Miracle-dz/UniFAR-FTUs huggingface dataset:
Excitation and voltage-gated modulation of single-mode dynamics in a planar nanogap spin Hall nano-oscillator

We experimentally study the dynamical modes excited by current-induced spin-orbit torque and its electrostatic gating effect in a 3-terminal planar nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating spin-wave and localized"bullet"modes are achieved and controlled by varying the applied in-plane magnetic field and driving current. The minimum linewidth shows a linear dependence on the actual temperature of the active area, confirming single-mode dynamics based on the nonlinear theory of spin-torque nano-oscillation with a single mode. The observed electrostatic gating tuning oscillation frequency arises from voltage-controlled magnetic anisotropy and threshold current of SHNO via modification of the nonlinear damping and/or the interfacial spin-orbit coupling of the magnetic multilayer. In contrast to previously observed two-mode coexistence degrading the spectral purity in Py/Pt-based SHNOs with a negligible IPMA, a single coherent spin-wave mode with a low driven current can be achieved by selecting the ferromagnet layer with a suitable IPMA because the nonlinear mode coupling can be diminished by bringing in the PMA field to compensate the easy-plane shape anisotropy. Moreover, the simulations demonstrate that the experimentally observed current and gate-voltage modulation of auto-oscillation modes are also closely associated with the nonlinear damping and mode coupling, which are determined by the ellipticity of magnetization precession. The demonstrated nonlinear mode coupling mechanism and electrical control approach of spin-wave modes could provide the clue to facilitate the implementation of the mutual synchronization map for neuromorphic computing applications in SHNO array networks.
Actually Sparse Variational Gaussian Processes

Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data. In practice however, for large datasets requiring many inducing variables, such as low-lengthscale spatial data, even sparse GPs can become computationally expensive, limited by the number of inducing variables one can use. In this work, we propose a new class of inter-domain variational GP, constructed by projecting a GP onto a set of compactly supported B-spline basis functions. The key benefit of our approach is that the compact support of the B-spline basis functions admits the use of sparse linear algebra to significantly speed up matrix operations and drastically reduce the memory footprint. This allows us to very efficiently model fast-varying spatial phenomena with tens of thousands of inducing variables, where previous approaches failed.
Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild

Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs). Recently, 3D pseudo-GTs have been widely used to train 3D human mesh estimation networks as the 3D pseudo-GTs enable 3D mesh supervision when training the networks on ITW datasets. However, despite the great potential of the 3D pseudo-GTs, there has been no extensive analysis that investigates which factors are important to make more beneficial 3D pseudo-GTs. In this paper, we provide three recipes to obtain highly beneficial 3D pseudo-GTs of ITW datasets. The main challenge is that only 2D-based weak supervision is allowed when obtaining the 3D pseudo-GTs. Each of our three recipes addresses the challenge in each aspect: depth ambiguity, sub-optimality of weak supervision, and implausible articulation. Experimental results show that simply re-training state-of-the-art networks with our new 3D pseudo-GTs elevates their performance to the next level without bells and whistles. The 3D pseudo-GT is publicly available1.
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator

With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid. We then use two lightweight deep neural networks (DNNs), taking the normalized representation as input and estimating the Laplacian pyramid of the corresponding LDR image. We optimize the tone mapping network by minimizing the normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with human judgments of tone-mapped image quality. In Stage two, the input HDR image is self-calibrated to compute the final LDR image. We feed the same HDR image but rescaled with different maximum luminances to the learned tone mapping network, and generate a pseudo-multi-exposure image stack with different detail visibility and color saturation. We then train another lightweight DNN to fuse the LDR image stack into a desired LDR image by maximizing a variant of the structural similarity index for multi-exposure image fusion (MEF-SSIM), which has been proven perceptually relevant to fused image quality. The proposed self-calibration mechanism through MEF enables our TMO to accept uncalibrated HDR images, while being physiology-driven. Extensive experiments show that our method produces images with consistently better visual quality. Additionally, since our method builds upon three lightweight DNNs, it is among the fastest local TMOs.
Determining the Antibacterial Effect of Mentha Longifolia Essential Oil on Cariogenic Bacteria and Its Compounds: an in vitro Study

