First Set Of Analyses Research Articles

Cognition and Other Non-Motor Symptoms in an At-Risk Cohort for Parkinson's Disease Defined by REM-Sleep Behavior Disorder and Hyposmia.

REM-sleep behavior disorder (RBD) and other non-motor symptoms such as hyposmia were proposed by the Movement Disorder Society as research criteria for prodromal Parkinson's disease (P-PD). Global cognitive deficit was later added. To compare non-motor symptoms, focusing on cognition, between a P-PD group and a matched control group. In this cross-sectional, case-control study, in a first set of analyses, we performed extensive cognitive testing on people with (n = 76) and a control group without (n = 195) probable RBD and hyposmia. Furthermore, we assessed motor and non-motor symptoms related to Parkinson's Disease (PD). After propensity score matching, we compared 62 P-PD with 62 age- and sex-matched controls. In addition, we performed regression analyses on the total sample (n = 271). In a second set of analyses, we used, a.o., the CUPRO to evaluate retrograde procedural memory and visuo-constructive functions. People with P-PD showed significantly poorer performances in global cognition, visuo-constructive and executive functions, mainly in mental flexibility (p < 0.001; p = 0.004; p = 0.003), despite similar educational levels (p = 0.415). We observed significantly more motor and non-motor symptoms (p < 0.001; p = 0.004), higher scores for depression (p = 0.004) and apathy (p < 0.001) as well as lower quality of life (p < 0.001) in P-PD. Our findings confirm that global cognitive, executive, and visuo-constructive deficits define the P-PD group. In addition, depression, apathy, and lower quality of life were more prevalent in P-PD. If replicated in other samples, executive and visuo-constructive deficits should be considered in non-motor P-PD. Determining specific patterns will support early recognition of PD, secondary prevention of complications and the development of neuroprotective treatments.

A first characterization of the microbiota-resilience link in swine

BackgroundThe gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.ResultsThis study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.ConclusionOur comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations.7uyXcYriZkKxPgmfB9NKtxVideo

ESR/U-series dating of palaeontological remains from the Neanderthal site of Mutzig-Rain (Alsace, France)

The Middle Paleolithic site of Mutzig-Rain, discovered in 1992, has been studied through several pits (called M1 to M15) and excavation area since 2009, evidencing several localities and delivering more than 5000 lithic artifacts and 12,000 well preserved paleontological remains. The main excavation M2 sequence corresponds to a collapsed rock shelter, containing several levels of Middle Paleolithic occupation. The faunal assemblage of the main archaeological levels (l.7a to 7c) from this sequence includes woolly mammoth, reindeer, horse, bison and woolly rhinoceros, reflecting both cold climate and a steppe context for the whole sequence, while microfauna from the lower levels (l.7d and l.10) indicate the occasional presence of forests that could correspond to a cooler climate. In 1995, U-series analyses on bones from M8 were attempted but the obtained results did not follow the sample stratigraphic order. Later, an OSL dating study has placed the M2 archaeological sequence as coeval to the onset of the Weichselian glacial stage (MIS5d-5a), between 100 and 80 ka.The good preservation of the abundant faunal remains at Mutzig-Rain has allowed their use for ESR/U-series dating studies and the results obtained on Mutzig horse teeth are given in the present paper. A first set of analyses of teeth carried out from the M2 excavation provided ages around 106-96 ka for levels M2 l.7, in agreement with the OSL data, despite quite systematic evidence of U-leaching from the dental tissues and the consequent use of the Accelerating Uptake (AU) model for the age calculation. A second set of analyses on teeth from the M2 and M8 localities confirms this uranium leaching trend, placing the deposition of these levels ca 133 -122 ka.Taking the whole set of ESR/U-series ages, which are slightly older than the OSL dates, would suggest that the stratigraphic sequence was deposited between ca 130 ka (M8 l. 6 and M2 l.10) and ca 100 ka (M2 l.7), i.e. on a time range covering the Eemian Interglacial (MIS5e) and the subsequent Early Weichselian Glacial stage (MIS5d-5a). However, as the faunal record indicates a cool climate and an open steppe-like environment for the whole archaeological sequence, the ESR/U-series dating to the Eemian Interglacial in the Mutzig-Rain sequence seems unlikely. In order to check the quality of the paleodosimetric reconstruction of the ESR/U-series analyses, an additional isochron study was performed for the two levels for which several teeth were analyzed: M2 l.7 (n = 4) and M8 l.6 (n = 3). This isochron study validates the proposed reconstruction and confirms the Early Weichselian age of these two levels. These results confirm the chronology established previously with OSL and places Mutzig-Rain as a reference site for the understanding of the Neanderthal Middle Palaeolithic occupation in Eastern France.

Explanations based on Item Response Theory (eXirt): A model-specific method to explain tree-ensemble model in trust perspective

Solutions based on tree-ensemble models represent a considerable alternative to real-world prediction problems, but these models are considered black box, thus hindering their applicability in problems of sensitive contexts (such as: health and safety). Explainable Artificial Intelligence (XAI) aims to develop techniques that generate explanations of black box models, since these models are normally not self-explanatory. Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with the proposal to explain black box models through global rankings of feature relevance, which based on different methodologies, generate global explanations that indicate how the model’s inputs explain its predictions. This research aims to present an innovative XAI method, called eXirt, capable of carrying out the process of explaining tree-ensemble models, based on Item Response Theory (IRT). In this context, 41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost, Random Forest, and Gradient Boosting), and 7 XAI methods (including eXirt) were used to generate explanations. In the first set of analyses, the 164 ranks of global feature relevance generated by eXirt were compared with 984 ranks of the other XAI methods present in the literature, being verified that the new method generated different explanations from other existing methods. In a second analysis, exclusive local and global explanations generated by eXirt were presented that help in understanding the model trust, since in this explanation it is possible to observe particularities of the model regarding difficulty (if the model had difficulty predicting the test dataset), discrimination (if the model understands the test dataset as discriminative) and guesswork (if the model got the test dataset right by chance). Thus, it was verified that eXirt is able to generate global explanations of tree-ensemble models and also local and global explanations of models through IRT, showing how this consolidated theory can be used in machine learning in order to obtain explainable and reliable models.

A state-wide analysis of characteristics and predictors of dual system involvement among child victims of human trafficking

BackgroundThe child welfare system is pivotal in identifying and responding to child (younger than age 18) human trafficking victims. Children who have experienced human trafficking—or are at risk of victimization—are increasingly recognized as also being served in the juvenile justice system. Yet little is known about human trafficking victimization among children in the juvenile justice population or among children with child welfare and juvenile justice involvement. ObjectiveBuilding on previous research with child welfare–and juvenile justice–involved children, we investigated characteristics and system experiences among children who have experienced a human trafficking abuse allegation in Florida. Participants and settingThe overall study population included all children born on or after January 1, 1993, who had at least one DCF-documented maltreatment allegation before March 1, 2020. This study examined a total of 12,167 allegations in the first set of analyses and the first human trafficking allegation for each child (N = 9300) in the second set of analyses. MethodsAnalyses are based on linked administrative data for a cohort of children involved with Florida's Departments of Juvenile Justice (DJJ) and/or Children and Families (DCF). We used descriptive and multivariate logistic regression analyses to document the characteristics of single and dual system–involved children and examine predictors of child welfare, juvenile justice, and dual system involvement. Results(1) Nearly half of children with a human trafficking abuse allegation are involved in DCF at the time of the allegation, (2) females who experienced trafficking are more likely to be involved in DCF (with or without DJJ involvement) than involved in no system, (3) Black children with an alleged trafficking incident were more likely than White children to be involved in both systems compared with DCF only, and (4) children who experienced labor trafficking abuse allegations were less likely than those who experienced sex trafficking to be involved with either system. ConclusionsThis study depicts child victims of sex and labor trafficking who are involved with one or both systems in one state.

149 Marker Effect P-Value for Large Genotype Populations with the Algorithm for Proven and Young

Abstract The single-step method (ssGBLUP) allows for simultaneous evaluations of populations composed of genotyped and non-genotyped animals. As a GBLUP-based method, the output of ssGBLUP is breeding values. However, interest might rely on the estimation of SNP effects and on how genome segments are associated with the phenotype of interest. When that is the case, SNP effects can be obtained from a linear transformation of breeding values, and P-values can be used as a measure of estimation certainty. P-values are derived from the prediction error variance of SNP effects, which relies on the prediction error (co)variance matrix of breeding values obtained from the inverse of the left-hand side of the mixed model equations (LHS). However, inverting the LHS becomes unfeasible with large, genotyped populations. Therefore, alternative strategies to overcome this problem and obtain p-values with increasing genotyped populations might be evaluated. In this study, we aimed to estimate marker effect p-values when the algorithm for proven and young (APY) was used to approximate the predictor error (co)variance for animals in a large, genotyped population. We estimate P-values of SNP marker effects for PWG in an Angus cattle population. The dataset was composed of around 844K phenotyped animals, 450K genotypes, and 1.8M pedigree records. Analyses were split into two sets. In the first set, a reduced genotype data of 50K was used, so p-values obtained from a direct-inverse of LHS using the regular G-1 (Exact_G) or APY G-1 (Exact_GAPY) were compared with APY G-1 and an approximation of the prediction error variance that did not require the inversion of the LHS (Approx_GAPY). In the second set of analyses, P-values with Approx_GAPY were estimated with the full genomic set of 450K (Approx_GAPY_450K), and computational requirements were recorded. All analyses were performed in three replicates. In the first set of analyses, genome-wide association with Exact_G uncovered two significant regions in chromosomes 7 and 20. Along all replicates, the same regions were also identified with Exact_GAPY and Approx_GAPY, indicating that similar resolutions are obtained with exact and approximated p-values. Results from Approx_GAPY_450K showed that, with a complete genome set, besides the two regions in chromosomes 7 and 20, two new regions in chromosomes 6 and 14 were identified. On average, the entire procedure for obtaining P-values with 450K genotyped animals had an elapsed wall clock time of 24h with a maximum memory usage of 87.6 GB. Altogether, our results suggest that with APY and the approximation of the prediction error variance, current computational boundaries for obtaining marker effect P-values for large genotyped populations should be lifted.

