Utah College of Applied Technology

Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

Abstract Here, Ruby et al. analyze an unprecedented amount of public family tree data from Ancestry and determine that the heritability of human longevity was well below 10%, lower than the widely-held belief that lifespan... Human life span is a phenotype that integrates many aspects of health and environment into a single ultimate quantity: the elapsed time between birth and death. Though it is widely believed that long life runs in families for genetic reasons, estimates of life span “heritability” are consistently low (∼15–30%). Here, we used pedigree data from Ancestry public trees, including hundreds of millions of historical persons, to estimate the heritability of human longevity. Although “nominal heritability” estimates based on correlations among genetic relatives agreed with prior literature, the majority of that correlation was also captured by correlations among nongenetic (in-law) relatives, suggestive of highly assortative mating around life span-influencing factors (genetic and/or environmental). We used structural equation modeling to account for assortative mating, and concluded that the true heritability of human longevity for birth cohorts across the 1800s and early 1900s was well below 10%, and that it has been generally overestimated due to the effect of assortative mating.

The COVID-19 pandemic continues to pose a major public health threat, especially in countries with low vaccination rates. To better understand the biological underpinnings of SARS-CoV-2 infection and COVID-19 severity, we formed the COVID-19 Host Genetics Initiative 1 . Here we present a genome-wide association study meta-analysis of up to 125,584 cases and over 2.5 million control individuals across 60 studies from 25 countries, adding 11 genome-wide significant loci compared with those previously identified 2 . Genes at new loci, including SFTPD , MUC5B and ACE2 , reveal compelling insights regarding disease susceptibility and severity.

Despite strides in characterizing human history from genetic polymorphism data, progress in identifying genetic signatures of recent demography has been limited. Here we identify very recent fine-scale population structure in North America from a network of over 500 million genetic (identity-by-descent, IBD) connections among 770,000 genotyped individuals of US origin. We detect densely connected clusters within the network and annotate these clusters using a database of over 20 million genealogical records. Recent population patterns captured by IBD clustering include immigrants such as Scandinavians and French Canadians; groups with continental admixture such as Puerto Ricans; settlers such as the Amish and Appalachians who experienced geographic or cultural isolation; and broad historical trends, including reduced north-south gene flow. Our results yield a detailed historical portrait of North America after European settlement and support substantial genetic heterogeneity in the United States beyond that uncovered by previous studies.

Abstract We present a massive investigation into the genetic basis of human lifespan. Beginning with a genome-wide association (GWA) study using a de-identified snapshot of the unique AncestryDNA database – more than 300,000 genotyped individuals linked to pedigrees of over 400,000,000 people – we mapped six genome-wide significant loci associated with parental lifespan. We compared these results to a GWA analysis of the traditional lifespan proxy trait, age, and found only one locus, APOE, to be associated with both age and lifespan. By combining the AncestryDNA results with those of an independent UK Biobank dataset, we conducted a meta-analysis of more than 650,000 individuals and identified fifteen parental lifespan-associated loci. Beyond just those significant loci, our genome-wide set of polymorphisms accounts for up to 8% of the variance in human lifespan; this value represents a large fraction of the heritability estimated from phenotypic correlations between relatives.

Abstract We conducted a meta‐analysis on the most commonly used forensic polygraph test, the Comparison Question Test. We captured as many studies as possible by using broad inclusion criteria. Data and potential moderators were coded from 138 datasets. The meta‐analytic effect size including inconclusive outcomes was 0.69 [0.66, 0.79]. We found significant moderator effects. Notably, level of motivation had a positive linear relationship with our outcome measures. Information Gain analysis of CQT outcomes representing the median accuracy showed a significant information increase over interpersonal deception detection across almost the complete range of base rates. Our results suggest that the CQT can be accurate, that experimental studies are generalizable, and no publication bias was detected. We discussed the limitations of the field research literature and problems within polygraph profession that lower field accuracy. We suggest some possible solutions.

