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The data of major and minor element compositions, and crystal size parameters related to:Petrology and Mineralogy of the Martian Olivine Gabbroic Shergottite Northwest Africa 13581: Insights into the Enriched Martian Mantle Source

With the rapid development of wearable cameras, a massive collection of egocentric video for first-person visual perception becomes available. Using egocentric videos to predict first-person activity faces many challenges, including limited field of view, occlusions, and unstable motions. Observing that sensor data from wearable devices facilitates human activity recognition, multi-modal activity recognition is attracting increasing attention. However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition. Nowadays, deep learning in real world has led to a focus on continual learning that often suffers from catastrophic forgetting. But the catastrophic forgetting problem for egocentric activity recognition, especially in the context of multiple modalities, remains unexplored due to unavailability of dataset. In order to assist this research, we present a multi-modal egocentric activity dataset for continual learning named UESTC-MMEA-CL, which is collected by self-developed glasses integrating a first-person camera and wearable sensors. It contains synchronized data of videos, accelerometers, and gyroscopes, for 32 types of daily activities, performed by 10 participants. Its class types and scale are compared with other publicly available datasets. The statistical analysis of the sensor data is given to show the auxiliary effects for different behaviors. And results of egocentric activity recognition are reported when using separately, and jointly, three modalities: RGB, acceleration, and gyroscope, on a base network architecture. To explore the catastrophic forgetting in continual learning tasks, four baseline methods are extensively evaluated with different multi-modal combinations. We hope the UESTC-MMEA-CL can promote future studies on continual learning for first-person activity recognition in wearable applications.

Background: Desert hedgehog (DHH), as a member of the Hedgehog (HH) family, is mainly involved in testicular development and peripheral nerve sheath formation. A DHH variant has been identified in patients with 46, XY gonadal dysgenesis (46, XY GD) with or without neuropathy, but few reports mention the involvement of other complications.Case presentation: Here, we report a Chinese female patient who was hospitalized at 14.3 years old due to slow breast development for more than 1 year. She had a female genitalia phenotype and breast development started at 13 years old but progressed slowly. She was not yet menarche on admission, and she had intermittent muscle cramps in her hands and feet. Her karyotype analysis was 46, XY and the SRY gene was positive. Surgical exploration revealed no uterus or ovaries, and the pathology of bilateral gonads was dysplastic testis tissue, which was consistent with partial gonadal dysgenesis (PGD). Genetic analysis identified a homozygous pathogenic variant in DHH exon 3 (c.1027T>C, p. Cys343Arg). During the 6-year follow-up, she received estrogen replacement therapy, resulting in breast development progression without gender dysphoria. However, her peripheral neuropathy became more obvious, and a nerve conduction study (NCS) indicated decreased nerve conduction velocity and action potential. In addition, she also suffered complications such as obesity, insulin resistance, fatty liver, and gastric ulcers.Conclusion: In the present study, we reported a case of 46, XY GD with minifascicular neuropathy caused by a DHH homozygous variant, and we summarized the reported cases worldwide. For the first time in such patients, we showed a comparison of NCS changes with age as well as the presence of multiple complications not previously reported.

Supplemental material, sj-docx-1-cat-10.1177_10760296221121297 for Association of Prolonged Disease Duration and TG/HDL-C Ratio in Accelerating Atherosclerosis in Patients with Takayasu's Arteritis by Juan Du, Yanlong Ren, Jiayi Liu, Taotao Li, Yaxin Zhang, Shiyu Yang, Tieduo Kang, Shangqiu Ning, Liying Chen, Xi Guo, Wenxian Liu and Lili Pan in Clinical and Applied Thrombosis/Hemostasis

Additional file 1: Supplementary Table 1. Medication of TAK patients with or without aortic valve involvement before first admission. Supplementary Table 2. Laboratory tests and disease activity scores of TAK patients with or without aortic valve involvement. Supplementary Table 3. Predictive factors of adverse events in TAK patients with surgical treatment for aortic valve lesion.

Additional file 1: Table S1. Oligonucleotide primers utilized in RT-PCR. Table S2. Tumor weight and tumor weight inhibition rate of mice in each group.

