Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of Bénard convection with and without rotation

An experimental study of the response of a thin uniformly heated rotating layer of fluid is presented. It is shown that the stability of the fluid depends strongly upon the three parameters that described its state, namely the Rayleigh number, the Taylor number and the Prandtl number. For the two Prandtl numbers considered, 6·8 and 0·025 corresponding to water and mercury, linear theory is insufficient to fully describe their stability properties. For water, subcritical instability will occur for all Taylor numbers greater than 5 × 104, whereas mercury exhibits a subcritical instability only for finite Taylor numbers less than 105. At all other Taylor numbers there is good agreement between linear theory and experiment. The heat flux in these two fluids has been measured over a wide range of Rayleigh and Taylor numbers. Generally, much higher Nusselt numbers are found with water than with mercury. In water, at any Rayleigh number greater than 104, it is found that the Nusselt number will increase by about 10% as the Taylor number is increased from zero to a certain value, which depends on the Rayleigh number. It is suggested that this increase in the heat flux results from a perturbation of the velocity boundary layer with an ‘Ekman-layer-like’ profile in such a way that the scale of boundary layer is reduced. In mercury, on the other hand, the heat flux decreases monotonically with increasing Taylor number. Over a range of Rayleigh numbers (at large Taylor numbers) oscillatory convection is preferred although it is inefficient at transporting heat. Above a certain Rayleigh number, less than the critical value for steady convection according to linear theory, the heat flux increases more rapidly and the convection becomes increasingly irregular as is shown by the temperature fluctuations at a point in the fluid. Photographs of the convective flow in a silicone oil (Prandtl number = 100) at various rotation rates are shown. From these a rough estimate is obtained of the dominant horizontal convective scale as a function of the Rayleigh and Taylor numbers.
Editorial: Oral complications in cancer patients

COPYRIGHT © 2023 González-Arriagada, Ottaviani, Dean, Ottaviani, Santos-Silva and Treister. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation

Cardiac magnetic resonance imaging (MRI) segmentation task refers to the accurate segmentation of ventricle and myocardium, which is a prerequisite for evaluating the soundness of cardiac function. With the development of deep learning in medical imaging, more and more heart segmentation methods based on deep learning have been proposed. Due to the fuzzy boundary and uneven intensity distribution of cardiac MRI, some existing methods do not make full use of multi-scale characteristic information and have the problem of ambiguity between classes. In this paper, we propose a dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation. The network uses feature fusion module to preserve boundary information, and adopts the direction field module to obtain the feature maps to improve the original segmentation features. Firstly, multi-scale feature information is obtained and fused through dilated convolutional layers of different scales while downsampling. Secondly, in the decoding stage, the edge fusion block integrates the edge features into the side output of the encoder and concatenates them with the upsampled features. Finally, the concatenated features utilize the direction field to improve the original segmentation features and generate the final result. Our propose method conducts comprehensive comparative experiments on the automated cardiac diagnosis challenge (ACDC) and myocardial pathological segmentation (MyoPS) datasets. The results show that the proposed cardiac MRI segmentation method has better performance compared to other existing methods.
Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

In this study, the Direction Of Departure (DOD) and Direction Of Arrival (DOA) of signals detection for Multi-Input Multi-Output (MIMO) radar is discussed. A novel signal subspace reconstruction model to match the signal subspace obtained based on the covariance matrix of the array output is developed to enhance the performance of the DOD and DOA detection. In the developed scheme, the technology of beamforming is first introduced to define an objective space in mathematics for the targets to be detected. By considering the orthogonality between the signal subspace and the noise subspace and defining a reconstruction index of the signal subspace, a multi-dimensional objective function of the DOD and DOA is established. Therefore, the problem of DOD and DOA detection is transformed into an optimization of the multi-dimensional objective function. Subsequently, the Quantum-Behaved Particle Swarm Optimization (QPSO) is employed to optimize the multi-dimensional objective function and to determine an optimal signal subspace. At the same time the DOD and DOA can be fast captured. A series of simulations demonstrate that the proposed method provides significant accuracy improvements in DOD and DOA detection, especially for low signal-to-noise ratio thresholds and small snapshots.
Early recognition of risk of critical adverse events based on deep neural decision gradient boosting