Statement of the Problem: Continuous use of chemical agents to reduce the number of cariogenic bacteria leads to adverse effects; therefore, in recent years, many studies have focused on plant-based substances. Purpose: This study explores the antibacterial effects of Mentha Longifolia (M. longifolia) essential oil on Streptococcus mutans (S. mutans), Streptococcus sobrinus (S. sobrinus), and Lactobacillus as cariogenic microorganisms and determines the compounds in it. Materials and Method: In this experimental study, S. mutans, S. sobrinus, and Lactobacillus isolates were collected from the saliva samples of five children with severe early childhood caries (S-ECC). The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of M. longifolia essential oil were determined by Broth microdilution method. Chlorhexidine 0.2% and phosphate-buffered normal saline (pH= 7.0) were used as positive and negative controls, respectively. The chemical composition of M. longifolia essential oil was evaluated by gas chromatography-mass spectrometry. The data were analyzed with a paired t-test and the p below 0.05 was considered significant. Results: The MIC and MBC ratios for S. mutans were 3.12% and 6.25%, for S. sobrinus were 6.25% and 12.5%, and Lactobacillus were 3.12% and 6.25%, respectively. Chemical analysis of M. longifolia essential oil showed that 34 various compounds. Piperitone oxide (27.59%), Transcariophylline (14.55%), and 2-cyclohexane-1-one (12.24%) were the major constituents. Conclusion: M. longifolia essential oil has both growth inhibitory and bactericidal effects on all the three species of bacteria. This antibacterial effect was similar against S. mutans and Lactobacillus, which was greater than S. sobrinus, thus, it can be used as a supplementary for caries prevention compounds.
Protective effects of melatonin receptor agonists on endotoxin-induced uveitis in rats

Objective(s): Melatonin has an important role in regulating a variety of physiological functions of the body. We investigated the protective effects of Agomelatine (AGO) and Ramelteon (RAME) on Endotoxin-Induced Uveitis (EIU) in rats. Materials and Methods: 70 rats were randomly divided into fourteen groups. Healthy group normal saline, (IP), Uveitis group (200 μg/kg lipopolysaccharide (LPS), SC), DEX group (200 μg/kg LPS plus 1 mg/kg dexamethasone, IP), AGO20 group received 200 μg/kg LPS plus 20 mg/kg AGO, AGO40 group received 200 μg/kg LPS plus 40 mg/kg AGO, RAME2 group received 200 μg/kg LPS plus 2 mg/kg RAME, and group RAME4 received 200 μg/kg LPS plus 4 mg/kg RAME. Each group had two subgroups: the 3rd and 24th hr. The eye tissues were collected and investigated biomicroscopically (clinical manifestations and scoring, molecularly(qRT-PCR analyses of tumor necrosis factor-α (TNF-α), vascular endothelial growth factor (VEGF), and caspase 3 and caspase 9 mRNA expression), biochemically (Superoxide dismutase activity (SOD), Glutathione (GSH), and malondialdehyde levels (MDA)) and histopathologically (staining with Harris Hematoxylin and Eosin Y). Results: Melatonin receptor agonist treatment reduced the clinical score count of ocular inflammation in the uveitic rats. TNF-α, VEGF, caspase 9, and caspase 3 levels markedly decreased in the uveitic rats. Melatonin receptor agonists significantly ameliorated fixed changes in GSH, SOD, and MDA levels. Melatonin receptor agonists also ameliorated histopathological injury in eye tissues associated with uveitis. Conclusion: Melatonin receptor agonists ameliorated the inflammatory response in EIU. These findings suggest that melatonin receptor agonists may represent a potential novel therapeutic drug for uveitis treatment.
Evaluation of the short-term outcomes of robotic-assisted radical resection for perihilar cholangiocarcinoma: a propensity-scored matching analysis

Abstract Background The application of robotic-assisted radical resection in perihilar cholangiocarcinoma (pCCA) remains poorly defined. This study aimed to evaluate the safety and efficacy of robotic-assisted radical resection for pCCA in our institute. Methods Between July 2017 and July 2022, pCCA patients undergoing robotic-assisted and open radical resection at First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were included. The short-term outcomes were compared by using propensity-scored matching (PSM) analysis. Results Eighty-six pCCA patients were enrolled. After PSM at a ratio of 1:2, 10 and 20 patients were assigned to the robotic-assisted and open groups, respectively. There were no significant disparities in the clinicopathological features between the two groups. The robotic-assisted group had significantly longer operation time (median: 548 vs 353 min, P = 0.004) and larger total number of lymph nodes examined (median: 11 vs 5, P = 0.010) than the open group. The robotic-assisted group tended to have a lower intraoperative blood loss (median: 125 vs 350 mL, P = 0.067), blood transfusion rates (30.0% vs 70.0%, P = 0.056), and post-operative overall morbidities (30.0% vs 70.0%, P = 0.056) than the open group, even though the differences were not statistically significant. There were no significant differences in the negative resection margin, post-operative major morbidities, or post-operative length-of-stay between the robotic-assisted and open groups (all P > 0.05). Conclusions Robotic-assisted radical resection of pCCA may get a larger total number of lymph nodes examined than open surgery. Provided robotic-assisted surgery may be a feasible and safe technique for selected pCCA patients.
Fast level set algorithm based on the LPT

