Miracle-dz/UniFAR-FTUs huggingface dataset:
CD204-positive macrophages accumulate in breast cancer tumors with high levels of infiltrating lymphocytes and programmed death ligand-1 expression

Although immunotherapy has been demonstrated to be promising in triple-negative (TN) breast cancer (BC), most BC cases are classified as non-TN. To enrich the responders for immunotherapy regardless of their subtypes, classification based on tumor-infiltrating lymphocyte (TIL) levels and programmed death ligand-1 (PD-L1) status may be useful. However, this classification has not been fully applied to BC. Furthermore, suppressive subsets in the local tumor microenvironment, such as tumor-associated macrophages (TAMs), which promote tumor progression, cannot be ignored to overcome immunotherapy resistance. The aims of the present study were to classify primary BC cases based on the TIL levels and PD-L1 status, and to identify suppressive immune subsets in each categorized group. A retrospective analysis of 73 patients with invasive BC was performed. The frequency of TILs was evaluated in HE-stained slides (10% cutoff), and PD-L1 levels (SP142; 1% cutoff), as well as immune subsets (CD3+, CD8+, FOXP3+, CD20+, CD68+ and CD204+ cells) were assessed using immunohistochemistry. It was revealed that 22% (16/73) of the tumors were categorized as TIL+PD-L1+, of which 69% (11/16) were TN type. By contrast, 66% (48/73) of the tumors were categorized as TIL−PD-L1−, of which 77% (37/48) were HR+ and HER2− types. The number of CD204+ M2-type macrophages was significantly associated with high histological grade (P=0.0246) and high Ki-67 (P=0.0152), whereas CD68+ macrophages were not associated with these factors. Furthermore, CD204+ macrophages and FOXP3+ Tregs accumulated in 88% (14/16) and 63% (10/16) of TIL+PD-L1+ tumors, respectively, compared with 20.8% (10/48) and 27.1% (13/48) of TIL−PD-L1− tumors. In conclusion, 22% of BC tumors were classified as TIL+PD-L1+ (69% were TN), which were enriched with suppressive immune subsets. These cell types may serve as potential novel immunotherapeutic targets.
Early Signs of Atherogenic Features in the HDL Lipidomes of Normolipidemic Patients Newly Diagnosed with Type 2 Diabetes

Cardiovascular disease (CVD) is the major cause of death in patients with type-2 diabetes mellitus (T2DM), although the factors that accelerate atherosclerosis in these patients are poorly understood. The identification of the altered quantity and quality of lipoproteins, closely related to atherogenesis, is limited in routine to a pattern of high triglycerides and low HDL-cholesterol (HDL-C) and in research as dysfunctional HDLs. We used the emerging NMR-based lipidomic technology to investigate compositional features of the HDLs of healthy individuals with normal coronary arteries, drug-naïve; recently diagnosed T2DM patients with normal coronary arteries; and patients with recent acute coronary syndrome. Patients with T2DM and normal serum lipid profiles even at diagnosis presented significant lipid alterations in HDL, characterized by higher triglycerides, lysophosphatidylcholine and saturated fatty acids; and lower cholesterol, phosphatidylcholine, phosphatidylethanolamine, sphingomyelin, plasmalogens and polyunsaturated fatty acids, an atherogenic pattern that may be involved in the pathogenesis of atherosclerosis. These changes are qualitatively similar to those found, more profoundly, in normolipidemic patients with established Coronary Heart Disease (CHD). We also conclude that NMR-based lipidomics offer a novel holistic exploratory approach for identifying and quantifying lipid species in biological matrixes in physiological processes and disease states or in disease biomarker discovery.
The impact of body mass index, central obesity and physical activity on lung function: results of the EpiHealth study

