Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Based Security for VANET with Blockchain

The security issue is one of the greatest challenges in vehicular ad hoc networks (VANETs) attracting a great deal of attention. Malicious onboard units (OBUs) can attack other OBUs with various manners to obtain illegal gains, such as jamming, eavesdropping spoofing and so on. To reduce the potential attackers in the network, we propose an indirect reciprocity security framework with a scalar reputation assigned to each OBU to evaluate their dangerous level to the VANET. A blockchain technique that uses consensus mechanism and encryption algorithms to protect information from being tampered is applied for the transmitter to record the behaviors of other OBUs. We also propose a reinforcement learning based action selection strategy for an OBU in the VANET to choose a reliable relay OBU or determine whether to follow the request of a source OBU or not. A hotbooting technique is applied for the OBUs with prior knowledge to accelerate the learning speed. Simulation results show that the proposed action selection strategy can efficiently increase the packet delivery ratio, the reputation and the utility of the each OBU.
Using U-Net for signal preprocessing in interferometric synthetic aperture radar

Synthetic Aperture Radar (SAR) interferometry is an active remote sensing technology that uses microwaves to characterize the earth's surface. SAR interferometry allows to measure the 3D profile of the earth's surface, recover surface topography, and determine topographic displacements over time. The microwave SAR signal is usually highly distorted. Distortions can be caused by, for example, atmospheric disturbances and various characteristics of earth's surface scatterers reflectance. Compensation for these distortions is performed by filtering the phase and evaluating the degree of coherence of the original images. This is an important step to improve the accuracy of the subsequent pphase-unwrapping operation. In this paper, we investigate the use of U-net neural networks for preprocessing the SAR interferogram at various parameters of the distortion of the SAR signal. Two neural networks filter the SAR interferogram and determine the degree of coherence, respectively.
The role of positive relationship events in romantic attachment avoidance.

Motivated by the Attachment Security Enhancement Model (Arriaga et al., 2018), the present research investigated the associations between positive relationship experiences and romantic attachment avoidance in three dyadic studies that combined multiple methods, including daily diaries, laboratory observations, and longitudinal follow-ups. Frequency of daily positive relationship events (but not external positive events) during a 21-day diary period predicted declines in romantic attachment avoidance (but not anxiety) from pre- to post-diary in fledgling couples (Study 1) and newlyweds (Study 2). Video-recorded discussions of fledgling couples' shared positive experiences revealed that behaviors validating the relationship (but not simply showing conversational interest) predicted lagged declines in romantic attachment avoidance (but not anxiety) over 1 month (Study 3). The associations were mediated by positive affect during the diary period in Studies 1 and 2, and by changes in positive affect from pre- to post-discussion in Study 3. Positive relationship experiences did not significantly interact with time in predicting romantic avoidance over a 1-year follow-up with quarterly assessments of attachment orientations in Study 1, over an 8-month follow-up with monthly assessments in Study 2, or over a 2-month follow-up with monthly assessments in Study 3. Altogether, these studies provide one of the most comprehensive tests of how positive relationship experiences in nondistressing contexts are linked to romantic attachment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Development and validation of the Highly Effective Health Behavior Pattern Inventory – Short Form

Objectives The purpose of this study was to develop and validate a short form (SF) of the highly effective health behavior pattern inventory (HEBPI) for chronic disease patients for comfort to use. Methods HEBPI-SF is a self-reported questionnaire based on highly effective health behaviors associated with building healthy habits. HEBPI-SF was validated by calculating adjusted odds ratios (aOR) for the 12 HEBPI-SF health behavior subscales and the maintenance of the 12 corresponding health habits. Results HEBPI-SF developed in this study comprised 12 subscales and 53 items, utilizing patient data (N = 724) derived from the original HEBPI (12 subscales and 131items). HEBPI-SF demonstrated high reliability (Cronbach’s α of 0.703–0.859) and showed that the 12 health behaviors included in the HEBPI are associated with the maintenance of the corresponding health habits (aOR: 2.29–8.42). The scores of the HEBPI-SF showed a positive correlation with physical, mental, social, spiritual and general health status, quality of life, and self-management strategies. Discussion HEBPI-SF is a reliable tool for the assessment of health behaviors of chronically ill patients. Considering previous findings of simple health behaviors significantly affecting health status, this survey tool for the assessment of health behaviors is useful for promotion and maintenance of healthy habits.
Low Golimumab Trough Levels at Week 6 are Associated with Poor Clinical, Endoscopic and Histological Outcomes in Ulcerative Colitis Patients: Pharmacokinetic and Pharmacodynamic Sub-analysis of the Evolution Study.

