The Public-Private Mix in National and International Development

Social welfare is a complex, multi-dimensional, field of practice that seeks to promote the well-being of people everywhere. But national responses to social welfare differ dramatically from one society to the next and, often, valid comparisons between different nations and systems of social welfare are difficult to undertake. This paper addresses that issue by introducing an innovative approach to welfare policy analysis using a Private-Public Development Mix (PPDM) model. The PPDM draws on all four of social welfare’s core institutions—the State, the family & household, the Market, and Civil Society Organizations (CSOs)—as well as four sets of social challenges for which national and international policy responses are needed. The utility of the model is demonstrated through analyses of public-private responses to poverty alleviation efforts in the United States and to advancing compulsory primary and middle school education among rural children living in two of China’s poorest regions

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively

Functional Analysis of the Kinome of the Wheat Scab Fungus Fusarium graminearum

As in other eukaryotes, protein kinases play major regulatory roles in filamentous fungi. Although the genomes of many plant pathogenic fungi have been sequenced, systematic characterization of their kinomes has not been reported. The wheat scab fungus Fusarium graminearum has 116 protein kinases (PK) genes. Although twenty of them appeared to be essential, we generated deletion mutants for the other 96 PK genes, including 12 orthologs of essential genes in yeast. All of the PK mutants were assayed for changes in 17 phenotypes, including growth, conidiation, pathogenesis, stress responses, and sexual reproduction. Overall, deletion of 64 PK genes resulted in at least one of the phenotypes examined, including three mutants blocked in conidiation and five mutants with increased tolerance to hyperosmotic stress. In total, 42 PK mutants were significantly reduced in virulence or non-pathogenic, including mutants deleted of key components of the cAMP signaling and three MAPK pathways. A number of these PK genes, including Fg03146 and Fg04770 that are unique to filamentous fungi, are dispensable for hyphal growth and likely encode novel fungal virulence factors. Ascospores play a critical role in the initiation of wheat scab. Twenty-six PK mutants were blocked in perithecia formation or aborted in ascosporogenesis. Additional 19 mutants were defective in ascospore release or morphology. Interestingly, F. graminearum contains two aurora kinase genes with distinct functions, which has not been reported in fungi. In addition, we used the interlog approach to predict the PK-PK and PK-protein interaction networks of F. graminearum. Several predicted interactions were verified with yeast two-hybrid or co-immunoprecipitation assays. To our knowledge, this is the first functional characterization of the kinome in plant pathogenic fungi. Protein kinase genes important for various aspects of growth, developmental, and infection processes in F. graminearum were identified in this study

The role of private nonprofit organizations in promoting compulsory education in rural China: Applying the public-private mix model

Often referred to as the “public-private mix” in social policy, the social structure of all societies is characterized by a mix of the government, market, household, and nongovernmental actors. This dissertation examines the nature of the public-private mix in China, one of the world’s most rapidly developing countries. Particular attention is given to the changing responsibilities of the state, the market, households, and the nonprofit sector with respect to the provision of compulsory education in rural China. Government documents, media contents, and scholarly literature are used to map the changing roles of the state, market and households in contemporary China. A snowball sample of 464 private nonprofit organizations (NPOs) is studied to examine the role of the emerging nonprofit sector. Government officials and nonprofit leaders are interviewed in an effort to better understand the unique challenges and opportunities that confront each partner in the public-private mix. The results suggest that, currently, the state remains the dominant provider, although the market, households and the nonprofit sector are taking on more responsibilities. The study demonstrates that there is a lack of a clear division of labor among the sectors, in that all four sectors are providing similar programs. The study argues that such a phenomenon is caused by China mobilizing the state, market and households to create the nonprofit sector. The state, market and households are each contributing to the development of NPOs and, in turn, are providing support for the organizations they created. China’s efforts at establishing a viable nonprofit sector face challenges of various types and intensity. Findings of this study suggest that the process of NPO formation in China is similar to that experienced by the country in implementing market reforms, which resulted in the establishment of a vibrant market sector. Based on these observations and analysis, the study proposes strategies to facilitate collaboration among different sectors aimed at improving social welfare provision in China. Finally, the study extends and refines the public-private mix model and offers a methodological approach to the study of welfare provision in other countries going through structural social and economic changes

Chitosan Microsphere Used as an Effective System to Deliver a Linked Antigenic Peptides Vaccine Protect Mice Against Acute and Chronic Toxoplasmosis

Multiple antigenic peptide (MAP) vaccines have advantages over traditional Toxoplasma gondii vaccines, but are more susceptible to enzymatic degradation. As an effective delivery system, chitosan microspheres (CS) can overcome this obstacle and act as a natural adjuvant to promote T helper 1 (Th1) cellular immune responses. In this study, we use chitosan microparticles to deliver multiple antigenic epitopes from GRA10 (G10E), containing three dominant epitopes. When G10E was entrapped within chitosan microparticles (G10E-CS), adequate peptides for eliciting immune response were loaded in the microsphere core and this complex released G10E peptides stably. The efficiency of G10E-CS was detected both in vitro, via cell culture, and through in vivo mouse immunization. In vitro, G10E-CS activated Dendritic Cells (DC) and T lymphocytes by upregulating the secretion of costimulatory molecules (CD40 and CD86). In vivo, Th1 biased cellular and humoral immune responses were activated in mice vaccinated with G10E-CS, accompanied by significantly increased production of IFN-γ, IL-2, and IgG, and decreases in IL-4, IL-10, and IgG1. Immunization with G10E-CS conferred significant protection with prolonged survival in mice model of acute toxoplasmosis and statistically significant decreases in cyst burden in murine chronic toxoplasmosis. The results from this study indicate that chitosan microspheres used as an effective system to deliver a linked antigenic peptides is a promising strategy for the development of efficient vaccine against T. gondii

Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach

Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods

Petrogenesis and tectonic implications of late Oligocene highly fractionated leucogranites in the Ailao Shan-Red River shear zone, SW China

We report for the first time the occurrence of leucogranite bodies that intruded the Ailaoshan metamorphic complex along the Ailao Shan-Red River (ASRR) shear zone. Leucogranites in Yuanyang, Southeast Yunnan province, are composed of quartz (~30%), alkali feldspar (~45%), plagioclase (~5%), muscovite (~13%) and biotite (~3%) and other minor silicate minerals (~3%, garnet, tourmaline). U–Pb dating of zircon from leucogranites yields a weighted mean age of 27.4 ± 0.5 Ma, suggesting a late Oligocene anatexis event. These leucogranites are strongly peraluminous with A/CNK values of 1.01–1.19 and have high SiO (73.3–77.0 wt%) and KO + NaO (7.87–10.08 wt%), and low CaO (0.41–0.99 wt%), MgO (0.04–0.21 wt%), TiO (0.01–0.06 wt%) and FeO (0.44–0.86 wt%) contents. They are geochemically similar to Himalayan leucogranites and are enriched in large-ion lithophile elements (e.g. Rb), and depleted in high-field-strength elements (e.g. Nb, Zr, and Hf). They have low Zr/Hf (12.3–22.5) and Nb/Ta (2.54–12.73) with negative Eu anomalies (Eu/Eu = 0.05–0.45), indicative of extensive fractional crystallization of plagioclase and zircon. In addition, these rocks have low Sr/Sr (t) (0.7072–0.7111), high ε(t) (−1.53 to −3.61). They contain zircon grains with ε (−5 to +1.8) and old two-stage Hf model ages of 1.24–1.02 Ga, implying that they were probably derived from partial melting of amphibolite (old crustal material) within the lower crust. In combination with previous studies of metamorphism of the ASRR shear zone, our study demonstrates that the leucogranites may have been generated in a highly dynamic setting involving both extrusion and extension that defined the beginning of left-lateral slip-strike movement of the ASRR shear zone at 27 Ma