Collaboration on evidence synthesis in Africa: a network study of growing research capacity

Background Evidence-based practice in medicine and social policy relies heavily on evidence synthesis. To translate evidence into practical guidelines for low- and middle-income countries, local expertise is essential. The objectives of this study are to assess the change in capacity for conducting evidence synthesis in Africa and to identify key African institutions for regional capacity-building. We take on a network perspective, considering that the position of an institution in the African evidence ecosystem is one constituent of its research capacity. Methods We systematically identified 3548 evidence synthesis publications between 2008 and 2019 with at least one author in Africa from the Web of Science Core Collection. These articles involved 3769 institutions. Longitudinal institution-level collaboration network data were constructed based on co-authorship information. We used social network analysis to examine the institutions’ connectivity and tendency for intra- and interregional collaboration. We also identified the degree- and betweenness-central African institutions and explored the structure and composition of their local network neighbourhoods. Results The number of African institutions involved in evidence synthesis has increased substantially over the last decade, from 31 in 2008 to 521 in 2019, and so has the number of evidence synthesis publications with authors in Africa. African institutions in the evidence ecosystem have also become more connected during this period. Although the amount of intercontinental collaboration continues to exceed that of regional collaboration, the tendency for African institutions to collaborate with partners in Africa is increasing. We identified seven institutions—in South Africa, Egypt and Uganda—as central to the collaboration networks between 2008 and 2019, all of whom showed a tendency to collaborate across sectors. Conclusion The development of more regionally based network-building initiatives would help to foster communities of practice and inter-institutional collaboration, strengthening regional research capacity. Moreover, the analysis in this study adds depth beyond a simple bibliometric analysis and illustrates that network analysis could provide a useful tool to evaluate the effectiveness of capacity-building strategies and programmes in the future

Modeling Transmission of Tuberculosis with MDR and Undetected Cases

This paper presents a novel mathematical model with multidrug-resistant (MDR) and undetected TB cases. The theoretical analysis indicates that the disease-free equilibrium is globally asymptotically stable if R0<1; otherwise, the system may exist a locally asymptotically stable endemic equilibrium. The model is also used to simulate and predict TB epidemic in Guangdong. The results imply that our model is in agreement with actual data and the undetected rate plays vital role in the TB trend. Our model also implies that TB cannot be eradicated from population if it continues to implement current TB control strategies

High hydrostatic pressure harnesses the biosynthesis of secondary metabolites via the regulation of polyketide synthesis genes of hadal sediment-derived fungi

Deep-sea fungi have evolved extreme environmental adaptation and possess huge biosynthetic potential of bioactive compounds. However, not much is known about the biosynthesis and regulation of secondary metabolites of deep-sea fungi under extreme environments. Here, we presented the isolation of 15 individual fungal strains from the sediments of the Mariana Trench, which were identified by internal transcribed spacer (ITS) sequence analysis as belonging to 8 different fungal species. High hydrostatic pressure (HHP) assays were performed to identify the piezo-tolerance of the hadal fungi. Among these fungi, Aspergillus sydowii SYX6 was selected as the representative due to the excellent tolerance of HHP and biosynthetic potential of antimicrobial compounds. Vegetative growth and sporulation of A. sydowii SYX6 were affected by HHP. Natural product analysis with different pressure conditions was also performed. Based on bioactivity-guided fractionation, diorcinol was purified and characterized as the bioactive compound, showing significant antimicrobial and antitumor activity. The core functional gene associated with the biosynthetic gene cluster (BGC) of diorcinol was identified in A. sydowii SYX6, named as AspksD. The expression of AspksD was apparently regulated by the HHP treatment, correlated with the regulation of diorcinol production. Based on the effect of the HHP tested here, high pressure affected the fungal development and metabolite production, as well as the expression level of biosynthetic genes which revealed the adaptive relationship between the metabolic pathway and the high-pressure environment at the molecular level

Validation of the children international IgA nephropathy prediction tool based on data in Southwest China

