Miracle-dz/UniFAR-FTUs huggingface dataset:
Relaxed Notions of Curvature and a Lumped Strain Method for Elastic Plates

The paper proposes an approximation method, lumped strain method (LSM), for elastic plates based on generalized notions of the Gaussian and mean curvatures and on a relaxation of the energy functional to the space of continuous piecewise linear functions. The method adapts ideas from the theory of $\Gamma $-convergence. We restrict our attention to the case of the simply supported plate and state the proof of convergence of the method. We give an application to the rhombic plate and compare the results with those obtained by standard finite element approximations.
CFD analysis of the effect of nozzle stand-off distance on turbulent impinging jets

Three-dimensional steady Reynolds Averaged Navier-Stokes simulations have been carried out to investigate the effect of the nozzle stand-off distance on the mean and turbulence characteristics of jets impinging vertically on flat surfaces. As part of the study, the performance of different turbulence models such as Realizable k–e, k–ω SST, and Reynolds Stress Model (RSM) were evaluated. Based on comparisons with experimental data, RSM was chosen to further evaluate the characteristics of impinging jets. The Reynolds number based on the jet exit velocity and nozzle diameter is 100 000. Three different nozzle height-to-diameter ratios, representing different types of impinging jets, were simulated and compared with available experimental data. A strong dependency of the jet characteristics on the nozzle height-to-diameter ratio was observed. The simulations show that an increase in this ratio results in larger shear stress and more distributed pressure on the wall, more development of the flow in the axial .
EHR Safari: Data is Contextual

In the last decade, machine learning (ML) has shown tremendous success in areas such as vision, language, strategic games, and more. Parallel to this, hospitals’ capacity for data collection has greatly increased with the adoption and continuing maturation of electronic health records (EHRs). The result of these trends has been a large degree of excitement and optimism about how ML will revolutionize healthcare once researchers get access to data. In this work, we present a cautionary tale of the instinct some computer scientists have to “let the data speak for itself. ” Using a popular, public EHR dataset as a case study, we demonstrate numerous examples where a non-clinician’s intuition may lead to incorrect – and potentially harmful – modeling assumptions. We explore both non-obvious quirks in the data (i.e., hypothetical incorrect assumptions) and examples of published papers that misunderstood the data generating process (i.e., actual incorrect assumptions). This case study is meant to serve as a cautionary tale to encourage every data scientist to approach their projects with the humility to know what they can do well and what they cannot. Without the guidance of stakeholders that understand the data generating process, data scientists run the risk of “garbage-in, garbage-out” analysis because their models are not measuring meaningful relationships.
High-Throughput Quantitative Screening of Glucose-Stimulated Insulin Secretion and Insulin Content Using Automated MALDI-TOF Mass Spectrometry

Type 2 diabetes (T2D) is a metabolic disorder characterized by loss of pancreatic β- cell function, decreased insulin secretion and increased insulin resistance, that affects more than 400 million people worldwide. Although several treatments are proposed to patients suffering from T2D, long-term control of glycemia remains a challenge. Therefore, identifying new potential drugs and targets that positively affect β-cell function and insulin secretion remains crucial. Here, we developed an automated approach to allow the identification of new compounds or genes potentially involved in β-cell function in a 384-well plate format, using the murine β-cell model Min6. Using MALDI-TOF mass spectrometry, we have implemented a high-throughput screening (HTS) strategy based on the automation of a cellular assay allowing to detect insulin secretion in response to glucose, quantitative detection of insulin, in a miniaturized system. As a proof of concept, we screened siRNA targeting well-know β-cell genes and 1600 chemical compounds and identified several molecules as potential regulators of insulin secretion and/or synthesis, demonstrating that our approach allows HTS of insulin secretion in vitro.
Interactive Visualization of Machine Learning Model Results Predicting Infection Risk

We present a novel visual-interactive interface to show results of a machine learning algorithm, which predicts the infection probability for patients in hospitals. The model result data is complex and needs to be presented in a clear and intuitive way to microbiology and infection control experts in hospitals. Our visual-interactive interface offers linked views which allow for detailed analysis of the model results. Feedback from microbiology and infection control experts showed that they were able to extract new insights regarding outbreaks and transmission pathways.
Controlling local moment formation and local moment interactions in bilayer graphene

