bluuebunny/crossref_metadata_2025 huggingface dataset:
WISEFUSE
Authors: Mahgoub Ashraf , Yi Edgardo Barsallo , Shankar Karthick , Minocha Eshaan , Elnikety Sameh , Bagchi Saurabh , Chaterji Somali
Published: July 2022

We characterize production workloads of serverless DAGs at a major cloud provider. Our analysis highlights two major factors that limit performance: (a) lack of efficient communication methods between the serverless functions in the DAG, and (b) stragglers when a DAG stage invokes a set of parallel functions that must complete before starting the next DAG stage. To address these limitations, we propose WISEFUSE, an automated approach to generate an optimized execution plan for serverless DAGs for a user-specified latency objective or $ budget. We introduce three optimizations: (1) Fusion combines in-series functions together in a single VM to reduce the communication overhead between cascaded functions. (2) Bundling executes a group of parallel invocations of a function in one VM to improve resource sharing among the parallel workers to reduce skew. (3) Resource Allocation assigns the right VM size to each function or function bundle in the DAG to reduce the E2E latency and cost. We implement WISEFUSE to evaluate it experimentally using three popular serverless applications with different DAG structures, memory footprints, and intermediate data sizes. Compared to competing approaches and other alternatives, WISEFUSE shows significant improvements in E2E latency and cost. Specifically, for a machine learning pipeline, WISEFUSE achieves P95 latency that is 67% lower than Photons, 39% lower than Faastlane, and 90% lower than SONIC without increasing the $ cost.
An Enterprise-Grade Open-Source Data Reduction Architecture for All-Flash Storage Systems
Authors: Ajdari Mohammadamin , Raaf Patrick , Kishani Mostafa , Salkhordeh Reza , Asadi Hossein , Brinkmann André
Published: July 2022

Data reduction technologies have proven their effectiveness to decrease the ever-growing demands on storage system capacities, but also introduce new complexity in the system I/O stack that can easily invalidate well-known best practices. In this paper, we conduct an extensive set of experiments on an enterprise all-flash storage (AFS) system equipped with an open-source data reduction module, i.e., RedHat VDO, and reveal novel observations on the performance gap between the state-of-the-art and the optimal AFS stack with integrated data reduction. We then offer cross-layer optimizations to enhance the performance of AFS, which range from deriving new optimal hardware RAID configurations up to modifications of the enterprise storage stack tailored to the major bottlenecks observed. By implementing all proposed optimizations in an enterprise AFS, we show up to 12.5x speedup over the baseline AFS with integrated data reduction, and up to 57x performance/cost improvement over an optimized AFS (with no data reduction) for 100% write, high-reduction workload scenarios.
Asymptotic Convergence Rate of Dropout on Shallow Linear Neural Networks
Authors: Senen-Cerda Albert , Sanders Jaron
Published: July 2022

We analyze the convergence rate of gradient flows on objective functions induced by Dropout and Dropconnect, when applying them to shallow linear Neural Networks (NNs) ---which can also be viewed as doing matrix factorization using a particular regularizer. Dropout algorithms such as these are thus regularization techniques that use {0,1} -valued random variables to filter weights during training in order to avoid coadaptation of features. By leveraging a recent result on nonconvex optimization and conducting a careful analysis of the set of minimizers as well as the Hessian of the loss function, we are able to obtain (i) a local convergence proof of the gradient flow and (ii) a bound on the convergence rate that depends on the data, the dropout probability, and the width of the NN. Finally, we compare this theoretical bound to numerical simulations, which are in qualitative agreement with the convergence bound and match it when starting sufficiently close to a minimizer.
Dremel
Authors: Zhao Chenxingyu , Chugh Tapan , Min Jaehong , Liu Ming , Krishnamurthy Arvind
Published: July 2022

LSM-tree-based key-value stores like RocksDB are widely used to support many applications. However, configuring a RocksDB instance is challenging for the following reasons: 1) RocksDB has a massive parameter space to configure; 2) there are inherent trade-offs and dependencies between parameters; 3) optimal configurations are dependent on workload and hardware; and 4) evaluating configurations is time-consuming. Prior works struggle with handling the curse of dimensionality, capturing relationships between parameters, adapting configurations to workload and hardware, and evaluating quickly.

We present a system, Dremel, to adaptively and quickly configure RocksDB with strategies based on the Multi-Armed Bandit model. To handle the large parameter space, we propose using fused features, which encode domain-specific knowledge, to work as a compact and powerful representation for configurations. To adapt to the workload and hardware, we build an online bandit model to identify the best configuration. To evaluate quickly, we enable multi-fidelity evaluation and upper-confidence-bound sampling to speed up configuration search. Dremel not only achieves up to ×2.61 higher IOPS and 57% less latency than default configurations but also achieves up to 63% improvement over prior works on 18 different settings with the same or smaller time budget.

