhance diagnoses in fields like oncology, cardiology, and neurology. These algorithms have found utility in making healthcare decisions that are often complicated by seemingly endless relationships between exogenous and endogenous variables. They have also found utility in the allocation of limited healthcare resources and the management of end-of-life issues. With the increase in computing power and the ability to test a virtually unlimited number of relationships, scientists and engineers have the unprecedented ability to increase the prognostic confidence that comes from complex data analysis. While these systems present exciting opportunities for the democratization and precision of healthcare, their use raises important moral and ethical considerations around Christian concepts of autonomy and hope. The purpose of this essay is to explore some of the practical limitations associated with AI in medicine and discuss some of the potential theological implications that machine-generated diagnoses may present. Specifically, this article examines how these systems may disrupt the patient and healthcare provider relationship emblematic of Christ's healing mission. Finally, this article seeks to offer insights that might help in the development of a more robust ethical framework for the application of these systems in the future.

Prediction and prognosis of adverse maternal and foetal/neonatal outcomes in pulmonary hypertension: an observational study and nomogram construction.

BACKGROUND: Pregnant women with pulmonary hypertension (PH) have higher mortality rates and poor foetal/neonatal outcomes. Tools to assess these risk factors are not well established. METHODS: Predictive and prognostic nomograms were constructed using data from a "Development" cohort of 420 pregnant patients with PH, recorded between January 2009 and December 2018. Logistic regression analysis established models to predict the probability of adverse maternal and foetal/neonatal events and overall survival by Cox analysis. An independent "Validation" cohort comprised data of 273 consecutive patients assessed from January 2019 until May 2022. Nomogram performance was evaluated internally and implemented with online software to increase the ease of use. RESULTS: Type I respiratory failure, New York Heart Association functional class, N-terminal pro-brain natriuretic peptide [Formula: see text] 1400 ng/L, arrhythmia, and eclampsia with pre-existing hypertension were independent risk factors for maternal mortality or heart failure. Type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, New York Heart Association functional class, and N-terminal pro-brain natriuretic peptide [Formula: see text] 1400 ng/L were independent predictors of pulmonary hypertension survival during pregnancy. For foetal/neonatal adverse clinical events, type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, parity, platelet count, fibrinogen, and left ventricular systolic diameter were important predictors. Nomogram application for the Development and Validation cohorts showed good discrimination and calibration; decision curve analysis demonstrated their clinical utility. CONCLUSIONS: The nomogram and its online software can be used to analyse individual mortality, heart failure risk, overall survival prediction, and adverse foetal/neonatal clinical events, which may be useful to facilitate early intervention and better survival rates.

[Single-modal neuroimaging computer aided diagnosis for schizophrenia based on ensemble learning using privileged information].

Neuroimaging technologies have been applied to the diagnosis of schizophrenia. In order to improve the performance of the single-modal neuroimaging-based computer-aided diagnosis (CAD) for schizophrenia, an ensemble learning algorithm based on learning using privileged information (LUPI) was proposed in this work. Specifically, the extreme learning machine based auto-encoder (ELM-AE) was first adopted to learn new feature representation for the single-modal neuroimaging data. Random project algorithm was then performed on the learned high-dimensional features to generate several new feature subspaces. After that, multiple feature pairs were built among these subspaces to work as source domain and target domain, respectively, which were used to train multiple support vector machine plus (SVM+) classifier. Finally, a strong classifier is learned by combining these SVM+ classifiers for classification. The proposed algorithm was evaluated on a public schizophrenia neuroimaging dataset, including the data of structural magnetic resonance imaging (sMRI) and functional MRI (fMRI). The results showed that the proposed algorithm achieved the best diagnosis performance. In particular, the classification accuracy, sensitivity and specificity of the proposed algorithm were 72.12% ± 8.20%, 73.50% ± 15.44% and 70.93% ± 12.93%, respectively, on the sMRI data, and it also achieved the classification accuracy of 72.33% ± 8.95%, sensitivity of 68.50% ± 16.58% and specificity of 75.73% ± 16.10% on the fMRI data. The proposed algorithm overcomes the problem that the traditional LUPI methods need the additional privileged information modality as source domain. It can be directly applied to the single-modal data for classification, and also can improve the classification performance. Therefore, it suggests that the proposed algorithm will have wider applications.

Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis.

BACKGROUND: Preoperative discrimination between nonmuscle-invasive bladder carcinomas (NMIBC) and the muscle-invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). PURPOSE: To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T2 -weighted (T2 W) and multi-b-value diffusion-weighted (DW) sequences. ASSESSMENT: A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. STATISTICAL TESTS: Chi-square test and Student's t-test were applied on clinical characteristics to analyze the significant differences between patient groups. RESULTS: Of the 1104 features, an optimal subset involving 19 features was selected from T2 W and DW sequences, which outperformed the other two subsets selected from T2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM-RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. DATA CONCLUSION: The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2 W sequence or DW sequence only. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489-1498.

Emerging imaging technologies in dermatology: Part II: Applications and limitations.

Clinical examination is critical for the diagnosis and identification of response to treatment. It is fortunate that technologies are continuing to evolve, enabling augmentation of classical clinical examination with noninvasive imaging modalities. This article discusses emerging technologies with a focus on digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional developing modalities. The most readily adopted technologies to date include total-body digital photography and dermoscopy, with some practitioners beginning to use confocal microscopy. In this article, applications and limitations are addressed. For a detailed discussion of the principles involved in these technologies, please refer to the first part of this review article.

Emerging imaging technologies in dermatology: Part I: Basic principles.

Dermatologists rely primarily on clinical examination in combination with histopathology to diagnose conditions; however, clinical examination alone might not be sufficient for accurate diagnosis and skin biopsies have associated morbidity. With continued technological advancement, there are emerging ancillary imaging technologies available to dermatologists to aid in diagnosis and management. This 2-part review article will discuss these emerging technologies including: digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional modalities in development. In this first installment, the authors describe the breadth of technologies available and the science behind them. Then, in the second article, the authors discuss the applications and limitations of these technologies and future directions.

Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies.

Kernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant- and microbiome-based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.

Lower-extremity resistance training on unstable surfaces improves proxies of muscle strength, power and balance in healthy older adults: a randomised control trial.

BACKGROUND: It is well documented that both balance and resistance training have the potential to mitigate intrinsic fall risk factors in older adults. However, knowledge about the effects of simultaneously executed balance and resistance training (i.e., resistance training conducted on unstable surfaces [URT]) on lower-extremity muscle strength, power and balance in older adults is insufficient. The objective of the present study was to compare the effects of machine-based stable resistance training (M-SRT) and two types of URT, i.e., machine-based (M-URT) and free-weight URT (F-URT), on measures of lower-extremity muscle strength, power and balance in older adults. METHODS: Seventy-five healthy community-dwelling older adults aged 65-80 years, were assigned to three intervention groups: M-SRT, M-URT and F-URT. Over a period of ten weeks, all participants exercised two times per week with each session lasting ~60 min. Tests included assessment of leg muscle strength (e.g., maximal isometric leg extension strength), power (e.g., chair rise test) and balance (e.g., functional reach test), carried out before and after the training period. Furthermore, maximal training load of the squat-movement was assessed during the last training week. RESULTS: Maximal training load of the squat-movement was significantly lower in F-URT in comparison to M-SRT and M-URT. However, lower-extremity resistance training conducted on even and uneven surfaces meaningfully improved proxies of strength, power and balance in all groups. M-URT produced the greatest improvements in leg extension strength and F-URT in the chair rise test and functional reach test. CONCLUSION: Aside from two interaction effects, overall improvements in measures of lower-extremity muscle strength, power and balance were similar across training groups. Importantly, F-URT produced similar results with considerably lower training load as compared to M-SRT and M-URT. Concluding, F-URT seems an effective and safe alternative training program to mitigate intrinsic fall risk factors in older adults. TRIAL REGISTRATION: This trial has been registered with clinicaltrials.gov ( NCT02555033 ) on 09/18/2015.

