Miracle-dz/UniFAR-FTUs huggingface dataset:
Unravelling the contribution of complex trauma to psychopathology and cognitive deficits: a cohort study

Background Complex traumas are traumatic experiences that involve multiple interpersonal threats during childhood or adolescence, such as repeated abuse. These traumas are hypothesised to cause more severe psychopathology and poorer cognitive function than other non-complex traumas. However, empirical testing has been limited to clinical/convenience samples and cross-sectional designs. Aims To investigate psychopathology and cognitive function in young people exposed to complex, non-complex or no trauma, from a population-representative longitudinal cohort, and to consider the role of pre-existing vulnerabilities. Method Participants were from the Environmental Risk Longitudinal Twin Study, a population-representative birth cohort of 2232 British children. At age 18 years (93% participation), we assessed lifetime exposure to complex and non-complex trauma, past-year psychopathology and current cognitive function. We also prospectively assessed early childhood vulnerabilities: internalising and externalising symptoms at 5 years of age, IQ at 5 years of age, family history of mental illness, family socioeconomic status and sex. Results Participants exposed to complex trauma had more severe psychopathology and poorer cognitive function at 18 years of age, compared with both trauma-unexposed participants and those exposed to non-complex trauma. Early childhood vulnerabilities predicted risk of later complex trauma exposure, and largely explained associations of complex trauma with cognitive deficits, but not with psychopathology. Conclusions By conflating complex and non-complex traumas, current research and clinical practice underestimate the severity of psychopathology, cognitive deficits and pre-existing vulnerabilities linked with complex trauma. A better understanding of the mental health needs of people exposed to complex trauma could inform the development of new, more effective interventions.
Red Flower Hell: a Minecraft MALMÖ Challenge to Support Introductory Programming Courses ∗

The Red Flower Hell challenge is designed for undergraduate courses, such as introductory programming or artificial intelligence, in which students can learn in a competitive way. The name Red Flower Hell comes from the objective of the challenge because the agent programs to be developed must collect as many red flowers as possible in a battle royale-like gorge in Minecraft created directly for this challenge. The battle royale feature is given by that lava flows down the hillsides in the gorge. In the first round of the challenge, agents only have to deal with lava. In the second round, agents also have to fight various Minecraft monsters, such as zombies or spiders. The agent versus agent fight has not yet been implemented. We have used Project MALMÖ for implementing Red Flower Hell. It is a Minecraft mod created by Microsoft for researching artificial general intelligence. The Red Flower Hell challenge was tested at a High-level programming language course at
Deep learning frameworks for structural topology optimization

Topology optimization has emerged as a popular approach to refine a component’s design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component’s performance during the optimization process. Recently, machine learning-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single machine learning model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend the current approaches to higher resolutions. In this thesis, we explore deep learning-based frameworks that are consistent with traditional topology optimization algorithms for three-dimensional topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each trying to learn a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better than current ML-based topology optimization methods.
PU21: A novel perceptually uniform encoding for adapting existing quality metrics for HDR

Standard image quality metrics, such as PSNR or SSIM, cannot be directly computed on linear high dynamic range colour values because such values non-linearly related to our perception of visible differences. In this work, we develop a new encoding function (PU21) to convert absolute high dynamic range (HDR) linear colour values into approximately perceptually uniform (PU) values, which can be used with standard quality metrics. The proposed PU21 function is based on a recent contrast sensitivity model, fitted to the measurements up to 10000cd/m2. Unlike the conventional simplified approach of deriving PU functions based on peak sensitivities, we model realistic coding artefacts to find visibility thresholds for our derivation. Furthermore, the new PU accounts for the effect of glare on image quality. The proposed PU21 improves the accuracy of quality predictions for standard metrics of PSNR, VSI, FSIM, SSIM, and MS-SSIM in their correlation with subjective scores on HDR images included in UPIQ, one of the largest HDR image quality datasets.
Anomaly detection in complex software architectures