51 The Minimal Effect of Depression on Cognitive Functioning when Accounting for Performance Validity

Objective:While much research has demonstrated a relationship between depression and cognitive deficits, most studies have neglected to include measurements of performance validity. The very small number of studies that have examined this relationship when accounting for performance validity have found that the relationship between depression and cognition is small or nonsignificant. The current study examined the relationship between depression (assessed through both clinical interview and self-report symptom measures) and multiple domains of cognition after accounting for noncredible performance on neuropsychological testing.Participants and Methods:Participants were veterans referred for outpatient clinical evaluation. Among other tests that varied across patients, the neuropsychological battery included: California Verbal Learning Test - second edition (CVLT-II) total immediate recall across trials 1–5, short delay free recall, and long delay free recall; Trail Making Test; FAS and Animal Fluency; Rey–Osterrieth Complex Figure Test (ROCF) copy and 3-minute delay recall; and Wisconsin Card Sorting Test (WCST) categories completed, total errors, and percent perseverative errors. These tests represent domains that have previously been examined in relation to depression (e.g., memory, processing speed, executive functioning). Evaluations were conducted for clinical purposes, so that some individuals who were not administered certain tests have missing data. The first set of regression analyses (N=206) included age, sex, and education at Step 1, Beck Depression Inventory-2 (BDI-2) total score at Step 2, and pass or failure of Trial 1 of the Test of Memory Malingering (TOMM) at Step 3 as predictors of performance on the 12 test indices. The second set of regression analyses (N=559) mirrored the first but with Major Depressive Disorder (MDD) diagnosis at Step 2 instead.Results:In the first set of analyses, after including TOMM in the model, only the relationship between BDI-2 and verbal fluency remained significant, but did not survive Bonferroni correction (p&lt;.004). In the second set of analyses, before including the TOMM, MDD diagnosis was significantly related only to worse performance on Trails A and CVLT-II Short and Long Delay Free Recall, with small effect sizes (rp=.06–.15). When TOMM Trial 1 was included in the model, MDD diagnosis became a nonsignificant predictor of CVLT-II Long Delay Free Recall but remained a significant predictor for Trails A and CVLT-II Short Delay Free Recall (p&lt;.05). After Bonferroni correction (p&lt;.004), with TOMM Trial 1 included in the model, MDD diagnosis remained a significant predictor only of CVLT-II Short Delay Free Recall, with a small effect size (rp=.17).Conclusions:After accounting for noncredible performance, there was little evidence for a relationship between depression diagnosis or symptoms and many cognitive domains. These results suggest that previously reported effects of depression on cognition are not mainly due to underlying neurological mechanisms, but rather to motivational factors. Future research could focus on the potential psychological mechanisms (e.g., negative attitudes, expectancy bias, low motivation, etc.) driving the relationship between depression and low effort on cognitive testing. If replicated, the current findings could be valuable to clinicians treating depressed individuals who have concerns about their cognitive functioning, by indicating psychoeducation and reassurance.

Loneliness and depression: bidirectional mendelian randomization analyses using data from three large genome-wide association studies.

Major depression (MD) is a serious psychiatric illness afflicting nearly 5% of the world's population. A large correlational literature suggests that loneliness is a prospective risk factor for MD; correlational assocationsof this nature may be confounded for a variety of reasons. This report uses Mendelian Randomization (MR) to examine potentially causal associations between loneliness and MD. We report on analyses using summary statistics from three large genome wide association studies (GWAS). MR analyses were conducted using three independent sources of GWAS summary statistics. In the first set of analyses, we used available summary statistics from an extant GWAS of loneliness to predict MD risk. We used two sources of outcome data: the Psychiatric Genomics Consortium (PGC) meta-analysis of MD (PGC-MD; N = 142,646) and the Million Veteran Program (MVP-MD; N = 250,215). Finally, we reversed analyses using data from the MVP and PGC samples to identify risk variants for MD and used loneliness outcome data from UK Biobank. We find robust evidence for a bidirectional causal relationship between loneliness and MD, including between loneliness, depression cases status, and a continuous measure of depressive symptoms. The estimates remained significant across several sensitivity analyses, including models that account for horizontal pleiotropy. This paper provides the first genetically-informed evidence that reducing loneliness may play a causal role in decreasing risk for depressive illness, and these findings support efforts to reduce loneliness in order to prevent or ameliorate MD. Discussion focuses on the public health significance of these findings, especially in light of the SARS-CoV-2 pandemic.

