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Objectives: This study aims to enhance supervised human activity recognition based on spatiotemporal graph convolutional neural networks by addressing two key challenges: (1) extracting local spatial feature information from implicit joint connections that is unobtainable through standard graph convolutions on natural joint connections alone. (2) Capturing long-range temporal dependencies that extend beyond the limited temporal receptive fields of conventional temporal convolutions. Methods: To achieve these objectives, we propose three novel modules integrated into the spatiotemporal graph convolutional framework: (1) a connectivity feature extraction module that employs attention to model implicit joint connections and extract their local spatial features. (2) A long-range frame difference feature extraction module that captures extensive temporal context by considering larger frame intervals. (3) A coordinate transformation module that enhances spatial representation by fusing Cartesian and spherical coordinate systems. Findings: Evaluation across multiple datasets demonstrates that the proposed method achieves significant improvements over baseline networks, with the highest accuracy gains of 2.76 % on the NTU-RGB+D 60 dataset (Cross-subject), 4.1 % on NTU-RGB+D 120 (Cross-subject), and 4.3 % on Kinetics (Top-1), outperforming current state-of-the-art algorithms. This paper delves into the realm of behavior recognition technology, a cornerstone of autonomous systems, and presents a novel approach that enhances the accuracy and precision of human activity recognition. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to investigate the role of ubiquilin-4 in predicting the immunotherapy response in gastric cancer. Methods: Retrospective RNA-sequencing and immunohistochemical analysis were performed for patients with gastric cancer who received programmed death-1 blockade therapy after recurrence. Multiplex immunohistochemistry identified immune cell types in gastric cancer tissues. We used immunocompetent 615 mice and immunodeficient nude mice to perform tumorigenic experiments. Results: Ubiquilin-4 expression was significantly higher in responders (p < 0.05, false discovery rate > 2.5) and showed slight superiority over programmed death ligand 1 in predicting programmed death-1 inhibitor therapy response (area under the curve: 87.08 vs. 72.50). Ubiquilin-4-high patients exhibited increased CD4+ and CD8+ T cells, T follicular helper cells, monocytes, and macrophages. Ubiquilin-4-overexpressed mouse forestomach carcinoma cells showed significantly enhanced growth in immunocompetent mice but not in immunodeficient mice. Upregulation or downregulation of ubiquilin-4 synergistically affected programmed death ligand 1 at the protein and messenger RNA levels. Functional enrichment analysis revealed significant enrichment of the Notch, JAK-STAT, and WNT signaling pathways in ubiquilin-4-high gastric cancers. Ubiquilin-4 promoted Numb degaration, activating the Notch signaling pathway and upregulating programmed death ligand 1. Conclusions: Ubiquilin-4 may contribute to immune escape in gastric cancer by upregulating programmed death ligand 1 expression in tumor cells through Notch signaling activation. Thus, ubiquilin-4 could serve as a predictive marker for programmed death ligand 1 inhibitor therapy response in gastric cancer. [ABSTRACT FROM AUTHOR]

Object detection holds a crucial role in medical diagnostics. Tasks like organ segmentation and malignancy diagnosis typically necessitate preliminary localization of corresponding anatomical structures. Precise positioning ensures that only pertinent regions require processing, leading to a potential reduction in computational and storage demands. Conventional image detection approaches necessitate numerous candidate boxes, resulting in redundant computations. Developing techniques capable of accurately detecting medical image objects without reliance on candidate boxes holds substantial practical significance. This paper introduces a 2D method for detecting medical image objects, which leverages multi-agent deep Q-network reinforcement learning and a multi-scale image representation. The method constructs a collaborative environment for multiple agents. These agents individually govern the upper-right corner and lower-left corner positions of the object detection frame, progressively converging toward the actual endpoint through iterative interactions. To expedite the detection process, a multi-scale image representation technique is employed. This method segments the process into three scales. Initially, within the coarse-scale space, the agent approximates the region containing the true endpoint, subsequently executing oscillatory movements. Progressively, it refines its approach within the fine-scale space, advancing toward the genuine endpoint with smaller iterative steps. The detection results demonstrate that collaborative detection among agents yields a 2.45 % higher intersection over union compared to non-collaborative detection. Agents exhibit varying step sizes and fields of view in different scale spaces, leading to a reduction in detection time by 0.12 s compared to single-scale comparison. Experimental outcomes demonstrate the superiority of the medical image target detection method proposed in this study over prevailing mainstream detection algorithms. [ABSTRACT FROM AUTHOR]

