Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning with Distributed Datasets: A Multiple Criteria Decision Making Approach

Large-scale data analysis is growing at an exponential rate as data pro-liferates in our societies. This abundance of data has the advantage of allowing the decision-maker to implement complex models in scenarios that were prohibitive before. At the same time, such an amount of data requires a distributed thinking approach. In fact, Deep Learning models require plenty of resources, and distributed training is needed. This paper presents a multicriteria approach for distributed learning. Our approach uses the Weighted Goal Programming approach in its Chebyshev formulation to build an ensemble of decision rules that optimize aprioristically deﬁned performance metrics. Such a formulation is beneﬁcial because it is both model and metric agnostic and provides an interpretable output for the decision-maker. We test our approach by showing a practical application in electricity demand forecasting. Our results suggest that when we allow for dataset split overlapping, the performances of our methodology are consistently above the baseline model trained on the whole dataset.
A NEW APPROACH TO HYBRID CLOUD

Hybrid computing systems have received considerable attention recently as an approach to significant performance gains in many problem domains. In order to succeed with hybrid cloud approach, there are a few challenges that must be addressed such as application complexity and security. As one of the first public cloud solutions, Amazon's EC2 has strongly contributed to this development. However, not only the cloud community is growing rapidly, but also the number of critiques is increasing. Especially in terms of data security and privacy, but also in other topics (such as availability, vendor lock-in, cost, or interoperability), public clouds carry inherent risks. An obvious yet not trivial solution to these issues is the use of both private and public delivery models, and combining them to a hybrid cloud.
Differential mechanisms of posterior cingulate cortex downregulation and symptom decreases in posttraumatic stress disorder and healthy individuals using real‐time fMRI neurofeedback

Intrinsic connectivity networks, including the default mode network (DMN), are frequently disrupted in individuals with posttraumatic stress disorder (PTSD). The posterior cingulate cortex (PCC) is the main hub of the posterior DMN, where the therapeutic regulation of this region with real‐time fMRI neurofeedback (NFB) has yet to be explored.
Numerical Simulation of Gas Transportation in Mechanized Top-Coal Caving with Gob-Side Entry Retaining

To solve the problem of gas accumulation in thick-seam top-coal caving mining face, goaf gas migration equations were established based on seepage theory and the law of mass conservation. Taking No.7602 working face of Wuyang coal mine as the study object, gas migration rules of goaf in ventilation of U + I and Y+I were simulated by Fluent, and comparative analysis and verification were carried out by in-situ monitoring data. Test indicates that after gob-side entry retaining, average gas concentration value of No.7602 working face Return airway decreased by 39% compared with similar working face, decreased by 39% in gas emission roadway, decreased by 42% in upper corner. Ventilation of U + I can successfully resolve the problem of gas accumulation in upper corner and working face. The conclusion has a good instruction value to gas control in thick-seam top-coal caving mining face.
British HIV Association guidelines for the management of HIV‐2 2021

1Consultant in HIV Medicine, Homerton University Hospital NHS Trust, London, UK 2UK Community Advisory Board representative 3Clinical Senior Lecturer in HIV, Barts and the London, Queen Mary University of London, London, UK 4Consultant in Sexual Health and HIV Medicine, Barts Health NHS Trust, London, UK 5Consultant Virologist, National Infection Service, Public Health England, UK 6Consultant Physician Sexual Health and HIV, Kings College Hospital, London, UK 7Consultant in Sexual Health and HIV Medicine, Barts Health NHS Trust, London, UK 8Specialist Registrar in Infectious Diseases and Microbiology, Manchester University NHS Foundation Trust, Manchester, UK 9Consultant in Infectious Diseases, North Manchester General Hospital, Manchester, UK
QUANTIFYING IMPACTS DURING BEAM AND FLOOR TRAINING IN PREADOLESCENT GIRLS FROM TWO STREAMS OF ARTISTIC GYMNASTICS

The primary aim of this study was to determine the effects of participation level (international and national), apparatus (beam and floor) and training phase (precompetition and competition) on estimates of training load in a convenient sample of gymnasts aged 7 to 13 years. Video analysis of training sessions along with accelerometer and force platform (500 Hz) peak ground reaction force data were used to establish differences between key gymnastic-specific and non-gymnastic-specific variables. International level gymnasts demonstrated higher incidence in hours of training, number of observed impacts, markers of the quality of periodised training program and lower relative injury rates. Marked differences were also observed between the two training phases. The demand for injury prevention strategies may be higher at the national level of gymnastics training.
Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.
Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.
Metric Goodness and Measurement Invariance of the Italian Brief Version of Interpersonal Reactivity Index: A Study With Young Adults

