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The meiotic chromosome axis coordinates chromosome organization and interhomolog recombination in meiotic prophase and is essential for fertility. In S. cerevisiae, the HORMAD protein Hop1 mediates the enrichment of axis proteins at nucleosome-rich islands through a central chromatin-binding region (CBR). Here, we use cryoelectron microscopy to show that the Hop1 CBR directly recognizes bent nucleosomal DNA through a composite interface in its PHD and winged helix-turn-helix domains. Targeted disruption of the Hop1 CBR-nucleosome interface causes a localized reduction of axis protein binding and meiotic DNA double-strand breaks (DSBs) in axis islands and leads to defects in chromosome synapsis. Synthetic effects with mutants of the Hop1 regulator Pch2 suggest that nucleosome binding delays a conformational switch in Hop1 from a DSB-promoting, Pch2-inaccessible state to a DSB-inactive, Pch2-accessible state to regulate the extent of meiotic DSB formation. Phylogenetic analyses of meiotic HORMADs reveal an ancient origin of the CBR, suggesting that the mechanisms we uncover are broadly conserved.

Abstract: Immune effector cell–associated hematotoxicity (ICAHT) is associated with morbidity and mortality after chimeric antigen receptor (CAR) T-cell therapy. To date, the factors associated with ICAHT are poorly characterized, and there is no validated predictive model of ICAHT as defined by current consensus criteria. Therefore, we performed comprehensive univariate analyses to identify factors associated with severe (grade 3-4) early ICAHT (eICAHT) in 691 patients who received commercial or investigational CAR T-cell therapy for hematologic malignancies. In univariate logistic regression, preinfusion factors associated with severe eICAHT included disease type (acute lymphoblastic leukemia), prelymphodepletion (pre-LD) blood counts including absolute neutrophil count (ANC), lactate dehydrogenase (LDH), and inflammatory (C-reactive protein [CRP], ferritin, and interleukin-6 [IL-6]) and coagulopathy biomarkers (D-dimer). Postinfusion laboratory markers associated with severe eICAHT included early and peak levels of inflammatory biomarkers (CRP, ferritin, and IL-6), coagulopathy biomarkers (D-dimer), peak cytokine release syndrome grade, and peak neurotoxicity grade. We trained (n = 483) and validated (n = 208) 2 eICAHT prediction models (eIPMs): eIPMPre including preinfusion factors only (disease type and pre-LD ANC, platelet count, LDH, and ferritin) and eIPMPost containing both preinfusion (disease type and pre-LD ANC, platelet count, and LDH) and early postinfusion (day +3 ferritin) factors. Both models generated calibrated predictions and high discrimination (area under the receiver operating characteristic curve in test set, 0.87 for eIPMPre and 0.88 for eIPMPost), with higher net benefit in decision curve analysis for eIPMPost. Individualized predictions of severe eICAHT can be generated from both eIPMs using our online tool (available at https://eipm.fredhutch.org).

Abstract: Measurable residual disease (MRD) by multiparametric flow cytometry (MFC) before allogeneic hematopoietic cell transplantation (HCT) identifies patients at high risk of acute myeloid leukemia (AML) relapse, often occurring early after allografting. To examine the role of MFC MRD testing to predict later relapses, we examined 935 adults with AML or myelodysplastic neoplasm/AML transplanted in first or second morphologic remission who underwent bone marrow restaging studies between day 70 and 100 after HCT and were alive and without relapse by day +100. Of 935 adults, 136 (15%) had MRD before HCT, whereas only 11 (1%) had MRD at day +70 to +100. In day +100 landmark analyses, pre-HCT and day +70 to +100 MFC MRD were both associated with relapse (both P < .001), relapse-free survival (RFS; both P < .001) overall survival (OS; both P < .001), and, for post-HCT MRD, nonrelapse mortality (P = .001) after multivariable adjustment. Importantly, although 126/136 patients (92%) with MRD before HCT tested negative for MRD at day +70 to +100, their outcomes were inferior to those without MRD before HCT and at day +70 to +100, with 3-year relapse risk of 40% vs 15% (P < .001), 3-year RFS of 50% vs 72% (P < .001), and 3-year OS of 56% vs 76% (P < .001), whereas 3-year nonrelapse mortality estimates were similar (P = .53). Thus, despite high MRD conversion rates, outcomes MRD positive/MRD negative (MRDneg) patients are inferior to those of MRDneg/MRDneg patients, suggesting all patients with pre-HCT MRD should be considered for preemptive therapies after allografting.