In view of the lower computational efficiency of level set, a new algorithm was presented to improve the computing speed of level set method, which combined the Log Polar Transform (LPT) with level set method. The property of the LPT is that it can realize the data compression and reduce the data size through non-uniform sampling of the object. So the pending image was projected into the log polar coordinate to implement the level set evolution, in order to reduce computational efforts. Finally, we compared our method with the direct method and the narrow band method. The results show that our method takes the less time to access the data than other two methods while still obtain the ideal segmentation results.
Public Priorities in Women's Health: Analysis of Request for Information Published to Inform “Advancing NIH Research on the Health of Women: A 2021 Conference”

Objectives: To assist with planning a congressionally requested conference on women's health research, the National Institutes of Health (NIH) Office of Research on Women's Health (ORWH) invited comments to characterize public concerns related to any or all of the specified public health issues: maternal morbidity and mortality (MMM); stagnant rates of cervical cancer survival; and the growing incidence of chronic debilitating conditions in women (CDCW). This analysis summarizes public priorities in women's health research. Materials and Methods: All comments received in response to a request for information were open coded and a master list of keywords was created, and comments were categorized. Comments addressing CDCW were categorized using a conceptual framework developed by the NIH. Results: Two hundred forty-seven comments were coded and analyzed. One hundred four comments (42%) addressed MMM; 182 comments (73%) discussed CDCW; and 27 comments (10%) addressed cervical cancer. Comments focused on CDCW most frequently addressed female-specific conditions (83%). The 10 most frequently identified keywords in order of frequency from the manual coding were as follows: (1) MMM, (2) racial disparities, (3) access to care, (4) provider training, (5) mental health, (6) Black or African American women, (7) screening, (8) quality of care, (9) time to diagnosis, and (10) social determinants of health. Conclusions: Comments demonstrate a broad range of concerns related to the health of women, including MMM, CDCW, and cervical cancer. A wide array of commenters included patients, advocacy groups, and academic and professional organizations originating from geographically diverse locations. These comments reflect a strong desire from the public to prioritize research on the health of women.
CLC Estimator: A Tool for Latent Construct Estimation via Congeneric Approaches in Survey Research

Abstract This article proposes the Shiny app ‘CLC Estimator’ –Congeneric Latent Construct Estimator– to address the problem of estimating latent unidimensional constructs via congeneric approaches. While congeneric approaches provide more rigorous results than suboptimal parallel-based scoring methods, most statistical packages do not provide easy access to congeneric approaches. To address this issue, the CLC Estimator allows social scientists to use congeneric approaches to estimate latent unidimensional constructs smoothly. The present app provides a novel solution to the challenge of limited access to congeneric estimation methods in survey research.
Explorando a transformada wavelet contínua

The continuous wavelet transform is a powerful tool for multiscale data analysis. It has wide applications in physics, mathematics, natural sciences and even great appeal in engineering, computing and areas of technology and innovation. As an introductory point of view, the wavelet transform has been widely addressed in previous works. Thus, this presentation emphasizes the use of this technique in theoretical and practical situations that are not usually explored, but situations that are fundamental to a better use, or even a correct use, of this tool. Consistent bases enable mainly extensions of wavelet application to new investigations, that are facilitated by the availability of free programs and tools, and even several of these in completely available distribution of resources (free softwares). The content of this article, by the authors' choice criteria, presents concepts and examples of techniques that can be of great interest for various studies in physics and other related areas. This text is aimed at researchers, teachers and graduate students, with the possibility of still meet the preparation needs of students at last undergraduation stage.
Advances in the phytochemical screening and biological potential of propolis

Propolis is a natural resinous product collected from different parts of plants by bees and mixed with their salivary secretions. The occurrence of more than 180 different chemotypes has flavonoids, phenolic acids, esters, and phenolic aldehydes, as well as balsamic resins, beeswax, pollen, and essential and aromatic oils, among others. Its biological potential documented throughout the world justifies the need, from time to time, to organize reviews on the subject, with the intention of gathering and informing about the update on propolis. In this review (CRD42020212971), phytochemical advances, in vitro, in vivo, and clinical biological assays of pharmacological interest are showcased. The focus of this work is to present propolis clinical safety assays, antitumor, analgesic, antioxidant, anti‐inflammatory, and antimicrobial activities. This literature review highlights propolis' promising biological activity, as it also suggests that studies associating propolis with nanotechnology should be further explored for enhanced bioprocessing applications.
Design strategies for the self-assembly of polyhedral shells

