Miracle-dz/UniFAR-FTUs huggingface dataset:
Thrombocytopenia Is an Independent Risk Factor for the Prognosis of Thrombotic Microangiopathy in Chinese Patients With Systemic Lupus Erythematosus

Objectives: This study aims to describe clinical characteristics and outcome of thrombotic microangiopathy (TMA) in Chinese patients with systemic lupus erythematosus (SLE), and investigate the risk factors. Methods: We conducted a retrospective single-center cohort and enrolled patients of TMA associated with SLE between January 2015 and December 2018. Demographic characteristics, clinical features, laboratory profiles, therapeutic strategies, and outcomes were collected. The risk factors of TMA in patients with SLE for mortality using multivariate analysis were estimated. Results: A total of 119 patients with a diagnosis of TMA were enrolled within the study period in our center, and SLE was found in 72 (60.5%) patients. The mean age was 29.2 ± 10.1 and 65 (92.3%) were women. Only 15 patients were found with definite causes, the other 57 cases remained with unclear reasons. Sixty-two patients got improved, while 10 patients died after treatment (mortality rate: 13.9%). Compared with the survival group, the deceased group had a higher prevalence of neuropsychiatric manifestations, infection with two or more sites, increased levels of C-reaction protein (CRP) and D-Dimer, and decreased platelet count. Multivariate analysis showed that the decrease of platelet count is the independent risk factor for in-hospital mortality for TMA in patients with SLE. The receiver operating characteristic (ROC) curve analysis displayed that a cutoff value of <18 × 109/L for platelet count could significantly increase the risk of death. Conclusions: Thrombotic microangiopathy often occurs in patients with active SLE with high mortality (13.9%), and thrombocytopenia, especially when the platelet count is lower than 18 × 109/L, is the risk factor for death.
Risk Factors, Incidence, and Prognosis of Thromboembolism in Cancer Patients Treated With Immune Checkpoint Inhibitors

In recent years, immune checkpoint inhibitors (ICIs) have become the standard treatment option for tumors. With the widespread application of ICIs, immune-related adverse events (irAEs) have gradually attracted the attention of researchers. Owing to the characteristics of ICIs, irAEs can affect each organ of the human body. Thromboembolism is uncommon in cancer patients receiving ICIs, but it may affect their survival. Most thromboembolic events do not cause serious effects after early prediction and treatment, but life-threatening toxic reactions are also observed. This condition should not be ignored because of vague and atypical symptoms, which make early diagnosis more challenging. This article focuses on the high-risk factors, underlying mechanisms, incidence, and prognosis of thromboembolism in patients using ICIs and briefly describes the intervention and treatment measures. This information would allow patients to effectively manage the side effects of thromboembolism during Immune checkpoint inhibitors treatment, ensuring the efficacy of ICIs and reducing mortality.
Validation of an Echidna Forelimb Musculoskeletal Model Using XROMM and diceCT

In evolutionary biomechanics, musculoskeletal computer models of extant and extinct taxa are often used to estimate joint range of motion (ROM) and muscle moment arms (MMAs), two parameters which form the basis of functional inferences. However, relatively few experimental studies have been performed to validate model outputs. Previously, we built a model of the short-beaked echidna (Tachyglossus aculeatus) forelimb using a traditional modelling workflow, and in this study we evaluate its behaviour and outputs using experimental data. The echidna is an unusual animal representing an edge-case for model validation: it uses a unique form of sprawling locomotion, and possesses a suite of derived anatomical features, in addition to other features reminiscent of extinct early relatives of mammals. Here we use diffusible iodine-based contrast-enhanced computed tomography (diceCT) alongside digital and traditional dissection to evaluate muscle attachments, modelled muscle paths, and the effects of model alterations on the MMA outputs. We use X-ray Reconstruction of Moving Morphology (XROMM) to compare ex vivo joint ROM to model estimates based on osteological limits predicted via single-axis rotation, and to calculate experimental MMAs from implanted muscles using a novel geometric method. We also add additional levels of model detail, in the form of muscle architecture, to evaluate how muscle torque might alter the inferences made from MMAs alone, as is typical in evolutionary studies. Our study identifies several key findings that can be applied to future models. 1) A light-touch approach to model building can generate reasonably accurate muscle paths, and small alterations in attachment site seem to have minimal effects on model output. 2) Simultaneous movement through multiple degrees of freedom, including rotations and translation at joints, are necessary to ensure full joint ROM is captured; however, single-axis ROM can provide a reasonable approximation of mobility depending on the modelling objectives. 3) Our geometric method of calculating MMAs is consistent with model-predicted MMAs calculated via partial velocity, and is a potentially useful tool for others to create and validate musculoskeletal models. 4) Inclusion of muscle architecture data can change some functional inferences, but in many cases reinforced conclusions based on MMA alone.
Influence of Statin Therapy on the Incidence of Cardiovascular Events, Cancer, and All-Cause Mortality in People Living With HIV: A Meta-Analysis

