Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of a pyroptosis-related classifier for risk prediction of acute myocardial infarction.

BACKGROUND Acute myocardial infarction (AMI) is a common cardiovascular disease that has a high mortality. Pyroptosis is a programmed cell death mediated by inflammasome. It remains to be clarified on the expression pattern and risk predictive role of pyroptosis-related genes in AMI. METHODS The gene expression data were extracted from the Gene Expression Omnibus (GEO), and pyroptosis-related genes were obtained from published articles. Pyroptosis-related differential expressed genes were selected between normal and AMI samples and then we explored their immune infiltration level using CIBERSORT. Univariate Cox and LASSO regression were applied to establish a classifier based on pyroptosis-related genes. ROC analysis was utilized to evaluate the classifier. RESULTS In this study, we obtained 20 pyroptosis-related genes which showed differential expression in AMI and normal samples. Among the differential expressed genes, GZMB was significantly positively associated with activated NK cells (R = 0.71, p < 0.01), while NLRP3 exhibited a negative correlation with resting NK cells (R = -0.66, p < 0.01). 9 genes (NLRP9, GSDMD, CASP8, AIM2, GPX4, NOD1, NOD2, SCAF11, GSDME) were eventually identified as a predictive risk classifier for AMI patients. With the classifier, patients at high and low risk could be discriminated. Further external validation showed the high accuracy of the classifier (AUC = 0.75). CONCLUSIONS Pyroptosis-related genes are closely related to immune infiltration in AMI, and a 9-gene classifier has good performance in predicting the risk of AMI with high accuracy, which could provide a new way for targeted treatment in AMI.
Spatial–temporal dynamics and recovery mechanisms of dried soil layers under Robinia pseudoacacia forest based on in‐situ field data from 2017 to 2020

Planting Robinia pseudoacacia in water‐limited regions can promote soil and water conservation and ecological service functions. However, it can cause the formation of below ground dried soil layers (DSLs), causing land degradation and tree mortality. To ascertain the spatial–temporal dynamics and recovery processes of DSLs, we monitored the deep soil water content (SWC) to a depth of 500 cm at 27 sites on a typical R. pseudoacacia forest from 2017 to 2020 and calculated the evaluation indices of DSLs based on plant‐available SWC (PASWC) and stable field capacity (SFC). Compared with PASWC‐identified DSLs, the degree of SFC‐identified DSLs was more severe, although the spatial–temporal characteristics were similar. Severe soil desiccation was identified as 79% of the 500 cm profile dried out below 101 cm. The mean thickness of DSL and mean SWC within a DSL were 396 cm and 9.0%, respectively, and the quantitative index reached Grade III (severe) DSLs. All DSL indices demonstrated weak or moderate variability in space and strong variability in time. The heterogeneity of rainfall events and microtopographic positions explain the spatial–temporal variation of DSL indices well. A heavy rainfall event (>50 mm) triggered the disappearance of DSLs at some sites, indicating the possibility of DSLs recovery based on field observations under natural condition. Microtopography (such as the swales) provides advantageous conditions to the recovery of DSLs by redistributing rainfall flow at the local scale. These results provide insights into DSLs recovery, which is useful for land development and rehabilitation in planted forests.
A Bayesian Approach for Benefit-Risk Assessment

An important aspect of the drug evaluation process is to have an integrated benefit-risk assessment to determine, using some quantitative measures, whether the benefit outweighs the risk for the target population. Chuang-Stein et al. proposed a five-category random variable along with three global measures of benefit-risk assessment. Assuming the cell probabilities follow a multinomial distribution, we propose a Bayesian approach for the longitudinal assessment of benefit-risk using these three global measures and a new measure. A Dirichlet distribution is used as the natural conjugate prior for multinomial cell probabilities, and the posterior distributions of cell-probabilities are recursively derived as the data from multiple visits become available. In a more generalized approach, a power prior is used through the likelihood function to discount the information from previous visits, and, again, the posterior distributions of the cell-probabilities at multiple visits are derived. The estimates of the posterior means and credible intervals for the four global measures are derived, and the decision rules based on the credible intervals are applied for the assessment of the four global measures. Using two simulated datasets generated under two different scenarios—one where benefit outweighs risk and the other where benefit does not outweigh risk—the performances of the four measures are evaluated using a Markov chain Monte Carlo (MCMC) technique. We illustrate of the methodology using clinical trial data.
Transform, Warp, and Dress: A New Transformation-guided Model for Virtual Try-on