We study local moment formation for adatoms on bilayer graphene (BLG) within a mean-field theory of the Anderson impurity model. The wavefunctions of the BLG electrons induce strong particle–hole asymmetry and band dependence of the hybridization, which is shown to result in unusual features in the impurity model phase diagram. We also study the effect of varying the chemical potential, as well as varying an electric field perpendicular to the bilayer; the latter modifies the density of states of electrons in BLG and, more significantly, shifts the impurity energy. We show that this leads to regimes in the impurity phase diagram where local moments can be turned on or off by applying modest external electric fields. Finally, we show that the RKKY interaction between local moments can be varied by tuning the chemical potential (as has also been suggested in monolayer graphene) or, more interestingly, by tuning the electric field so that it induces changes in the band structure of BLG.
Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text. Neural word embedding has been employed in literature, since it is less labor-intensive than automatic feature engineering. By ignoring the context of sentiment, most of the word-embedding models follow syntactic data of words. The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets (DFODLSAAT) model. The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language. At first, data cleaning and pre-processing steps are performed to convert the input tweets into a useful format. In addition, TF-IDF model is exploited as a feature extractor to generate the feature vectors. Besides, Attention-based Bidirectional Long Short Term Memory (ABLSTM) technique is applied for identification and classification of sentiments. At last, the hyperparameters of ABLSTM model are optimized using DFO algorithm. The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects. The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.
A Tutorial on Particle Filtering and Smoothing: Fifteen years later

Optimal estimation problems for non-linear non-Gaussian state-space models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete, up-to-date survey of this field as of 2008. Basic and advanced particle methods for filtering as well as smoothing are presented.
Vascular oxidative stress causes neutrophil arrest in brain capillaries, leading to decreased cerebral blood flow and contributing to memory impairment in a mouse model of Alzheimer’s disease

INTRODUCTION In this study, we explore the role of oxidative stress produced by NOX2-containing NADPH oxidase as a molecular mechanism causing capillary stalling and cerebral blood flow deficits in the APP/PS1 mouse model of AD. METHODS We inhibited NOX2 in APP/PS1 mice by administering a 10 mg/kg dose of the peptide inhibitor gp91-ds-tat i.p., for two weeks. We used in vivo two-photon imaging to measure capillary stalling, penetrating arteriole flow, and vascular inflammation. We also characterized short-term memory function and gene expression changes in cerebral microvessels. RESULTS We found that after NOX2 inhibition capillary stalling, as well as parenchymal and vascular inflammation, were significantly reduced. In addition, we found a significant increase in penetrating arteriole flow, followed by an improvement in short-term memory, and downregulation of inflammatory gene expression pathways. DISCUSSION Oxidative stress is a major mechanism leading to microvascular dysfunction in AD, and represents an important therapeutic target.
A comprehensive analysis of rheumatoid arthritis B cells reveals the importance of CD11c+ve double-negative-2 B cells as the major synovial plasma cell precursor

B cells are key pathogenic drivers of chronic inflammation in rheumatoid arthritis (RA). There is limited understanding of the relationship between synovial B cell subsets and pathogenic antibody secreting cells (ASCs). This knowledge is crucial for the development of targeted therapies. Here, we combine flow cytometry of circulating B cells with single-cell RNA and paired repertoire sequencing of over 27,000 synovial B cells from patients with established RA. Twelve B cell clusters were identified including previously recognised subsets, and a novel cluster that strongly expressed heat shock proteins. All lineages identified by trajectory analysis contribute to the DN2 B cell population, which is the major precursor to synovial ASCs. This was further supported by B cell receptor (BCR) lineage analysis, which revealed clonal relationships between DN2 cells and ASCs. This study advances our understanding of B cells in RA and reveals the origin of pathogenic ASCs in the RA synovium.
Inherent Limitations of Multi-Task Fair Representations

