bluuebunny/crossref_metadata_2025 huggingface dataset:
Biomarker testing strategies in non-small cell lung cancer in the real-world setting: analysis of methods in the Prospective Central Lung Cancer Biomarker Registry (LungPath) from the Spanish Society of Pathology (SEAP)
Authors: Martín-López Javier , Rojo Federico , Martínez-Pozo Antonio , Hernández-Iglesias Teresa , Carcedo David , de Alda Lucía Ruiz , García J Francisco , Salas Clara
Published: December 2021

Aims

The aim of this study is to extend the analysis of the Lung Cancer Biomarker Testing Registry (LungPath), by analysing the techniques used in the determination of epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-ros oncogene 1 (ROS1) and programmed death ligand-1 (PD-L1) for the diagnostic of patients with advanced non-small-cell lung cancer (NSCLC).

Methods

Information of the technique used for the determination of EGFR, ALK, ROS1 and PD-L1 was recorded from March 2018 to January 2019 from 44 centres, but only 34 centres matched with the 38 centres previously analysed, allowing to analyse the techniques used in 8970 matched determinations of EGFR, ALK, ROS1 and PD-L1. Therefore, a by-centre analysis studied the level of implementation of the techniques in the 44 centres, while a by-determination analysis made it possible to assess the overall frequency of the techniques used on the 9134 matched samples.

Results

By-centre analysis showed that only 46.5% and 25.6% of the centres used reflex strategies for ALK and ROS1 determination, respectively. By-determination analysis showed that 94.4% of EGFR determinations were performed by PCR, 80.7% of ALK determinations were performed by IHC with clone D5F3, while 55.7% of ROS1 determinations were performed by IHC with clone D4D6. 22C3 were the PD-L1 clone more used (43.5%) followed by SP263 clone (31.1%).

Conclusions

The real-world evidence obtained from LungPath shows the effort of Spanish hospitals in performing biomarker determination in NSCLC with different methodologies despite that next-generation sequencing (NGS) utilisation in the year of the analysis was low. Biomarker determination results could be optimised with the incorporation of sequencing methods such as NGS in pathology departments.
Improving the clinical interpretation of missense variants in X linked genes using structural analysis
Authors: Sallah Shalaw Rassul , Ellingford Jamie M , Sergouniotis Panagiotis I , Ramsden Simon C , Lench Nicholas , Lovell Simon C , Black Graeme C
Published: December 2021

Background

Improving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders.

Methods

We have developed aprotein-specific variant interpreter(ProSper) that combines genetic and protein structural data. This algorithm predicts missense variant pathogenicity by applying machine learning approaches to the sequence and structural characteristics of variants.

Results

ProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively.

Conclusion

ProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.
Hydraulic conductivity testing and destructive sampling of field-scale mine waste test piles
Authors: Gorakhki Mohammad R.H. , Bareither Christopher A. , Scalia Joseph
Published: December 2021

A commingled waste rock and tailings test pile and a waste rock test pile were evaluated to determine saturated hydraulic conductivity and destructively sampled to measure dry density. The commingled test pile contained a mixture of filtered tailings and waste rock blended to isolate waste rock particles as inclusions within the tailings matrix. Test piles were constructed in the shape of truncated 5 m high pyramids with 25 m base sides and flat 5 m × 5 m top surfaces, and instrumented to monitor water content (and additional geochemical indicator parameters) within the test pile and seepage from the base of the pile. Piles were decommissioned after 26 months of operation. Saturated hydraulic conductivities were measured using sealed double-ring infiltrometers (2.4 m square outer ring and 1 m square inner ring). Tensiometers and embedded water content sensors were used to measure progression of the wetting front, and the final location of the wetting front in the commingled test pile was directly measured during decommissioning. Field-measured saturated hydraulic conductivities were compared to laboratory-measured results intended to simulate the test piles. Despite having a lower average density, the commingled waste rock and tailings had a hydraulic conductivity approximately 2.5 times lower than the waste rock.
THE INFLUENCES OF STOCK MARKET ON EXCHANGE-TRADED FUND UNDER THE THRESHOLD EFFECT OF MONETARY POLICY IN CHINA
Authors: Yang Tzu-Yi , Hung Phan Van , Chang Chia-Jui , Nguyen Nguyen Phuc
Published: December 2021

This paper estimates the smooth transition autoregressive model with exogenous variables to evaluate the effects of stock market returns on the exchange-traded funds’ (ETFs) returns in China with reserve requirement ratio (RRR) from monetary policy as a transition variable. The sample used in this study lasts from March 4, 2005 to June 30, 2017. The empirical result points out that there is the effect of RRR value on the relationship between stock market returns and ETF return. Moreover, these effects are variable depending on the conversion and its changes over time in different variations of threshold intervals. Lastly, the larger the change of China’s stock market variables’ lag period, the smaller the impacts on Chinese ETF return.
New optical soliton solutions of fractional perturbed nonlinear Schrödinger equation in nanofibers
Authors: Saha Ray S. , Das N.
Published: December 2021

