Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of ARRB1 in prognosis and immunotherapy: A Pan-Cancer analysis

Background: β-arrestin1 (ARRB1), was originally identified as a multifunctional adaptor protein. Although ARRB1 has recently been shown to also play an important role in tumor growth, metastasis, inflammation, and immunity, its relationship with distinct tumor types and the tumor immune microenvironment remains unclear. Methods: We analyzed the ARRB1 expression profile and clinical characteristics in 33 cancer types using datasets from The Cancer Genome Atlas (TCGA) database. Clinical parameters such as patient survival, tumor stage, age, and gender were used to assess the prognostic value of ARRB1. The Human Protein Atlas (HPA) database was used to explore ARRB1 protein expression data. ESTIMATE and CIBERSORT algorithms were performed to assess immune infiltration. Furthermore, putative correlations between ARRB1 and tumor-infiltrating immune cells, the signatures of T-cell subtypes, immunomodulators, the tumor mutation burden (TMB), Programmed cell death ligand 1 (PD-L1), and microsatellite instability (MSI) were also explored. Gene functional enrichment was determined using GSEA. GSE40435 and GSE13213 cohorts were used to validate the correlation of ARRB1 with KIRC and LUAD clinicopathological parameters. Finally, the relationship between ARRB1 and immunotherapeutic responses was assessed using three independent immunotherapy cohorts, namely, GSE67501, GSE168204, and IMvigor210. Results: We found that ARRB1 expression levels were lower in 17 tumor tissues than in the corresponding normal tissues. We further found that ARRB1 expression was significantly correlated with tumor stage in BRCA, ESCA, KIRC, TGCT, and THCA, while in some tumors, particularly KIRC and LUAD, ARRB1 expression was associated with better prognosis. ARRB1 expression was also positively correlated with the stromal score or the immune score in some tumors. Regarding immune cell infiltration, ARRB1 expression in DLBC was positively correlated with M1 macrophage content and negatively correlated with B-cell infiltration. Additionally, there was a broad correlation between ARRB1 expression and three classes of immunomodulators. Furthermore, high ARRB1 expression levels were significantly correlated with some tumor immune-related pathways. Finally, ARRB1 expression was significantly associated with MSI, PD-L1, and TMB in some tumors and with the efficacy of immune checkpoint inhibitors (ICIs) in melanoma. Conclusion: ARRB1 has prognostic value in malignant tumors, especially in KIRC and LUAD. At the same time, ARRB1 was closely correlated with the tumor immune microenvironment and indicators of immunotherapy efficacy, indicating its great potential as a reliable marker for predicting the efficacy of immunotherapy.
Analysis of OSTBC-OFDM Combined with Dual-Polarization and Time-Diversity in Millimeter-Wave MIMO Channels with Rain Distortions

Various destructive weather and physical phenomena affect many parameters in the radio layer (i.e., affecting the wireless paths Over-The-Air (OTA)) of many outdoor-to-outdoor wireless systems. These destructive effects create polarization torsion and rotation of the signals propagating in space and cause the scattering of wireless spatial paths. The direct meaning is a significant degradation in system performance, especially in the Quality-of-Service (QoS). Under these challenging scenarios, intelligent utilization of advanced Multiple-Input-Multiple-Output (MIMO) techniques such as polarization-diversity and time-diversity at the transmitter, as well as at the receiver, and intelligent use of the Cross-Polarization-Isolation (XPI) mechanism, are essential. We prove that combining these techniques with the tuning of the XPI of the antennas creates optimal conditions in the wireless MIMO channels. This combination does not only improve the system’s performance, but also turns the destructive physical phenomena in the spatial-domain, into an advantage. In this article, we focus on formulating a wireless communication MIMO model in millimeter-Wave (mmWave) channels under rain distortions. We demonstrate the optimal use of combining Orthogonal-Space-Time-Coding (OSTBC) and Maximal-Ratio-Receive-Combiner (MRRC) with cross-polarization diversity techniques, that utilize the tuning of the XPI. An analytical exact optimal solution is proposed, that allows the tuning of the leading parameters to achieve global optimal performance, in terms of channel-capacity and Bit-Error-Rate (BER). In addition, we propose a process of approximation of feedback-closed-loop-MIMO. The feedback is employed between the transmitter and the receiver, in the scenario of changes in the channel-response-matrix in-between successive symbol-times. The feedback was designed to acheive global-maximum channel-capacity, while preserving the channel-path orthogonality in order to minimize the BER.
Coherent distributions for the rigid rotator

