Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening Biomarkers for Systemic Lupus Erythematosus Based on Machine Learning and Exploring Their Expression Correlations With the Ratios of Various Immune Cells

Background Systemic lupus erythematosus (SLE) is an autoimmune illness caused by a malfunctioning immunomodulatory system. China has the second highest prevalence of SLE in the world, from 0.03% to 0.07%. SLE is diagnosed using a combination of immunological markers, clinical symptoms, and even invasive biopsy. As a result, genetic diagnostic biomarkers for SLE diagnosis are desperately needed. Method From the Gene Expression Omnibus (GEO) database, we downloaded three array data sets of SLE patients’ and healthy people’s peripheral blood mononuclear cells (PBMC) (GSE65391, GSE121239 and GSE61635) as the discovery metadata (nSLE = 1315, nnormal = 122), and pooled four data sets (GSE4588, GSE50772, GSE99967, and GSE24706) as the validate data set (nSLE = 146, nnormal = 76). We screened the differentially expressed genes (DEGs) between the SLE and control samples, and employed the least absolute shrinkage and selection operator (LASSO) regression, and support vector machine recursive feature elimination (SVM-RFE) analyze to discover possible diagnostic biomarkers. The candidate markers’ diagnostic efficacy was assessed using the receiver operating characteristic (ROC) curve. The reverse transcription quantitative polymerase chain reaction (RT-qPCR) was utilized to confirm the expression of the putative biomarkers using our own Chinese cohort (nSLE = 13, nnormal = 10). Finally, the proportion of 22 immune cells in SLE patients was determined using the CIBERSORT algorithm, and the correlations between the biomarkers’ expression and immune cell ratios were also investigated. Results We obtained a total of 284 DEGs and uncovered that they were largely involved in several immune relevant pathways, such as type І interferon signaling pathway, defense response to virus, and inflammatory response. Following that, six candidate diagnostic biomarkers for SLE were selected, namely ABCB1, EIF2AK2, HERC6, ID3, IFI27, and PLSCR1, whose expression levels were validated by the discovery and validation cohort data sets. As a signature, the area under curve (AUC) values of these six genes reached to 0.96 and 0.913, respectively, in the discovery and validation data sets. After that, we checked to see if the expression of ABCB1, IFI27, and PLSCR1 in our own Chinese cohort matched that of the discovery and validation sets. Subsequently, we revealed the potentially disturbed immune cell types in SLE patients using the CIBERSORT analysis, and uncovered the most relevant immune cells with the expression of ABCB1, IFI27, and PLSCR1. Conclusion Our study identified ABCB1, IFI27, and PLSCR1 as potential diagnostic genes for Chinese SLE patients, and uncovered their most relevant immune cells. The findings in this paper provide possible biomarkers for diagnosing Chinese SLE patients.
Design and fatigue analysis of diagonally reinforced structures with a negative Poisson’s ratio lattice material

In this paper, a typical negative Poisson’s ratio lattice structure was studied and several diagonally reinforced 3D lattice structures were designed and analysed using the finite element method. On this basis, the fatigue life of the unreinforced and reinforced structures was calculated using SIMULIA Fe-safe software. Meanwhile, the fatigue failure process of the lattice structure was systematically simulated and quantitatively analysed by combining the multiaxial fatigue damage model. Results show that the enhancement design from the cell structure can provide an anti-diagonal shear enhancement effect. The structure A, B, and C can decrease the maximum von Mises stress by 95.8, 97.1, and 94.66%, and increase the compressibility by 80%, 56%, and 127%, respectively. The structure A enhancement yields the best overall performance in terms of structural stress, compressibility, and negative Poisson’s ratio properties enhancement. Compared with the unreinforced structure, the lifetime distribution of the structure A reinforced structure changes in both position and level. The overall lifetime has been improved from 102.813 of the unreinforced structure to 107 of the reinforced structure. Quantitative calculation of the fatigue damage is consistent with the fatigue life prediction results, which further validate the effectiveness of the diagonal enhancement method and the enhancement structure of the negative Poisson’s ratio lattice structure.
Preliminary Evaluation of a Brief Team Cohesion Manikin Scale