Exploratory Thermo-Mechanical Assessment of the Bottom Cap Region of the EU DEMO Water-Cooled Lead Lithium Central Outboard Blanket Segment

The Water-Cooled Lead Lithium (WCLL) Breeding Blanket (BB) is one of the two BB concept candidates to be selected as the driver blanket for the EU DEMO fusion reactor. In this regard, the development of a sound architecture of the WCLL Central Outboard Blanket (COB) Segment, ensuring the fulfilment of the thermal and structural design requirements, is one of the main goals of the EUROfusion consortium. To this purpose, an exploratory research campaign has been launched to preliminarily investigate the thermo-mechanical performances of the Bottom Cap (BC) region of the WCLL COB segment because of its peculiarities making its design different from the other regions. The assessment has been carried out considering the nominal BB operating conditions, the Normal Operation (NO) scenario, as well as a steady-state scenario derived from the in-box LOCA accident, the Over-Pressurization (OP) scenario. Starting from the reference geometric layout of the WCLL COB BC region, a first set of analyses has been launched in order to evaluate its structural performances under a previously calculated thermal field and to select potential geometric improvements. Then, the analysis of a complete BC region was conducted from both the thermal and structural standpoints, evaluating its structural behaviour in compliance with the RCC-MRx code. Finally, after some iterations and geometric updates, a promising geometric layout of the BC region has been obtained even though some criticalities still persist in the internal Stiffening Plates and First Wall. However, the obtained results clearly showed that the proposed layout is worthy to be further assessed to achieve a robust enough configuration. The work has been performed following a theoretical-numerical approach based on the Finite Element Method (FEM) and adopting the quoted Ansys commercial FEM code.

Childhood Sexual Abuse and Relationship Satisfaction: The Moderating Role of PTSD and Sexual-Related Posttraumatic Stress Symptoms

Research on childhood sexual abuse (CSA) has consistently demonstrated the long-term effects of such abuse, not only on survivors’ development, but also on the nature and quality of their adult relationships, particularly romantic ones. In this study we examined the moderating role of posttraumatic stress disorder (PTSD) and sexual-related posttraumatic stress symptoms (sexual-related PTSS) between CSA and relationship satisfaction. Survey data from 529 individuals who reported being currently in a romantic relationship were analyzed. In the first set of analyses, results demonstrated that participants with CSA reported significantly lower relationship satisfaction and significantly greater severity of PTSD and sexual-related PTSS than participants without CSA. Sexual-related PTSS but not PTSD moderated the association between CSA and participants’ relationship satisfaction, with the model of sexual-related PTSS explaining 20.8% of the variance in relationship satisfaction and the model of PTSD explaining 11.3% of this variance. In the second set of analyses conducted among survivors of CSA only, higher sexual-related PTSS severity was linked with ongoing abuse and with abuse by a non-family member. This study points to the potential contribution made by sexual-related PTSS to relationship satisfaction among survivors of CSA.

Effects of inherent surface roughness of additively manufactured lattice frame material on flow and thermal transport

Additively manufactured (AM) fiber-based lattice topologies provide superior thermal and mechanical performance. For true characterization of flow and thermal transport of AM lattices based on different unit cell topologies, it is imperative to understand the role of inherent surface roughness of AM parts on the same. To this end, the effect of inherent roughness on the endwalls and fibers of a tetrakaidecahedron unit cell-based lattice on the flow and thermal transport has been computationally studied here for Reynolds number of 9,806. The predictions were compared to the heat transfer coefficient values obtained from the experiments conducted on tetrakaidecahedron coupon additively manufactured in Stainless steel 420 (SS 420) using Binder-Jetting process. The AM parts were characterized using X-ray computed tomography (CT) scan and optical profilometer. For the computational study, artificial roughness was generated such that the roughness characteristics were similar to the AM parts. First set of analysis includes the as-designed smooth CAD geometry with desired lattice porosity of 0.886 and as-manufactured geometry with the rough endwalls and fibers having manufactured porosity of 0.856. The as-designed geometry underpredicted the velocity magnitudes and turbulent kinetic energy levels as compared to the as-manufactured counterpart. The overall heat transfer coefficient values were under-predicted by 26.8% and 13.9% from the experimental data, respectively. The second set of analysis included two geometries-(i) smooth endwall and rough fiber, and (ii) rough endwall and smooth fiber, both having manufactured lattice porosity of 0.856. The overall heat transfer coefficient values in the rough fiber-only and rough endwall-only deviated by 14.2% and 18.9% respectively. Amongst all the four geometries simulated, the as-manufactured part with roughness on both the endwalls and fibers provided the best agreement with the experimental results suggesting that the roughness effects cannot be overlooked for accurate thermal performance predictions. The rough fiber-only case provided better results than the rough endwall-only case suggesting that the roughness of the fibers accounting for the porosity deviations is important in dictating the overall lattice heat transfer performance. However, the endwall effects can become increasingly significant if the roughness scales become comparable to the global unit cell dimensions.

