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This paper is a detailed analysis of how socialist thinking can help International Health Organizations. WHO's health system is of great importance to people all over the world, and it holds a key position in the deal with international relations. But judging from the examples of the effects of various transnational epidemics discussed in this article, the current WHO health system is inadequate for this vital responsibility and fails to handle international relations well. This paper will give a detailed explanation of why WHO needs to apply socialist thought and argue the excellence of socialist thought in helping WHO better deal with international relations and international health issues. The deficiencies of WHO's health system are directly reflected in its policies. This paper will demonstrate how reforming WHO's policies with socialist ideology will improve international relations and better deal with international health issues.

We report the concise synthesis and discovery of a small library of 16 small-molecule sulfobetaine zwitterionic materials that exhibit LCST phase transitions in water.

Multimodal data analysis has attracted ever-increasing attention in computational biology and bioinformatics community recently. However, existing multimodal learning approaches need all data modalities available at both training and prediction stages, thus they cannot be applied to many real-world biomedical applications, which often have a missing modality problem as the collection of all modalities is prohibitively costly. Meanwhile, two diagnosis-related pieces of information are of main interest during the examination of a subject regarding a chronic disease (with longitudinal progression): their current status (diagnosis) and how it will change before next visit (longitudinal outcome). Correct responses to these queries can identify susceptible individuals and provide the means of early interventions for them. In this article, we develop a novel adversarial mutual learning framework for longitudinal disease progression prediction, allowing us to leverage multiple data modalities available for training to train a performant model that uses a single modality for prediction. Specifically, in our framework, a single-modal model (which utilizes the

In this work, for the one-pot two-step coupling reaction of aryl halides with bis(pinacol)diboron, we first applied a phosphorus-free N,S-chelated triazole sulfide palladium-catalyzed system. At the same time, we also carried out careful ligand design to explore the effect of the environment around the coordinating sulfur atom on the reaction. Experiments have shown that the N2-thioether substituted 1,2,3-triazlole palladium is an optimal catalyst The reaction could also reach up to quantitative yield in 4 h with only 1 mol% catalyst. Moreover, some low-activity aryl chlorides can also be coupled with bis(pinacolato)diboron under this catalytic system. We were able to obtain biaryls containing various functional groups in good to excellent yields.

Based on data from the China Education Panel Survey (CEPS), this study conducted an empirical analysis of the effect of parent educational expectations on the competence development of rural left-behind children and the underlying mediating mechanism. The research findings reveal that rural left-behind children have far less developed competencies than their non-left-behind counterparts and that parent educational expectations significantly and positively influence the competence development of left-behind children. Specifically, parent academic expectations impose the greatest impact on academic results of left-behind children; parent expectations of child education levels have the widest effects on various competencies of left-behind children; Parent involvement and teacher support exert a chain mediating effect on the relationship between parent educational expectations and the competence development of rural left-behind children. The mediation effect of teacher support or the concurrence of teacher support and parent involvement remarkably promoted the competence development of rural left-behind children, while parent involvement alone results in masking effects, which impede the improvement of their competencies to some extent.

Purpose The purpose of this paper is to design the novel oil–air distributor (N-OAD). Its structure design, oil feeding reliability, service life and viscosity properties of air bubble (AB) oil were analyzed. Meanwhile, the formation mechanism of AB oil was established based on Kelvin–Helmholtz instability. Design/methodology/approach First, oil–air distributor (OAD) and N-OAD were randomly selected for testing when the air pressure was 0.25 MPa and oil feeding was 100 times per hour. Then, the bubbles were found in the lubricant during the experiment, and the void fraction and viscosity properties of AB oil were tested by image processing method and the MARS 40 rheometer, respectively. Findings N-OAD has longer service life and higher working reliability than OAD. The key factors of AB oil formation were air pressure and oil feeding. And the void fraction of AB oil has different results on the viscosity at high and low shear rates. Originality/value The outcome of this research paper gives an insight to improve the reliability of oil–air lubrication systems and the safety factor of machine tool spindle operation.

Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems such as training neural networks due to low per-iteration computational complexity. In fact, the Newton or quasi-newton methods leveraging second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic quasi-Newton (SQN) methods have been developed to achieve the better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic quasi-Newton method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of $\mathcal{O}(n+n^{1/2}\epsilon^{-2})$ in the finite-sum setting to obtain an $\epsilon$-first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of $\mathcal{O}(\epsilon^{-3})$ is developed, which also matches the existing best result. Extensive experiments on benchmark datasets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization., Comment: 11 pages, accepted for publication by TNNLS. arXiv admin note: text overlap with arXiv:1902.02715 by other authors

Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to evaluate the robustness of the model from a more effective and accurate perspective. The existing universal attack methods fail to exploit the differences and connections between the instance and universal levels to produce dominant perturbations. To address this challenge, we propose a new universal attack method that unifies instance-specific and universal attacks from a feature perspective to generate a more dominant UAP. Specifically, we reformulate the UAP generation task as a minimax optimization problem and then utilize the instance-specific attack method to solve the minimization problem thereby obtaining better training data for generating UAP. At the same time, we also introduce a consistency regularizer to explore the relationship between training data, thus further improving the dominance of the generated UAP. Furthermore, our method is generic with no additional assumptions about the training data and hence can be applied to both data-dependent (supervised) and data-independent (unsupervised) manners. Extensive experiments demonstrate that the proposed method improves the performance by a significant margin over the existing methods in both data-dependent and data-independent settings. Code is available at https://github.com/lisenxd/AT-UAP.

Right lung transplantation in rats has been attempted occasionally, but the technical complexity makes it challenging to apply routinely. Additionally, basic research on inverted lobar lung transplantation is scarce because of the lack of a cost-effective experimental model. We first reported right lung transplantation in a rat model using left-to-right inverted anastomosis to imitate the principle of clinically inverted lung transplantation.Right lung transplantation was performed in 10 consecutive rats. By using a 3-cuff technique, the left lung of the donor rat was implanted into the right thoracic cavity of the recipient rat. The rat lung graft was rotated 180° along the vertical axis to achieve anatomic matching of right hilar structures. Another 10 consecutive rats had received orthotopic left lung transplantation as a control.All lung transplantation procedures were technically successful without intraoperative failure. One rat (10%) died of full pulmonary atelectasis after right lung transplantation, whereas all rats survived after left lung transplantation. No significant difference was observed in heart-lung block retrieval (8.6 ± 0.8 vs 8.4 ± 0.9 minutes), cuff preparation (8.3 ± 0.9 vs 8.7 ± 0.9 minutes), or total procedure time (58.2 ± 2.6 vs 56.6 ± 2.1 minutes) between the right lung transplantation and standard left lung transplantation groups (Right lung transplantation with a left-to-right inverted anastomosis in a rat model is technically easy to master, expeditious, and reproducible. It can potentially imitate the principle of clinically inverted lung transplantation and become an alternative to standard left lung transplantation.

Background: This study aims to investigate the factors contributing to the discrepancy in between biopsy Gleason score and radical prostatectomy Gleason score in patients diagnosed with prostate cancer. Methods: A total of 341 patients who underwent radical prostatectomy from 2011/04 to 2020/12 were identified. We only include patients with initial Gleason score of 6 after biopsy and enrolled 102 patients. Preoperative clinical variables and pathological variables were assessed to clarify the association with post-surgical Gleason score upgrading. The optimal cut-off points for significant continuous variables were then identified by obtaining the area under the receiver operating characteristic curve. Results: Upgrading was observed in 63 patients and non-upgrading in 39 patients. In the multiple variables assessed, smaller prostate volume (PV) (p value=0.0007), prostate specific antigen density (PSAD) (p value=0.0055), positive surgical margins (p value=0.0062) and pathological perineural invasion (p value=0.0038) were significant predictors of Gleason score upgrading. To further explore preclinical variables, a cut-off value for PV (38ml, p value=0.0017) and PSAD(0.26ng/ml2, p value=0.0013) were identified to be associated with Gleason score upgrading Conclusions: Smaller PV and elevated PSAD are associated with increased risk of Gleason score upgrading. The inverse relationship between PV and Gleason score upgrading might reflect the low androgenicity response of the prostate stroma.

For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.

Floating bodies are widely used in the field of offshore engineering. Existing studies show that the motion responses of a floating body in waves will change with the internal water in the cabins, and it is essential to analyze its hydrodynamic performance under various potential operating conditions. However, most of the research only considers the interaction between the floating body and the internal water in the upright position, and there has been little research on the inclined floating body caused by water partially filled in the broadside. In this study, a floating body with a plurality of longitudinal and transverse cabins was designed. The regular wave model test was carried out in a wave basin, and the numerical results were compared with the experimental results, which verified the accuracy of the model. The effects of wave direction, wave frequency, water-filling depth, and cabin division on the motion responses of the floating body are analyzed. The results show that the water inside the cabins has a significant impact on the roll motion. With the increase of the water-filling depth, the natural frequency of the roll motion decreases. Special attention should be paid to the impact on the wave direction and cabin division on the partially water-filled inclined floating body.

Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.

Supplementary Data from Trastuzumab Plus Endocrine Therapy or Chemotherapy as First-line Treatment for Patients with Hormone Receptor–Positive and HER2-Positive Metastatic Breast Cancer (SYSUCC-002)

Purpose:There is no research evidence demonstrate which is the better partner strategy, endocrine therapy or chemotherapy, to combine with anti-HER2 therapy as the first-line management of hormone receptor (HR)-positive (HR+) and HER2-positive (HER2+) metastatic breast cancer (MBC). We wished to ascertain if trastuzumab plus endocrine therapy is noninferior to trastuzumab plus chemotherapy.Patients and Methods:We conducted an open-label, noninferiority, phase III, randomized, controlled trial (NCT01950182) at nine hospitals in China. Participants, stratified by previous adjuvant endocrine therapy and disease status (recurrent disease vs. de novo metastasis), were assigned randomly (1:1) to receive trastuzumab plus endocrine therapy (per investigator's choice of oestrogen-receptor modulators or aromatase inhibitor, with/without concurrent ovarian suppression) or chemotherapy (per investigator's choice of taxanes, capecitabine, or vinorelbine). The primary endpoint was progression-free survival (PFS) with a noninferiority upper margin of 1.35 for the HR. The intention-to-treat population was used in primary and safety analyses.Results:A total of 392 patients were enrolled and assigned randomly to receive trastuzumab plus endocrine therapy (ET group, n = 196) or trastuzumab plus chemotherapy (CT group, n = 196). After a median follow-up of 30.2 months [interquartile range (IQR) 15.0–44.7], the median PFS was 19.2 months [95% confidence interval (CI), 16.7–21.7)] in the ET group and 14.8 months (12.8–16.8) in the CT group (hazard ratio, 0.88; 95% CI, 0.71–1.09; Pnoninferiority < 0.0001). A significantly higher prevalence of toxicity was observed in the CT group compared with the ET group.Conclusions:Trastuzumab plus endocrine therapy was noninferior to trastuzumab plus chemotherapy in patients with HR+HER2+ MBC.

Jasmine flower residue (JFR) is a by-product retained in the production process of jasmine tea and can be used as an unconventional feed due to its rich nutrient value. This study aimed to evaluate the effects of the addition of JFR to the diet of goats on their meat quality and flavor. Twenty-four castrated Nubian male goats were randomly divided into two groups and fed a mixed diet containing 10% JFR (JFR, n = 12) or a conventional diet (CON, n = 12) for 45 days. Meat quality and flavor were measured at the end of the treatment. The addition of JFR to the diet could reduce the shear force of the longissimus dorsi muscle, as well as, the cross-sectional area and diameter of muscle fibers, indicating that the addition of JFR improved meat quality. JFR also increased the content of glutamic acid and ω-3 polyunsaturated fatty acid (C18:3n3 and C20:5N3) and reduced the content of C24:1 and saturated fatty acid (C20:0 and C22:0). In addition, the use of JFR increased the content of acetaldehyde and hexanal in the meat. Furthermore, JFR introduced new volatile components in the meat. The umami, saltiness, and richness of the meat also improved. In conclusion, the addition of jasmine flower residue to the diet can improve the meat quality and flavor of goat.

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy.Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms.Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively.Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics.

Germplasm resource is the source of herbal medicine production. Cultivation of superior germplasm resources helps to resolve the serious conflict between long-term population persistence and growing market demand by producing materials with high quality consistently.Cibotium barometzis the original plant of cibotii rhizoma (“Gouji”), a traditional Chinese medicine used in the therapy of pain, weakness, and numbness of lower extremity. Long-history use ofCibotiumhas rendered wild populations of this species declined seriously in China. Without sufficient understanding of species and lineage diversity ofCibotium, it is difficult to propose a targeted conservation scheme at present, let alone selecting high-quality germplasm resources. In order to fill such a knowledge gap, this study sampledC. barometzand relative species throughout their distribution in China, performed genome skimming to obtain plastome data, and conducted phylogenomic analyses. We constructed a well-supported plastome phylogeny of ChineseCibotium, which showed that three species with significant genetic difference distributed in China, namelyC. barometz,C. cumingii, andC. sino-burmaense, a cryptic species endemic to NW Yunnan and adjacent region of NE Myanmar. Moreover, our results revealed two differentiated lineages ofC. barometzdistributed in the east and west side of a classic phylogeographic boundary that probably shaped by monsoons and landforms in China. We also evaluated the resolution of nine traditional barcode loci, and designed five new DNA barcodes based on the plastome data which can discriminate all these species and lineages of ChineseCibotiumaccurately. These novel findings integrated genetic basis will guide conservation planners and medicinal plant breeders to build systematic conservation plans and exploit germplasm resources ofCibotiumin China.