Anomaly detection is a crucial aspect of software architecture maintenance and building a stable system. With early problem detection operators can react quickly to reduce potential downtime risk resulting in data and money saving, therefore a reliable real-time anomaly detection system is highly desired. Unfortunately, currently used monitoring techniques are lacking behind fast-growing industry applications and scale of used architectures. This thesis aims at solving a problem of a renown company to design a new end-to-end solution for anomaly detection. We reviewed and discuss the best practices of designing such monitoring system, what anomaly detection techniques can be used, what metrics and features to collect and how to represent them. Collected logs and metrics by our system were preprocessed and released as a research dataset together with this work. The dataset records several days of anonymized runtime behaviour of 2 architectures with expert annotations of anomalous behaviour based on expert experience. We identified potential weaknesses of current state-of-the-art models and propose a modification called Timed workflow inference to address this issue. We also designed and implemented a new graph-based model Timed graph workflow to generalize some strict rules of other solutions. Our models were experimentally evaluated with other state-ofthe-art anomaly detection models using our dataset. The proposed solutions proved to be competitive and in several aspects even better.
Query Answerability Classifier for Direct Answer Module in Web Search Engines

In order determine when we can show direct answer module to user queries in web search engine, an independent classifier is designed in this study to assess the answerability of each user query. Real user queries are sampled from MS MARCO Question Answering and Natural Langauge Generation dataset [1] and manually labelled with query answerability to train and evaluate the classifier. As a result, the XGboost model has an overall better performance than the random forest model with prediction accuracy score 0.83 and F1 score 0.89. Once the classifier determines the user query is answerable, a MRC model may be used to find the direct answer within provided passages. Else, no direct answer shall be provided to this query.
Running heading: EFFECT OF A GAP-YEAR

Existing gap-year research indicates a number of benefits of a gap-year at the end of school and before university enrollment. Lifespan theory of control, however, suggests that direct goal investment, rather than delay, at developmental transitions is associated with more adaptive outcomes. Comparing these perspectives, we undertake two studies: one in Finland (N = 384, waves = 3) and one in Australia (N = 2259, waves = 5) both with an initial time wave in the last year of high-school. We explore the effects of a gap-year on both psychological and attainment outcomes using an extensive propensity score matching technique. The Finnish study found no difference in growth in goal commitment, effort, expectations of attainment and strain, or in actual university enrollment in those planning to enter university directly versus those who plan to take a gap-year after matching. The Australian study found no difference in growth in outlooks for the future and career prospects, and life satisfaction between gap-year youth and direct university entrants. However, the study did find that gap-year students were more likely to drop out of a university degree. Implication for theory and practice are discussed. Supplementary material: https://pdparker.github. io/gapyear
Machine Learning Methods for Quality Prediction in Manufacturing Inspection

The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from manufacturing production processes. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. Data pertaining to processes performed on a multi-model production line would contain significantly more features than that of an isolated process. The difficulty of analyzing such a large dataset makes it ideal for the application of data mining techniques to derive useful knowledge. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.
Efficacy and safety of cyclosporine in the management of coronavirus disease 2019: A protocol for systematic review and meta-analysis

Introduction Cyclosporine may improve the clinical course and outcomes of Coronavirus disease 2019 (COVID-19) due to its antiviral and anti-cytokine effects as shown in vitro. A few ongoing trials are exploring the benefit of adding it to the standard of care (SOC) of COVID-19 patients. Objectives The primary objective is to evaluate the severity of COVID-19, determined by oxygen saturation, intensive care unit (ICU) admission, or the World Health Organization COVID-19 clinical severity scale in patients treated with oral or intravenous cyclosporine added to SOC compared SOC alone or placebo. Secondary objectives include mortality, length of hospitalization, length of ICU stay, and laboratory measurements as well as the safety outcomes of cyclosporine. Methodology A systematic review and meta-analysis of randomized clinical trials and observational studies that compared cyclosporine to placebo or SOC in COVID-19 patients will be conducted. PubMed, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials, Google Scholar, and ClinicalTrials.gov will be explored for studies that satisfy pre-specified inclusion criteria. Quality assessment of all included studies will be performed. Meta-analyses will be done utilizing random effect models to estimate the effect of cyclosporine on the severity of COVID-19. Heterogeneity will be assessed utilizing Q statistics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines will be followed. Results The result of this synthesis will inform potential changes in the management of COVID-19 patients, especially regarding the role of calcineurin inhibitors. Additionally, it will serve as hypothesis generating for potential future prospective studies.
Investigation on the current situation of stroke patients in northernern Henan Province of China

