Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Massive MIMO Channel Estimation with Built-in Parameter Estimation

In order to reduce hardware complexity and power consumption, massive multiple-input multiple-output (MIMO) system uses low-resolution analog-to-digital converters (ADCs) to acquire quantized measurements $\boldsymbol y$. This poses new challenges to the channel estimation problem, and the sparse prior on the channel coefficients $\boldsymbol x$ in the angle domain is used to compensate for the information lost during quantization. By interpreting the sparse prior from a probabilistic perspective, we can assume $\boldsymbol x$ follows some sparse prior distribution and recover it using approximate message passing (AMP). However, the distribution parameters are unknown in practice and need to be estimated. Due to the increased computational complexity in the quantization noise model, previous works either use an approximated noise model or manually tune the noise distribution parameters. In this paper we treat both the signal and parameters as random variables and recover them jointly within the AMP framework. This leads to a much simpler parameter estimation method and allows us to work with the true quantization noise model. Experimental results show that the proposed approach achieves state-of-the-art performance under various noise levels and does not require parameter tuning, making it a practical and carefree approach for channel estimation.
Population biology and secondary production of the stout razor clam Tagelus plebeius (Bivalvia, Solecurtidae) on a sandflat in southeastern Brazil

The population biology and production of the stout razor clam Tagelus plebeius Lightfoot, 1786 were investigated on an intertidal sandflat on the southeast coast of Brazil (Enseada Beach, Sao Sebastiao, state of Sao Paulo) between April 1997 and April 1998. Two rectangular sites of 50 X 10 m parallel to the waterline were established, site A (upper intertidal level) and site B (middle intertidal level), where the samples were taken in an 0.5 x 0.5 m quadrat. High abundances were recorded in winter and spring, with no significant differences between the sites. The high bivalve abundances were related to the presence of very fine homogeneous sediment with low salinities. Tagelus plebeius had negative allometric growth, characteristic of deep burrowers for the relationships DM/SL and AFDM/SL. Parameters of the modified von Bertalanffy growth function were: L∞ = 67.01 mm, K = 1.73 year-1, t0 = -0.11 year, C = 0.43, WP = 0.96. The instantaneous mortality (Z) was 3.12 year-1, relatively high in comparison to other tropical bivalve populations. Secondary production was 1.53 g AFDM m-2 year-1, with a P/B ratio reaching 1.37 year-1. This high turnover ratio (P/B) was related to a rapid population replacement, connected with the short life span and high mortality of the species.
High- Versus Low-Energy Acetabular Fracture Outcomes in the Geriatric Population

Introduction: High-energy mechanisms of acetabular fracture in the geriatric population are becoming increasingly common as older adults remain active later in life. This study compared outcomes for high- versus low-energy acetabular fractures in older adults. Materials and Methods: We studied outcomes of 22 older adults with acetabular fracture who were treated at a level-I trauma center over a 4-year period. Fourteen patients were categorized as low-energy mechanism of injury, and 8 were identified as a high-energy mechanism. We analyzed patient demographics with univariate logistic regressions performed to assess differences in high- and low-energy group as well as patient characteristics compared with surgical outcomes. Results: Most high-energy mechanisms were caused by motor vehicle collision (n = 4, 50.0%), with most having posterior wall fractures (50.0%). Among patient characteristics, the mechanism of injury, hip dislocation, fracture types, and fracture gap had the largest differences between energy groups effect size (ES: 2.45, 1.43, 1.36, and 0.83, respectively). The high-energy group was more likely to require surgery (odds ratio [OR] = 2.80, 95% CI: 0.26-30.70), develop heterotopic bone (OR = 4.33, 95% CI: 0.33-57.65), develop arthritis (OR = 3.60, 95% CI: 0.45-28.56), and had longer time to surgery (mean = 4.8 days, standard deviation [SD] = 5.8 days) compared to low-energy group (mean = 2.5 days, SD = 2.3 days). Discussion: The results of this case series confirm previous findings that patients with high-energy acetabular fractures are predominantly male, younger, and have fewer comorbidities than those who sustained low-energy fractures. Our results demonstrate that the majority of the high-energy fracture patients also suffered a concurrent hip dislocation with posterior wall fracture and experienced a longer time to surgery than the low-energy group. Conclusion: Geriatric patients who sustained high-energy acetabular fractures tend to have higher overall rates of complications, including infection, traumatic arthritis, and heterotopic bone formation when compared with patients with a low-energy fracture mechanism.
Dynamic models for knowledge tracing & prediction of future performance

