Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformation-consistent semi-supervised learning for prostate CT radiotherapy

Deep supervised models often require a large amount of labelled data, which is difficult to obtain in the medical domain. Therefore, semi-supervised learning (SSL) has been an active area of research due to its promise to minimize training costs by leveraging unlabelled data. Previous research have shown that SSL is especially effective in low labelled data regimes, we show that outperformance can be extended to high data regimes by applying Stochastic Weight Averaging (SWA), which incurs zero additional training cost. Our model was trained on a prostate CT dataset and achieved improvements of 0.12 mm, 0.14 mm, 0.32 mm, and 0.14 mm for the prostate, seminal vesicles, rectum, and bladder respectively, in terms of median test set mean surface distance (MSD) compared to the supervised baseline in our high data regime.
Integration of large-scale renewable power into the Taiwan power system

This study implements the system impact analyses when a large amount of renewable generation is integrated into the Taiwan grid; these analyses include power flow, fault current, and transient stability. The simulation utilizes actual system parameters in Taiwan. According to the simulation results, this study proposes practical coping strategies for improving system frequency stability. They include the installation of energy storage systems, management of demand response, and adjustment of unit commitment.
Minimized feature overhead malware detection machine learning model employing MRMR‐based ranking

To deal with the huge amount of data, minimizing the overhead will play a key role in speedy and efficient malware detection. We propose a machine learning (ML) malware detection model with preprocessing to limit the feature overhead. The portable‐executable (PE) header information that retains meaningful and distinctive information has been considered to classify benign and malware files. The dataset is preprocessed by applying transformation, outlier detection and filling, and smoothing techniques. A maximum relevance minimum redundancy‐based feature selection method is deployed to assign the rank and score to each feature retaining the maximum relevant and minimal redundant information. Based on the obtained rank, many subsets of features have been created and investigated against support vector machine (SVM) and k‐nearest neighbors (k‐NN) with parametric tuning. The proposed ML model integrated with data preprocessing, feature selection, and SVM‐polynomial classifier has superior performance. This model is eliminating 63.8% feature overhead with accuracy above 99.1% for the benchmark datasets. To examine the robustness of the proposed model, new balanced and imbalanced datasets are created using new malware. The test results are encouraging with accuracy and specificity above 96.68%, 97.65%, and 91.57%, respectively. Interestingly, the proposed model is not trained using the newly created dataset.
Characterization of Acetylcholinesterase from Various Sources: A Mini Re

In this review, various aspects of acetylcholinesterase (AChE) are reviewed. Acetylcholinesterase (AChE, EC3.1.1.7) is an important neurotransmitter because it hydrolyses the neurotransmitter acetylcholine (ACh), it is also the target site of organophosphorus and carbamate insecticides. The discovery of the first neurotransmitter ACh was soon followed by the discovery of its hydrolysing enzyme, AChE. The role of AChE in terminating AChE-mediated neurotransmission made it the focus of intense research for much of the past century. The role of butyrylcholinesterase (BChE) is not known but it is assumed to participate in the growth and development and to scavenge the cholinergic toxins, it is also reported to have an auxiliary role in synaptic transmission. Acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) are the major groups of cholinesterase differentiated by their substrate preferences. Substrate binding specificity and catalytic efficiency are tangled in enzyme catalysis. The fact that an enzyme may exist in more than one molecular form has been accepted for a number of years.
La importancia de los registros en Latinoamérica

Pulmonary Hypertension (PH) is a particular hemodynamic disorder associated to several diseases. The registries or data-bases are able to identify epidemiological profiles, improve diagnoses, recognize better therapies and outcomes. Greater diversities of human populations, physical environments and unequal health resources access turn this task into a challenge in Latin America. The authors analyze the RELAHPII and the registry of HAPred.co in order to observe suitable features for a joint data-base. This tool could be amplified with other countries in the region.
Characterization of Cold and Heat Tolerance of Bactrocera tau (Walker)