Sample average approximation (SAA) is a tractable approach to deal with the chance constrained programming, a challenging issue in stochastic programming. The constraint is usually characterized by the 0/1 loss function which results in enormous difficulties in designing numerical algorithms. Most existing methods have been created based on the SAA reformulation, such as binary integer programming or the relaxation, and no viable algorithms have been developed to tackle SAA directly, not to mention theoretical guarantees. In this paper, we investigate a general 0/1 constrained optimization that includes SAA as a special case and thus provide a new way to address SAA. We first show that the new model has some advantageous statistic properties. Then by deriving the Bouligand tangent and Frechet normal cones of the 0/1 constraint, we establish several optimality conditions including the one that can be equivalently expressed by a system of equations, thereby allowing us to design a smoothing Newton type method. We show that the proposed algorithm has a locally quadratic convergence rate and nice numerical performance in comparison with two select algorithms and GUROBI.

This thesis explores the Chinese immigrants’ multilingual competencies, the development of their communication skills in Spain with the languages from different language groups among which the immigrants’ Spanish, English and Chinese skills’ development is the main part of the exploration. The study further examines the relationships between their multilingual development and the influenced factors such as family and school backgrounds, social environment, education, policy, etc. The paper also analyse different ways and processes of acquiring multilingual skills for Chinese immigrants in Spain and presents a number of suggestions for future work on the diversity on immigrant multilingualism. Additionally, the study reflects some effective approaches for second and third language acquisition in multilingual education especially for the languages from different language groups with large distance of structure as well as script. According to the development of the social and linguistic diversity, the multilingualism has to be studied in-depth with a diversity perspective. Generally , the study contributes to a better understanding of the language dynamics that take place in the contact between languages from different family groups in contexts of migration which reveal and enrich the principle of multilingual development on the diversity from immigration perspective which differs from the general multilingual development influenced by a variety of environmental factors and individual reasons. Esta tesis explora las competencias multilingües de los inmigrantes chinos, el desarrollo de sus habilidades de comunicación en España con los idiomas de diferentes grupos lingüísticos, aunque se centra en el desarrollo de las habilidades comunicativas en español, inglés y chino de los inmigrantes de origen chino. El estudio examina más a fondo las relaciones entre su desarrollo multilingüe y los factores que influyen su desarrollo, como los antecedentes familiares y escolares, el entorno social, la educación, las políticas, etc. A través de este estudio, analizando el desarrollo multilingüe en los idiomas de diferentes familias lingüísticas y los diversos orígenes de los inmigrantes chinos en España, nuestra propuesta distingue diferentes formas y procesos de adquisición de las habilidades multilingües para los inmigrantes chinos en España y presenta una serie de sugerencias para un futuro trabajo sobre la diversidad en el multilingüismo de los inmigrantes chinos. Además, el estudio refleja algunos enfoques efectivos para la adquisición de un segundo y tercer idioma en la educación multilingüe. De acuerdo con el desarrollo de la diversidad social y lingüística, el multilingüismo debe ser estudiado en profundidad con una perspectiva de diversidad. En general, el estudio contribuye a una mejor comprensión de las dinámicas lingüísticas que se producen en el contacto entre lenguas de diferentes grupos familiares en contextos de migración, que revelan y enriquecen el principio de desarrollo plurilingüe sobre la diversidad, desde la perspectiva migratoria, que se diferencia del multilingüismo general cuyo desarrollo es influenciado por una variedad de factores ambientales y razones individuales.

Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering performance. To address this drawback, we propose self-supervised discriminative feature learning for deep multi-view clustering (SDMVC). Concretely, deep autoencoders are applied to learn embedded features for each view independently. To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures. In a self-supervised manner, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning. During this process, global discriminative information can be mined to supervise all views to learn more discriminative features, which in turn are used to update the target distribution. Besides, this unified target distribution can make SDMVC learn consistent cluster assignments, which accomplishes the clustering consistency of multiple views while preserving their features' diversity. Experiments on various types of multi-view datasets show that SDMVC achieves state-of-the-art performance.

Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data. Learning such a representa-tion may be critical for developing explainable and human-controllable Deep Generative Models (DGMs) in artificialintelligence. However, disentanglement in GANs is not a triv-ial task, as the absence of sample likelihood and posteriorinference for latent variables seems to prohibit the forwardstep. Inspired by contrastive learning (CL), this paper, froma new perspective, proposes contrastive disentanglement ingenerative adversarial networks (CD-GAN). It aims at dis-entangling the factors of inter-class variation of visual datathrough contrasting image features, since the same factorvalues produce images in the same class. More importantly,we probe a novel way to make use of limited amount ofsupervision to the largest extent, to promote inter-class dis-entanglement performance. Extensive experimental resultson many well-known datasets demonstrate the efficacy ofCD-GAN for disentangling inter-class variation.

Unlike other cell types, developing B cells undergo multiple rounds of somatic recombination and hypermutation to evolve high-affinity antibodies. Reflecting the high frequency of DNA double-strand breaks, adaptive immune protection by B cells comes with an increased risk of malignant transformation. B lymphoid transcription factors (e.g., IKZF1 and PAX5) serve as metabolic gatekeepers by limiting glucose to levels insufficient to fuel transformation. We here identified aberrant expression of the lactonase PON2 in B cell acute lymphoblastic leukemia (B-ALL) as a mechanism to bypass metabolic gatekeeper functions. Compared to normal pre-B cells, PON2 expression was elevated in patient-derived B-ALL samples and correlated with poor clinical outcomes in pediatric and adult cohorts. Genetic deletion of Pon2 had no measurable impact on normal B cell development. However, in mouse models for BCR-ABL1 and NRASG12D-driven B-ALL, deletion of Pon2 compromised proliferation, colony formation, and leukemia initiation in transplant recipient mice. Compromised leukemogenesis resulted from defective glucose uptake and adenosine triphosphate (ATP) production in PON2-deficient murine and human B-ALL cells. Mechanistically, PON2 enabled glucose uptake by releasing the glucose-transporter GLUT1 from its inhibitor stomatin (STOM) and genetic deletion of STOM largely rescued PON2 deficiency. While not required for glucose transport, the PON2 lactonase moiety hydrolyzes the lactone-prodrug 3OC12 to form a cytotoxic intermediate. Mirroring PON2 expression levels in B-ALL, 3OC12 selectively killed patient-derived B-ALL cells but was well tolerated in transplant recipient mice. Hence, while B-ALL cells critically depend on aberrant PON2 expression to evade metabolic gatekeeper functions, PON2 lactonase activity can be leveraged as synthetic lethality to overcome drug resistance in refractory B-ALL.

Deep discriminative models (DDMs), e.g. deep regression forests and deep decision forests, have been extensively studied recently to solve problems such as facial age estimation, head pose estimation, etc.. Due to a shortage of well-labeled data that does not have noise and imbalanced distribution problems, learning DDMs is always challenging. Existing methods usually tackle these challenges through learning more discriminative features or re-weighting samples. We argue that learning DDMs gradually, from easy to hard, is more reasonable, for two reasons. First, this is more consistent with the cognitive process of human beings. Second, noisy as well as underrepresented examples can be distinguished by virtue of previously learned knowledge. Thus, we resort to a gradual learning strategy -- self-paced learning (SPL). Then, a natural question arises: can SPL lead DDMs to achieve more robust and less biased solutions? To answer this question, this paper proposes a new SPL method: easy and underrepresented examples first, for learning DDMs. This tackles the fundamental ranking and selection problem in SPL from a new perspective: fairness. Our idea is fundamental and can be easily combined with a variety of DDMs. Extensive experimental results on three computer vision tasks, i.e., facial age estimation, head pose estimation, and gaze estimation, show our new method gains considerable performance improvement in both accuracy and fairness. Source code is available at https://github.com/learninginvision/SPU., Comment: The article is submitted to TNNLS, and is under review

Data in the real world frequently involve binary status: truth or falsehood, positiveness or negativeness, similarity or dissimilarity, spam or non-spam, and to name a few, with applications into the regression, classification problems and so on. To characterize the binary status, one of the ideal functions is the Heaviside step function that returns one for one status and zero for the other. Hence, it is of dis-continuity. Because of this, the conventional approaches to deal with the binary status tremendously benefit from its continuous surrogates. In this paper, we target the Heaviside step function directly and study the Heaviside set constrained optimization: calculating the tangent and normal cones of the feasible set, establishing several first-order sufficient and necessary optimality conditions, as well as developing a Newton type method that enjoys locally quadratic convergence and excellent numerical performance.

Sparse optimization has seen an evolutionary advance in the past decade with extensive applications ranging from image and signal processing, statistics to machine learning. As a tractable approach, regularization is frequently used, leading to a regularized optimization where l0 norm or its continuous approximations that characterize the sparsity are punished in its objective. From the continuity of approximations to the discreteness of l0 norm, the most challenging model is the l0-regularized optimization. To conquer its hardness, numerous numerically effective methods have been proposed. However, most of them only enjoy that the (sub)sequence converges to a stationary point from the deterministic optimization perspective or the distance between each iterate and any given sparse reference point is bounded by an error bound in the sense of probability. We design a method SNL0: subspace Newton method for the l0-regularized optimization, and prove that its generated sequence converges to a stationary point globally under the strong smoothness condition. In addition, it is also quadratic convergent with the help of locally strong convexity, well explaining that our method, as a second order method, is able to converge very fast. Moreover, a novel mechanism to effectively update the penalty parameter is created, which allows us to get rid of the tedious parameter tuning task that is suffered by most regularized optimization methods.

Hand, foot, and mouth disease (HFMD) has spread widely and led to high disease burden in many countries. In this study, we aimed to analyze the interaction of the main pathogens of HFMD using a mathematical model. A dataset on reported HFMD cases was collected from April, 2009 to December, 2017 in Changsha City. A long-term etiological surveillance was conducted focusing on the pathogens of the disease including enterovirus A71 (EV71), coxsachievirus A16 (CA16), and other enteroviruses. A susceptible-infectious-recovered model was adopted to calculate the reproduction number during the ascending period of reported cases (defined as Rasc) and the descending period (defined as Rdes). About 214,178 HFMD cases (including clinically diagnosed cases and confirmed cases) were reported in Changsha City, among which 31 were death cases with a fatality of 0.01%. The number of reported HFMD cases increased yearly with a Linear model of “f(t) = 18542.68 + 1628.91t” where f(t) and t referred to number of reported cases and sequence of year, respectively. The fatality of the disease decreased yearly with a linear model of “f(t) = – 0.012 + 0.083/t”. About 5319 stool or anal swab specimens were collected from the reported cases. Among them, 1201 were tested EV71 positive, 836 were CA16 positive, and 1680 were other enteroviruses positive. Rasc and Rdes of HFMD was 1.34 (95% confidence interval [CI]: 1.28–1.40) and 0.73 (95% CI: 0.69–0.76), respectively. EV71 and CA16 interacted with each other, and the interaction between EV71 and other enteroviruses and the interaction between CA16 and other enteroviruses were both directional. However, during the reported cases decreasing period, interactions only occurred between EV71 and other enteroviruses and between CA16 and other enteroviruses. These interactions all decreased Rasc but increased Rdes of affected pathogens. The interactions of the pathogens exist in Changsha City. The effective reproduction number of the affected pathogen is adjusted and verges to 1 by the interaction.

Solutions to the linear complementarity problem (LCP) are naturally sparse in many applications such as bimatrix games and portfolio section problems. Despite that it gives rise to the hardness, sparsity makes optimization faster and enables relatively large scale computation. Motivated by this, we take the sparse LCP into consideration, investigating the existence and boundedness of its solution set as well as introducing a new merit function, which allows us to convert the problem into a sparsity constrained optimization. The function turns out to be continuously differentiable and twice continuously differentiable for some chosen parameters. Interestingly, it is also convex if the involved matrix is positive semidefinite. We then explore the relationship between the solution set to the sparse LCP and stationary points of the sparsity constrained optimization. Finally, Newton hard thresholding pursuit is adopted to solve the sparsity constrained model. Numerical experiments demonstrate that the problem can be efficiently solved through the new merit function.