BackgroundImmunoglobulin A nephropathy (IgAN) is one of the most common kidney diseases leading to renal injury. Of pediatric cases, 25%–30% progress into end-stage kidney disease (ESKD) in 20–25 years. Therefore, predicting and intervening in IgAN at an early stage is crucial. The purpose of this study was to validate the availability of an international predictive tool for childhood IgAN in a cohort of children with IgAN treated at a regional medical centre.MethodsAn external validation cohort of children with IgAN from medical centers in Southwest China was formed to validate the predictive performance of the two full models with and without race differences by comparing four measures: area under the curve (AUC), the regression coefficient of linear prediction (PI), survival analysis curves for different risk groups, and R2D.ResultsA total of 210 Chinese children, including 129 males, with an overall mean age of 9.43 ± 2.71 years, were incorporated from this regional medical center. In total, 11.43% (24/210) of patients achieved an outcome with a GFR decrease of more than 30% or reached ESKD. The AUC of the full model with race was 0.685 (95% CI: 0.570–0.800) and the AUC of the full model without race was 0.640 (95% CI: 0.517–0.764). The PI of the full model with race and without race was 0.816 (SE = 0.006, P &lt; 0.001) and 0.751 (SE = 0.005, P &lt; 0.001), respectively. The results of the survival curve analysis suggested the two models could not well distinguish between the low-risk and high-risk groups (P = 0.359 and P = 0.452), respectively, no matter the race difference. The evaluation of model fit for the full model with race was 66.5% and without race was 56.2%.ConclusionsThe international IgAN prediction tool has risk factors chosen based on adult data, and the validation cohort did not fully align with the derivation cohort in terms of demographic characteristics, clinical baseline levels, and pathological presentation, so the tool may not be highly applicable to children. We need to build IgAN prediction models that are more applicable to Chinese children based on their particular data

Developing and Evaluating Innovative Approaches for Estimating Causal Effects of Low-dose Aspirin on Pregnancy Outcomes

First trimester pregnancy loss occurs in one third of all pregnancies, and recurrent pregnancy loss is also prevalent in up to 30% of women with a prior history. Using intention-to-treat, the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial found that low-dose aspirin (LDA) led to 4.3 (95% CI -1.2 to 9.6) per 100 women at high risk of pregnancy loss. However, the estimated effect, which is based on the assignment to a treatment arm, rather than adherence to a particular treatment protocol, limits the understanding of potential benefits of LDA on pregnancy. Existing methods for adherence adjustment to estimate per-protocol effects in randomized trials are subject to the limitations of observational studies, including model mis-specification due to incorrect confounder selection, or from strong parametric assumptions. The objective of this dissertation is to evaluate and develop innovative approaches for estimating the adherence-adjusted effects of LDA on pregnancy. First, to mitigate the impact of incorrect confounder selection, we evaluated the performance of causal discovery methods in a simulation study using the data resampled from EAGeR. We found that, the evaluated causal discovery method yielded low accuracy in selecting sufficient confounder adjustment sets in the M- or Butterfly-structured causal diagrams. Second, to avoid strong parametric assumptions, we developed an R package implementing the augmented inverse probability weighting (AIPW), a doubly robust estimator supporting stacking machine learning. Our simulation study suggests that, our AIPW package has excellent performance compared to existing R packages implementing doubly robust estimators. Finally, we used the AIPW package with stacking machine learning to estimate per-protocol effects of LDA in a time-fixed setting from the EAGeR trial. Our results show that LDA led to 8.0 (95% CI 2.5 to 13.6) more pregnancies per 100 women who adhered to the randomized treatment assignment for at least 5/7 days per week over at least 80% person-week of follow-up, consistent with the previous analysis using parametric g-formula in a time-varying setting. In conclusion, this dissertation does not only provide additional evidence of the benefits of LDA on pregnancy, but also the state-of-the-art approaches for effect estimations in epidemiologic studies

An Efficient Trajectory Negotiation and Verification Method Based on Spatiotemporal Pattern Mining

In trajectory-based operations, trajectory negotiation and verification are conducive to using airspace resources fairly, reducing flight delay, and ensuring flight safety. However, most of the current methods are based on route negotiation, making it difficult to accommodate airspace user-initiated trajectory requests and dynamic flight environments. Therefore, this paper develops a framework for trajectory negotiation and verification and describes the trajectory prediction, negotiation, and verification processes based on a four-dimensional trajectory. Secondly, users predict flight trajectories based on aircraft performance and flight plans and submit them as requested flight trajectories to the air traffic management (ATM) system for negotiation in the airspace. Then, a spatiotemporal weighted pattern mining algorithm is proposed, which accurately identifies flight combinations that violate the minimum flight separation constraint from four-dimensional flight trajectories proposed by users, as well as flight combinations with close flight intervals and long flight delays in the airspace. Finally, the experimental results demonstrate that the algorithm efficiently verifies the user-proposed flight trajectory and promptly identifies flight conflicts during the trajectory negotiation and verification processes. The algorithm then analyzes the flight trajectories of aircrafts by applying various constraints based on the specific traffic environment; the flight combinations which satisfy constraints can be identified. Then, based on the results identified by the algorithm, the air traffic management system can negotiate with users to adjust the flight trajectory, so as to reduce flight delay and ensure flight safety

