Miracle-dz/UniFAR-FTUs huggingface dataset:
Lorenz System State Stability Identification using Neural Networks

Nonlinear dynamical systems such as Lorenz63 equations are known to be chaotic in nature and sensitive to initial conditions. As a result, a small perturbation in the initial conditions results in deviation in state trajectory after a few time steps. The algorithms and computational resources needed to accurately identify the system states vary depending on whether the solution is in transition region or not. We refer to the transition and non-transition regions as unstable and stable regions respectively. We label a system state to be stable if it's immediate past and future states reside in the same regime. However, at a given time step we don't have the prior knowledge about whether system is in stable or unstable region. In this paper, we develop and train a feed forward (multi-layer perceptron) Neural Network to classify the system states of a Lorenz system as stable and unstable. We pose this task as a supervised learning problem where we train the neural network on Lorenz system which have states labeled as stable or unstable. We then test the ability of the neural network models to identify the stable and unstable states on a different Lorenz system that is generated using different initial conditions. We also evaluate the classification performance in the mismatched case i.e., when the initial conditions for training and validation data are sampled from different intervals. We show that certain normalization schemes can greatly improve the performance of neural networks in especially these mismatched scenarios. The classification framework developed in the paper can be a preprocessor for a larger context of sequential decision making framework where the decision making is performed based on observed stable or unstable states.
CODA: Constructivism Learning for Instance-Dependent Dropout Architecture Construction

Dropout is attracting intensive research interest in deep learning as an efficient approach to prevent overfitting. Recently incorporating “structural” information when deciding which units to drop out produced promising results comparing to methods that ignore the structural information. However, a major issue of the existing work is that it failed to differentiate among instances when constructing the dropout architecture. This can be a significant deficiency for many applications. To solve this issue, we propose Constructivism learning for instance-dependent Dropout Architecture (CODA), which is inspired from a philosophical theory, constructivism learning. Specially, based on the theory we have designed a better drop out technique, Uniform Process Mixture Models, using a Bayesian nonparametric method Uniform process. We have evaluated our proposed method on 5 real-world datasets and compared the performance with other state-of-the-art dropout techniques. The experimental results demonstrated the effectiveness of CODA.
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM

Loop closure detection is an essential component of simultaneous localization and mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task; however, their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this article, we introduce the novel loop closure detection network (LCDNet) that effectively detects loop closures in light detection and ranging (LiDAR) point clouds by simultaneously identifying previously visited places and estimating the six degrees of freedom relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art loop closure detection and point cloud registration techniques by a large margin, especially while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city.
An Efficient and Flexible Spike Train Model Via Empirical Bayes

Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neurons demonstrate over-dispersed spiking behaviour. Although the Negative Binomial Generalized Linear Model (NB-GLM) provides a powerful tool for modeling over-dispersed spike counts, the maximum likelihood-based standard NB-GLM leads to highly variable and inaccurate parameter estimates. Thus, we propose a hierarchical parametric empirical Bayes method to estimate the neural spike responses among neuronal population. Our method integrates both Generalized Linear Models (GLMs) and empirical Bayes theory, which aims to (1) improve the accuracy and reliability of parameter estimation, compared to the maximum likelihood-based method for NB-GLM and Poisson-GLM; (2) effectively capture the over-dispersion nature of spike counts from both simulated data and experimental data; and (3) provide insight into both neural interactions and spiking behaviours of the neuronal populations. We apply our approach to study both simulated data and experimental neural data. The estimation of simulation data indicates that the new framework can accurately predict mean spike counts simulated from different models and recover the connectivity weights among neural populations. The estimation based on retinal neurons demonstrate the proposed method outperforms both NB-GLM and Poisson-GLM in terms of the predictive log-likelihood of held-out data.1
Causal Inference in medicine and in health policy, a summary

