Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of inhibitory learning theory-based exposure and response prevention and selective serotonin reuptake inhibitor in obsessive-compulsive disorder management: A treatment comparison

Background: The majority of treatment research on obsessive-compulsive disorder (OCD) has focused on emotional processing theory (EPT)-based exposure-based interventions. Despite the outcomes of EPT-based exposure and response prevention (ERP), a sizeable percentage of patients do not respond whereas 50%–60% of those who respond experience at least partial relapse at follow-up assessments. Inhibitory learning theory (ILT) provides a novel foundation for understanding how exposure therapy can be maximized to overcome such deficits but has not been adequately studied and compared to other evidence-based management in OCD. Aim: The aim of this study was to compare ILT-based ERP plus selective serotonin reuptake inhibitor (SSRI) with only SSRI treatment in OCD patients. Materials and Methods: The present study is pretest/posttest control group design with single masking, where participants (n = 32) diagnosed with OCD were randomly assigned into two treatment groups, namely ILT-based ERP plus SSRI and SSRI alone. Yale-Brown obsessive-compulsive scale was primary outcome measure. Intervention was done for 3 months. Thereafter, the participants were followed up for 6 months. Results: ILT-based ERP and SSRI are both effective treatments. There was no significant difference in treatment effects between combined treatments of SSRI plus ILT-based ERP and SSRI alone in immediate post assessment. However, combined treatment of SSRI plus ILT-based ERP had significantly better treatment effects on follow-ups than SSRI alone. Conclusions: SSRI combined with ILT-based strategies to maximize ERP is significantly better than SSRI alone in the treatment of OCD.
An Agent-based Software Framework for Machine Learning Tuning

— Nowadays, the challenge of knowledge discovery is to mine massive amounts of data available online. The most widely used approaches to tackle that challenge are based on machine learning techniques. In spite of being very powerful, those techniques require their parameters to be calibrated in order to generate models with better quality. Such calibration processes are time-consuming and rely on the skills of machine learning experts. Within this context, this research presents a framework based on software agents for automating the calibration of machine learning models. This approach integrates concepts from Agent Oriented Software Engineering (AOSE) and Machine Learning (ML). As a proof of concept, we first train a model for the IRIS dataset and then we show how our approach improves the quality of new models generated by our framework.
eTPM: A Trusted Cloud Platform Enclave TPM Scheme Based on Intel SGX Technology

Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined as trusted cloud, has become a hot research topic in cloud security. Currently, virtual TPM (vTPM) is commonly used in a trusted cloud to protect the integrity of the cloud environment. However, the existing vTPM scheme lacks protections of vTPM itself at a runtime environment. This paper proposed a novel scheme, which designed a new trusted cloud platform security component, ‘enclave TPM (eTPM)’ to protect cloud and employed Intel SGX to enhance the security of eTPM. The eTPM is a software component that emulates TPM functions which build trust and security in cloud and runs in ‘enclave’, an isolation memory zone introduced by SGX. eTPM can ensure its security at runtime, and protect the integrity of Virtual Machines (VM) according to user-specific policies. Finally, a prototype for the eTPM scheme was implemented, and experiment manifested its effectiveness, security, and availability.
Age-related changes in trunk muscle activity and spinal and lower limb kinematics during gait

