Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel compact ultra-wideband bandpass filter based on vialess vertical CPW/microstrip transitions

A novel compact ultra-wideband (UWB) bandpass filter based on vialess vertical coplanar waveguide (CPW)/microstrip transitions is proposed, designed, fabricated and measured. The represented filter consists of two back-to-back hybrid microstrip/slotline transitions derived from the microstrip to CPW. Due to this coupling configuration, a smooth UWB passband response with an insertion loss <0.85 dB is achieved. Moreover, two transmission zeros at lower and upper passband edges are introduced to provide sharp selectivity. For verification, a prototype is fabricated and measured. The simulated results are in good agreement with the measured one.
Validation of Earphone-Type Sensors for Non-Invasive and Objective Swallowing Function Assessment

Standard methods for swallowing function evaluation are videofluoroscopy (VF) and videoendoscopy, which are invasive and have test limitations. We examined the use of an earphone-type sensor to noninvasively evaluate soft palate movement in comparison with VF. Six healthy adults wore earphone sensors and swallowed barium water while being filmed by VF. A light-emitting diode at the sensor tip irradiated infrared light into the ear canal, and a phototransistor received the reflected light to detect changes in ear canal movement, including that of the eardrum. Considering that the soft palate movement corresponded to the sensor waveform, a Bland–Altman analysis was performed on the difference in time recorded by each measurement method. The average difference between the time taken from the most downward retracted position before swallowing to the most upward position during swallowing of the soft palate in VF was −0.01 ± 0.14 s. The Bland–Altman analysis showed no fixed or proportional error. The minimal detectable change was 0.28 s. This is the first noninvasive swallowing function evaluation through the ear canal. The earphone-type sensor enabled us to measure the time from the most retracted to the most raised soft palate position during swallowing and validated this method for clinical application.
DA$^{2}$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping

In this paper, we introduce DA<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The dataset contains about 9 M pairs of parallel-jaw grasps, generated from more than 6000 objects and each labeled with various grasp dexterity measures. In addition, we propose an end-to-end dual-arm grasp evaluation model trained on the rendered scenes from this dataset. We utilize the evaluation model as our baseline to show the value of this novel and nontrivial dataset by both online analysis and real robot experiments.
Homo and Heterotypic Cellular Cross-Talk in Epithelial Ovarian Cancer Impart Pro-Tumorigenic Properties through Differential Activation of the Notch3 Pathway

Simple Summary The omental metastatic spreading of epithelial ovarian cancer is spearheaded by complex cell–cell interactions present in the fluidic microenvironment (ascites), which is yet to be fully decoded. Using a unique co-culture model of SNFT (SKOV3 cells expressing a Notch3 luciferase reporter-sensor) and NIH3T3 cells (differentially overexpressing Jagged1 ligand), we demonstrated that incremental Jagged1 expression led to proportional Notch3 activation in SNFT. Differential Notch3 activation was also evident from co-culture of SNFT with other EOC cell lines and ascites-derived cancer-associated fibroblasts of HGSOC patients expressing varying levels of Jagged1. Amongst the top five modulated genes identified by the gene profiler array, both p21 and VEGFA showed enhanced expression (for p21)/secretion (for VEGFA) in SNFT when induced with Jagged1. Secreted VEGFA further reduced CSC differentiation in platinum-resistant A2780 cells. Pronounced VEGFA expression associated with Notch3 up-regulation in metastatic HGSOC tumors delineates an unknown role of the Notch3/VEGFA axis in EOC progression. Abstract An active fluidic microenvironment governs peritoneal metastasis in epithelial ovarian cancer (EOC), but its critical functional/molecular cues are not fully understood. Utilizing co-culture models of NIH3T3 cells (differentially overexpressing Jagged1) and SKOV3 cells expressing a Notch3 luciferase reporter-sensor (SNFT), we showed that incremental expression of Jagged1 led to proportional Notch3 activation in SNFT. With no basal luciferase activity, this system efficiently recorded dose-dependent Notch3 activation by rh-Jag1 peptide and the non-appearance of such induction in co-culture with NIH3T3Δjag1 cells indicates its sensitivity and specificity. Similar Notch3 modulation was shown for the first time in co-cultures with HGSOC patients’ ascites-derived cancer-associated fibroblasts and Jagged1-expressing EOC cell lines. NIH3T3J1-A and OVCAR3 co-cultured SNFT cells showed maximum proliferation, invasion, and cisplatin resistance among all the heterotypic/homotypic cellular partners. VEGFA and CDKN1A are the two most upregulated genes identified across co-cultures by the gene profiler array. Co-culture induced VEGFA secretion from SNFT cells which also reduced cancer stem cell differentiation in platinum-resistant A2780 cells. rh-Jag1-peptide promoted enhanced nuclear-cytoplasmic p21 expression. Additionally, metastatic HGSOC tumors had higher VEGFA than corresponding primary tumors. This study thus demonstrates the tumoral and non-tumoral cell-mediated differential Notch3 activation imparting its tumorigenic effects through two critical molecular regulators, VEGFA and p21, during EOC progression.
Echocardiography and MALDI-TOF Identification of Myosin-Binding Protein C3 A74T Gene Mutations Involved Healthy and Mutated Bengal Cats