A data science task can be deemed as making sense of the data or testing a hypothesis about it. The conclusions inferred from data can greatly guide us to make informative decisions. Big data has enabled us to carry out countless prediction tasks in conjunction with machine learning, such as identifying high risk patients suffering from a certain disease and taking preventable measures. However, healthcare practitioners are not content with mere predictions - they are also interested in the cause-effect relation between input features and clinical outcomes. Understanding such relations will help doctors treat patients and reduce the risk effectively. Causality is typically identified by randomized controlled trials. Often such trials are not feasible when scientists and researchers turn to observational studies and attempt to draw inferences. However, observational studies may also be affected by selection and/or confounding biases that can result in wrong causal conclusions. In this chapter, we will try to highlight some of the drawbacks that may arise in traditional machine learning and statistical approaches to analyze the observational data, particularly in the healthcare data analytics domain. We will discuss causal inference and ways to discover the cause-effect from observational studies in healthcare domain. Moreover, we will demonstrate the applications of causal inference in tackling some common machine learning issues such as missing data and model transportability. Finally, we will discuss the possibility of integrating reinforcement learning with causality as a way to counter confounding bias.
ANXIETY, RESILIENCE AND COPING STRATEGIES AMONG THE HEALTH CARE WORKERS DURING COVID-19 PANDEMIC

fear of infection, fatality, physical difficulties, lack of certainty around people's life, However it is considerably challenging for the health care workers, for their nature of work makes them more vulnerable to get infected than the normal population, It is common for the health care workers to experience anxiety in the face of current pandemic. thus the present study focuses on the anxiety levels and coping strategies among health care workers during pandemic . The objective of this study is to assess levels of anxiety, resilience and ways of coping among health care workers. A total of 40 samples shall be taken for the present study, The standardized tools to be used in the study are a) Covid-19 anxiety scale (Lee, S.A 2020) b) Brief Cope Inventory (Carver 1997) c) Brief Resilience scale (smith and colleagues 2008) The hypotheses of the current study states that 1)There is a significant level of anxiety among health care workers 2) There is a significant relationship between the levels of anxiety and coping strategy 3) There is a significant relationship between resilience and levels of anxiety, 4) There is a significant difference between gender and level of anxiety 5) There is a significant difference between gender and coping strategy. The present study shall be insightful in terms of understanding the prevalence of anxiety among health care workers, and the role of coping strategy plays in elevating or ameliorating anxiety, It can be helpful in planning the interventions for future studies. The present study shall be helpful in understanding the level of anxiety among health care workers and what kind of coping strategy are predominantly used by the health care workers, and how adaptive/ maladaptive coping strategy influence the levels of anxiety, thus it can be helpful in planning the appropriate interventions for the target population in the future studies. ORIGINAL RESEARCH PAPER Psychology ANXIETY, RESILIENCE AND COPING STRATEGIES AMONG THE HEALTH CARE WORKERS DURING COVID-19 PANDEMIC
The influence of RFI classification and cow age on body weight and body condition change, supplement intake, and grazing behavior of beef cattle winter grazing mixed-grass rangelands

© The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Transl. Anim. Sci. 2020.4:S68–S73 doi: 10.1093/tas/txaa100
Serum and cerebrospinal fluid biomarker profiles in acute SARS-CoV-2-associated neurological syndromes

Abstract Preliminary pathological and biomarker data suggest that SARS-CoV-2 infection can damage the nervous system. To understand what, where and how damage occurs, we collected serum and CSF from patients with COVID-19 and characterized neurological syndromes involving the PNS and CNS (n = 34). We measured biomarkers of neuronal damage and neuroinflammation, and compared these with non-neurological control groups, which included patients with (n = 94) and without (n = 24) COVID-19. We detected increased concentrations of neurofilament light, a dynamic biomarker of neuronal damage, in the CSF of those with CNS inflammation (encephalitis and acute disseminated encephalomyelitis) [14 800 pg/ml (400, 32 400)], compared to those with encephalopathy [1410 pg/ml (756, 1446)], peripheral syndromes (Guillain–Barré syndrome) [740 pg/ml (507, 881)] and controls [872 pg/ml (654, 1200)]. Serum neurofilament light levels were elevated across patients hospitalized with COVID-19, irrespective of neurological manifestations. There was not the usual close correlation between CSF and serum neurofilament light, suggesting serum neurofilament light elevation in the non-neurological patients may reflect peripheral nerve damage in response to severe illness. We did not find significantly elevated levels of serum neurofilament light in community cases of COVID-19 arguing against significant neurological damage. Glial fibrillary acidic protein, a marker of astrocytic activation, was not elevated in the CSF or serum of any group, suggesting astrocytic activation is not a major mediator of neuronal damage in COVID-19.
Health-related quality of life after gastric cancer treatment in Brazil: Narrative review and reflections