Objectives: To analyze the basic situations and clinical characteristics of stroke patients in our hospital from May 2018 to April 2021, lay a foundation for the prevention and reasonable treatment of stroke patients in northern Henan Province. Methods: The basic information of 835 stroke patients in our hospital was collected and classified according to age, gender, bad habits, accompanied diseases and drug use before admission to hospital, and severity of the stroke patients were also evaluated according to mRS scoring standard. Results: A total of 835 stroke patients were collected from May 2018 to April 2021 in our hospital. The age range of stroke patients was 28-95 years old, 96.29% stroke patients was above 40 years old; there were 202 stroke patients with smoking history and 225 stroke patients with drinking history; Among the 835 stroke patients, hypertension, cerebral infarction and diabetes mellitus were the main accompanied diseases. Antihypertensive drugs (506 cases), antiplatelet drugs (208 cases), statins (173 cases) and antidiabetic drugs (143 cases) were the main therapeutic drugs in stroke patients before admission in the northern Henan Province; the results of mRS scoring standard showed that among 835 stroke patients, there were 609 cases with milder symptoms, accounting for 82.84% (there were 330 stroke patients with 1 points, 279 stroke patients with 2 points, and 83 stroke patients with 3 points), and 120 cases with severe symptoms, accounting for 14.37% (55 cases with 4 points, 65 cases with 5 points). Conclusion: The age of stroke patients in northern Henan Province was mainly over 40 years old, most of stroke patients were in the early stage of stroke; smoking, drinking, hypertension and diabetes mellitus were main risk factors of stroke. And there was a sex difference between male stroke patients and female stroke patients in stroke risk factors smoking and hypertension. those data may help us for active prevention and rational drug use for stroke in clinic.
Dynamic, stage-course protein interaction network using high power CpG sites in Head and Neck Squamous Cell Carcinoma

Head and neck cancer is the sixth leading cause of cancer across the globe and is significantly more prevalent in South Asian countries, including Pakistan. Prediction of pathological stages of cancer can play a pivotal role in early diagnosis and personalized medicine. This project ventures into the prediction of different stages of head and neck squamous cell carcinoma (HNSCC) using prioritized DNA methylation patterns. DNA methylation profiles for each HNSCC stage (stage-I- IV) were used to extensively analyze 485,577 methylation CpG sites and prioritize them on the basis of the highest predictive power using a wrapper-based feature selection method, along with different classification models. We identified 68 high-power methylation sites which predicted the pathological stage of HNSCC samples with 90.62 % accuracy using a Random Forest classifier. We set out to construct a protein-protein interaction network for the proteins encoded by the 67 genes associated with these sites to study its network topology and also undertook enrichment analysis of nodes in their immediate neighborhood for GO and KEGG Pathway annotations which revealed their role in cancer-related pathways, cell differentiation, signal transduction, metabolic and biosynthetic processes. With information on the predictive power of each of the 67 genes in each HNSCC stage, we unveil a dynamic stage-course network for HNSCC. We also intend to further study these genes in light of functional datasets from CRISPR, RNAi, drug screens for their putative role in HNSCC initiation and progression.
Genome-wide contribution of common short-tandem repeats to Parkinson's disease genetic risk

Parkinson's disease (PD) is a complex neurodegenerative disorder with a strong genetic component, where most known disease-associated variants are single nucleotide polymorphisms (SNPs) and small insertions and deletions (Indels). DNA repetitive elements account for >50% of the human genome, however little is known of their contribution to PD etiology. While select short tandem repeats (STRs) within candidate genes have been studied in PD, their genome-wide contribution remains unknown. Here we present the first genome-wide association study (GWAS) of STRs in PD. Through a meta-analysis of 16 imputed GWAS cohorts from the International Parkinson's Disease Genomic Consortium (IPDGC), totalling 39,087 individuals (16,642 PD cases and 22,445 controls of European ancestry) we identified 34 genome-wide significant STR loci (p < 5.34x10-6), with the strongest signal located in KANSL1 (chr17:44205351:[T]11, p=3x10-39, OR=1.31 [CI 95%=1.26-1.36]). Conditional-joint analyses suggested that 4 significant STRs mapping nearby NDUFAF2, TRIML2, MIRNA-129-1 and NCOR1 were independent from known PD risk SNPs. Including STRs in heritability estimates increased the variance explained by SNPs alone. Gene expression analysis of STRs (eSTR) in RNASeq data from 13 brain regions, identified significant associations of STRs influencing the expression of multiple genes, including PD known genes. Further functional annotation of candidate STRs revealed that significant eSTRs within NUDFAF2 and ZSWIM7 overlap with regulatory features and are associated with change in the expression levels of nearby genes. Here we show that STRs at known and novel candidate PD loci contribute to PD risk, and have functional effects in disease-relevant tissues and pathways, supporting previously reported disease-associated genes and giving further evidence for their functional prioritization. These data represent a valuable resource for researchers currently dissecting PD risk loci.
A classification model to predict specialty drug use