In this article, the space-time fractional perturbed nonlinear Schrödinger equation (NLSE) in nanofibers is studied using the improved [Formula: see text] expansion method (ITEM) to explore new exact solutions. The perturbed nonlinear Schrodinger equation is a nonlinear model that occurs in nanofibers. The ITEM is an efficient method to obtain the exact solutions for nonlinear differential equations. With the help of the modified Riemann–Liouville derivative, an equivalent ordinary differential equation has been obtained from the nonlinear fractional differential equation. Several new exact solutions to the fractional perturbed NLSE have been devised using the ITEM, which is the latest proficient method for analyzing nonlinear partial differential models. The proposed method may be applied for searching exact travelling wave solutions of other nonlinear fractional partial differential equations that appear in engineering and physics fields. Furthermore, the obtained soliton solutions are depicted in some 3D graphs to observe the behaviour of these solutions.
Some novel optical solutions to the perturbed nonlinear Schrödinger model arising in nano-fibers mechanical systems
Authors: Ilhan Onur Alp , Manafian Jalil , Lakestani Mehrdad , Singh Gurpreet
Published: December 2021

This paper aims to compute solitary wave solutions and soliton wave solutions based on the ansatz methods to the perturbed nonlinear Schrödinger equation (NLSE) arising in nano-fibers. The improved [Formula: see text]-expansion method and the rational extended sinh–Gordon equation expansion method are used for the first time to obtain the new optical solitons of this equation. The obtained results give an accuracy interpretation of the propagation of solitons. We held a comparison between our results and those are in the previous work. The outcome indicates that perturbed NLSE arising nano-fibers is used in optical problems. Finally, via symbolic computation, their dynamic structure and physical properties were vividly shown by three-dimensional, density, and [Formula: see text]-curves plots. These solutions have greatly enriched the exact solutions of (2+1)-dimensional perturbed nonlinear Schrödinger equation in the existing literatures.
Performance optimization of liquid ring pumps based on Gappy POD surrogate model
Authors: Guo Guangqiang , Zhang Renhui , Yang Junhu , Wang Jingyi
Published: December 2021

To solve the problems of high calculation cost and difficult optimization caused by the complex gas–liquid two-phase flow in liquid ring pumps, an optimization method of a liquid ring pump impeller based on the proper orthogonal decomposition (POD) surrogate model was proposed. The impeller blade shape was parameterized by using the quartic Bezier curve. The sample was obtained from the uniform experimental design of blade shape design parameters. The POD surrogate model was constructed by the parameters of sample blade shape and its corresponding flow field data, which was used instead of CFD numerical simulation to predict the flow field variables under the perturbations of blade design parameters in the optimization process. And then, the gradient vector of the objective function to design variables was predicted quickly and accurately. The blade shape was updated continuously along the negative direction of the gradient vector, and then, the optimal design of the impeller was acquired. The calculation case results show that the relative error between the pressure field in the impeller predicted by the POD method and that of CFD simulation is less than 5%, and the calculation cost is only 1/8460 of CFD simulation. Compared with the original model, the flow field distribution in the optimized model is significantly improved, and the efficiency is promoted by 3.8%.
Investigation on the performance dependence of proton radiated SiGe HBTs with emitter area and temperature
Authors: Wei Yinlong , Lan Kuibo , Wang Zhi , Wei Junqing , Ma Zhenqiang , Qin Guoxuan
Published: December 2021

The DC and AC performances of proton radiated Silicon-Germanium (SiGe) Heterojunction Bipolar Transistors (HBTs) with different emitter areas at liquid nitrogen temperature (77 K), room temperature and heating hotplate (393 K) were presented in this work. Performance dependence on the emitter area and temperature was investigated. Results showed that SiGe HBTs with a large emitter area had more damage by proton radiation. Furthermore, the SiGe HBTs showed better tolerance to proton radiation at extreme temperatures than at room temperature. To reveal the underlying mechanism, the radiated SiGe HBTs were modeled based on the device structure and parameters. The electron density, Shockley–Read–Hall (SRH) recombination and carrier mobility were extracted from the device model and demonstrated to have major impacts on the performance dependence of the radiated SiGe HBTs. The results provide useful guidance for the application of SiGe HBTs at extreme environments.
A DELAY NONAUTONOMOUS PREDATOR–PREY MODEL FOR THE EFFECTS OF FEAR, REFUGE AND HUNTING COOPERATION
Authors: TIWARI PANKAJ KUMAR , VERMA MAITRI , PAL SOUMITRA , KANG YUN , MISRA ARVIND KUMAR
Published: December 2021

