bluuebunny/crossref_metadata_2025 huggingface dataset:
Auxiliary Stopping Area Layout Method for High-Speed Maglev Operated Bidirectionally on Single Track
Authors: Yu Yi , Chu Pengzi , Dong Danyang , Jiang Xi , Zhao Huahua , Lin Hui
Published: April 2021

Auxiliary stopping area (ASA) is the necessary emergency facility for train safety of the normal high-speed maglev. The study addresses the ASA layout problem of the high-speed maglev operated bidirectionally on single track. First, an optimization model of the ASA layout for unidirectional double-track lines considering train safety, operation efficiency, and construction cost is established, and two basic methods of the ASA layout are investigated based on the distance demand characteristic of ASAs. Then, the ASA layout problem of bidirectional single-track lines is analyzed, and an ASA two-way coordination layout algorithm (ASA-TWCLA) is proposed. Finally, a numerical experiment is carried out. The results suggest that under the premise of train safety and operation efficiency, compared with using the basic methods separately on the two directions, adopting the ASA-TWCLA algorithm can obtain a more economical ASA layout scheme for the same scenario.
PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution
Authors: Liu Huanyu , Liu Jiaqi , Li Junbao , Pan Jeng-Shyang , Yu Xiaqiong
Published: April 2021

Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.
Hierarchical Self-Attention Hybrid Sparse Networks for Document Classification
Authors: Huang Weichun , Tao Ziqiang , Huang Xiaohui , Xiong Liyan , Yu Jia
Published: April 2021

Document classification is a fundamental problem in natural language processing. Deep learning has demonstrated great success in this task. However, most existing models do not involve the sentence structure as a text semantic feature in the architecture and pay less attention to the contexting importance of words and sentences. In this paper, we present a new model based on a sparse recurrent neural network and self-attention mechanism for document classification. Subsequently, we analyze three variant models of GRU and LSTM for evaluating the sparse model in different datasets. Extensive experiments demonstrate that our model obtains competitive performance and outperforms previous models.
Chemical Analysis of the Ingredients of 20% Aqueous Ethanol Extract of Nardostachys jatamansi through Phytochemical Study and Evaluation of Anti-Neuroinflammatory Component
Authors: Kim Kwan-Woo , Yoon Chi-Su , Park Sung-Joo , Bae Gi-Sang , Kim Dong-Gu , Kim Youn-Chul , Oh Hyuncheol
Published: April 2021

Nardostachys spp. have been widely used in Asia as a folk medicine. In particular, the extracts of Nardostachys jatamansi, a species that grows in China, India, and Tibet, have been used to treat mental disorders, hyperlipidemia, hypertension, and convulsions. In this investigation, the potential of 20% aqueous ethanol extract of N. jatamansi (NJ20) as a botanical drug was explored by chemically investigating its constituents and its anti-neuroinflammatory effects on lipopolysaccharide- (LPS-) induced in vitro and in vivo models. Nine secondary metabolites were isolated and identified from NJ20, and quantitative analysis of these metabolites revealed desoxo-narchinol A as the major constituent. In LPS-challenged cells, pretreatment with NJ20 inhibited the LPS-induced excessive production of proinflammatory mediators, such as nitric oxide, prostaglandin E2, interleukin- (IL-) 1β, IL-6, and tumor necrosis factor-α. NJ20 also attenuated the overexpression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2. Additionally, pre-intraperitoneal injection of NJ20 downregulated the mRNA overexpression of IL-1β, IL-6, and iNOS in the prefrontal cortex, hypothalamus, and hippocampus of the LPS-stimulated C57BL/c mouse model. Chemical and biological investigations of NJ20 revealed that it is a potential inhibitor of LPS-induced neuroinflammatory responses in microglial cells and mouse models. The major active constituent of NJ20, desoxo-narchinol A, demonstrated anti-neuroinflammatory effects. Hence, our findings indicate that NJ20 may be a promising herbal mixture for developing a functional product and/or herbal drug for treating neuroinflammatory diseases.
The Impact of the COVID-19 Movement Restrictions on the Road Traffic in the Czech Republic during the State of Emergency
Authors: Simunek Milan , Smutny Zdenek , Dolezel Michal
Published: April 2021

