Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell Intrinsic IL-38 Affects B Cell Differentiation and Antibody Production

IL-38 is an IL-1 family receptor antagonist with an emerging role in chronic inflammatory diseases. IL-38 expression has been mainly observed not only in epithelia, but also in cells of the immune system, including macrophages and B cells. Given the association of both IL-38 and B cells with chronic inflammation, we explored if IL-38 affects B cell biology. IL-38-deficient mice showed higher amounts of plasma cells (PC) in lymphoid organs but, conversely, lower levels of plasmatic antibody titers. Exploring underlying mechanisms in human B cells revealed that exogenously added IL-38 did not significantly affect early B cell activation or differentiation into plasma cells, even though IL-38 suppressed upregulation of CD38. Instead, IL-38 mRNA expression was transiently upregulated during the differentiation of human B cells to plasma cells in vitro, and knocking down IL-38 during early B cell differentiation increased plasma cell generation, while reducing antibody production, thus reproducing the murine phenotype. Although this endogenous role of IL-38 in B cell differentiation and antibody production did not align with an immunosuppressive function, autoantibody production induced in mice by repeated IL-18 injections was enhanced in an IL-38-deficient background. Taken together, our data suggest that cell-intrinsic IL-38 promotes antibody production at baseline but suppresses the production of autoantibodies in an inflammatory context, which may partially explain its protective role during chronic inflammation.
The Prognostic Significance of FOXD1 Expression in Head and Neck Squamous Cell Carcinoma

It has been reported that forkhead box D1 (FOXD1) plays an established role in human early embryonic development and is broadly involved in various malignancies. However, there is limited information regarding FOXD1 expression in head and neck squamous cell carcinoma (HNSCC). This present study aimed to explore the clinical significance of FOXD1 in patients with HNSCC. Tissue microarrays of 334 primary HNSCC patients who underwent surgery between 2008 and 2010 at Sun Yat-sen University Cancer Center were investigated by immunohistochemistry regarding FOXD1 expression. χ2 test was used to estimate the relationship of FOXD1 expression with clinicopathologic characteristics. Univariate and multivariate analyses were performed to identify FOXD1 expression as an independent prognostic indicator of overall survival (OS) and disease-free survival (DFS). FOXD1 expression is closely associated with postoperative recurrence. HNSCC patients with high FOXD1 expression have poorer prognoses than the low-expression group (p < 0.05). According to multivariate analysis, FOXD1 was an independent prognostic factor for OS and DFS. The results revealed that FOXD1 could be a prognostic factor for HNSCC and might serve as a potential target for novel therapies.
The entropy of C2 surface diffeomorphisms in terms of Hausdorff dimension and a Lyapunov exponent

Abstract In this paper we prove that if the entropy of an ergodic measure preserved by a C2 surface diffeomorphism is positive then it is equal to the product of the Hausdorff dimension of the quotient measure defined by the family of stable manifolds and the positive Lyapunov exponent.
Filtering linear polarization converters based on multi-mode unit for harmonic suppression

In this article, a class of filtering linear polarization converters based on a single quasi-single-layer triangular resonator unit is proposed. By loading the stub-line and slot lines, the degenerate modes of the single fundamental resonant mode of an isosceles right-angle patch are separated into two for a second-order filtering characteristic, and two additional resonant modes are then generated for the second conversion band. Attributed to the mixed electric and magnetic coupling in the coupling section of the loaded stub-line, several conversion zeros are introduced at the same time. As the contribution of this work, multiple resonant modes and conversion zeros can be independently controlled by the corresponding parameters, enabling second-order single- and dual-band response with multi-mode high selectivity side-band and harmonic rejection characteristics out-of-band. With the detailed analysis of the surface current and field distributions, the polarization conversion mechanisms are revealed. Finally, the single-band and dual-band linear polarization converters are processed, and the measured and simulated results are in good agreement. As expected, the two polarization converters show excellent filtering features, which is beneficial for improving modulation accuracy and avoiding unwanted electromagnetic interference in various applications of multi-passband and multi-functional metasurfaces.
What we know, and what we need to find out about universal, school-based social and emotional learning programs for children and adolescents: A review of meta-analyses and directions for future research.

