Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating indicators of job performance: Distributions and types of analyses

Distributions o f job performance indicators have historically been assumed to be normally distributed (Aguinis & O'Boyle, 2014; Schmidt & Hunter, 1983; Tiffin, 1947). Generally, any evidence to the contrary has been attributed to errors in the measurement o f job performance (Murphy, 2008). A few researchers have been skeptical o f this assumption (Micceri, 1989; Murphy, 1999; Saal, Downey, & Lahey, 1980); yet, only recently has research demonstrated that in certain specific situations job performance is exponentially distributed (Aguinis, O'Boyle, Gonzalez-Mule, & Joo, 2016; O'Boyle & Aguinis, 2012). To date there have been few recommendations in the Industrial-Organizational Psychology literature about how to evaluate distributions o f job performance to determine whether they fit an exponential curve. There also has not been substantial justification in the literature as to why distributions o f job performance would be expected to be normally distributed versus exponentially distributed. Furthermore, recent research about job performance distributions has narrowly focused only on a few specific types o f work and on a few specific indicators o f performance. Thus, research concerning distributions o f job performance indicators is, to date, o f limited generalizability. The current research attempts to close the gaps in the literature by identifying high fidelity methods and applying them to classify distributions o f various indicators o f job performance on a continuous spectrum from normal to exponential. In this research, multiple types o f indicators o f performance (and indices computed from combinations o f indicators) were found to produce exponential distributions. More specifically, managerial indicators o f job performance were found to best fit a normal distribution whereas objective measures, as well as composite measures o f performance consisting o f objective and subjective indicators, were found to best fit an exponential distribution. This study provides researchers and practitioners with new suggestions for classifying job performance distributions as well as new techniques for better differentiating between top and bottom performers. APPROVAL FOR SCHOLARLY DISSEMINATION The author grants to the Prescott Memorial Library o f Louisiana Tech University the right to reproduce, by appropriate methods, upon request, any or all portions o f this Dissertation. It was understood that “proper request” consists o f the agreement, on the part o f the requesting party, that said reproduction was for his personal use and that subsequent reproduction will not occur without written approval o f the author o f this Dissertation. Further, any portions o f the Dissertation used in books, papers, and other works must be appropriately referenced to this Dissertation. Finally, the author o f this Dissertation reserves the right to publish freely, in the literature, at any time, any or all portions o f this Dissertation.
ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition

The recurrent neural network transducer (RNN-T) is a prominent streaming end-to-end (E2E) ASR technology. In RNN-T, the acoustic encoder commonly consists of stacks of LSTMs. Very recently, as an alternative to LSTM layers, the Conformer architecture was introduced where the encoder of RNN-T is replaced with a modified Transformer encoder composed of convolutional layers at the frontend and between attention layers. In this paper, we introduce a new streaming ASR model, Convolutional Augmented Recurrent Neural Network Transducers (ConvRNN-T) in which we augment the LSTM-based RNN-T with a novel convolutional frontend consisting of local and global context CNN encoders. ConvRNN-T takes advantage of causal 1-D convolutional layers, squeeze-and-excitation, dilation, and residual blocks to provide both global and local audio context representation to LSTM layers. We show ConvRNN-T outperforms RNN-T, Conformer, and ContextNet on Librispeech and in-house data. In addition, ConvRNN-T offers less computational complexity compared to Conformer. ConvRNN-T's superior accuracy along with its low footprint make it a promising candidate for on-device streaming ASR technologies.
Online optimization-based predictive flight control using first-order methods