The COVID-19 pandemic crisis has impacted numerous areas of people’s work and free-time activities. This article aims to present the main impacts of the COVID-19 movement restrictions on the road traffic in the Czech Republic, measured during the first epidemic wave, i.e., from 12 March to 17 May 2020. The state of emergency was imposed by the Czech government as a de jure measure for coping with the perceived crisis, although the measure eventually resulted only in a quite liberal de facto form of stay-at-home instruction. Unique country-scale traffic data of the first six months of 2020 from 37,002 km of roads, constituting 66% of all roads in the Czech Republic, were collected and analyzed. For the prediction of the prepandemic traffic conditions and their comparison with the measured values in the period of the state of emergency, a long-term traffic speed prediction ensemble model consisting of case-based reasoning, linear regression, and fallback submodels was used. The authors found out that the COVID-19 movement restrictions had a significant impact on the country-wide traffic. Traffic density was reduced considerably in the first three weeks, and the weekly average traffic speed in all road types increased by up to 21%, expectedly due to less crowded roads. The exception was motorways, where a different trend in traffic was found. In sum, during the first three weeks of the state of emergency, people followed government regulations and restrictions and changed their travel behavior accordingly. However, following this period, the traffic gradually returned to the prepandemic state. This occurred three weeks before the state of emergency was terminated. From a behavioral perspective, this article briefly discusses the possible causes of such discrepancies between de jure and de facto pandemic measures, i.e., the governmental communication strategy related to loosening of movement restrictions, media reality, and certain culture-related traits.
Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals
Authors: Jin Xinlei , Ye Dengpan , Chen Chuanxi
Published: April 2021

The deepfake technology is conveniently abused with the low technology threshold, which may bring the huge social security risks. As GAN-based synthesis technology is becoming stronger, various methods are difficult to classify the fake content effectively. However, although the fake content generated by GANs can deceive the human eyes, it ignores the biological signals hidden in the face video. In this paper, we proposed a novel video forensics method with multidimensional biological signals, which extracting the difference of the biological signal between real and fake videos from three dimensions. The experimental results show that our method achieves 98% accuracy on the main public dataset. Compared with other technologies, the proposed method only extracts fake video information and is not limited to a specific generation method, so it is not affected by synthetic methods and has good adaptability.
Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing
Authors: Huang Fangwan , Leng Xiuyu , Kasukurthi Mohan Vamsi , Huang Yulong , Li Dongqi , Tan Shaobo , Lu Guiying , Lu Juhong , Benton Ryan G. , Borchert Glen M. , Huang Jingshan
Published: April 2021

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians’ decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients’ cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected “breath by breath” by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.
Rotavirus in Calves and Its Zoonotic Importance
Authors: Geletu Umer Seid , Usmael Munera Ahmednur , Bari Fufa Dawo
Published: April 2021

Rotavirus is a major pathogen responsible for diarrheal disease in calves, resulting in loss of productivity and economy of farmers. However, various facets of diarrheal disease caused by rotavirus in calves in the world are inadequately understood, considering that diarrheal disease caused by rotavirus is a vital health problem in calves that interrupts production benefits with reduced weight gain and increased mortality, and its potential for zoonotic spread. The pathological changes made by rotavirus are almost exclusively limited to the small intestine that leads to diarrhea. It is environmentally distributed worldwide and was extensively studied. Reassortment is one of the important mechanisms for generating genetic diversity of rotaviruses and eventually for viral evolution. So, the primary strategy is to reduce the burden of rotavirus infections by practicing early colostrum’s feeding in newborn calves, using vaccine, and improving livestock management. Rotaviruses have a wide host range, infecting many animal species as well as humans. As it was found that certain animal rotavirus strains had antigenic similarities to some human strains, this may be an indication for an animal to play a role as a source of rotavirus infection in humans. Groups A to C have been shown to infect both humans and animals. The most commonly detected strains in both human and animals are G2, G3, G4, and G9, P [6]. Therefore, this review was made to get overview epidemiology status and zoonotic importance of bovine rotavirus.
3D Skeletal Human Action Recognition Using a CNN Fusion Model
Authors: Li Meng , Sun Qiumei
Published: April 2021

Smart homes have become central in the sustainability of buildings. Recognizing human activity in smart homes is the key tool to achieve home automation. Recently, two-stream Convolutional Neural Networks (CNNs) have shown promising performance for video-based human action recognition. However, such models cannot act directly on the 3D skeletal sequences due to its limitation to the 2D image video inputs. Considering the powerful effect of 3D skeletal data for describing human activity, in this study, we present a novel method to recognize the skeletal human activity in sustainable smart homes using a CNN fusion model. Our proposed method can represent the spatiotemporal information of each 3D skeletal sequence into three images and three image sequences through gray value encoding, referred to as skeletal trajectory shape images (STSIs) and skeletal pose image (SPI) sequences, and build a CNNs’ fusion model with three STSIs and three SPI sequences as input for skeletal activity recognition. Such three STSIs and three SPI sequences are, respectively, generated in three orthogonal planes as complementary to each other. The proposed CNN fusion model allows the hierarchical learning of spatiotemporal features, offering better action recognition performance. Experimental results on three public datasets show that our method outperforms the state-of-the-art methods.
Augmented Total Elbow Arthroplasty with Femoral Strut Allograft for Revision of Prosthetic Joint Infection with Distal Humerus Bone Loss and Incomplete Union of Periprosthetic Humeral Shaft Fracture
Authors: Monahan William R. , Alaimo Elizabeth , Mahylis Jared M.
Published: April 2021