This paper is an abridged version.
A Detailed Look at MIMO Performance in 60 GHz WLANs
Authors: Aggarwal Shivang , Neelamraju Srisai Karthik , Bhat Ajit , Koutsonikolas Dimitrios
Published: July 2022

One of the key enhancements in the upcoming 802.11ay standard for 60 GHz WLANs is the support for simultaneous transmissions of up to 8 data streams via SU- and MU-MIMO, which has the potential to enable data rates up to 100 Gbps. However, in spite of the key role MIMO is expected to play in 802.11ay, experimental evaluation of MIMO performance in 60 GHz WLANs has been limited to date, primarily due to lack of hardware supporting MIMO transmissions at millimeter wave frequencies. In this work, we fill this gap by conducting the first large-scale experimental evaluation of SU- and MU-MIMO performance in 60 GHz WLANs. Unlike previous studies, our study involves multiple environments with very different multipath characteristics. We analyze the performance in each environment, identify the factors that affect it, and compare it against the performance of SISO. Further, we seek to identify factors that can guide beam and user selection to limit the (often prohibitive in practice) overhead of exhaustive search. Finally, we propose two heuristics that perform both user and beam selection with low overhead, and show that they perform close to an Oracle solution and outperform previously proposed approaches in both static and mobile scenarios, regardless of the environment and number of users.
Tensor Completion with Nearly Linear Samples Given Weak Side Information
Authors: Yu Christina Lee , Xi Xumei
Published: July 2022

Tensor completion exhibits an interesting computational-statistical gap in terms of the number of samples needed to perform tensor estimation. While there are only Θ(tn) degrees of freedom in a t-order tensor with nt entries, the best known polynomial time algorithm requires O(nt/2) samples in order to guarantee consistent estimation. In this paper, we show that weak side information is sufficient to reduce the sample complexity to O(n). The side information consists of a weight vector for each of the modes which is not orthogonal to any of the latent factors along that mode; this is significantly weaker than assuming noisy knowledge of the subspaces. We provide an algorithm that utilizes this side information to produce a consistent estimator with O(n1+κ) samples for any small constant κ > 0. We also provide experiments on both synthetic and real-world datasets that validate our theoretical insights.
The Cerebellar Cortex
Authors: Hull Court , Regehr Wade G.
Published: July 2022

The cerebellar cortex is an important system for relating neural circuits and learning. Its promise reflects the longstanding idea that it contains simple, repeated circuit modules with only a few cell types and a single plasticity mechanism that mediates learning according to classical Marr-Albus models. However, emerging data have revealed surprising diversity in neuron types, synaptic connections, and plasticity mechanisms, both locally and regionally within the cerebellar cortex. In light of these findings, it is not surprising that attempts to generate a holistic model of cerebellar learning across different behaviors have not been successful. While the cerebellum remains an ideal system for linking neuronal function with behavior, it is necessary to update the cerebellar circuit framework to achieve its great promise. In this review, we highlight recent advances in our understanding of cerebellar-cortical cell types, synaptic connections, signaling mechanisms, and forms of plasticity that enrich cerebellar processing.
Functional Ultrasound Neuroimaging
Authors: Montaldo Gabriel , Urban Alan , Macé Emilie
Published: July 2022

Functional ultrasound (fUS) is a neuroimaging method that uses ultrasound to track changes in cerebral blood volume as an indirect readout of neuronal activity at high spatiotemporal resolution. fUS is capable of imaging head-fixed or freely behaving rodents and of producing volumetric images of the entire mouse brain. It has been applied to many species, including primates and humans. Now that fUS is reaching maturity, it is being adopted by the neuroscience community. However, the nature of the fUS signal and the different implementations of fUS are not necessarily accessible to nonspecialists. This review aims to introduce these ultrasound concepts to all neuroscientists. We explain the physical basis of the fUS signal and the principles of the method, present the state of the art of its hardware implementation, and give concrete examples of current applications in neuroscience. Finally, we suggest areas for improvement during the next few years.
[Retracted] Analysis and Application of Language Environment Function from the Perspective of English Linguistics
Authors: Zhu Lin
Published: July 2022