In Brazil, gastric cancer is the third most common type of cancer among men and fifth among women, with an estimated 13360 new cases among men and 7870 among women each year during the 2020-2022 period. This study presents reflections and attempts to add knowledge to the theme of quality of life (QoL) in patients with gastric adenocarcinoma and describes some of its characteristics in three regions of Brazil, with an evaluation of the disease’s impacts in various dimensions of life, as reported by the patients themselves. We performed a narrative review of the literature and a data analysis of studies on QoL in Brazilian patients treated for gastric adenocarcinoma from three different cities in three geographic regions: Brasília (the midwest), Jaú (the southeast), and Macapá (the north).
Mammography with a fully automated breast volumetric software as a novel method for estimating the preoperative breast volume prior to mastectomy

Purpose Increasing interest in maintaining a positive body image following breast cancer surgery has become an important aspect of reconstruction surgery. Volume matching of the reconstructed breast to natural breasts is the most important consideration. This study aimed to explore the feasibility of using mammography with a fully automated breast volumetric software to measure the preoperative breast volume in patients with breast cancer. Methods We evaluated patients who underwent a total mastectomy between July 2016 and February 2021. The specimen volume following total mastectomy was compared with breast volume estimates using a fully automated volumetric software (Quantra ver. 2.1.1) and 4 other previously described mammography-based prediction methods. The association between the estimates and mastectomy specimen volume was assessed using Pearson correlation and Bland-Altman analysis. Results Sixty-six patients were included. Compared with previously described mammography-based methods, Quantra estimates were more strongly correlated with mastectomy specimen volume in the entire, fatty, and dense breast groups (r = 0.920, 0.921, and 0.915, respectively; P < 0.001). In applying Quantra estimates for measuring preoperative breast volume, we adjusted a simple equation: mastectomy specimen volume = Quantra estimate × 0.8. Conclusion Mammography with a fully automated breast volumetric software can be useful for measuring preoperative breast volume in patients with breast cancer who undergo reconstruction surgery.
Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): The Netherlands Organisation for Health Research and Development (ZonMw) University of Amsterdam Research Priority Area Medical Integromics OnBehalf CAPACITY-COVID19 Registry Background The electrocardiogram (ECG) is an easy to assess, widely available and inexpensive tool that is frequently used during the work-up of hospitalized COVID-19 patients. So far, no study has been conducted to evaluate if ECG-based machine learning models are able to predict all-cause in-hospital mortality in COVID-19 patients. Purpose With this study, we aim to evaluate the value of using the ECG to predict in-hospital all-cause mortality of COVID-19 patients by analyzing the ECG at hospital admission, comparing a logistic regression based approach and a DNN based approach. Secondly, we aim to identify specific ECG features associated with mortality in patients diagnosed with COVID-19. Methods and results We studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw-format 12-lead ECGs recorded after admission (<72 hours) were collected, manually assessed, and annotated using pre-defined ECG features. Using data from five out of seven centers (n = 634), two mortality prediction models were developed: (a) a logistic regression model using manually annotated ECG features, and (b) a pre-trained deep neural network (DNN) using the raw ECG waveforms. Data from two other centers (n = 248) were used for external validation. Performance of both prediction models was similar, with a mean area under the receiver operating curve of 0.69 [95%CI 0.55–0.82] for the logistic regression model and 0.71 [95%CI 0.59–0.81] for the DNN in the external validation cohort. After adjustment for age and sex, ventricular rate (OR 1.13 [95% CI 1.01–1.27] per 10 ms increase), right bundle branch block (3.26 [95% CI 1.15–9.50]), ST-depression (2.78 [95% CI 1.03–7.70]) and low QRS voltages (3.09 [95% CI 1.02-9.38]) remained as significant predictors for mortality. Conclusion This study shows that ECG-based prediction models at admission may be a valuable addition to the initial risk stratification in admitted COVID-19 patients. The DNN model showed similar performance to the logistic regression that needs time-consuming manual annotation. Several ECG features associated with mortality were identified. Figure 1: Overview of methods, using and example case: (left) logistic regression and (right) deep learning. This specific case had a high probability of in-hospital mortality (above the threshold of 30%). Follow-up of this case showed that the patient had died during admission. Abstract Figure. Overview of ML methods used
Toward the identification of molecular markers associated with phytochemical traits in the Iranian sumac (Rhus coriaria L.) population