Reliability of Computer-Assisted and Manual Measurement Methods for Assessment of Graf Type 1 and Type 2 Hip Sonograms.

OBJECTIVES: The purpose of this study was to investigate the interobserver, intraobserver, and intermethod reliability of computer-assisted digital and manual measurements of hip sonograms. METHODS: Seventy-four hip sonograms were evaluated in this retrospective study. Five evaluators measured digital images and manual paper printouts according to the Graf method (Arch Orthop Trauma Surg 1984; 102:248-255). Interobserver and intraobserver reliability rates were calculated. Reliability criteria were graded on a numeric scale. RESULTS: The interobserver reliability of both computer-based and manual methods for alpha angle measurements was good to excellent, but the interobserver reliability was fair to poor for beta angle measurements. Intraobserver reliability was varied. Alpha angle measurements by both manual and computer-based methods had high concordance with each other, whereas beta angle measurements had low concordance. The intermethod variability did not differ between observers. CONCLUSIONS: The alpha angle measurements had high concordance with each other for both manual and computer-based methods, whereas the beta angle measurements had low concordance. This information should be taken into account in clinical practice. Overall, the two measurement methods were reliable and consistent with each other.

Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.

Aims: We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results: In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion: The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new data sets.

Computational approaches for the design of modulators targeting protein-protein interactions.

BACKGROUND: Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED: In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION: Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

A support vector machine-based algorithm to identify bisphosphonate-related osteonecrosis throughout the mandibular bone by using cone beam computerized tomography images.

OBJECTIVE: This study aimed to develop an algorithm to distinguish the patients with bisphosphonate-related osteonecrosis of the jaws (BRONJ) from healthy controls using CBCT images by evaluating both trabecular and cortical bone changes through the whole body of the mandibular bone. METHODS: Patient data set was created from axial CBCT images of 7 BRONJ patients (28 slices) and 8 healthy controls (27 slices). The healthy bone of healthy controls, bone sclerosis of BRONJ patients, bone necrosis of BRONJ patients, and normal appearing bone of BRONJ patients (NBP) were labeled on CBCT images by three maxillofacial radiologists. Proposed algorithm had preparation and background cancellation, mandibular bone segmentation and centerline determination, spatial transformation of gray values, and classification steps. RESULTS: Significant differences between the statistical moments (mean, variance, skewness, kurtosis, standard error, median, mode and coefficient of variance) of healthy and diseased (bone sclerosis and necrosis) groups were observed (p = 0.000, p < 0.05). Also, variations were noted between healthy controls and NBP of BRONJ patients (p = 0.000, p < 0.05).The statistical moments were utilized to develop the algorithm which has resulted with accuracy of 0.999, sensitivity of 0.998, specificity of 0.998, precision of 1, recall of 0.998, AUC of 1, and F1 score of 0.999 in identification of BRONJ patients from healthy ones. CONCLUSION: The proposed algorithm differentiated the mandibular bones of the healthy and the BRONJ patients with high accuracy in the present test sample.

Bestform-F - Best Function of Range of Motion: A Feasibility Study of a Multimodal Exercise Training Program for Older Adults in Retirement Homes.

Purpose: Mobility is a crucial factor for independence and quality of life in old age. Nevertheless, many old people in retirement homes do not meet the physical activity recommendations. The aim of the Bestform-F - Best Function of Range of Motion feasibility study (bestform-F) was to evaluate the feasibility of implementing a machine-based multimodal exercise training program in older residents in retirement homes. Materials and Methods: The participants (n = 77) were recruited from two retirement homes and took part in a six-month multimodal exercise training program (2x/week, 45 minutes) on pneumatic strength training machines, a balance platform and bicycle ergometers. Feasibility criteria were recruitment number ≥ 35 participants within six months, dropout rate < 40% of participants within six months of exercise, and training adherence ≥ 50% of participants taking part in at least 50% of offered training sessions. Additionally, physical performance, fear of falling, cognitive function, and quality of life were assessed at baseline and after six months. Results: For the bestform-F study, 77 (85.6 ± 6.6 years; 78% women) out of 215 eligible residents from two senior residences were recruited. The dropout rate over six months was 10% (8/77 participants). The training adherence rate for the finishing participants was 77% (53/69 participants). In addition to the achieved feasibility criteria, significant improvements were recorded in the Chair Stand Test, Six-Minute Walk Test, and fear of falling after six months. Conclusion: All feasibility criteria have been fulfilled. The high number of recruited participants, the low dropout rate, and high adherence to the training program confirm the feasibility of a multimodal machine-based exercise training program offered to residents in retirement homes. The results provide a basis for a cluster-randomized controlled trial aimed at further investigating the efficacy of the bestform-F program.

Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation.

BACKGROUND: The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. OBJECTIVE: The aim of this study was to evaluate machine learning-based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporated multifaceted clinical information on evolution of the disease. METHODS: This multicenter cohort study (COVIP study) obtained patient data from 151 intensive care units (ICUs) from 26 countries. Different models based on the Sequential Organ Failure Assessment (SOFA) score, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were derived as baseline models that included admission variables only. We subsequently included clinical events and time-to-event as additional variables to derive the final models using the same algorithms and compared their performance with that of the baseline group. Furthermore, we derived baseline and final models on a European patient cohort, which were externally validated on a non-European cohort that included Asian, African, and US patients. RESULTS: In total, 1432 elderly (≥70 years old) COVID-19-positive patients admitted to an ICU were included for analysis. Of these, 809 (56.49%) patients survived up to 30 days after admission. The average length of stay was 21.6 (SD 18.2) days. Final models that incorporated clinical events and time-to-event information provided superior performance (area under the receiver operating characteristic curve of 0.81; 95% CI 0.804-0.811), with respect to both the baseline models that used admission variables only and conventional ICU prediction models (SOFA score, P<.001). The average precision increased from 0.65 (95% CI 0.650-0.655) to 0.77 (95% CI 0.759-0.770). CONCLUSIONS: Integrating important clinical events and time-to-event information led to a superior accuracy of 30-day mortality prediction compared with models based on the admission information and conventional ICU prediction models. This study shows that machine-learning models provide additional information and may support complex decision-making in critically ill elderly COVID-19 patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04321265; https://clinicaltrials.gov/ct2/show/NCT04321265.

Digital Innovation in Medicinal Product Regulatory Submission, Review, and Approvals to Create a Dynamic Regulatory Ecosystem-Are We Ready for a Revolution?

The pace of scientific progress over the past several decades within the biological, drug development, and the digital realm has been remarkable. The'omics revolution has enabled a better understanding of the biological basis of disease, unlocking the possibility of new products such as gene and cell therapies which offer novel patient centric solutions. Innovative approaches to clinical trial designs promise greater efficiency, and in recent years, scientific collaborations, and consortia have been developing novel approaches to leverage new sources of evidence such as real-world data, patient experience data, and biomarker data. Alongside this there have been great strides in digital innovation. Cloud computing has become mainstream and the internet of things and blockchain technology have become a reality. These examples of transformation stand in sharp contrast to the current inefficient approach for regulatory submission, review, and approval of medicinal products. This process has not fundamentally changed since the beginning of medicine regulation in the late 1960s. Fortunately, progressive initiatives are emerging that will enrich and streamline regulatory decision making and deliver patient centric therapies, if they are successful in transforming the current transactional construct and harnessing scientific and technological advances. Such a radical transformation will not be simple for both regulatory authorities and company sponsors, nor will progress be linear. We examine the shortcomings of the current system with its entrenched and variable business processes, offer examples of progress as catalysts for change, and make the case for a new cloud based model. To optimize navigation toward this reality we identify implications and regulatory design questions which must be addressed. We conclude that a new model is possible and is slowly emerging through cumulative change initiatives that question, challenge, and redesign best practices, roles, and responsibilities, and that this must be combined with adaptation of behaviors and acquisition of new skills.

Trypan blue as an affordable marker for automated live-dead cell analysis in image cytometry.