Introduction Perioperative critical events will affect the quality of medical services and threaten the safety of patients. Using scientific methods to evaluate the perioperative risk of critical illness is of great significance for improving the quality of medical services and ensuring the safety of patients. Method At present, the traditional scoring system is mainly used to predict the score of critical illness, which is mainly dependent on the judgment of doctors. The result is affected by doctors' knowledge and experience, and the accuracy is difficult to guarantee and has a serious lag. Besides, the statistical prediction method based on pure data type do not make use of the patient's diagnostic text information and cannot identify comprehensive risk factor. Therefore, this paper combines the text features extracted by deep neural network with the pure numerical type features extracted by XGBOOST to propose a deep neural decision gradient boosting model. Supervised learning was used to train the risk prediction model to analyze the occurrence of critical illness during the perioperative period for early warning. Results We evaluated the proposed methods based on the real data of critical illness patients in one hospital from 2014 to 2018. The results showed that the critical disease risk prediction model based on multiple modes had faster convergence rate and better performance than the risk prediction model based on text data and pure data type. Discussion Based on the machine learning method and multi-modal data of patients, this paper built a prediction model for critical adverse events in patients, so that the risk of critical events can be predicted for any patient directly based on the preoperative and intraoperative characteristic data. At present, this work only classifies and predicts the occurrence of critical illness during or after operation based on the preoperative examination data of patients, but does not discuss the specific time when the patient was critical illness, which is also the direction of our future work.
Large pneumothorax following thoracic and lumbar tumor surgery: Risk factors and management strategies

Objective Large pneumothorax is a rare but dangerous complication following thoracic and lumbar tumor surgery. There is little discussion about the features of large pneumothorax following spinal tumor surgery. The purpose of this study was to analyze the characteristics of postoperative pneumothorax, identify factors related to large pneumothorax, and propose a management algorithm for prevention, diagnosis, and treatment. Methods Included in this retrospective study were 118 patients who developed pneumothorax after receiving thoracic and lumbar tumor surgery between January 2015 and October 2021. A measurement of lung compression ≥20% on chest CT or x-ray was defined as large pneumothorax, and potential risk factors for large pneumothorax were identified by univariate analysis. Results Spinal tumor history and intraoperative blood loss were risk factors for large pneumothorax. The common symptoms of postoperative pneumothorax were chest pain, chest tightness and dyspnea. The mean longest transverse diameter of tumors was 6.63 ± 2.4 cm. En bloc resection was performed in 70 patients, with a mean operation time of 6.9 ± 2.5 h and mean intraoperative blood loss of 1771 ± 1387 ml. The most common pathologies were chondrosarcoma, giant cell tumors of bone, and neurogenic tumors. Conclusion During surgery, an artificial dura mater patch and a prolene suture can be used to repair the pleural and lung defects. We recommend chest CT as the preferred method for identifying postoperative pneumothorax. If a patient presents severe dyspnea, a large pneumothorax or concurrent pleural effusion, application of chest drainage is strongly recommended.
Specific myeloid signatures in peripheral blood differentiate active and rare clinical phenotypes of multiple sclerosis

Current understanding of Multiple Sclerosis (MS) pathophysiology implicates perturbations in adaptive cellular immune responses, predominantly T cells, in Relapsing-Remitting forms (RRMS). Nevertheless, from a clinical perspective MS is a heterogeneous disease reflecting the heterogeneity of involved biological systems. This complexity requires advanced analysis tools at the single-cell level to discover biomarkers for better patient-group stratification. We designed a novel 44-parameter mass cytometry panel to interrogate predominantly the role of effector and regulatory subpopulations of peripheral blood myeloid subsets along with B and T-cells (excluding granulocytes) in MS, assessing three different patient cohorts: RRMS, PPMS (Primary Progressive) and Tumefactive MS patients (TMS) (n=10, 8, 14 respectively). We further subgrouped our cohort into inactive or active disease stages to capture the early underlying events in disease pathophysiology. Peripheral blood analysis showed that TMS cases belonged to the spectrum of RRMS, whereas PPMS cases displayed different features. In particular, TMS patients during a relapse stage were characterized by a specific subset of CD11c+CD14+ CD33+, CD192+, CD172+-myeloid cells with an alternative phenotype of monocyte-derived macrophages (high arginase-1, CD38, HLA-DR-low and endogenous TNF-a production). Moreover, TMS patients in relapse displayed a selective CD4 T-cell lymphopenia of cells with a Th2-like polarised phenotype. PPMS patients did not display substantial differences from healthy controls, apart from a trend toward higher expansion of NK cell subsets. Importantly, we found that myeloid cell populations are reshaped under effective disease-modifying therapy predominantly with glatiramer acetate and to a lesser extent with anti-CD20, suggesting that the identified cell signature represents a specific therapeutic target in TMS. The expanded myeloid signature in TMS patients was also confirmed by flow cytometry. Serum neurofilament light-chain levels confirmed the correlation of this myeloid cell signature with indices of axonal injury. More in-depth analysis of myeloid subsets revealed an increase of a subset of highly cytolytic and terminally differentiated NK cells in PPMS patients with leptomeningeal enhancement (active-PPMS), compared to those without (inactive-PPMS). We have identified previously uncharacterized subsets of circulating myeloid cells and shown them to correlate with distinct disease forms of MS as well as with specific disease states (relapse/remission).
Quadric-Based Polygonal Surface Simplification