Virtual try-on has recently emerged in computer vision and multimedia communities with the development of architectures that can generate realistic images of a target person wearing a custom garment. This research interest is motivated by the large role played by e-commerce and online shopping in our society. Indeed, the virtual try-on task can offer many opportunities to improve the efficiency of preparing fashion catalogs and to enhance the online user experience. The problem is far to be solved: current architectures do not reach sufficient accuracy with respect to manually generated images and can only be trained on image pairs with a limited variety. Existing virtual try-on datasets have two main limits: they contain only female models, and all the images are available only in low resolution. This not only affects the generalization capabilities of the trained architectures but makes the deployment to real applications impractical. To overcome these issues, we present Dress Code, a new dataset for virtual try-on that contains high-resolution images of a large variety of upper-body clothes and both male and female models. Leveraging this enriched dataset, we propose a new model for virtual try-on capable of generating high-quality and photo-realistic images using a three-stage pipeline. The first two stages perform two different geometric transformations to warp the desired garment and make it fit into the target person’s body pose and shape. Then, we generate the new image of that same person wearing the try-on garment using a generative network. We test the proposed solution on the most widely used dataset for this task as well as on our newly collected dataset and demonstrate its effectiveness when compared to current state-of-the-art methods. Through extensive analyses on our Dress Code dataset, we show the adaptability of our model, which can generate try-on images even with a higher resolution.
Object-less Vision-language Model on Visual Question Classification for Blind People

Despite the long-standing appearance of question types in the Visual Question Answering dataset, Visual Question Classification does not received enough public interest in research. Different from general text classification, a visual question requires an understanding of visual and textual features simultaneously. Together with the enthusiasm and novelty of Visual Question Classification, the most important and practical goal we concentrate on is to deal with the weakness of Object Detection on object-less images. We thus propose an Object-less Visual Question Classification model, OL–LXMERT, to generate virtual objects replacing the dependence of Object Detection in previous Vision-Language systems. Our architecture is effective and powerful enough to digest local and global features of images in understanding the relationship between multiple modalities. Through our experiments in our modified VizWiz-VQC 2020 dataset of blind people, our Object-less LXMERT achieves promising results in the brand-new multi-modal task. Furthermore, the detailed ablation studies show the strength and potential of our model in comparison to competitive approaches.
CruParamer: Learning on Parameter-Augmented API Sequences for Malware Detection

Learning on execution behaviour, i.e., sequences of API calls, is proven to be effective in malware detection. In this paper, we present CruParamer, a deep neural network based malware detection approach for Windows platform that performs learning on sequences of parameter-augmented APIs. It first employs rule-based and clustering-based classification to assess the sensitivity of a parameter to malicious behaviour, and further labels the API following the run-time parameters with varying degrees of sensitivities. Then, it encodes the APIs by concatenating the native embedding and the sensitive embedding of labelled APIs, for characterizing the relationship between successive labelled APIs and their correspondence in terms of security semantics. Finally, it feeds the sequences of API embedding into the deep neural network for training a binary classifier to detect malware. In addition to presenting the design, we have implemented CruParamer and evaluated it on two datasets. The results demonstrate that CruParamer outperforms naïve models when taking raw APIs as input, proving the effectiveness of CruParamer. Moreover, we have evaluated the impact of mimicry and adversarial attacks on our model, and the results verify the robustness of CruParamer.
Structural Changes in Monolayer Cobalt Oxides under Ambient Pressure CO and O2 Studied by In Situ Grazing-Incidence X-ray Absorption Fine Structure Spectroscopy

