Miracle-dz/UniFAR-FTUs huggingface dataset:
Tomographic Volumetric Additive Manufacturing of Silicon Oxycarbide Ceramics

Ceramics are highly chemically, thermally, and mechanically resistant. These remarkable propertiers make them useful across multiple industries; but also, difficult to mold into complex shapes. A possibility to make convoluted ceramic parts is to use preceramic polymers (PCPs) in liquid form. The PCP resin is first solidified in a desired geometry and then transformed into ceramic compounds through a pyrolysis step that preserves the shape. Light‐based additive manufacturing (AM) is a promising route to achieve solidification of the PCP resin. Different approaches, such as stereolithography, have already been proposed but they all rely on a layer‐by‐layer printing process which limits printing speed and object geometry. Herein, the fabrication of complex 3D centimeter‐scale ceramic parts by using tomographic volumetric printing is presented, which is fast and offers a high‐resolution and geometrical design freedom. First, a photosensitive polysiloxane preceramic resin that is solidified by projecting light patterns from multiple angles is formulated. Then, the obtained 3D printed parts are converted into ceramics by pyrolysis. The strength of this approach is demonstrated through the fabrication of smooth, dense microcomponents exhibiting overhangs and hollow geometries without the need of supporting structures. Their resistance to thermal stress and harsh chemical treatments is characterized.
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?

The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46%. High mortality is thought to be related in part to challenges in making a prompt diagnosis, which may in turn delay implementation of evidence-based therapies. A deep neural network (DNN) algorithm utilizing unbiased ventilator waveform data (VWD) may help to improve screening for ARDS. We first show that a convolutional neural network-based ARDS detection model can outperform prior work with random forest models in AUC (0.95+/-0.019 vs. 0.88+/-0.064), accuracy (0.84+/-0.026 vs 0.80+/-0.078), and specificity (0.81+/-0.06 vs 0.71+/-0.089). Frequency ablation studies imply that our model can learn features from low frequency domains typically used for expert feature engineering, and high-frequency information that may be difficult to manually featurize. Further experiments suggest that subtle, high-frequency components of physiologic signals may explain the superior performance of DL models over traditional ML when using physiologic waveform data. Our observations may enable improved interpretability of DL-based physiologic models and may improve the understanding of how high-frequency information in physiologic data impacts the performance our DL model.
XLM-K: Improving Cross-Lingual Language Model Pre-Training with Multilingual Knowledge

Cross-lingual pre-training has achieved great successes using monolingual and bilingual plain text corpora. However, most pre-trained models neglect multilingual knowledge, which is language agnostic but comprises abundant cross-lingual structure alignment. In this paper, we propose XLM-K, a cross-lingual language model incorporating multilingual knowledge in pre-training. XLM-K augments existing multilingual pre-training with two knowledge tasks, namely Masked Entity Prediction Task and Object Entailment Task. We evaluate XLM-K on MLQA, NER and XNLI. Experimental results clearly demonstrate significant improvements over existing multilingual language models. The results on MLQA and NER exhibit the superiority of XLM-K in knowledge related tasks. The success in XNLI shows a better cross-lingual transferability obtained in XLM-K. What is more, we provide a detailed probing analysis to confirm the desired knowledge captured in our pre-training regimen. The code is available at https://github.com/microsoft/Unicoder/tree/master/pretraining/xlmk.
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing

Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.
Spatially resolved properties of supernova host galaxies in SDSS-IV MaNGA

We crossmatch galaxies from Mapping Nearby Galaxies at Apache Point Observatory with the Open Supernova Catalog, obtaining a total of 132 SNe within MaNGA bundle. These 132 SNe can be classified into 67 Type Ia and 65 Type CC. We study the global and local properties of supernova host galaxies statistically. Type Ia SNe are distributed in both star-forming galaxies and quiescent galaxies, while Type CC SNe are all distributed along the star-forming main sequence. As the stellar mass increases, the Type Ia/CC number ratio increases. We find: (1) there is no obvious difference in the interaction possibilities and environments between Type Ia SN hosts and a control sample of galaxies with similar stellar mass and SFR distributions, except that Type Ia SNe tend to appear in galaxies which are more bulge-dominated than their controls. For Type CC SNe, there is no difference between their hosts and the control galaxies in galaxy morphology, interaction possibilities as well as environments; (2) compared to galaxy centers, the SN locations have smaller velocity dispersion, lower metallicity, and younger stellar population. This is a natural result of radius gradients for all these parameters. The SN location and its symmetrical position relative to the galaxy center, as well as regions with similar effective radii have very similar [Mg/Fe], gas-phase metallicity, gas velocity dispersion and stellar population age.
Investigating Non-local Features for Neural Constituency Parsing

Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1). Our parser also outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings.
Auditing AI models for Verified Deployment under Semantic Specifications