In fields such as aerospace or automotive, the use of classical control methods such as PID is still significant. The presence of constraints, however, impacts on the performance of these controllers that are usually designed to avoid constraint saturation. MPC techniques are the obvious alternative to handle constraint saturation and fully exploit the operative range of the system. Furthermore, MPC can be used as a fault-tolerant controller to handle actuator faults and control reallocation in a modular and systematic way. In this dissertation, we rely on MPC techniques to handle constraints and actuator faults, motivated by an aerospace application (i.e., the longitudinal control of an Airbus passenger aircraft). The proposed fault-tolerant strategy strongly relies on the MPC capability of handling constraints and directly controlling each actuator independently. The advantages of MPC in terms of performance and fault tolerance, however, are shadowed by the computational requirements of this technique. The presence of an optimizer compromises the performance of the controller in terms of computation time and increases its requirements in terms of hardware and software. In this dissertation, we design MPC-tailored optimizers suitable for online optimization. The proposed contributions aim to bring MPC closer to actual implementation on the next generation of aircraft.
Anatomy of local government/design education collaboration.

Abstract The Public Collaboration Lab (PCL) is an action research partnership between the London Borough of Camden (LBC) and the University of the Arts London, funded by the Arts and Humanities Research Council. The research explored the potential for, and value of, strategic collaboration between design education (DHE) and local government (LG), and how design research and practice can contribute to service, policy and social innovation in the LG context. The project prototyped and piloted a new way of working that identified and leveraged synergies in the operational objectives of LG and the learning objectives (research and teaching) of DHE. The poster shares findings from the research in the form of an ‘anatomy’1 of LG/DHE collaboration, which articulates and visualizes the internal workings of LG/DHE. The ‘anatomy’ serves as a resource for researchers and practitioners seeking to reflect on collaborative projects by inputting data to the models to visualize their activities; and as a generative tool, providing a set of templates that focus discussion around the planning of collaborative projects.
AI-based image quality assessment in CT

Medical imaging, especially computed tomography (CT), is becoming increasingly important in research studies and clinical trials and adequate image quality is essential for reliable results. The aim of this study was to develop an artificial intelligence (AI)-based method for quality assessment of CT studies, both regarding the parts of the body included (i.e. head, chest, abdomen, pelvis), and other image features (i.e. presence of hip prosthesis, intravenous contrast and oral contrast). Approach: 1,000 CT studies from eight different publicly available CT databases were retrospectively in- cluded. The full dataset was randomly divided into a training (n = 500), a validation/tuning (n = 250), and a testing set (n = 250). All studies were manually classified by an imaging specialist. A deep neural network network was then trained to directly classify the 7 different properties of the image. Results: The classification results on the 250 test CT studies showed accuracy for the anatomical regions and presence of hip prosthesis in the interval 98.4% to 100.0%. The accuracy for intravenous contrast was 89.6% and for oral contrast 82.4%. Conclusions: We have shown that it is feasible to develop an AI-based method to automatically perform a quality assessment regarding if correct body parts are included in CT scans, with a very high accuracy.
Monoallelic CRMP1 gene variants cause neurodevelopmental disorder

Collapsing response mediator proteins (CRMPs) are key for brain development and function. Here, we link CRMP1 to a neurodevelopmental disorder. We report heterozygous de novo variants in the CRMP1 gene in three unrelated individuals with muscular hypotonia, intellectual disability and/or autism spectrum disorder. Based on in silico analysis these variants are predicted to affect the CRMP1 structure. We further analyzed the effect of the variants on the protein structure/levels and cellular processes. We showed that the human CRMP1 variants are dominant-negative and impact the oligomerization of CRMP1 proteins. Moreover, overexpression of mutant-CRMP1 variants affect neurite outgrowth of murine cortical neurons. While altered CRMP1 levels have been reported in psychiatric diseases, genetic mutation in CRMP1 gene has never been linked to human disease. We report for the first-time mutations in the CRMP1 gene and emphasize its key role in brain development and function by linking directly to a human neurodevelopmental disease.
Functional genomics provide key insights to improve the diagnostic yield of hereditary ataxia