Coherent solutions of the classical Liouville equation for the rigid rotator are presented as positive phase-space distributions localized on the Lagrangian submanifolds of Hamilton-Jacobi theory. These solutions become Wigner-type quasiprobability distributions by a formal discretization of the left-invariant vector fields from their Fourier transform in angular momentum. The results are consistent with the usual quantization of the anisotropic rotator, but the expected value of the Hamiltonian contains a finite “zero point” energy term. It is shown that during the time when a quasiprobability distribution evolves according to the Liouville equation, the related quantum wave function should satisfy the time-dependent Schrodinger equation.
Drugs–Protein affinity‐score prediction using deep convolutional neural network

Drug discovery involves identifying novel drug–target (DT) interactions. Most proposed computer models for predicting drug–target interactions have emphasized binary classification, but the aim is to determine whether two drug targets interact. However, it is more practical but more challenging to anticipate the binding affinity, which evaluates the strength of a DT pair's association. The drug may not work if the binding affinity is not strong enough. Due to this reason, we need an expert system for predicting the affinity score between the drug and target protein. Advanced deep learning techniques can predict binding affinities because there are more new public affinity data in databases related to DT. This paper uses a comparative analysis of different drug and protein‐encoding techniques to predict DT binding affinities based on similarities between drugs and proteins. The validation results on the standard dataset show that the proposed model is an excellent way to predict how well DT binds and can be very helpful in the process of new drugs. Hence, the model on the DAVIS dataset achieved a higher concordance index, that is, 0.897, and the lowest mean square error, that is, 0.226; for the KIBA dataset, the concordance index score achieved is 0.867, and the mean square error is 0.191. The findings are compared to baseline methods using some evaluation parameters, including the mean squared error and the concordance index.
Coupled Inductor-Based Single-Switch Quadratic High Step-Up DC–DC Converters With Reduced Voltage Stress on Switch

In this article, two single-switch quadratic boost converters based on a three-winding coupled inductor (CI) are proposed. These converters provide a high voltage gain in a low duty cycle, reduced voltage stress on the power switch, continuous input current with a low di/dt, higher design flexibility, and limited current falling rate on the voltage multiplier diodes to minimize the reverse recovery loss. Moreover, the first proposed converter has two capacitors in the output reducing the need for a bulky output capacitor, while the second proposed converter has higher utilization of the CI and is able to provide a higher voltage gain. The operation principle and theoretical analysis, such as voltage stress, current stress, effect of leakage inductances on the voltage gain, and design considerations are presented in this article. Furthermore, the superiority of the proposed converters over the competitive high step-up quadratic converters are justified through a comparison study. Finally, a 300-W, 30- to 400-V laboratory experimental setup is implemented to validate the theoretical analysis.
Dwarf Mongoose Optimization with Machine-Learning-Driven Ransomware Detection in Internet of Things Environment