BACKGROUND AND AIMS Golimumab has an established exposure-response relationship in patients with ulcerative colitis (UC). However, the association of serum golimumab trough levels (TL) with objective markers of disease activity, such as endoscopic and histologic activity scores and concentration of biomarkers, remains less understood. This report describes the relationship of serum golimumab TL at the end of induction period (week 6) with clinical, endoscopic, histologic and biomarker parameters. METHODS This was an open-label, uncontrolled, prospective and interventional study. Moderate to severely active UC patients naïve to biological therapy were treated with golimumab. Serum golimumab TL and faecal calprotectin levels were measured at baseline (week 0 of induction) and week 6. RESULTS Thirty-four patients completed the induction phase (week 6) and were included in this analysis. Overall, 47.1% and 14.7% of patients achieved clinical response and remission with significantly higher serum golimumab TL in patients with early response or remission (3.7 μg/mL vs. 1.3 μg/mL, p=0.0013; and 3.1 μg/mL vs. 1.7 μg/mL, p=0.0164, respectively). In addition, golimumab TL were significantly higher in patients achieving histological remission (4.2 μg/mL vs. 1.7 μg/mL, p= 0.0049). Week 6 golimumab TL were inversely correlated with the total Mayo score (rs = -0.546; p=0.0008), the Mayo endoscopic subscore (rs = -0.381; p=0.0262), the Geboes histological activity score (rs = -0.464; p=0.0057) and faecal calprotectin levels (rs= -0.497; p=0.0044). CONCLUSIONS A higher early exposure to golimumab is associated with a better objective response in active UC patients and appears to drive the outcome at week 6. CLINICAL TRIAL IDENTIFICATION EudraCT 2014-003262-25.
Molecular Targeted Therapy of Head and Neck Cancer: Promising Molecular Targets of Post-Epidermal Growth Factor Receptor Era

Received March 24, 2014 Accepted June 4, 2014 Address for correspondence Chul-Ho Kim, MD, PhD Department of Otolaryngology, Ajou University School of Medicine, 164 World cup-ro, Yeongtong-gu, Suwon 443-380, Korea Tel +82-31-219-5269 Fax +82-31-219-5264 E-mail ostium@ajou.ac. kr Head and neck squamous cell carcinoma (HNSCC) affects over half a million people worldwide. Despite advances in biology and medicine, only half of the patients are alive in 5 years. The epidermal growth factor receptor (EGFR) is overexpressed in approximately 90% of HNSCC cases, and it is correlated with poor responses to therapy and worse prognosis. Multiple therapies targeting this pathway have been tested. However, only a minority of patients has showed meaningful responses to these agents and almost all who do develop acquired tumor resistance after a few months of treatment. Recently, a significant interest has focused on identifying mechanisms of acquired and de novo resistance of EGFR blockage. In addition, other inhibitors of EGFR that interfere with known molecular pathways activated in HNSCC have been studied extensively, either as single agents or in combination with other treatment modalities. Here we review some of EGFR resistance mechanisms and briefly discuss new molecular therapeutic strategies to overcome that resistance in HNSCC. Korean J Otorhinolaryngol-Head Neck Surg 2014;57(9):575-83
Diversity, Daily Activity Patterns, and Pollination Effectiveness of the Insects Visiting Camellia osmantha, C. vietnamensis, and C. oleifera in South China