We have used grazing incidence X-ray absorption fine structure spectroscopy at the cobalt K-edge to characterize monolayer CoO films on Pt(111) under ambient pressure exposure to CO and O2, with the aim of identifying the Co phases present and their transformations under oxidizing and reducing conditions. X-ray absorption near edge structure (XANES) spectra show clear changes in the chemical state of Co, with the 2+ state predominant under CO exposure and the 3+ state predominant under O2-rich conditions. Extended X-ray absorption fine structure spectroscopy (EXAFS) analysis shows that the CoO bilayer characterized in ultrahigh vacuum is not formed under the conditions used in this study. Instead, the spectra acquired at low temperatures suggest formation of cobalt hydroxide and oxyhydroxide. At higher temperatures, the spectra indicate dewetting of the film and suggest formation of bulklike Co3O4 under oxidizing conditions. The experiments demonstrate the power of hard X-ray spectroscopy to probe the structures of well-defined oxide monolayers on metal single crystals under realistic catalytic conditions.
Two Types of Liposomal Formulations Improve the Therapeutic Ratio of Prednisolone Phosphate in a Zebrafish Model for Inflammation

Glucocorticoids (GCs) are effective anti-inflammatory drugs, but their clinical use is limited by their side effects. Using liposomes to target GCs to inflammatory sites is a promising approach to improve their therapeutic ratio. We used zebrafish embryos to visualize the biodistribution of liposomes and to determine the anti-inflammatory and adverse effects of the GC prednisolone phosphate (PLP) encapsulated in these liposomes. Our results showed that PEGylated liposomes remained in circulation for long periods of time, whereas a novel type of liposomes (which we named AmbiMACs) selectively targeted macrophages. Upon laser wounding of the tail, both types of liposomes were shown to accumulate near the wounding site. Encapsulation of PLP in the PEGylated liposomes and AmbiMACs increased its potency to inhibit the inflammatory response. However, encapsulation of PLP in either type of liposome reduced its inhibitory effect on tissue regeneration, and encapsulation in PEGylated liposomes attenuated the activation of glucocorticoid-responsive gene expression throughout the body. Thus, by exploiting the unique possibilities of the zebrafish animal model to study the biodistribution as well as the anti-inflammatory and adverse effects of liposomal formulations of PLP, we showed that PEGylated liposomes and AmbiMACs increase the therapeutic ratio of this GC drug.
Functional and Structural Networks Decoupling in GTCS and Its Reorganization by Drugs

Objective:The present study aims to investigate the potential disturbance of functional and structural large-scale networks in drug-naive patients with generalized tonic-clonic seizures (GTCS) and the effects of drugs-receiving.Methods:In this study, 41 patients with GTCS, including 21 drug-naive patients and 20 patients with antiepileptic drugs (AEDs), and 29 healthy controls were recruited to construct large-scale brain networks based on the resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor image (DTI). The structural and functional connectivity and the coupling between structure and function were further investigated to identify the network features that responded to drugs.Results:Drug-naive patients showed extensively enhanced functional and structural connections compared with controls. Especially, abnormally enhanced connections between the default mode network (DMN) and the frontal parietal network (FPN) were observed. Besides, drug-receiving patients showed similar functional connection strength to that of the control group. However, drug-receiving and drug-naive patients demonstrated similar alterations in the structural network. Moreover, the structural and functional coupling was decreased in connections within DMN and DMN with other networks, and drug-receiving could reverse the decrease.Significance:These findings identified the pattern alteration of structural and functional connectivity in GTCS patients. AEDs treatment benefit for the seizures might originate from the effects of the functional network rather than the structural network. However, it can improve the abnormal brain network by affecting the coupling relationship. The coupling of structural and functional connectivity might be used to evaluate the efficacy of AEDs.
3D Chitosan-Gallic Acid Complexes: Assessment of the Chemical and Biological Properties