The internet of things (ransomware refers to a type of malware) is the concept of connecting devices and objects of all types on the internet. IoT cybersecurity is the task of protecting ecosystems and IoT gadgets from cyber threats. Currently, ransomware is a serious threat challenging the computing environment, which needs instant attention to avoid moral and financial blackmail. Thus, there comes a real need for a novel technique that can identify and stop this kind of attack. Several earlier detection techniques followed a dynamic analysis method including a complex process. However, this analysis takes a long period of time for processing and analysis, during which the malicious payload is often sent. This study presents a new model of dwarf mongoose optimization with machine-learning-driven ransomware detection (DWOML-RWD). The presented DWOML-RWD model was mainly developed for the recognition and classification of goodware/ransomware. In the presented DWOML-RWD technique, the feature selection process is initially carried out using an enhanced krill herd optimization (EKHO) algorithm by the use of dynamic oppositional-based learning (QOBL). For ransomware detection, DWO with an extreme learning machine (ELM) classifier can be utilized. The design of the DWO algorithm aids in the optimal parameter selection of the ELM model. The experimental validation of the DWOML-RWD method can be examined on a benchmark dataset. The experimental results highlight the superiority of the DWOML-RWD model over other approaches.
Atypical Sites of the Lipoma on the Hand and Fingers: Clinical and Imaging Features and Surgical Outcomes

Background: Lipomas are the most frequent benign soft tissue tumor that are rarely found in the hand and are exceptionally rare on the fingers. The aim of this study was to investigate lipomas of atypical locations, so that they can be taken into account when making a differential diagnosis of a tumor of the hand or fingers. Methods: We studied a group of 27 patients diagnosed with lipoma of the wrist, palm, and fingers. The diagnosis was made by clinical and imaging examinations. Surgical treatment was established based on symptoms of pain, paresthesias, functional impairment, or aesthetic concerns. Treatment outcomes were assessed clinically at 1.5 years post-intervention. Results: Sizes over 5 cm were recorded in five cases, with the largest lipoma being 8 by 5 by 3.5 cm in size and weighing 125 g, located in the palm. There was one case of spontaneous tendon rupture and one case of carpal tunnel syndrome. There was no recurrence recorded at 1.5-year follow-up. Conclusions: Lipomas in the palm and fingers are rare entities (with more men affected), and surgical treatment consists of complete removal of the tumor and providing definitive healing. Despite their rarity, clinicians should consider lipomas when making differential diagnoses of soft tissue tumors of the hand.
Homonegative Victimization and Perceived Stress among Adolescent Sexual Minority Males: The Attenuating Role of Peer and Family Support

Abstract Purpose: Research suggests social support may protect sexual minorities from the harmful effects of victimization that undermine mental and sexual health wellness; however, this relationship has been underexplored among a diverse youth population. We examined the association between lifetime homonegative victimization, perceived stress in the last month, and resilience factors among a diverse sample of adolescent sexual minority males. Methods: Data were collected between June 2018 and April 2020 as part of the MyPEEPS Mobile study, a multi-site randomized controlled trial evaluating the effectiveness of a mobile behavioral HIV prevention intervention for adolescents living in the U.S. We analyzed baseline survey data from 542 sexual minority males, aged 13–18 years. We used bivariate analyses to examine relationships among variables and multivariable linear regression models to determine if resilience factors attenuated the association between homonegative victimization and perceived stress. Results: Perceived stress was positively associated with younger age, internalized homophobia, experiencing verbal victimization, threats of being outed, and threats of physical violence. Relying on online friends for support and having good family relations both attenuated the relationship between verbal victimization and perceived stress. However, neither of these resilience factors significantly weakened the associations between perceived stress, threats of physical violence, and being outed. Conclusion: Resilience factors, including peer and family support, may play an attenuating role in the relationship between homonegative victimization and perceived stress among adolescent sexual minority males. ClinicalTrials.gov Identifier: NCT03167606
Using a Short Food Frequency Questionnaire to Evaluate Macronutrients, Fiber, Phosphorus, Potassium, and Calcium in Adults with Stages 3–5 Chronic Kidney Disease