Abstract Sumac (Rhus coriaria L.) is one of the important forest species dispersed in the northwest of Iran. It is one of the important spice in Iran and the Middle East because of active components containing organic acids, phenolic acids, flavonoids, anthocyanins, tannins and terpenoids. This study aimed to investigate population structure and linkage disequilibrium (LD) extent within Rhus coriaria L. genotypes using ISSR markers and identify molecular markers associated with phytochemical traits using association analysis. In the molecular part of the experiment, the genetic diversity of 75 sumac genotypes from five different areas of northwest Iran was assessed by 18 ISSR primers. In the phenotypic assessment, the fruits of the sumac genotypes were analyzed using HPLC‐LC/MSMS for determining phytochemical components including maleic acid, ellagic acid, maleic acid hexoside, gallic acid, coumaric acid, quercetin, caftaric acid, and linoleic acid. The phenotypic data analysis revealed the great phenotypic diversity among and within Iranian sumac populations for the studied phytochemical traits. The studied sumac genotypes were divided into two subpopulations based on molecular marker‐based structure analysis. A significant level of LD was observed in 11.64% of the ISSR marker pairs (p < .05). The mixed linear model procedure showed that 12 loci had a significant association with investigated traits. The ISSR loci identified in this study can be potentially used in marker‐assisted selection in sumac breeding programs.
Aberration correction in stimulated emission depletion microscopy to increase imaging depth in living brain tissue

Abstract. Significance: Stimulated emission depletion (STED) microscopy enables nanoscale imaging of live samples, but it requires a specific spatial beam shaping that is highly sensitive to optical aberrations, limiting its depth penetration. Therefore, there is a need for methods to reduce optical aberrations and improve the spatial resolution of STED microscopy inside thick biological tissue. Aim: The aim of our work was to develop and validate a method based on adaptive optics to achieve an a priori correction of spherical aberrations as a function of imaging depth. Approach: We first measured the aberrations in a phantom sample of gold and fluorescent nanoparticles suspended in an agarose gel with a refractive index closely matching living brain tissue. We then used a spatial light modulator to apply corrective phase shifts and validate this calibration approach by imaging neurons in living brain slices. Results: After quantifying the spatial resolution in depth in phantom samples, we demonstrated that the corrections can substantially increase image quality in living brain slices. Specifically, we could measure structures as small as 80 nm at a depth of 90 μm inside the biological tissue and obtain a 60% signal increase after correction. Conclusion: We propose a simple and robust approach to calibrate and compensate the distortions of the STED beam profile introduced by spherical aberrations with increasing imaging depth and demonstrated that this method offers significant improvements in microscopy performance for nanoscale cellular imaging in live tissue.
Synthesis of Nanosized Silica and Silver-Doped Silica Nanoparticles for Heat Transfer Fluids Applications

The nanosized silica (SiO2) with the size less than 100 nm have successfully been prepared by hydrolysis and condensation of tetraethyl orthosilicate (TEOS) via a modified Stöber method at room temperature. The experiment was conducted by controlling the amount of the catalyst used, i.e., ammonium hydroxide (NH4OH). The morphology observation of the synthesized silica nanoparticles was conducted by using a transmission electron microscope (TEM). It was found that the size of the silica depending on the amount of the catalyst used, with homogenous size ranging from 10 to 360 nm. The doping of silver nanoparticles was done by mixing the synthesized silica with the silver ions (Ag+) solutions. Then the sample was annealed for 75 min which results in the nucleation of the silver nanoparticles less than 20 nm onto the silica surfaces, depending on the temperature used.