Total elbow arthroplasty (TEA) prosthetic joint infection (PJI) in the setting of distal humerus bone loss poses a challenge for restoration of function. This can be complicated by a periprosthetic humeral fracture. Revision surgery in the setting of these pathologies possesses a significant challenge, especially when two or, in this case, all three problems are treated simultaneously. We present the clinical course, operative findings, and definitive treatment with the use of an augmented total elbow arthroplasty and femoral strut allograft reinforcement in detail. A review of the literature regarding the identification and management of infected TEA with augmented prosthesis and bone allograft augmentation of humerus fractures will be outlined in this case report.
The Impact of Green R&D Activities on SO2 Emissions: Evidence from China
Authors: Chen Shuxing , Ma Jiangshui , Ding Denglong , Yu Shengqi , Tang Yuee
Published: April 2021

Prior research on the effectiveness of improving environmental performance has acknowledged the importance of domestic research and development (R&D) activities. However, these studies remain general and ambiguous because they assume that all R&D operations are associated with environmental performance. The corresponding empirical evidence is inexact and ambiguous. In this study, we focus on the effect of green R&D activities on SO2 emission. Considering that heterogeneity exists in green R&D activities, we divide them into two groups for two purposes. The empirical results, explored based on China’s interprovincial data between 2000 and 2016, suggest that green R&D activities critically influence the reduction of SO2 emissions. However, the R&D activities of different purposes show statistically differentiated effects on SO2 emissions. Primarily, utility-type R&D activities show the most significant positive impact on SO2 emissions reduction. Subsequent research based on the panel threshold also indicates that the impact of green R&D activities on SO2 emissions shows nonlinear characteristics, depending on the technology’s absorptive ability.
Safety Effects of Horizontal Curve Design and Lane and Shoulder Width on Single Motorcycle Accidents in Norway
Authors: Kvasnes Sara , Pokorny Petr , Jensen Jan Kristian , Pitera Kelly
Published: April 2021

Factors related to the road infrastructure contribute to the occurrence of motorcycle accidents. This study investigates how design parameters of the existing rural two-lane road network in Norway influence the occurrence of single motorcycle accidents. The design elements considered in this study are horizontal curvature (curve type, degree of curvature, and adjacent curve requirements) and lane and shoulder widths. A matched case-control study design was applied to investigate the safety effects of these elements. Cases were defined as segments experiencing at least one single motorcycle accident during the study period from 2013 to 2017, while controls were defined as segments not experiencing an accident in the same period. In order to identify the segments, a GIS analysis was performed on data collected from the National Road Database (NVDB). In case-control studies, matching allows us to control for confounding variables. AADT and speed limit were used as matching variables in this study. A matching ratio of 4 : 1 (i.e., four controls per case) was used, resulting in 752 controls being matched to 188 cases. The results indicate horizontal alignment to have a more significant effect on single motorcycle accidents compared to lane and shoulder widths. Segments with several adjacent reverse curves, with high curvature (R < 200 m), have high odds for an accident. Further, if the requirements for adjacent curves are not fulfilled (i.e., considerable variation in adjacent curve radii), the odds increase even more. While the results are not statistically significant, trends seen indicate that wider lanes were associated with increased odds for an accident, while wider shoulders were associated with decreased odds. In comparison with a similar study considering passenger vehicles, the results of this study also indicate that horizontal alignment has a greater effect on single motorcycle accidents than on passenger vehicle accidents.
Eco-Friendly Adsorbent from Waste of Mint: Application for the Removal of Hexavalent Chromium
Authors: Khammour Fatima , Abdoul-Latif Fatouma Mohamed , Ainane Ayoub , Mohamed Jalludin , Ainane Tarik
Published: April 2021

