Nonexistence of Vortices for Rotating Two-Component Focusing Bose Gases

This paper is concerned with ground states of two-component Bose gases confined in a harmonic trap $V(x)=x_1^2+\Lambda^2 x_2^2$ rotating at the velocity $\Omega >0$, where $\Lambda\ge 1$ and $(x_1, x_2)\in R^2$. We focus on the case where the intraspecies interaction $(-a_1,-a_2)$ and the interspecies interaction $-\beta$ are both attractive, i.e, $a_1, a_2$ and $\beta$ are all positive. It is shown that for any $0<\Omega <\Omega ^*:=2$, ground states exist if and only if $0<a_1,\, a_2<a^*:=\|w\|^2_2$ and $00$ is the unique positive solution of $-\Delta w+ w-w^3=0$ in $R^2$. By developing the argument of refined expansions, we further prove the nonexistence of vortices for ground states as $\beta\nearrow\beta^*$, where $0<\Omega <\Omega ^*$ and $0<a_1,\, a_2<a^*$ are fixed.Comment: 59 pages, 1 figure, and all comments are welcom

Angularity in Higgs boson decays via H→gg\boldsymbol{H\to gg} at NNLL′^{'} accuracy

We present improved predictions of a class of event-shape distributions called angularity for a contribution from an effective operator $H\to gg$ in Higgs hadronic decay that suffers from large perturbative uncertainties. In the frame of Soft-Collinear Effective Theory, logarithmic terms of the distribution are resummed at NNLL$^{'}$ accuracy, for which 2-loop constant of gluon-jet function for angularity is independently determined by a fit to fixed-order distribution at NLO corresponding to ${\mathcal{O}}(\alpha_s^2)$ relative to the born rate. Our determination shows reasonable agreement with value in a thesis recently released. In the fit, we use an asymptotic form with a fractional power conjectured from recoil corrections at one-loop order and it improves the accuracy of determination in positive values of angularity parameter $a$. The resummed distribution is matched to the NLO fixed-order results to make our predictions valid at all angularity values. We also discuss the first moment and subtracted moment of angularity as a function of $a$ that allow to extract information on leading and subleading nonperturbative corrections associated with gluons.Comment: 33 pages, 12 figure

Analysis of lncRNA Expression in Patients With Eosinophilic and Neutrophilic Asthma Focusing on LNC_000127

Long non-coding RNA (lncRNA) is important in many diseases. Some studies have shown that lncRNA affects the pathogenesis of systemic inflammation of asthma. lncRNA regulates gene transcription, protein expression, and epigenetic regulation. However, lncRNAs associated with different airway phenotypes, such as eosinophilic (Eos) and neutrophilic (Neu) asthma have not been identified. The goal of this study was to determine the differences in circulating lncRNA signatures in Eos and Neu samples. Using RNA-sequencing (RNA-seq), lncRNA expression was evaluated in peripheral whole blood samples among Eos patients, Neu patients, and healthy individuals (Control). Bioinformatic analysis was used to predict relevant biological pathways. Quantitative PCR (qPCR) was used to measure gene expression in whole blood samples, Jurkat cells, and human CD4+ T cells. Finally, a novel lncRNA, LNC_000127, was inhibited by transfection of Jurkat cells with a lentiviral vector, and the effect was examined by Human Asthma RT2 Profiler™ PCR Array and western blotting. Compared to control samples, Eos samples contained 190 unique lncRNAs and Neu samples had 166 unique lncRNAs (difference ≥2-fold). KEGG pathway annotation data and GO terms revealed that different lncRNAs are involved in different mechanisms. LNC_000127, was highly expressed in Eos samples before treatment; its expression was increased in Jurkat cells and human CD4+ T cells following stimulation with PMA/CD28. Subsequent analyses revealed that LNC_000127 functions in the Th2 inflammation pathway. The results suggest that lncRNAs are involved in different phenotypes of asthma. Whether the different phenotypes of asthma can be recognized based on these lncRNAs (as biomarkers) requires further analysis. Targeting LNC_000127 may be effective for reducing Th2 inflammation in Eos asthma

Study the correlation of FIB-4 and sST2 levels with heart failure

Objective To investigate the correlation between hepatic fibrosis index-4 （FIB-4） and soluble growth stimulating gene 2 protein （sST2） with the incidence and severity of heart failure. Methods 114 patients with heart failure were selected in the heart failure group and 38 healthy controls in the same period were assigned into the control group. The differences in general conditions and the expression levels of FIB-4 and sST2 were compared between two groups. According to the NYHA cardiac function classification， all patients with heart failure were divided into grade Ⅱ-Ⅳ. According to the left ventricular ejection fraction （LVEF）， they were divided into heart failure with decreased ejection fraction （HFrEF）， heart failure with preserved ejection fraction （HFpEF） and heart failure with median ejection fraction （HFmrEF）. The levels of FIB-4 and sST2 were compared among all groups， and the receiver operating characteristic （ROC） curves of FIB-4 and sST2 and their combination and heart failure specificity were drawn. Results According to the New York Heart Association （NYHA） classification， the levels of FIB-4 in patients with heart failure were higher than those in healthy controls， and the levels of FIB-4 in grade Ⅳ patients were higher compared with those in grade Ⅱ and Ⅲ counterparts （all P &lt; 0.01）， whereas no significant difference was observed between gradeⅡand grade Ⅲ individuals （P &gt; 0.05）. Significant differences were found in the levels of sST2 among grade Ⅱ， grade Ⅲ and grade Ⅳ groups （all P &lt; 0 05）. There were statistical differences in the overall distribution of FIB-4 and sST2 among patients with heart failure of different types （P &lt; 0.05）. The ROC curve showed that the area under the ROC curve （AUC） of FIB-4 in the diagnosis of heart failure was 0.784， the AUC of heart failure diagnosed by ST2 was 0.910， and the AUC of these two combined in the diagnosis of heart failure was 0.922， and the specificity was higher than that of either single diagnosis （both P &lt; 0.001）. Conclusions FIB-4 is related to the grading of cardiac function， and the level of sST2 is positively correlated with NYHA and LVEF classification. The combination of FIB-4 and sST2 yields higher specificity in the diagnosis of heart failure and can be utilized to evaluate the severity of heart failure， which is of great significance for the diagnosis， treatment and prognosis of heart failure

Neutral operator with variable parameter and third-order neutral differential equation

Additional file 1. The TRS sequences of structural genes in the HP-PRRSV/SD16 genome (GenBank: JX087437)

Effects of Kevlar® 29 yarn twist on tensile and tribological properties of self-lubricating fabric liner

Yarn twist in textile technology is an important characteristic since it considerably affects the properties of knitted or woven fabrics. Many researchers have investigated the effect of staple-spun yarn twist on the properties of the yarns and fabrics. However, the effects of twist level of Kevlar® 29 filament yarn on the properties of yarn and its resin-impregnated self-lubricating fabric liner are not fully known yet. In this study, we have investigated the effects of Kevlar® 29 twist level on the tensile and tribological properties of the fabric liner (Kevlar® 29/polytetrafluoroethylene fabric-resin composite). Two unexpected findings about the effect of yarn twist have been observed, namely (1) asynchronous twist effect on the yarn’s and the liner’s tensile strength and (2) dissimilar yarn twist effect on the liner’s performance. These findings are mainly attributed to the synergic contributions of the yarn twist and strength and the interaction of the resin with the yarn orientation in the woven fabric structure of the liner

NICE 2023 Zero-shot Image Captioning Challenge

In this report, we introduce NICE project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and outcomes of NICE challenge 2023. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes