Do effective public governance and gender (in)equality matter for poverty?

In this study, we examined the relationship between determinants of governance and poverty reduction. We also investigated how female participation in the labour market helps alleviate poverty. We collected the balanced panel data of 29 countries over the period 2004–2016 from the World Bank database and Worldwide Governance Indicators database. Results indicated that robust governance is necessary for poverty reduction and that policy implementation timeliness is more likely to mitigate poverty. Moreover, the inclusion of females in the labour market and an efficient governance system contribute to enhanced well-being among the po

Characterizing Immunoglobulin Repertoire from Whole Blood by a Personal Genome Sequencer

In human immune system, V(D)J recombination produces an enormously large repertoire of immunoglobulins (Ig) so that they can tackle different antigens from bacteria, viruses and tumor cells. Several studies have demonstrated the utility of next-generation sequencers such as Roche 454 and Illumina Genome Analyzer to characterize the repertoire of immunoglobulins. However, these techniques typically require separation of B cell population from whole blood and require a few weeks for running the sequencers, so it may not be practical to implement them in clinical settings. Recently, the Ion Torrent personal genome sequencer has emerged as a tabletop personal genome sequencer that can be operated in a time-efficient and cost-effective manner. In this study, we explored the technical feasibility to use multiplex PCR for amplifying V(D)J recombination for IgH, directly from whole blood, then sequence the amplicons by the Ion Torrent sequencer. The whole process including data generation and analysis can be completed in one day. We tested the method in a pilot study on patients with benign, atypical and malignant meningiomas. Despite the noisy data, we were able to compare the samples by their usage frequencies of the V segment, as well as their somatic hypermutation rates. In summary, our study suggested that it is technically feasible to perform clinical monitoring of V(D)J recombination within a day by personal genome sequencers

Results of active surveillance of foodborne diseases in Pudong New Area of Shanghai, 2015-2018

Objective To understand the epidemic trend and characteristics of foodborne diseases caused by specific pathogens in Pudong New Area of Shanghai, so as to provide scientific evidence for disease prevention and control. Methods From 2015 to 2018, the infectious cases with diarrhea as the main complaint were collected from the sentinel hospital of foodborne disease active monitoring in Pudong New Area of Shanghai. Stool or anal swab samples were collected and tested for Vibrio parahaemolyticus, Salmonella, Shigella, diarrheogenic Escherichia coli, Campylobacter jejuni and Norovirus. At the same time, the profiles were collected and analyzed. Results In 2015-2018, 2 871 stool or anal swab samples of diarrhea cases were monitored and collected. The positive rate of pathogens was 23.55% (676/2 871), including Norovirus 20.04% (97/484), diarrheogenic Escherichia coli 11.84% (340/2 871), Campylobacter jejuni 7.21% (68/943), Vibrio parahaemolyticus 4.01% (115/2 871), Salmonella 3.27% (94/2 871) and Shigella 0.28% (8/2 871). The positive rate was higher in the third quarter, showing a significant peak in summer and autumn. Suspicious food was mainly mixed food (41.12%, 278/676), followed by aquatic animals and their products (22.19%, 150/676) and meat and meat products (10.95%, 74/676). Conclusion Norovirus and diarrheogenic Escherichia coli were the main pathogens of diarrhea cases in Pudong New Area of Shanghai. We should further improve the foodborne disease active monitoring system, carry out detailed epidemiological case investigation for specific pathogen positive cases, and provide technical support for effective prevention and control

Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.Comment: 9 pages,KDD,KDD201

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55 approximately 90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18 approximately 96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5 approximately 18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.published_or_final_versio

