julilili42/papers huggingface dataset:
1036-P: The Association between Maternal Gestational Weight Gain and Autonomic Nervous System Is Disturbed in GDM: Results from Two-Year-Old Children
2022-05-31

Objective: The intrauterine environment is known to affect the offspring's long-term risk for obesity and diabetes. Previous data show that maternal metabolism and gestational weight gain (GWG) are associated with autonomic nervous system (ANS) function in fetuses in utero, which can be assessed with heart rate variability (HRV) . We have now examined whether this association is also present in 2-year-old children and addressed the impact of gestational diabetes (GDM) . Research Design and Methods: We examined children of participants of the ongoing PREG study (NCT04270578) who had a 5-point 75g oral glucose tolerance test during pregnancy. To assess HRV, a 10-minute ECG was recorded in the offspring. We analyzed time domain (beats per minute (BPM) , Root Mean Square of successive differences (RMSSD)) , Standard Deviation of RR-Intervals (SDNN) and frequency domain parameters (low frequency (LF) , high frequency (HF) and LF/HF) of HRV. Results: We examined 67 children (33 girls, 34 boys, age 25.3±1.6 months) . During pregnancy, 30 of their mothers had GDM, which was treated according to national guidelines. There were no differences between groups in birth weight, weight at age 2, and body fat. We observed statistically significant associations of GWG with heart rate, RMSSD, SDNN, HF power, and HF/LF, indicating a lower parasympathetic tone in children of mothers with low GWG. This association was attenuated in GDM-exposed children. ANS function correlated with body weight and body fat only in children from normoglycemic pregnancies (NGT) . Conclusion: We found an impact of maternal GWG on offspring ANS function, which was missing in the presence of treated GDM. The balance of the ANS was related to offspring body composition only in children from NGT pregnancies. Our results point towards a critical impact of maternal weight gain during pregnancy on the developing ANS with longstanding consequences for body composition. Disclosure L.Fritsche: None. H.Preissl: None. M.Heni: Advisory Panel; Boehringer Ingelheim International GmbH, Research Support; Boehringer Ingelheim International GmbH, Sanofi, Speaker's Bureau; Amryt Pharma Plc, Boehringer Ingelheim International GmbH, Novo Nordisk. J.Hartkopf: None. J.Hummel: None. D.Löffler: None. H.Häring: None. A.Peter: None. A.L.Birkenfeld: None. R.Wagner: Advisory Panel; Akcea Therapeutics, Daiichi Sankyo, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Lilly, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. A.Fritsche: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. Funding The PREG study is supported in part by a grant from the Federal Ministry of Education and Research (BMBF) (01GI0925) to the German Center for Diabetes Research (DZD) and by grant from the Deutsche Diabetes Stiftung (380/02/16) to LF
180-OR: Empagliflozin Does Not Affect Pancreatic Fat Content and Insulin Secretion in Humans with Prediabetes
2022-05-31

Introduction: Pancreatic fat accumulation is a potential pathogenic factor in type 2 diabetes. Evidence from hypocaloric diet indicates that reduction of pancreatic fat could contribute to an improvement in ß-cell function. There is no established pharmacological approach to reduce pancreatic steatosis. We now tested the ability of empagliflozin to reduce pancreatic fat content in overweight and obese persons with prediabetes. Methods: In this double-blind, placebo-controlled randomized trial (NCT03227484) , 40 participants were 1:1 randomized to receive 25 mg empagliflozin qd or placebo for 8 weeks. Pancreatic fat was quantified by magnetic resonance imaging (6-point Dixon technique) . Insulin sensitivity and insulin secretion was estimated from 75g oral glucose tolerance test. The study was adequately powered to detect effects on pancreatic fat previously reported from the DiRECT trial. Results: There was no significant change in pancreatic fat content in either treatment group (empagliflozin group -0.88 % ± 2.71 %, placebo group 0.22 % ± 3.02 % [mean±SD], both p≥0.2) and the course of pancreatic fat content was comparable between treatments (p=0.2) . Insulin secretion, as assessed by Disposition Index and AUC-C-peptide0-30/AUC-glucose0-30 (adjusted for insulin sensitivity) did not differ between treatments (p=0.3 and p=0.7, respectively) . Discussion: This randomized, controlled trial did not detect effects of empagliflozin on pancreatic fat content and insulin secretion in persons with prediabetes. This implicates that the beneficial effects of empagliflozin on liver fat and systemic glucose metabolism do not rely on alterations in pancreatic fat. Consequently, further options for pancreatic fat reduction need to be evaluated. Disclosure J.Hummel: None. M.Heni: Advisory Panel; Boehringer Ingelheim International GmbH, Research Support; Boehringer Ingelheim International GmbH, Sanofi, Speaker's Bureau; Amryt Pharma Plc, Boehringer Ingelheim International GmbH, Novo Nordisk. J.Machann: None. C.Dannecker: None. S.Kullmann: Other Relationship; Novo Nordisk. A.L.Birkenfeld: None. H.Häring: None. A.Peter: None. A.Fritsche: Advisory Panel; Boehringer Ingelheim International GmbH, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. R.Wagner: Advisory Panel; Akcea Therapeutics, Daiichi Sankyo, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Lilly, Novo Nordisk, Sanofi-Aventis Deutschland GmbH. Funding This study was supported by Boehringer Ingelheim through an Independent Research Grant. Boehringer Ingelheim had role in the study design, in the collection, analysis or interpretation of data, or in writing of this abstract.
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
2022-05-31