Fear of predation may assert privilege to prey species by restricting their exposure to potential predators, meanwhile it can also impose costs by constraining the exploration of optimal resources. A predator–prey model with the effect of fear, refuge, and hunting cooperation has been investigated in this paper. The system’s equilibria are obtained and their local stability behavior is discussed. The existence of Hopf-bifurcation is analytically shown by taking refuge as a bifurcation parameter. There are many ecological factors which are not instantaneous processes, and so, to make the system more realistic, we incorporate three discrete time delays: in the effect of fear, refuge and hunting cooperation, and analyze the delayed system for stability and bifurcation. Moreover, for environmental fluctuations, we further modify the delayed system by incorporating seasonality in the fear, refuge and cooperation. We have analyzed the seasonally forced delayed system for the existence of a positive periodic solution. In the support of analytical results, some numerical simulations are carried out. Sensitivity analysis is used to identify parameters having crucial impacts on the ecological balance of predator–prey interactions. We find that the rate of predation, fear, and hunting cooperation destabilizes the system, whereas prey refuge stabilizes the system. Time delay in the cooperation behavior generates irregular oscillations whereas delay in refuge stabilizes an otherwise unstable system. Seasonal variations in the level of fear and refuge generate higher periodic solutions and bursting patterns, respectively, which can be replaced by simple 1-periodic solution if the cooperation and fear are also allowed to vary with time in the former and latter situations. Higher periodicity and bursting patterns are also observed due to synergistic effects of delay and seasonality. Our results indicate that the combined effects of fear, refuge and hunting cooperation play a major role in maintaining a healthy ecological environment.
Synthesis, characterization, and femtosecond third-order optical nonlinearity of Au@Ag core–shell nanoparticles
Authors: Lu Heng , Gu Bing
Published: December 2021

In this work, the Au@Ag bimetallic core–shell nanostructures were synthesized by a seed-mediated growth. The crystal structure, morphology, elemental composition, atomic concentration, and absorption spectrum of the as-synthesized nanoparticles were characterized by means of X-ray diffraction, transmission electron microscopy, energy dispersive spectroscopy, X-ray photoelectron spectroscopy, and ultraviolet–visible linear absorption spectrum, respectively. The femtosecond third-order optical nonlinearities of nanoparticle dispersions were investigated by carrying out the femtosecond-pulsed [Formula: see text]-scan measurements at 800[Formula: see text]nm. The experimental results indicate that Au@Ag core–shell nanoparticles exhibit the positive refractive nonlinearity and negative absorptive nonlinearity. The third-order nonlinear refraction indexes of Au and Au@Ag nanoparticles are measured to be [Formula: see text] and [Formula: see text][Formula: see text]cm2/GW, respectively. The results show that the bimetallic nanoparticle has potential possibility in nonlinear photonic applications.
Comparing the topology of phylogenetic network generators
Authors: Janssen Remie , Liu Pengyu
Published: December 2021

Phylogenetic networks represent evolutionary history of species and can record natural reticulate evolutionary processes such as horizontal gene transfer and gene recombination. This makes phylogenetic networks a more comprehensive representation of evolutionary history compared to phylogenetic trees. Stochastic processes for generating random trees or networks are important tools in evolutionary analysis, especially in phylogeny reconstruction where they can be utilized for validation or serve as priors for Bayesian methods. However, as more network generators are developed, there is a lack of discussion or comparison for different generators. To bridge this gap, we compare a set of phylogenetic network generators by profiling topological summary statistics of the generated networks over the number of reticulations and comparing the topological profiles.
DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost
Authors: Maruf Firda Aminy , Pratama Rian , Song Giltae
Published: December 2021

Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exomes for tumor samples only without the paired normal samples. To include these types of data for extensive studies on the process of tumorigenesis, it is necessary to develop an approach for identifying somatic mutations using tumor exome sequencing data only. In this study, we designed a machine learning approach using Deep Neural Network (DNN) and XGBoost to identify somatic mutations in tumor-only exome sequencing data and we integrated this into a pipeline called DNN-Boost. The XGBoost algorithm is used to extract the features from the results of variant callers and these features are then fed into the DNN model as input. The XGBoost algorithm resolves issues of missing values and overfitting. We evaluated our proposed model and compared its performance with other existing benchmark methods. We noted that the DNN-Boost classification model outperformed the benchmark method in classifying somatic mutations from paired tumor-normal exome data and tumor-only exome data.
Dual-View Conditional Variational Auto-Encoder for Emotional Dialogue Generation
Authors: Li Mei , Zhang Jiajun , Lu Xiang , Zong Chengqing
Published: December 2021

Emotional dialogue generation aims to generate appropriate responses that are content relevant with the query and emotion consistent with the given emotion tag. Previous work mainly focuses on incorporating emotion information into the sequence to sequence or conditional variational auto-encoder (CVAE) models, and they usually utilize the given emotion tag as a conditional feature to influence the response generation process. However, emotion tag as a feature cannot well guarantee the emotion consistency between the response and the given emotion tag. In this article, we propose a novel Dual-View CVAE model to explicitly model the content relevance and emotion consistency jointly. These two views gather the emotional information and the content-relevant information from the latent distribution of responses, respectively. We jointly model the dual-view via VAE to get richer and complementary information. Extensive experiments on both English and Chinese emotion dialogue datasets demonstrate the effectiveness of our proposed Dual-View CVAE model, which significantly outperforms the strong baseline models in both aspects of content relevance and emotion consistency.
Memorizing All for Implicit Discourse Relation Recognition
Authors: Munir Kashif , Bai Hongxiao , Zhao Hai , Zhao Junhan
Published: December 2021

Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clues from explicit connectives. An implicit discourse relation recognizer has to carefully tackle the semantic similarity of sentence pairs and the severe data sparsity issue. In this article, we learn token embeddings to encode the structure of a sentence from a dependency point of view in their representations and use them to initialize a baseline model to make it really strong. Then, we propose a novel memory component to tackle the data sparsity issue by allowing the model to master the entire training set, which helps in achieving further performance improvement. The memory mechanism adequately memorizes information by pairing representations and discourse relations of all training instances, thus filling the slot of the data-hungry issue in the current implicit discourse relation recognizer. The proposed memory component, if attached with any suitable baseline, can help in performance enhancement. The experiments show that our full model with memorizing the entire training data provides excellent results on PDTB and CDTB datasets, outperforming the baselines by a fair margin.
Teachers’ Sociodemographic Characteristics, Psychological Distress, Job Satisfaction, and Their Willingness to Include Children with Special Needs in Regular Classes
Authors: Nwosu Kingsley Chinaza , Wahl W. P. , Oparaugo Ursula Ifeoma , Ezennaka Anthony Obinna , Ibrahim Daniel Balami , Ahmed Ademola Oyeyemi , Ementa Ngozi Christiana , Alaribe Christopher Obinna , Akuneme Christiana Chioma , Nnaemeka Gloria Uzoamaka
Published: December 2021

Advancing inclusive education in countries where children with special needs (CWSNs) are yet to be meaningfully included in regular schools will ensure the progress of Goal 4 of the Sustainable Development Goals. One potent determinant to the effective inclusion of CWSNs is teachers’ intention and efforts to integrate them into their classes. Yet, there is still inadequate evidence in the existing body of literature that will help stakeholders understand the psychosocial variables that will impact their intentions and efforts to include CWSNs in their classes. In view of this, our study determined the association between teachers’ sociodemographic characteristics, psychological distress, job satisfaction, and their willingness to include CWSNs in their classes using a hierarchical regression model. The sample size is made up of 502 secondary school teachers in Anambra State. Our model showed the potency of sociobiological and motivational factors on teachers’ inclusive education willingness of CWSNs in their classes. Teachers’ age, years of teaching experience, and job satisfaction are significant predictors of their inclusion willingness for CWSNs. Teachers’ psychological distress did not significantly predict their inclusion willingness for CWSNs. The significance and implications of our findings were underscored.
Analysis of the Mechanism and Safety of Bisphosphonates in Patients with Lung Cancer and Bone Metastases
Authors: Zhang Gao , Gong Hu , Xu Hanqiao
Published: December 2021

Objective. To explore the mechanism and safety of bisphosphonates in patients with lung cancer and bone metastases. Method. A total of 104 patients with lung cancer and bone metastases in our hospital were selected and randomly divided into two groups: control group (

n
=
54

) and research group (

n
=
50

). Chemotherapy was given to the control group, and the research group was treated with bisphosphonate drugs. The quality of life, HAMA, HAMD score, VAS score, treatment effect, serum calcium and KPS score, inflammatory factor levels, and immune function were compared between the two groups. Result. The quality of life in both groups was significantly increased (

P
<
0.05

). The HAMA and HAMD scores of the research group decreased significantly than those of the control group after treatment (

P
<
0.05

). The VAS scores of the two groups were significantly reduced (

P
<
0.05

). The effective rates of treatment in the control group and the research group were 81.5% and 96.0%, respectively. Serum calcium was significantly decreased, and KPS score was significantly increased at weeks 1 and 6 after treatment, and the change was more obvious in the research group (

P
<
0.05

). The levels of inflammatory factors in the two groups were significantly reduced, and the immune indicators were significantly increased. Conclusion. Bisphosphonates have good effect on patients with lung cancer and bone metastases, which can improve anxiety and depression, reduce pain score, improve serum calcium level and immune function, and reduce inflammatory response. Therefore, bisphosphonate drug therapy is worth widely used.
Deep Learning-Based Magnetic Resonance Imaging in the Evaluation of Tumor, Node, and Metastasis Staging of Renal Cancer
Authors: Han Guocan , Lin Weifeng , Lin Wei
Published: December 2021