The Interpersonal Reactivity Index (IRI) is a widely used multidimensional measure to assess empathy across four main dimensions: perspective taking (PT) empathic concern (EC) personal distress (PD) fantasy (F). This study aimed to replicate the Italian validation process of the shortened IRI (Interpersonal Reactivity Index) scale in order to confirm its psychometric properties with a sample of young adults. The Gender Measurement Invariance of empathy in this age group was also an objective of the work in order to increase the data on this aspect. A total of 683 Italian university students participated in a non-probabilistic sampling. The 16-item version was confirmed in its four-factor structure but with changes to some items. The model showed good fits with both the CFA and the gender Measurement Invariance. The internal consistency measures were found to be fully satisfactory. Convergent validity was tested by the correlations with the Prosocialness Scale for Adults and The Toronto Alexithymia Scale-20. As hypothesized the measure proved good convergent validity with Prosocialness, i.e., the willingness to assist, help, share, care and empathy with others, and a relevant inverse association with the External Oriented Thinking, characterizing individuals with emotionally poor thinking. This research provided additional evidence for a link between alexithymia and poor empathic abilities in young adults.
High Reliability and the Management of Critical Infrastructures

Organisation theorists and practitioners alike have become greatly interested in high reliability in the management of large hazardous technical systems and society's critical service infrastructures. But much of the reliability analysis is centred in particular organisations that have command and control over their technical cores. Many technical systems, including electricity generation, water, telecommunications and other "critical infrastructures," are not the exclusive domain of single organisations. Our essay is organised around the following research question: How do organisations, many with competing, if not conflicting goals and interests, provide highly reliable service in the absence of ongoing command and control and in the presence of rapidly changing task environments with highly consequential hazards? We analyse electricity restructuring in California as a specific case. Our conclusions have surprising and important implications both for high reliability theory and for the future management of critical infrastructures organised around large technical systems.
Phytochemicals Mediate Autophagy Against Osteoarthritis by Maintaining Cartilage Homeostasis

Osteoarthritis (OA) is a common degenerative joint disease and is a leading cause of disability and reduced quality of life worldwide. There are currently no clinical treatments that can stop or slow down OA. Drugs have pain-relieving effects, but they do not slow down the course of OA and their long-term use can lead to serious side effects. Therefore, safe and clinically appropriate long-term treatments for OA are urgently needed. Autophagy is an intracellular protective mechanism, and targeting autophagy-related pathways has been found to prevent and treat various diseases. Attenuation of the autophagic pathway has now been found to disrupt cartilage homeostasis and plays an important role in the development of OA. Therefore, modulation of autophagic signaling pathways mediating cartilage homeostasis has been considered as a potential therapeutic option for OA. Phytochemicals are active ingredients from plants that have recently been found to reduce inflammatory factor levels in cartilage as well as attenuate chondrocyte apoptosis by modulating autophagy-related signaling pathways, which are not only widely available but also have the potential to alleviate the symptoms of OA. We reviewed preclinical studies and clinical studies of phytochemicals mediating autophagy to regulate cartilage homeostasis for the treatment of OA. The results suggest that phytochemicals derived from plant extracts can target relevant autophagic pathways as complementary and alternative agents for the treatment of OA if subjected to rigorous clinical trials and pharmacological tests.
Special Education and General Education Teacher Perceptions of Collaborative Teaching Responsibilities and Attitudes Towards an Inclusive Environment in Jordan

The purpose of this study was to investigate how general and special education instructors perceive their collaborative teaching responsibilities and their attitudes toward inclusive environments. A self-administered questionnaire was distributed to 300 teachers in accordance with the social interdependence theory and cooperative learning conceptual framework. The survey was composed of two parts. The first section examined collaborative teaching duties for both instructors. It included 29 items and four categories (planning, instruction, evaluation, and behavior management). The second section included 15 items to assess attitudes toward inclusion. The study enrolled a total of 233 teachers (123 in special education and 110 in general education) with a response rate of 78%. The results showed that there was agreement between general and special education on only one of the four domains (instruction). Additionally, special education teachers expressed a more favorable attitude toward inclusion than did general education teachers. The current situation’s implications were explored with an emphasis on the necessity for additional shared practical activities among teachers.
Lens Biometry in Congenital Lens Deformities: A Swept-Source Anterior Segment OCT Analysis