Credit card usage is essential in the current economic climate. It has become a necessary component of domestic, commercial, and international operations. Even though there are many advantages to using credit cards when done properly and sensibly, fraudulent activity can result in serious credit and financial harm. Credit card fraud is becoming more of an issue in the financial services industry because more unauthorized payments lead to significant losses. Because of the high amount of transactions and changing fraud patterns, traditional rule-based fraud detection techniques are no longer adequate. Machine learning (ML) techniques provide viable ways to analyze trends and anomalies in order to detect fraudulent transactions. This research looks at a number of machines learning methods, including both supervised and unsupervised training strategies, emphasizing their accuracy, effectiveness, and drawbacks. In order to increase detection rates, the study also looks at assessment metrics, data imbalance problems, and new hybrid models. Lastly, important issues including privacy issues, limitations on real-time detection, and changing fraud tactics are covered, highlighting the necessity of flexible and expandable fraud detection systems.

This study explores the meaning of weaving for Karen refugee women from Burma as they participated in their traditional practice of weaving in Salt Lake City, Utah. Participants included eight Karen refugee women. Data were collected through one focus group and in-depth, semi-structured, open-ended interviews, structured to capture past and present meanings related to weaving. Transcription and analysis of the interviews and focus group were guided by phenomenology. Three themes emerged from structured, qualitative analysis: “I am Karen people”; “When there is nobody I am not happy, but with a lot of people I am happy”; and “If I can sell a lot of things I am happy to weave all the time”. The findings demonstrate how potent one occupation can be for the well-being of a particular group of people in terms of cultural identity construction, empowerment, social support, and economic survival in a new environment.

Most signal transduction pathways in humans are regulated by protein kinases through phosphorylation of their protein substrates. Typical eukaryotic protein kinases are of two major types: those that phosphorylate-specific sequences containing tyrosine (~90 kinases) and those that phosphorylate either serine or threonine (~395 kinases). The highly conserved catalytic domain of protein kinases comprises a smaller N lobe and a larger C lobe separated by a cleft region lined by the activation loop. Prior studies find that protein tyrosine kinases recognize peptide substrates by binding the polypeptide chain along the C-lobe on one side of the activation loop, while serine/threonine kinases bind their substrates in the cleft and on the side of the activation loop opposite to that of the tyrosine kinases. Substrate binding structural studies have been limited to four families of the tyrosine kinase group, and did not include Src tyrosine kinases. We examined peptide-substrate binding to Src using paramagnetic-relaxation-enhancement NMR combined with molecular dynamics simulations. The results suggest Src tyrosine kinase can bind substrate positioning residues C-terminal to the phosphoacceptor residue in an orientation similar to serine/threonine kinases, and unlike other tyrosine kinases. Mutagenesis corroborates this new perspective on tyrosine kinase substrate recognition. Rather than an evolutionary split between tyrosine and serine/threonine kinases, a change in substrate recognition may have occurred within the TK group of the human kinome. Protein tyrosine kinases have long been therapeutic targets, but many marketed drugs have deleterious off-target effects. More accurate knowledge of substrate interactions of tyrosine kinases has the potential for improving drug selectivity.

Background: The prevalence of hypothyroidism (HT) has increased over time. To assess the effectiveness of treatment, we (1) studied thyrotropin (TSH) levels among patients receiving levothyroxine (LT4) and (2) determined the percentages of patients switching among LT4 formulations. Methods: Data on patients with HT receiving LT4 from the Optum™ Clinical and Claims Database were analyzed from March 2013 through February 2020. Eligible adult patients had ≥1 medical claim with an HT diagnosis and all patients were observed for ≥12 months. Patients included in Objective 1 were indexed on a randomly selected TSH result and had ≥2 results for TSH 1–15 months apart. Patients included in Objective 2 were indexed on a randomly selected LT4 pharmacy claim and had ≥2 LT4 claims ≥1 month apart and ≥1 claim during follow-up. Outcomes were the proportion of patients with low, normal, or high (<0.45, 0.45–4.5, or >4.5 mIU/L, respectively) TSH levels and the proportion of patients switching LT4 formulations, respectively. Data were stratified by age group, sex, and insurance type. All data reported were analyzed using descriptive statistics. Results: Of patients who were in the indexed TSH group, 81.1% [confidence intervals: 80.4–81.8; n / N = 9130/11,259] achieved normal TSH values. When stratified by age group, sex, and insurance type, ≥70% of patients in each of these subgroups exhibited normal mean TSH values at follow-up. For Objective 2 ( N = 25,076), 24.9% ( N = 6238) of the LT4-indexed group had ≥1 formulation switch in 12 months, of which 67.3% only switched once, and 41.4% ( N = 10,370) had ≥1 formulation switch in up to 24 months. A significantly higher proportion of Medicare vs. commercially insured patients had switched formulations (26.2% vs. 23.1%, p < 0.001). Conclusions: Most LT4-treated patients maintain normal TSH levels, which is an improvement vs. previous reports. Continued physician engagement and patient education are advised to further reduce the number of patients who maintain off-target TSH levels. Contrary to clinical recommendations, about 25% of patients receiving LT4 switched formulations within 1 year, with >40% switching within 2 years; among patients who switched, most only switched once.