This study was aimed to investigate the diagnostic accuracy of magnetic resonance imaging (MRI) based on deep dictionary learning in TNM (tumor, node, and metastasis) staging of renal cell carcinoma. In this study, 82 patients with renal cancer were selected as the research object. The results were diagnosed by deep dictionary learning MRI, and TNM staging was performed by professional imaging personnel. MRI image will be reconstructed after deep dictionary learning to improve its image recognition ability. The pathological diagnosis will be handed over to the physiological pathology laboratory of the hospital for diagnosis. The staging results were compared with the pathological diagnostic staging results, and the results were analyzed by consistency statistics to evaluate the diagnostic value. The results showed that T staging was significantly consistent with the pathological diagnosis. 2 cases were misdiagnosed, and the accuracy rate was 97.56%. Compared with the pathological diagnosis, N staging had less obvious consistency. 10 cases were misdiagnosed, and the accuracy rate was 87.80%. M staging was significantly consistent with the pathological diagnosis. 4 cases were misdiagnosed. The accuracy rate was 95.12%. After laparotomy, it was found that 37 patients had emboli and 45 patients had no emboli, while 40 patients had emboli and 42 patients had no emboli by MRI. The accuracy rate was 96.34%. The results showed that in the evaluation of TNM staging by MRI imaging based on deep dictionary learning in patients with renal cell carcinoma, the diagnostic results of N staging and M staging were highly consistent with the pathological diagnosis, while the diagnostic results of T staging were slightly less accurate, and the diagnostic consistency was good. The results can provide effective support for the clinical application of MRI imaging based on deep dictionary learning as the clinical diagnosis of TNM staging of renal cell carcinoma.
Adaptive Video Caption Detection and Extraction Method Based on Color Filtering Principle
Authors: Ren Peng , Zhao Genlin , Liu Yongjun
Published: December 2021

Based on the principle of color (RGB) filtering, an improved adaptive video caption detection and extraction method is proposed. Firstly, the principle of the color filtering algorithm used in the video caption detection and extraction method is analyzed, and then the algorithm is improved adaptively according to the caption pixel size to filter the noise. Finally, experiments verify the effect of this method in extracting subtitles from video. The experimental results show that the accuracy of detecting and extracting subtitles in color video is as high as 99.3%.
Osteochondritis Dissecans Lesion of the Trochlear Groove: A Case of Nonsurgical Management for a Rare Lesion
Authors: Krebs Paul , Walla Nicholas , Flanigan David
Published: December 2021

Osteochondritis dissecans (OCD) lesions are potential causes of knee pain in pediatric patients, with lesions most frequently found on the lateral and medial femoral condyles. This case discusses an OCD lesion of the trochlear groove, a rare location for OCD lesions, in an 11-year-old female athlete. The patient presents after several years of knee pain that had acutely worsened, and both X-ray and MRI demonstrated the lesion, with MRI confirming a stable lesion. While previous literature has leaned towards surgical management, this patient was successfully managed nonoperatively in a locked knee brace for 12 weeks. She then went through 4 weeks of physical therapy and a 4 week progression back into soccer activity with return to full activity in 5 months.
Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder
Authors: Junejo A. R. , Kaabar Mohammed K. A. , Li Xiang
Published: December 2021

Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respiratory syndrome type 2, HIV infection, and Hepatitis-C virus can cause directly and indirectly cardiovascular disease (CVD). Emphasis should be placed not only on the development of broad-spectrum molecules and antibodies but also on host factor therapy, including the reutilization of previously approved or developing drugs. Another new class of therapeutics with great antiviral therapeutic potential is molecular communication networks using deep learning autoencoder (DL-AEs). The use of DL-AEs for diagnosis and prognosis prediction of infectious and noninfectious diseases has attracted a particular attention. MCN is map to molecular signaling and communication that are found inside and outside the human body where the goal is to develop a new black box mechanism that can serve the future robust healthcare industry (HCI). MCN has the ability to characterize the signaling process between cells and infectious disease locations at various levels of the human body called point-to-point MCN through DL-AE and provide targeted drug delivery (TDD) environment. Through MCN, and DL-AE healthcare provider can remotely measure biological signals and control certain processes in the required organism for the maintenance of the patient’s health state. We use biomicrodevices to promote the real-time monitoring of human health and storage of the gathered data in the cloud. In this paper, we use the DL-based AE approach to design and implement a new drug source and target for the MCN under white Gaussian noise. Simulation results show that transceiver executions for a given medium model that reduces the bit error rate which can be learned. Then, next development of molecular diagnosis such as heart sounds is classified. Furthermore, biohealth interface for the inside and outside human body mechanism is presented, comparative perspective with up-to-date current situation about MCN.
RAP1GAP Functions as a Tumor Suppressor Gene and Is Regulated by DNA Methylation in Differentiated Thyroid Cancer
Authors: Faam Bita , Ghaffari Mohammad A. , Khorsandi Layasadat , Ghadiri Ata A. , Totonchi Mehdi , Amouzegar Atieh , Fanaei S. Ahmad , Azizi Fereidoun , Shahbazian Hajeih B. , Hashemi Tabar Mahmoud
Published: December 2021