Large-scale data about learners' behavior are being generated at high speed on various online learning platforms. Knowledge Tracing (KT) is a family of machine learning sequence models that are capable of using these data efficiently with the objective to identify the likelihood of future learning performance. This study provides an overview of KT models from a technical and an educational point of view. It focuses on data representation, evaluation, and optimization, and discusses the underlying model assumptions such that the strengths and weaknesses with regard to a specific application become visible. Based on the need for advanced analytical methods suited for large and diverse data, we briefly review big data analytics along with KT learning algorithms' efficiency, learnability and scalability. Challenges and future research directions are also outlined. In general, the overview can serve as a guide for researchers and developers, linking the dynamic knowledge tracing models and properties to the learner's knowledge acquisition process that should be accurately modeled over time. Applied KT models to online learning environments hold great potential for the online education industry because it enables the development of personalized adaptive learning systems. Keywords-big data applications; educational data mining; knowledge tracing; sequential supervised machine learning.
Computed Tomography (CT) Imaging Features of Patients with COVID-19: Systematic Review and Meta-Analysis

Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious disease, and its first outbreak was reported in Wuhan, China. A coronavirus disease (COVID-19) causes severe respiratory distress (ARDS). Due to the primary involvement of the respiratory system, chest CT is strongly recommended in suspected COVID-19 cases, for both initial evaluation and follow-up. Objective The aim of this review was to systematically analyze the existing literature on CT imaging features of patients with COVID-19 pneumonia. Methods A systematic search was conducted on PubMed, Embase, Cochrane Library, Open Access Journals (OAJ), and Google Scholar databases until April 15, 2020. All articles with a report of CT findings in COVID-19 patients published in English from the onset of COVID-19 outbreak to April 20, 2020, were included in the study. Result From a total of 5041 COVID-19-infected patients, about 98% (4940/5041) had abnormalities in chest CT, while about 2% have normal chest CT findings. Among COVID-19 patients with abnormal chest CT findings, 80% (3952/4940) had bilateral lung involvement. Ground-glass opacity (GGO) and mixed GGO with consolidation were observed in 2482 (65%) and 768 (18%) patients, respectively. Consolidations were detected in 1259 (22%) patients with COVID-19 pneumonia. CT images also showed interlobular septal thickening in about 691 (27%) patients. Conclusion Frequent involvement of bilateral lung infections, ground-glass opacities, consolidation, crazy paving pattern, air bronchogram signs, and intralobular septal thickening were common CT imaging features of patients with COVID-19 pneumonia.
Glutathione S-transferase T1 and M1 null genotype distribution among non-alcoholic fatty liver disease patients and its association with cytokine and adipokine profiles

Aim of the study Among the key genes involved in the development of non-alcoholic fatty liver disease (NAFLD) are genes encoding the synthesis of glutathione S-transferase (GST). Material and methods Deletion polymorphism of GSTT1 and GSTM1 genes was investigated in 104 NAFLD patients and 45 healthy individuals. Biochemical blood analysis, tumor necrosis factor-α (TNF-α), interleukin-10, leptin and adiponectin plasma levels were studied. Results The distribution of deletion vs. non-deletion genotypes of the GSTT1 gene in NAFLD patients was 18 (17.3%) vs. 86 (82.7%) patients and in healthy people it was 6 (13.3%) vs. 39 (86.7%) individuals. The genotype distribution of the GSTM1 gene was as follows: 52 (50.0%) NAFLD patients had null genotype vs. 52 patients (50.0%) with non-deletion genotype; in the control group – 23 (51.1%) vs. 22 (48.9%) individuals. Deletion of the GSTT1 gene in NAFLD patients was associated with twice as high (p = 0.01) TNF-α level in the blood as compared to patients with normal genotype. Higher concentration of leptin in blood by 37.1% (p = 0.04) was observed in patients with null genotype of the GSTM1 gene, as compared to those with normal genotype. Conclusions Deletion polymorphism of GSTT1 and GSTM1 genes distribution among NAFLD patients did not differ as compared to healthy individuals. Null-genotype GSTT1 gene carriers were characterized by higher TNF-α concentration and null-genotype GSTM1 gene carriers were characterized by elevated leptin level as compared to normal genotype carriers.
Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study

