bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel fluoroscopic landmark to significantly facilitate the visualization of foramen ovale in treating idiopathic trigeminal neuralgia
Authors: He Liangliang , Zhao WenXing , Su Po-Yi Paul , Guo Guili , Yue Jianning , Ni Jiaxiang , Yang Liqiang , Guan Zhonghui
Published: December 2020

Background and objectives

Access through the foramen ovale (FO) is essential in performing trigeminal ganglion injection, glycerol rhizolysis, balloon compression, and radiofrequency thermocoagulation (RFT) to treat idiopathic trigeminal neuralgia (ITN). However, identification of the FO under fluoroscopy can be difficult and time-consuming, and thus exposes patients to increased radiation and procedure risks. Here we present the ‘H-figure’ as a novel fluoroscopic landmark to quickly visualize the FO.

Methods

The H-figure landmark can be recognized as the medial border of the mandible and the lateral edge of the maxilla as the two vertical lines, and thesuperior line ofpetrous ridge oftemporal bone (S-P-T line) as the horizontal line, and the FO fluoroscopic view is then optimized at the center of the H-figure immediately above the S-P-T line. We applied this landmark in a clinical cohort of 136 patients with ITN who underwent fluoroscopy-guided RFT of the trigeminal ganglion. We also compared the H-figure method with the traditional method. The primary outcome was the total number of fluoroscopic images required to visualize the FO (as a proxy of radiation exposure). Secondary measures included the procedure time required to finalize the FO view and the sensory testing voltage for paresthesia.

Results

With the H-figure approach we were able to view the FO with an average of 4.2 fluoroscopic shots at an average time of 6.8 min. When compared with the non-H-figure traditional technique, the H-figure method required almost half the fluoroscopic shots in nearly half the procedure duration time, and paresthesia was evoked with half of the voltage.

Conclusion

The H-figure is an easy fluoroscopic landmark that can help to view the FO with less radiation and procedure time, and the needles placed with this approach can be closer to the target for the RFT treatment of patients with ITN.
Performance of human papillomavirus DNA detection in residual specimens taken forChlamydia trachomatisandNeisseria gonorrhoeaenucleic acid amplification testing in men who have sex with men
Authors: Nugent Diarmuid , Stirrup Oliver , Pett Sarah , Panwar Kavita , Checchi Marta , Mesher David , Soldan Kate , Beddows Simon , Gilson Richard
Published: December 2020

Objectives

Rectal swab specimens, either alone or pooled with first-void urine (FVU) and pharyngeal swab specimens, are used to test forChlamydia trachomatis(CT) andNeisseria gonorrhoeae(NG) infection in men who have sex with men (MSM). Following introduction of human papillomavirus (HPV) vaccination for MSM attending UK sexual health services (SHSs), HPV testing of residual CT/NG test specimens has been proposed to monitor HPV prevalence in this population. Performance of HPV detection in such specimens has not been evaluated previously.

Methods

MSM attending a UK SHS provided three specimens: (1) rectal swab for CT/NG, (2) pooled rectal/pharyngeal/FVU specimen for CT/NG and (3) dedicated anal swab for HPV. Specimen 3 and residual material from specimens 1 and 2 were tested for type-specific HPV DNA. HPV detection was by an in-house multiplex PCR and luminex-based genotyping assay.

Results

A total of 129 MSM were recruited with a mean age of 38.1 years; 24% were HIV-positive. Of the 129 MSM, 92 (71%) had any type-specific HPV DNA in ≥1 specimen; 80 (62%) had high risk (HR) HPV. Of 123 participants with sufficient residual pooled and dedicated specimens, 70 (56.9%) had detectable HPV on both, and 40 (32.5%) were negative on both; overall concordance was 89% (95% CI 83% to 94%), and kappa statistic was 0.78 (95% CI 0.66 to 0.89). Pooled samples had a 4.1% (95% CI −1.9% to 10.0%) higher test positivity rate than dedicated samples.Of 125 participants with sufficient residual rectal and specimens, 74 (59.2%) had detectable HPV on both, and 36 (28.8%) were negative on both; overall concordance was 88% (95% CI 81% to 93%), and kappa statistic was 0.74 (95% CI 0.61 to 0.86). Residual rectal samples had 5.6% (95%CI −0.6% to 11.8%) higher test positivity than dedicated samples.