Inactivation of tumor suppressor genes, such as RAP1GAP, by hypermethylation of their regulatory region can give rise to thyroid tumors. The aim of this study was to investigate the expression of the RAP1GAP gene and the DNA methylation patterns of its CpG74a, CpG74b, and CpG24 in an Iranian population with differentiated thyroid cancer (DTC). In this study, 160 individuals who underwent thyroidectomy in the Tehran Erfan Hospital between 2018 and 2020 were selected. DNA methylation patterns of selected CpG islands (CpG74a, CpG74b, and CpG24) were determined using methylation-specific PCR. The mRNA expression and protein level of ­RAP1GAP were also evaluated. SW1736 and B-CPAP cells were treated with 5-aza-2′-deoxycytidine (5-Aza) to demethylate these regions. The hypermethylation rates of CpG74a and CpG24 in DTC samples were significantly higher than in the control. The mRNA expression and protein level of ­RAP1GAP were significantly decreased in the DTC group. In the DTC group, hypermethylation in CpG74a was correlated with decreasing RAP1GAP expression (R2: 0.34; p = 0.043). CpG74a with a specificity of 86.4% has significant prediction power to distinguish between DTC and normal thyroid tissues. Additionally, hypermethylation of CpG74a was significantly associated with higher tumor stages (stage III-IV: 77%; stage I-II: 23%; p = 0.012). Increasing expression of RAP1GAP after demethylation with 15 µM of 5-Aza was observed in both cell lines. These results indicate that DNA hypermethylation in CpG74a can be considered as an epigenetic biomarker in DTC.
Weight Changes in Type 2 Diabetes and Cancer Risk: A Latent Class Trajectory Model Study
Authors: Jensen Britt W. , Watson Charlotte , Geifman Nophar , Baker Jennifer L. , Badrick Ellena , Renehan Andrew G.
Published: December 2021

Introduction: Body mass index (BMI) is often elevated at type 2 diabetes (T2D) diagnosis. Using latent class trajectory modelling (LCTM) of BMI, we examined whether weight loss after diagnosis influenced cancer incidence and all-cause mortality. Methods: From 1995 to 2010, we identified 7,708 patients with T2D from the Salford Integrated Record database (UK) and linked to the cancer registry for information on obesity-related cancer (ORC), non-ORC; and all-cause mortality. Repeated BMIs were used to construct sex-specific latent class trajectories. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression models. Results: Four sex-specific BMI classes were identified; stable-overweight, stable-obese, obese-slightly-decreasing, and obese-steeply-decreasing; comprising 41%, 45%, 13%, and 1% of women, and 45%, 37%, 17%, and 1% of men, respectively. In women, the stable-obese class had similar ORC risks as the obese-slightly-decreasing class, whereas the stable-overweight class had lower risks. In men, the obese-slightly-decreasing class had higher risks of ORC (HR = 1.86, 95% CI: 1.05–3.32) than the stable-obese class, while the stable-overweight class had similar risks No associations were observed for non-ORC. Compared to the stable-obese class, women (HR = 1.60, 95% CI: 0.99–2.58) and men (HR = 2.37, 95% CI: 1.66–3.39) in the obese-slightly-decreasing class had elevated mortality. No associations were observed for the stable-overweight classes. Conclusion: Patients who lost weight after T2D diagnosis had higher risks for ORC (in men) and higher all-cause mortality (both genders) than patients with stable obesity.
Value of Radiomics of Perinephric Fat for Prediction of Intraoperative Complexity in Renal Tumor Surgery
Authors: Mühlbauer Julia , Kriegmair Maximilian C. , Schöning Lale , Egen Luisa , Kowalewski Karl-Friedrich , Westhoff Niklas , Nuhn Philipp , Laqua Fabian C. , Baessler Bettina
Published: December 2021

Introduction: The aim of this study was to assess the value of computed tomography (CT)-based radiomics of perinephric fat (PNF) for prediction of surgical complexity. Methods: Fifty-six patients who underwent renal tumor surgery were included. Radiomic features were extracted from contrast-enhanced CT. Machine learning models using radiomic features, the Mayo Adhesive Probability (MAP) score, and/or clinical variables (age, sex, and body mass index) were compared for the prediction of adherent PNF (APF), the occurrence of postoperative complications (Clavien-Dindo Classification ≥2), and surgery duration. Discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC). In addition, the root mean square error (RMSE) and R2 (fraction of explained variance) were used as additional evaluation metrics. Results: A single feature logit model containing “Wavelet-LHH-transformed GLCM Correlation” achieved the best discrimination (AUC 0.90, 95% confidence interval [CI]: 0.75–1.00) and lowest error (RMSE 0.32, 95% CI: 0.20–0.42) at prediction of APF. This model was superior to all other models containing all radiomic features, clinical variables, and/or the MAP score. The performance of uninformative benchmark models for prediction of postoperative complications and surgery duration were not improved by machine learning models. Conclusion: Radiomic features derived from PNF may provide valuable information for preoperative risk stratification of patients undergoing renal tumor surgery.
The Burden of Pediatric Subarachnoid Hemorrhage Healthcare Disparities in Senegal: A Retrospective Cohort Study
Authors: Thioub Mbaye , Mbaye Maguette , Zolo Yvan , Sghiouar Manal , Diop Sagar , Wague Daouda , Sy El Hadji Cheikh Ndiaye , Thiam Alioune Badara , Tshimbombu Tshibambe Nathanael , Kanmounye Ulrick Sidney , Ba Momar Code
Published: December 2021