Study objectives Obesity is often associated with lower lung function; however, the interaction of lung function with central obesity and physical inactivity is less clear. As such, we investigated the effect on lung function of body size (body mass index (BMI)), central obesity (waist circumference (WC)) and self-reported physical activity. Methods Lung function, height, weight and WC were measured in 22 743 participants (12 791 women), aged 45–75 years, from the EpiHealth cohort study. Physical activity, gender and educational level were assessed using a questionnaire. Results Obesity, central obesity and physical inactivity were all associated with lower forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC). However, in participants without central obesity there was an increase in both FEV1 and FVC by BMI (% predicted FVC increasing from median 98%, interquartile range (IQR) 89–110% in underweight participants (BMI <20) to 103%, IQR 94–113% in obese participants (BMI ≥30)). In contrast, there was a decrease in % predicted FVC in participants with central obesity (from 98%, IQR 89–109% in the normal weight group to 95%, IQR 85–105% in the obese weight group). We further found a negative association between physical activity and lung function among those with low and high levels of physical activity (% predicted FEV1 97%, IQR 86–107% versus 103%, IQR 94–113%, respectively and % predicted FVC 96%, IQR 85–106% versus 103%, IQR 94–113%, respectively). All results remained when calculated by z-scores. Conclusions The association between BMI and lung function is dependent on the presence of central obesity. Independent of obesity, there is an association between physical activity and lung function. BMI and central obesity are related to lung function in the middle-aged and elderly. BMI association with lung function is dependent on the presence of central obesity, while the association of physical activity and lung function is independent of obesity. https://bit.ly/3eQl86C
Long-term fracture load of all-ceramic crowns: Effects of veneering ceramic thickness, application techniques, and cooling protocol

Background To evaluate, in vitro, the effects of the cooling protocol, application technique, and veneering ceramic thickness on the fracture resistance of ceramic crowns with Y-TZP frameworks. Material and Methods 80 frameworks were made from zirconia by the CAD/CAM technique and divided into 8 groups (n = 10) according to the factors: “application technique” (stratified-L and pressed -P), “thickness” (1 mm and 2 mm), and “cooling protocol” (slow-S and fast-F) of the feldspathic veneering ceramic. After, all crowns were cemented over G10 preparations with resin cement (Panavia F, Kuraray), mechanically cycled (2x106 cycles, 200 N, 3Hz), and subjected to the axial compression resistance test (0.5 mm/min, 10 kN). The data (N) underwent descriptive statistical analysis by 3-way ANOVA and Tukey’s test (5%). Fracture analysis was performed to determine the possible origin of failure. Results The factors “cooling protocol” (P=0.0058) and “application” technique (P=0.0001) influenced the fracture resistance of the crowns. For pressed veneer technique, the P2S (4608.9±464.5). A presented significantly higher results than that P2F(3621.1±523.0)BCD (Tukey’s test). For the stratified technique, this difference was not observed (P>0.05). The thickness of the veneering ceramic was not significant regardless of the cooling protocol and technique (P>0.05). The predominant failure mode was chipping of the ceramic veneer originating in the subsurface. Conclusions The pressed technique, used with a slow-cooling protocol, leads to the best outcome for the veneering of all-ceramic crowns. Key words:Zirconia, ceramics, cooling protocol, thickness, application technique.
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base

Acquiring commonsense knowledge and reasoning is recognized as an important frontier in achieving general Artificial Intelligence (AI). Recent research in the Natural Language Processing (NLP) community has demonstrated significant progress in this problem setting. Despite this progress, which is mainly on multiple-choice question answering tasks in limited settings, there is still a lack of understanding (especially at scale) of the nature of commonsense knowledge itself. In this paper, we propose and conduct a systematic study to enable a deeper understanding of commonsense knowledge by doing an empirical and structural analysis of the ConceptNet knowledge base. ConceptNet is a freely available knowledge base containing millions of commonsense assertions presented in natural language. Detailed experimental results on three carefully designed research questions, using state-of-the-art unsupervised graph representation learning ('embedding') and clustering techniques, reveal deep substructures in ConceptNet relations, allowing us to make data-driven and computational claims about the meaning of phenomena such as 'context' that are traditionally discussed only in qualitative terms. Furthermore, our methodology provides a case study in how to use data-science and computational methodologies for understanding the nature of an everyday (yet complex) psychological phenomenon that is an essential feature of human intelligence.
Neutron and Charged-Particle Induced Cross Sections for Radiochemistry in the Region of Samarium, Europium, and Gadolinium