With the growing awareness to fairness issues in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations. The goal of such representations is that a model trained on data under the representation (e.g., a classifier) will be guaranteed to respect some fairness constraints, while still being expressive enough to model the task well. Such representations are useful when they can be fixed for training models on various different tasks and also when they serve as data filtering between the raw data (available to the representation designer) and potentially malicious agents that use the data under the representation to learn predictive models and make decisions. A long list of recent research papers strive to provide tools for achieving these goals. However, we prove that in most cases, such goals are inaccessible! Roughly stated, we prove that no representation can guarantee the fairness of classifiers for different tasks trained using it (while retaining the needed expressive powers). The reasons for this impossibility depend on the notion of fairness one aims to achieve. For the basic ground-truth-independent notion of Demographic (or Statistical) Parity, the obstacle is conceptual; a representation that guarantees such fairness inevitably depends on the marginal (unlabeled) distribution of the relevant instances, and in most cases that distribution changes from one task to another. For more refined notions of fairness, that depend on some ground truth classification, like Equalized Odds (requiring equality of error rates between groups), fairness cannot be guaranteed by a representation that does not take into account the task specific labeling rule with respect to which such fairness will be evaluated (even if the marginal data distribution is known a priori). Furthermore, for tasks sharing the same marginal distribution, we prove that except for trivial cases, no representation can guarantee Equalized Odds fairness for any two different tasks while enabling accurate label predictions for both. Fair classification, group fairness, data representation, fair data representation, Demographic Parity, Equalized Odds.
Broadband reflective cholesteric liquid crystalline gels: volume distribution of reflection properties and polymer network in relation with the geometry of the cell photopolymerization

The ultraviolet (UV) light‐absorbing properties of the liquid crystal (LC) constituent during the photo‐induced elaboration of a cholesteric LC (CLC) gel may induce the broadening of the reflection bandwidth of the material, a situation that is promoted by asymmetrical irradiation conditions (only one side of the cell is irradiated). The in situ structure of the polymer network, included in the LC, was investigated by transmission electron microscopy and the temperature dependence of the reflection properties examined; it is shown that the network has a structure gradient that is at the origin of the broadening phenomenon. The smallest reflection wavelength is related to the cell side from which the UV light beam came in. A priori, this situation was unexpected since it is shown that this part of the gel is enriched with nematic (infinite‐pitch CLC) network‐forming material. The result is discussed in relation to the variation of the reflection band characteristics with polymer concentration, which offers the opportunity for indirect access to the volume distribution of the cholesteric periodicities. For applications, broadband reflective cholesteric gels may be of interest for reflective polarizer‐free displays or for the light management with smart electrically‐switchable reflective windows.
Dominant and Genome-Wide Formation of DNA:RNA Hybrid G-Quadruplexes in Living Yeast Cells

Guanine-rich nucleic acids form G-quadruplex (G4) structures that play a critical role in cellular processes. Previous studies have mostly focused on monomeric intramolecular G4s with four consecutive guanine tracts (G-tracts) from a single strand. However, this structural form has never been confirmed in eukaryotic cells. Here, we report the formation of hybrid G4s (hG4s), consisting of G-tracts from both DNA and RNA, in the genome of living yeast cells. Analysis of Okazaki fragment syntheses and G4-specific probing reveals that hG4s can efficiently form with as few as a single DNA guanine-guanine (GG) tract due to the participation of G-tracts from RNA. This finding increases the number of G4-forming sites in the yeast genome from 38 to 587,694, a more than 15,000-fold increase. Interestingly, hG4s still form and even dominate at genomic G4 sites that are theoretically capable of forming the monomeric intramolecular DNA G4s by themselves. Compared to DNA G4s (dG4s), hG4s exhibit a wider range of kinetics, higher prevalence, and greater structural diversity and stability. Most importantly, hG4 formation is tightly coupled to transcription through the involvement of RNA, allowing hG4s to function in a transcription-dependent manner. Overall, our study establishes hG4s as the overwhelmingly dominant G4 species in the yeast genome and emphasizes a renewal of the current perception of the structural form, formation mechanism, prevalence, and functional role of G4s in eukaryotic genomes. It also provides a sensitive and currently the only method for detecting the structural form of G4s in living cells. Significance The identification of hybrid G-quadruplexes (hG4s) has disclosed a previously unrecognized structural form of G4s as the most common and abundant G4 species in the yeast genome. It reveals not only a dominant rule governing the formation of G4s in eukaryotic genomes, but also a unique genotype that allows G4-mediated transcriptional regulation to take feedback from the output as input, thus allowing the creation of feedback loops at the transcriptome scale.
EEG Decoding Reveals Neural Predictions for Naturalistic Material Behaviors

