Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer

Objectives: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence. Methods: A total of 96 patients (ORN incidence ratio of 1:1) treated between 2011 and 2015 were selected from the local HNC toxicity database. Demographic, clinical and dosimetric data (based on the mandible dose–volume histogram) were considered as model variables. Prediction accuracy (measured using a stratified fivefold nested cross-validation), sensitivity, specificity, precision and negative predictive value were used to evaluate the prediction performance of a multivariate logistic regression (LR) model, a support vector machine (SVM) model, a random forest (RF) model, an adaptive boosting (AdaBoost) model and an artificial neural network (ANN) model. The different models were compared based on their prediction accuracy and using the McNemar’s hypothesis test. Results: The ANN model (77% accuracy), closely followed by the SVM (76%), AdaBoost (75%) and LR (75%) models, showed the highest overall prediction accuracy. The RF model (71%) showed the lowest prediction accuracy. However, based on the McNemar’s test applied to all model pair combinations, no statistically significant difference between the models was found. Conclusion: Based on our results, we encourage the use of ML-based prediction models for ORN incidence as has already been done for other HNC toxicity end points. Advances in knowledge: This research opens a new path towards personalised RT for HNC using ML to predict mandible ORN incidence.
Moving beyond Inhibition: Capturing a Broader Scope of the Self-Control Construct with the Self-Control Strategy Scale (SCSS)

Abstract The purpose of the present research was to develop a more comprehensive measure of self-control that reflects recent theoretical advancements that extend beyond inhibition. Across six samples (N = 1,946, 48.95% males, Ages 18-76, US-MTurkers/Israelis), we sought to develop and validate the Self-Control Strategies Scale (SCSS), as well as examine its predictive validity across important life domains (e.g., weight, physical activity, savings). The SCSS is comprised of eight self-control strategies that represent three categories: anticipatory control (situation selection, reward, punishment, pre-commitment), down-regulation of temptation (distraction, cognitive change, acceptance), and behavioral inhibition. Results indicate that there was a strong association between the widely used Brief Self-Control Scale (BSCS). and the behavioral inhibition strategy of the SCSS. While the behavioral inhibition strategy was a strong and consistent predictor of most self-control related outcomes, results further indicate that in some domains, but not others, certain strategies may be beneficial whereas others may be detrimental. While inhibition remains to be an important factor of self-control, our findings point to the importance of adapting the use of different strategies to different domains. The SCSS can therefore be used to gain a more fine-grained understanding of the self-control construct.
Knowledge of Chinese dentists on HPV, their willingness and barriers to recommend HPV vaccination to patients

ABSTRACT Objective: This study aimed to assess the knowledge of Chinese dentists on human papilloma virus (HPV), their willingness and factors prevention them from recommending patients for HPV vaccination, and to identify ways through which dentists can acquire HPV-related knowledge. Methods: This was a cross-sectional study based on Chinese dentists. The anonymous self-filled questionnaire method was used to collect demographic information of participants, their knowledge of HPV, willingness and barriers to recommending HPV vaccines to patients, and the strategies they can adopt to obtain HPV-related knowledge. Results: A total of 517 dentists completed the questionnaire, and 89.94% of dentists were willing to recommend HPV vaccination to patients. Participants aged 30–39 years, male dentists and dentist-in-charge had relatively lower knowledge scores than other groups. Besides, participants aged 30–39 years, working in private hospitals, and practicing in Southern China had a lower willingness to recommend HPV vaccination to patients. Recommending HPV vaccination to patients is not within the scope of practicing dentists, and concerns about the safety of the HPV vaccine were found to be the main barriers preventing its recommendation. Notably, 97.87% of dentists were eager to acquire HPV-related knowledge, and agreed for the first time to enroll in relevant educational courses. Conclusion: Chinese dentists had expressed a strong desire to recommend HPV vaccines to patients, even though their knowledge of HPV was scanty. Therefore, measures to improve their understanding of HPV vaccines and eliminate barriers suppressing their willingness to recommend HPV vaccines are urgently needed.
Reactive astrocytes in ALS display diminished intron retention