Many applications in computer graphics and related fields can benefit from automatic simplification of complex polygonal surface models. Applications are often confronted with either very densely over-sampled surfaces or models too complex for the limited available hardware capacity. An effective algorithm for rapidly producing high-quality approximations of the original model is a valuable tool for managing data complexity. In this dissertation, I present my simplification algorithm, based on iterative vertex pair contraction. This technique provides an effective compromise between the fastest algorithms, which often produce poor quality results, and the highest-quality algorithms, which are generally very slow. For example, a 1000 face approximation of a 100,000 face model can be produced in about 10 seconds on a PentiumPro 200. The algorithm can simplify both the geometry and topology of manifold as well as non-manifold surfaces. In addition to producing single approximations, my algorithm can also be used to generate multiresolution representations such as progressive meshes and vertex hierarchies for view-dependent refinement. The foundation of my simplification algorithm, is the quadric error metric which I have developed. It provides a useful and economical characterization of local surface shape, and I have proven a direct mathematical connection between the quadric metric and surface curvature. A generalized form of this metric can accommodate surfaces with material properties, such as RGB color or texture coordinates. I have also developed a closely related technique for constructing a hierarchy of well-defined surface regions composed of disjoint sets of faces. This algorithm involves applying a dual form of my simplification algorithm to the dual graph of the input surface. The resulting structure is a hierarchy of face clusters which is an effective multiresolution representation for applications such as radiosity.
Surfacing rates, swim speeds, and patterns of movement of Antarctic blue whales

During three surveys in the austral summers of 2013, 2015, 2019, data on Antarctic blue whale blow rates, dive times, swim speeds, and broadscale movements were collected using video photogrammetric tracking and intra-voyage photo-identification. A total of 24.4 hours of video observations were suitable for blow interval or movement analysis. Similar to other blue whale populations, Antarctic blue whale dive behaviour comprised a sequence of short dives followed by a long dive with a mean dive time for short dives of 17.6 s, and a mean long dive time of 189.3 s. Two separate methods were used to estimate the average blow rate for Antarctic blue whales, giving estimates of 59.7 and 63.2 blows per hour. The overall mean swim speed over the course of all suitable video track segments was 1.59 ms-1, but there were significant differences between years in the mean of the overall movement rate for each track; average movement rates were lower in 2015 compared to 2013 and 2019 (0.90 ms-1, 1.84 and 1.55 ms-1 respectively), with higher rates of turning in 2015. In 2019, there was faster overall movement through the study area in a consistent direction. The total number of photo-identified blue whales re-sighted intra-season in 2013 was nine (out of 50 identified individuals); in 2015 it was seven (out of 46); in 2019 two (out of 25). Whales remained for several days with little overall movement within the 2015 study area, whereas they were moving through the study area in 2019, which would explain the low number of intra-season resights. The predominant heading in 2019 was towards the area of Antarctic blue whale concentrations at the entrance to the Ross Sea observed in previous years. The photo-identification data also show a high proportion of resighted whales with coherent movements. This suggests that Antarctic blue whales might travel together, at least over periods of several days or sometimes up to a month. The differences between behaviours in 2015 and 2019 in particular may be related to differences in the characteristics of krill swarms between the study areas.
ASCA observations of the coronal x-ray emission of algol

We report a 1.7 day ASCA X-ray observation of the 2.87 day binary Algol (Beta Per), centered on the secondary eclipse. Spectra accumulated for different intensity states show a prominent He-like iron K line at 6.7 keV. A two-temperature variable abundance plasma model applied to the spectra yielded temperatures of approximately 8 and approximately 30 MK. The modeled coronal abundances of Fe, O, Mg, Si, S, Ar, and Ca were a factor of 2-3 below the solar photospheric value, and N less than 0.1. These model abundance anomalies are similar to those found from the ASCA spectra of other late-type stars and may indicate either true deviations from solar abundances or problems with the assumptions and atomic physics of the plasma models. The X-ray light curve shows a factor of 2 increase in flux over 13 hr beginning in the middle of the secondary eclipse, with a shallow eclipse centered on phase 0.45. The orbital light curve is similar to that observed by ROSAT 1 year earlier. The rise in flux is caused by an increase in the emission measure of the higher temperature component. The intensity variation is not associated with any major change in the abundances or temperature of the two components.
Efficient suppression of parkinsonian beta oscillations in a closed-loop model of deep brain stimulation with amplitude modulation