In recent years, more and more questions have been raised about the idea that a sentence alone can fully analyze the semantics without considering the context. When we talk about the acceptability of a sentence, we immediately and naturally think of the context in which the sentence can be used correctly. In the current English classroom teaching, teachers follow the traditional teaching procedures too much: first give detailed explanations, and then carry out mechanical exercises. The lack of flexible teaching methods and the creation of contexts affects the improvement of students’ communicative ability. During my long‐term learning of English, I have considered the following questions: how to make use of the function of context to make English learning diverse and challenging; how to create a variety of practical forms, such as creating a way to make students interested and able to participate in learning, in order to improve students’ language communication ability. This paper analyzes the current situation of English teaching, expounds the concept and classification of context and language application, summarizes the applied linguistics theory and related theory, and analyzes the context of English linguistics horizon function and application. This study combines context with English learning, and English learning “how to create and use context,” so as to improve students’ communicative competence.
Clinical Efficacy of Immunoglobulin Combined with Glucocorticoids in the Treatment of Oculomotor Myasthenia Gravis in Children and the Effect on Serum Immunity
Authors: Fan Lijun , Yang Yahui , Zhang Fan , Huang Fei
Published: July 2022

To investigate the effects of treatment with immunoglobulin on clinical outcomes and immune function in children with oculomotor myasthenia gravis. The clinical data of 100 pediatric patients with oculomotor myasthenia gravis treated in our hospital from January 2019 to December 2021 were selected as the subjects of this retrospective study and divided into a comparison group and a treatment group according to the different treatment methods. The comparison group was treated with glucocorticoids, and the treatment group was treated with immunoglobulin on the basis of the comparison group. The differences in the serum indexes, the effects of immune function, and the clinical efficacy of the two groups were observed and compared. It was found the comparison of immunoglobulin G (IgG), immunoglobulin A (IgA), and immunoglobulin M (IgM) after treatment was significantly different and lower in the treatment group than in the comparison group; the comparison of CD4+, CD3+, CD4+/CD8+, and NK cells after treatment was significantly different and higher in the treatment group than in the comparison group. The effective rate of 98.00% in the treatment group was significantly higher than that of 76.00% in the comparison group, and the difference was statistically significant. The clinical efficacy of the two groups showed that the fever, cough, sputum, myasthenia gravis crisis, and gastrointestinal reactions in the treatment group were significantly lower than those in the comparison group. The study indicates that comparative study of children with oculomotor myasthenia gravis treated with immunoglobulin combined with glucocorticoids is more effective, effectively improving the immune level of patients and reducing adverse reactions.
Differences in Nutritional and Psychological Habits in Hypertension Patients
Authors: Carreira-Míguez María , Ramos-Campo Domingo Jesús , Clemente-Suárez Vicente Javier
Published: July 2022

Multifactorial factors such as psychological and nutritional habits are controlling factors in hypertension. The aim of the present study was to analyze differences in nutritional and psychological habits in humans with hypertension. Fifty participants with hypertension (HG) (57.5 ± 13.6 years) and 100 participants as control group (CG) with no hypertension disease (48.9 ± 7.9 years) were interviewed via online questionnaire. Multifactorial items in nutrition habits and psychological profile were analyzed by a compendium of questionnaires; psychological measures refer to personality, anxiety, depression, loneliness, perceived stress, and psychological inflexibility; and a nutritional questionnaire to analyze eating habits and nutrition behaviors of the participants. CG showed significantly higher week vitality (p = 0.001), juice weekly consumption (p = 0.011), coffee weekly consumption (p = 0.050), fermented milk weekly consumption (p = 0.004), and fruit weekly consumption (p = 0.022) than HG. Lower values of weekly coffee consumption and week vitality were found in HG. According to the psychological profile, significant differences were found only depression values, finding HG more depressed than CG (p = 0.002). In conclusion, our results showed that people with better nutrition and mental health would present lower levels of blood pressure. Therefore, the combination of psychological therapy and nutritional recommendations for reducing the risk of having hypertension and improving the blood pressure levels may be needed for patient with hypertension.
Bioinformatics Analysis for Constructing a Six‐Immune‐Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma
Authors: Wang Jue , Jin Zongrui , Wu Guolin , Wang Jilong , Xu Banghao , Zhu Hai , Guo Ya , Wen Zhang
Published: July 2022

The present study was aimed at identifying the potential prognostic biomarkers of the immune‐related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA‐sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune‐related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3‐antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune‐related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six‐lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC.
Shrunken Pore Syndrome Is Associated with Renal Function Decline in Female Patients with Kidney Diseases
Authors: Wu Zhongcai , Wang Le , Li Yueqiang , Yao Ying , Zeng Rui
Published: July 2022