The influence of age on spinal muscle activation patterns and its relation to kinematics is poorly understood. We aimed at understanding age-related changes to spine and trunk muscle activity in addition to spinal and lower limb kinematics during treadmill walking under various conditions. An observational study was conducted evaluating asymptomatic young (n = 10; 3F, 7M; 26.3±2.5yrs) and older (n = 9; 3F, 6M; 67.1±4.2yrs) adults’ treadmill walking at 2km/h and 4km/h, each at 0, 1, 5, and 10% inclination. Unilateral (right side) electromyography (EMG) was recorded from deep and superficial multifidus (intramuscular) and erector spinae and abdominal obliques (surface); trunk and leg kinematics were also measured. Muscle activity was characterised by peak amplitude and duration of activity, and the time-point of peak amplitude in the gait cycle (0–100%). Peak activation in older adults was lower for the superficial multifidus (p<0.0001) and higher for the thoracolumbar (p<0.001) and lumbar erector spinae (p<0.01). The duration of activation was longer in older adults for all muscles (p<0.05) except the superficial multifidus, and longer during faster walking for all participants. The time-point of peak amplitude in the gait cycle was earlier in older participants for the external obliques (p<0.05). Walking speed appeared to influence muscle activity more than inclination. Older adults used less spine, trunk and lower limb motion, except at the ankle. Age-related differences within multifidus and between paravertebral and trunk muscles were inconsistent. Walking at 4km/h at 5–10% inclination may specifically target the lumbar paravertebral muscles.
Lost in the crowd: identifying targetable MHC class I neoepitopes for cancer immunotherapy

ABSTRACT Introduction: The recent development of checkpoint blockade immunotherapy for cancer has led to impressive clinical results across multiple tumor types. There is mounting evidence that immune recognition of tumor derived MHC class I (MHC-I) restricted epitopes bearing cancer specific mutations and alterations is a crucial mechanism in successfully triggering immune-mediated tumor rejection. Therapeutic targeting of these cancer specific epitopes (neoepitopes) is emerging as a promising opportunity for the generation of personalized cancer vaccines and adoptive T cell therapies. However, one major obstacle limiting the broader application of neoepitope based therapies is the difficulty of selecting highly immunogenic neoepitopes among the wide array of presented non-immunogenic HLA ligands derived from self-proteins. Areas covered: In this review, we present an overview of the MHC-I processing and presentation pathway, as well as highlight key areas that contribute to the complexity of the associated MHC-I peptidome. We cover recent technological advances that simplify and optimize the identification of targetable neoepitopes for cancer immunotherapeutic applications. Expert commentary: Recent advances in computational modeling, bioinformatics, and mass spectrometry are unlocking the underlying mechanisms governing antigen processing and presentation of tumor-derived neoepitopes.
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach

Classiﬁcation learning crucially depends on the correct label information in training data. We consider the problem that a respective uncertainty can neither be neglected nor it can be approximated by a statistical model. In the proposed approach each training data is equipped with a certainty value reﬂecting the probability of the label correctness. This information is used in the learning process for the classiﬁer. For this purpose, we adopt the cross-entropy cost function from deep learning for a modiﬁed learning vector quantization model. We show the usefulness of this knowledge integration in medical diagnostic data analysis for detection of Wilson’s disease as an example.
One Month of Cannabis Abstinence in Adolescents and Young Adults Is Associated With Improved Memory.

OBJECTIVE Associations between adolescent cannabis use and poor neurocognitive functioning have been reported from cross-sectional studies that cannot determine causality. Prospective designs can assess whether extended cannabis abstinence has a beneficial effect on cognition. METHODS Eighty-eight adolescents and young adults (aged 16-25 years) who used cannabis regularly were recruited from the community and a local high school between July 2015 and December 2016. Participants were randomly assigned to 4 weeks of cannabis abstinence, verified by decreasing 11-nor-9-carboxy-∆⁹-tetrahydrocannabinol urine concentration (MJ-Abst; n = 62), or a monitoring control condition with no abstinence requirement (MJ-Mon; n = 26). Attention and memory were assessed at baseline and weekly for 4 weeks with the Cambridge Neuropsychological Test Automated Battery. RESULTS Among MJ-Abst participants, 55 (88.7%) met a priori criteria for biochemically confirmed 30-day continuous abstinence. There was an effect of abstinence on verbal memory (P = .002) that was consistent across 4 weeks of abstinence, with no time-by-abstinence interaction, and was driven by improved verbal learning in the first week of abstinence. MJ-Abst participants had better memory overall and at weeks 1, 2, 3 than MJ-Mon participants, and only MJ-Abst participants improved in memory from baseline to week 1. There was no effect of abstinence on attention: both groups improved similarly, consistent with a practice effect. CONCLUSIONS This study suggests that cannabis abstinence is associated with improvements in verbal learning that appear to occur largely in the first week following last use. Future studies are needed to determine whether the improvement in cognition with abstinence is associated with improvement in academic and other functional outcomes. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT03276221.
Distribution of endogenous normal tau in the mouse brain