In the last decade, many advances have been made in the field of automatic temperature estimation, including wearable sensor technologies (WST), infrared thermography (IRT), and non-contact infrared thermometer (NCIT). In contrast with the WST and IRT, NCIT is inexpensive without the risk of potential skin irritation. Nevertheless, NCIT is limited in short valid estimation distance (<;12 cm), resulting in the non-satisfaction of the surging application requirements nowadays. This paper proposed an algorithm based on Neural Network Regression not only to reduce the error from 0.6° to 0.12°, which is close to the medical instrument level, but as well to lengthen the valid distance to the range between 50 cm and 100 cm. Furthermore, this study developed an embedded automatic body temperature estimation system which could continuously and unconsciously measure the human temperature in real-time. Integrated with face tracking and fuzzy-control of Pan-tilt unit, the system ensures that human face is focused while measuring. With wireless communication techniques, users can review their physiological Information via App and Web, which is beneficial to remote healthcare.

PURPOSE: This work seeks to develop exact confidence interval estimators for figures of merit that describe the performance of linear observers, and to demonstrate how these estimators can be used in the context of x-ray computed tomography (CT). The figures of merit are the receiver operating characteristic (ROC) curve and associated summary measures, such as the area under the ROC curve. Linear computerized observers are valuable for optimization of parameters associated with image reconstruction algorithms and data acquisition geometries. They provide a means to perform assessment of image quality with metrics that account not only for shift-variant resolution and nonstationary noise but that are also task-based. METHODS: We suppose that a linear observer with fixed template has been defined and focus on the problem of assessing the performance of this observer for the task of deciding if an unknown lesion is present at a specific location. We introduce a point estimator for the observer signal-to-noise ratio (SNR) and identify its sampling distribution. Then, we show that exact confidence intervals can be constructed from this distribution. The sampling distribution of our SNR estimator is identified under the following hypotheses: (i) the observer ratings are normally distributed for each class of images and (ii) the variance of the observer ratings is the same for each class of images. These assumptions are, for example, appropriate in CT for ratings produced by linear observers applied to low-contrast lesion detection tasks. RESULTS: Unlike existing approaches to the estimation of ROC confidence intervals, the new confidence intervals presented here have exactly known coverage probabilities when our data assumptions are satisfied. Furthermore, they are applicable to the most commonly used ROC summary measures, and they may be easily computed (a computer routine is supplied along with this article on the Medical Physics Website). The utility of our exact interval estimators is demonstrated through an image quality evaluation example using real x-ray CT images. Also, strong robustness is shown to potential deviations from the assumption that the ratings for the two classes of images have equal variance. Another aspect of our interval estimators is the fact that we can calculate their mean length exactly for fixed parameter values, which enables precise investigations of sampling effects. We demonstrate this aspect by exploring the potential reduction in statistical variability that can be gained by using additional images from one class, if such images are readily available. We find that when additional images from one class are used for an ROC study, the mean AUC confidence interval length for our estimator can decrease by as much as 35%. CONCLUSIONS: We have shown that exact confidence intervals can be constructed for ROC curves and for ROC summary measures associated with fixed linear computerized observers applied to binary discrimination tasks at a known location. Although our intervals only apply under specific conditions, we believe that they form a valuable tool for the important problem of optimizing parameters associated with image reconstruction algorithms and data acquisition geometries, particularly in x-ray CT.