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data. [ABSTRACT FROM AUTHOR]

Background: Many studies have used pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) as the primary endpoint for the short-term efficacy in gastric cancer, but whether it is a good indicator for overall survival is poorly understood. Methods: This study reviewed a multi-institution database of patients who underwent radical gastrectomy and achieved pCR after NAC. Cox regression models were used to identify clinicopathologic predictors of overall survival (OS) and disease-free survival (DFS). Survival curves were calculated by using the Kaplan–Meier method and compared by means of the log-rank test. Results: OS and DFS in patients with pCR were significantly higher than in those with non-pCR (both P < 0.001). Multivariable analysis confirmed pCR was an independent prognostic factor for OS and DFS (P = 0.009 and P = 0.002 for OS and DFS, respectively). However, the survival benefit for pCR was present only for ypN0 tumors (P = 0.004 and P = 0.001 for OS and DFS, respectively), and OS (P = 0.292) and DFS (P = 0.285) among patients with ypN+ gastric cancer could not be stratified by pCR. Conclusions: In our study, pCR is an independent prognostic factor for OS and DFS, but the survival benefit for pCR is present only for ypN0 tumors but not ypN+ tumors. [ABSTRACT FROM AUTHOR]

The cardiovascular disease is one of the most serious health problems around the world. Traditionally, detection of cardiac arrhythmia based on the visual inspection of electrocardiography (ECG) signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous arrhythmia detection techniques including signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing different myocardial dysfunctions through ECG signals. In this study we propose a novel technique for automatic detection of cardiac arrhythmia in one-lead ECG signals based upon complete ensemble empirical mode decomposition (CEEMD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, ECG signals are decomposed into a series of Intrinsic Mode Functions (IMFs) by using the CEEMD method without the preprocessing of QRS detection. The IMF6 and IMF7 of the ECG signals are extracted, which contain the majority of the ECG signals' energy and are considered to be the predominant IMFs. Second, four levels DWT is employed to decompose the predominant IMFs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3, in which the properties associated with the nonlinear ECG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in ECG system dynamics between normal versus abnormal individual heartbeats. Fourth, neural networks are then used to model, identify and classify ECG system dynamics between normal (healthy) and arrhythmia ECG signals. Finally, experiments are carried out on the MIT-BIH arrhythmia database to assess the effectiveness of the proposed method, in which 436 ECG signal fragments for one lead (MLII) from 28 persons of five classes of heart beats were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.81 % . Compared with various state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of myocardial dysfunction in the clinical application. [ABSTRACT FROM AUTHOR]

