[Hydrochemical Characteristics of Surface Water and Groundwater in Shiyan Urban Area and Sources of Nitrate].

Nitrate pollution in water bodies is a worldwide environmental problem, and identifying the sources of nitrate is of great significance to guarantee the sustainable use of water resources. A variety of water chemistry indicators and nitrate nitrogen and oxygen isotopes （N-NO and O-NO） were used to analyze the water chemistry characteristics of water bodies in Shiyan to identify the sources of nitrate in the water bodies and to calculate the contribution rate of nitrate from different pollution sources of the water bodies using the SIMMR model. The results showed that the hydrochemical types of surface water and groundwater in the study area were dominated by the HCO-Ca·Mg type, and the formation of nitrate in the water body was mainly affected by nitrification, with non-obvious denitrification.

Comparison of the accuracy of dynamic navigation and the free hand approaches in the placement of pterygoid implants in the completely edentulous maxilla: An in vitro study.

Background/purpose: Pterygoid implant is a promising solution for patients with a partially or fully edentulous atrophic maxilla. However, whether dynamic navigation system will improve the accuracy of pterygoid implant surgery is still unknown. This study aimed to compare the accuracy of dynamic navigation and free-hand approaches in pterygoid implant placement in completely edentulous maxilla models.

Kneadable dough-type hydrogel transforming from dynamic to rigid network to repair irregular bone defects.

Irregular bone defects, characterized by unpredictable size, shape, and depth, pose a major challenge to clinical treatment. Although various bone grafts are available, none can fully meet the repair needs of the defective area. Here, this study fabricates a dough-type hydrogel (DR-Net), in which the first dynamic network is generated by coordination between thiol groups and silver ions, thereby possessing kneadability to adapt to various irregular bone defects.

Virtual pterygoid implant planning in maxillary atrophic patients: prosthetic-driven planning and evaluation.

Purpose: The study aims to use cone beam computed tomography (CBCT) to (1) define the virtual valid length of pterygoid implants in maxillary atrophic patients from the prosthetic prioritized driven position and (2) measure the implant length engaged in the pterygoid process according to the HU difference of the pterygoid maxillary junction.

Materials And Methods: Virtual pterygoid implants were planned with CBCT of maxillary atrophic patients in the software. The entry and angulation of the implant were planned according to the prosthetic prioritized driven position in the 3D reconstruction image.

Nucleolus-Targeted Photodynamic Anticancer Therapy Using Renal-Clearable Carbon Dots.

Photodynamic therapy (PDT), which utilizes light excited photosensitizers (PSs) to generate reactive oxygen species (ROS) and consequently ablate cancer cells or diseased tissue, has attracted a great deal of attention in the last decades due to its unique advantages. In order to further enhance PDT effect, PSs are functionalized to target specific sub-cellular organelles, but most PSs cannot target nucleolus, which is demonstrated as a more efficient and ideal site for cancer treatment. Here, an effective carbon dots (C-dots) photosensitizer with intrinsic nucleolus-targeting capability, for the first time, is synthesized, characterized, and employed for in vitro and in vivo image-guided photodynamic anticancer therapy with enhanced treatment performance at a low dose of PS and light irradiation.

A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set.

Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction.

Growth differentiation factor 15 promotes blood vessel growth by stimulating cell cycle progression in repair of critical-sized calvarial defect.

Repair of large bone defects remains a challenge for surgeons, tissue engineering represents a promising approach. However, the use of this technique is limited by delayed vascularization in central regions of the scaffold. Growth differentiation factor 15(GDF15) has recently been reported to be a potential angiogenic cytokine and has an ability to promote the proliferation of human umbilical vein endothelial cells(HUVECs).

Chronic hepatitis B infection and risk of antituberculosis drug-induced liver injury: Systematic review and meta-analysis.

Background: Antituberculosis drug-induced liver injury (ATDILI) is a major safety concern for the treatment of tuberculosis (TB). The impact of chronic hepatitis B infection (CHBI) on the risk of ATDILI is still controversial. In this study, we aimed to assess systematically the influence of CHBI on the susceptibility to ATDILI.

A block mixture model to map eQTLs for gene clustering and networking.

To study how genes function in a cellular and physiological process, a general procedure is to classify gene expression profiles into categories based on their similarity and reconstruct a regulatory network for functional elements. However, this procedure has not been implemented with the genetic mechanisms that underlie the organization of gene clusters and networks, despite much effort made to map expression quantitative trait loci (eQTLs) that affect the expression of individual genes. Here we address this issue by developing a computational approach that integrates gene clustering and network reconstruction with genetic mapping into a unifying framework.