Introduction Parkinson's disease (PD) is a movement disorder characterized by the pathological beta band (15–30 Hz) neural oscillations within the basal ganglia (BG). It is shown that the suppression of abnormal beta oscillations is correlated with the improvement of PD motor symptoms, which is a goal of standard therapies including deep brain stimulation (DBS). To overcome the stimulation-induced side effects and inefficiencies of conventional DBS (cDBS) and to reduce the administered stimulation current, closed-loop adaptive DBS (aDBS) techniques were developed. In this method, the frequency and/or amplitude of stimulation are modulated based on various disease biomarkers. Methods Here, by computational modeling of a cortico-BG-thalamic network in normal and PD conditions, we show that closed-loop aDBS of the subthalamic nucleus (STN) with amplitude modulation leads to a more effective suppression of pathological beta oscillations within the parkinsonian BG. Results Our results show that beta band neural oscillations are restored to their normal range and the reliability of the response of the thalamic neurons to motor cortex commands is retained due to aDBS with amplitude modulation. Furthermore, notably less stimulation current is administered during aDBS compared with cDBS due to a closed-loop control of stimulation amplitude based on the STN local field potential (LFP) beta activity. Discussion Efficient models of closed-loop stimulation may contribute to the clinical development of optimized aDBS techniques designed to reduce potential stimulation-induced side effects of cDBS in PD patients while leading to a better therapeutic outcome.
A combination of the HLA-DRB1*03 phenotype and low plasma mannose-binding lectin predisposes to autoantibody formation in women with recurrent pregnancy loss

Introduction It is documented that a series of autoantibodies can be detected with increased frequency in women with recurrent pregnancy loss (RPL) and they may impact the pregnancy prognosis negatively. It is unknown whether the autoantibodies per se or the basic immune disturbances underlying autoantibody production, are the reason for this association. Our group has previously found that some genetically determined immunological biomarkers are associated with RPL and the same biomarkers are also in various degrees known to predispose to autoantibody production. The aim of this study was to clarify whether the RPL-associated immunogenetic biomarkers are associated with positivity for three major classes of autoantibodies associated with RPL. Methods In 663 patients with RPL in whom we had results for HLA-DRB1 typing and plasma mannose-binding lectin (p-MBL) measurement, it was investigated whether there is a correlation between positivity for the autoantibodies: anticardiolipin antibodies, β2 glycoprotein I antibodies, and lupus anticoagulant (jointly called antiphospholipid antibodies), thyroid-peroxidase antibodies, and antinuclear antibodies and each of the HLA-DRB1 alleles HLA-DRB1*03 or HLA-DRB1*07 either alone or in combination with low p-MBL defined as ≤500 µg/l. Results Although slightly higher frequencies of positivity of two or more autoantibodies were seen in patients with either p-MBL ≤500 µg/l or being positive for HLA-DRB1*03, none were significantly associated. However, in patients with the combination of low p-MBL and HLA-DRB1*03, presence of at least one autoantibody was significantly more frequent than in patients with no such combination (OR= 2.4; 95% CI 1.2-5.0, p = 0.01). In an analysis of which autoantibodies were most strongly associated with the low p-MBL/HLA-DRB1*03 combination, antinuclear antibodies were significantly more frequent in these patients (OR 2.0; 95% CI 1.0-3.9, p=0.05) whereas the other autoantibodies were also positively but more weakly associated with this combination. Discussion In conclusion, to clarify the pathogenetic background, underlying immunogenetic factors should be examined in autoantibody positive RPL patients (as well as other patients with autoimmune diseases) but the genetic background may be complex.
Gröbner Bases Theory and The Diamond Lemma

Commutative Gröbner bases theory is well known and widely used. In this thesis, we will discuss thoroughly its generalization to noncommutative polynomial ring k<X> which is also an associative free algebra. We introduce some results on monomial orders due to John Lawrence and the author. We show that a noncommutative monomial order is a well order while a onesided noncommutative monomial order may not be. Then we discuss the generalization of polynomial reductions, S-polynomials and the characterizations of noncommutative Gröbner bases. Some results due to Mora are also discussed, such as the generalized Buchberger’s algorithm and the solvability of ideal membership problem for homogeneous ideals. At last, we introduce Newman’s diamond lemma and Bergman’s diamond lemma and show their relations with Gröbner bases theory.