ABSTRACT Objective Predicting who is likely to become utilizers of specialty drugs allows care managers to have an early intervention and payers to have financial preparation for the upcoming spending. Our administrative claims-based predictive model is to predict the members who might use specialty drugs. Materials and Methods A national database* and a commercial health plan claim data were used to select a total 6.5 million people who were not taking any specialty drugs before the Target Prediction Window. There were about 136,700 members who were older than 65 in the study. We extracted 81 features from past history of medical, pharmacy claims, and demographic data to predict the specialty drug use in the following year. Members having at least three-month continuous enrollment either under medical or pharmacy plan in the previous year immediately before the start of the target prediction window and with no specialty drug taking history were eligible for this study. We trained and tuned on 75% of the data using an extreme gradient boosting binary classifier. We used the remaining 25% of the data to predict the outcomes and evaluate the performance. We also recorded the performance for the age group older than 65 years old. Results There were 3% of members who used specialty drugs in the cohort under the current study. The important features for prediction included age, monthly pharmacy payment, monthly medical payment, diseases, procedure, or drug-related codes. On the test data with members of all ages, model performance for the area under the receiving operator characteristics curve (AUROC) was 78.6%. For the test set on members older than 65 (prevalence rate 3.6%), we had an AUROC of 79.3%. Discussion There is no similar machine learning model in the field to predict specialty drug use. Our model provides an unparalleled opportunity to allow early intervention for people who might develop diseases that require specialty drug use. It is also important for health plans and providers to know their covered population who might use specialty drugs and predict the increased cost in the next year. Conclusion A predictive model of specialty drug use can be helpful for both payers and providers to prepare for a spending spike or have an early intervention. In return, this helps to improve patients' overall satisfaction.
Tagitinin C induces ferroptosis through PERK-Nrf2-HO-1 signaling pathway in colorectal cancer cells

Rationale: Colorectal cancer (CRC) is a common malignant tumor of the digestive system. However, the efficacy of surgery and chemotherapy is limited. Ferroptosis is an iron- and reactive oxygen species (ROS)-dependent form of regulated cell death (RCD) and plays a vital role in tumor suppression. Ferroptosis inducing agents have been studied extensively as a novel promising way to fight against therapy resistant cancers. The aim of this study is to investigate the mechanism of action of tagitinin C (TC), a natural product, as a novel ferroptosis inducer in tumor suppression. Methods: The response of CRC cells to tagitinin C was assessed by cell viability assay, clonogenic assay, transwell migration assay, cell cycle assay and apoptosis assay. Molecular approaches including Western blot, RNA sequencing, quantitative real-time PCR and immunofluorescence were employed as well. Results: Tagitinin C, a sesquiterpene lactone isolated from Tithonia diversifolia, inhibits the growth of colorectal cancer cells including HCT116 cells, and induced an oxidative cellular microenvironment resulting in ferroptosis of HCT116 cells. Tagitinin C-induced ferroptosis was accompanied with the attenuation of glutathione (GSH) levels and increased in lipid peroxidation. Mechanistically, tagitinin C induced endoplasmic reticulum (ER) stress and oxidative stress, thus activating nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2). As a downstream gene (effector) of Nrf2, heme oxygenase-1 (HO-1) expression increased significantly with the treatment of tagitinin C. Upregulated HO-1 led to the increase in the labile iron pool, which promoted lipid peroxidation, meanwhile tagitinin C showed synergistic anti-tumor effect together with erastin. Conclusion: In summary, we provided the evidence that tagitinin C induces ferroptosis in colorectal cancer cells and has synergistic effect together with erastin. Mechanistically, tagitinin C induces ferroptosis through ER stress-mediated activation of PERK-Nrf2-HO-1 signaling pathway. Tagitinin C, identified as a novel ferroptosis inducer, may be effective chemosensitizer that can expand the efficacy and range of chemotherapeutic agents.