Miracle-dz/UniFAR-FTUs huggingface dataset:
In-stent restenosis in acute coronary syndrome—a classic and a machine learning approach

Background In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML). Objectives The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI). Methods Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables. Results The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter. Conclusion ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.
Landmark processing by head direction cells

Head direction (HD) cells are neurons that increase their firing rate whenever a rat faces within a range of heading directions, irrespective of its location within the environment. Their direction-specific firing is multisensory, where visual cues have a dominant role in controlling the preferred firing direction (PFD) to which an HD cell fires. Many studies have examined the role of visual cues in controlling the firing of HD and other spatially modulated cells, however, little is known about how visual information is integrated with a spatial navigation signal. Therefore, the aim of this thesis is to investigate what properties of the visual environment are detected and used by the navigation system. To investigate this question, HD cells were recorded in the retrosplenial cortex (RSC) and the postsubiculum (PoS) of rats as they explored an arena with two oppositely positioned cards that varied in contrast, or the orientation, height and lateral position of a bar. The cue cards were rotated together to test the hypothesis that HD cells use differences in the visual properties of local cues to align their PFD. A second experiment tested the hypothesis that PoS HD cells process the configuration of landmarks, examining changes in the PFD when the spatial relation between multiple local cues was altered. The main finding from these experiments is that contrast and orientation were reliably used as landmarks, while height and lateral position had a weaker effect in controlling the activity of HD cells. The second finding is that PoS HD cells were sensitive to changes in the spatial arrangement of familiar cues, selecting the cues that changed their position over those that remained stationary. Overall, these results show that HD cells process fine-grained visual form which might be used for complex image analysis and landmark processing.
The gender gap in the relationship between Metabolic Syndrome and Restrictive Ventilatory Defect.

Abstract Background: Given the fundamental physiological differences between the sexes, this study aimed to investigate the effect of metabolic syndrome on ventilatory defects stratified by sex. Methods: We conducted a nationwide, pooled, cross-sectional study. Data of 45,788 participants (men, n=15,859; women, n=29,929) aged 30 years or more were obtained from the Taiwan Biobank. Age-sex adjusted and multivariate logistic regression models were used to estimate the risk of developing impaired pulmonary function (restrictive or obstructive ventilatory defect) in individuals with or without metabolic syndromes. Separate models were also used to estimate for the risk of metabolic syndrome scores and the risk of individual metabolic abnormalities on the risk of restrictive ventilatory defect. Results: The overall prevalence of metabolic syndrome was estimated to be 15.9% in Taiwan, much higher in men than in women (18.6% versus 14.4%). Significant association was observed on the effect of metabolic syndromes on the risk of restrictive ventilatory defect. The risk of developing restrictive ventilator defect was 35% higher in participants with metabolic syndromes (odds ratio, 1.35; 95% confidence interval, 1.26-1.45) than those without metabolic syndromes. Elevated blood pressure and triglycerides abnormality were important predictors of restrictive ventilator defect.Sex-stratified subgroup analyses of the individual metabolic abnormalities indicated that men with abdominal obesity and women with dysglycemia were more likely to develop restrictive ventilatory defect. Conclusion: Our study evidences suggested that metabolic syndromes were important predictors of impaired pulmonary function and increased risk of developing restrictive ventilatory defects, and its risk increased with increasing numbers of metabolic abnormalities.
A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems

It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.
Efficacy and safety of anti-CD38 monoclonal antibodies in patients with relapsed/refractory multiple myeloma: a systematic review and meta-analysis with trial sequential analysis of randomized controlled trials