Tau is a microtubule‐associated protein (MAP) that is localized to the axon. In Alzheimer's disease (AD), the distribution of tau undergoes a remarkable alteration, leading to the formation of tau inclusions in the somatodendritic compartment. While the abnormal aggregated tau has been extensively studied in human patient tissues and animal models of AD, how normal tau localizes to the axon, which would be the foundation to understand how the mis‐localization occurs, has not been well studied due to the poor detectability of normal unaggregated tau in vivo. Therefore, we developed immunohistochemical techniques that can detect normal mouse and human tau in brain tissues with high sensitivity. Using these techniques, we demonstrate the global distribution of tau in the mouse brain and confirmed that normal tau is exclusively localized to the axonal compartment in vivo. Interestingly, tau antibodies strongly labeled nonmyelinated axons such as hippocampal mossy fibers, while white matters generally exhibited low levels of immunoreactivity. Furthermore, mouse tau is highly expressed not only in neurons but also in oligodendrocytes. With super resolution imaging using the stimulated‐depletion microscopy, axonal tau appeared punctate rather than fibrous, indicating that tau decorates microtubules sparsely. Co‐labeling with presynaptic and postsynaptic markers revealed that normal tau is not localized to synapses but sparsely distributes in the axon. Taken together, this study reports novel antibodies to investigate the localization and mis‐localization of tau in vivo and novel findings of normal tau localization in the mouse brain.
Distributed Single Password Protocol Framework

Passwords are the most widely used factor in various areas such as secret sharing, key establishment, and user authentication. Single password protocols are proposed (starting with Belenkiy et. al [4]) to overcome the challenges of traditional password protocols and provide provable security against offline dictionary, man-in-the-middle, phishing, and honeypot attacks. While they ensure provable security, they allow a user securely to use a single low-entropy human memorable password for all her accounts. They achieve this with the help of a cloud or mobile storage device. However, an attacker corrupting both the login server and storage can mount an offline dictionary attack on user’s single password. In this work, we introduce a framework for distributed single password protocols (DiSPP) that analyzes existing protocols, improves upon them regarding novel constructions and distributed schemes, and allows exploiting alternative cryptographic primitives to obtain secure distributed single password protocols with various trade-offs. Previous single password solutions can be instantiated as part of our framework. We further introduce a secure DiSPP instantiation derived from our framework enforcing the adversary to corrupt several cloud and mobile storage devices in addition to the login server in order to perform a successful offline dictionary attack. We also provide a comparative analysis of different solutions derived from our framework.
Effects of anoxic exposure on the nuclear factor of activated T cell (NFAT) transcription factors in the stress‐tolerant wood frog

The wood frog, Lithobates sylvaticus (also known as Rana sylvatica), is used for studying natural freeze tolerance. These animals convert 65% to 70% of their total body water into extracellular ice and survive freezing for weeks in winter. Freezing interrupts oxygen delivery to organs; thus, wood frogs limit their ATP usage by depressing their metabolism and redirecting the available energy only to prosurvival processes. Here, we studied the nuclear factor of activated T cell (NFAT) transcription factor family in response to 24‐hour anoxia, and 4‐hour aerobic recovery in liver and skeletal muscle. Protein expression levels of NFATc1‐c4, calcineurin A and glycogen synthase kinase 3β (NFAT regulators), osteopontin, and atrial natriuretic peptide (ANP) (targets of NFATc3 and NFATc4, respectively) were measured by immunoblotting, and the DNA‐binding activities of NFATc1‐c4 were measured by DNA‐protein interaction ELISAs. Results show that NFATc4, calcineurin, and ANP protein expression as well as NFATc4 DNA binding increased during anoxia in liver where calcineurin and ANP protein levels and NFATc4 DNA binding remaining high after aerobic recovery. Anoxia caused a significant increase in NFATc3 protein expression but not DNA‐binding activity in muscle. Our results show that anoxia can increase NFATc4 transcriptional activity in liver, leading to the increase in expression of cytoprotective genes in the wood frog. Understanding the molecular mechanisms involved in mediating survival under anoxia/reoxygenation conditions in a naturally stress‐tolerant model, such as the wood frog, provides insightful information on the prosurvival regulatory mechanisms involved in combating stress. This information will also further our understanding of metabolic rate depression and answer the question of how frogs tolerate prolonged periods of oxygen deprivation and resume to full function upon recovery without facing any detrimental side effects as other animals would.
Flibanserin: A controversial drug for female hypoactive sexual desire disorder