Simple Summary The most common cardiomyopathy in feline is hypertrophic cardiomyopathy (HCM) that is recognized as an inheritance of autosomal dominant transmission in some cats. Feline HCM has been associated with the mutation of the myosin-binding protein C3 (MYBPC3) gene in several locations with a specific breed. Recently, the proteomic analyzed by mass spectrometry is a promising novel approach to characterize the specific pattern of peptide and protein for relatively clinical biomarkers. However, there has been only limited knowledge of differential peptides and proteins related to the MYBPC3 gene mutation. Therefore, this study aimed to investigate the MYBPC3 gene mutation in Bengal cats with HCM. In combined with mass spectrometry, we purposed to identify the potential peptide candidates and expected proteins. The obtained findings of specific peptides and proteins from mass spectrometry may contribute to the development of novel diagnostic tools for feline HCM related to the MYBPC3 gene mutation. Further, this observation appears to be useful information such as a database of peptidomics library in veterinary medicine for the future diagnosis of feline cardiomyopathy associated with gene mutation. Abstract This study aimed to identify the potential peptide candidates and expected proteins associated with MYBPC3-A74T gene mutations in Bengal cats and determine if peptidome profiles differ between healthy controls and cats with MYBPC3-A74T gene mutations. All animals were evaluated using echocardiography. DNA was isolated and followed by the screening test of MYBPC3 gene mutation. The MALDI-TOF mass spectrometry was conducted for analyzing the targeted peptide and protein patterns. The expected protein candidates were searched for within the NCBI database. Our results demonstrated that the MYBPC3-A74T gene mutation was dominant in Bengal cats but not in domestic shorthair cats. Correlations between baseline characteristics and echocardiographic parameters were discovered in Bengal cats. Mass spectrometry profiles of the candidate proteins were suspected to accompany the cat with the MYBPC3-A74T gene mutation, involving integral protein–membrane, organization of nucleus, DNA replication, and ATP-binding protein. Therefore, MYBPC3-A74T gene mutations occur frequently in Bengal cat populations. The high incidence of homozygotes for the mutation supports the causal nature of the MYBPC3-A74T mutation. In addition, peptidomics analysis was established for the first time under this condition to promise a complementary technique for the future clinical diagnosis of the MYBPC3-A74T mutation associated with physiological variables and cardiac morphology in cats.
1 kV, 10-kW SiC-Based Quadruple Active Bridge DCX Stage in a DC to Three-Phase AC Module for Medium-Voltage Grid Integration

Interfacing low voltage dc to medium voltage three-phase ac grid is often based on series-stackable modular converter architectures. To minimize energy storage requirements, it is advantageous to employ a quadruple active bridge (QAB) stage operating as a “dc transformer” in each stackable module. The QAB stage offers three isolated dc link voltages, which then allow for flexible stacking of three single-phase dc-to-ac inverter stages. Each of the module phases processes a pulsating power having a component at twice the line frequency. This presents a challenge in maintaining zero voltage switching (ZVS) on the secondary sides of the QAB during low-power portions of the line cycle. This article is focused on the design of the QAB stage. A detailed analysis of ZVS switching waveforms is presented, including effects of nonlinear device capacitances. It is shown how ZVS can be achieved at all times using a relatively small circulating current provided by the magnetizing inductance of the high-frequency transformer. Analytical expressions are given for the optimal values of the magnetizing inductance and the dead times of the QAB primary and secondary bridges. The approach is verified by experimental results on a 1 kV, 10-kW SiC-based prototype, demonstrating a relatively flat efficiency curve with a peak efficiency of 97.1% at 75% load.
Is MRI Essential Prior to Arthroscopic Management of Internal Derangement of the Knee

