Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

Importance Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights. Objective To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes. Design, Setting, and Participants This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020. Main Outcomes and Measures Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample. Results A total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates. Conclusions and Relevance In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.
Malaria Parasite Schizont Egress Antigen-1 Plays an Essential Role in Nuclear Segregation during Schizogony

Malaria is a deadly infectious disease. Rationally designed novel therapeutics will be essential for its control and eradication. ABSTRACT Malaria parasites cause disease through repeated cycles of intraerythrocytic proliferation. Within each cycle, several rounds of DNA replication produce multinucleated forms, called schizonts, that undergo segmentation to form daughter merozoites. Upon rupture of the infected cell, the merozoites egress to invade new erythrocytes and repeat the cycle. In human malarial infections, an antibody response specific for the Plasmodium falciparum protein PF3D7_1021800 was previously associated with protection against malaria, leading to an interest in PF3D7_1021800 as a candidate vaccine antigen. Antibodies to the protein were reported to inhibit egress, resulting in it being named schizont egress antigen-1 (SEA1). A separate study found that SEA1 undergoes phosphorylation in a manner dependent upon the parasite cGMP-dependent protein kinase PKG, which triggers egress. While these findings imply a role for SEA1 in merozoite egress, this protein has also been implicated in kinetochore function during schizont development. Therefore, the function of SEA1 remains unclear. Here, we show that P. falciparum SEA1 localizes in proximity to centromeres within dividing nuclei and that conditional disruption of SEA1 expression severely impacts the distribution of DNA and formation of merozoites during schizont development, with a proportion of SEA1-null merozoites completely lacking nuclei. SEA1-null schizonts rupture, albeit with low efficiency, suggesting that neither SEA1 function nor normal segmentation is a prerequisite for egress. We conclude that SEA1 does not play a direct mechanistic role in egress but instead acts upstream of egress as an essential regulator required to ensure the correct packaging of nuclei within merozoites. IMPORTANCE Malaria is a deadly infectious disease. Rationally designed novel therapeutics will be essential for its control and eradication. The Plasmodium falciparum protein PF3D7_1021800, annotated as SEA1, is under investigation as a prospective component of a malaria vaccine, based on previous indications that antibodies to SEA1 interfere with parasite egress from infected erythrocytes. However, a consensus on the function of SEA1 is lacking. Here, we demonstrate that SEA1 localizes to dividing parasite nuclei and is necessary for the correct segregation of replicated DNA into individual daughter merozoites. In the absence of SEA1, merozoites develop defectively, often completely lacking a nucleus, and, consequently, egress is impaired and/or aberrant. Our findings provide insights into the divergent mechanisms by which intraerythrocytic malaria parasites develop and divide. Our conclusions regarding the localization and function of SEA1 are not consistent with the hypothesis that antibodies against it confer protective immunity to malaria by blocking merozoite egress.
Simultaneous Inhibition of LSD1 and TGF-B Enables Eradication of Poorly Immunogenic Tumors with anti-PD-1 Treatment.

Epigenetic regulators are a class of promising targets in the combination with immune checkpoint inhibitors for cancer treatment, but the impact of the broad effects of perturbing epigenetic regulators on tumor immunotherapy remains to be fully explored. Here we show that ablation of the histone demethylase LSD1 in multiple tumor cells induces TGF-B expression, which exerts an inhibitory effect on T cell immunity through suppressing the cytotoxicity of intratumoral CD8+ T cells and consequently dampens the antitumor effect of LSD1 ablation-induced T cell infiltration. Importantly, concurrent depletion of LSD1 and TGF-B in combination with PD-1 blockade significantly increases both CD8+ T cell infiltration and cytotoxicity, leading to eradication of poorly immunogenic tumors and a long-term protection from tumor re-challenge. Thus, combining LSD1 inhibition with blockade of TGF-B and PD-1 may represent a promising triple combination therapy for treating certain refractory tumors.
Biofabrication of a three dimensional human‐based personalized neurofibroma model