The progression of chronic kidney disease (CKD) can be directly or indirectly accelerated by a poor diet and the diet’s influence on risk factors for this disease. There have been no food frequency questionnaires (FFQs) developed for the assessment of diet in patients with CKD in Taiwan. This study analyzed the validity of a short FFQ (SFFQ) with 42 items for estimating patient intake of macronutrients, fiber, phosphorus, potassium, and calcium against 3-day dietary records (3-day DRs) in Taiwanese patients with stages 3–5 CKD. In an interview, 107 participants with the help of a dietician filled out an SFFQ and reviewed a 3-day DR the patients had filled out prior to the interview. Partial Pearson correlation coefficients between SFFQ and 3-day DR were 0.722, 0.619, 0.593, 0.572, 0.450, 0.611 and 0.410 for protein, fat, carbohydrate, fiber, phosphorus, potassium, and calcium, respectively, after adjusting for energy intake. Cross-classification analysis revealed 63.5–83.2% similarity in cross-tool estimated intakes of macronutrients, fiber, phosphorus, potassium, and calcium in the same quartiles or adjacent ones. Bland–Altman plots revealed good agreement between the two tools along different intake levels. In conclusion, the newly developed SFFQ had moderate relative validity in estimating the usual intake of key nutrients related to nutrition management of patients with late-stage CKD, suggesting it can be used to assess dietary intakes in a population with CKD, especially in those residing in an Asian region.
Comparative Transcriptome Analysis of Two Peach Rootstocks Uncovers the Effect of Gene Differential Expression on Nitrogen Use Efficiency

Nitrogen is an important nutrient element that limits plant growth and yield formation, but excessive nitrogen has negative effects on plants and the environment. It is important to reveal the molecular mechanism of high NUE (nitrogen use efficiency) for breeding peach rootstock and variety with high NUE. In this study, two peach rootstocks, Shannong–1 (S) and Maotao (M), with different NUE were used as materials and treated with 0.1 mM KNO3 for transcriptome sequencing together with the control group. From the results of comparison between groups, we found that the two rootstocks had different responses to KNO3, and 2151 (KCL_S vs. KCL_M), 327 (KNO3_S vs. KCL_S), 2200 (KNO3_S vs. KNO3_M) and 146 (KNO3_M vs. KCL_M) differentially expressed genes (DEGs) were identified, respectively, which included multiple transcription factor families. These DEGs were enriched in many biological processes and signal transduction pathways, including nitrogen metabolism and plant hormone signal transduction. The function of PpNRT2.1, which showed up-regulated expression under KNO3 treatment, was verified by heterologous expression in Arabidopsis. The plant height, SPAD (soil and plant analyzer development) of leaf and primary root length of the transgenic plants were increased compared with those of WT, indicating the roles of PpNRT2.1 in nitrogen metabolism. The study uncovered for the first time the different molecular regulatory pathways involved in nitrogen metabolism between two peach rootstocks and provided gene reserve for studying the molecular mechanism of nitrogen metabolism and theoretical basis for screening peach rootstock or variety with high NUE.
Mentha piperita: Medicinal uses and pharmacological properties

Mentha piperita (Mint) is a well-known herb belong to family Lamiaceae. Mint is famous as “herb of hospitality” in Greek mythology. It is used worldwide for culinary purposes. Mentha piperita has many therapeutic effects and used in different ailments. It is used extensively in gastric problems. It consists many bioactive compounds which showed different pharmacological effects. It is also an aromatic herb and also fame for its fragrance. Essential oils have strong aroma and a complex composition. They are natural and volatile secondary metabolites. It is used in drinks, teas, jellies, toffees, syrups, and in ice-creams. Mint leaves possess a deep, fresh, and aromatic flavor with a cool smack.
Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot

Loop closure detection based on a residual network (ResNet) and a capsule network (CapsNet) is proposed to address the problems of low accuracy and poor robustness for mobile robot simultaneous localization and mapping (SLAM) in complex scenes. First, the residual network of a feature coding strategy is introduced to extract the shallow geometric features and deep semantic features of images, reduce the amount of image noise information, accelerate the convergence speed of the model, and solve the problems of gradient disappearance and network degradation of deep neural networks. Then, the dynamic routing mechanism of the capsule network is optimized through the entropy peak density, and a vector is used to represent the spatial position relationship between features, which can improve the ability of image feature extraction and expression to optimize the overall performance of networks. Finally, the optimized residual network and capsule network are fused to retain the differences and correlations between features, and the global feature descriptors and feature vectors are combined to calculate the similarity of image features for loop closure detection. The experimental results show that the proposed method can achieve loop closure detection for mobile robots in complex scenes, such as view changes, illumination changes, and dynamic objects, and improve the accuracy and robustness of mobile robot SLAM.
Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network

In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequency part of speech. In contrast, the inverse Mel (IMel) spectrogram, which focuses on the high-frequency part, is proposed to comprehensively analyze emotions. Because the convolutional neural network-stacked sparse autoencoder (CNN-SSAE) can extract deep optimized features, the Mel-IMel dual-channel complementary structure is proposed. In the first channel, a CNN is used to extract the low-frequency information of the Mel spectrogram. The other channel extracts the high-frequency information of the IMel spectrogram. This information is transmitted into an SSAE to reduce the number of dimensions, and obtain the optimized information. Experimental results show that the highest recognition rates achieved on the EMO-DB, SAVEE, and RAVDESS datasets were 94.79%, 88.96%, and 83.18%, respectively. The conclusions are that the recognition rate of the two spectrograms was higher than that of each of the single spectrograms, which proves that the two spectrograms are complementary. The SSAE followed the CNN to get the optimized information, and the recognition rate was further improved, which proves the effectiveness of the CNN-SSAE network.
Analysis of the effect of cognitive ability on academic achievement: Moderating role of self-monitoring

In this study, cognitive ability was classified into memory ability, representational ability, information processing ability, logical reasoning ability, and thinking conversion ability, and analyzed the effects of these five ability values on academic achievement. Structural equation modeling (SEM) was used to analyze the moderating effect of Self-monitoring between cognitive ability and Academic Achievement, using students’ Self-monitoring as moderating variables. The results of the study showed that cognitive ability can significantly and positively affect academic achievement, while Self-monitoring can significantly moderate the effect of cognitive ability on academic performance, with a significant moderating effect on math subjects and English subjects among achievement subjects, and the higher the value of cognitive ability, the stronger the moderating effect.
An experimental study of the vector fields of velocity and vorticity in turbulent flows

All turbulent flows are characterized by high levels of fluctuating vorticity. This is the feature that distinguishes turbulence from other stochastic fluid motions which are not categorized as turbulent. For this reason, vorticity dynamics play an essential role in the description of turbulence. It is important to measure and analyze vorticity in order to study the mechanisms that are responsible for the phenomena occurring in turbulent flow fields. The random vorticity fluctuations that characterize turbulence could not maintain themselves if the velocity fluctuations were two dimensional, since an important vorticity-generation mechanism known as vortex stretching is absent in two-dimensional flow. Therefore, three-dimensional measurements are necessary for the analysis of vorticity in turbulent flows. In the present work the three-dimensional velocity and vorticity vector fields in grid tur¬ bulence and the outer part of wall-boundary layer have been experimentally investigated at Reynolds-numbers Rex • 50 to 200. Data Interpretation is based upon a new calibration and data evaluation method, developed in the course of this work, capable of highly accurate measurements of the instantaneous, three-dimensional velocity vector in intensely turbulent flow fields by miniature four-hot-wire sensors. This sensor type is the main part of the developed vorticity probes, which consist of five four-hot-wire sensors. The Statistical properties of the measured velocity and vorticity time series are in good agreement with theory and other experimental and simulated data, confirming the reliability of the applied measuring technique. The results confirm the streng tendency for alignment between vorticity and the intermediate eigenvector of the rate of strain tensor, the positiveness of the enstrophygeneration term and the tendency of alignment in the strict sense between vorticity and the vortex stretching vector, i.e. to be parallel but not anti-parallel. In both flow types similar characteristics of the enstrophy production mechanisms have been observed, depending mainly on the Reynolds number Rex. Conditional sampling analyses reveal distinct dtfferences between regions of weak and streng vorticity, indicating the tendency for streng vorticity to be organized in coherent structures. The dominating vortex structures in boundary layers were found to form an angle of about 45* to the wall.