Aims: To investigate the lens biometric parameters in congenital lens deformities, using a novel technique of swept-source anterior segment optical coherence tomography (SS-ASOCT). Methods: This prospective study included patients with microspherophakia (MSP), coloboma lentis (CL), and posterior lenticonus (PL). For this cohort, 360-degree high-resolution lens images were obtained using the latest SS-ASOCT (CASIA2, Tomey Corp, Nagoya, Japan). The lens biometric parameters were calculated by the CASIA2 built-in software for anterior lens radius (ALR), posterior lens radius (PLR), anteroposterior distance (APD), anterior chamber depth (ACD), equatorial diameter (Eq Dia), rear projection length (RPL), and maximum diameter of the lesion (MDL). Results: This study included two eyes each with MSP and CL and one eye with PL. The lens of MSP was spherical and posteriorly dislocated, with decreased ALR and PLR, Eq Dia, but increased APD. In patients with CL, the coloboma was isolated, bilateral, inferior, and located toward the maldeveloped ciliary body. High astigmatism was mainly lenticular, and this was calculated by the ALR and PLR. Regarding the site of coloboma, a significant decrease in ALR was observed, while the PLR and APD were not affected. The PL eyes had a cone-shaped protrusion of the posterior lens surface with a subtle cataractous region around the apex. An extremely high posterior surface curvature was observed with a mean PLR of 1.67 mm. The RPL and MDL were about 1.80 and 0.4 mm, respectively, which were homogenous at different sections. Conclusions: The CASIA2 is a valuable option for in vivo crystalline lens measurement for congenital lens deformities, enabling the accurate diagnosis and providing illuminating insights into the pathogenesis of MSP, CL, and PL
A CLUSTERED CLASS DISTRIBUTION APPROACH FOR PROCESS MONITORING AND FAULT DETECTION

Clustering algorithms have been useful for fault detection of process systems. Among many algorithms, ART2 neural networks have provided good results for fault detection and identification. In this article, a new algorithm for fault detection is proposed that is based on clustered classes from ART2 neural networks. An index for detecting changes of process state is defined, to represent differences between recent class distribution and historical class distribution. The proposed algorithm was applied to detection of state change of an industrial wastewater treatment process and was shown to be effective in detecting a change in the process.
Rapid Enhancement of Subcortical Neural Responses to Sine-Wave Speech

The efferent auditory nervous system may be a potent force in shaping how the brain responds to behaviorally significant sounds. Previous human experiments using the frequency following response (FFR) have shown efferent-induced modulation of subcortical auditory function online and over short- and long-term time scales; however, a contemporary understanding of FFR generation presents new questions about whether previous effects were constrained solely to the auditory subcortex. The present experiment used sine-wave speech (SWS), an acoustically-sparse stimulus in which dynamic pure tones represent speech formant contours, to evoke FFRSWS. Due to the higher stimulus frequencies used in SWS, this approach biased neural responses toward brainstem generators and allowed for three stimuli (/bɔ/, /bu/, and /bo/) to be used to evoke FFRSWS before and after listeners in a training group were made aware that they were hearing a degraded speech stimulus. All SWS stimuli were rapidly perceived as speech when presented with a SWS carrier phrase, and average token identification reached ceiling performance during a perceptual training phase. Compared to a control group which remained naïve throughout the experiment, training group FFRSWS amplitudes were enhanced post-training for each stimulus. Further, linear support vector machine classification of training group FFRSWS significantly improved post-training compared to the control group, indicating that training-induced neural enhancements were sufficient to bolster machine learning classification accuracy. These results suggest that the efferent auditory system may rapidly modulate auditory brainstem representation of sounds depending on their context and perception as non-speech or speech.
Prognostic Value and Outcome for ETV6/RUNX1-Positive Pediatric Acute Lymphoblastic Leukemia: A Report From the South China Children’s Leukemia Group