Abstract Background Androgen receptor (AR) gene alterations, as detected by circulating tumor cell‐free DNA (cfDNA) genomic profiling, have been shown to emerge after a variable duration of androgen signaling inhibition. AR alterations were associated with inferior outcomes on treatment with androgen receptor pathway inhibitors (ARPI) in the first line metastatic castration‐resistant prostate cancer (mCRPC) setting in a phase 2 trial. Here in, we assessed the impact of these AR alterations on survival outcomes in a real‐world patient population of mCRPC experiencing disease progression on an ARPI. Methods In this IRB‐approved retrospective study, consecutively seen patients with a confirmed diagnosis of mCRPC, with disease progression on a treatment with ARPIs in the first line mCRPC setting, with no prior exposure to an ARPI in the castration sensitive setting, and with available cfDNA profiling from a CLIA certified laboratory were included. Patients were categorized based on AR status: wild‐type ( AR wt ) or alteration‐positive ( AR + ). The objective was to correlate overall survival (OS) after disease progression on the first‐line ARPI with the presence or absence of AR alterations. Kaplan–Meier and Cox Regression Tests were used as implemented in R‐Studio (v.4.2). Results A total of 137 mCRPC patients were eligible: 69 with AR wt versus 68 with AR + . The median OS posttreatment with the first ARPI was significantly higher for AR wt than AR + patients (30.1 vs. 15.2 mos; p &lt; 0.001). Of 108 patients who received a subsequent line of therapy, 63 received an alternate ARPI (AR + 39 vs. 24 AR wt ), while 20 received a taxane‐based therapy (11 AR + vs. 9 AR wt ). Among patients receiving an alternate ARPI, AR + had numerically shorter OS (16.8 vs. 30.4 mos, p = 0.1). Among patients receiving taxane‐based regimens, the OS was not significantly different between AR + and AR wt (14.5 vs. 10.1 mos, p = 0.18). Conclusion In this real‐world study, mCRPC patients with AR alterations on cfDNA had inferior OS after disease progression on the first ARPI, compared to those who did not, and may impact outcomes on a subsequent ARPI but not on subsequent taxane‐based therapy received. By providing survival estimates for patients with or without AR alterations, our data may aid in patient counseling, prognostication, treatment decision, and for designing future clinical trials in this setting.

Eye health in patients with myeloma.

Background and objective Increased glutamate concentration in the cerebrospinal fluid has been reported in severely head-injured patients, suggesting that an excessive release of glutamate may be involved in the process of neuronal damage. Ischaemic damage after subdural haematoma has been reported to be reduced by glutamate (N-methyl-D-aspartate: NMDA) receptor antagonists such as dizocilpine and CGS 19755; even though these drugs were given 20–30 min after insult. Excessive release of excitatory amino acids may produce the neural damage after subdural haematoma and NMDA receptor antagonists may become valuable therapeutic drugs. This study compared the effects of ketamine and dizocilpine, on intracranial pressure and histopathological changes after acute subdural haematoma produced by an injection of autologous blood (150 µL) in rats. Methods The control (n = 9), ketamine (n = 9) and dizocilpine (n = 9) groups, respectively, received saline, ketamine (total dose: 210 mg kg−1) or dizocilpine (total dose: 1.0 mg kg−1) from 0.5 to 8 h after acute subdural haematoma. A silicone group (n = 9) had the same volume of silicone injected subdurally. Results The volume of ischaemic damage in the silicone group (1.3 ± 1.2 mm3) was significantly smaller than in the control group (11.9 ± 3.8 mm3). Ketamine and dizocilpine did not increase intracranial pressure. Dizocilpine significantly decreased the volume of ischaemic damage (6.1 ± 3.8 mm3). Ketamine failed to significantly decrease damage (7.8 ± 5.0 mm3). Conclusions These results suggest that the factors elicited by the clotted blood contribute to the ischaemic damage after subdural haematoma, and that the glutamate receptor antagonist dizocilpine reduces the damage, while ketamine shows only a trend reduction of the damage.