Neurofibromas are the most characteristic feature of neurofibromatosis type 1 (NF1), a multisystemic disorder caused by aberrations in the neurofibromin gene (NF1). Despite significant progress over the last several years in understanding this disease, a suitable in vitro model to better mimic neurofibroma formation and growth has yet to be described. There is therefore a need to establish an in vitro, three dimensional model that allows the incorporation of multicellular lineages and the modulation of the cellular microenvironment—known to be important for cellular crosstalk and distribution of soluble factors—to study neurofibroma biology and morphogenesis. A self‐assembly approach was used to generate tissue‐engineered skins (TES) in which patient‐derived spheroids made of NF1‐associated Schwann cells and fibroblasts were seeded. We describe the first in vitro three dimensional neurofibroma model—directly derived from NF1 patients presenting with histopathological features—having an ECM protein expression profile quite similar to that of a native tumor. We observed efficient incorporation, proliferation, and migration of spheroids within NF1‐TES over time. This biotechnological approach could provide a unique tool for precision medicine targeting NF1 and for assessing the tumorigenic properties of each NF1 gene mutation linked to tumor formation.
Evaluating Visual Cues Modulates Their Representation in Mouse Visual and Cingulate Cortex

Choosing an action in response to visual cues relies on cognitive processes, such as perception, evaluation, and prediction, which can modulate visual representations even at early processing stages. In the mouse, it is challenging to isolate cognitive modulations of sensory signals because concurrent overt behavior patterns, such as locomotion, can also have brainwide influences. To address this challenge, we designed a task, in which head-fixed mice had to evaluate one of two visual cues. Choosing an action in response to visual cues relies on cognitive processes, such as perception, evaluation, and prediction, which can modulate visual representations even at early processing stages. In the mouse, it is challenging to isolate cognitive modulations of sensory signals because concurrent overt behavior patterns, such as locomotion, can also have brainwide influences. To address this challenge, we designed a task, in which head-fixed mice had to evaluate one of two visual cues. While their global shape signaled the opportunity to earn reward, the cues provided equivalent local stimulation to receptive fields of neurons in primary visual (V1) and anterior cingulate cortex (ACC). We found that mice evaluated these cues within few hundred milliseconds. During this period, ∼30% of V1 neurons became cue-selective, with preferences for either cue being balanced across the recorded population. This selectivity emerged in response to the behavioral demands because the same neurons could not discriminate the cues in sensory control measurements. In ACC, cue evaluation affected a similar fraction of neurons; emerging selectivity, however, was stronger than in V1, and preferences in the recorded population were biased toward the cue promising reward. Such a biased selectivity regime might allow the mouse to infer the promise of reward simply by the overall level of activity. Together, these experiments isolate the impact of task demands on neural responses in mouse cerebral cortex, and document distinct neural signatures of cue evaluation in V1 and ACC. SIGNIFICANCE STATEMENT Performing a cognitive task, such as evaluating visual cues, not only recruits frontal and parietal brain regions, but also modulates sensory processing stages. We trained mice to evaluate two visual cues, and show that, during this task, ∼30% of neurons recorded in V1 became selective for either cue, although they provided equivalent visual stimulation. We also show that, during cue evaluation, mice frequently move their eyes, even under head fixation, and that ignoring systematic differences in eye position can substantially obscure the modulations seen in V1 neurons. Finally, we document that modulations are stronger in ACC, and biased toward the reward-predicting cue, suggesting a transition in the neural representation of task-relevant information across processing stages in mouse cerebral cortex.
Measuring potential effects of the developmental burden associated with the vertebrate notochord

Abstract The notochord functions primarily as a supporting tissue to maintain the anteroposterior axis of primitive chordates, a function that is replaced entirely by the vertebral column in many vertebrates. The notochord still appears during vertebrate embryogenesis and plays a crucial role in the developmental pattern formation of surrounding structures, such as the somites and neural tube, providing the basis for the vertebrate body plan. The indispensable role of the notochord has often been referred to as the developmental burden and used to explain the evolutionary conservation of notochord; however, the existence of this burden has not been successfully exemplified so far. Since the adaptive value of target tissues appears to result in the evolutionary conservation of upstream structures through the developmental burden, we performed comparative gene expression profiling of the notochord, somites, and neural tube during the mid‐embryonic stages in turtles and chicken to measure their evolutionary conservation. When compared with the somites and neural tube, overall gene expression profiles in the notochord showed significantly lower or merely comparable levels of conservation. However, genes involved in inductive signalings, such as the sonic hedgehog (Shh) cascade and the formation of functional primary cilia, showed relatively higher levels of conservation in all the three structures analyzed. Collectively, these results suggest that shh signals are critical as the inductive source and receiving structures, possibly constituting the inter‐dependencies of developmental burden.
cMyBPC phosphorylation modulates the effect of omecamtiv mecarbil on myocardial force generation

