Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Approach for Hyperspectral Image Classification with Additive Noise for Remote Sensing and Airborne Surveillance

Hyperspectral imaging plays a vital role in homeland security, defence, medical imaging, and many other industrial applications. In general, Hyperspectral Images (HSI) are noisy. They require more accurate machine learning methods for object detection/prediction/classification and, more importantly, more stable prediction/classification accuracies in the presence of noise. This work focuses on improving Hyperspectral Image (HSI) prediction/classification in the presence of noise. It demonstrates that it can outperform deep learning (DL) state-of-the-art results with a modified 3D-DenseNet architecture. Specifically, we (a) propose a new model that we denote 3D-HSI-DenseNet that relatively boosts classification accuracy of the original version of then 3D-DenseNet, while generally improving the prediction stability of the network in the presence of noise (b) demonstrate that the proposed method has a faster training and testing time in comparison to the original DenseNet; (c) show that the proposed modified architecture of the DenseNet reduces the total number of the model parameters by nearly 1.5 times compared to the DenseNet architecture from the HSI classification literature; (d) experimentally show that Network in Network (NIN) can be used as a Dimensionality Reduction (DR) technique and it is as effective as PCA, without its limitations, for band reduction of HSI; (e) show the stability and robustness in classification performance of the proposed model trained with and without noise, by introducing different levels of noise into the testing data set.
Automated Generation, Execution, and Evaluation of Virtual Test Series

Virtual tests are playing an increasingly important role in the development and functional verification of technical systems, as they are safe, reproducible, and applicable throughout the entire product life cycle. The use of virtual environments is particularly helpful in cases where the operational environment of the system under test has a high variability with many influencing parameters. Complex system tests usually require large numbers of virtual experiments in order to achieve a sufficient coverage of the entire test space. Consequently, the modeling of this comprehensive scenario space rapidly becomes complex and inefficient. In addition, the effort for the manual execution of test series with many thousands of experiments increases and easily becomes unmanageable. Hence, an architecture for the automated generation, execution and evaluation of virtual test series is inevitable. Emerging from the technology of digital twins, this paper faces these challenges and presents a scalable approach for the realization of a scenario-based test procedure. The proposed architecture supports the systematic use of virtual tests for the functional verification of technical systems in appropriate quantity, quality, and time.
Combined use of the GlideScope and flexible fiberoptic bronchoscope after several failed intubation attempts for a retrosternal thyroid mass: a case report

A GlideScope® and a flexible fiberoptic bronchoscope (FOB) were successfully used in combination, to intubate a 73-year-old female patient in which repeated previous intubation attempts with conventional laryngoscope had failed. GlideScope®, and FOB were used sequentially. She had been scheduled for an elective total thyroidectomy to remove a huge retrosternal goiter. A computed tomography (CT) scan revealed a large necrotic mass (measuring 10 x 16 cm) with heterogeneous enhancement involving the thyroid gland and extending to the anterior mediastinum, causing significant tracheal compression and left deviation. Endotracheal intubation was successfully performed while she was awake using a combination of GlideScope and FOB. The operation lasted for two hours and was uneventful. The patient was then transferred to the intensive care unit (ICU) and kept intubated, sedated, and mechanically ventilated overnight. She was awakened and extubated the following day with no airway complications.
Morphological pattern of the dermal denticles of the Southern sawtail catshark Galeus mincaronei Soto, 2001

Dermal denticles cover the skin of sharks for protection and may have additional functions, depending on the environment. The aim of the present study was to analyze the morphological pattern of dermal denticles across the body of the poorly known deep‐water catshark Galeus mincaronei, which is endemic to Southern Brazil. Additionally, we aimed to identify differences between sexes and stages of sexual maturity. For this purpose, we analyzed patches of eight skin samples from body regions, that is, the head, body, and tail. Dermal denticles were imaged using scanning electron microscopy. Measurements of crown morphological variations (length and width) and density of dermal denticles were performed. Nine characters of dermal denticle morphology were selected to describe a morphological pattern using principal component analysis (PCA). The first PCA axis explained 45.2% of the variation, the second explained 33%, and the third explained 14.3%, together explaining 92.5% of the total variation in denticle morphology. We identified three groups of dermal denticle crown morphologies and their possible functions. The first was composed of nostril and snout denticles, which might suggest abrasion strength functions; the second was composed of most body denticles, such as those in the cranium, on the left side of the body, and on the pectoral, dorsal, and caudal fins, which might be related to generalized functions; and the third was composed of only caudal crest denticles, which might indicate defense functions with hydrodynamic properties in G. mincaronei. Likewise, our results reveal variations in dermal denticle morphology and arrangement along the three maturity stages considered in both sexes. Similarly, sexual dimorphism in the crown width and morphology of dermal denticles was observed in developing and mature individuals. Our results are the first to reveal the morphological variation of dermal denticles in relation to function, sex, and ontogeny of individual G. mincaronei.
Robust and Data-efficient Q-learning by Composite Value-estimation