A serious environmental disaster is looming on the horizon due to the indiscriminate release of heavy metals into the soil and wastewater from human industrial practices. In this study, waste mint (WM) was used to remove chromium(VI) from aqueous solution using batch experiments. The adsorbent material (WM) was characterized using scanning electron microscopy coupled with energy dispersive analysis of X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermal gravimetric analysis (TGA). The adsorption parameters optimized were as follows: pH solution (2–11), initial concentration of Cr(VI) (10–50 mg/L), adsorbent dose (0.1–10 g/L), and temperature conditions (298 K, 308 K, and 318 K). The experimental data fitted well to the fractional power kinetic model (0.97≤R2≤ 0.99) and Langmuir isotherm (R2 = 0.984) with a maximum adsorption capacity Qmax = 172.41 mg/g. The thermodynamic parameters for Cr(VI) sorption were also calculated, confirming that the adsorption process was spontaneous and accompanied by an exothermic adsorption (−4.83 ≤ ΔG ≤ −3.22 kJ/mol and ΔH = −28.93 kJ/mol). The Cr(VI) removal percentage was within the range of 41–98%, and the highest removal was noted at pH = 2. The results of the present study suggest that WM is a potential low-cost adsorbent for the removal of chromium(VI) from aqueous solutions.
An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks
Authors: Li Yanbin , Li Yue , Xu Huanliang , Ren Shougang
Published: April 2021

The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.
Multicriteria Decision Making to Continuous Software Improvement Based on Quality Management, Assurance, and Metrics
Authors: Sun Lei , Nazir Shah , Hussain Anwar
Published: April 2021

Software process improvement methodologies support to incessantly regulate and advance the process of software for its performance development. This development arises in terms of software product quality, time, changes reduction, and so on. The aim of software process improvement is to make software process effective and to increase quality of software product through continuous evaluation. Various approaches and frameworks are in practice for software process improvement. Early decision making concerning continuous software improvement can ultimately lead to successful software developments and growing software industry and business of software. Keeping in view the early decision making regarding continuous software improvement, the proposed study has considered a decision support system (DSS) for making decision based on multicriteria against quality management, assurance, and metrics. The necessary factors and alternatives for continuous software improvement were considered. The process of the proposed study was experimentally conducted, and results were good enough for supporting the study. The study will help practitioners in early decision making regarding software improvement based on quality management.
[Retracted] Using Clustering Analysis and Association Rule Technology in Cross‐Marketing
Authors: Cheng Yang , Cheng Ming , Pang Tao , Liu Sizhen
Published: April 2021

In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross‐marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.
National Survey of Patient Factors Associated with Colorectal Cancer Screening Preferences
Authors: Zhu Xuan , Parks Philip D. , Weiser Emily , Fischer Kristin , Griffin Joan M. , Limburg Paul J. , Finney Rutten Lila J.
Published: April 2021

Recommended colorectal cancer screening modalities vary with respect to safety, efficacy, and cost. Better understanding of the factors that influence patient preference is, therefore, critical for improving population adherence to colorectal cancer screening. To address this knowledge gap, we conducted a panel survey focused on three commonly utilized colorectal cancer screening options [fecal immunochemical test or guaiac-based fecal occult blood test (FIT/gFOBT), multi-target stool DNA (mt-sDNA) test, and colonoscopy] with a national sample of U.S. adults, ages 40–75 years and at average risk of colorectal cancer, in November 2019. Of 5,097 panelists invited to participate, 1,595 completed the survey (completion rate, 31.3%). Our results showed that when presented a choice between two colorectal cancer screening modalities, more respondents preferred mt-sDNA (65.4%) over colonoscopy, FIT/gFOBT (61%) over colonoscopy, and mt-sDNA (66.9%) over FIT/gFOBT. Certain demographic characteristics and awareness of and/or experience with various screening modalities influenced preferences. For example, uninsured people were more likely to prefer stool-based tests over colonoscopy [OR, 2.53; 95% confidence interval (CI), 1.22–5.65 and OR, 2.73; 95% CI, 1.13–7.47]. People who had heard of stool-based screening were more likely to prefer mt-sDNA over FIT/gFOBT (OR, 2.07; 95% CI, 1.26–3.40). People who previously had a stool-based test were more likely to prefer FIT/gFOBT over colonoscopy (OR, 2.75; 95% CI, 1.74–4.41), while people who previously had a colonoscopy were less likely to prefer mt-sDNA or FIT/gFOBT over colonoscopy (OR, 0.39; 95% CI, 0.24–0.63 and OR, 0.40; 95% CI, 0.26–0.62). Our survey demonstrated broad patient preference for stool-based tests over colonoscopy, contrasting the heavy reliance on colonoscopy for colorectal cancer screening in clinical practice and highlighting the importance of considering patient preference in colorectal cancer screening recommendations.