The performance of small groups of individuals is substantially influenced by team cohesion. Past research has examined the processes involved in the development of cohesion, the extent to which a group of individuals are drawn together as a team, between team members across large temporal scales. Questions remain, however, regarding the progression of team cohesion within a specific set of activities. To address these questions, brief measures that are able to reliably assess team cohesion are required. In the present study, a novel set of team cohesion scales, designed to be completed rapidly and at multiple times during a task, was examined. Three team cohesion manikin scales were developed to assess interpersonal attraction, commitment, and pride. Ratings from the scales were gathered as small groups of participants completed two sets of tasks, the first in which participants observed video clips of teams that varied in displayed cohesion, and the second where groups completed two activities. High reliability was observed across the scales when participants used them to rate groups observed in video clips. During group activities, significant relations were observed between scale scores and past measures of team cohesion. The present study provides preliminary evidence that brief self-report measures can be used to assess team cohesion when observing as well as participating in group activities.
A 19.6–39.4 GHz Broadband Low Noise Amplifier Based on Triple-Coupled Technique and T-Coil Network in 65-nm CMOS

This paper presents a differential 19.6–39.4 GHz broadband low-noise amplifier (LNA) in 65-nm CMOS technology. The LNA consists of two cascode stage and one common-source stage. To achieve a wide bandwidth and low average noise figure, inter-stage peak-gain distribution technique and transformer-based triple-coupled technique are developed. Besides, a new compact T-coil-based network is proposed to neutralize the parasitic capacitors and enlarge the gain. The measure results show that the 3-dB bandwidth is from 19.6 to 39.4 GHz, the maximum gain is 23.5 dB, and the noise figure (NF) is from 3.7 to 5.8 dB. The dc power comsumption is 46 mW with 1V supply voltage. The input P1dB is −17 dBm at 30 GHz.
Optimum-Path-to-Go Guidance of Command Adjusted Trajectory Projectiles.

Abstract : Execution of optimal guidance and control algorithms in real time with a microprocessor is feasible using polynomial networks to store, in compact and almost instantly retrievable form, information about a large number of pre-computed optimum two-point boundary-value (TPBV) trajectory solutions. The mathematical basis for computing these optimum solutions has been derived in this SBIR Phase I work using the calculus of variations to obtain adjoint differential equations that are initialized for the TPBV solution. Historically, these initializations have usually necessitated iterative computations because the adjoint equations are not analytically integrable. It is shown that polynomial networks fitted, off-line, to a data base of optimum trajectories can provide the appropriate initializations in real time. These polynomial networks offer optimum-path-to-go (OPTG) and target motion prediction capabilities that can significantly improve performance of maneuverable projectiles, particularly in cases of thruster impulse limitations and substantial uncertainties about target behavior. This report presents the derivation of the governing variational equations, a simulation for three-degree-of-freedom trajectory guidance analyses of command adjusted trajectory (CAT) projectiles engaging maneuvering targets, and results for a 120mm CAT projectile. The results show that the blended guidance law has a smaller miss distance and greater divert capability than the baseline guidance, and that pure OPTG guidance requires fewer divert squib firings than the baseline guidance.
A Comparative Study of Face Recognition Algorithms under Occlusion

Face recognition algorithms are used to automatically recognize human faces. It has got a wide variety of applications in many areas such as Surveillance, access, security, advertisement, healthcare, and etc. Many big tech companies have already adopted this technology and it has been proved as promising convenient biometric technology. In this paper, we are comparing the face recognition algorithms performance against the datasets that are reflecting considerable occlusion (the hidden part of the face, the face parts could be hidden with scarf, glasses, hair or any other object). We selected two publicly available datasets, the first one is the face disguise dataset that reflects major occlusion and the second one that is Specs on Faces (SoF) dataset that reflects partial occlusion. After the data collection, we run the data preprocessing techniques in which we removed the existing noise to the datasets and organized them into different sets. Afterward, we applied feature extraction algorithms and then we fed them into classifiers to get algorithm's performance. At the end of the experiments, we observed that the Local Binary Pattern Histogram (LBPH) algorithm outperforms the other two algorithms by securing 33.444% accuracy against the dataset with major occlusion and 98.504% accuracy against the dataset with partial occlusion, and Linear Discriminant Analysis (LDA) secured the second position against the dataset with major occlusion but third position against the dataset with partial occlusion.
Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States

Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.
Urine Proteome in Distinguishing Hepatic Steatosis in Patients with Metabolic-Associated Fatty Liver Disease