This article reviews 12 meta-analyses of universal, school-based social and emotional learning (SEL) programs for children from early childhood education through high school. The aims were to assess the breath and consistency of outcomes across meta-analyses, and the potential influence of different moderators (i.e., individual, programmatic, ecological, and methodological) on program impacts. Collectively, the meta-analyses were rated to be high quality, and included 524 unique reports conducted in many countries and involving an estimated one million students. Mean effects were consistently statistically significant across reviews on a range of outcomes including increased SEL skills, attitudes, prosocial behaviors, and academic achievement, and decreased conduct problems and emotional distress (post ds ranged from 0.09 to 0.70 and follow-up ds ranged from 0.07 to 0.33 depending on the outcome and the specific review). However, there was little consistency regarding the moderators examined, or findings when the same moderators were assessed across reviews. Moreover, there is little information on possible interactions between moderators. Research has yet to clarify which individual, contextual, methodological, and programmatic variables promote or hinder the development of different SEL skills for diverse school-aged children and youth. Recommendations to guide future research in identifying the conditions and mechanisms by which SEL programs are most effective are provided. Public Significance Statement This review of 12 meta-analyses, involving an estimated one million students from early childhood education through high school, shows that social and emotional learning (SEL) programs have consistent, positive impacts on a broad range of student outcomes including increased SEL skills, attitudes, prosocial behaviors, and academic achievement, and decreased REVIEW OF SEL PROGRAMS 5 conduct problems and emotional distress. However, there is little consistency regarding conditions and mechanisms by which these programs are most effective. By summarizing the substantial evidence base for SEL programs, and offering recommendations for future work, this paper will stimulate more research and practice related to such initiatives.
Predicting Advanced Air Mobility Adoption Globally by Machine Learning

Advanced air mobility (AAM) is a sustainable aviation initiative to deliver cargo and passengers in urban and regional locations by electrified drones. The widespread expectation is that AAM adoption worldwide will help to reduce pollution, reduce transport costs, increase accessibility, and enable a more reliable and resilient supply chain. However, most countries lack regulations that legalize AAM. A fragmented regulatory approach hampers the progress of business prospectors and international organizations concerned with human welfare. Therefore, amidst high uncertainty, knowledge of indicators that can predict the propensity for AAM adoption will help nations and organizations plan for drone use. This research finds predictive indicators by assembling a unique dataset of 36 economic, social, environmental, governance, land use, technology, and transportation indicators for 204 nations. Subsequently, the best of 12 different machine learning models ranks the predictive importance of the indicators. The gross domestic product (GDP) and the regulatory quality index (RQI) developed by the Worldwide Governance Indicators (WGI) project were the two top predictors. Just as importantly, the poor predictors were as follows: the social progress index developed by the Social Progress Imperative, the WGI rule-of-law index, land use characteristics such as rural and urban proportions, borders on open waterways, population density, technology accessibility such as electricity and cell phones, carbon dioxide emission level, aviation traffic, port traffic, tourist arrivals, and roadway fatalities.
Identifying Multi-Functional Enzyme by Hierarchical Multi-Label Classifier

Natural Science Foundation of China [61001013, 61102136, 60971085]; Natural Science Foundation of Fujian Province of China [2011J05158, 2010J01350]; Base Research Project of Shenzhen Bureau of Science, Technology, and Information [JC201006030858A]
A Magnetically Actuated Variable Stiffness Manipulator Based on Deployable Shape Memory Polymer Springs

and safe navigation. This study presents a new design of a magnetically actuated VSM based on shape memory polymer (SMP) springs. The VSM has a silicone backbone enclosed within a spring made of SMP that can change in length with stiffness change that is triggered by Joule heating. The stiffness and thermal characteristics of the VSM are studied using analytical models and experiments. Subsequently, a one-segment VSM and a two-segment VSM having outer diameters of 9 and 10 mm and lengths of 15 and 25 mm, respectively, capable of extending to four times their length are designed. The VSM can be deployed in a compact form and extended to achieve variable bending curvatures in soft and rigid states, which can facilitate instrument insertion and reduce operation invasiveness. Potential clinical applications are demonstrated by incorporating miniature camera, biopsy tool
Segmentation of speech into syllabic units

An algorithm has been designed to detect boundaries between syllabic segments of the speech signal, segments that contain but one peak or syllabic phoneme. A loudness function is defined, roughly corresponding to the short‐time signal energy, and this function exhibits alternating sequence of points of relatively major maxima and minima. No absolute energy criteria appear adequate to decide whether a given maximum in the function is in fact a syllabic peak. However, minima can be characterized as significant with reference to the magnitudes of neighboring maxima. The algorithm uses the difference between the magnitude of the convex hull of the loudness function and the function itself as a measure of significance and recursively subsegments to the total signal until a given threshold of difference is nowhere exceeded. The algorithm is used as the first stage of a sequential procedure for segmentation and phonetic labeling of speech for recognition applications. [Research supported by the Advanced Research.
SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