Sexual functioning is an integral part of human life. Female sexual dysfunction (FSD) adversely affects quality of life and general well-being. The Food and Drug Administration recently approved flibanserin, for treatment of female hypoactive sexual desire disorder (HSDD), regarded as the most common FSD, amid great controversy. A novel multifunctional serotonin agonist and antagonist, flibanserin, has been shown to be efficacious in treating HSDD but with a rather tenacious side effect profile. We review this interesting drug in its entirety. Data for the article were collected by reviewing articles on PubMed, the drug (Addyi) website, and related websites on the internet.
Using Non-Orthogonal Multiplexing for In-Band Full-Duplex Backhaul for 5G Broadcasting

Delivering broadcast-type services is one of the main use cases defined for the next generation 5G system. To deliver robust mobile broadcast services, single-frequency-network (SFN) is preferred due to the inherent spatial diversity. However, each SFN transmitter requires a backhaul link, which results in high infrastructure cost, especially for small cell networks with large number of transmitters. This paper proposes a cost-efficient in-band full-duplex backhaul solution using power-based non-orthogonal multiplexing technology (P-NOM). In the proposed solution, the backhaul data and mobile services are transmitted in different signal layers in a P-NOM system at the same time and using the same spectrum. In order to achieve SFN operation, careful timing control on the backhaul and service transmissions is designed. When comparing to in-band backhaul solutions using orthogonal multiplexing (OM) technologies, the proposed P-NOM based technology achieves higher spectrum efficiency and network scalability. In addition, the proposed technology can be directly applied for backhaul transmission in mixed unicast and broadcast delivery, which is a desired feature for 5G to achieve more efficient use of spectrum.
The Young Injured Gymnast: A Literature Review and Discussion

Gymnastics is a sport requiring grace, strength, and flexibility, resulting in a wide variety of injuries. This article is an overview of the sport of gymnastics and associated injury trends in the artistic gymnastics population. Injury rates in gymnastics range from 1.08 to 50.3 per 1000 h of exposure. More injuries occur in competition versus practice. Fewer injuries occur in recreational gymnastics than competitive gymnastics. The most common injury in gymnastics occurs in the lower extremity, and is an ankle sprain, followed by knee internal derangement. However, in men’s gymnastics the most common gymnastics injury occurs in the upper body (specifically the shoulder). The most common skills causing injury to a gymnast are front/back handsprings and saltos/flips. Unique injuries commonly seen when caring for the young gymnast include spondylolysis, ostoechondritis dissecans of the capetillium and talus, Gymnast wrist, Grip lock, Osgood‐Schlatter, patellofemoral syndrome, Sever’s disease, and ankle sprains. Research efforts should focus on injury rates after the 2006 rule change, injury prevention, men’s gymnastics injury patterns, concussions, hip injuries, and ACL injuries in gymnasts.
Usability Study of Mainstream Wearable Fitness Devices: Feature Analysis and System Usability Scale Evaluation