Background: In patients with metabolic-associated fatty liver disease (MAFLD), hepatic steatosis is the first step of diagnosis, and it is a risk predictor that independently predicts insulin resistance, cardiovascular risk, and mortality. Urine biomarkers have the advantage of being less complex, with a lower dynamic range and fewer technical challenges, in comparison to blood biomarkers. Methods: Hepatic steatosis was measured by magnetic resonance imaging (MRI), which measured the proton density fat fraction (MRI-PDFF). Mild hepatic steatosis was defined as MRI-PDFF 5–10% and severe hepatic steatosis was defined as MRI-PDFF > 10%. Results: MAFLD patients with any kidney diseases were excluded. There were 53 proteins identified by mass spectrometry with significantly different expressions among the healthy control, mild steatosis, and severe steatosis patients. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of these significantly changed urinary molecular features correlated with the liver, resulting in the dysregulation of carbohydrate derivative/catabolic/glycosaminoglycan/metabolic processes, insulin-like growth factor receptor levels, inflammatory responses, the PI3K–Akt signaling pathway, and cholesterol metabolism. Urine alpha-1-acid glycoprotein 1 (ORM1) and ceruloplasmin showed the most significant correlation with the clinical parameters of MAFLD status, including liver fat content, fibrosis, ALT, triglycerides, glucose, HOMA-IR, and C-reactive protein. According to ELISA and western blot (30 urine samples, normalized to urine creatinine), ceruloplasmin (ROC 0.78, p = 0.034) and ORM1 (ROC 0.87, p = 0.005) showed moderate diagnostic accuracy in distinguishing mild steatosis from healthy controls. Ceruloplasmin (ROC 0.79, p = 0.028) and ORM1 (ROC 0.81, p = 0.019) also showed moderate diagnostic accuracy in distinguishing severe steatosis from mild steatosis. Conclusions: Ceruloplasmin and ORM1 are potential biomarkers in distinguishing mild and severe steatosis in MAFLD patients.
A Proof-of-Concept Electrochemical Cytosensor Based on Chlamydomonas reinhardtii Functionalized Carbon Black Screen-Printed Electrodes: Detection of Escherichia coli in Wastewater as a Case Study

Herein, we report a proof-of-concept algal cytosensor for the electrochemical quantification of bacteria in wastewater, exploiting the green photosynthetic alga Chlamydomonas reinhardtii immobilized on carbon black (CB) nanomodified screen-printed electrodes. The CB nanoparticles are used as nanomodifiers, as they are able to sense the oxygen produced by the algae and thus the current increases when algae are exposed to increasing concentrations of bacteria. The sensor was tested on both standard solutions and real wastewater samples for the detection Escherichia coli in a linear range of response from 100 to 2000 CFU/100 mL, showing a limit of detection of 92 CFU/100 mL, in agreement with the maximum E. coli concentration established by the Italian law for wastewater (less than 5000 CFU/100 mL). This bacterium was exploited as a case study target of the algal cytosensor to demonstrate its ability as an early warning analytical system to signal heavy loads of pathogens in waters leaving the wastewater treatment plants. Indeed, the cytosensor is not selective towards E. coli but it is capable of sensing all the bacteria that induce the algae oxygen evolution by exploiting the effect of their interaction. Other known toxicants, commonly present in wastewater, were also analyzed to test the cytosensor selectivity, with any significant effect, apart from atrazine, which is a specific target of the D1 protein of the Chlamydomonas photosystem II. However, the latter can also be detected by chlorophyll fluorescence simultaneously to the amperometric measurements. The matrix effect was evaluated, and the recovery values were calculated as 105 ± 8, 83 ± 7, and 88 ± 7% for 1000 CFU/100 mL of E. coli in Lignano, San Giorgio, and Pescara wastewater samples, respectively.
Optimal Phase-Shift Control to Minimize Reactive Power for a Dual Active Bridge DC–DC Converter

In a dual active bridge (DAB) dc–dc converter, modulating the phase-shift ratios can suppress reactive power and increase the efficiency under non-unity voltage gain conditions. Derivation of the optimal phase-shift ratios to achieve the minimum reactive power is challenging because a DAB can operate in four different scenarios (forward/backward, buck/boost) and each scenario has five operating modes. This paper first introduces a transformation in which different DAB operating scenarios (forward/backward, buck/boost) can be equivalent to one another; then optimization of only one scenario (forward/buck) is required. Next, for the five modes in forward/buck scenario, based on a graphical method, this paper proves that each operating point of Modes 1, 2, and 5 can be mapped into that of Mode 3 with lower conduction losses, and only two modes require detailed analysis. The optimization process of these two modes are derived step-by-step. Global optimal phase-shift signals that minimize the reactive power under four different DAB operating scenarios (forward/backward, buck/boost) are presented. A simple table and control block diagram are presented for practical implementation of global reactive-power minimization control. Experimental comparison with other modulation schemes verifies the efficiency improvement of the proposed control method.
EGFR-Mutated Non-Small Cell Lung Cancer and Resistance to Immunotherapy: Role of the Tumor Microenvironment