Three-dimensional chitosan-gallic acid complexes were proposed and prepared for the first time by a simple adsorption process of gallic acid (GA) on three-dimensional chitosan structures (3D chitosan). Highly porous 3D devices facilitate a high GA load, up to 2015 mmol/kg at pH 4.0. The preservation of the redox state of GA released from 3D chitosan was confirmed by spectroscopic analyses. The antioxidant activity of 3D chitosan-GA complexes was assessed using the DPPH radical scavenging assay and was found to be dramatically higher than that of free chitosan. The mechanical property of 3D chitosan–GA complexes was also evaluated using a compression test. Finally, 3D chitosan–GA complexes showed a significant antimicrobial capacity against E. coli and S. aureus, selected, respectively, as a model strain for Gram-negative and Gram-positive bacteria. Our study demonstrated a new, simple, and eco-friendly approach to prepare functional chitosan-based complexes for nutraceutical, cosmeceutical, and pharmaceutical applications.
A study of the validity of three major business excellence models in the Asia Pacific region

The validity of three key Business Excellence (BE) models used in the Asia Pacific Region – the Australian Business Excellence Framework (ABEF), the Baldrige Criteria for Performance Excellence (BCPE), and the Singapore Quality Award Criteria (SQAC) – was studied based on data (item scores) of past applicants of national quality/BE awards. This study greatly enhances understanding of the following: (i) how measurement items in different BE models are related to their assigned categories (constructs); (ii) evidence that there exists a theory underlying BE criteria; and (iii) how theoretical models can be interpreted, from a practical perspective. We found that in general, the level of evidence of measurement validity of the ABEF was lower, compared to the other two BE models. It is recommended that more attention is given to enhancing the precision of measurement items in future model revisions. The heuristics used in this analysis can be applied as a generic method in many other studies.
Pediatric Diabetes on the Rise: Trends in Incident Diabetes During the COVID-19 Pandemic

Abstract Context The effects of the coronavirus disease 2019 (COVID-19) pandemic on the incident cases of pediatric type 1 diabetes (T1D) and type 2 diabetes (T2D) are not clear. Objective To identify trends in incidence and presentation of pediatric new-onset T1D and T2D during the COVID-19 pandemic. Methods A retrospective chart review was conducted. Demographics, anthropometrics, and initial laboratory results from patients ages 0 through 21 years who presented with new-onset diabetes to a pediatric tertiary care center were recorded. Results During the pandemic, incident cases of pediatric T1D increased from 31 in each of the prior 2 years to 46; an increase of 48%. Incident cases of pediatric T2D increased by 231% from 2019 to 2020. The number of incident cases of pediatric T2D increased significantly more than the number of incident cases of pediatric T1D (P = 0.009). Patients with T2D were more likely to present in diabetic ketoacidosis (DKA), though this was not statistically significant (P = 0.093). Severe DKA was higher compared with moderate DKA (P = 0.036) in incident cases of pediatric T2D. During the pandemic, for the first time, incident cases of T2D accounted for more than one-half of all newly diagnosed pediatric diabetes cases (53%). Conclusions There were more incident pediatric T1D and T2D cases as well as an increase in DKA severity in T2D at presentation during the COVID-19 pandemic. More importantly, incident T2D cases were higher than the incident T1D during the pandemic. This clearly suggests a disruption and change in the pediatric diabetes trends with profound individual and community health consequences.
Psychological first aid (PFA) and disasters

Abstract A disaster is commonly thought of as an occurrence that results in property damage and physical injuries that exceed the response capabilities of local resources. History teaches that disasters also result in a surge in demand for psychological support amongst survivors and disaster responders. This surge quickly exceeds local response capacities and has the potential to exceed even the mental health resources that may be imported from neighbouring jurisdictions and disaster relief agencies. Efficient and effective acute mental health intervention is, therefore, needed. However, the effectiveness of traditional multi-session counselling during and shortly after disasters has been questioned. Instead, the utilization of efficient and effective crisis-focussed psychological interventions has been suggested as acute phase alternatives. This paper asserts psychological first aid (PFA) may be considered a specific crisis-focussed disaster mental health intervention for use during and after disasters. PFA is designed for use in assessing and mitigating acute distress, while serving as a platform for psychological triage complementing more traditional psychological and psychiatric interventions. PFA may be employed by mental health clinicians as well as ‘peer responders’.
Tivozanib (T) as first-line (1L) treatment of metastatic renal cell carcinoma (mRCC): A real-world outcome review in North-West of England (NWE), UK.