Background. Shrunken pore syndrome (SPS) represents selective impairment of kidney filtration of low‐molecular‐weight molecules between 1 and 30 kDa and has been related to outcomes including morbidity, mortality, and cardiovascular events. However, the prevalence and kidney outcomes of SPS have not been investigated in patients with IgA nephropathy (IgAN) and membranous nephropathy (MN). Methods. We retrospectively collected information of 536 patients including 414 with IgAN and 122 with MN. SPS was mainly defined by cystatin C‐based eGFR < 70% of creatinine‐based eGFR using the CAPA‐LM equation pairs, while CKD‐EPI equations were also employed in sensitivity analyses. Prevalence rate of SPS and its association with end‐stage renal disease (ESRD) or severe eGFR decline (≥50% eGFR reduction or doubling of baseline creatinine) were investigated. Results. 44% (8%) patients were identified as possessing SPS using the CAPA‐LM definition. ESRD happened in 24 patients during the average follow‐up period of 27.7 months. Despite dramatic increase of incidence rate of ESRD for SPS, significant hazard ratio (HR) only existed in IgAN patients after multivariable adjustment (HR: 8.35, 95% CI: 2.10~33.26), but lost significance in sensitivity analyses. 36 patients were determined as having experienced severe eGFR decline after excluding transient creatinine fluctuation. SPS was associated with severe eGFR decline by Kaplan‐Meier survival analyses in the overall population as well as the IgAN, MN, male, and female subpopulations, which remained significant in multivariable adjustments in all groups except IgAN. However, only in female patients the association between SPS and eGFR decline remained significant in all the sensitivity analyses. Conclusions. SPS was independently associated with eGFR decline in female patients with IgAN and MN.
Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion
Authors: Kang Xiancai , Hua Chuangli
Published: July 2022

In order to explore how to realize network mining for associated users, an algorithm of associated user mining based on recommendation system is proposed. This method recommends key technical problems and solutions based on multi-information fusion to explore the research of user network data mining. The data mining algorithm of associated user network based on multi-information fusion is about 35% higher than the previous method. This article is combined with common scoring data to improve the accuracy of algorithm results. The experiment was carried out on the real data set FilmTrust to evaluate the proposed new algorithm, measure the prediction accuracy of the score prediction results, and compare the offline test results to verify the effectiveness of the new algorithm.
Evaluation of Students’ Educational Management Quality Based on Intuitionistic Fuzzy Information
Authors: Cao Fangjian
Published: July 2022

In order to further improve the scientificity and rationality of students’ education management quality evaluation, this paper proposes a method of students’ education management quality evaluation based on intuitive fuzzy information. This method makes a scientific judgment on the teaching management process and teaching results in combination with the objectives of students’ education and teaching management. On the basis of teaching evaluation, with the help of intuitionistic fuzzy information integration algorithm method, this paper focuses on using intuitionistic fuzzy-weighted average operator and normal distribution weighting method to construct the quality evaluation model of students’ education and teaching management, and develops the teaching quality evaluation system based on intuitionistic fuzzy theory. Through the two-level fuzzy comprehensive evaluation, the teacher evaluation result score is 81.45, which is an algebraic value from 0 to 100, and the score is between 80 and 90, which verifies that the classroom teaching evaluation result is good.
Music Score Recognition Method Based on Deep Learning
Authors: Lin Qin
Published: July 2022

In recent years, the recommendation application of artificial intelligence and deep music has gradually become a research hotspot. As a complex machine learning algorithm, deep learning can extract features with value laws through training samples. The rise of deep learning network will promote the development of artificial intelligence and also provide a new idea for music score recognition. In this paper, the improved deep learning algorithm is applied to the research of music score recognition. Based on the traditional neural network, the attention weight value improved convolutional neural network (CNN) and high execution efficiency deep belief network (DBN) are introduced to realize the feature extraction and intelligent recognition of music score. Taking the feature vector set extracted by CNN-DBN as input set, a feature learning algorithm based on CNN&DBN was established to extract music score. Experiments show that the proposed model in a variety of different types of polyphony music recognition showed more accurate recognition and good performance; the recognition rate of the improved algorithm applied to the soundtrack identification is as high as 98.4%, which is significantly better than those of other classic algorithms, proving that CNN&DBN can achieve better effect in music information retrieval. It provides data support for constructing knowledge graph in music field and indicates that deep learning has great research value in music retrieval field.
Periapical Healing following Root Canal Treatment Using Different Endodontic Sealers: A Systematic Review
Authors: Khandelwal Akshay , Janani Krishnamachari , Teja KavalipurapuVenkata , Jose Jerry , Battineni Gopi , Riccitiello Francesco , Valletta Alessandra , Palanivelu Ajitha , Spagnuolo Gianrico
Published: July 2022