Deep discriminative models (e.g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation. Most existing methods pursue robust and unbiased solutions either through learning discriminative features, or reweighting samples. We argue what is more desirable is learning gradually to discriminate like our human beings, and hence we resort to self-paced learning (SPL). Then, a natural question arises: can self-paced regime lead deep discriminative models to achieve more robust and less biased solutions? To this end, this paper proposes a new deep discriminative model--self-paced deep regression forests with consideration on underrepresented examples (SPUDRFs). It tackles the fundamental ranking and selecting problem in SPL from a new perspective: fairness. This paradigm is fundamental and could be easily combined with a variety of deep discriminative models (DDMs). Extensive experiments on two computer vision tasks, i.e., facial age estimation and head pose estimation, demonstrate the efficacy of SPUDRFs, where state-of-the-art performances are achieved., Comment: ECCV 2020

Facial age estimation is an important and challenging problem in computer vision. Existing approaches usually employ deep neural networks (DNNs) to fit the mapping from facial features to age, even though there exist some noisy and confusing samples. We argue that it is more desirable to distinguish noisy and confusing facial images from regular ones, and alleviate the interference arising from them. To this end, we propose self-paced deep regression forests (SP-DRFs) -- a gradual learning DNNs framework for age estimation. As the model is learned gradually, from simplicity to complexity, it tends to emphasize more on reliable samples and avoid bad local minima. Moreover, the proposed capped-likelihood function helps to exclude noisy samples in training, rendering our SP-DRFs significantly more robust. We demonstrate the efficacy of SP-DRFs on Morph II and FG-NET datasets, where our model achieves state-of-the-art performance., 7 pages, 5 figures, 2 tables

Li Li,1,* Changqiang Wu,2,* Lili Pan,1 Xin Li,1 Anren Kuang,1 Huawei Cai,1 Rong Tian11Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, People’s Republic of China; 2Sichuan Key Laboratory of Medical Imaging & School of Medical Imaging, North Sichuan Medical College, Nanchong 637000, People’s Republic of ChinaCorrespondence: Huawei CaiDepartment of Nuclear Medicine, West China Hospital, Sichuan University, No. 37, Guoxue Lane, Wuhou District, Chengdu 610041, People’s Republic of ChinaTel +86 1 388 071 1669Email hw.cai@yahoo.comRong TianDepartment of Nuclear Medicine, West China Hospital, Sichuan University, No. 37, Guoxue Lane, Wuhou District,Chengdu 610041, People’s Republic of ChinaTel +86 1 898 060 1586Fax +86 0 288 542 2187Email rongtiannuclear@126.com*These authors contributed equally to this workBackground: The early and accurate detection afforded by imaging techniques significantly reduces mortality in cancer patients. However, it is still a great challenge to achieve satisfactory performance in tumor diagnosis using any single-modality imaging method. Magnetic resonance imaging (MRI) has excellent soft tissue contrast and high spatial resolution, but it suffers from low sensitivity. Fluorescence imaging has high sensitivity, but it is limited by penetration depth. Thus, the combination of the two modes could result in synergistic benefits. Here, we design and characterize a novel dual-modality MR/near-infrared fluorescence imaging (MR/NIRFI) nanomicelle and test its imaging properties in mouse models of breast cancer.Methods: The nanomicelles were prepared by incorporating superparamagnetic iron oxide (SPIO) nanoparticles into 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-5000] micelles to which an NIRF dye and a tumor-targeted peptide (N3-Lys-bombesin, Bom) were conjugated. The nanomicelles were characterized for particle size, zeta potential and morphology. The transverse relaxivity, targeting specificity and imaging ability of the nanomicelles for MR/NIRFI were also examined.Results: The fabricated nanomicelles displayed a well-defined spherical morphology with a mean diameter of 145±56 nm and a high transverse relaxivity (493.9 mM−1·s,−1 3.0T). In MRI, the T2 signal reduction of tumors in the Bom-targeted group was 24.1±5.7% at 4 hrs postinjection, whereas only a 0.1±3.4% (P=0.003) decrease was observed in the nontargeted group. In NIRFI, the contrast increased gradually in the targeted group, and the tumor/muscle ratio increased from 3.7±0.3 at 1 hr to 4.7±0.1 at 2 hrs and to 6.4±0.2 at 4 hrs. No significant changes were observed in the nontargeted group at any time points.Conclusion: Considering all our results, we conclude that these novel MR/NIRFI dual-modality nanomicelles could be promising contrast agents for cancer diagnosis.Keywords: SPIO nanoparticles, magnetic resonance imaging, near-infrared fluorescence imaging, tumor diagnosis