In the past few years, off-policy reinforcement learning methods have shown promising results in their application to robot control. Q-learning based methods, however, still suffer from poor data-efficiency and are susceptible to stochasticity or noise in the immediate reward, which is limiting with regard to real-world applications. We alleviate this problem by proposing two novel off-policy Temporal-Difference formulations: (1) Truncated Q-functions which represent the return for the first n steps of a target-policy rollout with respect to the full action-value and (2) Shifted Q-functions, acting as the farsighted return after this truncated rollout. This decomposition allows us to optimize both parts with their individual learning rates, achieving significant learning speedup and robustness to variance in the reward signal, leading to the Composite Q-learning algorithm. We show the efficacy of Composite Q-learning in the tabular case and furthermore employ Composite Q-learning within TD3. We compare Composite TD3 with TD3 and TD3(∆), which we introduce as an off-policy variant of TD(∆). Moreover, we show that Composite TD3 outperforms TD3 as well as TD3(∆) significantly in terms of data-efficiency in multiple simulated robot tasks and that Composite Q-learning is robust to stochastic immediate rewards.
EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.
Polygenic autoimmune disease risk alleles impacting B cell tolerance act in concert across shared molecular networks in mouse and in humans

Most B cells produced in the bone marrow have some level of autoreactivity. Despite efforts of central tolerance to eliminate these cells, many escape to periphery, where in healthy individuals, they are rendered functionally non-responsive to restimulation through their antigen receptor via a process termed anergy. Broad repertoire autoreactivity may reflect the chances of generating autoreactivity by stochastic use of germline immunoglobulin gene segments or active mechanisms may select autoreactive cells during egress to the naïve peripheral B cell pool. Likewise, it is unclear why in some individuals autoreactive B cell clones become activated and drive pathophysiologic changes in autoimmune diseases. Both of these remain central questions in the study of the immune system(s). In most individuals, autoimmune diseases arise from complex interplay of genetic risk factors and environmental influences. Advances in genome sequencing and increased statistical power from large autoimmune disease cohorts has led to identification of more than 200 autoimmune disease risk loci. It has been observed that autoantibodies are detectable in the serum years to decades prior to the diagnosis of autoimmune disease. Thus, current models hold that genetic defects in the pathways that control autoreactive B cell tolerance set genetic liability thresholds across multiple autoimmune diseases. Despite the fact these seminal concepts were developed in animal (especially murine) models of autoimmune disease, some perceive a disconnect between human risk alleles and those identified in murine models of autoimmune disease. Here, we synthesize the current state of the art in our understanding of human risk alleles in two prototypical autoimmune diseases – systemic lupus erythematosus (SLE) and type 1 diabetes (T1D) along with spontaneous murine disease models. We compare these risk networks to those reported in murine models of these diseases, focusing on pathways relevant to anergy and central tolerance. We highlight some differences between murine and human environmental and genetic factors that may impact autoimmune disease development and expression and may, in turn, explain some of this discrepancy. Finally, we show that there is substantial overlap between the molecular networks that define these disease states across species. Our synthesis and analysis of the current state of the field are consistent with the idea that the same molecular networks are perturbed in murine and human autoimmune disease. Based on these analyses, we anticipate that murine autoimmune disease models will continue to yield novel insights into how best to diagnose, prognose, prevent and treat human autoimmune diseases.
Simultaneous prediction of multiple physical parameters using gated recurrent neural network: Porosity, water saturation, shale content

Reservoir parameter prediction is of significant value to oil and gas exploration and development. Artificial intelligence models are developing rapidly in reservoir parameter prediction. Unfortunately, current research has focused on multi-input single-output prediction models. Meaning, these models use a large amount of logging or seismic data to predict the petrophysical properties of a single reservoir. Another prominent problem is that most mechanistic learning studies have focused on using logging data (e.g., gamma ray and resistivity) to make predictions of reservoir parameters. Although these studies have yielded promising accuracy, a great shortcoming is the inability to obtain such data in logs by seismic inversion. The value of our research work is to achieve a complete description of the reservoir using the elastic parameters from the seismic inversion. We developed a deep learning method based on gated recurrent neural network (GRNN) suitable for simultaneous prediction of porosity, saturation and shale content in the reservoir. GRNN is based on Gated Recurrent Unit (GRU), which can automatically update and reset the hidden state. The input parameters to the model are compressive wave velocity, shear wave velocity and density. The model is trained to fit nonlinear relationships between input parameters and multiple physical parameters. We employed two wells: one for testing and the other for training. 20% of the data in the training wells were used as the validation set. In preprocessing, we performed z-score whitening on the input data. During the training phase, the model hyperparameters were optimized based on the mean absolute error (MAE) box plots of the validation set. Experiments on the test data show that the model has superior robustness and accuracy compared to the conventional recurrent neural network (RNN). In the GRNN prediction results of the test set, the MAE is 0.4889 and the mean squared error (MSE) is 0.5283. Due to the difference in input parameters, our prediction is weaker than the research method using logging data. However, our proposed method has higher practical value in exploration work.
Model development and update of portable NIRS instrument for assessment of internal quality attributes of two navel orange varieties

