The Influence of Training Habits on The Lower Kinematics of Junior School Freshmen (Girls)

This study is aimed to find out the influence of training habits on the lower limb (hip, knee, ankle) kinematics of junior school girl students, and compare it with the parameter of adults to find out the characteristics of the lower limb kinematics in Juvenile stage, and more desired to explore the law about it to provide the basis for physical training in juvenile stage. Thirty junior school girl students age at 13 to 14 years old participated in this study, of which 15 participants have exercise habits and 15 participants without exercise habits. The Vicon kinematics analysis system (Oxford, Metrics, Ltd., Oxford, UK) with a shooting frequency of 200Hz was used to collect the three-dimensional kinematics of the hip, knee and ankle joint. The study found that the exercise group in step and pace were higher 7.1% and 6.4% respectively than non-exercise group, but in step frequency, 7.7% lower than non-exercise group. In terms of joint angle, compared with participants without exercise habits, participants with exercise habit showed decreased angle of ankle with dorsiflexion, increased angle of the ankle with plantarflexion, and significant peak angle of plantarflexion; meantime, exerted increased angle of eversion and decreased angle inversion which were more similar to the kinematics parameters of adult women. During the push-off period, there was an obvious increase in non-exercise group’s angle of ankle with eversion, which may be one of the reasons for the phenomenon of “outer eight feet” in the juvenile. The physical parameters of the participants with exercise were more approximated to the adults’, indicating that exercise habits have positive effects on the stability of joint, such as the joint force can be better controlled, improving the walking stability, and avoiding injury

Form-NLU: Dataset for the Form Language Understanding

Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases.Comment: Accepted by SIGIR 202

Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies

A functional genomic approach to actionable gene fusions for precision oncology

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize similar to 100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology

Integrated genomic characterization of oesophageal carcinoma

Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

The Nehari Manifold for a Fractional p-Kirchhoff System Involving Sign-Changing Weight Function and Concave-Convex Nonlinearities

In this paper, we are concerned with the following fractional p-Kirchhoff system with sign-changing nonlinearities: M(∫R2nux-uyp/x-yn+psdxdy)-Δpsu=λa(x)uq-2u+α/(α+β)f(x)uα-2uvβ,  in  Ω, M(∫R2n|v(x)-v(y)|p/|x-y|n+psdxdy)-Δpsv=μb(x)vq-2v+(β/α+β)f(x)uαvβ-2v,  in  Ω, and u=v=0,  in  Rn∖Ω, where Ω is a smooth bounded domain in Rn, n>ps, s∈(0,1), λ, μ are two real parameters, 10, l>0, h≥1,a(x),b(x)∈L(α+β)/(α+β-q)(Ω) are sign changing and either a±=max⁡{±a,0}≢0 or b±=max⁡{±b,0}≢0, f∈L(Ω¯) with f∞=1, and f≥0. Using Nehari manifold method, we prove that the system has at least two solutions with respect to the pair of parameters (λ,μ)

Infinitely Many Solutions for a Superlinear Fractional p-Kirchhoff-Type Problem without the (AR) Condition

In this paper, we investigate the existence of infinitely many solutions to a fractional p-Kirchhoff-type problem satisfying superlinearity with homogeneous Dirichlet boundary conditions as follows: [a+b(∫R2Nux-uypKx-ydxdy)]Lpsu-λ|u|p-2u=gx,u, in  Ω, u=0, in  RN∖Ω, where Lps is a nonlocal integrodifferential operator with a singular kernel K. We only consider the non-Ambrosetti-Rabinowitz condition to prove our results by using the symmetric mountain pass theorem

Accurate determination of interfacial thermal resistance inside particle-laden composites based on high-throughput computation and machine learning

Particle-laden composites are typical thermal interfacial materials (TIMs) in the electronic applications, which are widely used in the electron packaging fields. The effective thermal conductivity (effective TC) of the particle-laden composites is dominant by the particle-matrix and particle-particle interfacial thermal resistance (ITR). The reliable identification of ITR is essential for the accurate prediction of TC of the composites, which has potential in the design of TIMs. In this work, we propose an efficient strategy to identify the interfacial thermal resistance in the particle-laden composites combining the numerical simulation, high-throughput computation, machine learning algorithm and simple experimental measurement. Firstly, the high-throughput computation is conducted based on the numerical modeling of the standard samples, in which the input parameters are ITRs in the composites. Afterwards, a prototypical function-based machine learning strategy is employed on the database to describe the numerical relation between the effective TC and the input parameters. Finally, comparing the numerical predictions from the machine learning model with the experimental measurement of the effective TC, a high-throughput screening of the ITRs is executed for the identification of their values. The reliability of the strategy is validated by an example of Al2O3-AlN/silicone composites, showing that the particle-particle ITR is higher than particle-matrix ITR