The aim of the present study was to combine image cytometry and trypan blue (TB) exclusion staining for a reproducible high-throughput detection of dead cells, enabling TB as an inexpensive marker, to be affordable for many studies and creating the possibility to combine fluorochromes without or with less spectral overlap. Capillary blood was drawn from a healthy volunteer, red blood cells were lysed and leukocyte cell death was induced. Samples were stained with CD45-FITC, CD14-PE, TB and DAPI, and then analyzed using image cytometry (iCys). TB quenching control tests were performed using DAPI and CD45-FITC. Images were generated in .TIF and .JPEG format using iCys image cytometer. The images were analyzed using CellProfiler (CP) modules to optimize the analysis based on the aims of each phase of this study. CellProfiler Analyst (CPA) was used to classify cells throughout machine learning and to calculate sensibility of the classification. A sensitivity of 0.94 for dead cells and 0.99 for live cells was calculated using CPA. We did not see any quenching effects of the FITC staining. DAPI signal was reduced in the presence of TB. The results of the present study revealed that TB serves as a dead cell marker in an image cytometric analysis, being able to be combined with other fluorescence markers without loss of fluorescence intensity signal or overlapping emission spectrum. SCANNING 38:857-863, 2016. © 2016 Wiley Periodicals, Inc.

Sound stream segregation: a neuromorphic approach to solve the "cocktail party problem" in real-time.

The human auditory system has the ability to segregate complex auditory scenes into a foreground component and a background, allowing us to listen to specific speech sounds from a mixture of sounds. Selective attention plays a crucial role in this process, colloquially known as the "cocktail party effect." It has not been possible to build a machine that can emulate this human ability in real-time. Here, we have developed a framework for the implementation of a neuromorphic sound segregation algorithm in a Field Programmable Gate Array (FPGA). This algorithm is based on the principles of temporal coherence and uses an attention signal to separate a target sound stream from background noise. Temporal coherence implies that auditory features belonging to the same sound source are coherently modulated and evoke highly correlated neural response patterns. The basis for this form of sound segregation is that responses from pairs of channels that are strongly positively correlated belong to the same stream, while channels that are uncorrelated or anti-correlated belong to different streams. In our framework, we have used a neuromorphic cochlea as a frontend sound analyser to extract spatial information of the sound input, which then passes through band pass filters that extract the sound envelope at various modulation rates. Further stages include feature extraction and mask generation, which is finally used to reconstruct the targeted sound. Using sample tonal and speech mixtures, we show that our FPGA architecture is able to segregate sound sources in real-time. The accuracy of segregation is indicated by the high signal-to-noise ratio (SNR) of the segregated stream (90, 77, and 55 dB for simple tone, complex tone, and speech, respectively) as compared to the SNR of the mixture waveform (0 dB). This system may be easily extended for the segregation of complex speech signals, and may thus find various applications in electronic devices such as for sound segregation and speech recognition.

Development of a functional anatomical subtalar pronator and supinator strength training machine.

BACKGROUND: The strength training industry has failed in designing a machine for exercising the pronators and supinators, despite their substantial medio-lateral bracing function. OBJECTIVE: The present study documents the muscle strength generation capabilities of the pronators and supinators within their functional anatomic movement plane, using an innovative strength training machine with an oblique axis. METHODS: By using two force transducers, the angle-torque relationship of the pronators and supinators of 18 healthy male subjects was identified during maximum voluntary isometric contractions at five anatomical joint angles. Surface EMG was recorded from anterior tibial (TA), peroneus longus (PL) and soleus (SOL) muscles. RESULTS: The pronator strength curve showed an inverted U-shaped characteristic, whereas the supinator curve descends from pronated to supinated position. Compared to the muscle activities for one-leg heel raise and toe raise, PL (108-131%) and TA (59-83%), respectively, showed highest activity during pronations. The most activated supinator is SOL (67% of a one-leg heel raise). CONCLUSIONS: Differences in the shape of the pronator and supinator strength curves revealed that two different variable cams have to be implemented for matching the human torque capability. We anticipate our study to be a starting point for preventive machine-based training interventions.