Fetal inflammatory response and risk for psychiatric disorders

Abstract Inflammation contributes to numerous neuropsychiatric disorders, especially those that first appear in childhood. Maternal intrauterine environment, including the placenta, has a role in brain development and risk for neuropsychiatric disorders. This study examines the link between fetal inflammatory syndrome (FIRS), which is placental inflammation in the peri-partem period, and neuropsychiatric disorders during childhood.This is a retrospective cohort study using data from electronic medical records over a 19-year period at one women’s hospital. The study includes 4851 children born with placentas meeting criteria for and 31,927 controls identified with normal placentas born during the same period. To be diagnosed with FIRS placenta must contain chorionic vasculitis and/or funisitis. Children had to be in study period for at least 5 years. The primary outcome of the study is incidence of neuropsychiatric disorders during childhood. The secondary outcomes were psychiatric medications prescribed, and psychiatric hospitalizations and treatment. Children born to placentas meeting criteria for FIRS were more likely to be diagnosed with neuropsychiatric disorders (OR = 1.21, CI 95% [1.09,1.35]). Specifically, they were more likely to be diagnosed with autism spectrum disorder (OR = 1.35, CI 95% [1.08, 1.67]), ADHD (OR = 1.27, CI 95% [1.07, 1.49]), conduct disorder (OR = 1.50, CI 95% [1.24, 1.81]), PTSD (OR = 2.46. CI 95% [1.21, 5.04]), adjusting for maternal history of psychiatric disorders, intra-partem substance use, and prescriptions of anti-inflammatory drugs. Children born with placental inflammation are at an increased risk to develop neuropsychiatric disorders. This has profound implications for future research, and early detection, monitoring, and treatment in these children

Insight into the adaptation mechanisms of high hydrostatic pressure in physiology and metabolism of hadal fungi from the deepest ocean sediment

ABSTRACTHigh hydrostatic pressure (HHP) influences the life processes of organisms living at depth in the oceans. While filamentous fungi are one of the essential members of deep-sea microorganisms, few works have explored their piezotolerance to HHP. Here, we obtained three homogeneous Aspergillus sydowii from terrestrial, shallow, and hadal areas, respectively, to compare their pressure resistance. A set of all-around evaluation methods including determination of growth rate, metabolic activity, and microscopic staining observation was established and indicated that A. sydowii DM1 from the hadal area displayed significant piezotolerance. Global analysis of transcriptome data under elevated HHP revealed that A. sydowii DM1 proactively modulated cell membrane permeability, hyphae morphology, and septal quantities for seeking a better livelihood under mild pressure. Besides, differentially expressed genes were mainly enriched in the biosynthesis of amino acids, carbohydrate metabolism, cell process, etc., implying how the filamentous fungi respond to elevated pressure at the molecular level. We speculated that A. sydowii DM1 could acclimatize itself to HHP by adopting several strategies, including environmental response pathway HOG-MAPK, stress proteins, and cellular metabolisms.IMPORTANCEFungi play an ecological and biological function in marine environments, while the physiology of filamentous fungi under high hydrostatic pressure (HHP) is an unknown territory due to current technologies. As filamentous fungi are found in various niches, Aspergillus sp. from deep-sea inspire us to the physiological trait of eukaryotes under HHP, which can be considered as a prospective research model. Here, the evaluation methods we constructed would be universal for most filamentous fungi to assess their pressure resistance, and we found that Aspergillus sydowii DM1 from the hadal area owned better piezotolerance and the active metabolisms under HHP indicated the existence of undiscovered metabolic strategies for hadal fungi. Since pressure-related research of marine fungi has been unexpectedly neglected, our study provided an enlightening strategy for them under HHP; we believed that understanding their adaptation and ecological function in original niches will be accelerated in the perceivable future

Sidereal filtering based on single differences for mitigating GPS multipath effects on short baselines

Carrier-phase multipath effects are one of the most significant error sources in precise Global Positioning System (GPS) positioning applications. A new sidereal filtering algorithm based on single differences is developed to mitigate multipath effects for short-baseline high-rate GPS applications such as structural deformation monitoring. This method differs from traditional sidereal filtering in that our method operates on the single differences rather than the coordinates or double differences. A multipath model for the single differences on the reference day is established for each satellite and is used to remove multipath errors from observations of subsequent days by taking advantage of the sidereal repeatability of multipath signals. Using both simulated and real GPS observations, we demonstrate that this method is insensitive to different weighting strategies used in computing single differences from double differences. Applying the proposed method can reduce the root mean square (RMS) of positioning noises by 82\% on average. Compared to sidereal filtering (in either coordinate or double differences domain) and aspect repeat time adjustment, this method can further reduce the RMS values by 13 and 7\%, respectively. Wavelet spectra have shown that the proposed method is more effective in mitigating multipath errors of both long and short periods. This method is also more advantageous in that it is applicable when different GPS satellites are observed on different days

Experimental demonstration of subband switching in Tb/s elastic optical network based on optical SCFDM superchannel

We experimentally demonstrate subband switching with a frequency spacing of 10-GHz in a Tb/s elastic optical network based on optical single-carrier frequency-division-multiplexing (SCFDM) superchannel. ? 2012 IEEE.EI