The genetic basis of swine inflammation and necrosis syndrome and its genetic association with post-weaning skin damage and production traits.

The swine inflammation and necrosis syndrome (SINS) is a syndrome visually characterized by the presence of inflamed and necrotic skin at extreme body parts, such as the teats, tail, ears, and claw coronary bands. This syndrome is associated with several environmental causes, but knowledge of the role of genetics is still limited. Moreover, piglets affected by SINS are believed to be phenotypically more susceptible to chewing and biting behaviors from pen mates, which could cause a chronic reduction in their welfare throughout the production process. Our objectives were to 1) investigate the genetic basis of SINS expressed on piglets' different body parts and 2) estimate SINS genetic relationship with post-weaning skin damage and pre and post-weaning production traits. A total of 5,960 two to three-day-old piglets were scored for SINS on the teats, claws, tails, and ears as a binary phenotype. Later, those binary records were combined into a trait defined as TOTAL_SINS. For TOTAL_SINS, animals presenting no signs of SINS were scored as 1, whereas animals showing at least one affected part were scored as 2. Apart from SINS traits, piglets had their birth weight (BW) and weaning weight (WW) recorded, and up to 4,132 piglets were later evaluated for combined skin damage (CSD), carcass backfat (BF), and loin depth (LOD). In the first set of analyses, the heritability of SINS on different body parts was estimated with single-trait animal-maternal models, and pairwise genetic correlations between body parts were obtained from two-trait models. Later, we used four three-trait animal models with TOTAL_SINS, CSD, and an alternative production trait (i.e., BW, WW, LOD, BF) to access trait heritabilities and genetic correlations between SINS and production traits. The maternal effect was included in the BW, WW, and TOTAL_SINS models. The direct heritability of SINS on different body parts ranged from 0.08 to 0.34, indicating that reducing SINS incidence through genetic selection is feasible. The direct genetic correlation between TOTAL_SINS and pre-weaning growth traits (BW and WW) was favorable and negative (from -0.40 to -0.30), indicating that selection for animals genetically less prone to present signs of SINS will positively affect the piglet's genetics for heavier weight at birth and weaning. The genetic correlations between TOTAL_SINS and BF and between TOTAL_SINS and LOD were weak or not significant (-0.16 to 0.05). However, the selection against SINS was shown to be genetically correlated with CSD, with estimates ranging from 0.19 to 0.50. That means that piglets genetically less likely to present SINS signs are also more unlikely to suffer CSD after weaning, having a long-term increase in their welfare throughout the production system.

The Impact of Baseline Anxiety on Drug Placebo Separation and Drug/Placebo Response in an Acute Schizophrenia Clinical Trial-A Post-hoc Analysis.

We sought to evaluate the impact of baseline anxiety levels on drug placebo separation and drug and placebo response in acutely psychotic schizophrenic subjects. In this post-hoc analysis, modified intent-to-treat Positive and Negative Syndrome Scale data were obtained from a phase 2, multi-center, 5 week, randomized, double-blind, placebo-controlled trial of KarXT in hospitalized adults with DSM-5 schizophrenia experiencing an acute exacerbation or relapse of symptoms. We investigated the impact of anxiety on drug placebo separation and drug and placebo response in 2 ways. In the first set of analyses, we dichotomized the data based on the absence or presence of anxiety symptoms. In the second set of analyses, we categorized subjects by levels of anxiety. All analyses were conducted using generalized linear models with normal distribution and identity link function. On average, subjects entering the trial were suffering from a moderate level of anxiety. Subjects with no baseline anxiety had a significant increase in placebo response, a decrease in drug response and did not separate drugs from placebo. With increasing levels of baseline anxiety, a larger drug placebo difference was observed. Our analyses identified that absence of anxiety at baseline was associated with a loss of signal at end of treatment between drug and placebo driven by a differential effect on placebo and treatment response. The effect observed was not related to the overall baseline symptom severity and was not mediated by improvement in anxiety itself. Interpretation of the results is caveated by the retrospective nature of the analyses.