Objective: The transplantation of hearts from donors who experienced intracranial bleeding (ICB) has been associated with inferior long-term survival in both single-center analyses and, more recently, with the United Network for Ogan Sharing Registry. The purpose of this study was to further explore this relationship through propensity score matching in recipients receiving donor hearts from ICB and non-ICB donors in a large national registry. Methods: We performed a retrospective cohort analysis of the United Network for Organ Sharing Registry Organ Procurement and Transplantation Network between 2006 and 2018 for adult candidates wait-listed for isolated heart transplantation. Recipients were stratified into 2 groups: ICB and non-ICB donors. Propensity score matching was performed to estimate causal effects by using observational data. Kaplan-Meier analysis was used to estimate survival posttransplant. Cox proportional hazards modeling was used to evaluate the independent effect of ICB as a cause of death. Results: A total of 25,315 candidates met inclusion criteria. ICB heart donors (n = 5529) were older (median age, 42 vs 27 years; P

Objective: Biologic valves dominate tricuspid valve replacement, yet data on different valve types are lacking. We compare the survival and durability of porcine and pericardial tricuspid prostheses. Methods: A retrospective review of consecutive patients undergoing tricuspid valve replacement with porcine (N = 542) or pericardial (N = 144) prostheses between 1975 and 2022 was performed using a prospectively maintained institutional database. Concurrent procedures were included. Cox proportional hazards and logistic regression were performed. Results: Patients who received the porcine prosthesis, compared with pericardial, were younger (56 ± 17 years vs 63 ± 15 years) and more likely to present urgently (55% porcine, 44% pericardial); however, there were no differences in redo status or concomitant operations. Ten-year survival was not significantly different between the porcine and pericardial groups (35% ± 3% vs 28% ± 4%, respectively, P = .2). The 10-year cumulative incidence of structural valve deterioration (porcine 9% ± 2%, pericardial 11% ± 3%, P = .8), reoperation for structural valve deterioration (porcine 5% ± 1%, pericardial 4% ± 2%, P = .06), and severe regurgitation (porcine 4% ± 1%, pericardial 5% ± 2%, P = .7) were not significantly different between groups. The failure mode was similar, with no difference in severe stenosis (porcine 32/47 [68%], pericardial 11/16 [69%], P = .9) or severe regurgitation (porcine 18/47 [38%], pericardial 7/16 [44%], P = .7). On regression analysis, valve type was not associated with survival (P = .6). Valve type was not associated with structural valve deterioration (P = .1) or reoperation for structural valve deterioration (P = .9). Conclusions: In our series, there were no differences in survival or durability between porcine and pericardial valves. In most patients undergoing tricuspid valve replacement, the choice of porcine versus pericardial prosthesis is unlikely to affect clinical outcomes.

Objective: Randomized data support transplantation of hearts from donors after circulatory death. This may lead to a sizeable increase in the donor pool. Regional variations in donors after circulatory death heart use were examined to help elucidate barriers to donor pool expansion. Methods: The United Network for Organ Sharing deceased donor dataset was queried for adult (age ≥ 18 years) donors after circulatory death donors of at least 1 organ between January 2020 and December 2023. Donors were stratified by the extent their respective cardiac allografts progressed through the donation process. United Network for Organ Sharing region-level use rates and annual trends were assessed. Results: Of 17,239 adult donors after circulatory death donors who donated at least 1 organ for transplant during the study period, 1196 (9.4%) were heart donors. Regional donors after circulatory death heart donor pursuit rates ranged from 97% to 100%, consent attainment rates from 94% to 99%, and heart recovery rates from 5% to 10%. The transplantation rate of recovered organs ranged from 90% to 97%. Multivariable logistic regression demonstrated United Network for Organ Sharing region to be independently associated with donors after circulatory death heart use after controlling for baseline differences in donor risk. Conclusions: Transplantation of donors after circulatory death heart allografts has increased in the United States since 2020, but the overall number of hearts procured and transplanted from donors after circulatory death donors remains low. The operational barriers to transplantation of donors after circulatory death hearts require further investigation. Further, significant regional variation exists regarding rates of progression of donors after circulatory death hearts through the donation process. Sharing of successful practices among Organ Procurement Organizations and transplant centers will facilitate maximal use of this new donor pool.