We have developed a set of modeled nuclear reaction cross sections for use in radiochemical diagnostics. Systematics for the input parameters required by the Hauser-Feshbach statistical model were developed and used to calculate neutron and proton induced nuclear reaction cross sections in the mass region of samarium, europium and gadolinium (62 {le} Z {le} 64, 82 {le} N {le} 96).
Short-Term Core Strengthening Program Improves Functional Movement Score in Untrained College Students

Functional movement is an important part of developing athletes’ but also untrained individuals’ performance. Its monitoring also proved useful in identifying functional limitations and asymmetries, and also in determining the intervention effects. The quasi-experimental pre-test post-test study investigated the effects of core stability training program on the Functional Movement Screen (FMS) score in untrained students after six weeks. The intervention (INT) and control (CG) groups included 73 and 65 male students, respectively. Functional movement patterns were evaluated using the FMS including seven components scores representing seven basic functional patterns. Both groups significantly improved almost all FMS components scores, but the INT increased the mean performance of the hurdle step (partial ŋ2 × 100 = 4%, p = 0.02), in-line lunge (partial ŋ2 × 100 = 3%, p = 0.05), rotatory stability (partial ŋ2 × 100 = 4%, p = 0.02) and total FMS (partial ŋ2 × 100 = 3%, p = 0.04) significantly more than the CG. This justifies that core strengthening can improve FMS in untrained individuals even with the short duration programs.
How useful are virtual fracture clinics?

Background Due to the overwhelming demand for trauma services, resulting from increasing emergency department attendances over the past decade, virtual fracture clinics (VFCs) have become the fashion to keep up with the demand and help comply with the BOA Standards for Trauma and Orthopaedics (BOAST) guidelines. In this article, we perform a systematic review asking, “How useful are VFCs? ”, and what injuries and conditions can be treated safely and effectively, to help decrease patient face to face consultations. Our primary outcomes were patient satisfaction, clinical efficiency and cost analysis, and clinical outcomes. Methods We performed a systematic literature search of all papers pertaining to VFCs, using the search engines PubMed, MEDLINE, and the Cochrane Database, according to the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) checklist. Searches were carried out and screened by two authors, with final study eligibility confirmed by the senior author. Results In total, 21 records were relevant to our research question. Six orthopaedic injuries were identified as suitable for VFC review, with a further four discussed in detail. A reduction of face to face appointments of up to 50% was reported with greater compliance to BOAST guidelines (46.4%) and cost saving (up to £212,000). Conclusions This systematic review demonstrates that the VFC model can help deliver a safe, more cost-effective, and more efficient arm of the trauma service to patients. Cite this article: Bone Joint Open 2020;1-11:683–690.
Extracting electron scattering cross sections from swarm data using deep neural networks

Electron-neutral scattering cross sections are fundamental quantities in simulations of low temperature plasmas used for many technological applications today. From these microscopic cross sections, several macro-scale quantities (called ‘swarm’ parameters) can be calculated. However, measurements as well as theoretical calculations of cross sections are challenging. Since the 1960s, researchers have attempted to solve the inverse swarm problem of obtaining cross sections from swarm data; but the solutions are not necessarily unique. To address these issues, we examine the use of deep learning models which are trained using the previous determinations of elastic momentum transfer, ionization and excitation cross sections for different gases available on the LXCat website and their corresponding swarm parameters calculated using the BOLSIG+ solver for the numerical solution of the Boltzmann equation for electrons in weakly ionized gases. We implement artificial neural network (ANN), convolutional neural network (CNN) and densely connected convolutional network (DenseNet) for this investigation. To the best of our knowledge, there is no study exploring the use of CNN and DenseNet for the inverse swarm problem. We test the validity of predictions by all these trained networks for a broad range of gas species and we deduce that DenseNet effectively extracts both long and short term features from the swarm data and hence, it predicts cross sections with significantly higher accuracy compared to ANN. Further, we apply Monte Carlo dropout as Bayesian approximation to estimate the probability distribution of the cross sections to determine all plausible solutions of this inverse problem.
Message Passing-Based Structured Sparse Signal Recovery for Estimation of OTFS Channels With Fractional Doppler Shifts