Improvements in functional genomic annotation have led to a critical mass of neurogenetic discoveries. This is exemplified in hereditary ataxia, a heterogeneous group of disorders characterised by incoordination from cerebellar dysfunction. Associated pathogenic variants in more than 300 genes have been described, leading to a detailed genetic classification partitioned by age-of-onset. Despite these advances, up to 75% of patients with ataxia remain molecularly undiagnosed even following whole genome sequencing, as exemplified in the 100,000 Genomes Project. This study aimed to understand whether we can improve our knowledge of the genetic architecture of hereditary ataxia by leveraging functional genomic annotations, and as a result, generate insights that raise the diagnostic yield. To achieve these aims, we used publicly-available multi-omics data to generate 294 genic features, capturing information relating to a genes structure, genetic variation, tissue- specific, cell-type-specific and temporal expression, as well as protein products of a gene. We studied these features across genes typically causing childhood-onset, adult-onset or both types of disease first individually, then collectively. This led to the generation of testable hypotheses which we investigated using whole genome sequencing data from 2,322 individuals presenting with ataxia and 6,387 non-neurological probands in the 100,000 Genomes Project. Using this approach, we demonstrated a surprisingly high short tandem repeat (STR) density within childhood-onset genes suggesting that we may be missing pathogenic repeat expansions within this cohort. This was verified in both childhood- and adult-onset ataxia patients from the 100,000 Genomes Project who were unexpectedly found to have a trend for higher repeat sizes even at naturally-occurring STRs in known ataxia genes, implying a role for STRs in pathogenesis. Using unsupervised analysis, we found significant similarities in genomic annotation across the gene panels, which suggested adult- and childhood-onset patients should be screened using a common diagnostic gene set. We tested this within the 100,000 Genomes Project by assessing the burden of pathogenic variants among childhood-onset genes in adult-onset patients and vice versa. This demonstrated a significantly higher burden of rare, potentially pathogenic variants in conventional childhood-onset genes among individuals with adult-onset ataxia. Our analysis has implications for the current clinical practice in genetic testing for hereditary ataxia. We suggest that the diagnostic rate for hereditary ataxia could be increased by removing the age-of-onset partition, and through a modified screening for repeat expansions in naturally-occurring STRs within known genes, in effect treating these regions as candidate pathogenic loci.
Suspected adverse drug reactions of the type 2 antidiabetic drug class dipeptidyl‐peptidase IV inhibitors (DPP4i): Can polypharmacology help explain?

To interpret the relationship between the polypharmacology of dipeptidyl‐peptidase IV inhibitors (DPP4i) and their suspected adverse drug reaction (ADR) profiles using a national registry. A retrospective investigation into the suspected ADR profile of four licensed DPP4i in the United Kingdom using the National MHRA Yellow Card Scheme and OpenPrescribing databases. Experimental data from the ChEMBL database alongside physiochemical (PC) and pharmacokinetic (PK) profiles were extracted and interpreted. DPP4i show limited polypharmacology alongside low suspected ADR rates. We found a minimal statistical difference between the unique ADR profiles ascribed to the DPP4i except for total ADRs (χ2; p < .05). Alogliptin consistently showed the highest suspected ADR rate per 1 000 000 items prescribed. Saxagliptin showed the lowest suspected ADR rate across all organ classes but did not reach statistical difference (χ2; p > .05). We confirmed the Phase III clinical trial data that showed gastrointestinal and skin reactions are the most reported ADRs across the DPP4i class and postulated underlying mechanisms for this based on possible drug interactions. The main pharmacological mechanism behind the ADRs is attributed to interactions with DPP4 activity and/or structure homolog (DASH) proteins which augment the immune‐inflammatory modulation of DPP4.
Evaluating a novel, integrative dashboard for health professionals’ performance in managing deteriorating patients: a quality improvement project

