The effect of intermittent theta burst stimulation for cognitive dysfunction: a meta-analysis.

BACKGROUND: Growing evidence suggests that cognitive dysfunction significantly impacts patients' quality of life. Intermittent theta burst stimulation (iTBS) has emerged as a potential intervention for cognitive dysfunction. However, consensus on the iTBS protocol for cognitive impairment is lacking. METHODS: We conducted searches in the Cochrane Central Register of Controlled Trials, EMBASE, PubMed, Chinese National Knowledge Infrastructure, Wanfang Database and the Chongqing VIP Chinese Science and Technology Periodical Database from their inception to January 2024. Random-effects meta-analyzes were used to calculate standardized mean differences and 95% confidence intervals. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: Twelve studies involving 506 participants were included in the meta-analysis. The analysis showed a trend toward improvement of total cognitive function, activities of daily living and P300 latency compared to sham stimulation in patients with cognitive dysfunction. Subgroup analysis demonstrated that these effects were restricted to patients with post-stroke cognitive impairment but not Alzheimer's disease or Parkinson's disease. Furthermore, subthreshold stimulation also exhibited a significant improvement. CONCLUSIONS: The results suggest that iTBS may improve cognitive function in patients with cognitive dysfunction, although the quality of evidence remains low. Further studies with better methodological quality should explore the effects of iTBS on cognitive function.

Efficacy of Intermittent Theta-Burst Stimulation and Transcranial Direct Current Stimulation in Treatment of Post-Stroke Cognitive Impairment.

BACKGROUND: The efficacy of intermittent theta-burst stimulation (iTBS) and transcranial direct current stimulation (tDCS) combined with cognitive training in the treatment of post-stroke cognitive impairment (PSCI) requires further investigation. METHODS: We randomly assigned 60 patients with PSCI to receive iTBS (n = 21), tDCS (n = 19), or cognitive training alone (n = 20). Cognitive function was evaluated by the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), and the performance of activities of daily living (ADL) was assessed with the modified Barthel Index (MBI). Of these patients, 14 participated in the functional near-infrared spectroscopy (fNIRS) measurement. RESULTS: After six weeks of treatment, cognitive function improved in all three groups of PSCI patients. Compared with patients receiving only cognitive training, the cognitive function of patients in the iTBS combined with cognitive training (p = 0.003) and tDCS combined with cognitive training groups (p = 0.006) showed greater improvement. The cognitive improvement from tDCS was related to the activation of the frontopolar cortex (FPC), while the improvement of cognition by iTBS was based on the activation of the stimulation site (the dorsolateral prefrontal cortex) and some distant regions. CONCLUSIONS: Both iTBS and tDCS in addition to cognitive training appear to improve cognitive function and quality of life of patients with PSCI, compared to cognitive training alone. tDCS improved cognitive function by improving the patient's valuation, motivation, and decision-making substructures, while iTBS improved patients' assessment and decision-making abilities, improving cognitive control and, ultimately, overall cognitive function.

Lower Limb Activity Recognition Based on sEMG Using Stacked Weighted Random Forest.

The existing surface electromyography-based pattern recognition system (sEMG-PRS) exhibits limited generalizability in practical applications. In this paper, we propose a stacked weighted random forest (SWRF) algorithm to enhance the long-term usability and user adaptability of sEMG-PRS. First, the weighted random forest (WRF) is proposed to address the issue of imbalanced performance in standard random forests (RF) caused by randomness in sampling and feature selection. Then, the stacking is employed to further enhance the generalizability of WRF. Specifically, RF is utilized as the base learner, while WRF serves as the meta-leaning layer algorithm. The SWRF is evaluated against classical classification algorithms in both online experiments and offline datasets. The offline experiments indicate that the SWRF achieves an average classification accuracy of 89.06%, outperforming RF, WRF, long short-term memory (LSTM), and support vector machine (SVM). The online experiments indicate that SWRF outperforms the aforementioned algorithms regarding long-term usability and user adaptability. We believe that our method has significant potential for practical application in sEMG-PRS.

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at https://github.com/Ma-Xinzhi/EEG-TransNet.

The effect of exercise rehabilitation with exergames combined with ice therapy in the treatment of obese patients with gout: protocol for a clinical trial.