Heart valve disorders (HVDs) are the major causes of cardiovascular diseases (CVD), which may be detected at the early stage using routine auscultation examination. The phonocardiogram (PCG) is a graphical representation of the physiological condition of the heart, which differs with respect to heart diseases. It is closely related to valve functionality which provides vital information for the diagnosis of CVD. However, visual inspection of PCG is tedious and error-prone, which makes it necessary and urgent to develop an automated system for the detection of HVDs with PCG recordings. In the present study we propose a novel method for the identification and classification of normal and abnormal non-segmented PCG recordings based on hybrid signal processing tools and deterministic learning theory. First, PCG signal and its first derivative are decomposed into a set of frequency subbands with a number of decomposition levels by using the tunable Q-factor wavelet transform method. Second, fast and adaptive multivariate empirical mode decomposition decomposes the subbands of the PCG signal and its derivative into scale-aligned intrinsic mode components (IMFs). The first two IMFs are extracted, which contain most of the energy of the PCG signal and its derivative and are considered to be the predominant IMFs. Third, Shannon energy is used to extract the characteristic envelope of predominant IMFs. The properties associated with the nonlinear PCG system dynamics are preserved. They are utilized to derive features, which demonstrate significant difference in PCG system dynamics between normal versus abnormal individual heartbeats. Fourth, neural networks are then used to model, identify and classify PCG system dynamics between normal and abnormal PCG signals based on deterministic learning theory. Finally, experiments have been carried out on a publicly available PCG database to verify the effectiveness of the proposed method, which include two types of classification, one for binary classification (normal vs. abnormal) and the other for multi-class classification (normal vs. aortic stenosis vs. mitral regurgitation vs. mitral stenosis vs. mitral valve prolapse). The overall average accuracy for binary, four-class and five-class classification are reported to be 97.75, 98.69 and 98.48%, respectively. The proposed method has obtained the highest overall accuracy in comparison to other state-of-the-art approaches using the same database, which can serve as an assistant diagnostic tool for the automated detection of HVDs in clinical applications. [ABSTRACT FROM AUTHOR]

L-glutamate is an important component of protein. It can prevent gastrointestinal damage caused by NSAIDs. We constructed two-phase enteric-coated granules of aspirin and L-glutamate compound by extrusion spheronization method and fluidized bed coating. The subliminal effective dose of L-glutamate is 100 mg/kg tested by model of gastric ulcer of rats induced by aspirin and drug administration. HPLC-UV and UV–Vis methods were adopted to determine content and cumulative release of aspirin and L-glutamate as quality analysis method indexes. The prescription and process optimization were carried out with yield, sphericity and dissolution. The two-phase compound granules have good sphericity of 0.93 ± 0.05 (aspirin pellets) and 0.94 ± 0.02 (L-glutamate pellets), content of salicylic acid (0.24 ± 0.03)%, dissolution of aspirin (2.36 ± 0.11)%. Quality evaluation and preliminary stability meet the commercial requirements. The stored environment of compound preparation should be sealed in a cool and dark place. [ABSTRACT FROM AUTHOR]

Terahertz computed tomography (THz CT) demonstrates its advantages in aspects of nonmetallic and nonpolar materials penetration, 3D internal structure visualization, etc. To perform satisfied reconstruction results, it is necessary to obtain complete measurements from many different views. However, this process is time-consuming and we usually obtain incomplete projections for THz CT in practice, which generates artifacts in the final reconstructed images. To address this issue, dictionary learning-based THz CT reconstruction (DLTR) model is proposed in this study. Especially, the image patches are extracted from other state-of-the-art reconstructed images to train the initial dictionary by using the K-SVD algorithm. Then, the dictionary can be adaptively updated during THz CT reconstruction. Finally, the updated dictionary is used for further updating reconstructed images. In order to verify the accuracy and quality of DLTR method, the filtered back-projection (FBP), simultaneous algebraic reconstruction technique (SART), and total variation (TV) reconstruction are chosen as comparisons. The experiment results show that the DLTR method has a good capability for noise suppression and structures preservation. [ABSTRACT FROM AUTHOR]

Heart disease prevention is one of the most important tasks for healthcare problems since more than 50 million people around the world are at the risk of cardiovascular disease. Traditionally, electrocardiography (ECG) signals play an important role in the diagnosis of cardiac disorder and arrhythmia detection since they reflect all the electrical activities of the heart. In the present study, we propose a novel technique for automatic detection of cardiac arrhythmia with one-lead ECG signals based upon tunable Q-factor wavelet transform (TQWT), variational mode decomposition (VMD), phase space reconstruction (PSR), and neural networks. First, ECG signals are decomposed into a set of frequency sub-bands with a number of decomposition levels by using the TQWT method without any preprocessing of QRS detection. Second, VMD is employed to decompose the sub-band of ECG signals into different intrinsic modes, in which the first four intrinsic modes contain the majority of the ECG signals' energy and are considered to be the predominant intrinsic modes. They are selected to construct the reference variable for analysis. Third, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear ECG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance has been utilized to derive features, which demonstrates significant difference in ECG system dynamics between normal versus abnormal individual heartbeats. Fourth, neural networks are then used to model, identify and classify ECG system dynamics between normal (healthy) and arrhythmia ECG signals. Finally, experiments are carried out on the MIT-BIH arrhythmia database to verify the effectiveness of the proposed method, in which 436 ECG signal fragments for one lead (MLII) from 28 persons of five classes of heart beats were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.72%. Compared with various state-of-the-art methods, the proposed method demonstrates superior performance and has the potential to serve as a candidate for the automatic detection of myocardial dysfunction in the clinical application. [ABSTRACT FROM AUTHOR]