Significance Nature is able to self-assemble complex supramolecular structures from elementary building blocks rarely making mistakes. For example, adenovirus nucleocapsids are formed from different proteins that assemble and fold into a regular icosahedral shell. One of the goals of nanotechnology is to emulate these strategies to produce new synthetic materials. Unfortunately, most artificial designs fail because the units misassemble into incorrect, and often irregular, structures. We here propose a design strategy for self-assembling finite-size shells where the interaction between the units is obtained from the solution to a Boolean satisfiability problem (SAT). We demonstrate that this quite general approach excels in self-assembling different Archimedean polyhedral shells with high yield, revealing some general rules guiding the design of successful assembles.
SARS-CoV-2 Infection of Human Neurons Is TMPRSS2 Independent, Requires Endosomal Cell Entry, and Can Be Blocked by Inhibitors of Host Phosphoinositol-5 Kinase

COVID-19 is a disease caused by the coronavirus SARS-CoV-2. Millions of patients display neurological symptoms, including headache, impairment of memory, seizures, and encephalopathy, as well as anatomical abnormalities, such as changes in brain morphology. ABSTRACT 2019 coronavirus disease (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In addition to respiratory illness, COVID-19 patients exhibit neurological symptoms lasting from weeks to months (long COVID). It is unclear whether these neurological manifestations are due to an infection of brain cells. We found that a small fraction of human induced pluripotent stem cell (iPSC)-derived neurons, but not astrocytes, were naturally susceptible to SARS-CoV-2. Based on the inhibitory effect of blocking antibodies, the infection seemed to depend on the receptor angiotensin-converting enzyme 2 (ACE2), despite very low levels of its expression in neurons. The presence of double-stranded RNA in the cytoplasm (the hallmark of viral replication), abundant synthesis of viral late genes localized throughout infected cells, and an increase in the level of viral RNA in the culture medium (viral release) within the first 48 h of infection suggested that the infection was productive. Productive entry of SARS-CoV-2 requires the fusion of the viral and cellular membranes, which results in the delivery of the viral genome into the cytoplasm of the target cell. The fusion is triggered by proteolytic cleavage of the viral surface spike protein, which can occur at the plasma membrane or from endosomes or lysosomes. We found that SARS-CoV-2 infection of human neurons was insensitive to nafamostat and camostat, which inhibit cellular serine proteases, including transmembrane serine protease 2 (TMPRSS2). Inhibition of cathepsin L also did not significantly block infection. In contrast, the neuronal infection was blocked by apilimod, an inhibitor of phosphatidyl-inositol 5 kinase (PIK5K), which regulates early to late endosome maturation. IMPORTANCE COVID-19 is a disease caused by the coronavirus SARS-CoV-2. Millions of patients display neurological symptoms, including headache, impairment of memory, seizures, and encephalopathy, as well as anatomical abnormalities, such as changes in brain morphology. SARS-CoV-2 infection of the human brain has been documented, but it is unclear whether the observed neurological symptoms are linked to direct brain infection. The mechanism of virus entry into neurons has also not been characterized. Here, we investigated SARS-CoV-2 infection by using a human iPSC-derived neural cell model and found that a small fraction of cortical-like neurons was naturally susceptible to infection. The productive infection was ACE2 dependent and TMPRSS2 independent. We also found that the virus used the late endosomal and lysosomal pathway for cell entry and that the infection could be blocked by apilimod, an inhibitor of cellular PIK5K.
Arteriovenous malformation of the spermatic cord mimicking reducible inguinal hernia

Arteriovenous malformations are a vascular anomaly most often found in the central nervous system; however, they can present ubiquitously in any organs or tissues. We present the case of a 55‐year‐old man who developed a tender, reducible inguinal bulge and underwent laparoscopic transabdominal preperitoneal inguinal hernia repair under the clinical diagnosis of an inguinal hernia. Intraoperative observation revealed no hernia sac, but a poorly defined spermatic cord mass, appearing to be responsible for the patient's symptoms, was found and removed. The pathology of the mass was consistent with the diagnosis of an arteriovenous malformation of the spermatic cord. Surgeons should keep in mind the small possibility of arteriovenous malformations in patients with clinical presentation of an inguinal hernia, as they may cause massive bleeding during and after the operation unless handled appropriately.
Development of multigrain breakfast cereal using extrusion technology

Designed experiments were conducted to prepare extrudates from different cereals blends using twin screw extruder. Linear model was developed using Response Surface methodology (RSM) to study the effect of the variables (rice, wheat, maize) on SEI (sectional expansion index), WAI (water absorption index), WSI (water solubility index), texture and color. Maize had a significant positive linear effect on the SEI and a significant negative effect on the texture of the extrudate. Rice had a positive linear effect on the WAI and WSI Combination of maize and wheat had a significant effect on the color of the extrudates. Maximum desirability of 0.659 was recorded at the 45.4% rice, 27.3% maize and 27.3% wheat levels. The predicted responses in terms of SEI, WAI, WSI, texture and color were 5.14,5.27g/g,11.24 per cent, 44.81N, 28.65 respectively. The predicted values registered non-significant difference from the experimental values.