Consistent reconstruction of cortical surfaces from longitudinal brain MR images

Accurate and consistent reconstruction of cortical surfaces from longitudinal human brain MR images is of great importance in studying longitudinal subtle change of the cerebral cortex. This paper presents a novel deformable surface method for consistent and accurate reconstruction of inner, central and outer cortical surfaces from longitudinal brain MR images. Specifically, the cortical surfaces of the group-mean image of all aligned longitudinal images of the same subject are first reconstructed by a deformable surface method, which is driven by a force derived from the Laplace’s equation. And then the longitudinal cortical surfaces are consistently reconstructed by jointly deforming the cortical surfaces of the group-mean image to all longitudinal images. The proposed method has been successfully applied to two sets of longitudinal human brain MR images. Both qualitative and quantitative experimental results demonstrate the accuracy and consistency of the proposed method. Furthermore, the reconstructed longitudinal cortical surfaces are used to measure the longitudinal changes of cortical thickness in both normal and diseased groups, where the overall decline trend of cortical thickness has been clearly observed. Meanwhile, the longitudinal cortical thickness also shows its potential in distinguishing different clinical groups

Multimodal classification of Alzheimer's disease and mild cognitive impairment

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers

The Protective Antibodies Induced by a Novel Epitope of Human TNF-α Could Suppress the Development of Collagen-Induced Arthritis

Tumor necrosis factor alpha (TNF-α) is a major inflammatory mediator that exhibits actions leading to tissue destruction and hampering recovery from damage. At present, two antibodies against human TNF-α (hTNF-α) are available, which are widely used for the clinic treatment of certain inflammatory diseases. This work was undertaken to identify a novel functional epitope of hTNF-α. We performed screening peptide library against anti-hTNF-α antibodies, ELISA and competitive ELISA to obtain the epitope of hTNF-α. The key residues of the epitope were identified by means of combinatorial alanine scanning and site-specific mutagenesis. The N terminus (80–91 aa) of hTNF-α proved to be a novel epitope (YG1). The two amino acids of YG1, proline and valine, were identified as the key residues, which were important for hTNF-α biological function. Furthermore, the function of the epitope was addressed on an animal model of collagen-induced arthritis (CIA). CIA could be suppressed in an animal model by prevaccination with the derivative peptides of YG1. The antibodies of YG1 could also inhibit the cytotoxicity of hTNF-α. These results demonstrate that YG1 is a novel epitope associated with the biological function of hTNF-α and the antibodies against YG1 can inhibit the development of CIA in animal model, so it would be a potential target of new therapeutic antibodies

Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features

Neuroimage measures from magnetic resonance (MR) imaging, such as cortical thickness, have been playing an increasingly important role in searching for bio-markers of Alzheimer’s disease (AD). Recent studies show that, AD, mild cognitive impairment (MCI) and normal control (NC) can be distinguished with relatively high accuracy using the baseline cortical thickness. With the increasing availability of large longitudinal datasets, it also becomes possible to study the longitudinal changes of cortical thickness and their correlation with the development of pathology in AD. In this study, the longitudinal cortical thickness changes of 152 subjects from four clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) are measured by our recently-developed 4D (spatial+temporal) thickness measuring algorithm. It is found that the four clinical groups demonstrate very similar spatial distribution of GM loss on cortex. To fully utilizing the longitudinal information and better discriminate the subjects from four groups, especially between Stable-MCI and Progressive-MCI, three different categories of features are extracted for each subject, i.e., (1) static cortical thickness measures computed from the baseline and endline, (2) cortex thinning dynamics, such as the thinning speed (mm/year) and the thinning ratio (endline/baseline), and (3) network features computed from the brain network constructed based on the correlation between the longitudinal thickness changes of different ROIs. By combining the complementary information provided by features from all three different categories, two classifiers are trained to diagnose AD and to predict the conversion to AD in MCI subjects, respectively. In the leave-one-out cross-validation, the proposed method can distinguish AD patients from NC at an accuracy of 96.1%, and can detect 81.7% (AUC=0.875) of the MCI converters at 6-months ahead of their conversions to AD. Also, by analyzing the brain network built via longitudinal cortical thickness changes, a significant decrease (P<0.02) of the network clustering coefficient (associated with the development of AD pathology) is found in the Progressive-MCI group, which indicates the degenerated wiring efficiency of the brain network due to AD. More interestingly, the decreasing of network clustering coefficient of the olfactory cortex region was also found in the AD patients, which suggests the olfactory dysfunction. Although the smell identification test is not performed in ADNI, this finding is consistent with other AD-related olfactory studies