Prevention Relevance:

Our national survey demonstrated broad patient preference for stool-based tests over colonoscopy, contrasting the heavy reliance on colonoscopy for colorectal cancer screening in clinical practice and highlighting the importance of considering patient preference in colorectal screening recommendations.
Overexpression of Capns1 Predicts Poor Prognosis and Correlates with Tumor Progression in Renal Cell Carcinoma
Authors: Zhuang Qianfeng , Fan Min , Shen Jie , Chen Zhen , Xue Dong , Lu Hao , Xu Renfang , He Xiaozhou , Hou Jianquan
Published: April 2021

Introduction: Calpain small subunit 1 (Capns1) has shown its correlation with the metastasis and invasion of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. However, the expression and function of Capns1 in human renal cell carcinoma (RCC) have not been clarified. This study aimed to examine the expression of Capns1 in RCC tissues and cell lines and to assess its role performed in RCC. Methods: Capns1 expression was evaluated in 75 pairs of RCC and matched adjacent non-tumor tissues by immunohistochemistry. The prognostic value of Capns1 in RCC was assessed by Kaplan-Meier and Cox regression analyses. The action of Capns1 in the proliferation, adhesion, migration, and invasion of RCC cells and the effects on matrix metalloproteinase (MMP) 2 and 9 expression were evaluated after Capns1 silence. Results: Capns1 expression was significantly higher in RCC tissues compared with the adjacent non-tumor tissues. Multivariate analysis showed that Capns1 overexpression was an independent poor prognostic marker in RCC. The silencing of Capns1 prohibited cell adhesion and impaired the migration and invasion ability of 786-O cells in vitro. Furthermore, Capns1 silence reduced MMP2 and MMP9 expression. Conclusion: Capns1 overexpression predicts poor prognosis and correlates with tumor progression in RCC. Capns1 expression might serve a prognostic marker and therapeutic target for RCC.
Factors Influencing Discussion of Cancer Genetic Testing with Health-Care Providers in a Population-Based Survey
Authors: Makhnoon Sukh , Yu Robert , Cunningham Sonia A. , Peterson Susan K. , Shete Sanjay
Published: April 2021

Introduction: Discussion of cancer genetic testing with health-care providers (HCPs) is necessary to undergo testing to inform cancer risk assessment and prevention. Given the rapid evolution in genetic testing practice in oncology, we describe the current landscape of population-level cancer genetic testing behaviors. Methods: A questionnaire including items regarding discussion of cancer genetic testing with HCPs was administered to a nonprobability sample (N = 2,029) of the Texas population. Results: Overall, 11% of respondents discussed cancer genetic testing with HCPs. In multivariable analysis, discussion was significantly related to having a personal history of breast/ovarian/colon cancer (OR = 11.57, 95% CI = 5.34–25.03), personal history of other cancer (OR = 3.18, 95% CI = 1.69–5.97), and health information-seeking behaviors (OR = 1.73, 95% CI = 1.12–2.66). Surprisingly, respondents who believed that inherited predispositions in addition to other modifiable risk factors cause cancer were less likely to discuss genetic testing compared to those who did not believe that inherited cancer predispositions cause cancer (OR = 0.54, 95% CI = 0.36–0.79). Discussion: The high discussion rate may be attributed to increased public awareness of genetic testing and adoption of more inclusive clinical genetic testing guidelines. The findings suggest that efforts to increase public awareness of the utility of genetic testing on personalized cancer risk assessment and cancer prevention are needed.
Kisspeptin Levels in Girls with Precocious Puberty: A Systematic Review and Meta-Analysis
Authors: Cintra Rafael Guerra , Wajnsztejn Rubens , Trevisan Camila Martins , Zaia Victor , Laganà Antonio Simone , Bianco Bianca , Montagna Erik
Published: April 2021

Background/Aims: Kisspeptin (KP) is a key player in the regulation of the release of gonadotropin-releasing hormone (GnRH), which increases the secretion of gonadotropin during puberty to establish reproductive function and regulate the hypothalamic-pituitary-gonadal axis. Premature activation of GnRH secretion leads to idiopathic/central gonadotropin-dependent precocious puberty (CPP). We aimed to compare the blood KP concentrations in girls with CPP and healthy controls. Methods: A systematic review and meta-analysis was performed. We searched MEDLINE, EMBASE, The Cochrane Library, and SciELO. Random-effects model and standardized mean difference (SMD) were used. Heterogeneity was assessed through I2. Meta-regression considered patient age, KP fraction, and analytical method for KP measurement. Results: The 11 studies included comprised 316 CPP patients and 251 controls. Higher KP levels in the CPP group were found (SMD 1.53; CI 95% = 0.56–2.51). Subgroup analysis revealed association with patient age (p = 0.048), indicating a positive correlation between elevation in KP concentration and age in CPP group. A group of patients with precocious thelarche (PT) from 5 of the included studies comprising 121 patients showed higher levels of KP (1.10; −0.25–2.45: CI 95%) and high heterogeneity (I2 = 91%). The CPP/PT ratio for KP level indicates KP 36% higher on CPP than PT patients. Conclusions: A consistent difference in KP levels between girls with CPP and controls was identified. While there are important limitations in KP assays which argue against its use as a diagnostic tool, the KP levels in CPP versus control and PT children are consistent with the predicted mechanisms and pathophysiology of CPP.
Association between Dipeptidyl Peptidase-4 Inhibitor Prescription and Erythropoiesis-Stimulating Agent Hyporesponsiveness in Hemodialysis Patients with Diabetes Mellitus
Authors: Hasegawa Takeshi , Zhao Junhui , Bieber Brian , Zee Jarcy , Pisoni Ronald L. , Robinson Bruce M. , Hanafusa Norio , Nangaku Masaomi
Published: April 2021