The healing of the periapical tissues is crucial to the success of root canal treatment. The review studies effectively examine various endodontic root canal sealants in terms of periapical healing. This systematic review was formulated following the PRISMA 2020 guidelines and registered in the international prospective register of systematic reviews (PROSPERO) number‐CRD42021239192. To find relevant articles, PubMed Central and Medline databases (until February 2022) were searched. Studies that evaluated healing following the application of different endodontic sealers were analysed. A primary outcome measure was the resolution of periapical lesions following the endodontic treatment. In vivo studies comparing radiographic treatment outcomes and articles with a minimum of 6‐month follow‐up were included. A total of 9 clinical trial studies that met all the inclusion criteria were included in the analysis. The overall risk of bias was high in four studies out of nine studies. Periapical lesions showed significant healing after endodontic treatment regardless of sealer type, although bioceramic and bioactive sealers had shown better results.
[Retracted] Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision
Authors: Li Dan , Sun Jinping , Wang Hongdong , Shi Hanqin , Liu Weiwei , Wang Likai
Published: July 2022

According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.
NUCB2/Nesfatin-1 Regulation of Chronic Visceral Hyperalgesia
Authors: Gu Qiaoyan , Lei Yuan , Wu Jianming , He Ting , Li Juanjuan , Song Shanshan
Published: July 2022

Objective. We previously described that different concentration Nucleobindin-2 (NUCB2)/Nesfatin-1 gradients differently regulated visceral hypersensitivity in irritable bowel syndrome. Therefore, this study is aimed at evaluating the effect of NUCB2/Nesfatin-1 on model rats with chronic visceral hyperalgesia. Methods. Neonatal and mature Sprague-Dawley rats were randomly divided into the healthy control and chronic visceral hyperalgesia model groups. The model was built by combining maternal separation with the acetic acid enema. The models were identified by the distension volume threshold to reach

abdominal
 
withdraw
 
reflex
 

AWR

 
score
=
3

, histological staining, and myeloperoxidase (MPO) detection. The visceral sensitivity to chronic visceral hyperalgesia was then evaluated. Result. Rats in the model group responded more strongly to pulling stimulation than healthy controls; the distension volume threshold causing AWR3 response in model rats was lower than the control group before NUCB2/Nesfatin-1 intervention. After intervention, the distension volume threshold was significantly lower in the NUCB2/Nesfatin-1 central intervention group than in the NUCB2/Nesfatin-1 peripheral intervention group, and the peak value of external oblique muscle electrical activity was significantly higher. Additionally, compared with the male intervention group, in the female intervention group, the volume threshold was significantly lower and the peak value was higher. Conclusion. NUCB2/Nesfatin-1 could regulate visceral sensitivity in chronic visceral hyperalgesia model rats; its regulatory effect correlated with the type of NUCB2/Nesfatin-1 intervention approaches (central or peripheral) and sex (male or female).
Comprehensive Computational Analysis of Honokiol Targets for Cell Cycle Inhibition and Immunotherapy in Metastatic Breast Cancer Stem Cells
Authors: Skolastika Skolastika , Hanif Naufa , Ikawati Muthi , Hermawan Adam
Published: July 2022

Breast cancer stem cells (BCSCs) play a critical role in chemoresistance, metastasis, and poor prognosis of breast cancer. BCSCs are mostly dormant, and therefore, activating them and modulating the cell cycle are important for successful therapy against BCSCs. The tumor microenvironment (TME) promotes BCSC survival and cancer progression, and targeting the TME can aid in successful immunotherapy. Honokiol (HNK), a bioactive polyphenol isolated from the bark and seed pods of Magnolia spp., is known to exert anticancer effects, such as inducing cell cycle arrest, inhibiting metastasis, and overcoming immunotherapy resistance in breast cancer cells. However, the molecular mechanisms of action of HNK in BCSCs, as well as its effects on the cell cycle, remain unclear. This study aimed to explore the potential targets and molecular mechanisms of HNK on metastatic BCSC (mBCSC)-cell cycle arrest and the impact of the TME. Using bioinformatics analyses, we predicted HNK protein targets from several databases and retrieved the genes differentially expressed in mBCSCs from the GEO database. The intersection between the differentially expressed genes (DEGs) and the HNK-targets was determined using a Venn diagram, and the results were analyzed using a protein-protein interaction network, hub gene selection, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, genetic alteration analysis, survival rate, and immune cell infiltration levels. Finally, the interaction between HNK and two HNK-targets regulating the cell cycle was analyzed using molecular docking analysis. The identified potential therapeutic targets of HNK (PTTH) included CCND1, SIRT2, AURKB, VEGFA, HDAC1, CASP9, HSP90AA1, and HSP90AB1, which can potentially inhibit the cell cycle of mBCSCs. Moreover, our results showed that PTTH could modulate the PI3K/Akt/mTOR and HIF1/NFkB/pathways. Overall, these findings highlight the potential of HNK as an immunotherapeutic agent for mBCSCs by modulating the tumor immune environment.
Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing
Authors: Wu Yanping , Zheng Changlong , Hao Meihui , Wang Linlin
Published: July 2022