Purpose To analyzed the outcome of ETV6/RUNX1-positive pediatric acute B lymphoblastic leukemia (B-ALL) with the aim of identifying prognostic value. Method A total of 2,530 pediatric patients who were diagnosed with B-ALL were classified into two groups based on the ETV6/RUNX1 status by using a retrospective cohort study method from February 28, 2008, to June 30, 2020, at 22 participating ALL centers. Results In total, 461 (18.2%) cases were ETV6/RUNX1-positive. The proportion of patients with risk factors (age <1 year or ≥10 years, WB≥50×109/L) in ETV6/RUNX1-positive group was significantly lower than that in negative group (P<0.001), while the proportion of patients with good early response (good response to prednisone, D15 MRD < 0.1%, and D33 MRD < 0.01%) in ETV6/RUNX1-positive group was higher than that in the negative group (P<0.001, 0.788 and 0.004, respectively). Multivariate analysis of 2,530 patients found that age <1 or ≥10 years, SCCLG-ALL-2016 protocol, and MLL were independent predictor of outcome but not ETV6/RUNX1. The EFS and OS of the ETV6/RUNX1-positive group were significantly higher than those of the negative group (3-year EFS: 90.11 ± 4.21% vs 82 ± 2.36%, P<0.0001, 3-year OS: 91.99 ± 3.92% vs 88.79 ± 1.87%, P=0.017). Subgroup analysis showed that chemotherapy protocol, age, prednisone response, and D15 MRD were important factors affecting the prognosis of ETV6/RUNX1-positive children. Conclusions ETV6/RUNX1-positive pediatric ALL showed an excellent outcome but lack of independent prognostic significance in South China. However, for older patients who have the ETV6/RUNX1 fusion and slow response to therapy, to opt for more intensive treatment.
The Used of Pedal-Pad Effective Amplitude Transmissibility Value to Predict Size of Comfortable Pedal-Pads

Vibration transmissibility from a car body to pedal-pad could result to drivers discomfort and fatigue, which may lead to health problems. Vibration transmissibility occurs when the vehicle engine is turned on and vibration is transmitted from the car body to pedal-pad in this research Pedal-pad effective Amplitude Transmissibility (PEAT) value was used to predict suitable size of the pedal-pad that could provide comfortable operation of the pedal. In this study, the variables are the three different sizes of pedal-pads and data was recorded while the car was moving on the road at constant speed with three different sizes of pedal-pads. The data was measured in root mean square (r.m.s) unit, of the frequency weighted acceleration (m/s2) for every minute. The finding of the study shows that percentage PEATr.m. s of the three different sizes of pedal-pad is greater than 100%. It shows that vibrations to pedal-pads are greater than vibration from the car body consequentially it means that the foot on the pedal is exposed to higher vibration transmissibility than the foot on the floor.
Immune Checkpoint Inhibition in Non-Melanoma Skin Cancer: A Review of Current Evidence

Immuno-oncology is a rapidly evolving field with growing relevance in the treatment of numerous malignancies. The prior study of immunotherapy in dermatologic oncology has largely focused on cutaneous melanoma. However, recent focus has shifted to the use of immunotherapy to treat non-melanoma skin cancers (NMSCs), such as basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and Merkel cell carcinoma (MCC). NMSCs represent the most ubiquitous cancers globally and, while they have a lower propensity to develop into advanced disease than cutaneous melanoma, their absolute mortality burden has recently surpassed that of melanoma. Patients with advanced NMSC are now benefiting from the successes of immunotherapy, including checkpoint inhibition with anti-CTLA-4 and anti-PD-1 monoclonal antibodies. In this review, we discuss the existing clinical evidence for immunotherapy in the treatment of NMSCs, with an emphasis on checkpoint inhibitor therapies. We highlight key studies in the field and provide up-to-date clinical evidence regarding ongoing clinical trials, as well as future study directions. Our review demonstrates that checkpoint inhibitors are positioned to provide unparalleled results in the previously challenging landscape of advanced NMSC treatment.
Solving the location-routing problem with simultaneous pickup and delivery by simulated annealing

The study concerns the location-routing problem with simultaneous pickup and delivery (LRPSPD) in which the pickup and delivery take place at the same time for each customer. The goal is to determine the facility locations and vehicle routes in order to minimise the total system cost as a sum of facility opening cost, vehicle fixed cost and vehicle travel cost. A simulated annealing (SA) heuristic is proposed for the problem and extensive computational experiments are conducted. The results show that the proposed SA effectively solves LRPSPD and outperforms existing exact approaches in terms of solution quality.
Review of Immunohistochemistry Biomarkers in Pancreatic Cancer Diagnosis

Pancreatic cancer is one of the cancer types with poor prognosis and high rate of mortality. Diagnostic modalities for early detection of pancreatic cancer have been among the academic concerns. On account of the potential role of immunohistochemistry (IHC) biomarkers in overcoming certain limitations of imaging diagnostic tools in discriminating pancreatic cancer tissues from benign ones, a growing scholarly attention has been given to the diagnostic efficacy of IHC biomarkers for pancreatic cancer. This review will analyze and synthesize published articles to provide an insight into potential IHC biomarkers for pancreatic cancer diagnosis.