Background: Magnetic Resonance imaging (MRI) is a frequently used, non-invasive investigation for the diagnosis of knee meniscal and anterior cruciate ligament (ACL) injuries. This investigation is not available at our institution and is fairly expensive costing about seven thousand rupees. Patients have to be outsourced to get this investigation causing significant delay and expense. This study was designed to compare the accuracy of clinical examination and MRI by arthroscopic exami-nation in patients with knee meniscal and ACL injuries.If clinical diagnosis could be as accurate as MRI, the need for this expensive and time consuming investigation could be avoided. Patients and Methods: This study had prospective observational design. Thirty patients who were diagnosed clinically and by MRI to have meniscal or ACL injury and needed arthroscopic intervention, were included in the study on the basis of intention to treat. Accuracy of clinical and MRI diagnosis as had been made earlier were compared after arthroscopic exami-nation.4 by 4 statistical table was used to determine the sensitivity, specificity, positive predictive value(PPV), negative pre-dictive value (NPV) and accuracy of clinical and MRI diagnosis for meniscal and ACL injuries. Results: In case of meniscal injuries, clinical examination had sensitivity of 91%, specificity of 22%, positive predictive value of 73%, negative predictive value of 50% and accuracy of 70% and MRI had sensitivity of 94%, specificity of 9%, positive predictive value of 64%, negative predictive value of 50% and accuracy of 63%. Similarly in case of ACL injuries, Clinical examination had sensitivity of 84%, specificity of 100%, positive predictive value of 100%, negative predictive value of 78% and accuracy of 90% and MRI had sensitivity of 94%, specificity of 58%, positive predictive value of 77%, negative predictive value of 87% and accuracy of 80%. Conclusion: The accuracy of diagnosis of meniscal and ACL injuries by clinical examination and MRI were arthroscopi-cally compared and found to be fairly matched . It is suggested that routine use of MRI just to confirm the clinical diagnosis of these injuries should be reconsidered.
Microscale Thermophoresis as a Tool to Study Protein Interactions and Their Implication in Human Diseases

The review highlights how protein–protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.
Learning to Optimize in Model Predictive Control

Sampling-based Model Predictive Control (MPC) is a flexible control framework that can reason about non-smooth dynamics and cost functions. Recently, significant work has focused on the use of machine learning to improve the performance of MPC, often through learning or fine-tuning the dynamics or cost function. In contrast, we focus on learning to optimize more effectively. In other words, to improve the update rule within MPC. We show that this can be particularly useful in sampling-based MPC, where we often wish to minimize the number of samples for computational reasons. Unfortunately, the cost of computational efficiency is a reduction in performance; fewer samples results in noisier updates. We show that we can contend with this noise by learning how to update the control distribution more effectively and make better use of the few samples that we have. Our learned controllers are trained via imitation learning to mimic an expert which has access to substantially more samples. We test the efficacy of our approach on multiple simulated robotics tasks in sample-constrained regimes and demonstrate that our approach can outperform a MPC controller with the same number of samples.
Infrastructure-Enabled Autonomy: An Attention Mechanism for Occlusion Handling

Although there has been tremendous progress in autonomous driving, navigating environments and predicting the behavior of other drivers in the presence of occlusions remains challenging. Cities have started investing in infrastructure sensors that could provide information about occluded spaces. We propose a framework that integrates infrastructure-to-vehicle communication in autonomous vehicle decision making, improving operational safety and mobility in challenging environments. By framing the problem as a partially observable Markov decision process in which querying an infrastructure sensor is a data-gathering action, we reduce the computational complexity associated with sensor processing while maintaining equivalent performance compared to an omniscient actor and demonstrate the value of infrastructure communication through a series of experiments.
Constrained Variable Impedance Control using Quadratic Programming

This paper proposes a quadratic programming (QP)-based variable impedance control (VIC) algorithm to solve contact-rich trajectory tracking problems with impedance, position and velocity constraints. To the best of our knowledge, the impedance constraints which are significant to ensure the worst contact compliance have never been considered in other previous works. To handle the impedance constraints of the VIC algorithm, a novel impedance model where the impedance parameters are directly served as the control input is established. The impedance-constrained VIC design problem is then formulated as a QP problem which can be efficiently solved. To handle the position and velocity constraints, a complementary force is introduced into the novel impedance model. The complementary force will appear to prevent the constraints violation when the robot approaches the constrained area. The design problem of the complementary force is also transformed into a QP problem. Combing these two QP solutions, the VIC algorithm with both impedance, position and velocity constraints can be obtained. Finally, various experiments are conducted to show the effectiveness of the proposed QP-based constrained VIC algorithm.
Molecular Survey of Trypanosoma congolense “Forest-Type” in Nigerian Cattle