An advanced smart building platform should meet the needs of humanization and provide the best information and communication technology integration capabilities to enhance the digital upgrading and transformation of the construction industry. From the perspective of the technology governance of the smart city in Yilan County, this study proposes the direction of the sustainable development of intelligence in Lan-yang area and discusses the intelligent disaster prevention of the IoT smart city in Yilan, Taiwan. In the study, data related to technology governance were collected through literature review and validation of empirical fire drills, including the meaning of smart city, the development process of promoting smart city in Taiwan, and technology governance and smart city development. The content and analysis of empirical fire drills demonstrated the specific achievements of the smart city technology governance development in Yilan County, Taiwan.

Learning to improve AUC performance for imbalanced data is an important machine learning research problem. Most methods of AUC maximization assume that the model function is linear in the original feature space. However, this assumption is not suitable for nonlinear separable problems. Although there have been several nonlinear methods of AUC maximization, scaling up nonlinear AUC maximization is still an open question. To address this challenging problem, in this paper, we propose a novel large-scale nonlinear AUC maximization method (named as TSAM) based on the triply stochastic gradient descents. Specifically, we first use the random Fourier feature to approximate the kernel function. After that, we use the triply stochastic gradients w.r.t. the pairwise loss and random feature to iteratively update the solution. Finally, we prove that TSAM converges to the optimal solution with the rate of O(1/t) after t iterations. Experimental results on a variety of benchmark datasets not only confirm the scalability of TSAM, but also show a significant reduction of computational time compared with existing batch learning algorithms, while retaining the similar generalization performance.

Semisupervised support vector machine (S

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is the key to the battery health management system. However, problems of unstable model output and extensive calculation limit the prediction accuracy. This article proposes a Particle Swarm Optimization Random Forest (PSO-RF) prediction method to improve the RUL prediction accuracy. First, the battery capacity extracted from the lithium-ion battery data set of the National Aeronautics and Space Administration (NASA) and the University of Maryland Center for Advanced Life Cycle Engineering (CALCE) is set as the battery life health factor. Then, a PSO-RF prediction model is established based on the optimal parameters for the number of trees and the number of random features to split by the PSO algorithm. Finally, the experiment is verified on the NASA and CALCE data sets. The experiment results indicate that the method predicts RUL with Mean Absolute Error (MAE) less than 2%, Root Mean Square Error (RMSE) less than 3%, and goodness of fit greater than 94%. This method solves the problem of parameter selection in the RF algorithm.

A two-dimensional (2D) simplified model of the very large floating structure (VLFS) is formulated based on Huston's interpretation of the Kane methodology. In this proposed model, the VLFS is considered as a series of discrete floating bodies connected by elastic hinges. The rotation stiffness of elastic hinges has a great influence on the dynamic responses of VLFS and its value is determined based on the vertical displacements equivalent between the simply supported beam model and the elastically hinged multiple bodies model with the same boundary conditions on a concentrated load. Reduced Kane equations are used in the actual dynamic analysis, once initial conditions and mechanical analysis of the system have been formulated. Validation of the Kane-based method and the reliability of the corresponding program developed are established by several comparative studies on a continuous structure and a hinged structure with three parts. The predictions based on the proposed method are essentially identical to the model test data and calculation results provided by related literature.

Purpose: There is no research evidence demonstrate which is the better partner strategy, endocrine therapy or chemotherapy, to combine with anti-HER2 therapy as the first-line management of hormone receptor (HR)-positive (HR+) and HER2-positive (HER2+) metastatic breast cancer (MBC). We wished to ascertain if trastuzumab plus endocrine therapy is noninferior to trastuzumab plus chemotherapy. Patients and Methods: We conducted an open-label, noninferiority, phase III, randomized, controlled trial (NCT01950182) at nine hospitals in China. Participants, stratified by previous adjuvant endocrine therapy and disease status (recurrent disease vs. de novo metastasis), were assigned randomly (1:1) to receive trastuzumab plus endocrine therapy (per investigator's choice of oestrogen-receptor modulators or aromatase inhibitor, with/without concurrent ovarian suppression) or chemotherapy (per investigator's choice of taxanes, capecitabine, or vinorelbine). The primary endpoint was progression-free survival (PFS) with a noninferiority upper margin of 1.35 for the HR. The intention-to-treat population was used in primary and safety analyses. Results: A total of 392 patients were enrolled and assigned randomly to receive trastuzumab plus endocrine therapy (ET group, n = 196) or trastuzumab plus chemotherapy (CT group, n = 196). After a median follow-up of 30.2 months [interquartile range (IQR) 15.0–44.7], the median PFS was 19.2 months [95% confidence interval (CI), 16.7–21.7)] in the ET group and 14.8 months (12.8–16.8) in the CT group (hazard ratio, 0.88; 95% CI, 0.71–1.09; Pnoninferiority < 0.0001). A significantly higher prevalence of toxicity was observed in the CT group compared with the ET group. Conclusions: Trastuzumab plus endocrine therapy was noninferior to trastuzumab plus chemotherapy in patients with HR+HER2+ MBC.