Missing observations are always a challenging problem that we have to deal with in diseases that require follow-up. In hospital records for vesicoureteral reflux (VUR) and recurrent urinary tract infection (rUTI), the number of complete cases is very low on demographic and clinical characteristics, laboratory findings, and imaging data. On the other hand, deep learning (DL) approaches can be used for highly missing observation scenarios with its own missing ratio algorithm. In this study, the effects of multiple imputation techniques MICE and FAMD on the performance of DL in the differential diagnosis were compared. The data of a retrospective cross-sectional study including 611 pediatric patients were evaluated (425 with VUR, 186 with rUTI, 26.65% missing ratio) in this research. CNTK and R 3.6. 3 have been used for evaluating different models for 34 features (physical, laboratory, and imaging findings). In the differential diagnosis of VUR and rUTI, the best performance was obtained by deep learning with MICE algorithm with its values, respectively, 64.05% accuracy, 64.59% sensitivity, and 62.62% specificity. FAMD algorithm performed with accuracy = 61.52, sensitivity = 60.20, and specificity was found out to be 61.00 with 3 principal components on missing imputation phase. DL-based approaches can evaluate datasets without doing preomit/impute missing values from datasets. Once DL method is used together with appropriate missing imputation techniques, it shows higher predictive performance.
The concluding chapter: recircumscription of Goodenia (Goodeniaceae) to include four allied genera with an updated infrageneric classification

Abstract Close scrutiny of Goodenia (Goodeniaceae) and allied genera in the ‘Core Goodeniaceae’ over recent years has clarified our understanding of this captivating group. While expanded sampling, sequencing of multiple regions, and a genome skimming reinforced backbone clearly supported Goodenias.l. as monophyletic and distinct from Scaevola and Coopernookia, there appears to be no synapomorphic characters that uniquely characterise this morphologically diverse clade. Within Goodenias.l. , there is strong support from nuclear, chloroplast and mitochondrial data for three major clades (Goodenia Clades A, B and C) and various subclades, which lead to earlier suggestions for the possible recognition of these as distinct genera. Through ongoing work, it has become evident that this is impractical, as conflict remains within the most recently diverged Clade C, likely due to recent radiation and incomplete lineage sorting. In light of this, it is proposed that a combination of morphological characters is used to circumscribe an expanded Goodenia that now includes Velleia, Verreauxia, Selliera and Pentaptilon, and an updated infrageneric classification is proposed to accommodate monophyletic subclades. A total of twenty-five new combinations, three reinstatements, and seven new names are published herein including Goodenia subg. Monochila sect. Monochila subsect. Infracta K.A. Sheph. subsect. nov. Also, a type is designated for Goodenia subg. Porphyranthus sect. Ebracteolatae (K.Krause) K.A. Sheph. comb. et stat. nov., and lectotypes or secondstep lectotypes are designated for a further three names.
Timing of Seed Dispersal in Pinus sylvestris Stands in Central Sweden

The objective of the study presented here was to describe the variation in timing of seed dispersal from Scots pine (Pinus sylvestris L.) seed trees in central Sweden. Seeds were collected in traps at two sites, for three years at one, and four years at the other . The traps were emptied from March to August each year at 1–2 week intervals during the main period of seed dispersal. The annual seed fall varied between 200 000 and 1.6 million seeds per hectare. The seed fall started in mid to late April, shortly after the heat sum had started to accumulate. The most intensive seed fall took place in early to mid May. The peak period, when 50% of the total seed dispersal occurred, lasted for 18–28 days at the different sites and years. The variation in timing among years seemed to be mainly due to climatic factors – high temperatures promoted seed dispersal, for instance. The results may be useful for planning the time of scarifi cation to optimise the natural regeneration of Scots pine. The data suggest that scarifi cation in the spring, no later than mid-May, would generally create a good seed-bed for most of the current year’s seeds, whereas scarifi cation in late May or June would bury a large proportion of this cohort.
Close2U: An App for Monitoring Cancer Patients with Enriched Information from Interaction Patterns

The management of cancer patients' symptoms in doctor consultations is a cornerstone in clinical care, this process being fundamental for the follow-up of the evolution of these. This article presents an application that allows collecting periodically and systematically the data of cancer patients and their visualization by the medical team. In this article, we made the analysis, design, implementation, and final evaluation by analyzing the correlation of this data collection with interaction patterns to determine how the user information can be enriched with information from the interaction patterns. We have followed an agile methodology based on the iterative and incremental development of successive prototypes with increased fidelity, where the requirements and solutions have evolved over time according to the need and assessments made. The comprehensive analysis of the patient's condition allowed us to perform a first analysis of the correlation of the states of patients concerning mood, sleeping quality, and pain with the interaction patterns. A future goal of this project is to optimize the process of data collection and the analysis of information. Another future goal is to reduce the time dedicated to reporting the evolution of symptoms in face-to-face consultations and to help professionals in analyzing the patient's evolution even in the period that has not been attended in person.
Transmission of HIV and HCV within Former Soviet Union Countries