Background The quality of recording and documentation of deteriorating patient management by health professionals has been challenged during the COVID-19 pandemic. Non-adherence to escalation and documentation guidelines increases risk of serious adverse events. Electronic health record (EHR)-integrated dashboards are auditing tools of patients’ status and clinicians’ performance, but neither the views nor the performance of health professionals have been assessed, relating to management of deteriorating patients. Objective To develop and evaluate a real-time dashboard of deteriorating patients’ assessment, referral and therapy. Settings Five academic hospitals in the largest National Health Service (NHS) trust in the UK (Barts Health NHS Trust). Intervention The dashboard was developed from EHR data to investigate patients with National Early Warning Score (NEWS2)>5, assessment, and escalation of deteriorating patients. We adopted the Plan, Do, Study, Act model and Standards for Quality Improvement Reporting Excellence framework to evaluate the dashboard. Design Mixed methods: (1) virtual, face-to-face, interviews and (2) retrospective descriptive EHR data analysis. Results We interviewed three nurses (two quality and safety and one informatics specialists). Participants perceived the dashboard as a facilitator for auditing NEWS2 recording and escalation of care to improve practice; (2) there is a need for guiding clinicians and adjusting data sources and metrics to enhance the functionality and usability. Data analysis (2019–2022) showed: (1) NEWS2 recording has gradually improved (May 2021–April 2022) from 64% to 83%;(2) referral and assessment completion increased (n: 170–6800 and 23–540, respectively). Conclusion The dashboard is an effective real-time data-driven method for improving the quality of managing deteriorating patients. Integrating health systems, a wider analysis NEWS2 and escalation of care metrics, and clinicians’ learning digital solutions will enhance functionality and experience to boost its value. There is a need to examine the generalisability of the dashboard through further validation and quality improvement studies.
Shared and ethnic background site-specific dietary patterns in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Dietary patterns (DPs) synthesize multiple related dietary components in one or more combined variables. A drawback of DPs is their limited reproducibility across subpopulations, especially adopting a posteriori DPs, derived using standard multivariate methods [e.g., factor analysis (FA)]. Standard approaches assessing reproducibility of FA-based DPs mostly rely on correlation coefficients/agreement measures between pairs of factors and do not consider any statistical model. Multi-study factor analysis builds upon standard FA model to identify DPs shared across all subpopulations and those specific to some subpopulations. Pattern reproducibility is investigated from a different perspective: a shared DP identified within multi-study factor analysis is "reproducible" since it is common to all subpopulations. Bayesian multi-study factor analysis (BMSFA) has been developed to improve DP retention and identification, two critical issues as the number of subpopulations analyzed increases. Using baseline (2008-2011) 24-hour dietary recalls from the Hispanic Community Health Study/Study of Latinos (n=16,415), we applied the BMSFA on 42 common nutrients to identify shared and subpopulation-specific DPs where subpopulations were defined as the cross-classification of ethnic background and study site (EBS). Overall, 4 shared DPs were identified: Plant-based foods, Processed foods, Dairy products, and Seafood. At the subpopulation level, we identified 12 EBS-specific DPs, one for each EBS category, primarily representing variants of foods from animal sources. Different nuances were expressed by subsets of fairly similar EBS-specific DPs, including an Animal vs. vegetable source, an Animal source only, and a Poultry vs. dairy products overarching DPs. Shared DPs from BMSFA were similar to their counterparts from standard FA and frequentist multi-study factor analysis; EBS-specific DPs from BMSFA were better characterized than those from frequentist multi-study factor analysis. In conclusion, the BMSFA successfully captured sources of both dietary homogeneity and heterogeneity in a large well-characterized study of US Hispanics/Latino adults by ethnic background and site.
Polygenic Risk Score Comparative Analyses Reveals Risk Disparity of Genetic Predisposition to Chronic Kidney Disease- A Multi Ancestry Approach

Polygenic Risk Score (PRS) models are used extensively to find the population/individual risk towards disease. These predictive scores are of great help as risk scores if predicted earlier the life of individual can be saved from the chronic/ complex diseases. In this empirical assessments study, the polygenic risk score was calculated in three different ancestries (SAS, EAS and African Americans) based on more than three hundred markers. The risk score we observed indicated that average population risk scores are varied but on cumulating the ancestries the average risk score increased ~1.3 times than individual population average risk. The parameter which varies greatly while calculating the PRS is the ancestry; it should be prerequisite that individuals of same ancestry should be taken as a one population groups while calculating the scores.
Preconception Care for Women with Medicaid: Self-report vs. Claims-based Utilization Measures