With the widespread of Small Private Online Courses (SPOC) in colleges and universities, course organizers who provide high‐quality personalized course activities need to understand learners' learning status and characteristics, and then optimize the course teaching. However, little research has been done on different learners' group behavior characteristics, such as which indicators can reflect learner groups' behavior, and what are the typical behavior characteristics of different learner groups. In this work, we established a Python Language Foundation self‐built SPOC course, and 109 undergraduates' learning behavior data were collected and analyzed. From 74‐dimensional behavior log data consisting of 72 video‐viewing, course video score, and comprehensive score, Principal Component Analysis was performed to reduce dimensionality. Agglomerative hierarchical clustering was used to divide learners into different categories, and the results showed that 15 groups are clustered. According to the analysis of the four indicators for group characteristics, which are the completion and viewing‐stability of task‐point videos, unit test excellence, and comprehensive score, it is identified into five primary types, including positive type, regular type, negative type, semi‐negative type, and a fluctuation type. Experiments conducted on a real data set across different academic years and courses show that the proposed approach has better clustering accuracy and practicability. It is expected that this work may be used to strengthen the personalized learning support services system in educational institutions and develop a tool that integrates exploration and analysis work onto the web platform.
A three dimensional MHD model of the Earth’s magnetosphere

We have simulated the steady state magnetospheric configuration which results when solar wind plasma impinges on a magnetic dipole. In our calculation, the MHD equations were solved self-consistently by a large scale code which employs Rusanov’s method to produce a three-dimensional model of the magnetospheric fields and plasma. This simulation, in which the interplanetary magnetic field was assumed to be zero, reproduced many of the observed magnetospheric features including the bow shock, magnetopause and plasma sheet. In particular, the calculated magnetic field is consistent with quiet time observations. The currents in the simulated magnetosphere are remarkably similar to those from semi-empirical magnetic field models.
Image restoration of FACED microscopy by generative adversarial network

We report the use of conditional generative adversarial network (cGAN) for restoring undersampled images captured in free-space angular-chirp-enhanced delay (FACED) microscopy. We show that this deep-learning approach allows the wider imaging field of view (FOV) along FACED axis, without substantially sacrificing the imaging resolution, photon-budget and speed even with lower density of scanning foci. This study could show the potential of further extending the applicability of FACED imaging to a wider range of biological applications that require extended FOV imaging.
Spatial deconvolution technique to obtain velocity profiles from chord integrated spectra

Passive spectroscopy is used to measure the plasma parameters on the ZaP experiment at the University of Washington. Twenty spectral intensities, which are functions of the plasma’s density, velocity, and temperature along the viewing chord, are recorded on a charged coupled device. The instrument function is different for each viewing chord. A deconvolution technique based on a shell model, which includes the effects of the instrument function, is developed to deduce the local plasma parameters. The error analysis for this technique is also developed. The technique is able to model complicated plasma parameter profiles and is able to deduce the local plasma parameters and position of the plasma.
What if: A retrospective reconstruction of resection cavity stereotactic radiosurgery to mimic neoadjuvant stereotactic radiosurgery

Introduction Neoadjuvant stereotactic radiosurgery (NaSRS) of brain metastases has gained importance, but it is not routinely performed. While awaiting the results of prospective studies, we aimed to analyze the changes in the volume of brain metastases irradiated pre- and postoperatively and the resulting dosimetric effects on normal brain tissue (NBT). Methods We identified patients treated with SRS at our institution to compare hypothetical preoperative gross tumor and planning target volumes (pre-GTV and pre-PTV) with original postoperative resection cavity volumes (post-GTV and post-PTV) as well as with a standardized-hypothetical PTV with 2.0 mm margin. We used Pearson correlation to assess the association between the GTV and PTV changes with the pre-GTV. A multiple linear regression analysis was established to predict the GTV change. Hypothetical planning for the selected cases was created to assess the volume effect on the NBT exposure. We performed a literature review on NaSRS and searched for ongoing prospective trials. Results We included 30 patients in the analysis. The pre-/post-GTV and pre-/post-PTV did not differ significantly. We observed a negative correlation between pre-GTV and GTV-change, which was also a predictor of volume change in the regression analysis, in terms of a larger volume change for a smaller pre-GTV. In total, 62.5% of cases with an enlargement greater than 5.0 cm3 were smaller tumors (pre-GTV < 15.0 cm3), whereas larger tumors greater than 25.0 cm3 showed only a decrease in post-GTV. Hypothetical planning for the selected cases to evaluate the volume effect resulted in a median NBT exposure of only 67.6% (range: 33.2–84.5%) relative to the dose received by the NBT in the postoperative SRS setting. Nine published studies and twenty ongoing studies are listed as an overview. Conclusion Patients with smaller brain metastases may have a higher risk of volume increase when irradiated postoperatively. Target volume delineation is of great importance because the PTV directly affects the exposure of NBT, but it is a challenge when contouring resection cavities. Further studies should identify patients at risk of relevant volume increase to be preferably treated with NaSRS in routine practice. Ongoing clinical trials will evaluate additional benefits of NaSRS.
Integrated proteome sequencing, bulk RNA sequencing and single-cell RNA sequencing to identify potential biomarkers in different grades of intervertebral disc degeneration