Background Following the collapse of the Union of Soviet Socialist Republic (USSR) in 1991, trans-border mobility increased within the former Soviet Union (FSU) countries. In addition, drug-trafficking and injection drug use began to rise, leading to the propagation and transmission of blood-borne infections within and across the FSU countries. To examine the transmission of blood-borne infections within this region, we analyzed the phylogenetic relationship of publically available sequences of two blood-borne viruses, hepatitis C virus (HCV) and human immunodeficiency virus (HIV), from FSU countries. Methods We analysed 614 and 295 NS5B sequences from HCV genotypes 1b and 3a, respectively, from 9 FSU countries. From 13 FSU countries, we analysed 347 HIV gag and 1282 HIV env sequences. To examine transmission networks and the origins of infection, respectively, phylogenetic and Bayesian analyses were performed. Results Our analysis shows intermixing of HCV and HIV sequences, suggesting transmission of these viruses both within and across FSU countries. We show involvement of three major populations in transmission: injection drug user, heterosexual, and trans-border migrants. Conclusion This study highlights the need to focus harm reduction efforts toward controlling transmission of blood-borne infections among the abovementioned high-risk populations in the FSU countries.
Phytochemical Evaluation, Embryotoxicity, and Teratogenic Effects of Curcuma longa Extract on Zebrafish (Danio rerio)

Curcuma longa L. is a rhizome plant often used as traditional medicinal preparations in Southeast Asia. The dried powder is commonly known as cure-all herbal medicine with a wider spectrum of pharmaceutical activities. In spite of the widely reported therapeutic applications of C. longa, research on its safety and teratogenic effects on zebrafish embryos and larvae is still limited. Hence, this research was aimed to assess the toxicity of C. longa extract on zebrafish. Using a reflux flask, methanol extract of C. longa was extracted and the identification and quantification of total flavonoids were carried out with HPLC. Twelve fertilized embryos were selected to test the embryotoxicity and teratogenicity at different concentration points. The embryos were exposed to the extract in the E3M medium while the control was only exposed to E3M and different developmental endpoints were recorded with the therapeutic index calculated using the ratio of LC50/EC50. C. longa extract was detected to be highly rich in flavonoids with catechin, epicatechin and naringenin as the 3 most abundant with concentrations of 3,531.34, 688.70 and 523. 83μg/mL respectively. The toxicity effects were discovered to be dose-dependent at dosage above 62. 50μg/mL, while at 125. 0μg/mL, mortality of embryos was observed and physical body deformities of larvae was recorded among the hatched embryos at higher concentrations. Teratogenic effect of the extract was severe at higher concentrations producing physical body deformities such as kink tail, bend trunk, enlarged yolk sac edema. Finally, the Therapeutic Index (TI) values calculated were approximately same for different concentration points tested. Overall, the result revealed that plants having therapeutic potential could also pose threats when consumed at higher doses especially on the embryos. Therefore, detailed toxicity analysis should be carried out on medicinal plants to ascertain their safety on the embryos and its development.
N-acetyl cysteine treatment preserves mitochondrial indices of functionality in the brain of hyperammonemic mice

Aim of the study Acute or chronic live failure could result in hyperammonemia and hepatic encephalopathy (HE). HE is a clinical complication characterized by severe cognitive dysfunction and coma. The ammonium ion (NH4+) is the most suspected toxic molecule involved in the pathogenesis of HE. NH4+ is a neurotoxic agent. Different mechanisms, including oxidative/nitrosative stress, inflammatory response, excitotoxicity, and mitochondrial impairment, are proposed for NH4+-induced neurotoxicity. N-acetyl cysteine (NAC) is a well-known thiol-reductant and antioxidant agent. Several investigations also mentioned the positive effects of NAC on mitochondrial function. In the current study, the effect of NAC treatment on brain mitochondrial indices and energy status was investigated in an animal model of HE. Material and methods Acetaminophen (APAP)-induced acute liver failure was induced by a single dose of the drug (800 mg/kg, i.p.) to C57BL/6J mice. Plasma and brain levels of NH4+ were measured. Then, brain mitochondria were isolated, and several indices, including mitochondrial depolarization, ATP level, lipid peroxidation, glutathione content, mitochondrial permeabilization, and dehydrogenase activity, were assessed. Results A significant increase in plasma and brain NH4+ was evident in APAP-treated animals. Moreover, mitochondrial indices of functionality were impaired, and mitochondrial oxidative stress biomarkers were significantly increased in APAP-treated mice. It was found that NAC treatment (100, 200, and 400 mg/kg, i.p.) significantly mitigated mitochondrial impairment in the brain of APAP-treated animals. Conclusions These data suggest the effects of NAC on brain mitochondrial function and energy status as a pivotal mechanism involved in its neuroprotective properties during HE.
Generating contextual embeddings for emergency department chief complaints

Abstract Objective We learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints. Materials and methods Retrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k = 1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE). Results The models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together. Discussion Despite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space. Conclusion Such a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.