This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm.
3D Modeling of Sculpture Nano-Ceramics under Sparse Image Sequence
Authors: Yang Zeyin
Published: July 2022

To improve the analysis ability of point cloud 3D reconstruction of sparse images of nano-ceramic sculpture points, an automatic cloud 3D reconstruction method of nano-ceramic sculpture points based on sparse image sequence is proposed. Firstly, 3D angle detection and edge contour feature extraction methods are used to analyze 3D point cloud features of nano-ceramic sculpture point save image; secondly, the point cloud of the fuel economy image of nano-ceramic sculpture points is merged and the sloping action method is used to shape degradation to realize the information increase and fusion filtering of the fuel economy image of nano-ceramic sculpture points; finally, combined with the local mean denoising method, image is refined to improve the ability of sparse image outline structure of nano-ceramic sculpture points. The simulation results show that this method has high accuracy, good image matching ability, and high signal-to-noise ratio.
3D Finite Element Analysis of Seismic Response of Rigid Pile Composite Foundation with Microprobe Group
Authors: Lin Li , Zhang Jichao , Li Yadong , Rao Zhuwei , Sen Zheng
Published: July 2022

In order to study the seismic performance of the rigid pile composite foundation, a three-dimensional finite element analysis of the seismic response of the rigid pile composite foundation with microprobe group was proposed. The three-dimensional finite element method is used to analyze the dynamic response law of pile group rigid pile composite foundation under the action of the earthquake, especially the internal force response of the pile body. It is worth noting that when the internal force response of pile decreases significantly, the maximum internal force of the whole pile is also significantly smaller than the maximum internal force of pile foundation. However, when the pile body is located on a layered foundation and the modulus of adjacent soil layers differs greatly, the earthquake action will cause the pile body to generate a large internal force. The seismic performance of the rigid pile composite foundation is feasible, and it can be extended and applied to the similar seismic performance test of other composite foundations.
Mycoplasma pneumoniae Infections and Primary Immune Deficiencies
Authors: Poddighe Dimitri , Demirkaya Erkan , Sazonov Vitaliy , Romano Micol
Published: July 2022

Mycoplasma pneumoniae (M. pneumoniae) is one of the leading causes of community-acquired pneumonia in children and is also implicated in a variety of reactive extrapulmonary diseases. Recurrent and/or severe respiratory infections are one of the most frequent manifestations of several types of primary immunodeficiency. Here, we reviewed the medical literature to assess the potential relevance of M. pneumoniae in the infections observed in children affected with combined, humoral, and innate primary immune deficiencies. M. pneumoniae does not result to be epidemiologically prevalent as a cause of pneumonia in children affected by primary immunodeficiencies, but this infection can have a persistent or severe course in this category of patients. Indeed, the active search of M. pneumoniae could be useful and appropriate especially in children with humoral immune deficiencies. Indeed, most cases of M. pneumoniae infection in primary immunodeficiencies are described in patients affected by a/hypo-gammaglobulinemia.
Dance Art Scene Classification Based on Convolutional Neural Networks
Authors: Li Le
Published: July 2022

Digital multimedia resources have become an important part of people’s daily cultural life. Automatic scene classification of a large number of dance art videos is the basis for scene semantic based video content retrieval. In order to improve the accuracy of scene classification, the videos are identified using a deep convolutional neural network based on differential evolution for dance art videos. First, the Canny operator is used in YCbCr colour space to detect the human silhouette in the key frames of the video. Then, the AdaBoost algorithm based on cascade structure is used to implement human target tracking and labelling, and the construction and updating of weak classifiers are analysed. Next, a differential evolution algorithm is used to optimise the structural parameters of the convolutional neural network, and an adaptive strategy is adopted for the scaling factor of the differential evolution algorithm to improve the optimisation solution accuracy. Finally, the improved deep convolutional neural network is used to train the classification of the labelled videos in order to obtain stable scene classification results. The experimental results show that by reasonably setting the crossover rate of differential evolution and the convolutional kernel size of the convolutional neural network, high scene classification performance can be obtained. The high accuracy and low root-mean-square error validate the applicability of the proposed method in dance art scene classification.
CFSBFDroid: Android Malware Detection Using CFS + Best First Search-Based Feature Selection
Authors: Sharma Ravi Mohan , Agrawal Chaitanya. P , Kumar Vinod , Mulatu Adugna Necho
Published: July 2022