Background Wearable devices have the potential to promote a healthy lifestyle because of their real-time data monitoring capabilities. However, device usability is a critical factor that determines whether they will be adopted on a large scale. Usability studies on wearable devices are still scarce. Objective This study aims to compare the functions and attributes of seven mainstream wearable devices and to evaluate their usability. Methods The wearable devices selected were the Apple Watch, Samsung Gear S, Fitbit Surge, Jawbone Up3, Mi Band, Huawei Honor B2, and Misfit Shine. A mixed method of feature comparison and a System Usability Scale (SUS) evaluation based on 388 participants was applied; the higher the SUS score, the better the usability of the product. Results For features, all devices had step counting, an activity timer, and distance recording functions. The Samsung Gear S had a unique sports track recording feature and the Huawei Honor B2 had a unique wireless earphone. The Apple Watch, Samsung Gear S, Jawbone Up3, and Fitbit Surge could measure heart rate. All the devices were able to monitor sleep, except the Apple Watch. For product characteristics, including attributes such as weight, battery life, price, and 22 functions such as step counting, activity time, activity type identification, sleep monitoring, and expandable new features, we found a very weak negative correlation between the SUS scores and price (r=−.10, P=.03) and devices that support expandable new features (r=−.11, P=.02), and a very weak positive correlation between the SUS scores and devices that support the activity type identification function (r=.11, P=.02). The Huawei Honor B2 received the highest score of mean 67.6 (SD 16.1); the lowest Apple Watch score was only 61.4 (SD 14.7). No significant difference was observed among brands. The SUS score had a moderate positive correlation with the user’s experience (length of time the device was used) (r=.32, P<.001); participants in the medical and health care industries gave a significantly higher score (mean 61.1, SD 17.9 vs mean 68.7, SD 14.5, P=.03). Conclusions The functions of wearable devices tend to be homogeneous and usability is similar across various brands. Overall, Mi Band had the lowest price and the lightest weight. Misfit Shine had the longest battery life and most functions, and participants in the medical and health care industries had the best evaluation of wearable devices. The perceived usability of mainstream wearable devices is unsatisfactory and customer loyalty is not high. A consumer’s SUS rating for a wearable device is related to their personal situation instead of the device brand. Device manufacturers should put more effort into developing innovative functions and improving the usability of their products by integrating more cognitive behavior change techniques.
Bridging deep and kernel methods

There has been some exciting major progress in recent years in data analysis methods, including a variety of deep learning architectures, as well as further advances in kernel-based learning methods, which have demonstrated predictive superiority. In this paper we provide a brief motivated survey of recent proposals to explicitly or implicitly combine kernel methods with the notion of deep learning networks
Temporal changes in genetic diversity and forage yield of perennial ryegrass in monoculture and in combination with red clover in swards

Agricultural grasslands are often cultivated as mixtures of grasses and legumes, and an extensive body of literature is available regarding interspecific interactions, and how these relate to yield and agronomic performance. However, knowledge of the impact of intraspecific diversity on grassland functioning is scarce. We investigated these effects during a 4-year field trial established with perennial ryegrass (Lolium perenne) and red clover (Trifolium pratense). We simulated different levels of intraspecific functional diversity by sowing single cultivars or by combining cultivars with contrasting growth habits, in monospecific or bispecific settings (i.e. perennial ryegrass whether or not in combination with red clover). Replicate field plots were established for seven seed compositions. We determined yield parameters and monitored differences in genetic diversity in the ryegrass component among seed compositions, and temporal changes in the genetic composition and genetic diversity at the within plot level. The composition of cultivars of both species affected the yield and species abundance. In general, the presence of clover had a positive effect on the yield. The cultivar composition of the ryegrass component had a significant effect on the yield, both in monoculture, and in combination with clover. For the genetic analyses, we validated empirically that genotyping-by-sequencing of pooled samples (pool-GBS) is a suitable method for accurate measurement of population allele frequencies, and obtained a dataset of 22,324 SNPs with complete data. We present a method to investigate the temporal dynamics of cultivars in seed mixtures grown under field conditions, and show how cultivar abundances vary during subsequent years. We screened the SNP panel for outlier loci, putatively under selection during the cultivation period, but none were detected.