BACKGROUND: Gout remains a leading cause of inflammatory arthritis worldwide, and the main risk factor for gout is persistent hyperuricemia. The clinical management of gout is mostly drug-based, and other treatment options are often ignored. This research proposal will explore whether exergames combined with ice therapy can help patients with gout to lose weight, relieve pain, improve the range of movement, improve quality of life, decrease uric acid level, decrease kinesiophobia and improve mental health of patients with gout. METHODS: This experiment will use a two-arm randomized controlled design. The study setting is at the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia (USM). Obese patients with gout (N = 30) will be randomly assigned to the control group (receive an exergames intervention) and intervention group (receive an exergames intervention combined with ice therapy). The outcomes measurement will be conducted before (baseline) and after intervention (4 weeks). Then, it will be followed up at 12 weeks. DISCUSSION: To our knowledge, no study has investigated the effect of exergames and ice therapy among gout patients. This study is expected to demonstrate that exercise rehabilitation facilitated by exergames with ice therapy is more effective in gout management compared to a conventional rehabilitation intervention. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR2300070029). Registered on 31 March 2023.

Activation changes in patients with post-stroke cognitive impairment receiving intermittent theta burst stimulation: A functional near-infrared spectroscopy study.

BACKGROUND: Intermittent theta burst stimulation (iTBS) has demonstrated efficacy in patients with cognitive impairment. However, activation patterns and mechanisms of iTBS for post-stroke cognitive impairment (PSCI) remain insufficiently understood. OBJECTIVE: To investigate the activation patterns and potential benefits of using iTBS in patients with PSCI. METHODS: A total of forty-four patients with PSCI were enrolled and divided into an iTBS group (iTBS and cognitive training) or a control group (cognitive training alone). Outcomes were assessed based on the activation in functional near-infrared spectroscopy (fNIRS), as well as Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) and the modified Barthel Index (MBI). RESULTS: Thirty-eight patients completed the interventions and assessments. Increased cortical activation was observed in the iTBS group after the interventions, including the right superior temporal gyrus (STG), left frontopolar cortex (FPC) and left orbitofrontal cortex (OFC). Both groups showed significant improvements in LOTCA and MBI after the interventions (p < 0.05). Furthermore, the iTBS group augmented superior improvement in the total score of MBI and LOTCA compared to the control group, especially in visuomotor organization and thinking operations (p < 0.05). CONCLUSION: iTBS altered activation patterns and improved cognitive function in patients with PSCI. The activation induced by iTBS may contribute to the improvement of cognitive function.

Effects of robot-assisted upper limb training combined with intermittent theta burst stimulation (iTBS) on cortical activation in stroke patients: A functional near-infrared spectroscopy study.

BACKGROUND: The therapeutic effect and mechanism of robot-assisted upper limb training (RT) combined with intermittent theta burst stimulation (iTBS) for stroke patients are unclear. OBJECTIVE: The purpose of this study was to evaluate changes in brain activation after combination therapy and RT alone using functional near-infrared spectroscopy (fNIRS). METHODS: Patients were randomly assigned to two groups (iTBS + RT Group, n = 18, and RT Group, n = 18). Training was conducted five times a week for four weeks. fNIRS was used to measure changes in oxyhemoglobin in both the primary motor cortex (M1) and pre-motor and supplementary motor area (pSMA) during affected limb movement. Fugl-Meyer Assessment-Upper Extremity (FMA-UE) was employed for evaluating the function of upper limbs. RESULTS: Thirty-two patients with subacute stroke completed the study. The cortex of both hemispheres was extensively activated prior to treatment in the RT group. After training, overactivation decreased. The brain activation of the combined treatment group transferred to the affected side after the treatment. There was a notable enhancement in the FMA-UE scores for both groups, with the combined group's progress significantly surpassing that of the RT group. CONCLUSION: RT combined with iTBS can improve the motor function of stroke patients and promote the balance between cerebral hemispheres.

Effects of Combined Use of Intermittent Theta Burst Stimulation and Cognitive Training on Poststroke Cognitive Impairment: A Single-Blind Randomized Controlled Trial.