Introduction: Dipeptidyl peptidase-4 (DPP-4) has been hypothesized to improve responsiveness to erythropoiesis-stimulating agent (ESA). We aimed to describe the trend in DPP-4 inhibitor prescription patterns and assess the association between DPP-4 inhibitor prescription and ESA hyporesponsiveness (eHypo) in Japanese hemodialysis (HD) patients with diabetes mellitus (DM). Methods: We analyzed data from the Japan Dialysis Outcomes and Practice Patterns Study phase 4–6 (2009–2017) on patients with DM who underwent HD thrice per week for at least 4 months. The primary exposure of interest was having a DPP-4 inhibitor prescription. The primary analysis outcomes were a binary indicator of eHypo (mean hemoglobin <10 and mean ESA dose >6,000 units/week over 4 months) and the natural log-transformed ESA resistance index (ERI). We used conditional logistic regression to compare within-patient changes in eHypo before and after initial DPP-4 inhibitor prescription. We used linear generalized estimating equation models to compare continuous ERI outcomes while accounting for within-patient repeated measurements with an exchangeable correlation structure. Results: There was a monotonic increase in DPP-4 inhibitor prescription according to study year up to 20% in 2017. Moreover, 12.8% of patients with a DPP-4 inhibitor prescription were ESA hyporesponsive before the initial DPP-4 inhibitor prescription. After DPP-4 inhibitor prescription, the odds of eHypo and mean log-ERI remained unchanged in the whole cohort of our study. The interaction analysis of DPP-4 inhibitor and sideropenia showed that DPP-4 inhibitors attenuated eHypo in the patients without iron deficiency. Conclusion: Our findings indicate a recent increase in DPP-4 inhibitor prescription among Japanese HD patients with DM. DPP-4 inhibitors could improve ERI in patients undergoing HD without iron deficiency.
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Authors: Frye Charles G. , Simon James , Wadia Neha S. , Ligeralde Andrew , DeWeese Michael R. , Bouchard Kristofer E.
Published: April 2021

Despite the fact that the loss functions of deep neural networks are highly nonconvex, gradient-based optimization algorithms converge to approximately the same performance from many random initial points. One thread of work has focused on explaining this phenomenon by numerically characterizing the local curvature near critical points of the loss function, where the gradients are near zero. Such studies have reported that neural network losses enjoy a no-bad-local-minima property, in disagreement with more recent theoretical results. We report here that the methods used to find these putative critical points suffer from a bad local minima problem of their own: they often converge to or pass through regions where the gradient norm has a stationary point. We call these gradient-flat regions, since they arise when the gradient is approximately in the kernel of the Hessian, such that the loss is locally approximately linear, or flat, in the direction of the gradient. We describe how the presence of these regions necessitates care in both interpreting past results that claimed to find critical points of neural network losses and in designing second-order methods for optimizing neural networks.
Sinning in the Rain: Weather Shocks, Church Attendance, and Crime
Authors: Moreno-Medina Jonathan
Published: April 2021

This paper provides evidence of the causal effect of church attendance on petty crime by using quasi-random variation in the number of Sundays when it precipitated at the specific time of most religious services. Using a novel strategy, I find a narrow time window when most individuals attend church. Based on a panel between 1980 and 2016, I find that one more Sunday with precipitation at the time of church increases yearly drug-related, alcohol-related, and white-collar crimes. I do not find an effect for violent or property crimes. These effects are driven by more religious counties. Previous evidence showing negative effects of church attendance on the demand for alcohol and drugs is consistent with a demand-driven interpretation of the results presented.
Collective Reputation in Trade: Evidence from the Chinese Dairy Industry
Authors: Bai Jie , Gazze Ludovica , Wang Yukun
Published: April 2021