With the fast development of smartphone technology and mobile applications, the mobile phone has become the most powerful tool to access the Internet and get various services with one click. Meanwhile, susceptibilities of the application are the primary hazard to the security of Android devices. Due to these weaknesses, an attacker can easily hack the confidential data of the mobile phone. The malware application automatically performs fraudulent activities on mobile phones without the user's knowledge. Thus, these attacks are the major threats to the security of mobile phones. To detect malicious applications installed on Android smartphones, we have conducted a study that focuses on permissions and intent-based mechanisms. The study was done in three phases: in the first phase, the dataset was created by extracting intents and permissions from APK files; in the second phase, correlation-based feature selection (CFS) and best first search (BFS) were combined to select the most representative features from the feature space of the extracted dataset; and in the third phase, machine learning (ML) techniques were trained and tested against the preprocessed dataset obtained in the second phase. The accuracy, precision, recall, F1 score, and error metrics of seven machine learning techniques (REPTree, Rule PART, RF, SMO, SGD, MCC, and LMT) were demonstrated over the Android dataset.
Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks
Authors: He Jianwei , Fan Chengli
Published: July 2022

The appearance of a large number of image editing software packages allows people to easily tamper with image content information, resulting in a significant decrease in image credibility. A color image mosaic detection model based on CNN is proposed in this study. The cascade network structure of shallow thin neurons replaces the single network structure of deep multineurons in this study, and it compensates for the shortcomings of the previous image tampering detection algorithm using the single network structure of deep multineurons by relearning the characteristics of difficult samples. A multiscale convolution layer and a residual module are included in the model at the same time. Feature maps with different receptive fields can be fused with the multiscale convolution layer. By establishing a short connection between the input and output feature maps, the residual module can effectively reduce the risk of gradient disappearance in the model’s training process while also speeding up the network’s convergence speed. The simulation results show that this algorithm has an accuracy of 92.14% and an F1 value of 95.7%. This detection method outperforms other detection methods in terms of detection ability, reliability, and usability. This research gives users more information on which to base their judgments on when judging color mosaic images.
CLK2 Expression Is Associated with the Progression of Colorectal Cancer and Is a Prognostic Biomarker
Authors: Lin Jiarui , Lin Guixing , Chen Binbin , Yuan Jinpeng , Zhuang Yezhong
Published: July 2022

Background. CLK2 is a splicing regulator and expressed ubiquitously in various malignancies. The study is aimed at exploring the potential roles of CLK2 in the development of colorectal cancer (CRC). Methods. Real‐time PCR and analyses of The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) database were utilized to evaluate the CLK2 gene transcription level and protein level of colorectal cancer (CRC) tissue. The chi‐squared and logistic regression tests were used to evaluate the relationship between CLK2 and clinicopathologic features. Kaplan‐Meier survival curve and Cox regression analysis were performed to explore the prognostic significance of CLK2. The association between CLK2 expression and immune landscapes was explored by CIBERSORT and ESTIMATE. Furthermore, GSEA (Gene Set Enrichment Analysis) and alternative splicing (AS) analyses were performed to investigate the relationship between CLK2 expression and downstream signaling pathway. Results. The CLK2 expression was upregulated in CRC in both transcript and protein level. The elevated expression of CLK2 was correlated with local invasion and poor prognosis. Furthermore, CLK2 induced tumor cell adhesion and thereby promotes local invasion of CRC. The CLK2 expression significantly inhibited plasma cells and eosinophil infiltration and showed no relationship with immune and stromal scores of CRC samples. CLK2 might involve in Notch signaling pathway by regulating the AS of CTBP1. Conclusions. CLK2 might be a potential prognostic biomarker and therapeutic target for colorectal cancer.
Prohibitins: A Key Link between Mitochondria and Nervous System Diseases
Authors: Jiang Tianlin , Wang Jiahua , Li Chao , Cao Guiyun , Wang Xiaohong
Published: July 2022