This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data: adaptscore_acute.csv A csv file that contains the estimated adaptation scores for the acute data set with HLA I model. adaptscore_leftout.csv A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model adaptscore_training.csv A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model adaptscore_training_hla1_without_clin.csv A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation) adaptscore_training_seed2.csv A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
2022-06-01

For the last few decades, several major subfields of artificial intelligence including computer vision, graphics, and robotics have progressed largely independently from each other. Recently, however, the community has realized that progress towards robust intelligent systems such as self-driving cars requires a concerted effort across the different fields. This motivated us to develop KITTI-360, successor of the popular KITTI dataset. KITTI-360 is a suburban driving dataset which comprises richer input modalities, comprehensive semantic instance annotations and accurate localization to facilitate research at the intersection of vision, graphics and robotics. For efficient annotation, we created a tool to label 3D scenes with bounding primitives and developed a model that transfers this information into the 2D image domain, resulting in over 150k images and 1B 3D points with coherent semantic instance annotations across 2D and 3D. Moreover, we established benchmarks and baselines for several tasks relevant to mobile perception, encompassing problems from computer vision, graphics, and robotics on the same dataset, e.g., semantic scene understanding, novel view synthesis and semantic SLAM. KITTI-360 will enable progress at the intersection of these research areas and thus contribute towards solving one of today's grand challenges: the development of fully autonomous self-driving systems.
BEHAVE: Dataset and Method for Tracking Human Object Interactions
2022-06-01

Modelling interactions between humans and objects in natural environments is central to many applications including gaming, virtual and mixed reality, as well as human behavior analysis and human-robot collaboration. This challenging operation scenario requires generalization to vast number of objects, scenes, and human actions. Unfortunately, there exist no such dataset. Moreover, this data needs to be acquired in diverse natural environments, which rules out 4D scanners and marker based capture systems. We present BEHAVE dataset, the first full body human-object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. We record ~15k frames at 5 locations with 8 subjects performing a wide range of interactions with 20 common objects. We use this data to learn a model that can jointly track humans and objects in natural environments with an easy-to-use portable multi-camera setup. Our key insight is to predict correspondences from the human and the object to a statistical body model to obtain human-object contacts during interactions. Our approach can record and track not just the humans and objects but also their interactions, modeled as surface contacts, in 3D. Our code and data can be found at: http://virtualhumans.mpi-inf.mpg.de/behave.
Realizing the symmetry-protected Haldane phase in Fermi–Hubbard ladders
2022-06-01

Abstract Topology in quantum many-body systems has profoundly changed our understanding of quantum phases of matter. The model that has played an instrumental role in elucidating these effects is the antiferromagnetic spin-1 Haldane chain 1,2 . Its ground state is a disordered state, with symmetry-protected fourfold-degenerate edge states due to fractional spin excitations. In the bulk, it is characterized by vanishing two-point spin correlations, gapped excitations and a characteristic non-local order parameter 3,4 . More recently it has been understood that the Haldane chain forms a specific example of a more general classification scheme of symmetry-protected topological phases of matter, which is based on ideas connected to quantum information and entanglement 5–7 . Here, we realize a finite-temperature version of such a topological Haldane phase with Fermi–Hubbard ladders in an ultracold-atom quantum simulator. We directly reveal both edge and bulk properties of the system through the use of single-site and particle-resolved measurements, as well as non-local correlation functions. Continuously changing the Hubbard interaction strength of the system enables us to investigate the robustness of the phase to charge (density) fluctuations far from the regime of the Heisenberg model, using a novel correlator.
Reversal of synapse loss in Alzheimer mouse models by targeting mGluR5 to prevent synaptic tagging by C1Q
2022-06-01