Auditing trained deep learning (DL) models prior to deployment is vital for preventing unintended consequences. One of the biggest challenges in auditing is the lack of human-interpretable specifications for the DL models that are directly useful to the auditor. We address this challenge through a sequence of semantically-aligned unit tests, where each unit test verifies whether a predefined specification (e.g., accuracy over 95%) is satisfied with respect to controlled and semantically aligned variations in the input space (e.g., in face recognition, the angle relative to the camera). We enable such unit tests through variations in a semantically-interpretable latent space of a generative model. Further, we conduct certified training for the DL model through a shared latent space representation with the generative model. With evaluations on four different datasets, covering images of chest X-rays, human faces, ImageNet classes, and towers, we show how AuditAI allows us to obtain controlled variations for certified training. Thus, our framework, AuditAI, bridges the gap between semantically-aligned formal verification and scalability. A blog post accompanying the paper is at this link https://developer.nvidia. com/blog/nvidia-research-auditing-ai-models-for-verified-deployment-under-semantic-specifications
The effect of boundary conditions on the stability of two-dimensional flows in an annulus with permeable boundary

Our aim is to study the effect of the outflow boundary conditions on the stability of incompressible flows in a domain with permeable boundary. For this purpose, we examine the stability of the Couette flow with the radial throughflow between permeable cylinders. Most earlier studies of this flow employed the boundary conditions that prescribe all components of the flow velocity on both cylinders.Taking these boundary conditions as a reference point, we investigate the effect of imposing different outflow boundary conditions. These conditions prescribe the normal stress and either the tangential velocity or the tangential stress. It turns out that both sets of boundary conditions make the corresponding steady flows more unstable. In particular, it is shown that even the classical (purely azimuthal) Couette flow becomes unstable to two-dimensional perturbations if one of the cylinders is permeable and the normal stress (rather than normal velocity) is prescribed on that cylinder.
Review of Clustering-Based Recommender Systems

Recommender systems are one of the most applied methods in machine learning and find applications in many areas, ranging from economics to the Internet of things. This article provides a general overview of modern approaches to recommender system design using clustering as a preliminary step to improve overall performance. Using clustering can address several known issues in recommendation systems, including increasing the diversity, consistency, and reliability of recommendations; the data sparsity of user-preference matrices; and changes in user preferences over time. This work will be useful for both beginners in the field of recommender systems and specialists in related fields that are interested in examining the applicability of recommender systems. This review is focused on the analysis of the scientific literature on the topics of recommender systems and clustering models that have appeared in recent years and contains a representative list of the literature for the further exploration of this topic. In the first part, a brief introduction to the so-called classic or traditional recommendation algorithms is given, along with an overview of the clustering problem.
Renormalization of the flavor-singlet axial-vector current and its anomaly at $N^3LO$ in QCD

The renormalization constant $Z_J$ of the flavor-singlet axial-vector current with a non-anticommuting $\gamma_5$ in dimensional regularization is determined to order $\alpha_s^3$ in QCD with massless quarks. In addition, the equality between the $\overline{\mathrm{MS}}$ renormalization constant $Z_{F\tilde{F}}$ associated with the operator $\big[F \tilde{F} \big]_{R}$ and that of $\alpha_s$ is verified explicitly to hold true at 4-loop order.
An Investigation of Language Model Interpretability via Sentence Editing

Pre-trained language models (PLMs) like BERT are being used for almost all language-related tasks, but interpreting their behavior still remains a significant challenge and many important questions remain largely unanswered. In this work, we re-purpose a sentence editing dataset, where faithful high-quality human rationales can be automatically extracted and compared with extracted model rationales, as a new testbed for interpretability. This enables us to conduct a systematic investigation on an array of questions regarding PLMs’ interpretability, including the role of pre-training procedure, comparison of rationale extraction methods, and different layers in the PLM. The investigation generates new insights, for example, contrary to the common understanding, we find that attention weights correlate well with human rationales and work better than gradient-based saliency in extracting model rationales. Both the dataset and code will be released to facilitate future interpretability research.
Selected transient receptor potential channel genes’ expression in peripheral blood mononuclear cells of multiple sclerosis