OBJECTIVE: Poststroke cognitive impairment substantially affects patients' quality of life. This study explored the therapeutic efficacy of intermittent theta burst stimulation combined with cognitive training for poststroke cognitive impairment. DESIGN: The experimental group received intermittent theta burst stimulation and cognitive training, whereas the control group only received cognitive training, both for 6 wks. The outcome measures were the Loewenstein Occupational Therapy Cognitive Assessment, modified Barthel Index, transcranial Doppler ultrasonography, and functional near-infrared spectroscopy. RESULTS: After therapy, between-group comparisons revealed a substantial difference in the Loewenstein Occupational Therapy Cognitive Assessment scores ( P = 0.024). Improvements in visuomotor organization and thinking operations were more noticeable in the experimental group than in the other groups ( P = 0.017 and P = 0.044, respectively). After treatment, the resistance index of the experimental group differed from that of the control group; channels 29, 37, and 41 were activated ( P < 0.05). The active locations were the left dorsolateral prefrontal cortex, prefrontal polar cortex, and left Broca's region. CONCLUSIONS: Intermittent theta burst stimulation combined with cognitive training had a superior effect on improving cognitive function and everyday activities compared with cognitive training alone, notably in visuomotor organization and thinking operations. Intermittent theta burst stimulation may enhance cognitive performance by improving network connectivity.

The efficacy of exoskeleton robotic training on ambulation recovery in patients with spinal cord injury: A meta-analysis.

OBJECTIVE: To discuss the efficacy of exoskeleton robotic training on ambulation recovery in patients with spinal cord injury (SCI). METHODS: PubMed, Embase, and Cochrane Central Register of Controlled Trials were searched systematically from their inception to April 2022 for studies on exoskeleton robotic training in patients with SCI. The language was restricted to English. The retrieved studies were screened to select eligible clinical trials. Meta-analysis was performed using Review Manager 5.4. RESULTS: Eleven randomized clinical trials (RCTs) involving 456 participants were included in the meta-analysis. The results of the meta-analysis showed that exoskeleton robotic training was more effective in improving FIM [SMD = 0.58, 95%CI = (0.07, 1.10), P = 0.03], LEMS [MD = 4.64, 95%CI = (3.58, 5.70), P<0.05], MAS [MD = 0.76, 95%CI = (0.48, 1.03), P<0.05] and BBS [MD = -3.11, 95%CI = (-12.59, 6.36), P<0.05] in patients with SCI, compared to conventional gait training(CGT). Subgroup analysis showed that the exoskeleton robotic could significantly improve the walking endurance and walking speed of patients with a duration of injury within 6 months. The sensitivity of inverted funnel plot analysis is low, suggesting that the analysis results of this study are relatively stable. CONCLUSION: Exoskeleton robotic training improves ambulation in patients with SCI, especially for patients with a course of injury within six months.

A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding.

Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.

The effect and optimal parameters of repetitive transcranial magnetic stimulation on lower extremity motor function in stroke patient: a systematic review and meta-analysis.

PURPOSE: This study aimed to evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) in treating lower limb motor dysfunction after stroke and explore the optimal stimulation parameters. METHODS: PubMed, Embase, Cochrane Library, and other relevant databases were systematically queried for randomised controlled trials (RCTs) investigating the efficacy of rTMS in addressing lower limb motor dysfunction post-stroke. The search encompassed records from inception to July 2022. The assessed outcomes encompassed parameters such as the Fugl-Meyer motor function score for lower limbs, balance function, and Barthel index (BI). Three independent researchers were responsible for research selection, data extraction, and quality assessment. Study screening, data extraction, and bias evaluation were performed independently by two reviewers. Data synthesis was undertaken using Review Manager 5.3, while Stata version 14.0 software was employed for generating the funnel plot. RESULTS: A total of 13 studies and 428 patients were included. The meta-analysis indicated that rTMS had a positive effect on the BI (MD = 5.87, 95% CI [0.99, 10.76], p = 0.02, I2 = 86%, N of studies = 8, N of participants = 248). Subgroup analysis was performed on the stimulation frequency, treatment duration, and different stroke stages (stimulation frequency was low-frequency (LF)-rTMS (MD = 4.45, 95% CI [1.05, 7.85], p = 0.01, I2 = 0%, N of studies = 4, N of participants = 120); treatment time ≤ 15 d: (MD = 4.41, 95% CI [2.63, 6.18], p < 0.00001, I2 = 0%, N of studies = 4, N of participants = 124); post-stroke time ≤6 months: (MD = 4.37, 95% CI [2.42, 6.32], p < 0.0001, I2 = 0%, N of studies = 5, N of participants = 172). CONCLUSION: LF-rTMS had a significant improvement effect on BI score, while high-frequency (HF)-rTMS and iTBS had no significant effect. And stroke time ≤6 months in patients with treatment duration ≤15 d had the best treatment effect.