Mamidi et al. investigate the effect of cardiac myosin-binding protein C (cMyBPC) phosphorylation on the response to omecamtiv mecarbil (OM), a candidate heart failure therapy. They show that OM uncouples myocardial force dynamics from cMyBPC phosphorylation, suggesting important therapeutic constraints.
Dynamic sampling bias and overdispersion induced by skewed offspring distributions

Natural populations often show enhanced genetic drift consistent with a strong skew in their offspring number distribution. The skew arises because the variability of family sizes is either inherently strong or amplified by population expansions, leading to so-called ‘jackpot’ events. The resulting allele frequency fluctuations are large and, therefore, challenge standard models of population genetics, which assume sufficiently narrow offspring distributions. While the neutral dynamics backward in time can be readily analyzed using coalescent approaches, we still know little about the effect of broad offspring distributions on the dynamics forward in time, especially with selection. Here, we employ an exact asymptotic analysis combined with a scaling hypothesis to demonstrate that over-dispersed frequency trajectories emerge from the competition of conventional forces, such as selection or mutations, with an emerging time-dependent sampling bias against the minor allele. The sampling bias arises from the characteristic time-dependence of the largest sampled family size within each allelic type. Using this insight, we establish simple scaling relations for allele frequency fluctuations, fixation probabilities, extinction times, and the site frequency spectra that arise when offspring numbers are distributed according to a power law n−(1+α). To demonstrate that this coarse-grained model captures a wide variety of non-equilibrium dynamics, we validate our results in traveling waves, where the phenomenon of ‘gene surfing’ can produce any exponent 1 < α < 2. We argue that the concept of a dynamic sampling bias is useful generally to develop both intuition and statistical tests for the unusual dynamics of populations with skewed offspring distributions, which can confound commonly used tests for selection or demographic history.
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification

Since their inception, transformer-based language models have led to impressive performance gains across multiple natural language processing tasks. For Arabic, the current state-of-the-art results on most datasets are achieved by the AraBERT language model. Notwithstanding these recent advancements, sarcasm and sentiment detection persist to be challenging tasks in Arabic, given the language’s rich morphology, linguistic disparity and dialectal variations. This paper proffers team SPPU-AASM’s submission for the WANLP ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment polarity detection of Arabic tweets. The study proposes a hybrid model, combining sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. The proposed system achieves a F1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm and sentiment detection tasks, respectively. Simulation results show that the proposed system outperforms multiple existing approaches for both the tasks, suggesting that the amalgamation of context-free and context-dependent text representations can help capture complementary facets of word meaning in Arabic. The system ranked second and tenth in the respective sub-tasks of sarcasm detection and sentiment identification.
Incorporating Orientations into End-to-end Driving Model for Steering Control

In this paper, we present a novel end-to-end deep neural network model for autonomous driving that takes monocular image sequence as input, and directly generates the steering control angle. Firstly, we model the end-to-end driving problem as a local path planning process. Inspired by the environmental representation in the classical planning algorithms(i.e. the beam curvature method), pixel-wise orientations are fed into the network to learn direction-aware features. Next, to handle the imbalanced distribution of steering values in training datasets, we propose an improvement on a cost-sensitive loss function named SteeringLoss2. Besides, we also present a new end-to-end driving dataset, which provides corresponding LiDAR and image sequences, as well as standard driving behaviors. Our dataset includes multiple driving scenarios, such as urban, country, and off-road. Numerous experiments are conducted on both public available LiVi-Set and our own dataset, and the results show that the model using our proposed methods can predict steering angle accurately.
Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems

Deep learning-based models are utilized to achieve state-of-the-art performance for recommendation systems. A key challenge for these models is to work with millions of categorical classes or tokens. The standard approach is to learn end-to-end, dense latent representations or embeddings for each token. The resulting embeddings require large amounts of memory that blow up with the number of tokens. Training and inference with these models create storage, and memory bandwidth bottlenecks leading to significant computing and energy consumption when deployed in practice. To this end, we present the problem of \textit{Memory Allocation} under budget for embeddings and propose a novel formulation of memory shared embedding, where memory is shared in proportion to the overlap in semantic information. Our formulation admits a practical and efficient randomized solution with Locality sensitive hashing based Memory Allocation (LMA). We demonstrate a significant reduction in the memory footprint while maintaining performance. In particular, our LMA embeddings achieve the same performance compared to standard embeddings with a 16$\times$ reduction in memory footprint. Moreover, LMA achieves an average improvement of over 0.003 AUC across different memory regimes than standard DLRM models on Criteo and Avazu datasets
Linear Constraints

A linear argument must be consumed exactly once in the body of its function. A linear type system can verify the correct usage of resources such as ﬁle handles and manually managed memory. But this veriﬁcation requires bureaucracy. This paper presents linear constraints , a front-end feature for linear typing that decreases the bureaucracy of working with linear types. Linear constraints are implicit linear arguments that are to be ﬁlled in automatically by the compiler. Linear constraints are presented as a qualiﬁed type system, together with an inference algorithm which extends ghc ’s existing constraint solver algorithm. Soundness of linear constraints is ensured by the fact that they desugar into Linear Haskell.
Ion-molecule reactions

Abstract : The fact that ion-molecule reactions can occur in gases has been known since early in this century, but until recently such reactions excited comparatively little curiosity, and research in this field proceeded at a slow pace. The first theoretical work of relevance here appeared in Langevin's famous mobility papers of 1903 and 1905, in which he considered the clustering of molecules about gaseous ions and orbiting collisions between ions and molecules. Approximately the first half of this volume is devoted to a discussion of all the known experimental methods for making quantitative studies of ion-molecule reactions, and a critical analysis is made of the accuracy and reliability of these methods. Considerable space is then devoted to the theoretical foundations of the subject and to the various models which have been adopted in computations of reaction rates. A review is given of the measurements in the earth's atmosphere from which information on ion-molecule reactions has been deduced, and this is followed by a discussion of ion-molecule chemistry. Finally, experimental data on 230 ion-molecule reactions are displayed in tabular and graphical form. All the available data on these reactions were critically analyzed, and the best values chosen for inclusion. In many cases, in which more than one good measurement appears to have been made on a given reaction several data are presented to indicate the magnitude of the disagreements among the separate results.
Modulated rotating waves and triadic resonances in spherical fluid systems: The case of magnetized spherical Couette flow

The existence of triadic resonances in the magnetized spherical Couette system is related to the development of modulated rotating waves, which are quasiperiodic flows understood in terms of bifurcation theory in systems with symmetry. In contrast to previous studies in spherical geometry, the resonant modes are not inertial waves but related to the radial jet instability, which is strongly equatorially antisymmetric. We propose a general framework in which triadic resonances are generated through successive Hopf bifurcations from the base state. The study relies on an accurate frequency analysis of different modes of the flow, for solutions belonging to two different bifurcation scenarios. The azimuthal and latitudinal nonlinear coupling among the resonant modes is analyzed and interpreted using spherical harmonics, and the results are compared with previous studies in spherical geometry.
High Resolution Soft X-ray Spectroscopy and the Quest for the Hot (5–10 MK) Plasma in Solar Active Regions

We discuss the diagnostics available to study the 5–10 MK plasma in the solar corona, which is key to understanding the heating in the cores of solar active regions. We present several simulated spectra, and show that excellent diagnostics are available in the soft X-rays, around 100 Å, as six ionization stages of Fe can simultaneously be observed, and electron densities derived, within a narrow spectral region. As this spectral range is almost unexplored, we present an analysis of available and simulated spectra, to compare the hot emission with the cooler component. We adopt recently designed multilayers to present estimates of count rates in the hot lines, with a baseline spectrometer design. Excellent count rates are found, opening up the exciting opportunity to obtain high-resolution spectroscopy of hot plasma.