The existence of collective reputation implies an important externality. Among firms trading internationally, quality shocks about one firm's products could affect the demand of other firms from the same origin country. We study such a reputation spillover in the context of a large-scale scandal that affected the Chinese dairy industry in 2008. Leveraging detailed firm-product level administrative data and official quality inspection reports, we document sizable reputation spillovers on uncontaminated firms. We further investigate potential mechanisms that could mediate the strength of collective reputation, including information accuracy, observability of the supply chain, and prior export experience.
The TFP Channel of Credit Supply Shocks
Authors: Ben Zeev Nadav
Published: April 2021

Recent work stresses a potentially important relation between credit supply shocks and aggregate TFP based on factor misallocation. I take three steps to examine this relation. First, using state-of-the-art credit supply shock and aggregate TFP measures, I show that an adverse credit supply shock has a weak and very short-lived effect on aggregate TFP. Second, using firm-level data, I show that firm-level capital stock responses to an adverse credit supply shock produce an insignificant and negligible capital-misallocation–induced TFP response. Third, using employment data by fine firm-size category classification, I also find a negligible labor-misallocation–induced TFP response. These findings suggest that the TFP channel of credit supply shocks has a limited role in their transmission to the real economy.
Revision of the genus Dziriblatta Chopard, 1936 (Blattodea, Ectobiidae, Ectobiinae). III. The species of the subgenus Dziriblatta
Authors: BOHN HORST
Published: April 2021

The present paper is the third part of a revision of the genus Dziriblatta and treats the nominal subgenus Dziriblatta and its species. The subgenus contains 10 species of which 8 are new to science. The species can be distributed to two species groups: the bolivari-species group with Dz. (Dz.) algerica, Dz. (Dz.) bolivari, Dz. (Dz.) brevisacculata, spec. nov., Dz. (Dz.) curvisetosa, spec. nov., and Dz. (Dz.) pilleata, spec. nov.—and the lobososacculata-species group with Dz. (Dz.) altotuberculata, spec. nov., Dz. (Dz.) lobososacculata, spec. nov., Dz. (Dz.) planotuberculata, spec. nov., Dz. (Dz.) ramososacculata, spec. nov., and Dz. (Dz.) undulata, spec. nov.. The descriptions of the species are complemented by numerous images showing their most important structures, their distribution in southern Spain and Morocco, and their supposed phylogenetic relationships. A determination key allows the discrimination of the males.
Diagnostics of Manitischeria gen. nov., an Old-World genus of leaf-mining Tischeriidae, composed of new species and species formerly in Tischeria Zeller
Authors: STONIS JONAS R. , DIŠKUS ARŪNAS , SOLIS M. ALMA , MONRO ALEXANDRE K.
Published: April 2021

We describe a new genus, Manitischeria Diškus & Stonis, gen. nov., and five new species: Manitischeria selindica Stonis & Diškus, sp. nov., M. namibiensis Stonis & Diškus, sp. nov. from Africa, and M. brachiata Diškus & Stonis, sp. nov., M. symbolica Diškus & Stonis, sp. nov., and M. baryshnikovae Diškus & Stonis, sp. nov. from South East Asia. We discuss the diagnostics of Manitischeria gen. nov. composed of these new species and others transferred from Tischeria Zeller. Species are mostly trophically associated with Malvaceae, but also Rhamnaceae and Betulaceae. We list 18 currently known species of Manitischeria gen. nov., including M. ptarmica (Meyrick), the type species, and provide 13 new combinations and the first documentation of genitalia of some, previously little-known species. New species are illustrated with photographs or drawings of the adults, genitalia, and the leaf mines when available. We briefly discuss the use of herbarium specimens to discover lepidopteran leaf mines, host plant, and distribution data.
New genus and new species of the subfamily Metrodorinae from China (Orthoptera: Tetrigidae)
Authors: LU XIANG-YI , DENG WEI-AN
Published: April 2021

The genus Concavetettix Deng, gen. nov. (type species: Concavetettix yunnanensis Deng, sp. nov.) is described from Daweishan, Pingbian County, Yunnan Province, China. The genus Macromotettix Günther, 1939 is reviewed. Three new species of the genus, M. microptera Deng, sp. nov., M. zhengi Deng, sp. nov. and M. brachyptera Deng, sp. nov. are described with detailed illustrations of external morphology. One new name is proposed: Macromotettix napoensis Deng, nom. nov.. Additionally, an updated key to species of the genus Macromotettix is given.
Assessment of a Ground-Based Lightning Detection and Near-Real-Time Warning System in the Rural Community of Swayimane, KwaZulu-Natal, South Africa
Authors: Mahomed Maqsooda , Clulow Alistair D. , Strydom Sheldon , Mabhaudhi Tafadzwanashe , Savage Michael J.
Published: April 2021