Modeling Expression Plasticity of Genes that Differentiate Drug-sensitive from Drug-resistant Cells to Chemotherapeutic Treatment.

By measuring gene expression at an unprecedented resolution and throughput, RNA-seq has played a pivotal role in studying biological functions. Its typical application in clinical medicine is to identify the discrepancies of gene expression between two different types of cancer cells, sensitive and resistant to chemotherapeutic treatment, in a hope to predict drug response. Here we modified and used a mechanistic model to identify distinct patterns of gene expression in response of different types of breast cancer cell lines to chemotherapeutic treatment.

A computational algorithm for functional clustering of proteome dynamics during development.

Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development.

A model framework for identifying genes that guide the evolution of heterochrony.

Heterochrony, the phylogenic change in the time of developmental events or rate of development, has been thought to play an important role in producing phenotypic novelty during evolution. Increasing evidence suggests that specific genes are implicated in heterochrony, guiding the process of developmental divergence, but no quantitative models have been instrumented to map such heterochrony genes. Here, we present a computational framework for genetic mapping by which to characterize and locate quantitative trait loci (QTLs) that govern heterochrony described by four parameters, the timing of the inflection point, the timing of maximum acceleration of growth, the timing of maximum deceleration of growth, and the length of linear growth.

Systems mapping of genes controlling chemotherapeutic drug efficiency for cancer stem cells.

Cancer can be controlled effectively by using chemotherapeutic drugs to inhibit cancer stem cells, but there is considerable inter-patient variability regarding how these cells respond to drug intervention. Here, we describe a statistical framework for mapping genes that control tumor responses to chemotherapeutic drugs as well as the efficacy of treatments in arresting tumor growth. The framework integrates the mathematical aspects of the cancer stem cell hypothesis into genetic association studies, equipped with a capacity to quantify the magnitude and pattern of genetic effects on the kinetic decline of cancer stem cells in response to therapy.

A bi-Poisson model for clustering gene expression profiles by RNA-seq.

With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data.

Delivering systems pharmacogenomics towards precision medicine through mathematics.

The latest developments of pharmacology in the post-genomic era foster the emergence of new biomarkers that represent the future of drug targets. To identify these biomarkers, we need a major shift from traditional genomic analyses alone, moving the focus towards systems approaches to elucidating genetic variation in biochemical pathways of drug response. Is there any general model that can accelerate this shift via a merger of systems biology and pharmacogenomics? Here we describe a statistical framework for mapping dynamic genes that affect drug response by incorporating its pharmacokinetic and pharmacodynamic pathways.

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information.

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity.

A maximum likelihood approach to functional mapping of longitudinal binary traits.

Despite their importance in biology and biomedicine, genetic mapping of binary traits that change over time has not been well explored. In this article, we develop a statistical model for mapping quantitative trait loci (QTLs) that govern longitudinal responses of binary traits. The model is constructed within the maximum likelihood framework by which the association between binary responses is modeled in terms of conditional log odds-ratios.

Systems mapping of HIV-1 infection.

Mathematical models of viral dynamics in vivo provide incredible insights into the mechanisms for the nonlinear interaction between virus and host cell populations, the dynamics of viral drug resistance, and the way to eliminate virus infection from individual patients by drug treatment. The integration of these mathematical models with high-throughput genetic and genomic data within a statistical framework will raise a hope for effective treatment of infections with HIV virus through developing potent antiviral drugs based on individual patients' genetic makeup. In this opinion article, we will show a conceptual model for mapping and dictating a comprehensive picture of genetic control mechanisms for viral dynamics through incorporating a group of differential equations that quantify the emergent properties of a system.

A multivalent three-point linkage analysis model of autotetraploids.

Because of its widespread occurrence and role in shaping evolutionary processes in the biological kingdom, especially in plants, polyploidy has been increasingly studied from cytological to molecular levels. By inferring gene order, gene distances and gene homology, linkage mapping with molecular markers has proven powerful for investigating genome structure and organization. Here we review and assess a general statistical model for three-point linkage analysis in autotetraploids by integrating double reduction, a phenomenon that commonly occurs in autopolyploids whose chromosomes are derived from a single ancestral species.

A quantitative model of transcriptional differentiation driving host-pathogen interactions.

Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen.

Towards a comprehensive picture of the genetic landscape of complex traits.

The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development.

Dynamic modeling of genes controlling cancer stem cell proliferation.

The growing evidence that cancer originates from stem cells (SC) holds a great promise to eliminate this disease by designing specific drug therapies for removing cancer SC. Translation of this knowledge into predictive tests for the clinic is hampered due to the lack of methods to discriminate cancer SC from non-cancer SC. Here, we address this issue by describing a conceptual strategy for identifying the genetic origins of cancer SC.