SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

Determination of HCV genotypes and viral loads in chronic HCV infected patients of Hazara Pakistan

Hepatitis C Virus (HCV) genotype and viral load are two significant predictive variables knowledge of which might persuade treatment decisions. The objective of the present study was to identify the distribution of different HCV genotypes circulating in the study area and to estimate viral load in chronically HCV infected patients. Out of total 305 HCV positive patients, 177 (58%) were males and 128 (42%) were females. Frequency breakup of the HCV positive patients was 169, 69, 38 and 29 from Abbottabad, Mansehra, Haripur and Battagram districts respectively. Out of the total 305 tested serum samples, 255 (83.06%) were successfully genotyped whereas 50 (16.4%) samples were found with unclassified genotypes. Among typable genotypes, 1a accounted for 21 (6.8%) 1b for 14 (4.6%), 2a for 4 (1.31%) 3a for 166 (54.42%) and genotype 3b for (8.19%). Twenty five (8.19%) patients were infected with mixed HCV genotypes. Viral load distribution was classified into three categories based on its viral load levels such as low (< 60, 0000 IU/mL), intermediate (60,0000-80,0000 IU/mL) and high (> 80,0000 IU/mL). The baseline HCV RNA Viral load in HCV genotype 3 infected patients was 50 (26.17%), 46 (24.08%) and 95 (49.73%) for low, intermediate and high categories respectively. For genotypes other than 3, these values for low, intermediate and high viral load categories were 50 (43.85), 35 (30.70) and 29 (25.43) respectively. Pre-treatment viral load in patients with untypable genotype was 19 (38.00%), 5 (20.00%) and 11 (44.00%) for low, intermediate and high viral load categories. Viral load distribution was also categorized sex wise; for males it was 58 (32.76%), 26 (14.68%) and 93 (52.54%) whereas for females it was 40 (31.25%), 34 (26.56%) and 54 (42.18%) for low, intermediate and high viral load respectively. In conclusion HCV genotype 3a is the most prevalent genotype circulating in Hazara Division like other parts of pakistan. Pre-treatment viral load is significantly high (p 0.014) in patients infected with HCV genotype 3 as compared to other genotypes

Knowledge, attitudes, and practices among nurses in Pakistan towards diabetic foot

Introduction: Diabetic foot ulcers are a pressing complication of diabetes mellitus. Wound care requires a significant proportion of healthcare resources. It is imperative, therefore, for healthcare professionals to possess sound knowledge of the disease along with a positive attitude to ensure better clinical practice. Our literature search revealed a scarcity of data pertaining to diabetic foot ulcers. Therefore, this study aims to evaluate the knowledge and attitudes of nurses regarding diabetic foot care. Methods: A cross-sectional study design was employed, a pre-validated and pre-tested questionnaire was used to collect data from a sample size of 250 nurses working at two tertiary care hospitals in Karachi, Pakistan. The study was conducted over a period of three months (January to March 2018) and included all nurses who possessed at least one year of clinical experience in diabetic ulcer care. The statistical software employed was SPSS version 19 (IBM Corp., Armonk, NY, US). Non-parametric tests and descriptive statistics were used for data analysis and statistical significance was assumed at a p-value of less than 0.5. Results: Only 54% of the nurses in our study possessed adequate knowledge of diabetic foot ulcers. The mean score of knowledge was 74.9 (±9.5). Macdonald’s standard criteria for learning outcomes was used to gauge the knowledge levels of our study population. Nurses performed best in the domain of ulcer care with 65.3% of the participants possessing good knowledge of the topic. The overall attitude of nurses towards patients with diabetic ulcers was positive. Conclusion: This study highlights important gaps in nurses’ knowledge and sheds light on the lack of evidence-based practice. Poor knowledge can compromise healthcare standards, even with the presence of positive attitudes. Hence, a comprehensive revision of nursing curricula across local tertiary hospitals for allowing nurses to update their knowledge is warrante

6-(4-Nitro­phen­oxy)hexa­nol

The title compound, C12H17NO4, features an almost planar mol­ecule (r.m.s. deviation for all non-H atoms = 0.070 Å). All methyl­ene C—C bonds adopt an anti­periplanar conformation. In the crystal structure the mol­ecules lie in planes parallel to (12) and the packing is stabilized by O—H⋯O hydrogen bonds

Human Activity Recognition from Wearable Sensor Data Using Self-Attention

Human Activity Recognition from body-worn sensor data poses an inherent challenge in capturing spatial and temporal dependencies of time-series signals. In this regard, the existing recurrent or convolutional or their hybrid models for activity recognition struggle to capture spatio-temporal context from the feature space of sensor reading sequence. To address this complex problem, we propose a self-attention based neural network model that foregoes recurrent architectures and utilizes different types of attention mechanisms to generate higher dimensional feature representation used for classification. We performed extensive experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-subject-out evaluation. We also observe that the sensor attention maps produced by our model is able capture the importance of the modality and placement of the sensors in predicting the different activity classes.Comment: Accepted for publication at the 24th European Conference on Artificial Intelligence (ECAI-2020); 8 pages, 4 figure

Under Biological Invasion: Impacts of Litter Decomposition Mediated by Invasive Plant Species on Soil Nutrients and Functional Growth Traits of both Invasive and Native Plant Species