Short-form development is an important topic in psychometric research, which requires researchers to face methodological choices at different steps. The statistical techniques traditionally used for shortening tests, which belong to the so-called exploratory model, make assumptions not always verified in psychological data. This article proposes a machine learning-based autonomous procedure for short-form development that combines explanatory and predictive techniques in an integrative approach. The study investigates the item-selection performance of two autoencoders: a particular type of artificial neural network that is comparable to principal component analysis. The procedure is tested on artificial data simulated from a factor-based population and is compared with existent computational approaches to develop short forms. Autoencoders require mild assumptions on data characteristics and provide a method to predict long-form items' responses from the short form. Indeed, results show that they can help the researcher to develop a short form by automatically selecting a subset of items that better reconstruct the original item's responses and that preserve the internal structure of the long-form.

Introduction: Individuals with esophageal and gastroesophageal junction (GEJ) cancers are at especially high risk of malnutrition. However, most patients with malnutrition do not receive adequate nutritional support. We conducted a single-arm trial to test the implementation of Support Through Remote Observation and Nutrition Guidance (STRONG), a multilevel digital intervention to improve nutritional outcomes for patients with locally advanced esophageal and gastroesophageal junction cancer. Methods: Participants received five nutritional counseling sessions with a dietitian, logged daily food intake through the Fitbit app, and completed five study assessments related to patient malnutrition, nutrition-related symptoms, and quality-of-life outcomes. We assessed the feasibility, acceptability, and usability of STRONG against a priori benchmarks. Results: Participants (N = 17) had a median age of 68 years, and 71 % were male. Feasibility benchmarks were met for participants completing the baseline assessment (94 %), completing four out of five assessments (82 %), and participant retention (85 %). Among participants who only received an oral diet during the study period, adherence to dietetic appointments (89 %) and food intake tracking (78 %) were high. Participant recruitment rate (47 %) was slightly below the benchmark (50 %). All participants found the intervention to be acceptable. Usability of the intervention was high, with 69 % and 92 % of participants reporting high satisfaction with tracking food intake through the Fitbit and the dietitian-led nutrition counseling sessions, respectively. Conclusion: Our study demonstrated that STRONG could be implemented with high feasibility, acceptability, and usability for esophageal and GEJ cancer patients. Findings from this study can guide a future efficacy study to assess the impact of STRONG on patient outcomes. Clinical trial registration: The Support Through Remote Observation and Nutrition Guidance Program for Gastroesophageal Cancer Patients (STRONG-GEC) study was registered on clinicaltrials.gov (NCT05438940) in June 2022 prior to participant enrollment.

In this article, we explore the use of Large Language Models (LLMs) for predicting factor loadings in personality tests through the semantic analysis of test items. By leveraging text embeddings generated from LLMs, we evaluate the semantic similarity of test items and their alignment with hypothesized factorial structures without depending on human response data. Our methodology involves using embeddings from four different personality test to examine correlations between item semantics and their grouping in principal factors. Our results indicate that LLM-derived embeddings can effectively capture semantic similarities among test items, showing moderate to high correlation with the factorial structure produced by humans respondents in all tests, potentially serving as a valid measure of content validity for initial survey design and refinement. This approach offers valuable insights into the robustness of embedding techniques in psychological evaluations, showing a significant correlation with traditional test structures and providing a novel perspective on test item analysis.

ABSTRACT The study aimed to evaluate the applicability of the association of two classes of drugs. Ten healthy cats were used, who underwent echocardiographic evaluation and measurement of systemic blood pressure at baseline and after administration of dexmedetomidine at a dose of (10 mcg/kg) and butorphanol at a dose of (0.4mg/kg) intramuscularly. In the results obtained, there was a 12% decrease in free wall thickness in diastole in M mode (p=0.017), in addition to a 47% decrease in heart rate (p< 0.0001) and a 30% increase in systemic blood pressure (p

IntroductionEvidence increasingly shows that facial emotion recognition (FER) is impaired in refractory mesial temporal lobe epilepsy (rMTLE), especially in patients with a right focus. This study explores FER in both mild (mMTLE) and refractory forms, examining the influence of epileptic focus lateralization on FER.Methods50 MTLE patients, categorized by epilepsy severity and focus lateralization, were compared with healthy controls. FER was assessed using the Ekman Faces Test (EFT), which evaluates recognition of six basic emotions, alongside a battery of cognitive and mood tests.ResultsmMTLE patients showed selective deficits in recognizing fear and anger, while rMTLE patients displayed broader deficits, affecting all emotions except surprise. Patients with a right focus underperformed across all negative emotions, whereas those with a left focus showed deficits mainly in fear and anger. Analysis indicated that early epilepsy onset was associated with poorer FER in right-focused patients; febrile seizures and mesial temporal sclerosis significantly impacted FER in left-focused patients.ConclusionMTLE affects circuits of FER even in mild subjects, although to a lesser extent than in refractory ones. Earlier onset of MTLE could disrupt the development of FER, possibly interfering during a critical phase of maturation of its circuits, when the focus is right. Conversely, left MTLE may cause less damage to FER circuits, requiring additional factors such as a history of febrile seizures and/or mesial temporal sclerosis for significant impact. Clinically, refractory and right-sided MTLE might be viewed as risk factors of FER deficits.