Material properties, such as softness or stickiness, determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190 ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150 ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment toward increased processing of unexpected material behaviors. SIGNIFICANCE STATEMENT In the real world, we can make accurate predictions about how an object's material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (e.g., a glass shattering when falling to the ground) or unexpected material behaviors (e.g., a glass melting on impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.
CRACD loss promotes small cell lung cancer tumorigenesis via EZH2-mediated immune evasion

The mechanisms underlying immune evasion and immunotherapy resistance in small cell lung cancer (SCLC) remain unclear. Herein, we investigate the role of CRACD tumor suppressor in SCLC. We found that CRACD is frequently inactivated in SCLC, and Cracd knockout (KO) significantly accelerates SCLC development driven by loss of Rb1, Trp53, and Rbl2. Notably, the Cracd-deficient SCLC tumors display CD8+ T cell depletion and suppression of antigen presentation pathway. Mechanistically, CRACD loss silences the MHC-I pathway through EZH2. EZH2 blockade is sufficient to restore the MHC-I pathway and inhibit CRACD loss-associated SCLC tumorigenesis. Unsupervised single-cell transcriptomic analysis identifies SCLC patient tumors with concomitant inactivation of CRACD, impairment of tumor antigen presentation, and downregulation of EZH2 target genes. Our findings define CRACD loss as a new molecular signature associated with immune evasion of SCLC cells and proposed EZH2 blockade as a viable option for CRACD-negative SCLC treatment.
CD4 T cells and neutrophils contribute to epithelial-mesenchymal transition in breast cancer

Epithelial-mesenchymal transition (EMT) is a central oncogenic mechanism, contributing both to transformation and metastatic dissemination. Inflammation and innate immune cells are known to favor EMT induction, but the role of adaptive immunity still remains unclear. Using an original murine mammary tumor model in immune cell subpopulation depletion experiments, we demonstrated that tumor cells maintain their epithelial phenotype in mice deficient for adaptive immune response, but undergo EMT in the presence of T-cells. This phenotypic conversion involves the major contribution of CD4 T cells, but not CD8 T cells nor B cells, undoubtedly demonstrating the pro-EMT role of CD4 T cells specifically among adaptive immune cells. Moreover, combined intra-tumor immune infiltrate and transcriptomic analyses of murine mammary tumors with various EMT phenotype revealed an inverse correlation between mesenchymal tumor cell and intratumoral neutrophil proportions, due to the reduced ability of mesenchymal cells to recruit neutrophils. Last, selective in vivo depletion of neutrophils and transcriptomic analysis of human breast tumor cohorts demonstrated the pro-EMT role of neutrophils and suggest a cooperation with CD4 T cells in EMT promotion. Collectively, our data highlight a novel mechanism of EMT regulation by both innate and adaptive immune compartments.
Identification of a new substrate for the ribosome associated endoribonuclease Rae1 reveals a link to the B. subtilis response and sensitivity to chloramphenicol

Rae1 is a well-conserved endoribonuclease among Gram-positive bacteria, cyanobacteria and the chloroplasts of higher plants. We have previously shown that Rae1 cleaves the Bacillus subtilis yrzI operon mRNA in a translation-dependent manner, within a short open reading frame (ORF) called S1025, encoding a 17-amino acid (aa) peptide of unknown function. Here, we map a new Rae1 cleavage site in the bmrBCD operon mRNA encoding a multidrug transporter, within a previously unannotated 26-aa short ORF that we have named bmrX. Similar to S1025, Rae1 cleavage within bmrX is both translation- and reading frame-dependent. Both mRNAs were previously shown to be induced by the presence of the protein synthesis inhibitor chloramphenicol (Cm). Strikingly, a rae1 deletion strain shows greater resistance to Cm than the wild-type strain, while its over-expression leads to increased Cm sensitivity, suggesting a link to translation quality control. Consistent with this, we show that cleavage by Rae1 promotes ribosome rescue by the tmRNA. Globally, our data point to a role of Rae1 in mRNA surveillance by eliminating mRNAs that encounter problems with translation.