Miaomei Yu,1 Lili Pan,1 Chen Sang,2 Qinfeng Mu,1 Lu Zheng,1 Guanghua Luo,1 Ning Xu31Comprehensive Laboratory, the Third Affiliated Hospital of Soochow University, Changzhou 213003, People’s Republic of China; 2Department of Cardiothoracic Surgery, the Third Affiliated Hospital of Soochow University, Changzhou 213003, People’s Republic of China; 3Section of Clinical Chemistry and Pharmacology, Institute of Laboratory Medicine, Lunds University, Lund S‑22185, SwedenObjective: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high mortality-to-incidence ratios. Apolipoprotein M (ApoM), a member of the apolipoprotein family, is mainly synthesized in the liver, whereas its role in HCC has not been elucidated. Here, we examined the effect of ApoM on the biological behavior of HCC cells and the possible mechanisms.Methods: We used CRISPR/Cas9 technology to knock out ApoM in SMMC7721 cells. Differentially expressed genes before and after ApoM knockout (KO) were analyzed by GeneChip microarrays and confirmed by qRT-PCR. Cell assays of proliferation, apoptosis, migration and invasion were performed in SMMC7721 cells, and the expression of epithelial–mesenchymal transition (EMT) markers was performed by western blot. And we performed functional recovery experiments by overexpressing vitamin D receptor (VDR) in SMMC7721.Results: The ApoM-KO SMMC7721 cell line was successfully constructed using the CRISPR/Cas9 technology. Our results showed that silencing ApoM suppressed apoptosis and promoted proliferation, migration, invasion and EMT of SMMC7721 cells. The microarray data revealed that a total of 1,868 differentially expressed genes were identified, including VDR. The qRT-PCR and western blot verification results demonstrated that knocking out ApoM could significantly reduce the expression of VDR. The functional recovery experiments indicated that VDR overexpression could offset the inhibition of cell apoptosis and the promotion of cell proliferation, migration, invasion and EMT caused by knocking out ApoM in SMMC7721 cells.Conclusion: ApoM could function as a tumor suppressor to inhibit the growth and metastasis of SMMC7721 cells via VDR signaling in HCC.Keywords: apolipoprotein M, CRISPR/Cas9, hepatocellular carcinoma cells, GeneChip microarrays, vitamin D receptor

We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior for multimodal image generation, which leads to a new Latent Dirichlet Allocation based GAN (LDAGAN). In detail, for the generative process modelling, LDAGAN defines a generative mode for each sample, determining which generative sub-process it belongs to. For the adversarial training, LDAGAN derives a variational expectation-maximization (VEM) algorithm to estimate model parameters. Experimental results on real-world datasets have demonstrated the outstanding performance of LDAGAN over other existing GANs.