Microglia-mediated synaptic loss contributes to the development of cognitive impairments in Alzheimer’s disease (AD). However, the basis for this immune-mediated attack on synapses remains to be elucidated. Treatment with the metabotropic glutamate receptor 5 (mGluR5) silent allosteric modulator (SAM), BMS-984923, prevents β-amyloid oligomer–induced aberrant synaptic signaling while preserving physiological glutamate response. Here, we show that oral BMS-984923 effectively occupies brain mGluR5 sites visualized by [ 18 F]FPEB positron emission tomography (PET) at doses shown to be safe in rodents and nonhuman primates. In aged mouse models of AD ( APPswe/PS1 Δ E9 overexpressing transgenic and App NL-G-F / hMapt double knock-in), SAM treatment fully restored synaptic density as measured by [ 18 F]SynVesT-1 PET for SV2A and by histology, and the therapeutic benefit persisted after drug washout. Phospho-TAU accumulation in double knock-in mice was also reduced by SAM treatment. Single-nuclei transcriptomics demonstrated that SAM treatment in both models normalized expression patterns to a far greater extent in neurons than glia. Last, treatment prevented synaptic localization of the complement component C1Q and synaptic engulfment in AD mice. Thus, selective modulation of mGluR5 reversed neuronal gene expression changes to protect synapses from damage by microglial mediators in rodents.
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning
2022-06-01

The goal of open-world compositional zero-shot learning (OW-CZSL) is to recognize compositions of state and objects in images, given only a subset of them during training and no prior on the unseen compositions. In this setting, models operate on a huge output space, containing all possible state-object compositions. While previous works tackle the problem by learning embeddings for the compositions jointly, here we revisit a simple CZSL baseline and predict the primitives, i.e. states and objects, independently. To ensure that the model develops primitive-specific features, we equip the state and object classifiers with separate, non-linear feature extractors. Moreover, we estimate the feasibility of each composition through external knowledge, using this prior to remove unfeasible compositions from the output space. Finally, we propose a new setting, i.e. CZSL under partial supervision (pCZSL), where either only objects or state labels are available during training, and we can use our prior to estimate the missing labels. Our model, Knowledge-Guided Simple Primitives (KG-SP), achieves state of the art in both OW-CZSL and pCZSL, surpassing most recent competitors even when coupled with semi-supervised learning techniques. Code available at: https://github.com/ExplainableML/KG-SP.
Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology
2022-06-01

Abstract Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype‐driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision‐making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal‐perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype‐driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care.
Nicotinamide‐N‐methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma
2022-06-01

Abstract Background The metabolic enzyme nicotinamide‐N‐methyltransferase (NNMT) is highly expressed in various cancer entities, suggesting tumour‐promoting functions. We systematically investigated NNMT expression and its metabolic interactions in clear cell renal cell carcinoma (ccRCC), a prominent RCC subtype with metabolic alterations, to elucidate its role as a drug target. Methods NNMT expression was assessed in primary ccRCC ( n = 134), non‐tumour tissue and ccRCC‐derived metastases ( n = 145) by microarray analysis and/or immunohistochemistry. Findings were validated in The Cancer Genome Atlas (kidney renal clear cell carcinoma [KIRC], n = 452) and by single‐cell analysis. Expression was correlated with clinicopathological data and survival. Metabolic alterations in NNMT‐depleted cells were assessed by nontargeted/targeted metabolomics and extracellular flux analysis. The NNMT inhibitor (NNMTi) alone and in combination with the inhibitor 2‐deoxy‐D‐glucose for glycolysis and BPTES (bis‐2‐(5‐phenylacetamido‐1,3,4‐thiadiazol‐2‐yl)ethyl‐sulfide) for glutamine metabolism was investigated in RCC cell lines (786‐O, A498) and in two 2D ccRCC‐derived primary cultures and three 3D ccRCC air–liquid interface models. Results NNMT protein was overexpressed in primary ccRCC ( p = 1.32 × 10 –16 ) and ccRCC‐derived metastases ( p = 3.92 × 10 –20 ), irrespective of metastatic location, versus non‐tumour tissue. Single‐cell data showed predominant NNMT expression in ccRCC and not in the tumour microenvironment. High NNMT expression in primary ccRCC correlated with worse survival in independent cohorts (primary RCC—hazard ratio [HR] = 4.3, 95% confidence interval [CI]: 1.5–12.4; KIRC—HR = 3.3, 95% CI: 2.0–5.4). NNMT depletion leads to intracellular glutamine accumulation, with negative effects on mitochondrial function and cell survival, while not affecting glycolysis or glutathione metabolism. At the gene level, NNMT‐depleted cells upregulate glycolysis, oxidative phosphorylation and apoptosis pathways. NNMTi alone or in combination with 2‐deoxy‐D‐glucose and BPTES resulted in inhibition of cell viability in ccRCC cell lines and primary tumour and metastasis‐derived models. In two out of three patient‐derived ccRCC air–liquid interface models, NNMTi treatment induced cytotoxicity. Conclusions Since efficient glutamine utilisation, which is essential for ccRCC tumours, depends on NNMT, small‐molecule NNMT inhibitors provide a novel therapeutic strategy for ccRCC and act as sensitizers for combination therapies.
Probabilistic Numerics
2022-06-01