Plastic materials, known for their lightweight, formability, transparency, and durability, are the state of the art for building façade applications. Recent advances in Large-Scale Robotic 3D Printing (LSR3DP) have enabled the production of bespoke, translucent façade components. While research has largely focused on individual panel properties, there is a gap in developing a comprehensive strategy for integrating these components into a complete façade system. This paper explores the potential of combining custom 3D-printed façade elements with standard curtain wall connections. Quantitative analysis involves constructing and testing a 1 m x 1 m LSR3DP façade assembly for air and water tightness, benchmarking its performance against a conventional curtain wall. Qualitatively, the approach is evaluated through a mock-up, highlighting the architectural possibilities of blending standard and non-standard façade elements. The findings demonstrate that this hybrid system is both technically viable and opens new design possibilities for architects and façade engineers.

IntroductionMissing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or regression, commonly used to handle missing data, rely upon assumptions that may not hold on psychological data and can lead to distorted results.MethodsThis study aims to evaluate the effectiveness of transformer-based deep learning for missing data imputation, comparing ReMasker, a masking autoencoding transformer model, with conventional imputation techniques (mean and median imputation, Expectation–Maximization algorithm) and machine learning approaches (K-nearest neighbors, MissForest, and an Artificial Neural Network). A psychometric dataset from the COVID distress repository was used, with imputation performance assessed through the Root Mean Squared Error (RMSE) between the original and imputed data matrices.ResultsResults indicate that machine learning techniques, particularly ReMasker, achieve superior performance in terms of reconstruction error compared to conventional imputation techniques across all tested scenarios.DiscussionThis finding underscores the potential of transformer-based models to provide robust imputation in psychometric research, enhancing data integrity and generalizability.

Heart failure is a serious and challenging medical condition characterized by the inability of the heart to pump blood effectively, leading to reduced blood flow to organs and tissues. Several underlying causes may be linked to this, including coronary artery disease, hypertension, or previous heart attacks. Therefore, it is a chronic condition that requires ongoing management and medical attention. HF affects >64 million individuals worldwide. Heart transplantation remains the gold standard of treatment for patients with end-stage cardiomyopathy. The recruitment of marginal donors may be considered an asset at the age of cardiac donor organ shortage. Primary graft dysfunction (PGD) is becoming increasingly common in the new era of heart transplantations. PGD is the most common cause of death within 30 days of cardiac transplantation. Mechanical Circulatory Support (MCS), particularly venoarterial extracorporeal membrane oxygenation (V-A ECMO), is the only effective treatment for severe PGD. VA-ECMO support ensures organ perfusion and provides the transplanted heart with adequate rest and recovery. In the new era of heart transplantation, early use allows for increased patient survival and careful management reduces complications.

Abstract Self‐organizing memristive nanowire (NW) networks are promising candidates for neuromorphic‐type data processing in a physical reservoir computing framework because of their collective emergent behavior, which enables spatiotemporal signal processing. However, understanding emergent dynamics in multiterminal networks remains challenging. Here experimental spatiotemporal characterization of memristive NW networks dynamics in multiterminal configuration is reported, analyzing the activation and relaxation of network's global and local conductance, as well as the inherent spatial nonlinear transformation capabilities. Emergent effects are analyzed i) during activation, by investigating the spatiotemporal dynamics of the electric field distribution across the network through voltage mapping; ii) during relaxation, by monitoring the evolution of the conductance matrix of the multiterminal system. The multiterminal approach also allowed monitoring the spatial distribution of nonlinear activity, demonstrating the impact of different network areas on the system's information processing capabilities. Nonlinear transformation tasks are experimentally performed by driving the network into different conductive states, demonstrating the importance of selecting proper operating conditions for efficient information processing. This work allows a better understanding of the local nonlinear dynamics in NW networks and their impact on the information processing capabilities, providing new insights for a rational design of self‐organizing neuromorphic systems.