Supplemental Digital Content is available in the text, Background: BCL-2 Associated X (BAX) is an important modulator of apoptosis. The associations between BAX gene polymorphism and cancer susceptibility and prognosis in different ethnic groups and types of cancer have yielded controversial results. To reconcile the results, a systematic review followed by meta-analysis was performed to assess the associations. Methods: A systematic search of Medline database (PubMed), EMBASE, China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang databases for publications on BAX polymorphisms, and susceptibility and prognosis was carried out until July 2017. Retrieved 14 articles met the inclusions. Summary odds ratios (ORs) and hazard ratios (HRs) with their 95% confidence intervals (CIs) were harnessed to determine the strength of correlation between BAX polymorphisms and cancer susceptibility and prognosis, which were combined using fixed- or random-effects models as appropriate. Results: A total of 12 trials involving 3321 cases and 3209 controls were included in our pooled analysis regarding the polymorphisms and the susceptibility of cancers. Overall, results of the present meta-analysis demonstrated that there was no significant association between BAX polymorphisms and susceptibility of cancers (OR = 1.052, 95% CI: 0.827–1.339, P = .679, A vs G). Even in a stratified analysis by ethnicity and the sources of control groups, the results were consistent. Four retrospective studies of 549 cases qualified for meta-analysis were identified to set forth the associations of the polymorphisms with cancer prognosis. Our results suggested that BAX gene polymorphisms were significantly associated with unfavorable prognosis (HR = 1.735, 95% CI: 1.368–2.202, P = .000, GG vs GA/AA). Conclusion: There is no significant association between BAX gene polymorphism and cancer susceptibility, but it probably contributes to increased adverse prognosis to cancer.

The iterative hard thresholding (IHT) algorithm is a popular greedy-type method in (linear and nonlinear) compressed sensing and sparse optimization problems.In this paper, we give an improved iterative hard thresholding algorithm for solving the nonnegative sparsity optimization (NSO) by employing the Armijo-type stepsize rule, which automatically adjusts the stepsize and support set and leads to a sufficient decrease of the objective function each iteration.Consequently, the improved IHT algorithm enjoys several convergence properties under standard assumptions. Those include the convergence to $\alpha$-stationary point (also known as $L$-stationary point in literature if the objective function has Lipschitz gradient) and the finite identification of the true support set. We also characterize when the full sequence converges to a local minimizer of NSO and establish its linear convergence rate. Extensive numerical experiments are included to demonstrate the good performance of the proposed algorithm.

Article describes a kind of the quick proofing process of carton. The digital mode of the quick proofing process of carton is scientific practical and accuracy, which is based on computer-aided design (CAD) technology. It can design carton Structure profile, which can be virtually proofed in the software. Combined digital proofing with digital mould technology, it can build a integration mode of software and hardware of carton device . Using the program to drive carton device,it can be realized that the digital proofing and cutting and creasing process of carton.

This paper studies the bending deformation of plastic honeycomb panels, with analysis of the plastic honeycomb panel with the basic structure and some of the features and application fields and future. This Article obtained the relationship between honeycomb apertures and bending strength by SimulationXpress computer simulation MOR experiments, draws a continuous aperture - strain curve, Analysis of the pore size of honeycomb panels - strain curve characteristics. By analyzing the bending deformation of cellular mechanical properties of plate and found the plastic deformation of honeycomb plate bending model of an ideal honeycomb aperture, which for plastic honeycomb panels in furniture and other applications, such as the construction sector has an important significance. Honeycomb plate by drawing a continuous curve of the bending deformation, which is the choice of plastic honeycomb panel bending strength and pore structure within the cellular significance in the design. This is the field of plastic honeycomb panels in furniture and other engineering applications have some reference and guidance.

With the high-speed development of modernization, the pace of life is also accelerating. Catering to the development of modernization, a lot of convenient fast food is also becoming increasingly popular among the consumers. But the convenient instant food, especially instant noodles which require “secondary processing” ,need large package volume, which takes a lot of room in the shelves and is often not conducive to carrying and transporting. Therefore, this topic primarily aims at design thing a series of new green collapsible paper fast-food boxes, to solve problems such as the space-saving, transportation stacking, being portable and so on, in the meanwhile meeting the needs of sustainable development. The design of collapsible fast-food boxes is based on AtriosCAD. And the structure adopts the most commonly used packaging carton folding method, so the packaging form is simple and processing cost is low. This design uses folding structure and the volume when open is as large as when it is on transportation, which makes it easy to be transported and carried, greatly reducing logistics costs; this structure uses the novel folding method, which enables consumers to use the box simply and conveniently.