Meningioma classification by immunohistochemistry: A replicability study

IntroductionMeningiomas account for nearly 40% of intracranial tumors. Recently, the immunohistochemistry (IHC) markers S100B, SCGN, ACADL and MCM2 have been shown to be associated with underlying biological subtypes of meningioma (MG1-MG4). We aimed to evaluate these IHC markers in a clinical setting. Research questionAre the new proposed IHC markers clinically useful? MethodsIn total, 244 patients with meningiomas with tissue in TMAs were included and the IHC markers S100B, SCGN, ACADL and MCM2 were analyzed. Two sets of analyses were performed; the first included all samples with any staining considered positive, the second only samples with >10% immunopositivity. PFS and OS were analyzed in correlation to immunopositivity in the second analysis set. ResultsIn the first set of analyses only 26.2% of samples could be to allocate to one group. No further analyses were performed with this selection. In the second set of analyses 52.0% could be allocated to a group. There was an enrichment of WHO grade 2 and 3 tumors in MG3 and MG4 as compared to MG1 (24.1% and 25.7% vs. 12.1%). Both the molecular group (p ​= ​0.032) and WHO grade (p ​= ​0.005) had significant impact on PFS, but only WHO grade predicted OS (p ​= ​0.033). ConclusionWe studied the proposed new method of classifying meningiomas into groups MG1, MG2, MG3 and MG4 using IHC markers, but found difficulties applying the classification system in our material mainly due to lack of exclusivity of markers. Thus, in its present form the classification method lacks clinical applicability.

Workplace Deceptions During the Pandemic: Differences in Conspiracy Beliefs, Psychological Functioning, and Covid-19 Experiences.

The global pandemic has disrupted virtually all countries on health, psychological functioning, and economies, to name a few. Accurate information has also fallen victim to the pandemic, which has been rife with misinformation and conspiracy theories. The current study investigated Covid-19 deceptions related to employment. With complete anonymity via MTurk, 389 participants from the United States rated their likelihood of deception regarding hypothetical four workplace scenarios. The first set of analyses examined differences between high and low risk of deceptions for each scenario based on participants' self-appraisals. The largest differences were found for general conspiracy beliefs and affective disorders, specifically major depression and generalized anxiety. The second set of analyses focused across the workplace scenarios on two operationalized groups with Likely-Deceptive (n = 189) vastly outnumbering Likely-Genuine (n = 55). Personal experiences with Covid-19 dramatically increased deceptions. Testing positive for Covid-19 increased the odds of being in the Likely-Deceptive by twelve-fold. Two discriminant models examined cognitive misbeliefs and psychological functioning. When both were combined, depression and Covid-19 misinformation produced the strongest structure coefficients followed closely by general conspiracy beliefs and generalized anxiety. The far-ranging implications of these findings are discussed.

Impact of Timing of Autologous Hematopoietic Cell Transplantation in Primary Central Nervous System Lymphoma: A University of California Hematologic Malignancies Consortium Retrospective Analysis

Background: Primary central nervous system lymphoma (PCNSL) is potentially curable when high-dose methotrexate-based induction is followed with intensive consolidation, which can consist of non-myeloablative chemotherapy (NMA-C) or myeloablative chemotherapy (MAC) followed by autologous hematopoietic cell transplantation (autoHCT). Although the efficacy of autoHCT has been demonstrated, very limited data is available regarding the optimal timing of autoHCT, i.e., to transplant in first complete remission (CR1) or second or later CR (CR2+). Moreover, the choice of consolidation differs between centers. Recently, the CALGB 51101 trial showed that MAC leads to improved progression-free survival (PFS) compared to NMA-C, but limiting this finding are that more patients who were randomized to receive NMA-C went off treatment prior to consolidation, either as a result of progression or death. To clarify the optimal timing of autoHCT in PCNSL and to support the findings of the CALGB 51101 trial, we conducted a multi-center retrospective analysis. Methods: We retrospectively analyzed patients ≥18 years treated at the University of California campuses in Davis, Fresno, Irvine, and Los Angeles who were diagnosed between January 2005-July 2021. Patients were divided into three cohorts: cohort A included those who underwent autoHCT in CR1, cohort B included those who underwent autoHCT in CR2+, and cohort C included those who underwent NMA-C in CR1 (irrespective of whether or not they underwent autoHCT in CR2+). In the first set of analyses, outcomes were compared between cohort A and B, and in the second set, outcomes were compared between cohort A and C. CR was defined by the lack of enhancement on magnetic resonance imaging. Patients in cohort A and B who were not in CR at the time of autoHCT, and those in cohort C not in CR at the time of NMA-C, were excluded. The primary endpoint was 2-year overall survival (OS), and secondary endpoints were 2-year progression-free survival (PFS), non-relapse mortality (NRM) and cumulative incidence of relapse (CIR). Time-to-event analyses were conducted using the Kaplan-Meier method and were compared by a log-rank test. NRM and CIR were calculated through a competing risk model, with NRM serving as a competing risk for CIR and vice versa. Univariate analyses were performed via Cox proportional hazards model. Patients alive at the end of the study were censored at their last follow-up date. Results: 69 unique patients were included, and their baseline characteristics are described in Table 1. 36 patients were included in cohort A, 10 in cohort B, and 28 in cohort C, 5 of whom underwent autoHCT in CR2+. The median follow-up times of cohorts A, B, and C were 67, 73, and 52 months, respectively. The 2-year OS from the time of diagnosis in cohort A was 94.3% (95% CI, 86.9-100) vs 90.0% (95% CI, 73.2-100) in cohort B (P=0.42), and 84.0% (95% CI, 70.7-99.8) in cohort C (P=0.072). The 2-year PFS from the time of transplant in cohorts A vs B were 91.2% (95% CI, 82.2-100) vs 67.5 (95% CI, 43-100) (P=0.008). The 2-year PFS from the time of diagnosis in cohorts A vs C were 91.4% (95% CI, 82.6-100) vs. 80.5% (95% CI, 66.4-97.6) (P=0.0016) (Figure 1). The 2-year NRM and CIR for cohorts A vs B were 5.8% vs 21.2% (P=0.179) and 2.9% vs 11.3% (P=0.037), respectively. The 2-year NRM and CIR for cohorts A vs C were 5.8% vs 3.6% (P=0.74) and 2.9% vs 16.0% (P<0.001) respectively. Univariate analyses including all 69 unique patients were performed for OS and PFS, and there were no significant associations based on age <60 vs ≥60 years, gender, ECOG performance status ≤1 vs ≥2, center of origin, and thiotepa/busulfan/cyclophosphamide (TBC) vs thiotepa/carmustine (TT-BCNU) conditioning. Conclusion: Although limited by the sample size and retrospective nature, whether autoHCT for PCNSL was performed in CR1 or CR2+ did not impact OS in our analysis, but CIR was higher and PFS was lower when autoHCT was performed in CR2+. Supporting the Alliance trial, autoHCT in CR1 produced longer PFS than NMA-C and patients deemed appropriate candidates for MAC consolidation in CR1 should be considered for autoHCT in this setting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Leveraging low-density crossbred genotypes to offset crossbred phenotypes and their impact on purebred predictions.