An invasive plant, Solidago canadensis is quickly encroaching across Eastern China and has become a crucial concern in the alteration of native ecosystem structure and function. However, the impact on invaded soil from S. canadensis litter is still under consideration. This study evaluated the effects of different levels of litter mass (Control: L0, 5 g: L5, 10 g: L10, 15 g: L15, and 20 g: L20) of invasive S. canadensis on the functional traits of two congeneric plant species (S. canadensis and S. decurrens), as well as resulting variations in soil nutrient levels. Our results indicated that shoot and root length, fresh and dry biomass, leaf chlorophyll and leaf nitrogen were significantly higher at L15 compared to the other treatments in the experiment. Additionally, in the L20 treatment all traits were decreased drastically, although these were higher than the control treatment, i.e. L0. Soil nutrients increased as the level of litter mass was raised in the soil. Furthermore, our study showed that high litter mass from S. canadensis can adversely impact the functional traits of both plant species. Further studies are required to assess the allelopathic effect of litter mass, as well as biological and physicochemical properties of field soil where high quantities of the invasive plant litter are found.National Natural Science Foundation of China [31971427, 32071521]; Jiangsu Planned Projects for Postdoctoral Research Funds [2021K384C]; Carbon peak and Carbon neutrality Technology Innovation Foundation of Jiangsu Province [BK20220030]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Jiangsu Collaborative Innovation Center of Technology and Material of Water TreatmentThis work was supported by the National Natural Science Foundation of China (31971427, 32071521), Jiangsu Planned Projects for Postdoctoral Research Funds (2021K384C), Carbon peak and Carbon neutrality Technology Innovation Foundation of Jiangsu Province(BK20220030), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment

A novel computational fractional modeling approach for the global dynamics and optimal control strategies in mitigating Marburg infection

Marburg virus disease poses a significant risk to global health, impacting both humans and non-human primates. This study has yielded an optimal control model for potentially mitigating the transmission of the Marburg infection. The proposed mathematical model includes fractional-order derivatives in the Caputo sense. Initially, we analyzed the model without control measures, examining its key characteristics regarding local and global stabilities. Subsequently, we extended the model by incorporating suitable time-dependent optimal control variables. We have also introduced two timedependent control measures: psi 1 for the prevention of human-to-human Marburg transmission, and psi 2 to enhance the rate of quarantine of exposed individuals. We performed simulation analysis for both cases i.e., with and without optimal controls using the two-step Newton polynomial approximation study between classical and fractional cases validate the biological significance of the fractional operator and effectiveness of the proposed optimal control strategies.Web of Science95131941315

Comparative analysis of inflow forecasting using machine learning and statistical techniques: case study of Mangla reservoir and Marala Headworks

This study, under the context of a global perspective, focuses on the Indus Basin Irrigation System (IBIS) of Pakistan specifically the Jhelum and Chenab rivers inflows. The IBIS operation relies on seasonal planning strategies, informed by forecasts of river inflows at key stations by the Indus River System Authority (IRSA). In this study, Artificial Intelligence (AI) models including Generalized Regression Neural Network (GRNN), and Multi-Layer Feedforward Neural Network (MLFN) along with the statistical model Autoregressive Integrated Moving Average (ARIMA) were used to forecast the inflows of both rivers for 5 years (2020–2024) with a lead time of 1 year. Historic flow data of 59 years (10 daily from 1966 to 2024) were collected from IRSA. The collected data from 1966 to 2014 are used for calibration/training and from 2015 to 2020 are used for validation/testing of selected models for both study locations. The results of correlation and error estimation depicted that Artificial Neural Network (ANN) models predicted better inflows than the ARIMA model. The average RMSE and R2 of ANN models is 9.68 and 0.92 and the average RMSE and R2 of ARIMA Model is 10.17 and 0.88, this results in improvement of average RMSE and R2 by 4.82% and 4.35% in case of ANN Models when compared with ARIMA Model. Qualitative analysis shows that ANN techniques better predicted the high and low flows when compared with statistical methods. Specifically, the application of the ANN models has enhanced the precision of forecasted inflows assessment compared to the probabilistic inflow forecasting methods used by IRSA. The average RMSE and R2 in case of IRSA forecast is 11.47 and 0.88 and the average RMSE and R2 in case of ANN Models is 10.30 and 0.92, this results in improvement of average RMSE and R2 by 10.20% and 4.35% in case of ANN Models when compared with IRSA forecast. This study highlights the need for utilization of ANN models in place of probabilistic inflow forecasting methods to improve the accuracy of time series inflow forecasts

Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud

State-of-the-art progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. A hybrid cloud is an infrastructure consisting of a private cloud (managed by the organization) and a public cloud (managed by the cloud service provider). The use of hybrid cloud enables electronic health records to be exchanged between medical institutions and supports multipurpose usage of electronic health records. Along with the benefits, cloud-based electronic health records also raise the problems of security and privacy specifically in terms of electronic health records access. A comprehensive and exploratory analysis of privacy-preserving solutions revealed that most current systems do not support fine-grained access control or consider additional factors such as privacy preservation and relationship semantics. In this article, we investigated the need of a privacy-aware fine-grained access control model for the hybrid cloud. We propose a privacy-aware relationship semantics–based XACML access control model that performs hybrid relationship and attribute-based access control using extensible access control markup language. The proposed approach supports fine-grained relation-based access control with state-of-the-art privacy mechanism named Anatomy for enhanced multipurpose electronic health records usage. The proposed (privacy-aware relationship semantics–based XACML access control model) model provides and maintains an efficient privacy versus utility trade-off. We formally verify the proposed model (privacy-aware relationship semantics–based XACML access control model) and implemented to check its effectiveness in terms of privacy-aware electronic health records access and multipurpose utilization. Experimental results show that in the proposed (privacy-aware relationship semantics–based XACML access control model) model, access policies based on relationships and electronic health records anonymization can perform well in terms of access policy response time and space storage