BACKGROUND AND OBJECTIVE: Increased glutamate concentration in the cerebrospinal fluid has been reported in severely head-injured patients, suggesting that an excessive release of glutamate may be involved in the process of neuronal damage. Ischaemic damage after subdural haematoma has been reported to be reduced by glutamate (N-methyl-D-aspartate: NMDA) receptor antagonists such as dizocilpine and CGS 19755; even though these drugs were given 20-30 min after insult. Excessive release of excitatory amino acids may produce the neural damage after subdural haematoma and NMDA receptor antagonists may become valuable therapeutic drugs. This study compared the effects of ketamine and dizocilpine, on intracranial pressure and histopathological changes after acute subdural haematoma produced by an injection of autologous blood (150 microL) in rats. METHODS: The control (n = 9), ketamine (n = 9) and dizocilpine (n = 9) groups, respectively, received saline, ketamine (total dose: 210 mg kg-1) or dizocilpine (total dose: 1.0 mg kg-1) from 0.5 to 8 h after acute subdural haematoma. A silicone group (n = 9) had the same volume of silicone injected subdurally. RESULTS: The volume of ischaemic damage in the silicone group (1.3 +/- 1.2 mm3) was significantly smaller than in the control group (11.9 +/- 3.8 mm3). Ketamine and dizocilpine did not increase intracranial pressure. Dizocilpine significantly decreased the volume of ischaemic damage (6.1 +/- 3.8 mm3). Ketamine failed to significantly decrease damage (7.8 +/- 5.0 mm3). CONCLUSIONS: These results suggest that the factors elicited by the clotted blood contribute to the ischaemic damage after subdural haematoma, and that the glutamate receptor antagonist dizocilpine reduces the damage, while ketamine shows only a trend reduction of the damage.

State-of-the-art filtered backprojection (FBP) algorithms often define the filtering operation to be performed along oblique filtering lines in the detector. A limited scan field of view leads to the truncation of those filtering lines, which causes artifacts in the final reconstructed volume. In contrast to the case where filtering is performed solely along the detector rows, no methods are available for the case of oblique filtering lines. In this work, the authors present two novel truncation correction methods which effectively handle data truncation in this case. Method 1 (basic approach) handles data truncation in two successive preprocessing steps by applying a hybrid data extrapolation method, which is a combination of a water cylinder extrapolation and a Gaussian extrapolation. It is independent of any specific reconstruction algorithm. Method 2 (kink approach) uses similar concepts for data extrapolation as the basic approach but needs to be integrated into the reconstruction algorithm. Experiments are presented from simulated data of the FORBILD head phantom, acquired along a partial-circle-plus-arc trajectory. The theoretically exact M-line algorithm is used for reconstruction. Although the discussion is focused on theoretically exact algorithms, the proposed truncation correction methods can be applied to any FBP algorithm that exposes oblique filtering lines.

OBJECTIVES: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data. DESIGN: A cross-sectional study. SETTING: AncestryDNA customers in the USA who consented to research. PARTICIPANTS: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test. RESULTS: We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study. CONCLUSIONS: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

OBJECTIVE: Hispanic/Latino(x) and African American/Black older adults experience disproportionate cardiometabolic disease burdens when compared with their non-Hispanic White counterparts. Sources of resilience such as social networks have been found to mitigate the risk of this disease and its end points like high-sensitivity C-reactive protein (hs-CRP). However, little is known about the social network infrastructure among these groups. Moreover, existing work has largely ignored the degree to which members of one's network are connected to one another (network density), which may be important for navigating structural barriers within interdependent groups. The objective of this study was to understand the association between network density and 5-year hs-CRP (blood spot) and whether this association was moderated by race-ethnicity. METHODS: A subsample of Hispanic/Latino(x), African American/Black, and non-Hispanic White older adults ( N = 1431) from the National Social Life Health and Aging Project was used. Multivariable regression was used to estimate the association between network density and its interaction with race-ethnicity, with hs-CRP 5 years later. RESULTS: Although no main effect of network density on 5-year hs-CRP was found, results revealed a significant network density by race-ethnicity interaction (Wald χ2 (2, 1242) = 3.31, p = .037). Simple slopes analyses revealed that Hispanic/Latino(x) older adults with high network density had significantly lower hs-CRP levels when compared with their same-ethnic counterparts with low network density ( b = -0.73, standard error = 0.31, 95% confidence interval = -1.33 to -0.13, p = .018). CONCLUSIONS: Results demonstrate population-level differences in social network structure and differential associations of this infrastructure with health. Implications for the Hispanic Mortality Paradox are discussed.