Introduction: Pediatric aneurysms are uncommon but potentially deadly clinical conditions with varied etiology and outcomes. In low-resource countries, numerous barriers prevent the timely diagnosis and management of pediatric aneurysmal subarachnoid hemorrhage (aSAH). Thus, this study aimed to assess the mortality of pediatric aSAH stemming from limited access to pediatric neurological surgery care in Senegal. Methods: Pediatric aSAH patients admitted at the authors’ institution from 2012 to 2020 were recruited. Spearman Rho’s correlation, McNemar’s test, and Wilcoxon signed-rank test were used. Odds ratios and their 95% confidence intervals were calculated, and the population attributable fraction (PAF) was used to quantify aSAH mortality attributable to lack of surgical care. Results: Twenty-four pediatric patients (12 females and 12 males) aged 12.2 (95% CI = 10.0–14.3) years presented with aSAH. Most patients had a single aneurysm measuring 12.6 (6.1–19.0) mm with 1 patient having 2. The median WFNS grade was 3 (range [1–4]), and the mean Fisher grade was 4 (range [1–4]). Fifteen patients (62.5%) had surgical treatment on day 15.0 (IQR = 23.0) of hospitalization. The overall mortality rate was 20.8%, and the PAF of mortality for lack of surgical treatment during hospitalization was 0.08. Conclusion: Eight percent of deaths among pediatric aSAH patients who do not receive surgical treatment are attributable to lack of access to surgical treatment. Health system strengthening policies should be implemented to address this health inequity.
Folded Electronic Textiles: Weaving, Knitting, Pleating and Coating Three-Dimensional Sensor Structures
Authors: Greinke Berit , Wood Emma , Skach Sophie , Vilas Arantza , Vierne Pauline
Published: December 2021

This article reports findings from a one-month design research project in which four textile and fashion designers engaged in collaborative exploration of technical and artistic knowledge inherent in folds and folding. They experimented with textile technologies and construction methods to design, fabricate and analyze three-dimensional dynamic materiality in electronic textiles (e-textiles). The focus was on sensor structures achieved by knitting, weaving, pleating and coating techniques. The analysis of measurements of electrical resistance between one and 36 sensing areas per sample demonstrate the added value of folds integrated into textile sensor structures. The authors discuss types of folding and their aesthetic, textural and sensorial advantages. Detailed information on design, fabrication and experimental results is provided in supplemental materials to the article.
Two new species of Anaglyptus Mulsant from China (Coleoptera: Cerambycidae: Cerambycinae: Anaglyptini)
Authors: WANG YINGQI , ZHAO WEIWEI , XIE GUANGLIN
Published: December 2021

Two new Anaglyptus species, Anaglyptus mucheni sp. nov. and Anaglyptus zhangjunqii sp. nov., are described and illustrated from China.
Rapid spin up and spin down of flow along slopes
Authors: Peterson Henry G. , Callies Jörn
Published: December 2021

The near-bottom mixing that allows abyssal waters to upwell tilts isopycnals and spins up flow over the flanks of mid-ocean ridges. Meso- and large-scale currents along sloping topography are subjected to a delicate balance of Ekman arrest and spin down. These two seemingly disparate oceanographic phenomena share a common theory, which is based on a one-dimensional model of rotating, stratified flow over a sloping, insulated boundary. This commonly used model, however, lacks rapid adjustment of interior flows, limiting its ability to capture the full physics of spin up and spin down of along-slope flow. Motivated by two-dimensional dynamics, the present work extends the one-dimensional model by constraining the vertically integrated cross-slope transport and allowing for a barotropic cross-slope pressure gradient. This produces a closed secondary circulation by forcing Ekman transport in the bottom boundary layer to return in the interior. The extended model can thus capture Ekman spin up and spin down physics: the interior return flow is turned by the Coriolis acceleration, leading to rapid rather than slow diffusive adjustment of the along-slope flow. This transport-constrained one-dimensional model accurately describes twodimensional mixing-generated spin up over an idealized ridge and provides a unified framework for understanding the relative importance of Ekman arrest and spin down of flow along a slope.
Comparison of Ventilated and Unventilated Air Temperature Measurements in Inland Dronning Maud Land on the East Antarctic Plateau
Authors: Morino Shohei , Kurita Naoyuki , Hirasawa Naohiko , Motoyama Hideaki , Sugiura Konosuke , Lazzara Matthew , Mikolajczyk David , Welhouse Lee , Keller Linda , Weidner George
Published: December 2021