Background: Preconception care may improve perinatal outcomes and reduce disparities, but there is no standard population measure of preconception care utilization (PCU). Objective: We compared claims-based PCU from Medicaid Analytic Extract (MAX) data to self-report in the Pregnancy Risk Assessment and Monitoring System (PRAMS) survey. Methods: Among Medicaid-enrolled women ages 15-45 with births during 2012, we identified preconception services in MAX using 55 ICD9 codes published by Health and Human Services. We estimated the proportion reporting preconception care from 26 PRAMS states and compared this to the states' proportion who received services in MAX. We fit mixed-effects logistic regression models of the probability of PCU on demographic factors (age, race/ethnicity) and diagnoses (depression, diabetes, or hypertension), separately for each dataset. Finally, we computed the population proportions receiving care by state (MAX) and the empirical Bayes means of the state-level effects (MAX and PRAMS). Results: Among 652,929 deliveries in MAX from the included states, 28.1% received at least one preconception service. In PRAMS, 23.6% (95% CI [22.1, 25.3]) of Medicaid-covered respondents reported preconception care. In both datasets, PCU rates were higher for Black non-Hispanic vs. White non-Hispanic women (PRAMS OR 2.05 [1.60, 1.62]; MAX OR 1.51 [1.49, 1.54]) and for those with diabetes (PRAMS OR 1.82 [1.16, 2.85]; MAX OR 1.34 [1.29, 1.40]) or hypertension (OR 1.85 [1.41, 2.44]; MAX OR 1.22 [1.18, 1.27]). In PRAMS, Asian (OR 3.37 [2.28, 4.98]) and Hispanic women (OR 2.07 [1.5, 2.80]) were more likely to report PCU than White non-Hispanic women, but in MAX they were less likely to receive services. The correlation between the PRAMS state-specific effects and those from MAX was 0.31 (p = 0.124). Conclusions: Claims-based estimates of PCU are moderately concordant with self-reported rates at the state level; however, rates measured through Medicaid claims vs. self-report diverge in some groups.
A DISEASE MODEL FOR DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG) WITH MUTATIONS IN TP53 AND ITS APPLICATION FOR DRUG REPURPOSING.

1.1Brain cancers are ones of most aggressive and difficult to treat cancers. Despite numerous studies of the cellular mechanisms of gliomas, it is difficult to stop tumor growth. A complex genetic and epigenetic nature of many gliomas and poorly known pathways of human neuron precursors maturation suggest turning to big data analysis to find new insights and directions for drug development. We developed in silico molecular models and predicted molecular switches in signaling cascades that maintain multipotency of neuronal precursor cells in diffuse intrinsic pontine glioma (DIPG) driven by the H3K27M mutation and mutations in the TP53 gene. Oncogenes and biomarkers were predicted based on transcriptomics and mutational genomics data from a cohort of 30 patients with DIPG analyzed using Elsevier artificial intelligence methods and a collection of manually curated cancer hallmark pathways. The molecular models of DIPG with mutations in TP53 and histone 3 gene describe the mechanism of oligodendrocyte dedifferentiation due to activation of transcriptional factors OLIG2, SOX2 and POU5F1, epithelial-to-mesenchymal transition via strong EGFR and TGFR signaling, enhanced cell response to hypoxia via HIF1A signaling, and enhanced angiogenesis by VEGFA overexpression. Using in silico analysis, we identified drugs capable of inhibiting mutant TP53: vorinostat, cisplatin, paclitaxel, and statins were top ranked drugs. The predicted drugs and oncogenes had individual patient-level differences that can be visualized with created DIPG model and may be useful for future research in the field of personalized medicine.