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Sudden deafness refers to a sudden, unexplained sensorineural hearing loss that occurs within 72 hours. It can cause considerable distress to the sufferer's daily life. At present, the treatment of sudden deafness is mainly glucocorticoid, including systemic glucocorticoid and intravympinal injection. However, the clinical evidence for both is not strong and there are some side effects. Acupuncture and moxibustion is a kind of complementary and alternative therapy with a long history in China, which has certain clinical effect on sudden deafness. This study will comprehensively search, summarize, analyze and comprehensively evaluate the existing evidence of clinical research on acupuncture treatment of sudden deafness, and present the research overview.

Temporal grounding aims to retrieve moments of the described event within an untrimmed video by a language query. Typically, existing methods assume annotations are precise and unique, yet one query may describe multiple moments in many cases. Hence, simply taking it as a one-vs-one mapping task and striving to match single-label annotations will inevitably introduce false negatives during optimization. In this study, we reformulate this task as a one-vs-many optimization problem under the condition of single positive labels. The unlabeled moments are considered unobserved rather than negative, and we explore mining potential positive moments to assist in multiple moment retrieval. In this setting, we propose a novel Diverse Temporal Grounding framework, termed DTG-SPL, which mainly consists of a positive moment estimation (PME) module and a diverse moment regression (DMR) module. PME leverages semantic reconstruction information and an expected positive regularization to uncover potential positive moments in an online fashion. Under the supervision of these pseudo positives, DMR is able to localize diverse moments in parallel that meet different users. The entire framework allows for end-to-end optimization as well as fast inference. Extensive experiments on Charades-STA and ActivityNet Captions show that our method achieves superior performance in terms of both single-label and multi-label metrics., Comment: The source codes are available at https://github.com/zhouhaocv/DTG-SPL

Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often available in real-world applications, the valuable knowledge of known anomalies should also be effectively exploited. However, utilizing a few known anomalies during training may cause another issue that the model may be biased by those known anomalies and fail to generalize to unseen anomalies. In this paper, we tackle supervised anomaly detection, i.e., we learn AD models using a few available anomalies with the objective to detect both the seen and unseen anomalies. We propose a novel explicit boundary guided semi-push-pull contrastive learning mechanism, which can enhance model's discriminability while mitigating the bias issue. Our approach is based on two core designs: First, we find an explicit and compact separating boundary as the guidance for further feature learning. As the boundary only relies on the normal feature distribution, the bias problem caused by a few known anomalies can be alleviated. Second, a boundary guided semi-push-pull loss is developed to only pull the normal features together while pushing the abnormal features apart from the separating boundary beyond a certain margin region. In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively. Code will be available at https://github.com/xcyao00/BGAD., Accepted to CVPR 2023

Additional file 1. Figure S1. Analysis of the ubiquitinated site-containing proteins in rice from published studies. Figure S2. Location of all E3 ligase encoding genes on the rice chromosome. Figure S3. Different types of ubiquitin E3 ligase-encoding genes in rice and the number of RT-PCR cloned and chemically synthesized E3 genes in this study. Figure S4. Confirmation of the interaction between OsUBC14 and its candidate E3s. Figure S5. E3 ubiquitin ligase activity of OsRING77, OsRING113, OsPUB28, OsPUB46, OsPUB49 and OsPUB69 in vitro. Figure S6. Ubiquitination assay of GST-OsSKIPa by MBP-OsPUB46. Figure S7. E3 ubiquitin ligase activity of OsRFPH2-10, P3IP1 and OsRING336 in vitro. Figure S8. The E3 ubiquitin ligase activity of OsRING116 in vitro and ubiquitination analysis of rTGA2.1. Figure S9. Transcript level of OPAL1 in individual overexpression lines and the editing types of OsFBK16. Figure S10. Degradation assay of OsPAL5 and OsPAL6 by OsFBK16 in vivo. Figure S11. Transcript level of OsPAL6 in individual overexpression lines.

Additional file 7. Review history.

The measurement criterion for mesopic vision

Accurate pedestrian classification and localization have received considerable attention due to their wide applications such as security monitoring, autonomous driving, etc. Although pedestrian detectors have made great progress in recent years, the fixed Intersection over Union (IoU) based assignment-regression manner still limits their performance. Two main factors are responsible for this: 1) the IoU threshold faces a dilemma that a lower one will result in more false positives, while a higher one will filter out the matched positives; 2) the IoU-based GT-Proposal assignment suffers from the inconsistent supervision problem that spatially adjacent proposals with similar features are assigned to different ground-truth boxes, which means some very similar proposals may be forced to regress towards different targets, and thus confuses the bounding-box regression when predicting the location results. In this paper, we first put forward the question that \textbf{Regression Direction} would affect the performance for pedestrian detection. Consequently, we address the weakness of IoU by introducing one geometric sensitive search algorithm as a new assignment and regression metric. Different from the previous IoU-based \textbf{one-to-one} assignment manner of one proposal to one ground-truth box, the proposed method attempts to seek a reasonable matching between the sets of proposals and ground-truth boxes. Specifically, we boost the MR-FPPI under R$_{75}$ by 8.8\% on Citypersons dataset. Furthermore, by incorporating this method as a metric into the state-of-the-art pedestrian detectors, we show a consistent improvement., Comment: This manuscript is waiting for further improvement

Introduction: Erectile dysfunction refers to the continued inability of the penis to reach and maintain sufficient erections to achieve a satisfactory sex life and last at least 6 months. As part of traditional Chinese medicine, acupuncture has been widely used in clinical practice. In order to evaluate, the exact effect of acupuncture on the clinical efficacy of patients with Post-stroke Erectile dysfunction (PSED), this experiment uses randomized controlled experiments. Methods/design: This pragmatic randomized controlled trial will recruit 103 patients who are diagnosed with PSED. Simple randomization to conventional treatment with a 1:1 allocation ratio will be used. Ten 30-min acupuncture sessions will be provided to patients assigned to the Intervention group. All participants will continue to receive conventional treatment. The selection of outcomes will be evaluated by International Erectile Function Index-5 (IIEF-5) score at week 8. Discussion: This trial may provide evidence regarding the clinical effectiveness, safety, and cost-effectiveness of acupuncture for patients with PSED. Trial registration: ClinicalTrials.gov, ChiCTR2000030231, Registered on February 25, 2020.

Current guidelines favor dual anti-platelet therapy with ticagrelor 90 mg BID (T90BID) over clopidogrel 75 mg QD (C75QD) in addition to aspirin for acute coronary syndrome. However, an increased risk of ticagrelor-related adverse events prompted the evaluation of low-dose regimens. This study (NCT03381742) retrospectively analyzed the data from 11 hospitals on 3,043 patients with coronary artery disease, who received C75QD, T90BID, ticagrelor 45 mg BID (T45BID), or ticagrelor 90 mg QD (T90QD). Compared with C75QD, both T45BID and T90QD showed significantly higher inhibition of platelet aggregation (P P 12 receptor reactivity. There were no significant differences between T45BID and T90QD in the trough plasma concentrations of ticagrelor and its active metabolite. Similar efficacy and safety outcomes were observed in the propensity score-matched analysis. In conclusion, the low-dose ticagrelor regimen, either T45BID or T90QD, may provide a more attractive benefit-risk profile than C75QD or T90BID.

Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and scale variation. In this paper, we propose W$^3$Net, which attempts to address above challenges by decomposing the pedestrian detection task into \textbf{\textit{W}}here, \textbf{\textit{W}}hat and \textbf{\textit{W}}hether problem directing against pedestrian localization, scale prediction and classification correspondingly. Specifically, for a pedestrian instance, we formulate its feature by three steps. i) We generate a bird view map, which is naturally free from occlusion issues, and scan all points on it to look for suitable locations for each pedestrian instance. ii) Instead of utilizing pre-fixed anchors, we model the interdependency between depth and scale aiming at generating depth-guided scales at different locations for better matching instances of different sizes. iii) We learn a latent vector shared by both visual and corpus space, by which false positives with similar vertical structure but lacking human partial features would be filtered out. We achieve state-of-the-art results on widely used datasets (Citypersons and Caltech). In particular. when evaluating on heavy occlusion subset, our results reduce MR$^{-2}$ from 49.3$\%$ to 18.7$\%$ on Citypersons, and from 45.18$\%$ to 28.33$\%$ on Caltech., Comment: This work is being revised. The updated version will be upload few months later

We extend the state-of-the-art Cascade R-CNN with a simple feature sharing mechanism. Our approach focuses on the performance increases on high IoU but decreases on low IoU thresholds--a key problem this detector suffers from. Feature sharing is extremely helpful, our results show that given this mechanism embedded into all stages, we can easily narrow the gap between the last stage and preceding stages on low IoU thresholds without resorting to the commonly used testing ensemble but the network itself. We also observe obvious improvements on all IoU thresholds benefited from feature sharing, and the resulting cascade structure can easily match or exceed its counterparts, only with negligible extra parameters introduced. To push the envelope, we demonstrate 43.2 AP on COCO object detection without any bells and whistles including testing ensemble, surpassing previous Cascade R-CNN by a large margin. Our framework is easy to implement and we hope it can serve as a general and strong baseline for future research., Comment: BMVC 2019 Camera Ready

In recent years, spectral clustering has become one of the most popular clustering algorithms for image segmentation. However, it has restricted applicability to large-scale images due to its high computational complexity. In this paper, we first propose a novel algorithm called Fast Spectral Clustering based on quad-tree decomposition. The algorithm focuses on the spectral clustering at superpixel level and its computational complexity is O(nlogn) + O(m) + O(m^(3/2)); its memory cost is O(m), where n and m are the numbers of pixels and the superpixels of a image. Then we propose Multiscale Fast Spectral Clustering by improving Fast Spectral Clustering, which is based on the hierarchical structure of the quad-tree. The computational complexity of Multiscale Fast Spectral Clustering is O(nlogn) and its memory cost is O(m). Extensive experiments on real large-scale images demonstrate that Multiscale Fast Spectral Clustering outperforms Normalized cut in terms of lower computational complexity and memory cost, with comparable clustering accuracy.

Organic light-emitting diodes (OLEDs) based on solution-processable small molecules are attracting intense attention, as such technology combines the merits of low-cost solution processability of polymers and finely defined structural uniformity of small molecules. Small-molecule tetraphenylethene (TPE) derivatives are excellent solid-state light emitters featuring aggregation-induced emission (AIE) characteristics, however those that can be used in solution-processable devices are very rare. To address this issue, herein, a series of novel star-shaped bipolar TPE derivatives are synthesized and characterized. Their thermal stabilities, photophysical properties, electronic structures, electrochemical behaviors, and application in solution-processed OLEDs are investigated systematically. These luminogens exhibit AIE characteristics and excellent fluorescence quantum yields up to 95% in the solid state. Nondoped OLEDs are successfully fabricated through a spin-coating method. The solution-processed OLEDs [ITO (130 nm)/PEDOT:PSS (40 nm)/emitter (70 nm)/TPBi (30 nm)/Ba (4 nm)/Al (120 nm)] adopting star- shaped TPE derivatives as light-emitting layers show peak luminance of 11665 cd m-2 and high electroluminescence (EL) efficiencies up to 8.3 cd A-1, 2.6 % and 7.5 lm W-1. These results demonstrate a promising avenue towards efficient nondoped OLEDs based on solution-processable AIE-active small molecules.

In Chinese, thousands of bankchecks are validated due to manual processing every day to find out the bankchecks, which are invalidate to the bank's rules or contain error input information. It is necessary to relieve people from these intensive and tedious works. This paper presents a technique, which is the preprocess stage in our auto-validating system, for extracting the user-entered courtesy amount item from Chinese bankcheck images. Seal imprints in different types and positions cause the main difficulty in the extracting procedure. Instead of removing lines in bi-level images, we proposed a line removal algorithm based on crossing shape analysis in gray-level image. The handwritten information is extracted by a new recursive thresholding algorithm, which can continue removing the brighter background until only the darkest object is left. The proposed methods have been evaluated on real-life Chinese checks. And the results demonstrate the effectiveness of the proposed preprocessing techniques.

A dual-polarization frequency-modulated continuous-wave (FMCW) reflectometer is established on J-TEXT for measuring density profile. The frequency of reflectometer covers both Q band and V band. In order to measure wider density range, ordinary mode polarization and extraordinary mode polarization are utilized at the same time. For the FMCW reflectometer, temporal resolution depends on sweeping rate of the microwave source. Benefited from HTO (hyperabrupt varactortuned oscillator) source, a full frequency sweep period of the reflectometer could be less than 40 μs. Electron density profile from 0-6.0×1019 m-3 can be detected, which covers the whole low field side in J-TEXT. To reconstruct the full density profile, the position of the zero density should be confirmed first, which is determined from where the intermediate frequencies change transiently. Meanwhile, we observe the propagation of left-hand extraordinary wave from data in X-mode reflectometer.