Objectives The current study aims to evaluate the safety and efficacy of anti-CD38 monoclonal antibodies (mAbs) among patients with relapsed/refractory multiple myeloma (RRMM) through meta-analysis. Methods As of June 2023, we searched PubMed, Web of Science, Embase and the Cochrane Library. Randomized controlled trials (RCTs) which compared the clinical outcomes of anti-CD38 mAbs plus immunomodulatory drugs (IMiDs) or proteasome inhibitors (PIs) plus dexamethasone and IMiDs (or PIs) and dexamethasone alone for RRMM patients were included. Efficacy outcomes were mainly evaluated with progression-free survival (PFS) and overall survival (OS). The safety was analyzed with hematologic and nonhematologic treatment-emergent adverse events (TEAEs). All results were pooled using hazard ratio (HR), relative risk (RR), and their 95% confidence interval (CI) and prediction interval (PI). Results This meta-analysis included 11 RCTs in total. Compared with IMiDs (or PIs) and dexamethasone alone, anti-CD38 mAbs in combination with IMiDs (or PIs) and dexamethasone significantly prolonged PFS (HR: 0.552, 95% CI = 0.461 to 0.659, 95% PI = 0.318 to 0.957) and OS (HR: 0.737, 95% CI = 0.657 to 0.827, 95% PI = 0.626 to 0.868) in patients with RRMM. Additionally, RRMM patients receiving anti-CD38 mAbs in combination with IMiDs (or PIs) and dexamethasone achieved higher rates of overall response (RR: 1.281, 95% CI = 1.144 to 1.434, 95% PI = 0.883 to 1.859), complete response or better (RR: 2.602, 95% CI = 1.977 to 3.424, 95% PI = 1.203 to 5.628), very good partial response (VGPR) or better (RR: 1.886, 95% CI = 1.532 to 2.322, 95% PI = 0.953 to 3.731), and minimum residual disease (MRD)-negative (RR: 4.147, 95% CI = 2.588 to 6.644, 95% PI = 1.056 to 16.283) than those receiving IMiDs (or PIs) and dexamethasone alone. For TEAEs, the rates of hematologic and nonhematologic TEAEs, including thrombocytopenia, neutropenia, upper respiratory tract infection (URTI), pneumonia, bronchitis, dyspnea, diarrhea, pyrexia, back pain, arthralgia, fatigue, insomnia, and hypertension, were higher in the anti-CD38 mAbs in combination with IMiDs (or PIs) and dexamethasone group than in the IMiDs (or PIs) and dexamethasone group. Conclusion Our study showed that anti-CD38 mAbs in combination with IMiDs (or PIs) and dexamethasone improved PFS and OS, and achieved higher rates of overall response, complete response or better, VGPR or better, and MRD-negative, as well as higher rates of thrombocytopenia, neutropenia, URTI, pneumonia, bronchitis, dyspnea, diarrhea, pyrexia, back pain, arthralgia, fatigue, insomnia, and hypertension in RRMM patients. Systematic review registration https://www.crd. york.ac. uk/PROSPERO/, identifier CRD42023431071.
Construction and characterization of protein libraries composed of secondary structure modules

Only a minute fraction of all possible protein sequences can exist in the genomes of all life forms. To explore whether physicochemical constraints or a lack of need causes the paucity of different protein folds, we set out to construct protein libraries without any restriction of topology. We generated different libraries (all -helix, all -strand, and -helix plus -strand) with an average length of 100 amino acid residues, composed of designed secondary structure modules ( -helix, -strand, and -turn) in various proportions, based primarily on the patterning of polar and nonpolar residues. We wished to explore that part of sequence space that is rich in secondary structure. The analysis of randomly chosen clones from each of the libraries showed that, despite the low sequence homology to known protein sequences, a substantial proportion of the library members containing -helix modules were indeed helical, possess a defined oligomerization state, and showed cooperative chemical unfolding behavior. On the other hand, proteins composed of mainly -strand modules tended to form amyloid-like fibrils and were among the least soluble proteins ever reported. We found that a large fraction of members in non-strand–containing protein libraries that are distant from natural proteins in sequence space possess unexpectedly favorable properties. These results reinforce the efficacy of applying binary patterning to design proteins with native-like properties despite lack of restriction in topology. Because of the intrinsic tendency of -strand modules to aggregate, their presence requires precise topologic arrangement to prevent fibril formation.
Application of arti ﬁ cial intelligence in endoscopic gastrointestinal tumors

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that arti ﬁ cial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the ef ﬁ ciency of early tumor screening, but also signi ﬁ cantly improves the survival rate of patients after treatment. With the aid of ef ﬁ cient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various arti ﬁ cial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clari ﬁ ed from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer
scDMV: a zero–one inflated beta mixture model for DNA methylation variability with scBS-seq data

Abstract Motivation The utilization of single-cell bisulfite sequencing (scBS-seq) methods allows for precise analysis of DNA methylation patterns at the individual cell level, enabling the identification of rare populations, revealing cell-specific epigenetic changes, and improving differential methylation analysis. Nonetheless, the presence of sparse data and an overabundance of zeros and ones, attributed to limited sequencing depth and coverage, frequently results in reduced precision accuracy during the process of differential methylation detection using scBS-seq. Consequently, there is a pressing demand for an innovative differential methylation analysis approach that effectively tackles these data characteristics and enhances recognition accuracy. Results We propose a novel beta mixture approach called scDMV for analyzing methylation differences in single-cell bisulfite sequencing data, which effectively handles excess zeros and ones and accommodates low-input sequencing. Our extensive simulation studies demonstrate that the scDMV approach outperforms several alternative methods in terms of sensitivity, precision, and controlling the false positive rate. Moreover, in real data applications, we observe that scDMV exhibits higher precision and sensitivity in identifying differentially methylated regions, even with low-input samples. In addition, scDMV reveals important information for GO enrichment analysis with single-cell whole-genome sequencing data that are often overlooked by other methods. Availability and implementation The scDMV method, along with a comprehensive tutorial, can be accessed as an R package on the following GitHub repository: https://github.com/PLX-m/scDMV.
Symmetries of modified Dirac operators in supergravity flux backgrounds

Modifications of Dirac operators in supergravity flux backgrounds are considered. Modified spin curvature operators and squares of modified Dirac operators corresponding to Schr\"odinger-Lichnerowicz-like formulas are obtained for different types of flux modifications. Symmetry operators of modified massless and massive Dirac equations are found in terms of modified Killing-Yano and modified conformal Killing-Yano forms. Extra constraints for symmetry operators in terms of different types of fluxes and modified Killing-Yano forms are determined.
Constraint-Informed Learning for Warm Starting Trajectory Optimization

Future spacecraft and surface robotic missions require increasingly capable autonomy stacks for exploring challenging and unstructured domains and trajectory optimization will be a cornerstone of such autonomy stacks. However, the nonlinear optimization solvers required remain too slow for use on relatively resource constrained flight-grade computers. In this work, we turn towards amortized optimization, a learning-based technique for accelerating optimization run times, and present TOAST: Trajectory Optimization with Merit Function Warm Starts. Offline, using data collected from a simulation, we train a neural network to learn a mapping to the full primal and dual solutions given the problem parameters. Crucially, we build upon recent results from decision-focused learning and present a set of decision-focused loss functions using the notion of merit functions for optimization problems. We show that training networks with such constraint-informed losses can better encode the structure of the trajectory optimization problem and jointly learn to reconstruct the primal-dual solution while also yielding improved constraint satisfaction. Through numerical experiments on a Lunar rover problem, we demonstrate that TOAST outperforms benchmark approaches in terms of both computation times and network prediction constraint satisfaction.
Structure-Guided Automated Reasoning

Algorithmic meta-theorems state that problems that can be formalized in a fixed logic can be solved efficiently on classes of structures with certain properties. A prominent example is Courcelle's Theorem, which states that all problems expressible in monadic second-order logic can be solved efficiently on structures of small treewidth. Such theorems are usually proven by a generic algorithm for the model-checking problem of the given logic, which is often complex and rarely leads to highly efficient solutions. Alternatively, we can solve the model-checking problem by grounding the given logic to propositional logic, for which dedicated solvers are available. Such encodings will, however, usually not preserve the input's treewidth. This paper investigates whether all problems definable in monadic second-order logic can efficiently be encoded into SAT such that the input's treewidth bounds the treewidth of the resulting formula. We answer this in the affirmative and, hence, provide an alternative proof of Courcelle's Theorem. Our technique can naturally be extended: There are treewidth-aware reductions from the optimization version of Courcelle's Theorem to MaxSAT and from the counting version of the theorem to #SAT. By using encodings to SAT, we obtain, ignoring polynomial factors, the same running time for the model-checking problem as we would with dedicated algorithms. We complement our upper bounds with new lower bounds based on ETH; and we show that the block size of the input's formula and the treewidth of the input's structure are tightly linked. We also provide matching upper and lower bounds for a fragment of guarded MSO, only using SAT-based techniques.
Dipole coupling of a bilayer graphene quantum dot to a high-impedance microwave resonator

We implement circuit quantum electrodynamics (cQED) with quantum dots in bilayer graphene, a maturing material platform for semiconductor qubits that can host long-lived spin and valley states. The presented device combines a high-impedance ($Z_\mathrm{r} \approx 1 \mathrm{k{\Omega}}$) superconducting microwave resonator with a double quantum dot electrostatically defined in a graphene-based van der Waals heterostructure. Electric dipole coupling between the subsystems allows the resonator to sense the electric susceptibility of the double quantum dot from which we reconstruct its charge stability diagram. We achieve sensitive and fast detection with a signal-to-noise ratio of 3.5 within 1 ${\mu}\mathrm{s}$ integration time. The charge-photon interaction is quantified in the dispersive and resonant regimes by comparing the coupling-induced change in the resonator response to input-output theory, yielding a maximal coupling strength of $g/2{\pi} = 49.7 \mathrm{MHz}$. Our results introduce cQED as a probe for quantum dots in van der Waals materials and indicate a path toward coherent charge-photon coupling with bilayer graphene quantum dots.
Beam Foreseeing in Millimeter-Wave Systems with Situational Awareness: Fundamental Limits via Cramér-Rao Lower Bound

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G NR beam management framework. We introduce an analytical framework based on the Cram\'{e}r-Rao Lower Bound, enabling the quantification of 6D position-related information of geometric reflectors. This includes both 3D locations and 3D orientation biases, facilitating accurate determinations of the beamforming gain achievable by each reflector or candidate beam. This framework empowers us to predict beam alignment performance at any given location in the environment, ensuring uninterrupted wireless access. Our analysis offers critical insights for choosing the most effective beam and antenna module strategies, particularly in scenarios where communication stability is threatened by blockages. Simulation results show that our approach closely approximates the performance of an ideal, Oracle-based solution within the existing 5G NR beam management system.