Phonocardiogram (PCG) plays an important role in evaluating many cardiac abnormalities, such as the valvular heart disease, congestive heart failure and anatomical defects of the heart. However, effective cardiac auscultation requires trained physicians whose work is tough, laborious and subjective. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) detection of PCG recordings without any segmentation of heart sound signals. Hybrid signal processing and artificial intelligence tools, including tunable Q-factor wavelet transform (TQWT), variational mode decomposition (VMD), phase space reconstruction (PSR) and neural networks, are utilized to extract representative features in order to model, identify and detect abnormal patterns in the dynamics of PCG system caused by heart disease. First, heart sound signal is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Second, VMD is employed to decompose the subband of the heart sound signal into different intrinsic modes, in which the first four intrinsic modes contain the majority of the heart sound signal's energy and are considered to be the predominant intrinsic modes. They are selected to construct the reference variable for analysis. Third, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear PCG system dynamics are preserved. Three-dimensional PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in PCG system dynamics between normal and abnormal heart sound signals. Finally, PhysioNet/CinC Challenge heart sound database is used for evaluation and the synthetic minority over-sampling technique method is applied to balance the datasets. By using the 10-fold cross-validation style, experimental results demonstrate that the proposed features with dynamical neural networks based classifier yield classification performance with sensitivity, specificity, overall score and accuracy values of 97.73 % , 98.05 % , 97.89 % , and 97.89 % , respectively. The results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic anomaly detection in the clinical application. [ABSTRACT FROM AUTHOR]

On October 10, 2018, a big landslide occurred on the right bank of the Jinsha River in Baige Village, Polo Township, Jiangda County, Tibet (hereafter called the Baige landslide), which blocked the Jinsha River, forming a barrier lake. Afterward, the landslide dam broke, producing a flood. On November 3, the rear wall of this landslide failed again, also blocking the Jinsha River and creating a bigger barrier lake. Then, by local people, a discharge channel was excavated on the top of the landslide dam, making lake water cross over the discharge channel. As the water flow gradually increased, the landslide dam broke again, producing a more severe flood, resulting in huge economic losses downstream. The purpose of this study is to understand the cause of this landslide and predict the future stability of its head scarp, providing some support for the control scheme in the later stage. A digital orthophoto map (DOM) and a digital elevation model (DEM) of the landslide were created using an unmanned aerial vehicle. Then based on the DOM and DEM, the geometric characteristics of the landslide were described. Multi-phase Planet 5 images were used to infer the development process of the landslide. Finally, the cause of the landslide was analyzed based on the rainfall data and the limit equilibrium calculation. The results show that the Baige landslide was a self-weight creeping event, and its development and trigger were independent of the rainfall. Before the landslide, the slope experienced five stages of evolution: steady deformation, slow deformation, rapid deformation, steady deformation, and rapid deformation. The limit equilibrium calculation indicates that the stability coefficient of the middle section of the head scarp is the lowest, thus which should be cut down as a priority. This study provides a typical example of a self-weight creep type landslide, and an important reference for prediction and prevention of similar large landslides in the Tibetan Plateau, southwestern China. [ABSTRACT FROM AUTHOR]