Prohibitins (PHBs) are conserved proteins in eukaryotic cells, which are mainly located in the inner mitochondrial membrane (IMM), cell nucleus, and cell membrane. PHBs play crucial roles in various cellular functions, including the cell cycle regulation, tumor suppression, immunoglobulin M receptor binding, and aging. In addition, recent in vitro and in vivo studies have revealed that PHBs are important in nervous system diseases. PHBs can prevent apoptosis, inflammation, mitochondrial dysfunction, and autophagy in neurological disorders through different molecules and pathways, such as OPA-1, PINK1/Parkin, IL6/STAT3, Tau, NO, LC3, and TDP43. Therefore, PHBs show great promise in the protection of neurological disorders. This review summarizes the relevant studies on the relationship between PHBs and neurological disorders and provides an update on the molecular mechanisms of PHBs in nervous system diseases.
Effects of Stone Removal via Different Approaches in the Treatment of Incarcerated Upper Ureteral Calculi: A Comparative Study
Authors: Yuan Xiaoliang , Wei Hanping , Liu Xiaowu , Jiao Zhimin , Wu Tingchun , Shi Honglei
Published: July 2022

Objective. The aim of this study is to investigate the clinical effects of percutaneous nephrolithotomy and transurethral ureteroscopic lithotripsy in the treatment of incarcerated upper ureteral calculi. Methods. This study retrospectively reviewed 400 patients with incarcerated upper ureteral calculi admitted to the hospital from January 2016 to December 2021. Among them, 200 patients treated with percutaneous nephrolithotomy were included in the percutaneous group and 200 patients treated with transurethral ureteroscopic lithotripsy were included in the transurethral group. Perioperative indicators and stone clearance rates on day 7 and 1 month after operation and the reoperation rate were compared between the two groups. The incidence of postoperative complications was recorded. Results. The operation time and postoperative hospital stay of the percutaneous group were longer than those of the transurethral group (

P
<
0.05

). There was no significant difference in intraoperative blood loss, 24 h postoperative pain score, stone clearance rates on day 3 and day 14 after operation, or the reoperation rate between the two groups (

P
>
0.05

). Postoperative complications in the two groups were mainly grade I and II. The total incidence of complications in the percutaneous group was significantly lower than that in the transurethral group (

P
<
0.05

). Conclusion. Both percutaneous nephrolithotomy and transurethral ureteroscopic lithotripsy are effective in the treatment of incarcerated upper ureteral calculi. The former can reduce the incidence of postoperative complications, but the operation time and postoperative hospital stay are longer.
Self‐Psychological Control and Creativity of Music Creation in Multimusic Environment
Authors: Xu Caihong
Published: July 2022

This study aimed to solve the difficulties in the research of self‐psychology, three executive controls, and creativity in multimusic environment. In today’s teaching and performance, the study of piano adaptation has practical significance in broadening artistic vision, improving performance technology, and training symphonic music thinking. A research method of self‐psychological three executive controls and creativity generation for creation in multimusic environment is proposed. A computer composition algorithm is based on the hidden Markov model and interactive genetic algorithm. By integrating melody elements and rhythm into the traditional Markov model with only notes or rhythm as the unit, a new hidden Markov music prediction model is established, and the interactive genetic algorithm is used to optimize the music segments. Simulation results show that the algorithm can use a small dataset to quickly create music clips with certain melody logic and in line with users’ personality. It is proved that the method based on the hidden Markov model and interactive genetic algorithm can meet the research needs of self‐psychological three execution control and creativity generation in multimusic environment.
Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention
Authors: Xu Rui , Wang Zhizhen , Liu Zhenbing , Han Chu , Yan Lixu , Lin Huan , Xu Zeyan , Feng Zhengyun , Liang Changhong , Chen Xin , Pan Xipeng , Liu Zaiyi
Published: July 2022

Automatic tissue segmentation in whole‐slide images (WSIs) is a critical task in hematoxylin and eosin‐ (H&E‐) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heterogeneity, large intraclass variability and small interclass variability make the classification task challenging. In this paper, we propose a novel bilinear convolutional neural network‐ (Bilinear‐CNN‐) based model with a bilinear convolutional module and a soft attention module to tackle this problem. This method investigates the intraclass semantic correspondence and focuses on the more distinguishable features that make feature output variations relatively large between interclass. The performance of the Bilinear‐CNN‐based model is compared with other state‐of‐the‐art methods on the histopathological classification dataset, which consists of 107.7 k patches of lung cancer. We further evaluate our proposed algorithm on an additional dataset from colorectal cancer. Extensive experiments show that the performance of our proposed method is superior to that of previous state‐of‐the‐art ones and the interpretability of our proposed method is demonstrated by Grad‐CAM.