The orthogonal time frequency space (OTFS) modulation has emerged as a promising modulation scheme for high mobility wireless communications. To enable efficient OTFS detection in the delay-Doppler (DD) domain, the DD domain channels need to be acquired accurately. To achieve the low latency requirement in future wireless communications, the time duration of the OTFS block should be small, therefore fractional Doppler shifts have to be considered to avoid significant modelling errors due to the assumption of integer Doppler shifts. However there lack investigations on the estimation of OTFS channels with fractional Doppler shifts in the literature. In this work, we develop a channel estimator for OTFS with particular attention to fractional Doppler shifts, and both bi-orthogonal waveform and rectangular waveform are considered. Instead of estimating the DD domain channel directly, we estimate the channel gains and (fractional) Doppler shifts that parameterize the DD domain channel. The estimation is formulated as a structured sparse signal recovery problem with a Bayesian treatment. Based on a factor graph representation of the problem, an efficient message passing algorithm is developed to recover the structured sparse signal (thereby the OTFS channel). The Cramer-Rao Lower Bound (CRLB) for the estimation is developed and the effectiveness of the algorithm is demonstrated through simulations.
Assessing the Acceptance of Clinical Decision Support Tools using an Integrated Technology Acceptance Model

With the medical development in recent decades, the multiplicity of different diseases and an increased number of patients, the use of an advanced healthcare information technology (HIT) such as a clinical decision support system (CDSS) has been of necessary to help general practitioners (GPs). CDSS may fail due to the failure to understand the factors influencing the GP’s acceptance of CDSS. Identifying factors that promote acceptance of CDSS can be a vital aspect for its successful implementation. This study seeks to identify factors that influence the acceptance of CDSS in Saudi Arabia by GPs. This study relies mainly on the unified theory of acceptance and use of technology (UTAUT) which has been integrated with a task-technology fit (TTF) model and has applied a qualitative method to collect the data through using semi-structured interviews with 12 GPs. The study’s results indicated that performance expectancy, effort expectancy, facilitating conditions, technology fit for the task, technology characteristics and task characteristics have all influenced the acceptance of CDSS. The results also indicated the need to extend the UTAUT model to investigate and explore other factors in GPs’ acceptance of CDSS.
Feature Learning in Infinite-Width Neural Networks

As its width tends to infinity, a deep neural network's behavior under gradient descent can become simplified and predictable (e.g. given by the Neural Tangent Kernel (NTK)), if it is parametrized appropriately (e.g. the NTK parametrization). However, we show that the standard and NTK parametrizations of a neural network do not admit infinite-width limits that can learn features, which is crucial for pretraining and transfer learning such as with BERT. We propose simple modifications to the standard parametrization to allow for feature learning in the limit. Using the *Tensor Programs* technique, we derive explicit formulas for such limits. On Word2Vec and few-shot learning on Omniglot via MAML, two canonical tasks that rely crucially on feature learning, we compute these limits exactly. We find that they outperform both NTK baselines and finite-width networks, with the latter approaching the infinite-width feature learning performance as width increases. More generally, we classify a natural space of neural network parametrizations that generalizes standard, NTK, and Mean Field parametrizations. We show 1) any parametrization in this space either admits feature learning or has an infinite-width training dynamics given by kernel gradient descent, but not both; 2) any such infinite-width limit can be computed using the Tensor Programs technique.
Density and non-density of $C^\infty_c \hookrightarrow W^{k,p}$ on complete manifolds with curvature bounds