Stroke Lower limb dysfunction is a common complication after stroke, seriously affecting the daily life of patients.Lower limb motor function improved significantly within 6 months after low frequency treatment.To maximise motor gains in patients with lower limb motor dysfunction following stroke in the shortest possible time, it is recommended to initiate early rehabilitation therapy using low-frequency transcranial magnetic stimulation during the subacute phase of stroke.

Three-Dimensional Gait Analysis and sEMG Measures for Robotic-Assisted Gait Training in Subacute Stroke: A Randomized Controlled Trial.

Background: The efficacy of robotic-assisted gait training (RAGT) should be considered versatilely; among which, gait assessment is one of the most important measures; observational gait assessment is the most commonly used method in clinical practice, but it has certain limitations due to the deviation of subjectivity; instrumental assessments such as three-dimensional gait analysis (3DGA) and surface electromyography (sEMG) can be used to obtain gait data and muscle activation during walking in stroke patients with hemiplegia, so as to better evaluate the rehabilitation effect of RAGT. Objective: This single-blind randomized controlled trial is aimed at analyzing the impact of RAGT on the 3DGA parameters and muscle activation in patients with subacute stroke and evaluating the clinical effect of improving walking function of RAGT. Methods: This randomized controlled trial evaluated the improvement of 4-week RAGT on patients with subacute stroke by 3DGA and surface electromyography (sEMG), combined with clinical scales: experimental group (n = 18, 20 sessions of RAGT) or control group (n = 16, 20 sessions of conventional gait training). Gait performance was evaluated by the 3DGA, and clinical evaluations based on Fugl-Meyer assessment for lower extremity (FMA-LE), functional ambulation category (FAC), and 6-minute walk test (6MWT) were used. Of these patients, 30 patients underwent sEMG measurement synchronized with 3DGA; the cocontraction index in swing phase of the knee and ankle of the affected side was calculated. Results: After 4 weeks of intervention, intragroup comparison showed that walking speed, temporal symmetry, bilateral stride length, range of motion (ROM) of the bilateral hip, flexion angle of the affected knee, ROM of the affected ankle, FMA-LE, FAC, and 6MWT in the experimental group were significantly improved (p < 0.05), and in the control group, significant improvements were observed in walking speed, temporal symmetry, stride length of the affected side, ROM of the affected hip, FMA-LE, FAC, and 6MWT (p < 0.05). Intergroup comparison showed that the experimental group significantly outperformed the control group in walking speed, temporal symmetry of the spatiotemporal parameters, ROM of the affected hip and peak flexion of the knee in the kinematic parameters, and the FMA-LE and FAC in the clinical scale (p < 0.05). In patients evaluated by sEMG, the experimental group showed a noticeable improvement in the cocontraction index of the knee (p = 0.042), while no significant improvement was observed in the control group (p = 0.196), and the experimental group was better than the control group (p = 0.020). No noticeable changes were observed in the cocontraction index of the ankle in both groups (p > 0.05). Conclusions: Compared with conventional gait training, RAGT successfully improved part of the spatiotemporal parameters of patients and optimized the motion of the affected lower limb joints and muscle activation patterns during walking, which is crucial for further rehabilitation of walking ability in patients with subacute stroke. This trial is registered with ChiCTR2200066402.

AI bot to detect fake COVID-19 vaccine certificate.

As the world is now fighting against rampant virus COVID-19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non-COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture-based feature extraction for extracting logo, symbol and for the signature we extract Crest-Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1-score, and computation time over state-of-art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201-LBP) outperforms with an accuracy of 0.94.

Synchronous analyses between electroencephalogram and surface electromyogram based on motor imagery and motor execution.

The functional coupling of the cerebral cortex and muscle contraction indicates that electroencephalogram (EEG) and surface electromyogram (sEMG) signals are coherent. The objective of this study is to clearly describe the coupling relationship between EEG and sEMG through a variety of analysis methods. We collected the EEG and sEMG data of left- or right-hand motor imagery and motor execution from six healthy subjects and six stroke patients. To enhance the coherence coefficient between EEG and sEMG signals, the algorithm of EEG modification based on the peak position of sEMG signals is proposed. Through analyzing a variety of signal synchronization analysis methods, the most suitable coherence analysis algorithm is selected. In addition, the wavelet coherence analysis method based on time spectrum estimation was used to study the linear correlation characteristics of the frequency domain components of EEG and sEMG signals, which verified that wavelet coherence analysis can effectively describe the temporal variation characteristics of EEG-sEMG coherence. In the task of motor imagery, the significant EEG-sEMG coherence is mainly in the imagination process with the frequency distribution of the alpha and beta frequency bands; in the task of motor execution, the significant EEG-sEMG coherence mainly concentrates before and during the task with the frequency distribution of the alpha, beta, and gamma frequency bands. The results of this study may provide a theoretical basis for the cooperative working mode of neurorehabilitation training and introduce a new method for evaluating the functional state of neural rehabilitation movement.

Altered succinylation of mitochondrial proteins, APP and tau in Alzheimer's disease.

Abnormalities in brain glucose metabolism and accumulation of abnormal protein deposits called plaques and tangles are neuropathological hallmarks of Alzheimer's disease (AD), but their relationship to disease pathogenesis and to each other remains unclear. Here we show that succinylation, a metabolism-associated post-translational protein modification (PTM), provides a potential link between abnormal metabolism and AD pathology. We quantified the lysine succinylomes and proteomes from brains of individuals with AD, and healthy controls. In AD, succinylation of multiple mitochondrial proteins declined, and succinylation of small number of cytosolic proteins increased. The largest increases occurred at critical sites of amyloid precursor protein (APP) and microtubule-associated tau. We show that in vitro, succinylation of APP disrupted its normal proteolytic processing thereby promoting Aß accumulation and plaque formation and that succinylation of tau promoted its aggregation to tangles and impaired microtubule assembly. In transgenic mouse models of AD, elevated succinylation associated with soluble and insoluble APP derivatives and tau. These findings indicate that a metabolism-linked PTM may be associated with AD.

A meta-analysis of the therapeutic effect of intranasal salmon calcitonin on osteoporosis.

OBJECTIVE: To evaluate the efficacy and safety of intranasal salmon calcitonin in the treatment of osteoporosis. METHODS: Eight Chinese and English databases were searched by electronic search (from the establishment of the database to October 2019). The literature was screened according to the inclusion criteria and exclusion criteria, the quality was evaluated according to Cochrane software, and the Review Manager 5.2 software was used for statistical analysis. RESULTS: A total of 374 documents were retrieved and 12 (12 original studies) were included after the screening, with a total sample capacity of 1068 cases. Meta-analysis showed that the intranasal salmon calcitonin had obvious advantages in reducing blood calcium, improving the ratio of serum creatinine and alkaline phosphatase. In addition, the intranasal salmon calcitonin had no obvious advantages in other indicators. It cannot be illustrated that the combination of intranasal salmon calcitonin and other conventional drugs is more effective than the simple use of conventional drugs. CONCLUSION: The intranasal salmon calcitonin is superior to conventional drugs in reducing blood calcium, increasing creatinine ratio, and alkaline phosphatase, but its advantages in other indicators such as improving the bone mineral density (BMD) of lumbar vertebrae and hip have not been confirmed, and it is not clear that the combination of intranasal salmon calcitonin and other conventional drugs is better than the simple conventional drugs.

In-network generalized trustworthy data collection for event detection in cyber-physical systems.

Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

A model of higher accuracy for the individual haplotyping problem based on weighted SNP fragments and genotype with errors.