Camellia spp. , which are self-incompatible plants, are some of the most important woody species producing edible oil in Southeast Asian countries. However, the demand for camellia oil currently exceeds the supply due to low product yields that have resulted from a decrease in pollination services. Although Camellia osmantha, C. vietnamensis, and C. oleifera are cultivated in South China, little is known about the correspondence between pollinator abundance and pollinator services for this plant genus. In this study, the diversity, daily activity patterns, and pollination effectiveness of insects visiting C. osmantha, C. vietnamensis and C. oleifera were investigated. A total of 24 species, belonging to four orders and 11 families, of visiting insects were identified. Apis cerana cerana Fabricius, Vespa bicolor Fabricius, V. velutina Lepeletier, V. ducalis Smith, and Phytomia zonata Fabricius were the dominant pollinators. The daily activity peaks of the five visiting insects were between 10:00 and 14:00, which may have been related to the pattern of floral resource production (particularly nectar). Cross-pollination by insects significantly increased the fruit production rates of C. osmantha, C. vietnamensis, and C. oleifera. Therefore, the wild bees and flies that pollinate wild and cultivated Camellia plants should be protected in South China.
Non-invasive techniques to assess restrictive lung disease in workers exposed to free crystalline silica

Objectives: To compare the reliability of spirometry and body plethysmography in detecting restrictive lung disease in clay excavation workers exposed to free crystalline silica (FCS). The exhaled breath condensate (EBC) biomarkers of oxidative stress were also assessed in order to evaluate early lung damage. Methods: The study involved 62 workers (58 males and 4 females) at a company that extracts and processes clay. Results: Body plethysmography (total lung capacity below the lower normal limit) and spirometry respectively indicated restrictive pattern prevalence rates of 22.6% and 1.6%. EBC 4-hydroxynonenale levels were not sufficiently sensitive to highlight a restrictive deficit, but did distinguish low and high rates of occupational exposure. There was no correlation between plethysmography values and the intensity or duration of exposure. Conclusions: Only one out of 14 cases of restrictive deficit diagnosed on the basis of body plethysmography values was also identified by means of spirometry. This finding supports the need to use body plethysmography in the health surveillance of clay workers exposed to FCS.
Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study

Background The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze and interpret. Machine learning methods can be useful in overcoming this analytical challenge. However, different methods employ distinct strategies to handle complex datasets. It is unclear whether one method is more appropriate than others for modeling postmortem microbiomes and their ability to predict attributes of interest in death investigations, which require understanding of how the microbial communities change after death and may represent those of the once living host. Methods and findings Postmortem microbiomes were collected by swabbing five anatomical areas during routine death investigation, sequenced and analyzed from 188 death cases. Three machine learning methods (boosted algorithms, random forests, and neural networks) were compared with respect to their abilities to predict case attributes: postmortem interval (PMI), location of death, and manner of death. Accuracy depended on the method used, the numbers of anatomical areas analyzed, and the predicted attribute of death. Conclusions All algorithms performed well but with distinct features to their performance. Xgboost often produced the most accurate predictions but may also be more prone to overfitting. Random forest was the most stable across predictions that included more anatomic areas. Analysis of postmortem microbiota from more than three anatomic areas appears to yield limited returns on accuracy, with the eyes and rectum providing the most useful information correlating with circumstances of death in most cases for this dataset.
Anaerobic Performance after a Low-Carbohydrate Diet (LCD) Followed by 7 Days of Carbohydrate Loading in Male Basketball Players

Despite increasing interest among athletes and scientists on the influence of different dietary interventions on sport performance, the association between a low-carbohydrate, high-fat diet and anaerobic capacity has not been studied extensively. The aim of this study was to evaluate the effects of a low-carbohydrate diet (LCD) followed by seven days of carbohydrate loading (Carbo-L) on anaerobic performance in male basketball players. Fifteen competitive basketball players took part in the experiment. They performed the Wingate test on three occasions: after the conventional diet (CD), following 4 weeks of the LCD, and after the weekly Carbo-L, to evaluate changes in peak power (PP), total work (TW), time to peak power (TTP), blood lactate concentration (LA), blood pH, and bicarbonate (HCO3−). Additionally, the concentrations of testosterone, growth hormone, cortisol, and insulin were measured after each dietary intervention. The low-carbohydrate diet procedure significantly decreased total work, resting values of pH, and blood lactate concentration. After the low-carbohydrate diet, testosterone and growth hormone concentrations increased, while the level of insulin decreased. After the Carbo-L, total work, resting values of pH, bicarbonate, and lactate increased significantly compared with the results obtained after the low-carbohydrate diet. Significant differences after the low-carbohydrate diet and Carbo-L procedures, in values of blood lactate concentration, pH, and bicarbonate, between baseline and post exercise values were also observed. Four weeks of the low-carbohydrate diet decreased total work capacity, which returned to baseline values after the carbohydrate loading procedure. Moreover, neither the low-carbohydrate feeding nor carbohydrate loading affected peak power.
Editorial Comment.