Climate change projections of increases in lightning activity are an added concern for lightning-prone countries such as South Africa. South Africa’s high levels of poverty, lack of education, and awareness, as well as a poorly developed infrastructure, increase the vulnerability of rural communities to the threat of lightning. Despite the existence of national lightning networks, lightning alerts and warnings are not disseminated well to such rural communities. We therefore developed a community-based early warning system (EWS) to detect and disseminate lightning threats and alerts in a timely and comprehensible manner within Swayimane, KwaZulu-Natal, South Africa. The system is composed of an electrical field meter and a lightning flash sensor with warnings disseminated via audible and visible alarms on site and with a remote server issuing short message services (SMSs) and email alerts. Twelve months of data (February 2018–February 2019) were utilized to evaluate the performance of the EWS’s detection and warning capabilities. Diurnal variations in lightning activity indicated the influence of solar radiation, causing convective conditions with peaks in lightning activity occurring during the late afternoon and early evening (between 1400 and 2100) coinciding with students being released from school and when most workers return home. In addition to detecting the threat of lightning, the EWS was beneficial in identifying periods that exhibited above-normal lightning activity, with two specific lightning events examined in detail. Poor network signals in rural communities presented an initial challenge, delaying data transmission to the central server until rectified using multiple network providers. Overall, the EWS was found to disseminate reliable warnings in a timely manner.
The Invisible Cage: Workers’ Reactivity to Opaque Algorithmic Evaluations
Authors: Rahman Hatim A.
Published: April 2021

Existing research has shown that people experience third-party evaluations as a form of control because they try to align their behavior with evaluations’ criteria to secure more favorable resources, recognition, and opportunities from external audiences. Much of this research has focused on evaluations with transparent criteria, but increasingly, algorithmic evaluation systems are not transparent. Drawing on over three years of interviews, archival data, and observations as a registered user on a labor platform, I studied how freelance workers contend with an opaque third-party evaluation algorithm—and with what consequences. My findings show the platform implemented an opaque evaluation algorithm to meaningfully differentiate between freelancers’ rating scores. Freelancers experienced this evaluation as a form of control but could not align their actions with its criteria because they could not clearly identify those criteria. I found freelancers had divergent responses to this situation: some experimented with ways to improve their rating scores, and others constrained their activity on the platform. Their reactivity differed based not only on their general success on the platform—whether they were high or low performers—but also on how much they depended on the platform for work and whether they experienced setbacks in the form of decreased evaluation scores. These workers experienced what I call an “invisible cage”: a form of control in which the criteria for success and changes to those criteria are unpredictable. For gig workers who rely on labor platforms, this form of control increasingly determines their access to clients and projects while undermining their ability to understand and respond to factors that determine their success.
Democratic Disarray: Organizational Messaging Coherence and the Local Echoing Press During the 2020 Iowa Democratic Caucus
Authors: Wiemer Eric C. , Scacco Joshua M. , Berkelaar Brenda
Published: April 2021

The Iowa caucuses are the inaugural event of the American presidential nomination process. When the state Democratic Party failed to report the 2020 caucus results in a timely manner and manage the consequences, the crisis situation threatened the legitimacy of the party and the integrity of the results. This research presents an in-depth case of the Iowa Democratic Party’s public communication response regarding an event described by the Des Moines Register as “hell” and a “results catastrophe.” Specifically, we were interested in how the Iowa Democratic Party responded to the crisis event and the extent to which the party organization was successful in disseminating favorable messaging about the caucus process to the local press. Drawing on organizational crisis management and echoing press perspectives, this analysis uses network and qualitative analytic approaches to assess message development, dissemination, and ultimately adoption. A local event with national implications presents a critical case in investigating how a political party, due to its institutional role in American elections and unique organizational structure, struggled to respond to the crisis.
Giovanni Battista Morgagni and the Morgagni Hernia
Authors: Hughes Byron D. , Nakayama Don
Published: April 2021

Giovanni Morgagni remains an eminent figure in the field of pathological anatomy. Born in Forli, Italy, he excelled as a child. He entered medical school at the age of 16 years old in Bologna. By the age of 31 he held the chair position at the University of Padua. During his tenure, he discovered many anatomical and pathological findings, with the most widely known discovery being the Morgagni Hernia. Morgagni first described this eponymic hernia in an adult stonecutter during an autopsy. In addition to his many discoveries, his most esteemed written contribution to the field of medicine came in the form of a five-volume book titled De Sedibus et Causis Morborum per Anatomen Indagatis, in which he correlated cadaveric anatomy and symptomatology revealed upon autopsy. He remained on faculty at the University of Padua for over five decades until his death in 1771.