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.
LITESPARK-022: A phase 3 study of pembrolizumab + belzutifan as adjuvant treatment of clear cell renal cell carcinoma (ccRCC).
2022-06-01

TPS4602 Background: Patients with locally advanced renal cell carcinoma (RCC) may experience recurrence after surgery. In the phase 3 KEYNOTE-564 (NCT03142334) trial, adjuvant pembrolizumab demonstrated significant improvement in disease-free survival (DFS) in ccRCC, leading to FDA approval of pembrolizumab (November 17, 2021) for adjuvant treatment of patients with RCC at intermediate-high or high risk for recurrence following nephrectomy or following nephrectomy and resection of metastatic lesions. Despite benefit of pembrolizumab, there is an unmet need for more effective adjuvant treatment for patients at risk for recurrence after definitive surgery. The HIF-2α inhibitor belzutifan (MK-6482) has shown activity and good tolerability in patients with advanced ccRCC and von Hippel-Lindau (VHL) disease–associated RCC. Combining belzutifan with pembrolizumab may be a therapeutic option as adjuvant treatment of ccRCC. This global, multicenter, double-blind, randomized, phase 3 study LITESPARK-022 (NCT05239728) is designed to compare the efficacy and safety of belzutifan + pembrolizumab with that of placebo + pembrolizumab as adjuvant treatment of ccRCC after nephrectomy. Methods: Approximately 1600 patients with histologically or cytologically confirmed RCC (intermediate-high [pT2, grade 4 or sarcomatoid, N0, M0 or pT3, any grade, N0, M0], high [pT4, any grade, N0, M0 or pT, any stage/grade, N+, M0] or M1 NED [patients who present with the primary kidney tumor and solid, isolated, soft tissue metastases that can be resected at the time of nephrectomy or ≤2 years from nephrectomy) with a clear cell component and had not previously received systemic therapy will be enrolled. Patients must have undergone nephrectomy and/or metastasectomy ≤12 weeks before randomization and be tumor free, confirmed by CT/MRI. Stratification factors are tumor grade (1 or 2 vs 3 or 4) and risk type (intermediate-high vs high vs M1 NED). Patients will be randomly assigned 1:1 to receive belzutifan 120 mg orally once daily + pembrolizumab 400 mg IV every 6 weeks (Q6W) or oral placebo + pembrolizumab 400 mg IV Q6W. Pembrolizumab treatment will be administered for up to 9 doses (̃1 year); belzutifan and placebo may be continued for a maximum of 54 weeks. Disease recurrence will be evaluated radiologically Q12W from randomization through year 2, Q16W in years 3-5, and Q24W in years 6 and beyond. Adverse events (AEs) will be monitored by NCI CTCAE v5.0 through 30 days (90 days for serious AEs) after cessation of study drug. The primary end point is DFS, as assessed by investigator, defined as time from randomization to first documented event of local recurrence, occurrence of distant kidney cancer metastasis, or death from any cause, whichever occurs first. The key secondary end point is overall survival. Other secondary end points are safety, disease recurrence–specific survival and patient-reported outcomes. Clinical trial information: NCT05239728.
Drug screen in iPSC-Neurons identifies nucleoside analogs as inhibitors of (G4C2)n expression in C9orf72 ALS/FTD
2022-06-01

An intronic (G<sub>4</sub>C<sub>2</sub>)<sub>n</sub> expansion in C9orf72 causes amyotrophic lateral sclerosis and frontotemporal dementia primarily through gain-of-function mechanisms: the accumulation of sense and antisense repeat RNA foci and dipeptide repeat (DPR) proteins (poly-GA/GP/GR/PA/PR) translated from repeat RNA. To therapeutically block this pathway, we screen a library of 1,430 approved drugs and known bioactive compounds in patient-derived induced pluripotent stem cell-derived neurons (iPSC-Neurons) for inhibitors of DPR expression. The clinically used guanosine/cytidine analogs decitabine, entecavir, and nelarabine reduce poly-GA/GP expression, with decitabine being the most potent. Hit compounds nearly abolish sense and antisense RNA foci and reduce expression of the repeat-containing nascent C9orf72 RNA transcript and its mature mRNA with minimal effects on global gene expression, suggesting that they specifically act on repeat transcription. Importantly, decitabine treatment reduces (G<sub>4</sub>C<sub>2</sub>)<sub>n</sub> foci and DPRs in C9orf72 BAC transgenic mice. Our findings suggest that nucleoside analogs are a promising compound class for therapeutic development in C9orf72 repeat-expansion-associated disorders.