Surface temperature measurements with naturally ventilated (NV) sensors over the Antarctic Plateau are largely subject to systematic errors caused by solar radiative heating. Here we examined the radiative heating error in Dronning Maud Land on the East Antarctic Plateau using both the newly installed automatic weather stations (AWSs) at NDF and Relay Station and the existing AWSs at Relay Station and Dome Fuji. Two types of NV shields were used in these AWSs: a multiplate radiation shield and a simple cylinder-shaped shield. In austral summer, the temperature bias between the force-ventilated (FV) sensor and the NV sensor never reached zero because of continuous sunlight. The hourly mean temperature errors reached up to 8°C at noon on a sunny day with weak wind conditions. The errors increased linearly with increasing reflected shortwave radiation and decreased nonlinearly with increasing wind speed. These features were observed in both the multiplate and the cylinder-shaped shields. The magnitude of the errors of the multiplate shield was much larger than that of the cylinder-shaped shield. To quantify the radiative errors, we applied an existing correction model based on the regression approach and successfully reduced the errors by more than 70% after the correction. This indicates that we can use the corrected temperature data instead of quality controlled data, which removed warm bias during weak winds in inland Dronning Maud Land.
Using Social Media as a Survey Recruitment Strategy for Post-Secondary Students During the COVID-19 Pandemic
Authors: Purewal Simran , Ardiles Paola , Di Ruggiero Erica , Flores John Vincent L. , Mahmood Sana , Elhagehassan Hussein
Published: December 2021

The COVID-19 pandemic rapidly forced Canadian post-secondary students into remote learning methods, with potential implications on their academic success and health. In recent years, the use of social media to promote research participation and as a strategy for communicating health messages has become increasingly popular. To better understand how the pandemic has impacted this population, we used social media platforms to recruit students to participate in a national bilingual COVID-19 Health Literacy Survey. The purpose of the survey was to assess the health literacy levels and online information-seeking behaviors of post-secondary students in relation to the coronavirus. This paper outlines the social media recruitment strategies used for promoting participation in the survey among Canadian post-secondary students during the pandemic. Facebook, Twitter, and Instagram accounts were created to promote the online survey. The objective of this paper is to examine the use of Instagram, Facebook, and Twitter as survey recruitment strategies tailored to students. Data analytics from these platforms were analyzed using descriptive statistics. We found that the most commonly used platform for survey dissemination was Twitter, with 64800 total impressions recorded over 3 months. The use of social media as a survey recruitment strategy showed promise in the current context of COVID-19 where many students are participating in online learning and for a study population that actively uses these platforms to seek out information.
Negative emotionality and peer status: Evidence for bidirectional longitudinal influences during the elementary school years
Authors: Bengtsson Hans , Arvidsson Åsa , Nyström Beatrice
Published: December 2021

Prior research indicates that high negative emotionality in combination with low peer status is conducive of clinically identified problems in childhood. This three-wave longitudinal study examined how negative emotionality and peer status are linked over time in middle and late childhood. Participants were recruited from second grade ( n = 90, mean age = 8.85) and fourth grade ( n = 119, mean age = 10.81) and were followed across a period of 2 years. Cross-lagged structural models examining concurrent and longitudinal associations between teacher-reported negative emotionality and peer ratings of likability were analyzed separately for externalizing emotion (anger) and internalizing emotion (sadness and fear). Both analyses provided support for a conceptual model in which high negative emotionality lowers peer status, and low peer status, in turn, through a feedback loop, increases negative emotionality over time. Bidirectional influences are interpreted as reflecting a transactional process involving the effects of negative emotionality on social behavior. The findings highlight the need for active efforts to help children with high negative emotionality gain acceptance from classmates.
Effects of different sedatives/analgesics on stress responses in patients undergoing craniotomy and bone flap decompression
Authors: Guo Qingduo , Ma Meina , Yang Qiuying , Yu Hong , Wang Xupeng , Wu Chunling , Li Rui
Published: December 2021

Objective

To explore the effects of sedation and analgesia with dexmedetomidine and other drugs on the stress response in patients with cerebral hemorrhage after craniotomy hematoma removal and bone flap decompression and insertion of an indwelling endotracheal catheter.

Methods

A total of 180 patients with cerebral hemorrhage with consciousness disturbance who underwent emergency surgery were included in this study. They were divided into six groups treated with propofol, dexmedetomidine, lidocaine, sufentanil, dezocine, and remifentanil, respectively. Intravenous medication was given after recovery of spontaneous respiration, and stress responses were compared among the group.

Results

Serum concentrations of norepinephrine, epinephrine, and cortisol and systolic blood pressure were significantly correlated with drug treatment. Serum norepinephrine concentrations differed significantly among the groups, except between the sufentanil and propofol groups. There were significant differences in serum epinephrine concentrations among all groups, and significant differences in serum cortisol concentrations among all groups, except the propofol, dexmedetomidine, and lidocaine groups.

Conclusion

Dexmedetomidine can reduce the stress response in patients with intracerebral hemorrhage undergoing emergency craniotomy and bone flap decompression, and can reduce adverse events from an indwelling endotracheal catheter 3 hours post-operation.