Transient receptor potential channels have responsibilities in many cellular processes such as cytokine production, cell differentiation, and cytotoxicity by affecting intracellular cation levels or intracellular signal pathways. Multiple sclerosis is a chronic autoimmune central nervous system (CNS) disease caused by environmental and genetic factors. In this study, we aim to investigate TRPV1-TRPV4, TRPM2, TRPM4, TRPM7, TRPC6, and TRPA1 mRNA expression levels, which are associated with the inflammatory process, in the peripheral blood mononuclear cells (PBMCs) of relapsing-remitting multiple sclerosis (RRMS) patients. Thirty-five healthy controls and age–gender matched thirty patients with RRMS were involved in the study. TRPC6, TRPA1, TRPM2, TRPM4, TRPM7, TRPV1, TRPV2, TRPV3, and TRPV4 PBMCs mRNA expression levels were determined by qPCR. In the present study, the TRPC6, TRPM7, TRPV1, TRPV3, and TRPV4 mRNA expressions of RRMS patients in PBMCs decreased at a significant level compared to the healthy control group (p = .000, p = .000, p = .044, p = .000, p = .004, respectively). The decreased expression of TRPC6, TRPM7, TRPV1, TRPV3, and TRPV4 in PBMCs may be associated with the pathogenesis of MS. Further studies are required to understand the mechanism of the relation between these TRP channels and MS and other autoimmune diseases.
T cell dysfunction in glioblastoma: a barrier and an opportunity for the development of successful immunotherapies

Purpose of review Immunotherapies such as immune checkpoint blockade have revolutionized cancer treatment, but current approaches have failed to improve outcomes in glioblastoma and other brain tumours. T cell dysfunction has emerged as one of the major barriers for the development of central nervous system (CNS)-directed immunotherapy. Here, we explore the unique requirements that T cells must fulfil to ensure immune surveillance in the CNS, and we analyse T cell dysfunction in glioblastoma (GBM) through the prism of CNS-resident immune responses. Recent findings Using comprehensive and unbiased techniques such as single-cell RNA sequencing, multiple studies have dissected the transcriptional state of CNS-resident T cells that patrol the homeostatic brain. A similar approach has revealed that in GBM, tumour-infiltrating T cells lack the hallmarks of antigen-driven exhaustion typical of melanoma and other solid tumours, suggesting the need for better presentation of tumour-derived antigens. Consistently, in a mouse model of GBM, increasing lymphatic drainage to the cervical lymph node was sufficient to promote tumour rejection. Summary For the success of future immunotherapy strategies, further work needs to explore the natural history of dysfunction in GBM tumour-infiltrating T cells, establish whether these originate from CNS-resident T cells and how they can be manipulated therapeutically.
Efficacy and safety of ivermectin for the treatment of COVID-19: a systematic review and meta-analysis

Summary Background Ivermectin became a popular choice for COVID-19 treatment among clinicians and the public following encouraging results from pre-print trials and in vitro studies. Early reviews recommended the use of ivermectin based largely on non-peer-reviewed evidence, which may not be robust. This systematic review and meta-analysis assessed the efficacy and safety of ivermectin for treating COVID-19 based on peer-reviewed randomized controlled trials (RCTs) and observational studies (OSs). Methods MEDLINE, EMBASE and PubMed were searched from 1 January 2020 to 1 September 2021 for relevant studies. Outcomes included time to viral clearance, duration of hospitalization, mortality, incidence of mechanical ventilation and incidence of adverse events. RoB2 and ROBINS-I were used to assess risk of bias. Random-effects meta-analyses were conducted. GRADE was used to evaluate quality of evidence. Results Three OSs and 14 RCTs were included in the review. Most RCTs were rated as having some concerns in regards to risk of bias, while OSs were mainly rated as having a moderate risk of bias. Based on meta-analysis of RCTs, the use of ivermectin was not associated with reduction in time to viral clearance, duration of hospitalization, incidence of mortality and incidence of mechanical ventilation. Ivermectin did not significantly increase incidence of adverse events. Meta-analysis of OSs agrees with findings from RCT studies. Conclusions Based on very low to moderate quality of evidence, ivermectin was not efficacious at managing COVID-19. Its safety profile permits its use in trial settings to further clarify its role in COVID-19 treatment. Protocol registration The review was prospectively registered in PROSPERO (CRD42021275302).
ENZYMATIC HYDROLYSIS OF CELLULOSE IN COCONUT COIR: PRE-TREATED VIA SONICATION

Coconut coir is composed of cellulose, lignin, pectin, and hemicellulose. Production of biofuels is one possible application of this lignocellulosic waste. Physical-chemical pretreatment is required in order to remove lignin and to increase cellulose digestability. Commonly, the coconut coir was pretreated primarily with NaOH solution (5-11%) in autoclave at 121°C for 1 hour. The aims of this work were to find an enhanced pretreatment procedure for the coconut coir, and to study kinetics of the cellulose hydrolysis process at elevated temperature. For this purpose, we used NaOH solution (5% w/v) and ultrasonic bath for 1 hour at 50°C, which can successfully replace autoclave pretreatment. Moreover, the temperature of the ultrasonic pretreatment coincided with the temperature of hydrolysis reaction carried out subsequently. Two commercial enzymes cellulase and β-galactosidase were applied simultaneously as biocatalyst for the enzymatic hydrolysis of cellulose. One of the advantages of the ultrasound-assisted pretreatment was the lowest weight loss of the coir 48% (w/w). When using autoclave, the weight loss was much higher about 66-67% (w/w) of the coir samples. First order kinetic equation for the enzymatic hydrolysis of cellulose to glucose was derived, and kinetic constants were obtained for the product released at different modes of operation.
Intrahepatic cholangiocyte regeneration from an Fgf‐dependent extrahepatic progenitor niche in a zebrafish model of Alagille Syndrome