Simple Summary Insects are often stressed by adverse factors in their natural environment. Temperature is a crucial driver of insect activity, adaptability, and distribution, and therefore, it greatly impacts the invasive success of alien pests. Bactrocera tau (Walker) is an invasive polyphagous herbivore of vegetables and fruits, now a pest of global importance. This study provides useful information about B. tau’s cold- and heat tolerance to extremely low and high temperatures. Its different life stages (i.e., egg, larvae, pupae, and adult) had high survival rates under adverse temperatures spanning −5 to 0 °C and 39 to 42 °C. These findings suggest that B. tau possesses a wide temperature threshold range for survival, which likely contributes to its better establishment and expansion in new regions. Meanwhile, fitted curves were used to quantify B. tau’s tolerance potential as a function of both stress intensity (heat or cold) and exposure duration. The information generated in this study will contribute to our understanding of thermal tolerance in B. tau and could also provide insights for devising phytosanitary control approaches. Abstract Bactrocera tau (Walker) (Diptera: Tephritidae) is a serious, economically important invasive pest that has spread and been established in many regions worldwide. Temperature is a crucial abiotic factor governing insect activity, fitness, and geographical distribution. Yet, surprisingly, the tolerance of B. tau to extreme cold and heat stress remains unclear. Here, we measured the supercooling point (SCP) of different life stages of B. tau. Further, several life stages of B. tau (egg, 1st, 2nd, and 3rd instar larvae, 1-day-old pupae, and 3-day-old adult) were subjected to six low temperatures (−9, −7, −5, −3, −1, and 0 °C) and six high temperatures (39, 40, 41, 42, 43, and 44 °C) for various durations (0.5, 1.0, 2.0, and 4.0 h), and three-way survival–time–temperature relationships were investigated. We found that the SCPs differed significantly among different life stages of B. tau, being the lowest for SCP of eggs, at −25.82 ± 0.51 °C. There was no significant effect of sex on the mean SCPs of B. tau adults, except for 45- to 50-day-old flies. In addition, an interaction effect was uncovered between tested temperatures and exposure duration upon B. tau mortality at different life stages. Eggs exhibited the strongest cold tolerance, yet the weakest heat tolerance. The 3rd instar larvae were the most heat- and cold tolerant among larval stages, followed by the 2nd and 1st instar larvae. The upper limit of the chill injury zone (ULCIZ) for 3-day-old adult and 1-day-old pupae was −2.51 °C and −2.50 °C, respectively, while their corresponding lower limit of thermal injury zone (LLTIZ) was 39.39 °C and 38.29 °C. This paper presents valuable data to provide an integrated knowledge for understanding the cold and heat tolerance potential of B. tau and ensure the proper implementation of post-harvest phytosanitary protocols for this pest’s disinfestation.
Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods

Background The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods. Methods A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype. Results Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001). Conclusions The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.
A 7-Bit Two-Step Flash ADC With Sample-and-Hold Sharing Technique

A 7-bit 3 GS/s two-channel time-interleaved two-step flash analog-to-digital converter (ADC) with 7-GHz effective resolution bandwidth (ERBW) is presented. A reference-embedding flash ADC for a fine stage having only a single capacitive digital-to-analog converter improves the power efficiency and area efficiency as well as the input bandwidth. The proposed sample-and-hold sharing structure not only improves the input bandwidth by removing the effect of the input capacitance of the fine ADC (FADC) but also eliminates the gain error between the coarse ADC and the FADC. The advanced sequential slope-matching offset calibration technique in the eight-time interpolated FADC improves the gain of the voltage-to-time converter and the interpolation linearity. A prototype ADC implemented in a 40-nm CMOS process occupies 0.03 mm2, including offset calibration circuitry. The measured peak differential non-linearity (DNL) and integral non-linearity (INL) after calibration are 0.53 and 0.47 LSB, respectively. With a 1.49-GHz input, the measured signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) are 39.94 and 55.78 dB, respectively. The ERBW without and with time skew calibration is 4.8 and 7 GHz, respectively. The power consumption is 6.8 mW under a supply voltage of 0.9 V, leading to a figure of merit (FoM) of 28 fJ/conversion-step at 3 GS/s.
Therapeutic Potential of Janus Kinase Inhibitors for the Management of Interstitial Lung Disease