Conclusions

We observed high concordance between the dedicated and residual STI test specimens. Our data support the strategy of testing residual specimens for HPV prevalence monitoring in MSM to evaluate the impact of the targeted vaccination programme.
Smoking behaviours and indoor air quality: a comparative analysis of smoking-permitted versus smoke-free homes in Dhaka, Bangladesh
Authors: Ferdous Tarana , Siddiqi Kamran , Semple Sean , Fairhurst Caroline , Dobson Ruaraidh , Mdege Noreen , Marshall Anna-Marie , Abdullah S M , Huque Rumana
Published: December 2020

Introduction

Exposure to secondhand smoke (SHS) is a health risk to non-smokers. Indoor particulate matter (PM2.5) is associated with SHS exposure and is used as a proxy measure. However, PM2.5 is non-specific and influenced by a number of environmental factors, which are subject to geographical variation. The nature of association between SHS exposure and indoor PM2.5—studied primarily in high-income countries (HICs) context—may not be globally applicable. We set out to explore this association in a low/middle-income country setting, Dhaka, Bangladesh.

Methods

A cross-sectional study was conducted among households with at least one resident smoker. We inquired whether smoking was permitted inside the home (smoking-permitted homes, SPH) or not (smoke-free homes, SFH), and measured indoor PM2.5 concentrations using a low-cost instrument (Dylos DC1700) for at least 22 hours. We describe and compare SPH and SFH and use multiple linear regression to evaluate which variables are associated with PM2.5 level among all households.

Results

We surveyed 1746 households between April and August 2018; 967 (55%) were SPH and 779 (45%) were SFH. The difference between PM2.5 values for SFH (median 27 µg/m3, IQR 25) and SPH (median 32 µg/m3, IQR 31) was 5 µg/m3 (p<0.001). Lead participant’s education level, being a non-smoker, having outdoor space and smoke-free rule at home and not using kerosene oil for cooking were significantly associated with lower PM2.5.

Conclusions

We found a small but significant difference between PM2.5 concentrations in SPH compared with SFH in Dhaka, Bangladesh—a value much lower than observed in HICs.
Fatal bacterial septicaemia after immobilisation of a captive brown bear (Ursus arctos)
Authors: Schlohsarczyk Elfi Katrin , Schmidt Nadine , Prenger‐Berninghoff Ellen , Herkommer Leonie Franziska , Henrich Manfred
Published: December 2020

A four‐year‐old female brown bear (Ursus arctos) was immobilised via blowpipe targeting the right hind limb to perform a chemical contraception. Few days after anaesthesia, the animal developed lameness and a reduced general condition, and died 1 week after immobilisation when alopecia and swelling of the injection area were observed for the first time. Besides a severe azotaemia, alopecia and reddening of the craniomedial skin, subcutis and skeletal muscles of the entire right hind limb showed a phlegmon, oedema and haemorrhage. Additional widespread haemorrhages were found. Histology revealed a suppurative and necrohaemorrhagic myositis of the affected limb, a suppurative iliofemoral lymphadenitis, blood resorption in the mesenteric lymph nodes and haemorrhage in the adrenal gland cortex. Microbiological investigation revealed high amounts of Escherichia coli, Streptococcus dysgalactiae and Clostridium septicum in skin, musculature, heart, lung, liver and spleen, indicating septicaemia, which led to death due to toxaemic shock.
Distinct intestinal microbial communities of two sympatric anadromous Arctic salmonids and the effects of migration and feeding
Authors: Element Geraint , Engel Katja , Neufeld Josh D. , Casselman John M. , Van Coeverden de Groot Peter J. , Walker Virginia K.
Published: December 2020

Although intestinal microbial communities from anadromous Arctic char (Salvelinus alpinus (Linnaeus, 1758)) in Kitikmeot, Nunavut, Canada, differ depending on the timing and location of capture, determinants of gut microbiota in other wild Arctic salmonids are largely unknown. Using high-throughput 16S rRNA gene sequence data, we compared intestinal microbiota from Arctic char to those from a related and sympatric salmonid, lake whitefish (Coregonus clupeaformis (Mitchill, 1818)). Shifts in lake whitefish gut microbial community composition were observed between brackish water and freshwater, similar to impacts of salinity reported previously for Arctic char. Despite these similarities, gut community profiles for the two salmonids differed, with whitefish having higher diversities and increased proportions of taxa affiliated with potential pathogens. Geography seemed to have a greater impact on freshwater whitefish gut microbiota than on corresponding Arctic char. Additionally, microbiota diversity was significantly more affected by feeding behavior in whitefish compared with sympatric Arctic char. As sampled whitefish were at their northern range limits and grew slowly, we speculate that they, and their microbial consortia, could be more vulnerable to certain abiotic and biotic factors than Arctic char, which are well adapted to conditions found in these high latitude environments and have the most northern distribution of any freshwater fish.
Stand density biases the estimation of the site index especially on dry sites
Authors: Toraño Caicoya Astor , Pretzsch Hans
Published: December 2020

The site index (SI) has been widely used in forest management and silviculture. It relies on the assumption that the height of dominant trees in a stand is independent from the local density. However, research on climate change suggests that under certain moisture stress conditions, this may not hold. Here, based on 29 plots from five long-term research experiments, we tested the effect of local stand density on the SI of Norway spruce (Picea abies (L.) H. Karst). With generalized additive models (GAMM), we analyzed the effect of stand structure and climate predictors on SI. The two evaluated models revealed that local stand density and age had a significant effect on SI (p ≤ 0.001), showing a clear negative trend especially significant on sites with poor and dry soils, which may reduce the SI by a maximum of approximately 4 m for an increase in density of between 400 and 600 trees/ha. We stress that the physiological characteristics of Norway spruce, flat-rooting system and xeromorphism, especially when growing in pure stands, may explain these effects. Thus, density control and growth in mixtures may help to reduce the water stress and losses in height growth under future climate conditions.
Securing Multimedia by Using DNA-Based Encryption in the Cloud Computing Environment
Authors: Namasudra Suyel , Chakraborty Rupak , Majumder Abhishek , Moparthi Nageswara Rao
Published: December 2020

Today, the size of a multimedia file is increasing day by day from gigabytes to terabytes or even petabytes, mainly because of the evolution of a large amount of real-time data. As most of the multimedia files are transmitted through the internet, hackers and attackers try to access the users’ personal and confidential data without any authorization. Thus, maintaining a strong security technique has become a significant concerned to protect the personal information. Deoxyribonucleic Acid (DNA) computing is an advanced field for improving security, which is based on the biological concept of DNA. A novel DNA-based encryption scheme is proposed in this article for protecting multimedia files in the cloud computing environment. Here, a 1024-bit secret key is generated based on DNA computing and the user's attributes and password to encrypt any multimedia file. To generate the secret key, the decimal encoding rule, American Standard Code for Information Interchange value, DNA reference key, and complementary rule are used, which enable the system to protect the multimedia file against many security attacks. Experimental results, as well as theoretical analyses, show the efficiency of the proposed scheme over some well-known existing schemes.
Correlation Discrepancy Insight Network for Video Re-identification
Authors: Ruan Weijian , Liang Chao , Yu Yi , Wang Zheng , Liu Wu , Chen Jun , Ma Jiayi
Published: December 2020

Video-based person re-identification (ReID) aims at re-identifying a specified person sequence from videos that were captured by disjoint cameras. Most existing works on this task ignore the quality discrepancy across frames by using all video frames to develop a ReID method. Additionally, they adopt only the person self-characteristic as the representation, which cannot adapt to cross-camera variation effectively. To that end, we propose a novel correlation discrepancy insight network for video-based person ReID, which consists of an unsupervised correlation insight model (CIM) for video purification and a discrepancy description network (DDN) for person representation. Concretely, CIM is constructed by using kernelized correlation filters to encode person half-parts, which evaluates the frame quality by the cross correlation across frames for selecting discriminative video fragments. Furthermore, DDN exploits the selected video fragments to generate a discrepancy descriptor using a compression network, which aims at employing the discrepancies with other persons’ to facilitate the representation of the target person rather than only using the self-characteristic. Due to the advantage in handling cross-domain variation, the discrepancy descriptor is expected to provide a new pattern for the object representation in cross-camera tasks. Experimental results on three public benchmarks demonstrate that the proposed method outperforms several state-of-the-art methods.
Adaptive Attention-based High-level Semantic Introduction for Image Caption
Authors: Liu Xiaoxiao , Xu Qingyang
Published: December 2020

There have been several attempts to integrate a spatial visual attention mechanism into an image caption model and introduce semantic concepts as the guidance of image caption generation. High-level semantic information consists of the abstractedness and generality indication of an image, which is beneficial to improve the model performance. However, the high-level information is always static representation without considering the salient elements. Therefore, a semantic attention mechanism is used for the high-level information instead of conventional of static representation in this article. The salient high-level semantic information can be considered as redundant semantic information for image caption generation. Additionally, the generation of visual words and non-visual words can be separated, and an adaptive attention mechanism is employed to realize the guidance information of image caption generation switching between new fusion information (fusion of image feature and high-level semantics) and a language model. Therefore, visual words can be generated according to the image features and high-level semantic information, and non-visual words can be predicted by the language model. The semantics attention, adaptive attention, and previous generated words are fused to construct a special attention module for the input and output of long short-term memory. An image caption can be generated as a concise sentence on the basis of accurately grasping the rich content of the image. The experimental results show that the performance of the proposed model is promising for the evaluation metrics, and the captions can achieve logical and rich descriptions.
MMFN
Authors: Nie Weizhi , Liang Qi , Wang Yixin , Wei Xing , Su Yuting
Published: December 2020

In recent years, research into 3D shape recognition in the field of multimedia and computer vision has attracted wide attention. With the rapid development of deep learning, various deep models have achieved state-of-the-art performance based on different representations. There are many modalities for representing a 3D model, such as point cloud, multiview, and panorama view. Deep learning models based on these different modalities have different concerns, and all of them have achieved high performance for 3D shape recognition. However, all of these methods ignore the multimodality information in conditions where the same 3D model is represented by different modalities. Thus, we can obtain a better descriptor by guiding the training to consider these multiple representations. In this article, we propose MMFN, a novel multimodal fusion network for 3D shape recognition that employs correlations between the different modalities to generate a fused descriptor, which is more robust. In particular, we design two novel loss functions to help the model learn the correlation information during training. The first is correlation loss, which focuses on the correlations among different descriptors generated from different structures. This approach reduces the training time and improves the robustness of the fused descriptor of the 3D model. The second is instance loss, which preserves the independence of each modality and utilizes feature differentiation to guide model learning during the training process. More specifically, we use the weighted fusion method, which applies statistical methods to obtain robust descriptors that maximize the advantages of the information from the different modalities. We evaluated the proposed method on the ModelNet40 and ShapeNetCore55 datasets for 3D shape classification and retrieval tasks. The experimental results and comparisons with state-of-the-art methods demonstrate the superiority of our approach.
Learning a Deep Agent to Predict Head Movement in 360-Degree Images
Authors: Zhu Yucheng , Zhai Guangtao , Min Xiongkuo , Zhou Jiantao
Published: December 2020

Virtual reality adequately stimulates senses to trick users into accepting the virtual environment. To create a sense of immersion, high-resolution images are required to satisfy human visual system, and low latency is essential for smooth operations, which put great demands on data processing and transmission. Actually, when exploring in the virtual environment, viewers only perceive the content in the current field of view. Therefore, if we can predict the head movements that are important behaviors of viewers, more processing resources can be allocated to the active field of view. In this article, we propose a model to predict the trajectory of head movement. Deep reinforcement learning is employed to mimic the decision making. In our framework, to characterize each state, features for viewport images are extracted by convolutional neural networks. In addition, the spherical coordinate maps and visited maps are generated for each viewport image, which facilitate the multiple dimensions of the state information by considering the impact of historical head movement and position information. To ensure the accurate simulation of visual behaviors during the watching of panoramas, we stipulate that the model imitates the behaviors of human demonstrators. To allow the model to generalize to more conditions, the intrinsic motivation is employed to guide the agent’s action toward reducing uncertainty, which can enhance robustness during the exploration. The experimental results demonstrate the effectiveness of the proposed stepwise head movement predictor.
Part-based Structured Representation Learning for Person Re-identification
Authors: Li Yaoyu , Yao Hantao , Zhang Tianzhu , Xu Changsheng
Published: December 2020

Person re-identification aims to match person of interest under non-overlapping camera views. Therefore, how to generate a robust and discriminative representation is crucial for person re-identification. Mining local clues from human body parts to describe pedestrians has been extensively studied in existing methods. However, existing methods locate human body parts coarsely and do not consider the relations among different local parts. To address the above problem, we propose a Part-based Structured Representation Learning (PSRL) for better exploiting local clues to improve the person representation. There are two important modules in our architecture: Local Semantic Feature Extraction and Structured Person Representation Learning. The Local Semantic Feature Extraction module is designed to extract local features from human body semantic regions. After obtaining the local features, the Structured Person Representation Learning is proposed to fuse the local features by considering the person structure. To model the underlying person structure, a graph convolutional network is employed to capture the relations of different semantic regions. The generated structured feature encodes underlying person structure information, and local semantic feature can solve the misalignment problem caused by pose variations in feature matching. By combining them together, we can improve the descriptive ability of the generated representation. Extensive evaluations on four standard benchmarks show that our proposed method achieves competitive performance against state-of-the-art methods.
Fog-based Secure Service Discovery for Internet of Multimedia Things
Authors: Liang Haoran , Wu Jun , Zheng Xi , Zhang Mengshi , Li Jianhua , Jolfaei Alireza
Published: December 2020

The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and tampering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the tampering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.
The identity in everyone's pocket
Authors: Vachon Phil
Published: December 2020

Keeping users secure through their smartphones.
Weighting Passages Enhances Accuracy
Authors: Muntean Cristina Ioana , Nardini Franco Maria , Perego Raffaele , Tonellotto Nicola , Frieder Ophir
Published: December 2020

We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR.
Past, present, and past as present in India's predictive policing
Authors: Narayan Shivangi
Published: December 2020

In India, law enforcement's use of big data to thwart crime has instead amplified the discriminatory presence of caste, religion, gender, and other social markers within a system that is supposedly objective and neutral.
Talking about race in CS education
Authors: Guzdial Mark
Published: December 2020

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Mark Guzdial suggests computer science education needs to change, to better serve the needs of students and society.
Preparation of Scallion-Derived Porous Carbon with Regular Pore Structure for High-Performance Supercapacitors
Authors: Song Chuanlin , Kuihua Han , Gao Yang , Teng Zhaocai , Wang Meimei , Li Jinxiao
Published: December 2020

On the premise of ensuring high specific surface area, further improving the pore structure of biomass-based activated carbon is highly desired for supercapacitors applications. And the properties of many biomass-based activated carbons need to be improved by further optimizing the preparation process. Herein, the scallions were used as raw materials to prepare activated carbon electrode materials via activation with KOH or synchronous activation and graphitization with K2FeO4 for supercapacitors in this study. Based on the large specific surface area (3298.76 m2 g−1) and outstanding hierarchical porous structure, the final product presented a high capacitance of 381.51 F g−1 at a current density of 1 A g−1 and a prominent energy density of 54.3 W h kg−1 at the power density of 179.33 W kg−1. Besides, it also exhibited a low equivalent series resistance (0.27 Ω). When K2FeO4 was used as the activator, the samples exhibited a better rate performance (77.8% at a current density of 10 A g−1) and a long cycle stability (98.9% retention after 5000 cycles at 5 A g−1). Hence, the scallion-based activated carbon is a perspectives material for clean energy storage/conversion and this synchronous strategy provide a guideline for a much-simplified and green manufacture process.
G protein-coupled receptor-G protein interactions: a single-molecule perspective
Authors: Calebiro Davide , Koszegi Zsombor , Lanoiselée Yann , Miljus Tamara , O’Brien Shannon
Published: December 2020

G protein-coupled receptors (GPCRs) regulate many cellular and physiological processes, responding to a diverse range of extracellular stimuli including hormones, neurotransmitters, odorants, and light. Decades of biochemical and pharmacological studies have provided fundamental insights into the mechanisms of GPCR signaling. Thanks to recent advances in structural biology, we now possess an atomistic understanding of receptor activation and G protein coupling. However, how GPCRs and G proteins interact in living cells to confer signaling efficiency and specificity remains insufficiently understood. The development of advanced optical methods, including single-molecule microscopy, has provided the means to study receptors and G proteins in living cells with unprecedented spatio-temporal resolution. The results of these studies reveal an unexpected level of complexity, whereby GPCRs undergo transient interactions among themselves as well as with G proteins and structural elements of the plasma membrane to form short-lived signaling nanodomains that likely confer both rapidity and specificity to GPCR signaling. These findings may provide new strategies to pharmaceutically modulate GPCR function, which might eventually pave the way to innovative drugs for common diseases such as diabetes or heart failure.
A Two-Step Newton-Type Method for Solving System of Absolute Value Equations
Authors: Shi Lei , Iqbal Javed , Arif Muhammad , Khan Alamgir
Published: December 2020

In this paper, we suggest a Newton-type method for solving the system of absolute value equations. This new method is a two-step method with the generalized Newton method as predictor. Convergence of the proposed method is proved under some suitable conditions. At the end, we take several numerical examples to show that the new method is very effective.
Teriflunomide Safety and Efficacy in Advanced Progressive Multiple Sclerosis
Authors: Moreira Ferreira Vanessa F. , Caefer Danielle , Erlich-Malona Natalie , Healy Brian C. , Chitnis Tanuja , Stankiewicz James M.
Published: December 2020

Objectives. To explore the safety and efficacy profile of teriflunomide in progressive multiple sclerosis. Methods. We conducted a single-center retrospective observational analysis of a progressive multiple sclerosis population, assessing safety and efficacy in patients treated at least one year with teriflunomide or glatiramer acetate. Sustained progression of expanded disability status scale and sustained worsening of timed 25-foot walk were compared using a Cox proportional hazards model. Results. Teriflunomide group (n=29) mean characteristics:age=58years (SD±7.6),disease duration=16.7years (SD±9.5),expanded disability status score=5.9(SD±1.3), andfollow−up=32.4months (SD±13.6). Glatiramer acetate group (n=30) mean characteristics:age=52.4years (SD±11.3),disease duration=15.1years (SD±10.4),expanded disability status score=5.7(SD±1.6), andfollow−up=46.9months (SD±43.9). Both treatments were well tolerated without serious side effects. After adjustment for age, sex, and baseline expanded disability status score, sustained expanded disability status score progression did not differ between groups (hazard ratio=1.17; 95% confidence interval: 0.45, 3.08;p=0.75). Sustained timed 25-foot walk worsening after adjustment also did not differ (hazard ratio=0.56; 95% confidence interval: 0.2, 1.53;p=0.26). Conclusion. In an advanced progressive multiple sclerosis population, no substantial differences in tolerability, safety, sustained EDSS progression, or sustained T25FW worsening over time were observed between glatiramer acetate and teriflunomide-treated groups. The small sample precluded definitive determination.
Efficacy of Manipulative Acupuncture Therapy Monitored by LSCI Technology in Patients with Severe Bell’s Palsy: A Randomized Controlled Trial
Authors: Zhong Weizheng , Yu Haibo , Rao Xiaodan , Wu Jianhuang , Gou Yanhua , Cui Han , Huang Xingxian , Wang Ling
Published: December 2020

To determine if the effect of manipulative acupuncture monitored by laser speckle contrast imaging (LSCI) can improve facial blood perfusion in patients with severe Bell’s palsy. This randomized, single‐blind, controlled trial included 120 newly diagnosed patients (within 14 days) with severe Bell’s palsy (House–Brackmann grading system (HBGS) ≥ grade IV). The patients were randomized (1 : 1) to receive either acupoints acupuncture combined with manipulations of twirling, lifting, and thrusting treatments (manipulative acupuncture) or acupoints acupuncture therapy alone (simple acupuncture). These treatments consisted of a total of 24 sessions, three times per week, and each treatment lasted for 30 min. Following 8 weeks of treatment and 6 months after the initial onset of facial palsy, facial nerve functioning was scored (HBGS) and clinical efficacy was measured. The patients’ facial blood perfusion significantly improved following manipulative acupuncture assisted by LSCI compared with that at baseline (P < 0.01). At the conclusion of the 8‐week treatment, both groups showed improvement; however, the recovery rate was significantly different (manipulative acupuncture 53.3% vs. simple acupuncture 33.9%, P < 0.05). Follow‐up analysis at 6 months after the onset of facial palsy revealed a significantly higher recovery rate (91.7% vs. 78.0%; P < 0.05). In addition, the number of treatments in the observation group was less than that in the simple acupuncture therapy group (P < 0.05). Compared with simple acupuncture therapy, manipulative acupuncture therapy led to a more significant recovery rate in the treatment of severe Bell’s palsy and required a shorter course of treatment. This trial was registered with ChiCTR1800019463.
A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport
Authors: Qian Yingmiao , Chen Shuhang , Li Jianchang , Ren Qinxin , Zhu Jinfu , Yuan Ruijia , Su Hao
Published: December 2020

Due to the increasing number of people traveling by air, the passenger flow at the airport is increasing, and the problem of passenger drop-off and pickup has a huge impact on urban traffic. The difficulty of taking a taxi at the airport is still a hot issue in the society. Aiming at the problem of optimizing the allocation of taxi resource, this paper is based on the cost-benefit analysis method to determine the factors that affect the taxi driver’s decision-making. The mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. A conclusion has been drawn that the allocation of airport taxi resource should be arranged closely related to drivers’ revenue and the layout of airport line.
Study of Lophomonas blattarum Infection in Kidney Transplant Patients in Mashhad City, Iran
Authors: Gheisari Zahra , Berenji Fariba , Nazemian Fatemeh , Shamsian Seyed Ali Akbar , Jarahi Lida , Parian Mahmoud , Hosseini Farash Bibi Razieh , Salehi Sangani Ghodratollah
Published: December 2020

Background. Lophomonas blattarum is a flagellate protozoan which is known as an emerging parasite in the human respiratory system. Organ transplant recipients are considered as immunocompromised patients due to prescription of immunosuppressive drugs. This group of patients is susceptible to opportunistic infection as well as lophomoniasis. This study aims to investigate the prevalence and clinical manifestation of pulmonary infections caused by L. blattarum in kidney transplant recipients. Methods. This is a case-control study including 50 kidney transplant recipients and 50 controls. The sputum samples were collected from 50 kidney transplant recipients with bronchopulmonary infection signs suspected to lophomoniasis admitted in Montaserieh and Imam Reza hospitals, Mashhad, Iran. 50 healthy individuals as the control group were matched for sex and age with case ones. The consent form, checklist, and required information were provided for each patient. All samples were microscopically examined for the flagellated protozoan, L. blattarum, using direct smear. Results. Among 50 kidney transplant recipients suspected to lophomoniasis, L. blattarum was identified in sputum samples of 4 (8%) participants of the case group including one female and three males. None of the samples were positive among the control group. Symptoms in patients of this study were high fever (4 out of 4 patients), cough (3 out of 4 patients), and dyspnea (2 out of 4 patients). Three patients showed a positive response to metronidazole treatment. Conclusion. The results of this study suggest that L. blattarum should be considered as a pathogenic agent in kidney transplant recipients. It is necessary to examine sputum samples in posttransplant pneumonia patients, especially in those resistant to antibacterial therapy.
Research on Lung Nodule Detection Based on Improved Target Detection Network
Authors: Li Ye , Wu Qian , Sun Hongwei , Wang Xuewei
Published: December 2020

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.
An Intelligent Group Decision Evaluation Model with Interval-Valued Intuitionistic Fuzzy Entropy Technology for Microblog User Influence
Authors: Zeng Lihua , Yang Tonghua , Ren Haiping , Xiong Neal
Published: December 2020

Aiming at the shortcomings of some existing interval-valued intuitionistic fuzzy entropy, this paper proposes an interval-valued intuitionistic fuzzy entropy, which contains not only the interval distance between membership and nonmembership but also the average hesitancy information. On this basis, the impact evaluation of microblog users is studied. Through the multilevel decomposition of user influence, the coverage of microblog, user interaction, and user activity are constructed as the first level indicators, and nine secondary indicators are selected as the comprehensive evaluation index system of microblog influence. Considering the highly dynamic and unstructured characteristics of social network data, the idea of interval-valued intuitionistic fuzzy is introduced to transform the evaluation of social network users’ influence into interval-valued intuitionistic fuzzy multiattribute group decision-making problem. Finally, the model is applied to dynamic evaluation of Sina Weibo users’ influence to verify the effectiveness of the model.
A Smart Privacy-Preserving Learning Method by Fake Gradients to Protect Users Items in Recommender Systems
Authors: Luo Guixun , Zhang Zhiyuan , Zhang Zhenjiang , Liu Yun , Wang Lifu
Published: December 2020

In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, we show that our algorithm can be reduced to a time-delay SGD, which can be proved to have a good convergence so that the accuracy will not decline. Our algorithm achieves a good tradeoff between the privacy and accuracy.
Allicin Attenuated Advanced Oxidation Protein Product-Induced Oxidative Stress and Mitochondrial Apoptosis in Human Nucleus Pulposus Cells
Authors: Xiang Qian , Cheng Zhangrong , Wang Juntan , Feng Xiaobo , Hua Wenbin , Luo Rongjin , Wang Bingjin , Liao Zhiwei , Ma Liang , Li Gaocai , Lu Saideng , Wang Kun , Song Yu , Li Shuai , Wu Xinghuo , Yang Cao , Zhang Yukun
Published: December 2020

Intervertebral disc degeneration (IDD) is one of the most common chronic degenerative musculoskeletal disorders. Oxidative stress-induced apoptosis of the nucleus pulposus (NP) cells plays a key role during IDD progression. Advanced oxidation protein products (AOPP), novel biomarkers of oxidative stress, have been reported to function in various diseases due to their potential for disrupting the redox balance. The current study is aimed at investigating the function of AOPP in the oxidative stress-induced apoptosis of human NP cells and the alleviative effects of allicin during this process which was known for its antioxidant properties. AOPP were demonstrated to hamper the viability and proliferation of NP cells in a time- and concentration-dependent manner and cause cell apoptosis markedly. High levels of reactive oxygen species (ROS) and lipid peroxidation product malondialdehyde (MDA) were detected in NP cells after AOPP stimulation, which resulted in depolarized mitochondrial transmembrane potential (MTP). Correspondingly, higher levels of AOPP were discovered in the human degenerative intervertebral discs (IVD). It was also found that allicin could protect NP cells against AOPP-mediated oxidative stress and mitochondrial dysfunction via suppressing the p38-MAPK pathway. These results disclosed a significant role of AOPP in the oxidative stress-induced apoptosis of NP cells, which could be involved in the primary pathogenesis of IDD. It was also revealed that allicin could be a promising therapeutic approach against AOPP-mediated oxidative stress during IDD progression.
Control of Rock Block Fragmentation Based on the Optimization of Shaft Blasting Parameters
Authors: Li Qingxiang , Luo Zhanyou , Huang Man , Pan Jiangbo , Wang Guoshu , Cheng Yunxin
Published: December 2020

In the construction of shaft, the blockage of the mucking shaft may cause the mud-water inrush disaster. Oversized rock fragmentation is the main cause for the blockage of the mucking shaft in the raise boring machine (RBM) construction method. The influence degree of blasting parameters on rock fragmentation after blasting is quantified by adopting analytic hierarchy process (AHP). On this basis, the shaft blasting maximum rock fragmentation control model based on double hidden layer BP neural network is proposed. Results show that the maximum rock fragmentation discharged from the mucking shaft after blasting should not exceed 1/3 of the diameter of the slag chute. The influence weight of the minimum resistance line that accounts to 15.6%, when AHP is applied for the quantification of the blasting parameters, can be regarded as the most important blasting parameter. The average absolute errors between the predicted value and the actual value of the largest block size control model of the shaft blasting are only 2.6%. The inversion analysis of the model can rapidly obtain the required blasting parameters, which can be used to guide the construction of the tunnel ventilation shaft.
Crowd Counting and Abnormal Behavior Detection via Multiscale GAN Network Combined with Deep Optical Flow
Authors: Song Beibei , Sheng Rui
Published: December 2020