African animal trypanosomosis is an important livestock disease in Nigeria which is considered as a threat to the on-going effort on poverty alleviation in the continent. The disease is caused by several Trypanosoma species which are protozoan parasites transmitted by tsetse. Trypanosomosis is characterized by tissue injury including overwhelming activation of Classical Myeloid cells that results in destruction of the liver and uncontrolled parasite growth. Reduction in production and sometimes death are disease sequella. Hence, proper surveillance of the disease using a sensitive tool is very necessary for monitoring and control of trypanosomosis. Therefore this study concentrated on providing knowledge on the true infection rate of Trypanosoma congolense “forest type” and its effect on body weight and haematological parameters. Blood sample was collected from 180 cattle, DNA was extracted and PCR technique was adopted for prevalence study. Haematological analysis was carried out using Auto-haemo-analyser. Data generated were analysed using SAS statistical package. A prevalence rate of 91.67% was recorded. There were significant (p<0.05) differences between Body-weight, Red Blood Cell count, Mean Corpuscular Volume and Monocyte number of infected and un-infected cattle. The haematology reveals that hosts were able to respond to anaemia compensatorily as perceived in the macro-cytic, normo-chromic status of the peripheral blood erythrocytic generation. The study concluded that the true prevalence of trypanosome infection is high and has both epidemiological and economic importance. This study `provides information that could facilitate future monitoring and control of the disease in the study area.
Global human mobility and knowledge transfer: Highly skilled return migrants as agents of transnational learning

Global human mobility constitutes a key mechanism for knowledge transfer. This study examines the micro-dynamics of knowledge transfer in the developed-developing country migratory context. It highlights the agentic role of return migrantsintransformingoverseaslearningintorelevantknowl-edge in their home contexts. Drawing on situated and relational theories of knowledge and learning, the study views knowledge transfer as a relevance discovery process. It looks at a group of highly skilled migrants who had returned from developed coun-triestoEthiopia.Despitetheirhighskill,thefocusofknowledge transfer was mostly in non-technical fields that include a broad range of organizational knowledge and work practices. These were made relevant to the local context through the returnees’ ‘work of reconciliation’, involving ‘engagement’, ‘alignment’ and ‘imagination’. The study challenges the standard assumption of a one-way linear flow of knowledge from developed to developing countries. It sheds new light on the migration-development link by highlighting the ‘aspirational’ aspect of migrant transnational learning. actors’(Giddens, 1984)who operate acrossthe host and home country professional and social contexts. The study examines a group of highly skilled migrants who returned from developed countries to Ethiopia. The developed-developing country migratory context reveals the relational forces and interactive dynam-ics of transnational learning and knowledge transfer. This is not only because of the challenges to knowledge transfer business and knowledge transfer activities in their origin home communities (Lituchy, This study examines the experience of a group of highly skilled return migrants to shed light on their transnational learning experience and the micro-processes of knowledge transfer.
The Mediating Role of Psychological Well-Being in the Relationship between Self-Care Knowledge and Disease Self-Management in Patients with Hypertensive Nephropathy

Objective: This study aimed to investigate the correlation between self-care knowledge, psychological well-being, and disease self-management in patients with hypertensive nephropathy, and to assess the effect of psychological well-being as a mediator of self-care knowledge and disease self-management. Methods: This is a cross-sectional study. The 220 patients with hypertensive nephropathy were recruited from a teaching hospital in Taiwan using purposive sampling. The average age was 70.14 (SD = 11.96) years old. Among them, 128 (58.2%) were male and 92 (41.8%) were female. Instruments included a hypertensive nephropathy self-care knowledge scale, the World Health Organization-5 Well-Being Index, and the chronic kidney disease self-management instrument. The mediating effect was determined with linear regression models and the Sobel test. Results: The total explanatory variation of age, systolic blood pressure, psychological well-being, and self-care knowledge on the disease self-management was 27.7%. Psychological well-being was the most important explanatory factor and alone explains 16%. Psychological well-being was a partial mediator of self-care knowledge and quality of life in patients with hypertensive nephropathy, with a total effect of 23.2%. Conclusions: This study showed that older patients with hypertensive nephropathy and those with a higher systolic blood pressure had lower levels of disease self-management. The higher the patients’ self-care knowledge and psychological well-being, the better their disease self-management.
Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

In recent years, detecting driver fatigue has been a significant practical necessity and issue. Even though several investigations have been undertaken to examine driver fatigue, there are relatively few standard datasets on identifying driver fatigue. For earlier investigations, conventional methods relying on manual characteristics were utilized to assess driver fatigue. In any case study, such approaches need previous information for feature extraction, which could raise computing complexity. The current work proposes a driver fatigue detection system, which is a fundamental necessity to minimize road accidents. Data from 11 people are gathered for this purpose, resulting in a comprehensive dataset. The dataset is prepared in accordance with previously published criteria. A deep convolutional neural network–long short-time memory (CNN–LSTM) network is conceived and evolved to extract characteristics from raw EEG data corresponding to the six active areas A, B, C, D, E (based on a single channel), and F. The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization. The suggested approach may be utilized to construct automatic fatigue detection systems because of their precision and high speed.