Abstract Interstitial lung disease (ILD) refers to a heterogeneous group of diseases characterized by lung fibroblast proliferation, interstitial inflammation, and fibrosis-induced lung damage. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway is known to be activated by pro-fibrotic/pro-inflammatory cytokines such as IL-6 and IL-13, whose levels are elevated in ILD. The overexpression of growth factors such as transforming growth factor β1 in ILD activates the JAK/STAT pathway through classical or non-classical pathways, promotes macrophage activation, increases the release of pro-inflammatory and pro-fibrosis factors, and facilitates fibroblast differentiation into myofibroblasts. These findings implicate that the JAK/STAT pathway plays an important role in the course of ILD. Recent evidence also suggests that JAK inhibition alleviates excessive inflammation and pulmonary fibrosis. Accordingly, the JAK inhibitors may serve as promising drugs for the treatment of JAK/STAT-induced ILD.
From grass-root change agents and academics to design thinkers preventing radicalization: a learner's journey

In recent years more and more youngsters and other vulnerable citizens tend to radicalise either through political (right wing, left wing), religious, or single-issue movements such as animal rights or ecological concerns. The CommUnity consortium set out to prevent this decision to radicalise through arts-based community-building via design thinking approaches. In this paper, how UCLL and IDP developed the design thinking course through design thinking, the Covid-19 adaptations, the bridge between different fields of knowledge and practice, and the innovative approaches in the prevention narrative are highlighted. Finally, the monitoring reports are analysed to define the community-builders design thinking learning trajectory following the phases of newcomer, adapter, leader, and grandmaster.
Strengthening Soft Skills in Online Learning

Soft skills are needed to prepare students for the world of work. Thus, this research contributes as a reference in developing students' soft skills during this pandemic, which requires learning to be done virtually. By providing special training to students to improve their soft skills during this pandemic, the world of education will create graduates who still have soft skills that are competent and meet the needs of the industrial world. This study applied an experimental design to determine the influence of holding special training for students regarding soft skills abilities. This study revealed a very good result where the soft skills ability samples have increased after attending the training that was designed. The pre-test results showed an average value of 1.95 at a rate of 5. After participating in training, a post-test assessment was carried out, getting an average value of 4.49. The samples used in this research were students willing to participate in the training without any coercion from any party. This research provides strength in soft skill competencies for students in a pandemic situation to maintain the quality of graduates.
P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA2DS2-VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.
Bioactive Compounds for Fibromyalgia-like Symptoms: A Narrative Review and Future Perspectives

Fibromyalgia (FM) is a prevalent, chronic condition without a cure or reliable therapy. The etiopathogenesis of this syndrome is ambiguous, which has heightened the challenge of discovering treatments to minimize patients’ painful symptoms. FM is characterized by diffuse musculoskeletal pain usually accompanied by functional pain syndromes, such as fatigue, sleep disturbances, cognitive difficulties, and mood issues. Currently available treatment options for FM are limited. Recent studies have suggested a potential role for dietary bioactive compounds in FM management. We performed a narrative review to evaluate the existing evidence regarding the dietary bioactive compounds for FM, and we proposed molecular mechanisms on this topic. The inclusion criteria were (i) human, in vivo, or in vitro studies, (ii) studies related to the effect of bioactive compounds on FM-like symptoms, (iii) peer-reviewed literature, and (iv) publications until February 2022 in PubMed and Google Scholar. Exclusion criteria were (i) study designs using CCI, SNI, or SNL models because they are more NP models rather than FM models, and (ii) studies published in a language other than English. Keywords were dietary bioactive compounds, fibromyalgia, cell, animals, humans. Here, we report the effects of commonly consumed bioactive compounds (capsaicin, ginger, curcumin, n-3 PUFA, grape seed extract, naringin, and genistein) on FM-like symptoms in cellular, animal, and human studies. Cellular studies demonstrated that these bioactive compounds reduce pro-inflammatory production and increase antioxidant capacity of neurons or myoblasts that regulate apoptosis/cell survival. Animal studies showed that these regularly consumed bioactive compounds have an effect on FM-like symptoms, as evidenced by decreased pain hypersensitivity and fatigue as well as improved social behaviors. Further studies are warranted to allow meaningful comparison and quantification of the efficacy of these bioactive compounds on FM-like symptoms across studies, in terms of actual changes in antioxidant capacity, pain hypersensitivity, fatigue, and social behaviors. To date, human studies regarding the efficacy of these bioactive compounds on FM-like symptoms are limited and inconclusive. Our review identifies this important knowledge gap and proposes that the development and use of improved preclinical FM models are needed, particularly concerning the usage of female animals to better mimic FM pathophysiology and symptomatology.
Automated optic disk detection in fundus images using a combination of deep learning and local histogram matching