335 Background: The TIVO-1 study demonstrated improved PFS in mRCC patients (pts) undergoing 1L treatment with T compared with sorafenib. We previously presented outcomes of our experience of T in 4 cancer centres in NWE. Here we present updated outcomes with longer follow-up (FU). Methods: mRCC pts commencing 1L T March 2017- May 2019 were identified. Outcomes of interest included overall response rate (ORR), survival (OS, PFS) and toxicity. Data cut off for this update was 30/11/2020. Results: 113 pts were identified with characteristics as in the table. Median FU was 25.9 mo (18.3-44.7mo), 18% pts remain on treatment at data cut-off. 88.5% commenced T at full dose, of which 67% maintained dose intensity. 11.5% commenced T with dose reduction due to prior TKI toxicities or comorbidities. Median number of cycles was 7 (1-33). In terms of efficacy, ORR was 29% (CR 0%, PR29%, SD39%, PD26%, NE 6%) and median PFS was 9.0 months (95% C.I. 6.0-12.1mo). Median PFS by IMDC risk group was: F= 23.0 months (95% C.I. 7.6-38.4mo); I= 10.0 months (95% C.I. 6.3-13.7mo); P= 3.0 months (95% C.I. 1.5.- 4.5mo), p value<0.0001. Median OS was 25.0 months (95% C.I. 15.4-34.6mo). Median OS by IMDC risk group was: F= NR with 72% alive at data cut-off; I= 26.0 months (95% C.I. 17.9-34.1mo); P= 7.0 months (95% C.I. 4.4-9.6mo), p value<0.0001. Adverse events (AEs) of any grade occurred in 77% (≥G3 13%) including fatigue 42% (≥G3 0%), diarrhoea 19% (≥G3 < 1%), mucositis 24% ( ≥G3 2%), anorexia 12% (≥G3 < 1%); and hypertension 7% (≥G3 2%). Notable ≥G3 events included abnormal liver function 3%; vascular events 2% and seizure < 1%. 18% of patients discontinued treatment due to toxicity with no treatment-related deaths. Conclusions: Our real world experience of 1L T suggests comparable activity with randomized data and other TKIs, particularly in F and I IMDC risk groups. The low incidence of serious AEs makes it an attractive option if unsuitable for combination therapies. [Table: see text]
Cross clinical-experimental-computational qualification of in silico drug trials on human cardiac purkinje cells for proarrhythmia risk prediction

The preclinical identification of drug-induced cardiotoxicity and its translation into human risk are still major challenges in pharmaceutical drug discovery. The ICH S7B Guideline and Q&A on Clinical and Nonclinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential promotes human in silico drug trials as a novel tool for proarrhythmia risk assessment. To facilitate the use of in silico data in regulatory submissions, explanatory control compounds should be tested and documented to demonstrate consistency between predictions and the historic validation data. This study aims to quantify drug-induced electrophysiological effects on in silico cardiac human Purkinje cells, to compare them with existing in vitro rabbit data, and to assess their accuracy for clinical pro-arrhythmic risk predictions. The effects of 14 reference compounds were quantified in simulations with a population of in silico human cardiac Purkinje models. For each drug dose, five electrophysiological biomarkers were quantified at three pacing frequencies, and results compared with available in vitro experiments and clinical proarrhythmia reports. Three key results were obtained: 1) In silico, repolarization abnormalities in human Purkinje simulations predicted drug-induced arrhythmia for all risky compounds, showing higher predicted accuracy than rabbit experiments; 2) Drug-induced electrophysiological changes observed in human-based simulations showed a high degree of consistency with in vitro rabbit recordings at all pacing frequencies, and depolarization velocity and action potential duration were the most consistent biomarkers; 3) discrepancies observed for dofetilide, sotalol and terfenadine are mainly caused by species differences between humans and rabbit. Taken together, this study demonstrates higher accuracy of in silico methods compared to in vitro animal models for pro-arrhythmic risk prediction, as well as a high degree of consistency with in vitro experiments commonly used in safety pharmacology, supporting the potential for industrial and regulatory adoption of in silico trials for proarrhythmia prediction.