Low back pain (LBP) is a prevalent health problem worldwide that affects over 80% of adults during their lifetime. Intervertebral disc degeneration (IDD) is a well-recognized leading cause of LBP. IDD is classified into five grades according to the Pfirrmann classification system. The purpose of this study was to identify potential biomarkers in different IDD grades through an integrated analysis of proteome sequencing (PRO-seq), bulk RNA sequencing (bRNA-seq) and single-cell RNA sequencing (scRNA-seq) data. Eight cases of grade I-IV IDD were obtained. Grades I and II were considered non-degenerative discs (relatively normal), whereas grades III and IV were considered degenerative discs. PRO-seq analysis was performed to identify differentially expressed proteins (DEPs) in various IDD grades. Variation analysis was performed on bRNA-seq data to differentiate expressed genes (DEGs) in normal and degenerated discs. In addition, scRNA-seq was performed to validate DEGs in degenerated and non-degenerated nucleus pulposus (NP). Machine learning (ML) algorithms were used to screen hub genes. The receiver operating characteristic (ROC) curve was used to validate the efficiency of the screened hub genes to predict IDD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze function enrichment and signaling pathways. Protein-protein interaction (PPI) network was used to prioritize disease-related proteins. SERPINA1, ORM2, FGG and COL1A1 were identified through PRO-seq as the hub proteins involved in regulating IDD. ML algorithms selected ten hub genes, including IBSP, COL6A2, MMP2, SERPINA1, ACAN, FBLN7, LAMB2, TTLL7, COL9A3, and THBS4 in bRNA-seq. Since serine protease inhibitor clade A member 1 (SERPINA1) was the only common gene, its accuracy in degenerated and non-degenerated NP cells was validated using scRNA-seq. Then, the rat degeneration model of caudal vertebra was established. The expression of SERPINA1 and ORM2 was detected using immunohistochemical staining of human and rat intervertebral discs. The results showed that SERPINA1 was poorly expressed in the degenerative group. We further explored the potential function of SERPINA1 by Gene Set Enrichment Analysis (GSEA) and cell-cell communication. Therefore, SERPINA1 can be used as a biomarker to regulate or predict the progress of disc degeneration.
Environment classification and hierarchical lane detection for structured and unstructured roads

This study presents a hierarchical lane detection system with the ability to deal with both structured and unstructured roads. The proposed system classifies the environment first before applying suitable algorithms for different types of roads. Instead of dealing with all situations with one complicated algorithm, this hierarchical architecture makes it possible to achieve high accuracy with relatively simple and efficient lane detection algorithms. For environment classification, pixels with lane-marking colours are extracted as feature points. Eigenvalue decomposition regularised discriminant analysis is utilised in model selection and maximum likelihood estimation of Gaussian parameters in high-dimensional feature space. For structured roads, the extracted feature points are reused for lane detection. Moving vehicles that have the same colours as the lane markings are eliminated from the feature points before the authors perform angles of inclination and turning points searching to locate the lane boundaries. For unstructured roads, mean-shift segmentation is applied to divide the scene into regions. Possible candidate pairs for road boundaries are elected from the region boundaries, and Bayes rule is used to choose the most probable candidate pairs as the lane boundaries. The experimental results have shown that the classification mechanism can effectively choose the correct lane detection algorithm according to the current environment setting, and the system is able to robustly find the lane boundaries on different types of roads in various weather conditions.
Two additional Colletotrichum species causing leaf spot of rambutan (Nephelium lappaceum)

Abstract Rambutan (Nephelium lappaceum) is an important fruit tree. Recently, leaf spots were observed in a tree in Los Baños, Laguna, Philippines. This study aims to identify the causal agent of rambutan leaf spot in the Philippines. Two fungal isolates, MBRAM001B and MBRAM003B, were isolated from the diseased leaves on potato dextrose agar medium. Colony growth and conidia characteristics of both isolates showed high resemblance with Colletotrichum species in the C. gloeosporioides species complex. Molecular characterisation of the isolates using the partial internal transcribed spacer rDNA and TUB2 regions identified the pathogens as C. tropicale MBRAM001B and C. siamense MBRAM003B. Leaf spots developed on rambutan leaves three days after inoculation and the same pathogens were isolated from the diseased leaves, establishing Koch’s Postulate. In conclusion, this study identified C. tropicale MBRAM001B and C. siamense MBRAM003B as causal agents of leaf spots in rambutan species in the Philippines. The survival and possible infection of these two Colletotrichum species on rambutan fruits and other fruit trees are worth further investigating.
Solving the fundamental equation of Petri net using the decomposition into functional subnets

The technique of solution of the fundamental equation of Petri net based on decomposition into functional subnets is proposed. Solutions of the fundamental equation of the entire Petri net are calculated out of solutions of the fundamental equations of functional subnets for dual Petri net. Acceleration of computations obtained is exponential with the respect to dimension of Petri net.