Retinal images have been used in the diagnosis of many ocular diseases such as glaucoma and diabetic retinopathy. Here, automatic detection of optic disk (OD) is essential in deriving clinical parameters to assist clinical diagnosis. In fact, detecting OD center and its boundary is the essential step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. In this study, we proposed a new approach for localizing OD by combining local histogram matching and the concept of deep learning. The algorithm is composed of 4 steps, Image partitioning, Local histogram matching and validation, Convolutional Neural Network (CNN) classification, and OD detection. Here, we used OD of the five reference retinal images in each dataset to extract the histograms of each color channel. Then, we calculated the mean of histograms for each channel as template for creating some OD candidates. An AlexNet-like CNN was applied to classify candidates as ODs or nonODs. The candidates used as an input to feed the CNN for final classification. In this study, we worked on three databases (one rural, MUMS-DB, and two publicly available databases, DRIVE, STARE) including 520 retinal images to evaluate the proposed method. The accuracy of our algorithm was 100%, 90%, and 95% for the DRIVE, STARE, and MUMS-DB respectively. It is shown that this method provides higher detection rates than the existing methods that have reported.
Scales for assessing therapeutic adherence and competence in dialectical behaviour therapy for PTSD: development and analysis of psychometric properties

ABSTRACT Background The assessment of therapeutic adherence and competence is essential to understand mechanisms that contribute to treatment outcome. Nevertheless, their assessment is often neglected in psychotherapy research. Aims/Objective To develop an adherence and a treatment-specific competence rating scale for Dialectical Behaviour Therapy for Posttraumatic Stress Disorder (DBT-PTSD), and to examine their psychometric properties. Global cognitive behavioural therapeutic competence and disorder-specific therapeutic competence were assessed using already existing scales to confirm their psychometric properties in our sample of patients with PTSD and emotion regulation difficulties. Method Two rating scales were developed using an inductive procedure. 155 videotaped therapy sessions from a multicenter randomised controlled trial were rated by trained raters using these scales, 40 randomly chosen videotapes involving eleven therapists and fourteen patients were doubly rated by two raters. Results Both the adherence scale (Patient-level ICC = .98; αs = .65; α p = .75) and the treatment-specific competence scale (Patient-level ICC = .98; αs = .78; α p = .82) for DBT-PTSD showed excellent interrater – and good reliability on the patient level. Content validity, including relevance and appropriateness of all items, was confirmed by experts in DBT-PTSD for the new treatment-specific competence scale. Conclusion Our results indicate that both scales are reliable instruments. They will be useful to examine possible effects of adherence and treatment-specific competence on DBT-PTSD treatment outcome.
Therapeutic Properties and Use of Extra Virgin Olive Oil in Clinical Nutrition: A Narrative Review and Literature Update

Extra virgin olive oil (EVOO) is a cornerstone of the Mediterranean diet (MedD). In this narrative review, we synthesize and illustrate the various characteristics and clinical applications of EVOO and its components—such as oleic acid, hydroxytyrosol, and oleuropein—in the field of clinical nutrition and dietetics. The evidence is split into diet therapy, oleic acid-based enteral nutrition formulations and oral supplementation formulations, oleic acid-based parenteral nutrition, and nutraceutical supplementation of minor components of EVOO. EVOO has diverse beneficial health properties, and current evidence supports the use of whole EVOO in diet therapy and the supplementation of its minor components to improve cardiovascular health, lipoprotein metabolism, and diabetes mellitus in clinical nutrition. Nevertheless, more intervention studies in humans are needed to chisel specific recommendations for its therapeutic use through different formulations in other specific diseases and clinical populations.