The variation of fruit among batches influences the performance of the portable near infrared spectroscopy (NIRS) instrument and then determines the success or failure for practical application in fruit industry. Model development and update methods were investigated for determining soluble solids contents (SSC) and titrable acidity (TA) of navel orange. The pretreatment and variable selection methods were explored for building partial least square regression (PLSR) models. The best models, developed by the combination of second derivative (2D) and variable sorting for normalization (VSN), could predict SSC but not TA. The root mean square error of prediction (RMSEP), coefficient of determination for prediction (Rp2) and ratio of prediction to deviation (RPD) for SSC were 0.66 °Brix, 0.66 and 1.73. Model maintain methods of model update (MU) and slope and bias correction (SBC) achieved the best results in predicting SSC for two external validation sets with Rp2, RMSEP and RPD of 0.54, 0.83 °Brix, 1.60 and 0.52, 0.83 °Brix, 1.65, respectively. The results suggested model development and update with MU and SBC could improve the robustness of the portable NIRS instrument in predicting SSC of navel orange.
Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of “virtual hearts” using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
Maintenance of Intestinal Homeostasis in Diarrhea-Predominant Irritable Bowel Syndrome by Electroacupuncture Through Submucosal Enteric Glial Cell-Derived S-Nitrosoglutathione

Objective: To determine whether electroacupuncture (EA) maintains intestinal homeostasis in diarrhea-predominant irritable bowel syndrome (IBS-D) rats by repairing intestinal barrier function through enteric glial cell (EGC)-derived S-nitrosoglutathione (GSNO). Methods: Sprague–Dawley rats were randomly divided into a control group (n = 10) and an IBS-D group (n = 20). These rats received senna solution by gavage and chronic unpredictable mild stress for 14 days and were further divided into a model group (n = 10) and an EA group (n = 10). Rats in the EA group were electroacupunctured at ST25 (Tianshu), ST36 (Zusanli), and LR3 (Taichong) for 20 min every day for 14 days. The abdominal withdrawal reflex (AWR), the percentage of time spent in open arms (OT%) in the elevated plus maze test, and the diarrhea index (DI) were measured. Histopathological examination was performed to evaluate the pathological features of the colon after sacrificing the rats. Transmission electron microscopy was used to observe the EGC in the muscle and submucosal layers. Enzyme-linked immunosorbent assay was performed to detect GSNO expression in the colon. Double immunofluorescence labeling was used to detect the colocalized GFAP and GSNO expressions in the muscle and submucosal layers. Plasma FITC-dextran was used to measure intestinal permeability, whereas western blot was used to detect ZO-1 and occludin expressions in the colon. Results: OT% and ZO-1 and occludin expressions were significantly lower than those of the control group, whereas AWR scores, DI, GSNO expression in the colon, colocalized GFAP and GSNO expressions in the submucosal layer, and intestinal permeability were significantly higher than those of the control group. Structural EGC abnormalities were observed in the model group. After EA treatment, OT% and ZO-1 and occludin expressions increased significantly, whereas AWR scores, DI, GSNO expression, colocalized GFAP and GSNO expressions in the submucosal layer, and intestinal permeability decreased significantly. The EGC structure was then restored to its normal state. Conclusion: EA treatment downregulates the submucosal EGC–derived GSNO expressions, repairs the intestinal barrier by upregulating the ZO-1 and occludin expression, and improves IBS-D symptoms, including visceral hypersensitivity, anxiety, and diarrhea, suggesting a potential role for EGC-derived GSNO in the regulation of intestinal homeostasis in IBS-D rats.
High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram

Background Post-stroke cognitive impairment (PSCI) after lacunar infarction was worth attention in recent years. An easy-to-use score model to predict the risk of PSCI was rare. This study aimed to explore the association between serum amyloid A (SAA) and cognitive impairment, and it also developed a nomogram for predicting the risk of PSCI in lacunar infarction patients. Methods A total of 313 patients with lacunar infarction were enrolled in this retrospective study between January 2021 and December 2021. They were divided into a training set and a validation set at 70%:30% randomly. The Chinese version of the Mini-Mental State Examination (MMSE) was performed to identify cognitive impairment 3 months after discharge. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for PSCI in the training set. A nomogram was developed based on the five variables, and the calibration curve and the receiver operating characteristic (ROC) curve were drawn to assess the predictive ability of the nomogram between the training set and the validation set. The decision curve analysis (DCA) was also conducted in both sets. Results In total, 52/313 (16.61%) participants were identified with PSCI. The SAA levels in patients with PSCI were significantly higher than non-PSCI patients in the training set (P < 0.001). After multivariate analysis, age, diabetes mellitus, white blood count, cystatin C, and SAA were independent risk predictors of PSCI. The nomogram demonstrated a good discrimination performance between the training set (AUC = 0.860) and the validation set (AUC = 0.811). The DCA showed that the nomogram had a well clinical utility in the two sets. Conclusion The increased SAA is associated with PSCI in lacunar infarction patients, and the nomogram developed with SAA can increase prognostic information for the early detection of PSCI.