Chemotherapy is the preferred treatment for advanced stage gastric cancer (GC) patients, and developing chemoresistance is a tremendous challenge to efficacy of GC treatment. The treatments of anti-tumor chemo-agents recruit more tumor-associated macrophages (TAMs) which are highly implicated in the chemoresistance development, but the underlying molecular mechanism is unclear. Here, we demonstrate that hypoxia-inducible factor 1α (HIF1α) in GC cells is activated upon 5-fluorouracil (5-FU) treatment and results in much more accumulation of M2-type TAMs which protect tumor cells from chemo-agents. Mechanistically, in the GC cells under the 5-FU treatment, reactive oxygen species is accumulated and then induces the activation of HIF1α signaling to drive the expression of high-mobility group box 1, which leads to more macrophage's infiltration into GC tumor. In turn, the recruited TAMs exhibit tumor-protected M2-type phenotype and promote the chemoresistance of GC cells via producing growth differentiation factor 15 (GDF15) to exacerbate the fatty acid β-oxidation in tumor cells. Blocking GDF15 using antibody or inhibiting FAO of tumor cells by etomoxir efficiently gave rise to the tumor cell sensitivity to 5-FU. Therefore, our study demonstrates a novel insight in understanding the cross talking between tumor cells and immune microenvironment and provides new therapeutic targets for clinic treatments of gastric cancer. [ABSTRACT FROM AUTHOR]

Adsorption of biochars (BC) produced from cash crop residuals is an economical and practical technology for removing nutrients from agricultural runoff. In this study, BC made of orange tree trunks and tea tree twigs from the Laoguanhe Basin were produced and modified by aluminum chloride (Al-modified) and ferric sulfate solutions (Fe-modified) under various pyrolysis temperatures (200°C–600°C) and residence times (2–5 h). All produced and modified BC were further analyzed for their abilities to adsorb ammonia and phosphorus with initial concentrations of 10–40 mg/L and 4–12 mg/L, respectively. Fe-modified Tea Tree BC 2h/400°C showed the highest phosphorus adsorption capacity of 0.56 mg/g. Al-modified Orange Tree BC 3h/500°C showed the best performance for ammonia removal with an adsorption capacity of 1.72 mg/g. FTIR characterization showed that P = O bonds were formed after the adsorption of phosphorus by modified BC, N-H bonds were formed after ammonia adsorption. XPS analysis revealed that the key process of ammonia adsorption was the ion exchange between K+ and NH4+. Phosphorus adsorption was related to oxidation and interaction between PO43 and Fe3+. According to XRD results, ammonia was found in the form of potassium amide, while phosphorus was found in the form of iron hydrogen phosphates. The sorption isotherms showed that the Freundlich equation fits better for phosphorus adsorption, while the Langmuir equation fits better for ammonia adsorption. The simulated runoff infiltration experiment showed that 97.3% of ammonia was removed by Al-modified Orange tree BC 3h/500°C, and 92.9% of phosphorus was removed by Fe-modified Tea tree BC 2h/400°C. [ABSTRACT FROM AUTHOR]

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals' energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn's widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application. [ABSTRACT FROM AUTHOR]

A detailed study on petrology and mineral chemistry of 12 mantle xenoliths from Late Cretaceous basaltic lava flows at Daxizhuang has been conducted to constrain the nature and secular evolution of the Mesozoic lithospheric mantle beneath the eastern North China Craton (NCC). The Daxizhuang mantle xenoliths are mainly anhydrous spinel lherzolite, with subordinate olivine websterite and rare spinel wehrlite. Based on petrographic and geochemical characteristics, the Daxizhuang spinel lherzolite can be subdivided into two groups. Group 1 lherzolite has olivine with Fo contents from 89.4 to 90.6 and LREE-depleted through spoon-shaped to LREE-enriched REE (rare earth element) patterns in clinopyroxene grains, reflecting variable degrees of partial melt extraction (up to 18%) overprinted by later incipient metasomatism. In contrast, Group 2 lherzolite is strongly metasomatized and enriched in iron, as evidenced by relatively lower Fo contents of olivine (84.9 to 88.2) than that from the Group 1 lherzolite and convex-upward trace element patterns in clinopyroxene grains. The Daxizhuang olivine websterite and spinel wehrlite are rich in Fe, Al and low Mg# in olivine and pyroxenes, and slight Ti-enrichment in spinel. Numerical modeling of the Mg# shows that the Daxizhuang olivine websterite and wehrlite may have resulted from the reaction between host residual lherzolite and evolved, silicate melts at high melt/rock ratios. The result of this study indicates that the lithospheric mantle beneath Daxizhuang underwent multiple metasomatism through asthenosphere-lithosphere interaction, which plays an important role in the transformation of the lithospheric mantle of the NCC at least during Late Cretaceous time. [ABSTRACT FROM AUTHOR]

Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer. Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer. [ABSTRACT FROM AUTHOR]

Hand gestures are spatio-temporal patterns which can be characterized by collections of spatio-temporal features. Recognition of hand gestures is to find the re-occurrences of such spatio-temporal patterns through pattern matching. However, dynamic hand gestures have many obstacles for accurate recognition, including poor lighting conditions, camera's inability to capture dynamic gesture in focus, occlusion due to finger movement, color variations due to lighting conditions. The Microsoft Kinect device provides an effective way to solve the above issues and also provides the skeleton for more convenient hand localization and tracking. The aim of this study is to develop a new trajectory-based method for hand gesture recognition using Kinect. In the first step, trajectory-based hand gesture features including spatial position and direction of fingertips, are derived from Kinect. The properties associated with the hand motion dynamics are preserved in these features. In the second step, radial basis function (RBF) neural networks are employed to model and approximate the hand motion dynamics derived from different hand gestures which represent Arabic numbers (0–9) and English alphabets (A–Z). The trained patterns of the approximated hand motion dynamics is stored in constant RBF networks. In the last step, a bank of dynamical estimators is constructed for all the training patterns, in which the constant RBF networks are embedded in. By comparing the set of estimators with a test gesture pattern, a set of recognition errors are generated, in which the average L 1 norms of the errors are taken as the recognition measure based on the smallest error principle. Finally, experiments are carried out to assess the performance of the proposed method compared with other state-of-the-art approaches. By using the twofold and tenfold cross-validation styles, the correct recognition rates for Arabic numbers (0–9) and English alphabets (A–Z) are reported to be 95.83 % , 97.25 % , and 91.35 % , 92.63 % , respectively. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals can be used to identify the human brain in different disease conditions. Nonetheless, it is difficult to detect the subtle and vital differences in EEG simply by visual inspection because of the non-stationary nature of EEG signals. Specifically, in order to find the epileptogenic focus for medical treatment in the case of a partial epilepsy, an intelligent system that can accurately and automatically detect and discriminate focal and non focal groups of EEG signals is required. This will assist clinicians in locating epileptogenic foci before surgery. In this study we propose a novel method for classification between focal and non focal EEG signals based upon empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third and fourth IMFs components are extracted which contain most of the EEG signals' energy and are considered to be the predominant IMFs. Second, phase space of the two IMFs componets is reconstructed, in which the properties associated with the EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between the focal and non focal groups of EEG signals. Third, neural networks are then used as the classifier with feature vectors as the input to distinguish between focal and non focal EEG signals based on the difference of system dynamics between the two groups. Finally, experiments are carried out on the Bern Barcelona database to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved accuracy on the 50 pairs and 3750 pairs of EEG signals is reported to be 96 % and 95.37 % , respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of focal EEG signals in the clinical application. [ABSTRACT FROM AUTHOR]