prostate biopsy. Decipher has a higher cost and should be performed only in select patients. If we had used this approach in every patient at NCCN FIR, we could have avoided radical prostatectomy in 75% (94 of 125) without missing AP findings. This study is not devoid of limitations. 1) AP served as a surrogate for oncologic outcomes but this bias is common to most studies as in the Western world patients at IR are usually offered curative therapy at diagnosis and sparse data are available on oncologic outcomes in patients at FIR who are on AS. 2) The tissue used for the Decipher test was obtained from the area with the highest Gleason finding in the pathology specimen and not from biopsy samples. However, the concordance rate between biopsy and RP samples reached 71% for the mean Decipher score of all biopsy cores, and 86% for the core with the highest sampled Gleason finding and percent of tumor involvement. Finally, mpMRI findings involve a certain degree of interobserver and intra-observer variability.
Pillar-Based Cooperative Perception from Point Clouds for 6G-Enabled Cooperative Autonomous Vehicles

3D object detection is a significant aspect of the perception module in autonomous driving; however, with current technology, data sharing between vehicles and cloud servers for cooperative 3D object detection under the strict latency requirement is limited by the communication bandwidth. The sixth-generation (6G) networks have accelerated the transmission rate of the sensor data significantly with extreme low-latency and high-speed data transmission. However, which sensor data format and when to transmit it are still challenging. To address these issues, this study proposes a cooperative perception framework combined with a pillar-based encoder and Octomap-based compression at edges for connected autonomous vehicles to reduce the amount of missing detection in blind spots and further distances. This approach satisfies the constraints on the accuracy of the task perception and provides drivers or autonomous vehicles with sufficient reaction time by applying fixed encoders to learn a representation of point clouds (LiDAR sensor data). Extensive experiment results show that the proposed approach outperforms the previous cooperative perception schemes running at 30 Hz, and the accuracy of the object bounding box results in further distances (greater than 12 m). Furthermore, this approach achieves a lower total delay for the procession of the fusion data and the transmission of the cooperative perception message. To the best of our knowledge, this study is the first to introduce a pillar-based encoder and Octomap-based compression framework for cooperative perception between vehicles and edges in connected autonomous driving.
Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy

Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People's mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human's various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system's constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients' facts which will cause a problem in sharing information over a network. So, this research paper's approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review

Abstract Objective To summarize the research literature about safety concerns with consumer-facing health apps and their consequences. Materials and Methods We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into natural categories. The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. Results Of the 74 studies identified, the majority were reviews of a single or a group of similar apps (n = 66, 89%), nearly half related to disease management (n = 34, 46%). A total of 80 safety concerns were identified, 67 related to the quality of information presented including incorrect or incomplete information, variation in content, and incorrect or inappropriate response to consumer needs. The remaining 13 related to app functionality including gaps in features, lack of validation for user input, delayed processing, failure to respond to health dangers, and faulty alarms. Of the 52 reports of actual or potential consequences, 5 had potential for patient harm. We also identified 66 reports about gaps in app development, including the lack of expert involvement, poor evidence base, and poor validation. Conclusions Safety of apps is an emerging public health issue. The available evidence shows that apps pose clinical risks to consumers. Involvement of consumers, regulators, and healthcare professionals in development and testing can improve quality. Additionally, mandatory reporting of safety concerns is needed to improve outcomes.