Learning Large Causal Structures from Inverse Covariance Matrix via Matrix Decomposition

Learning causal structures from observational data is a fundamental yet highly complex problem when the number of variables is large. In this paper, we start from linear structural equation models (SEMs) and investigate ways of learning causal structures from the inverse covariance matrix. The proposed method, called $\mathcal{O}$-ICID (for {\it Independence-preserving} Decomposition from Oracle Inverse Covariance matrix), is based on continuous optimization of a type of matrix decomposition that preserves the nonzero patterns of the inverse covariance matrix. We show that $\mathcal{O}$-ICID provides an efficient way for identifying the true directed acyclic graph (DAG) under the knowledge of noise variances. With weaker prior information, the proposed method gives directed graph solutions that are useful for making more refined causal discovery. The proposed method enjoys a low complexity when the true DAG has bounded node degrees, as reflected by its time efficiency in experiments in comparison with state-of-the-art algorithms

Evidence of Mineral Dust Altering Cloud Microphysics and Precipitation

Multi-platform and multi-sensor observations are employed to investigate the impact of mineral dust on cloud microphysical and precipitation processes in mesoscale convective systems. It is clearly evident that for a given convection strength,small hydrometeors were more prevalent in the stratiform rain regions with dust than in those regions that were dust free. Evidence of abundant cloud ice particles in the dust sector, particularly at altitudes where heterogeneous nucleation process of mineral dust prevails, further supports the observed changes of precipitation. The consequences of the microphysical effects of the dust aerosols were to shift the precipitation size spectrum from heavy precipitation to light precipitation and ultimately suppressing precipitation

Leishmania donovani visceral leishmaniasis diagnosed by metagenomics next-generation sequencing in an infant with acute lymphoblastic leukemia: a case report

BackgroundVisceral leishmaniasis (VL) is a neglected vector-borne tropical disease caused by Leishmania donovani (L. donovani) and Leishmania infantum (L. infantum). Due to the very small dimensions of the protozoa impounded within blood cells and reticuloendothelial structure, diagnosing VL remains challenging.Case presentationHerein, we reported a case of VL in a 17-month-old boy with acute lymphoblastic leukemia (ALL). The patient was admitted to West China Second University Hospital, Sichuan University, due to repeated fever after chemotherapy. After admission, chemotherapy-related bone marrow suppression and infection were suspected based on clinical symptoms and laboratory test results. However, there was no growth in the conventional peripheral blood culture, and the patient was unresponsive to routine antibiotics. Metagenomics next-generation sequencing (mNGS) of peripheral blood identified 196123 L. donovani reads, followed by Leishmania spp amastigotes using cytomorphology examination of the bone marrow specimen. The patient was given pentavalent antimonials as parasite-resistant therapy for 10 days. After the initial treatment, 356 L. donovani reads were still found in peripheral blood by mNGS. Subsequently, the anti-leishmanial drug amphotericin B was administrated as rescue therapy, and the patient was discharged after a clinical cure.ConclusionOur results indicated that leishmaniasis still exists in China. Unbiased mNGS provided a clinically actionable diagnosis of a specific infectious disease from an uncommon pathogen that eluded conventional testing

Legacy Phosphorus Across Canada: Insights from a 60-Year Dataset

Human activities over decades of agriculture and urbanization have altered phosphorus (P) cycling, posing a threat to water quality and ecosystem function. Algal blooms have become a pervasive problem in both small and large waterbodies across Canada. Despite concerted efforts to reduce P loading to surface waters, there has yet to be a noticeable improvement in water quality. This can be attributed to the accumulation of legacy P in the landscape as a result of excessive use of synthetic fertilizers and the production of livestock manure. These legacy P can reach the waterbodies decades after implementing P management practices. Therefore, to better understand long-term P dynamics and their drivers, it is crucial to develop long-term datasets of P inputs and outputs. We developed a 60-year (1961–2021), 250-meter grid resolution data of P components and P surplus across Canada. P surplus is the difference between P inputs (fertilizer inputs, livestock manure, detergent, and human waste) and non-hydrological P output (crop uptake). Our result shows the different drivers of P surplus across Canada. In Ontario and Quebec, the P surplus decreased from nutrient regulation programs in 1981 and subsequently rebounded in 2006 due to an increase in P fertilizer use. In prairie provinces, low P inputs and increasing crop yields have led to the mining of the P stores in the soils. This new, longer dataset will improve our understanding of long-term P dynamics and allow for explicit consideration of the impacts of legacy P on environmental outcomes.This research was undertaken thanks, in part, with support from the Global Water Futures Program funded by the Canada First Research Excellence Fund (CFREF)

Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data

<p>Abstract</p> <p>Background</p> <p>Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. However, the potential impact of gene expression profiling in cancer diagnosis, prognosis and development of personalized treatment may not be fully exploited due to the lack of consensus gene signatures and poor understanding of the underlying molecular mechanisms.</p> <p>Methods</p> <p>We developed a novel approach to derive gene signatures for breast cancer prognosis in the context of known biological pathways. Using unsupervised methods, cancer patients were separated into distinct groups based on gene expression patterns in one of the following pathways: apoptosis, cell cycle, angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and NF-κB. The survival probabilities were then compared between the patient groups to determine if differential gene expression in a specific pathway is correlated with differential survival.</p> <p>Results</p> <p>Our results revealed expression of cell cycle genes is strongly predictive of breast cancer outcomes. We further confirmed this observation by building a cell cycle gene signature model using supervised methods. Validated in multiple independent datasets, the cell cycle gene signature is a more accurate predictor for breast cancer clinical outcome than the previously identified Amsterdam 70-gene signature that has been developed into a FDA approved clinical test MammaPrint^®.</p> <p>Conclusion</p> <p>Taken together, the gene expression signature model we developed from well defined pathways is not only a consistently powerful prognosticator but also mechanistically linked to cancer biology. Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data