Lung cancer is a leading cause of cancer-related deaths worldwide. About 10–30% of patients with non-small cell lung cancer (NSCLC) harbor mutations of the EGFR gene. The Tumor Microenvironment (TME) of patients with NSCLC harboring EGFR mutations displays peculiar characteristics and may modulate the antitumor immune response. EGFR activation increases PD-L1 expression in tumor cells, inducing T cell apoptosis and immune escape. EGFR-Tyrosine Kinase Inhibitors (TKIs) strengthen MHC class I and II antigen presentation in response to IFN-γ, boost CD8+ T-cells levels and DCs, eliminate FOXP3+ Tregs, inhibit macrophage polarization into the M2 phenotype, and decrease PD-L1 expression in cancer cells. Thus, targeted therapy blocks specific signaling pathways, whereas immunotherapy stimulates the immune system to attack tumor cells evading immune surveillance. A combination of TKIs and immunotherapy may have suboptimal synergistic effects. However, data are controversial because activated EGFR signaling allows NSCLC cells to use multiple strategies to create an immunosuppressive TME, including recruitment of Tumor-Associated Macrophages and Tregs and the production of inhibitory cytokines and metabolites. Therefore, these mechanisms should be characterized and targeted by a combined pharmacological approach that also concerns disease stage, cancer-related inflammation with related systemic symptoms, and the general status of the patients to overcome the single-drug resistance development.
Higher frequency of mantram repetition practice is associated with enhanced clinical benefits among United States Veterans with posttraumatic stress disorder

ABSTRACT Background: There is increasing support for the use of meditation-based treatments for US military Veterans with posttraumatic stress disorder (PTSD). The Mantram Repetition Program (MRP), which is a portable meditative practice that features mindful repetition of a sacred phrase, is associated with significant reductions in PTSD symptom severity. Although regular practice is emphasized in meditation-based interventions, associations between frequency of practice and clinical outcomes are often not reported. Objectives: This study will examine whether the frequency of mantram repetition is associated with greater improvements in clinical outcomes. Methods: Veterans with PTSD participating in MRP (N = 160; combined experimental groups from two randomized controlled trials). Participants completed pre- and post-treatment self-report measures of anger and well-being and a clinician-administered interview of PTSD severity (CAPS-IV-TR). Veterans also reported average daily mantram repetition practice at post-treatment. We conducted a series of hierarchal multiple regression analyses. Results: When controlling for race/ethnicity and pre-treatment severity, higher frequency of mantram repetition practice was associated with significantly greater improvements (small effect sizes) in PTSD symptom severity (F(3,128) = 6.60, p < . 001, β = . 21, p = . 007), trait anger (F(3,128) = 31.23, p < . 001, β = . 25, p < . 001), state anger (F(3,110) = 17.62, p < . 001, β = . 16, p = . 04), mental health well-being (F(3,128) = 28.38, p < . 001, β = . 14, p = . 04), and spiritual well-being (F(3,127) = 13.15, p < . 001, β = . 23, p = . 003), but not physical health well-being. Conclusions: Higher frequency of mantram repetition practice appears to have beneficial effects on clinical outcomes for Veterans with PTSD. Strategies that promote skills practice may be an important target for improving clinical outcomes for meditation-based interventions. HIGHLIGHTS Higher frequency of meditation practice during Mantram Repetition Program was associated with greater reductions in PTSD symptoms and anger as well as improvements in well-being. Strategies to promote at-home meditation practice may optimize the benefits of MRP.
Sensitivity and Distortion Studies of Positron Emission Trancking System (PeTrack)

Positron Emission Tracking (PeTrack) is a new real-time 3D tracking technique. The project studied the sensitivity and distortion of the PeTrack system with four different detector field of view (FOV) configurations using Monte-Carlo simulation software (GATE). The simulation studies demonstrated the clear boundaries for FOV and also revealed a local distortion up to 1 mm for PeTrack. The simulation also showed that the detector misalignment could introduce the distortion to the system. The results of simulations lead us to calibrate the PeTrack system to obtain the global scaling factors. However, since PeTrack has a non-uniform distortion across FOV, the global scaling factors were helpful to only some extent. The value of calculated average error after global scaling correction is less than 1 mm. The PeTrack system is also co-registered with an x-ray imaging C-arm to evaluate the tracking performance.