The objectives of this study were to 1) investigate the predictability and bias of genomic breeding values (GEBV) of purebred (PB) sires for CB performance when CB genotypes imputed from a low-density panel are available, 2) assess if the availability of those CB genotypes can be used to partially offset CB phenotypic recording, and 3) investigate the impact of including imputed CB genotypes in genomic analyses when using the algorithm for proven and young (APY). Two pig populations with up to 207,375 PB and 32,893 CB phenotypic records per trait and 138,026 PB and 32,893 CB genotypes were evaluated. PB sires were genotyped for a 50K panel, whereas CB animals were genotyped for a low-density panel of 600 SNP and imputed to 50K. The predictability and bias of GEBV of PB sires for backfat thickness (BFX) and average daily gain recorded (ADGX) recorded on CB animals were assessed when CB genotypes were available or not in the analyses. In the first set of analyses, direct inverses of the genomic relationship matrix (G) were used with phenotypic datasets truncated at different time points. In the next step, we evaluated the APY algorithm with core compositions differing in the CB genotype contributions. After that, the performance of core compositions was compared with an analysis using a random PB core from a purely PB genomic set. The number of rounds to convergence was recorded for all APY analyses. With the direct inverse of G in the first set of analyses, adding CB genotypes imputed from a low-density panel (600 SNP) did not improve predictability or reduce the bias of PB sires' GEBV for CB performance, even for sires with fewer CB progeny phenotypes in the analysis. That indicates that the inclusion of CB genotypes primarily used for inferring pedigree in commercial farms is of no benefit to offset CB phenotyping. When CB genotypes were incorporated into APY, a random core composition or a core with no CB genotypes reduced bias and the number of rounds to convergence but did not affect predictability. Still, a PB random core composition from a genomic set with only PB genotypes resulted in the highest predictability and the smallest number of rounds to convergence, although bias increased. Genotyping CB individuals for low-density panels is a valuable identification tool for linking CB phenotypes to pedigree; however, the inclusion of those CB genotypes imputed from a low-density panel (600 SNP) might not benefit genomic predictions for PB individuals or offset CB phenotyping for the evaluated CB performance traits. Further studies will help understand the usefulness of those imputed CB genotypes for traits with lower PB-CB genetic correlations and traits not recorded in the PB environment, such as mortality and disease traits.

Computational wear of knee implant polyethylene insert surface under continuous dynamic loading and posterior tibial slope variation based on cadaver experiments with comparative verification