Alagille Syndrome (ALGS) is a congenital disorder caused by mutations in the Notch ligand gene JAGGED1, leading to neonatal loss of intrahepatic duct (IHD) cells and cholestasis. Cholestasis can resolve in certain patients with ALGS, suggesting regeneration of IHD cells. However, the mechanisms driving IHD cell regeneration following Jagged loss remains unclear. Here, we show that cholestasis due to developmental loss of IHD cells can be consistently phenocopied in zebrafish with compound jagged1b and jagged2b mutations or knockdown.
Multifaceted roles of CCL20 (C-C motif chemokine ligand 20): mechanisms and communication networks in breast cancer progression

ABSTRACT Emerging studies have demonstrated notable roles of CCL20 in breast cancer progression. Based on these findings, CCL20 has become a potential therapeutic target for cancer immunotherapy. Accordingly, studies utilizing monoclonal antibodies to target CCL20 are currently being experimented. However, the existence of cytokine network in the tumor microenvironment collectively regulates tumor progression. Hence, a deeper understanding of the role of CCL20 and the underlying signaling pathways regulating the functions of CCL20 may provide a novel strategy for therapeutic interventions. This review provides the current knowledge on how CCL20 interacts with breast cancer cells to influence tumor progression via immunosuppression, angiogenesis, epithelial to mesenchymal transition, migration/invasion and chemoresistance. As a possible candidate biomarker, we also reviewed signal pathways and other factors in the tumor microenvironment regulating the tumor-promoting functions of CCL20. These new insights may be useful to design new potent and selective CCL20 inhibitors against breast cancer in the future. Graphical Abstract
Gastroduodenal Intussusception Due to Vonoprazan-induced Gastric Polyps

A 66-year-old woman presented with abdominal discomfort and iron-deficiency anemia (hemoglobin, 8.3 g/dL). She had been taking 10 mg vonoprazan daily for 3 years for the treatment of gastroesophageal reflux disease. Helicobacter pylori tests (serum anti-IgG antibody and histology) were negative. Abdominal computed tomography revealed polypoid lesions arising from the gastric wall in the duodenal bulb (Picture 1). Esophagogastroduodenoscopy revealed more than 30 pedunculated polyps in the gastric body (Picture 2). At the time of endoscopy, intussusception due to the gastric polyps had likely spontaneously reduced. A biopsy of the polyps revealed fundic gland mucosa with cystic glands, which was consistent with fundic gland polyps, and parietal cell protrusion. We suspected that the gastric polyps were related to proton pump inhibition, and vonoprazan was discontinued. Twelve months later, follow-up endoscopy revealed a marked decrease in the number and size of the polyps (Picture 3). The serum gastrin levels before and after the cessation of vonoprazan were 830 and 130 (pg/mL), respectively. The anemia improved with iron treatment and did not recur. Long-term proton-pump inhibitor use has been reported to induce gastric fundic grand polyp development
Intra- and Inter-Limb Strength Asymmetry in Soccer: A Comparison of Professional and Under-18 Players

(1) Background: the present study examined the isokinetic peak torque exerted by both knee extensors and flexors, anterior–posterior imbalance and the magnitude and direction of inter-limb asymmetry in professional and academy soccer players. (2) Methods: one hundred soccer players (professional = 50, elite academy = 50) volunteered to take part in this investigation. An isokinetic dynamometer was used to measure the knee extensor (quadriceps) and flexors muscle (hamstrings) torques of the limbs as well as inter-limb asymmetries—using a standard percentage difference equation. (3) Results: professional players exhibited significantly greater (effect size [ES] = large) strength levels in the quadriceps and hamstrings under both testing conditions, significantly higher (small to moderate) intra-limb ratio values for 60°·s−1 but not for the 300°·s−1 test condition, significantly (small to moderate) lower inter-limb asymmetry values for all test conditions, with the exception of the hamstrings at 60°·s−1 and the direction of asymmetry was poor to slight, indicating that limb dominance was rarely the same between groups. (4) Conclusions: this study shows that isokinetic assessments, i.e., peak torque exerted by both knee extensors and flexors and intra-limb ratio, and the subsequent inter-limb asymmetry, i.e., magnitude and direction, can differentiate between professional and academy soccer players.