Gait measures have received increasing attention in the evaluation of patients with knee osteoarthritis (OA). Comprehending gait parameters is an essential requirement for studying the causes of knee disorders. The aim of this work is to develop a new method to distinguish between asymptomatic (AS) and osteoarthritic knee gait patterns using gait analysis via deterministic learning. Spatiotemporal parameters and three-dimensional knee joint rotations and translations are measured and compared in 19 patients with knee OA and 28 AS control subjects during level walking. The classification approach consists of two stages: a training stage and a classification stage. In the training stage, gait features representing gait dynamics, including knee rotations and translations, are derived from the kinematic data of the knees in six-degree-of-freedom. Gait dynamics underlying gait patterns of AS control subjects and patients with knee OA are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. Gait patterns of AS control subjects and patients with knee OA constitute a training set. In the classification stage, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test knee OA gait pattern to be classified, a set of classification errors are generated. The average L 1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test knee OA gait pattern according to the smallest error principle. Finally, experiments are carried out to demonstrate that the proposed method can effectively separate the gait patterns between the groups of AS control subjects and patients with knee OA. By using the two-fold cross-validation and leave-one-out cross-validation styles, the correct classification rates for knee OA gait patterns are reported to be 95.7 and 97.9%, respectively. [ABSTRACT FROM AUTHOR]

Background: To compare the efficacy of oxaliplatin-based and oxaliplatin-free adjuvant chemotherapies in patients with different Lauren type gastric cancers after D2 gastrectomy. Methods: From our established gastric cancer database, patients with pathological stage II and III gastric cancer who received adjuvant chemotherapy after D2 gastrectomy at Zhongshan Hospital of Fudan University were analyzed. Patients who received different adjuvant chemotherapy regimens were divided into two subgroups: oxaliplatin-based and oxaliplatin-free subgroup. Clinical outcomes were analyzed according to pathological stage and different Lauren types. Results: From Jan 2010 to June 2017, a total of 580 patients met all the eligibility criteria and were enrolled. The median DFS for all the patients was 24.37 months and the median OS was 56.70 months. In patients with intestinal type gastric cancer, the median DFS of the oxaliplatin-based subgroup was significantly longer than that of oxaliplatin-free subgroup (48.73 vs. 18.33 months, P < 0.001). The median OS was not reached in the oxaliplatin-based subgroup and 54.33 months in the oxaliplatin-free subgroup (P = 0.006). In patients with diffuse type gastric cancer, neither DFS nor OS differed significantly between two subgroups. In multivariate analysis, oxaliplatin-based adjuvant chemotherapy was independent positive predictor of DFS (HR 0.40; 95% CI 0.28–0.59; P < 0.001) and OS (HR 0.35; 95% CI 0.20–0.62; P < 0.001) in patients with intestinal type gastric cancer. Conclusions: The results of our study suggested that oxaliplatin-based adjuvant chemotherapy was more effective in patients with intestinal type gastric cancer after D2 gastrectomy but showed no more survival benefit in patients with diffuse type. [ABSTRACT FROM AUTHOR]

Purpose: Pancreatic cancer (PC) is an aggressive type of cancer that exhibits a rapid progression. Previously LOX-1, which is a type II trans-membrane glycoprotein that is expressed in endothelial cells, has been found to be involved in the development of several types of cancer. As yet, however, the expression of LOX-1 and its functional consequences in PC have not been documented. The present study was aimed at investigating the prognostic relevance of LOX-1 expression in PC patients and at resolving its role in PC metastasis. Methods: LOX-1 expression was assessed by immunohistochemistry on a tissue microarray containing samples from 98 PC patients. Kaplan-Meier analyses were performed to compare survival curves, whereas Cox regression analyses were performed to explore the independent prognostic value of LOX-1 expression on the overall survival (OS) of PC patients. Harrel's concordance index was applied to calculate the predictive accuracy of established models. In addition, in vitro scratch wound healing and Transwell assays were used to assess the effect of LOX-1 expression silencing and over-expression on PC cell migration and invasion, whereas Cell Counting Kit-8 (CCK8) and Flow Cytometry (FCM) assays were used to assess its effects on PC cell proliferation and apoptosis. Results: We found that LOX-1 is highly expressed in the PC tumor tissues tested and is related to the occurrence of lymph node metastases, higher TNM stages and a poor OS. We also found that LOX-1 expression may serve as an independent prognostic factor for the OS of PC patients. Our in vitro assays revealed that LOX-1 expression may promote the migration and invasion of PC cells through epithelial-mesenchymal transition (EMT). No effect on PC cell proliferation was noted. Conclusions: From our data we conclude that a high LOX-1 expression in PC tissues is indicative for the occurrence of lymph node metastases, high TNM stages and a poor prognosis. LOX-1 may serve as an independent prognostic biomarker. Our in vitro assays additionally revealed that LOX-1 may enhance the migration and invasion of PC cells through EMT. LOX-1 may also serve as a novel therapeutic target. [ABSTRACT FROM AUTHOR]