Algorithm design of iris outer edge location is one of keys in iris recognition. It dose ask for an excellent performance as well as an acceptable running time. Low-level technologies and simple classifiers are employed in this paper to meet these needs. First of all, we propose a gradient computation for point detection to ensure a shorter running time. In this part, points can be located as many as possible. Then classifiers distinguish points on fake boundaries and iris boundaries. The number of points will be dramatically cut down. One classifier bases on the characteristic shape of irises, one bases on the distances between points and pupil's center and the other bases on gray gradient of each point. At last, LSE (least square method) is employed to fit a curve of iris boundaries. The algorithm has been verified by experiments. The correct rate exceeds to 99% for public databases CASIA, while the running time is shortened to about 80ms.

Urban and rural coordinate employment is a complex and huge dynamic economic system. Much of the information in it are neither black nor white, but grey situation. So, it was appropriate use to be analysis. Evaluation model on urban-rural employment was set, by upgraded grey thesis for appraisement, and then give a qualitative analysis to Shaanxi province.

Iris image preprocessing, including iris localization and iris image quality evaluation, is the key step in iris recognition and has a close relationship to the accuracy of matching. So far, there are many iris localization algorithms having been proposed. In this paper, we propose a new iris localization algorithm, in which we adopt edge points detecting and curve fitting. After this, we set an integral iris image quality evaluation system that is necessary in the automatic iris recognition system. All the procedures of the algorithm are proved to be valid through our experiment on 2400 iris images from CASIA (The Institute of Automation, Chinese Academy of Sciences) database.

Objectives Understanding the mechanisms of fibrosis development in the heart during hypertension is pivotal to find new therapies to counteract heart failure. In this study, we aimed to define the role of Cathepsin S (Cat S) in the development of cardiac inflammation and fibrosis induced by angiotensin II (Ang II). Methods Cat S -/- and age-matched wild-type (WT) C57BL/6J mice were infused continuously with Ang II or saline for 7 days. Immunohistochemical staining and real-time PCR were performed for cardiac fibrosis and inflammation detection. LC3 level either in Ang II-infused hearts or in cultured macrophages were determined by Western Blot and transmission electron microscopy. NF-kB transcriptional activity of macrophages was measured after adenovirus infection with NF-kB luciferase reporter. Results Cat S -/- mice showed severe cardiac fibrosis, including elevated expression of collagen I and α-SMA as compared with WT mice. Moreover, macrophage infiltration and expression of inflammatory cytokines (TNF-α, TGF-β1 and IL-1β) were significantly higher in Cat S -/- than WT hearts. These Ang II-induced effects in Cat S -/- mouse hearts was associated with abnormal accumulation of autophagosomes and reduced clearance of damaged mitochondria, which led to increased levels of ROS and activation of NF-kB in macrophages. Conclusions Cat S is a lysosomal protease expressed in human atherosclerotic and aneurysmal tissues that may contribute to inflammatory diseases. Clearance of damaged mitochondria prevents ROS production and inflammation. Ang II infusion increased Cat S expression in macrophages, and Cat S knockout led to significant accumulation of autophagosomes, increased macrophage production of mitochondrial ROS and activation of NF-kB, which led to macrophage infiltration and expression of pro-inflammatory cytokines and cardiac fibrosis. We provide novel insights into the roles of Cat S in regulating cardiac fibrosis and the inflammatory response to Ang II in mouse by modulating mitophagy degradation.

The demonstration prototype Model SRO-80-1000 is the first spiral-wound RO pure water unit designed and produced in China. The unit is composed of five SRC-4012 modules with cellulose acetate membranes. Each module contains two SRC-401 membrane elements. The effective membrane areas of the unit are about 75 m 2 in total. The unit was applied to the production of primary pure water in the Huzhou Electrodialyzer Plant, Zhejiang Province, and was operated for an accumulated time of over 2,000 h (with some modules operating for over 3,000 h). The operating results show that its performance is stable, average salt rejection is about 90% with a water productivity of 1.3–1.4 m 3 /h, recovery more than 70% and a flux decline slope of −0.01. Long-term operating performance of the unit is described in detail, and the feed water pretreatment is introduced.