Objective: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. Methods: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared $\ell _2$ -norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. Results: We have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. Conclusion: All these promising experimental results have demonstrated the effectiveness of our new method. Significance: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online. 1 1 The code package of this paper have been made publicly available online at https://github.com/lyujian/Improved-Prediction-of-Cognitive-Outcomes

Citation counts are commonly used to evaluate the scientific impact of a publication on the general premise that more citations probably mean more endorsements. However, two questionable assumptions underpin this idea: a) that all authors contributed equally to the paper; and b) that the endorsement is positive. Obviously, neither of these assumptions hold true. Hence, with this study, we examine two components of citations—their purpose, i.e., the reason for the citation, and polarity, being the author’s attitude toward the cited work. Our findings provide a new perspective on the scientific impact of highly-cited publications. Our methodology consists of three steps. Firstly, a pre-trained model composed of a Word2Vec—a well-known word embedding approach—and a convolutional neural network (CNN) is used to identify citation polarity and purpose. Secondly, in a set of highly-cited papers, we compare eight categories of purpose from foundational to critical and three categories of polarity: positive, negative, and neutral. We further explore how different types of papers—those discussing discoveries or those discussing utilitarian topics—influence the evaluation of scientific impact of papers. Finally, we mine and discover the knowledge (e.g. method, concept, tool or data) to explain the actual scientific impact of a highly-cited paper. To demonstrate how combining citation polarity with purpose can provide far greater details of a paper’s scientific impact, we undertake a case study with 370 highly-cited journal articles spanning “Biochemistry & Molecular Biology” and “Genetics & Heredity”. The results yield valuable insights into the assumption about citation counts as a metric for evaluating scientific impact.

As an emerging machine learning and information retrieval technique, the matrix completion has been successfully applied to solve many scientific applications, such as collaborative prediction in information retrieval, video completion in computer vision, \emph{etc}. The matrix completion is to recover a low-rank matrix with a fraction of its entries arbitrarily corrupted. Instead of solving the popularly used trace norm or nuclear norm based objective, we directly minimize the original formulations of trace norm and rank norm. We propose a novel Schatten $p$-Norm optimization framework that unifies different norm formulations. An efficient algorithm is derived to solve the new objective and followed by the rigorous theoretical proof on the convergence. The previous main solution strategy for this problem requires computing singular value decompositions - a task that requires increasingly cost as matrix sizes and rank increase. Our algorithm has closed form solution in each iteration, hence it converges fast. As a consequence, our algorithm has the capacity of solving large-scale matrix completion problems. Empirical studies on the recommendation system data sets demonstrate the promising performance of our new optimization framework and efficient algorithm.

Summary Pollinator abundance has been declining worldwide as a result of land-use change, habitat destruction, pollution, and pesticide use with important impacts on global biodiversity, food security, and human livelihoods. While the collapse of plant-pollinator systems has been related to the complex network structure, the role of positive plant-reward-pollinator feedbacks (i.e., an extinction cascade based on feedbacks between plant and pollinator decline) remains poorly investigated. Here, we combine a phenomenological model with empirical plant-pollinator networks around the globe to show that plant-pollinator systems may undergo critical transitions to an undesired state with low species abundance as a result of increasing disturbance-induced mortality through positive plant-reward-pollinator feedbacks. Network complexity (i.e., connectance and/or species richness) enhances the capacity of plant-pollinator communities to withstand external disturbances. Our findings highlight the importance of critically evaluating land-use practices and maintaining complexity in plant-pollinator communities to conserve them and the diverse services they provide worldwide.

Zhang, Qiong, Wu, Fang-Yuan, Huang, Yao-Heng, Ma, Jin-Lin, Yang, Shi-Xiong (2022): Camellia piloflora (Theaceae), a new yellow camellia from Guangxi, South China. Phytotaxa 574 (2): 179-184, DOI: 10.11646/phytotaxa.574.2.7, URL: http://dx.doi.org/10.11646/phytotaxa.574.2.7

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.