We investigate the density of compactly supported smooth functions in the Sobolev space $W^{k,p}$ on complete Riemannian manifolds. In the first part of the paper, we extend to the full range $p\in [1,2)$ the most general results holding in the Hilbertian case. In particular, we obtain the density under a quadratic Ricci lower bound (when $k=2$) or a suitably controlled growth of the derivatives of $\mathrm{Riem}$ only up to order $k-3$ (when $k>2$). To this end, we prove a gradient regularity lemma that might be of independent interest. In the second part of the paper, when $p>n$ we give the first counterexample to the density property on manifolds whose sectional curvature is bounded from below by a negative constant.
Oblate electron holes are not attributable to anisotropic shielding

Shielding mechanisms' influence on the ratio of perpendicular to parallel scale lengths of multidimensional plasma electron hole equilibria are analyzed theoretically and computationally. It is shown that the ``gyrokinetic'' model, invoking perpendicular polarization, is based on a misunderstanding and cannot explain the observational trend that greater transverse extent accompanies lower magnetic field. Instead, the potential in the wings of the hole, outside the region of trapped-electron depletion, has isotropic shielding giving $\phi\propto {\rm e}^{-r/L}/r$, with the shielding length $L$ equal to the Debye length for holes much slower than the electron thermal speed. Particle in cell simulations confirm the analysis.
C R ] 2 8 N ov 2 02 0 VERIFICATION OF RECURSIVE BITCOIN CONTRACTS

Smart contracts — computer protocols that regulate the exchange of crypto-assets in trustless environments — have become popular with the spread of blockchain technologies. A landmark security property of smart contracts is liquidity : in a non-liquid contract, it may happen that some assets remain frozen, i.e. not redeemable by anyone. The relevance of this issue is witnessed by recent liquidity attacks to Ethereum, which have frozen hundreds of USD millions. We address the problem of verifying liquidity on BitML, a DSL for smart contracts with a secure compiler to Bitcoin, featuring primitives for currency transfers, contract renegotiation and consensual recursion. Our main result is a veriﬁcation technique for liquidity. We ﬁrst transform the inﬁnite-state semantics of BitML into a ﬁnite-state one, which focusses on the behaviour of a chosen set of contracts, abstracting from the moves of the context. With respect to the chosen contracts, this abstraction is sound, i.e. if the abstracted contract is liquid, then also the concrete one is such. We then verify liquidity by model-checking the ﬁnite-state abstraction. We implement a toolchain that automatically veriﬁes liquidity of BitML contracts and compiles them to Bitcoin, and we assess it through a benchmark of representative contracts.
Vertical gingival display changes associated with upper premolars extraction orthodontic treatment: A prospective clinical trial

Background Extraction of upper bicuspids have been anecdotally blamed to increase the vertical gingival display (VGD) anteriorly. However, the extraction may be needed in some cases in order to correct the underlying orthodontic problem. Objectives: To investigate and compare vertical gingival display (VGD) changes associated with upper (first vs second) premolars extraction during orthodontic treatment. Material and Methods Design: A prospective clinical trial. Setting: Postgraduate dental teaching clinics at Jordan University of Science and Technology (JUST). Sample population: Sixty orthodontic patients were included in the study. They were treated with upper first or second premolars extraction according to the underlying problem and the individualized treatment plan of each patient. Records (radiographs, study casts and clinical photographs) were taken for all subjects pre- and post- orthodontic treatment. Outcome measures: Pre- and post-treatment VGD, lip length in static and dynamic positions and the amount of upper teeth retractions were recorded. The paired and the independent t- test were used to detect differences within/between groups. Factors affecting VGD were investigated using backward stepwise linear regression analysis. Results In both static and dynamic captures, VGD increased after orthodontic treatment in both premolars extraction groups. Pre- and post-treatment variables differed significantly in groups 1 and 2. VGD changes were similar in both treatment groups. A significant association was found between VGD change during orthodontic treatment and upper canine retraction (P<0.001), pre-treatment ANB angle (P<0.01) and upper incisor retraction(P<0.05). Conclusions The amount of anterior VGD increases after upper premolars extraction. The increase in VGD after first and second premolars extractions was comparable. The increase in VGD after orthodontic treatment is associated with the amount of canine retraction, pre-treatment ANB and the amount of incisor retraction. Key words:Vertical gingival display, tooth extraction, dental esthetics.