Abstract Reactive astrocytes are implicated in amyotrophic lateral sclerosis (ALS), although the mechanisms controlling reactive transformation are unknown. We show that decreased intron retention (IR) is common to human-induced pluripotent stem cell (hiPSC)-derived astrocytes carrying ALS-causing mutations in VCP, SOD1 and C9orf72. Notably, transcripts with decreased IR and increased expression are overrepresented in reactivity processes including cell adhesion, stress response and immune activation. This was recapitulated in public-datasets for (i) hiPSC-derived astrocytes stimulated with cytokines to undergo reactive transformation and (ii) in vivo astrocytes following selective deletion of TDP-43. We also re-examined public translatome sequencing (TRAP-seq) of astrocytes from a SOD1 mouse model, which revealed that transcripts upregulated in translation significantly overlap with transcripts exhibiting decreased IR. Using nucleocytoplasmic fractionation of VCP mutant astrocytes coupled with mRNA sequencing and proteomics, we identify that decreased IR in nuclear transcripts is associated with enhanced nonsense mediated decay and increased cytoplasmic expression of transcripts and proteins regulating reactive transformation. These findings are consistent with a molecular model for reactive transformation in astrocytes whereby poised nuclear reactivity-related IR transcripts are spliced, undergo nuclear-to-cytoplasmic translocation and translation. Our study therefore provides new insights into the molecular regulation of reactive transformation in astrocytes.
First-principles calculations of the structural stability of Fe2P

An investigation into the structural stabilities of Fe2P under high pressure is conducted using first-principles calculations based on density functional theory. Our results demonstrate that the stable phase of Fe2P should be the Pnma with the lowest total energy at lower pressure, and the P6̄2m and Pnma phases would transform to the P3̄mphase with larger coordination number of iron at 125 GPa and 153 GPa respectively. A magnetic moment collapse is observed at about 28 GPa in the P6̄2m phase.
Indentation and bifurcation of inflated membranes

We study pneumatically inflated membranes indented by rigid indenters of different sizes and shapes. When the volume of the inflated membrane is beyond a critical value, a symmetric deformation mode becomes unstable and the system follows a path of asymmetric deformation. This bifurcation is analysed analytically for a two-dimensional membrane with either a line or plane indenter for which the stable deformation path is determined by computing the total system potential energy of different configurations. An axisymmetric membrane with indenters of different shapes and sizes is further investigated numerically. In this case, a cylindrical indenter can always trigger bifurcation while a small spherical indenter tends to be encapsulated rather than induce an asymmetric deformation mode. This result suggests that the observed bifurcation behaviour can be actively tuned and even triggered selectively by tuning indenter shape and size. We also demonstrate the effects of friction and biased bifurcation analytically through the example of a two-dimensional membrane with a line indenter.
A brief review on Group A Streptococcus pathogenesis and vaccine development

Streptococcus pyogenes, also known as Group A Streptococcus (GAS), is a Gram-positive human-exclusive pathogen, responsible for more than 500 000 deaths annually worldwide. Upon infection, GAS commonly triggers mild symptoms such as pharyngitis, pyoderma and fever. However, recurrent infections or prolonged exposure to GAS might lead to life-threatening conditions. Necrotizing fasciitis, streptococcal toxic shock syndrome and post-immune mediated diseases, such as poststreptococcal glomerulonephritis, acute rheumatic fever and rheumatic heart disease, contribute to very high mortality rates in non-industrialized countries. Though an initial reduction in GAS infections was observed in high-income countries, global outbreaks of GAS, causing rheumatic fever and acute poststreptococcal glomerulonephritis, have been reported over the last decade. At the same time, our understanding of GAS pathogenesis and transmission has vastly increased, with detailed insight into the various stages of infection, beginning with adhesion, colonization and evasion of the host immune system. Despite deeper knowledge of the impact of GAS on the human body, the development of a successful vaccine for prophylaxis of GAS remains outstanding. In this review, we discuss the challenges involved in identifying a universal GAS vaccine and describe several potential vaccine candidates that we believe warrant pursuit.
An electronic circular dichroism spectroscopy method for the quantification of L- and D-amino acids in enantiomeric mixtures

Rapid quantitative analysis of single chiral amino acid (aa) was achieved using circular dichroism (CD) with data analysis by standard calibration curve. The absolute concentrations of D- and L-aas in enantiomeric mixtures were determined by CD and achiral liquid chromatography (LC) method. It is worth noting that CD and LC were used independently, not online LC/CD in this study. The errors of the experimental results were less than 10%. The method is also applicable to the quantification of non-aa chiral molecules, such as chiral nucleoside and chiral quinine. With this study, we provide a new method for the chiral quantitative analysis of enantiomeric aas mixtures.
Impact of Exchange Rate Fluctuations on India’s Manufacturing Exports: An Empirical Investigation on Long-Run Relation