Background: Possible influences of statin therapy on the risk of cardiovascular events, cancer, and all-cause mortality in people living with HIV (PLWH) remain unclear. We performed a meta-analysis to systematically evaluate the efficacy of statin in PLWH. Methods: Relevant cohort studies were retrieved via a search of the Medline, the Embase, and the Web of Science databases until June 14, 2021. The data were combined with a random-effects model by incorporating the between-study heterogeneity. Results: A total of 12 multivariate cohort studies with 162,252 participants were eligible for the meta-analysis and 36,253 (22.3%) of them were statin users. Pooled results showed that statin use was independently related to a reduced mortality risk in PLWH [adjusted risk ratio (RR): 0.56, 95% CI: 0.44 to 0.72, p < 0.001, I2 = 41%]. In addition, results of the meta-analysis showed that statin use was not significantly associated with a reduced risk of cardiovascular events in PLWH compared to the statin non-users (RR: 1.14, 95% CI: 0.80 to 1.63, p = 0.48, I2 = 42%). However, statin use was significantly related to a reduced risk of cancer in PLWH (RR: 0.73, 95% CI: 0.58 to 0.93, p = 0.009, I2 = 49%). Sensitivity analyses by excluding one study at a time showed consistent results. No significant publication biases were observed. Conclusion: Statin use is associated with reduced all-cause mortality in PLWH. In addition, statin use is related to a reduced risk of cancer, although the risk of cardiovascular events seems not significantly affected.
Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning

Ca2+ sparks are the elementary Ca2+ release events in cardiomyocytes, altered properties of which lead to impaired Ca2+ handling and finally contribute to cardiac pathology under various diseases. Despite increasing use of machine-learning algorithms in deciphering the content of biological and medical data, Ca2+ spark images and data are yet to be deeply learnt and analyzed. In the present study, we developed a deep residual convolutional neural network method to detect Ca2+ sparks. Compared to traditional detection methods with arbitrarily defined thresholds to distinguish signals from noises, our new method detected more Ca2+ sparks with lower amplitudes but similar spatiotemporal distributions, thereby indicating that our new algorithm detected many very weak events that are usually omitted when using traditional detection methods. Furthermore, we proposed an event-based logistic regression and binary classification model to classify single cardiomyocytes using Ca2+ spark characteristics, which to date have generally been used only for simple statistical analyses and comparison between normal and diseased groups. Using this new detection algorithm and classification model, we succeeded in distinguishing wild type (WT) vs RyR2-R2474S± cardiomyocytes with 100% accuracy, and vehicle vs isoprenaline-insulted WT cardiomyocytes with 95.6% accuracy. The model can be extended to judge whether a small number of cardiomyocytes (and so the whole heart) are under a specific cardiac disease. Thus, this study provides a novel and powerful approach for the research and application of calcium signaling in cardiac diseases.
Tests and model calibration of high‐strength steel tubular beam‐to‐column and column‐base composite joints for moment‐resisting structures

Performance‐based engineering (PBE) methodologies allow for the design of more reliable earthquake‐resistant structures. Nonetheless, to implement PBE techniques, accurate finite element models of critical components are needed. With these objectives in mind, initially, we describe an experimental study on the seismic behaviour of both beam‐to‐column (BTC) and column‐base (CB) joints made of high‐strength steel S590 circular columns filled with concrete. These joints belonged to moment‐resisting frames (MRFs) that constituted the lateral‐force‐resisting system of an office building. BTC joints were conceived as rigid and of partial strength, whereas CB joints were designed as rigid and of full strength. Tests on a BTC joint composed of an S275 steel composite beam and high‐strength steel concrete‐filled tubes were carried out. Moreover, two seismic CB joints were tested with stiffeners welded to the base plate and anchor bolts embedded in the concrete foundation as well as where part of a column was embedded in the foundation with no stiffeners. A test programme was carried out with the aim of characterising these joints under monotonic, cyclic and random loads. Experimental results are presented by means of both force–interstory drift ratio and moment–rotation relationships. The outcomes demonstrated the adequacy of these joints to be used for MRFs of medium ductility class located in zones of moderate seismic hazard. Then, a numerical calibration of the whole joint subassemblies was successfully accomplished. Finally, non‐linear time‐history analyses performed on 2D MRFs provided useful information on the seismic behaviour of relevant MRFs. Copyright © 2015 John Wiley & Sons, Ltd.
The Container Tree Nursery Manual Volume 7: Seedling Processing, Storage and Outplanting

This manual is based on the best current knowledge of container nursery management and should be used as a general reference. Recommendations were made using the best information available at the time and are, therefore, subject to revision as more knowledge becomes available. Much of the information in this manual was primarily developed from information on growing western and southern conifer seedlings in the United States. Because of the wide variation in individual species responses, container nursery managers will need to adapt these principles and procedures to their own crop requirements. There is no substitute for individual experience, and recommended cultural practices should be tested before being implemented on an operational scale.
Non shakeout patterns of industry evolution. The case of turboprop engine industry

A careful case study of the entire history of the turboprop engine industry (1948-1997) is offered as an example of non shakeout pattern. The persistence of high concentration is not associated to the exit of smaller manufacturers, but instead a stable coexistence of generalist and specialist strategies emerges, in sharp contrast to the pattern observed in the otherwise similar jet engine industry. This paper operationalises the relevant variables of a more general theory of industry life cycle by taking into account the lack of creation of significant increasing returns in R&D, manufacturing or marketing, which is commonly found in industries which produce systemic products.
Nanocomputation of Mechanical Properties in Nanobio Membrane

It is very essential to know mechanical properties in different regions of nanobio membrane as one of the most important parts of living systems. Here the coarse-grained (CG) simulations method have been used to study the pressure profile in a system including nanobio membrane and water. CG simulations have become an important tool to study many biomolecular processes, exploring scales inaccessible to traditional models of atomistic resolution. One of the major simplifications of CG models is the representation of the solvent, which is either implicit or modeled explicitly as a van der Waals particle. The effect of polarization has been ignored in the initial CG water molecules model. Given the important role of water as a solvent in biological systems, its treatment is very important to the properties derived from simulation studies. Till now two models have been parameterized to simulate water: i) standard MARTINI water and ii) polarizable coarse-grained water model. Both of mentioned water models are proper to be used in combination with the CG MARTINI force field. In this work both of these models have been used for simulation. One micro second CG molecular dynamics simulation has been done for two separate systems. Each system includes water and hydrated 1-palmitoyl-2-oleoyl-1-sn-3-phosphatidylcholine (POPC) lipid nanobio membrane. The difference between two systems is in simulated water models that one system has standard MARTINI water and the other one has polarizable water. In each case pressure profile calculation has been done via Virial pressure theorem. Results indicate that using polarizable water model leads to higher picks in pressure profile in water region near surface of nanobio membrane. This can be related to density of polarizable water and also may play role as a small barrier.
Recent Progress, Emerging Techniques, and Future Research Prospects of Bangla Machine Translation: A Systematic Review

Machine Translation (MT), the way of translating texts or documents from a source language to a target language automatically without human intervention, has gained popularity in the growing information technology-based era of globalization. Bangla is a major language, and several MT studies with different tools and techniques have been investigated in the last two decades. Considering the importance of the Bangla language and its prospects in MT studies, this study provides a comprehensive review of existing Bangla MT studies to meet the timely demand. Specifically, at first, the basic ideas of different MT methods (Rule-based, Example-based, Statistical, Neural, and Hybrid) and performance measures of MT are presented as a background study of the present review. Then an overview of the Bangla language and a brief description of the available Bangla-English corpora are provided. Next, a description of the existing Bangla MT studies is provided categorically following the common strategic fashion to create a valuable reference for current researchers in the field that is also suitable for non-expert users. The achieved performances of individual methods are also compared in a tabular form. Finally, a number of future research prospects are revealed from the studies, encouraging researchers and practitioners to develop a better and comprehensive Bangla MT system.
Generation of Personalized Knowledge Graphs Based on GCN

Education must follow the principle of teaching students in accordance with their aptitude. In this paper, we propose a novel method to generate personalized knowledge graphs based on graph convolutional network. We have summarized the methods of evaluating the difficulty of exercises, and apply them to the generation of knowledge graph. After that, the adjacency matrix corresponding to the knowledge graph and the eigenvectors corresponding to the nodes are used as inputs of the graph convolutional network, and the semi-supervised leaning node classification is adopted to continuously iterate the training to optimize the graph convolution neural network model. Meanwhile, the graph convolutional neural network is used to generate personalized knowledge graph for each student, more accurate personalized services can be provided. The experimental results show that our method can make a better to realize in-depth personalized services.
Continental-scale contributions to the global CFC-11 emission 1 increase between 2012 and 2017 2 3 4

The early detection of a global emission increase of CFC-11 after 2012 (Montzka et al., 17 2018) alerted society to a possible violation of the Montreal Protocol on Substances that Deplete 18 the Ozone Layer (MP). This early alert resulted in parties participating in the MP taking urgent 19 actions (United Nations Environment Programme (UNEP), 2019). As a result, atmospheric 20 measurements made in 2019 suggest a sharp decline in global CFC-11 emissions (Montzka et al., 21 2021). Despite the success in the early detection and mitigation of some of this problem, regions 22 fully responsible for the recent global emission changes of CFC-11 have not yet been identified. 23 Roughly two thirds (60 ± 40 %) of the emission increase between 2008 2012 and 2014 2017 24 and two thirds (60 ± 30 %) of emission decline between 2014 2017 and 2019 was explained by 25 regional emission changes in eastern mainland China (Park et al., 2021; Rigby et al., 2019). Here, 26 we used atmospheric CFC-11 measurements made from two global aircraft surveys, the HIAPER 27 Pole-to-Pole Observations (HIPPO) in November 2009 – September 2011 and the Atmospheric 28 Tomography Mission (ATom) in August 2016 – May 2018, in combination with the global CFC29 11 measurements made by the U.S. National Oceanic and Atmospheric Administration during 30 these two periods, to derive global and regional emission changes of CFC-11. Our results suggest 31 Asia accounted for the largest fractions of global CFC-11 emissions in both periods, 43 (37 – 52) % 32 during November 2009 – September 2011 and 57 (49 – 62) % during August 2016 – May 2018. 33 Asia was also primarily responsible for the emission increase between these two periods, 34 accounting for 86 (59 – 115) % of the global CFC-11 emission rise between the two periods. 35 Besides eastern mainland China, we find that temperate western Asia and tropical Asia also 36 contributed significantly to global CFC-11 emissions during both periods and likely to the global 37 CFC-11 emission increase between these periods. Besides Asia, the atmospheric observations also 38 provide strong constraints on CFC-11 emissions from North America and Europe, suggesting that 39 each of them accounted for 10 – 15 % of global CFC-11 emissions during the HIPPO period and 40 smaller fractions in the ATom period. For South America, Africa, and Australia, the derived 41 regional emissions had larger dependence on the prior assumptions of emissions and emission 42 changes, due to a lower sensitivity of the observations considered here to emissions from these 43 regions. However, significant increases in CFC-11 emissions from the southern hemispheric lands 44 https://doi.org/10.5194/acp-2021-793 Preprint. Discussion started: 29 September 2021 c © Author(s) 2021. CC BY 4.0 License.
Degraded Visual Environment Tracker

Compressive Sensing (CS) has proven its ability to reduce the number of measurements required to reproduce images with similar quality to those reconstructed by observing the Shannon-Nyquist sampling criteria. By exploiting spatial redundancies, it was shown that CS can be used to denoise and enhance image quality. In this thesis we propose a method that incorporates an efficient use of CS to locate a specific object in zero-visibility environments. This method was developed after multiple implementations of dictionary learning, reconstruction, detection, and tracking algorithms in order to identify the shortcomings of existing techniques and enhance our results. We show that with the use of an over-complete dictionary of the target our technique can perceive the location of the target from hidden information in the scene. This thesis will summarize the previously implemented algorithms, detail the shortcomings evident in their outputs, explain our setups, and present quantified results to support its efficacy in the results section.
Teaching Outside Science Specialism: Plight of Public-School Science Major Teachers in Montevista District

The intent of this study was to describe the experiences, coping mechanisms and insights of the public school science teachers who encountered problems and challenges in teaching subjects outside from their field of specialization which answered the three fundamental research questions on what are the experiences of science teachers teaching subjects which is outside from their field of specialization, how do science teachers cope with their challenges, and insights of science teachers. The study utilized the qualitative phenomenological design with two data collection techniques, the in-depth interview and focus group discussion. There were 14 participants in this study who were science teachers from secondary schools in Montevista district. Triangulation was addressed through gathering of data from more than one source to gain a fuller perspective of the study. Essential themes were generated on the experiences of teachers, coping mechanisms and insights of science teachers. Previous endeavor cited mainly on the problems that the teacher encountered from teaching beyond the science specialism like Biology in major teaching and preparing lesson in the field of Physics and other science related areas. However, I have not come across various studies that deal with the challenges or problems of the science teachers teaching outside their science specialization.
International Journal of Electrical and Computer Engineering (IJECE)

Received Jan 22, 2022 Revised Aug 9, 2022 Accepted Aug 23, 2022 Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.