MOTIVATION: In genetic studies of complex diseases, haplotypes provide more information than genotypes. However, haplotyping is much more difficult than genotyping using biological techniques. Therefore effective computational techniques have been in demand. The individual haplotyping problem is the computational problem of inducing a pair of haplotypes from an individual's aligned SNP fragments. Based on various optimal criteria and including different extra information, many models for the problem have been proposed. Higher accuracy of the models has been an important issue in the study of haplotype reconstruction. RESULTS: The current article proposes a highly accurate model for the single individual haplotyping problem based on weighted fragments and genotypes with errors. The model is proved to be NP-hard even with gapless fragments. Based on the characteristics of Single Nucleotide Polymorphism (SNP) fragments, a parameterized algorithm of time complexity O(nk(2)2(k(2)) + m log m + mk(1)) is developed, where m is the number of fragments, n is the number of SNP sites, k(1) is the maximum number of SNP sites that a fragment covers (no more than n and usually smaller than 10) and k(2) is the maximum number of the fragments covering a SNP site (usually no more than 19). Extensive experiments show that this model is more accurate in haplotype reconstruction than other models. AVAILABILITY: The program of the parameterized algorithm can be obtained by sending an email to the corresponding author.

Endogenous BMP-4/ROS/COX-2 Mediated IPC and Resveratrol Alleviated Brain Damage.

The objective of the study was to examine the therapeutic role of combined ischemic preconditioning (IPC) and resveratrol (RES) on brain ischemia/reperfusion injury (BI/RI) by modulating endogenous bone morphogenetic protein-4 (BMP-4)/reactive oxygen species (ROS)/cyclooxygenase-2 (COX-2) in rats. Sprague Dawley (SD) rats were pretreated with 20 mg/kg RES (20 mg/kg RES was administered once a day via intraperitoneal injection 7 days prior to the I/R procedure) and IPC (equal volumes of saline were administered once a day by intraperitoneal injection over 7 days, and the bilateral common carotid arteries were separated for clamp 5 minutes followed by 5 minutes of reperfusion prior to the I/R procedure), and then subjected to 2 hours of ischemia and 22 hours of reperfusion. Blood and cerebral tissues were collected, cerebral pathological injuries and infarct sizes were investigated, serum interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels were measured, the activities of superoxide dismutase (SOD) and ROS were calculated, the contents of methane dicarboxylic aldehyde (MDA), IL-6, TNF-α and hemodynamic change were estimated, and expression levels of b-cell lymphoma-2 (Bcl-2), bcl-2-associated x (Bax), BMP-4 and COX-2 were assessed in cerebral tissues. IPC, RES and a combination of IPC and RES preconditioning ameliorated the pathological damage and infarct sizes, reduced cerebral oxidative stress damage, alleviated inflammatory damage, restrained apoptosis, and downregulated the expression levels of BMP-4 and COX-2 compared with those of the ischemia/reperfusion (I/R) group. This study suggested a combined strategy that could enhance protection against BI/RI in clinical brain disease.

Combined Ischemic Preconditioning and Resveratrol Improved Bloodbrain Barrier Breakdown via Hippo/YAP/TAZ Signaling Pathway.

BACKGROUND: Transient Ischemia/Reperfusion (I/R) is the main reason for brain injury and results in disruption of the Blood-Brain Barrier (BBB). It had been reported that BBB injury is one of the main risk factors for early death in patients with cerebral ischemia. Numerous investigations focus on the study of BBB injury which have been carried out. OBJECTIVE: The objective of this study was to investigate the treatment function of the activation of the Hippo/Yes-Associated Protein (YAP) signaling pathway by combined Ischemic Preconditioning (IPC) and resveratrol (RES) before brain Ischemia/Reperfusion (BI/R) improves Blood-Brain Barrier (BBB) disruption in rats. METHODS: Sprague-Dawley (SD) rats were pretreated with 20 mg/kg RES and IPC and then subjected to 2 h of ischemia and 22 h of reperfusion. The cerebral tissues were collected; the cerebral infarct volume was determined; the Evans Blue (EB) level, the brain Water Content (BWC), and apoptosis were assessed; and the expressions of YAP and TAZ were investigated in cerebral tissues. RESULTS: Both IPC and RES preconditioning reduced the cerebral infarct size, improved BBB permeability, lessened apoptosis, and upregulated expressions of YAP and transcriptional co-activator with PDZ-binding motif (TAZ) compared to the Ischemia/Reperfusion (I/R) group, while combined IPC and RES significantly enhanced this action. CONCLUSION: combined ischemic preconditioning and resveratrol improved blood-brain barrier breakdown via Hippo/YAP/TAZ signaling pathway.