Aiming at the problem of low performance of crowd abnormal behavior detection caused by complex backgrounds and occlusions, this paper proposes a single-image crowd counting and abnormal behavior detection via multiscale GAN network. The proposed method firstly designed an embedded GAN module with a multibranch generator and a regional discriminator to initially generate crowd-density maps; and then our proposed multiscale GAN module is added to further strengthen the generalization ability of the model, which can effectively improve the accuracy and robustness of the prediction detection and counting. On the basis of single-image crowd counting, synthetic optical-flow feature descriptor is adopted to obtain the crowd motion trajectory, and the classification of abnormal behavior is finally implemented. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness of crowd counting and abnormal behavior detection in real complex scenarios compared with the existing mainstream algorithms, which is suitable for engineering applications.
Simulation of Particle-Fluid Interaction in Fractal Fractures Based on the Immersed Boundary-Lattice Boltzmann Method
Authors: Liu Shenggui , Tang Songlei , Huang Jinkuang , Lv Mindong , Li Yingjun
Published: December 2020

In order to reveal the influence of particles on fluid flow characteristics in rough fractures under fluid-solid coupling, a range of fracture systems with varying roughness were generated using the Weierstrass-Mandelbrot function. Fluid-particle interactions in rough fractal fractures were simulated using the immersed boundary-lattice Boltzmann method. In this paper, the effects of fluid viscosity, particle size, particle quantity, fracture fractal dimension, and particle grading composition are studied. Results illustrate that increasing fluid viscosity hinders the movement of particles, resulting in the decreasing of particle velocity. As particle size and particle quantity increase, the particle occupation of the channel area grows larger, which lead to lower permeability of the channel. Increasing fracture fractal dimension surges the curvature of the fluid channel, but permeability has a negative exponential correlation to fractal dimension. With increasing particle grading composition, the blocking effect of particles on fracture flow also increases with increasing particle proportion.
Market Power, NAIRU, and the Phillips Curve
Authors: Zweig Derek
Published: December 2020

We explore the relationship between unemployment and inflation in the United States (1949-2019) through both Bayesian and spectral lenses. We employ Bayesian vector autoregression (“BVAR”) to expose empirical interrelationships between unemployment, inflation, and interest rates. Generally, we do find short-run behavior consistent with the Phillips curve, though it tends to break down over the longer term. Emphasis is also placed on Phelps’ and Friedman’s NAIRU theory using both a simplistic functional form and BVAR. We find weak evidence supporting the NAIRU theory from the simplistic model, but stronger evidence using BVAR. A wavelet analysis reveals that the short-run NAIRU theory and Phillips curve relationships may be time-dependent, while the long-run relationships are essentially vertical, suggesting instead that each relationship is primarily observed over the medium-term (2-10 years), though the economically significant medium-term region has narrowed in recent decades to roughly 4-7 years. We pay homage to Phillips’ original work, using his functional form to compare potential differences in labor bargaining power attributable to labor scarcity, partitioned by skill level (as defined by educational attainment). We find evidence that the wage Phillips curve is more stable for individuals with higher skill and that higher skilled labor may enjoy a lower natural rate of unemployment.
BurstBiRank: Co-Ranking Developers and Projects in GitHub with Complex Network Structures and Bursty Interactions
Authors: Yan Dengcheng , Shao Zhen , Zhang Yiwen , Qi Bin
Published: December 2020

With the wide adoption of social collaborative coding, more and more developers participate and collaborate on platforms such as GitHub through rich social and technical relationships, forming a large-scale complex technical system. Like the functionalities of critical nodes in other complex systems, influential developers and projects usually play an important role in driving this technical system to more optimized states with higher efficiency for software development, which makes it a meaningful research direction on identifying influential developers and projects in social collaborative coding platforms. However, traditional ranking methods seldom take into account the continuous interactions and the driving forces of human dynamics. In this paper, we combine the bursty interactions and the bipartite network structure between developers and projects and propose the BurstBiRank model. Firstly, the burstiness between each pair of developers and projects is calculated. Secondly, a weighted developer-project bipartite network is constructed using the burstiness as weight. Finally, an iterative score diffusion process is applied to this bipartite network and a final ranking score is obtained at the stationary state. The real-world case study on GitHub demonstrates the effectiveness of our proposed BurstBiRank and the outperformance of traditional ranking methods.