After the collapse of the fixed exchange rate system of Bretton Woods, the fluctuations of exchange rate and its impact on macroeconomic performance and trade in countries around the world are becoming an increasing debate among researchers and policymakers. This study empirically investigates whether fluctuations in real exchange rate may affect real exports in the Indian context. The study has employed autoregressive distributed lag (ARDL) bound test procedure to analyse the long-run relationship among variables using quarterly data from 2005Q1 to 2017Q4. The results of the ARDL bound test reveal that real exports are cointegrated with relative prices, real exchange rate volatility and world real GDP. The study has found negative but insignificant impact of exchange rate volatility on exports, but world GDP as a proxy of foreign economic activity and real effective exchange rate as relative prices have positive and statistically significant impact on Indian manufacturing exports. Further, the ARDL short-run error correction model implies that while the model may temporarily deviate from its long-run equilibrium, the deviations adjust towards the equilibrium level in the long run. JEL Codes: F01, F31, F14
A new α chain variant HB Tonosho [α110(G17)ALA→THR] : subunit dissociation during cation exchange chromatography for HB A1c assay

A new α chain variant, α110(G17)Ala→Thr, was detected because of subunit dissociation during the determination of the Hb A1c by automated cation exchange high performance liquid chromatography. The abnormal hemoglobin overlapped the cathodic edge of the band of Hb A in isoelectrofocusing. It was slightly unstable in the isopropanol test and had a slightly increased oxygen affinity. The abnormal α chain eluted slightly faster than the normal α chain in reversed phase high performance liquid chromatography. The amino acid substitution was determined by purification of S-alkylated αT-12, 13 tryptic peptide, chymotryptic digestion, and sequencing of an octapeptide α110-117. The abnormal α chain comprised about 14% of the total α chain. A biosynthetic study did not suggest selective loss of the abnormal chain in reticulocytes.
A unified framework for attribute extraction in electronic medical records

Electronic medical records(EMR) contain a lot of medical diagnosis information; In order to mine the value of data, it is necessary to extract the attributes of the electronic medical record. The deep learning method has been widely used in attribute extraction tasks and has achieved remarkable results in general text datasets. However, in specific medical fields, such as our electronic medical record extraction task, attribute extraction often lacks a lot of high-quality annotation data; besides, the attributes in the corpus can be divided into two types: discriminative attribute and extractive attribute, there is a strong correlation between some attributes. Independent Modeling each attribute cannot use this information, which will lead to insufficient information that the model can learn. This paper proposes a unified framework for medical record attribute extraction based on ALBERT, uses a large amount of general corpus as external knowledge for pre-training and fine-tuning, and adopts multi-task learning to make all attributes share the underlying coding and train. Experiments show that this framework is greatly improved than the traditional LSTM-CRF model; it performs better in practical application scenarios.
Study on Optimized Dispatching of Warship Equipment Maintenance Task Based on Ant Colony Algorithm

Based on the analysis of warship maintenance resources, the optimized task of model of warship maintenance is founded, and the resolution procedure of model with ant colony algorithm is also presented. This method that pheromone update strengthen ants’ ability in searching for better path ensures problems better solved. Finally, the example verifies this method effective.
A community health volunteer delivered problem-solving therapy mobile application based on the Friendship Bench ‘Inuka Coaching’ in Kenya: A pilot cohort study

Abstract Background Sub-Saharan Africa (SSA) has the largest care gap for common mental disorders (CMDs) globally, heralding the use of cost-cutting approaches such as task-shifting and digital technologies as viable approaches for expanding the mental health workforce. This study aims to evaluate the effectiveness of a problem-solving therapy (PST) intervention that is delivered by community health volunteers (CHVs) through a mobile application called ‘Inuka coaching’ in Kenya. Methods A pilot prospective cohort study recruited participants from 18 health centres in Kenya. People who self-screened were eligible if they scored 8 or higher on the Self-Reporting Questionnaire-20 (SRQ-20), were aged 18 years or older, conversant in written and spoken English, and familiar with the use of smart mobile devices. The intervention consisted of four PST mobile application chat-sessions delivered by CHVs. CMD measures were administered at baseline, 4-weeks (post-treatment), and at 3-months follow-up assessment. Results In all, 80 participants consented to the study, of which 60 participants (female, n = 38; male, n = 22) completed their 4-week assessments, and 52 participants completed their 3-month follow-up assessment. The results showed a significant improvement over time on the Self-Reporting Questionnaire-20 (SRQ-20). Higher-range income, not reporting suicidal ideation, being aged over 30 years, and being male were associated with higher CMD symptom reduction. Conclusion To our knowledge, this report is the first to pilot a PST intervention that is delivered by CHVs through a locally developed mobile application in Kenya, to which clinically meaningful improvements were found. However, a randomised-controlled trial is required to robustly evaluate this intervention.
The Impact of Selected Risk Factors on The Occurrence of Highly Pathogenic Avian Influenza in Commercial Poultry Flocks in Poland

Abstract Introduction Introduction of highly pathogenic avian influenza virus (HPAIV) into a country and its further spread may have a devastating impact on the poultry industry and lead to serious economic consequences. Various risk factors may increase the probability of HPAI outbreak occurrence but their relative influence is often difficult to determine. The study evaluates how the densities of selected poultry species and proximity to the areas inhabited by wild birds impacted HPAI outbreak occurrence during the recently reported epidemics in Poland. Material and methods The analysis was developed using these risk factors in the locations of affected and randomly chosen unaffected commercial farms. Generalised linear and non-linear models, specifically logistic regression, classification tree and random forest, were used to indicate the most relevant risk factors, to quantify their association with HPAI outbreak occurrence, and to develop a map depicting spatial risk distribution. Results The most important risk factors comprised the densities of turkeys, geese and ducks. The abundance of these species of poultry in an area increased the probability of HPAI occurrence, and their farming intensity in several areas of central, western, eastern and northern Poland put these areas at the highest risk. Conclusion The results may improve the targeting of active surveillance, strengthen biosecurity in the areas at risk and contribute to early detection of HPAI in outbreak reoccurrences.
Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images

ABSTRACT Since DCNNs (deep convolutional neural networks) have been successfully applied to various academic and industrial fields, semantic segmentation methods, based on DCNNs, are increasingly explored for remote-sensing image interpreting and information extracting. It is still highly challenging due to the presence of irregular target shapes, and similarities of inter – and intra-class objects in large-scale high-resolution satellite images. A majority of existing methods fuse the multi-scale features that always fail to provide satisfactory results. In this paper, a dual attention deep fusion semantic segmentation network of large-scale satellite remote-sensing images is proposed (DASSN_RSI). The framework consists of novel encoder-decoder architecture, and a weight-adaptive loss function based on focal loss. To refine high-level semantic and low-level spatial feature maps, the deep layer channel attention module (DLCAM) and shallow layer spatial attention module (SLSAM) are designed and appended with specific blocks. Then the DUpsampling is incorporated to fuse feature maps in a lossless way. Peculiarly, the weight-adaptive focal loss (W-AFL) is inferred and embedded successfully, alleviating the class-imbalanced issue as much as possible. The extensive experiments are conducted on Gaofen image dataset (GID) datasets (Gaofen-2 satellite images, coarse set with five categories and refined set with fifteen categories). And the results show that our approach achieves state-of-the-art performance compared to other typical variants of encoder-decoder networks in the numerical evaluation and visual inspection. Besides, the necessary ablation studies are carried out for a comprehensive evaluation.
A numerical parametric study to enhance thermal hydraulic performance of a novel alternating offset oblique microchannel

Abstract A novel alternating offset oblique microchannel (AOOMC) is proposed aimed at enhancing the thermal–hydraulic performance (THP) in microchannels. 3-D, conjugate numerical simulations are carried out over a range of Reynolds number from 200 to 800. The effect of three geometric parameters, relative oblique channel width relative fin offset width and oblique angle on THP is evaluated. THP shows an increasing–decreasing trend with and increases with increase in and for the range tested. The optimal geometry, and showcases the highest THP of 2.41 at Re = 800 which is superior to other enhanced microchannel geometries from the literature.
Quantitative Structure‐Activity Relationship (QSAR) Analysis Using the Partial Least Squares (PLS) Method: The Binding of Polycyclic Aromatic Hydrocarbons (PAH) to the Rat Liver 2,3,7,8‐Tetrachlorodibenzo‐P‐Dioxin (TCDD) Receptor

It is demonstrated that it is possible to obtain quantitative structure-activity relationships from different molecular descriptors for the binding affinity of ten polycyclic aromatic hydrocarbons (PAH) for the rat liver 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) receptor, by the use of the partial least squares method. The variables used as descriptors range from theoretical and measured electronic properties to simple descriptors of the molecular geometry. A two component model, explaining 85% of the variance of the binding affinity of the ten PAH for the TCDD receptor is obtained when all descriptor variables are included in the analysis. When only molecular geometry descriptors are utilized in the modelling, a one component model explaining 75% of the variance is obtained.
Structure, physical, dielectric and magnetic properties of Zn1– x Co x O nanoparticles prepared via coprecipitation method

Abstract Zn1– x Co x O nanoparticles have been prepared by coprecipitation method. X-ray diffraction study confirms the polycrstalline nature with hexagonal wurtzite ZnO structure of the Zn1– x Co x O nanoparticles. The W–H analysis results confirms the mean crystallite size of the Co-doped samples was increased from 62 to 72 nm. All samples have nanocrystalline particles were investigated by field emission scanning electron microscopy. Optical absorption studies showed a red shift absorption spectra due to the incorporation of Co atoms in ZnO lattice. Dielectric constant and dielectric loss were decreasing with increasing Co content. Magnetic hysteresis loops of prepared nanoparticles exhibit paramagnetic behavior at room temperature.