BackgroundThe effect of posterior tibial slope on the maximum contact pressure and wear volume of polyethylene (PE) insert were not given special attention. The effects of flexion angle, Anterior-Posterior (AP) Translation, and Tibial slope on the max contact pressure and wear of PE insert of TKR were investigated under loadings which were obtained in cadaver experiments by using Archard’s wear law. This study uses not only loads obtained from cadaver experiments but also dynamic flexion starting from 0 to 90 degrees.MethodWear on knee implant PE insert was investigated using a 2.5 size 3 dimensional (3D) cruciate sacrificing total knee replacement model and Finite Element Method (FEM) under loadings and AP Translation data ranging from 0 to 90 flexion angles validated by cadaver experiments. Two types of analyses were done to measure the wear effect on knee implant PE insert. The first set of analyses included the flexion angles dynamically changing with the knee rotating from 0 to 90 angles according to the femur axis and the transient analyses for loadings changing with a certain angle and duration.ResultsIt is seen that the contact pressure on the PE insert decreases as the cycle increases for both Flexion and Flexion+AP Translation. It is clear that as the cycle increases, the wear obtained for both cases increases. The loadings acting on the PE insert cannot create sufficient pressure due to the AP Translation effect at low speeds and have an effect to reduce the wear, while the effect increases with the wear as the cycle increases, and the AP Translation now contributes to the wear at high speeds. It is seen that as the posterior tibial slope angle increases, the maximum contact pressure values slightly decrease for the same cycle.ConclusionsThis study indicated that AP Translation, which changes direction during flexion, had a significant effect on both contact pressure and wear. Unlike previous similar studies, it was seen that the amount of wear continues to increase as the cycle increases. This situation strengthens the argument that loading and AP Translation values that change with flexion shape the wear effects on PE Insert.

Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net.

Glioma segmentation is believed to be one of the most important stages of treatment management. Recent developments in magnetic resonance imaging (MRI) protocols have led to a renewed interest in using automatic glioma segmentation with different MRI image weights. U-Net is a major area of interest within the field of automatic glioma segmentation. This paper examines the impact of different input MRI image-weight on the U-Net output performance for glioma segmentation. One hundred forty-nine glioma patients were scanned with a 1.5T MRI scanner. The main MRI image-weights acquired are diffusion-weighted imaging (DWI) weighted images (b50, b500, b1000, Apparent diffusion coefficient (ADC) map, Exponential apparent diffusion coefficient (eADC) map), anatomical image-weights (T2, T1, T2-FLAIR), and post enhancement image-weights (T1Gd). The U-Net and data augmentation are used to segment the glioma tumors. Having the Dice coefficient and accuracy enabled us to compare our results with the previous study. The first set of analyses examined the impact of epoch number on the accuracy of U-Net, and n_epoch = 20 was selected for U-Net training. The mean Dice coefficient for b50, b500, b1000, ADC map, eADC map, T2, T1, T2-FLAIR, and T1Gd image weights for glioma segmentation with U-Net were calculated 0.892, 0.872, 0.752, 0.931, 0.944, 0.762, 0.721, 0.896, 0.694 respectively. This study has found that, DWI image-weights have a higher diagnostic value for glioma segmentation with U-Net in comparison with anatomical image-weights and post enhancement image-weights. The results of this investigation show that ADC and eADC maps have higher performance for glioma segmentation with U-Net.

The multiple indicator multiple cause model for cognitive neuroscience: An analytic tool which emphasizes the behavior in brain-behavior relationships.

Cognitive neuroscience has inspired a number of methodological advances to extract the highest signal-to-noise ratio from neuroimaging data. Popular techniques used to summarize behavioral data include sum-scores and item response theory (IRT). While these techniques can be useful when applied appropriately, item dimensionality and the quality of information are often left unexplored allowing poor performing items to be included in an itemset. The purpose of this study is to highlight how the application of two-stage approaches introduces parameter bias, differential item functioning (DIF) can manifest in cognitive neuroscience data and how techniques such as the multiple indicator multiple cause (MIMIC) model can identify and remove items with DIF and model these data with greater sensitivity for brain–behavior relationships. This was performed using a simulation and an empirical study. The simulation explores parameter bias across two separate techniques used to summarize behavioral data: sum-scores and IRT and formative relationships with those estimated from a MIMIC model. In an empirical study participants performed an emotional identification task while concurrent electroencephalogram data were acquired across 384 trials. Participants were asked to identify the emotion presented by a static face of a child across four categories: happy, neutral, discomfort, and distress. The primary outcomes of interest were P200 event-related potential (ERP) amplitude and latency within each emotion category. Instances of DIF related to correct emotion identification were explored with respect to an individual’s neurophysiology; specifically an item’s difficulty and discrimination were explored with respect to an individual’s average P200 amplitude and latency using a MIMIC model. The MIMIC model’s sensitivity was then compared to popular two-stage approaches for cognitive performance summary scores, including sum-scores and an IRT model framework and then regressing these onto the ERP characteristics. Here sensitivity refers to the magnitude and significance of coefficients relating the brain to these behavioral outcomes. The first set of analyses displayed instances of DIF within all four emotions which were then removed from all further models. The next set of analyses compared the two-stage approaches with the MIMIC model. Only the MIMIC model identified any significant brain–behavior relationships. Taken together, these results indicate that item performance can be gleaned from subject-specific biomarkers, and that techniques such as the MIMIC model may be useful tools to derive complex item-level brain–behavior relationships.