Iron-modified corn straw biochar was used as an adsorbent to remove phosphorus from agricultural runoff. When agricultural runoffs with a total phosphorus (TP) concentration of 1.86 mg·L to 2.47 mg·L were filtered at a hydraulic retention time of 2 h through a filtration column packed with the modified biochar, a TP removal efficiency of over 99% and an effluent TP concentration of less than 0.02 mg·L were achieved. The isotherms of the phosphorus adsorption by the modified biochar fitted the Freundlich equation better than the Langmuir equation. The mechanism of the phosphorus adsorbed by the modified biochar was analyzed by using various technologies, i.e. scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR). The results indicated that the surface of the modified biochar was covered by small iron granules, which were identified as FeO. The results also showed that new iron oxides were formed on the surface of the modified biochar after the adsorption of phosphorus. Moreover, new bonds of Fe-O-P and P-C were found, which suggested that the new iron oxides tend to be Fe(PO)(OH). Aside from removing phosphorus, adding the modified biochar into soil also improved soil productivity. When the modified biochar-to-soil rate was 5%, the stem, root, and bean of broad bean plants demonstrated increased growth rates of 91%, 64%, and 165%, respectively. [ABSTRACT FROM AUTHOR]

Self-made cation exchange resin supported nanoscale zero-valent iron (R-nZVI) was used to remove phosphorus in rainwater runoff. 80% of phosphorus in rainwater runoff from grassland was removed with an initial concentration of 0.72 mg·L phosphorus when the dosage of R-nZVI is 8 g per liter rainwater, while only 26% of phosphorus was removed when using cation exchange resin without supported nanoscale zero-valent iron under the same condition. The adsorption capacity of R-nZVI increased up to 185 times of that of the cation exchange resin at a saturated equilibrium phosphorous concentration of 0.42 mg·L. Various techniques were implemented to characterize the R-nZVI and explore the mechanism of its removal of phosphate. Scanning electron microscopy (SEM) indicated that new crystal had been formed on the surface of R-nZVI. The result from inductive coupled plasma (ICP) indicated that 2.1% of nZVI was loaded on the support material. The specific surface area was increased after the load of nanoscale zero-valent iron (nZVI), according to the measurement of BET-N method. The result of specific surface area analysis also proved that phosphorus was removed mainly through chemical adsorption process. X-ray photoelectron spectroscopy (XPS) analysis showed that the new product obtained from chemical reaction between phosphate and iron was ferrous phosphate. [ABSTRACT FROM AUTHOR]

Since first described in 1982, intraductal papillary mucinous neoplasm (IPMN) has been the preferred term to describe the proliferation of the pancreatic ductal epithelium. It is totally different from pancreatic carcinoma in epidemiology, histology, pathology and prognosis. According to the site of involvement, IPMNs are classified into three categories, i.e. main duct type, branch duct type, and combined type. Most branch duct IPMNs are benign, whereas the other two types are often malignant. A large branch duct IPMN and marked dilation of the main pancreatic duct indicate the presence of adenoma at least. The additional existence of large mural nodules increases the possibility of malignancy in all types. The prognosis is more favorable after complete resection of benign and non-invasive malignant IPMNs. Malignant IPMNs that become more aggressive after parenchymal invasion necessitate adequate lymph node dissection. On the other hand, asymptomatic branch duct IPMNs without mural nodules can be observed without the need for resection for a considerable period of time. Our review addresses available data, current understanding, controversy, and future directions about IPMNs. [ABSTRACT FROM AUTHOR]