A predictive model for kidney transplant graft survival using machine learning

Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.Comment: This work has been published: Pahl ES, Street WN, Johnson HJ, Reed AI. "A Predictive Model for Kidney Transplant Graft Survival Using Machine Learning." 4th International Conference on Computer Science and Information Technology (COMIT 2020), November 28-29, 2020, Dubai, UAE. ISBN: 978-1-925953-30-5. Volume 10, Number 16.10.5121/csit.2020.10160

DING Proteins from Phylogenetically Different Species Share High Degrees of Sequence and Structure Homology and Block Transcription of HIV-1 LTR Promoter

Independent research groups reported that DING protein homologues isolated from bacterial, plant and human cells demonstrate the anti-HIV-1 activity. This might indicate that diverse organisms utilize a DING-mediated broad-range protective innate immunity response to pathogen invasion, and that this mechanism is effective also against HIV-1. We performed structural analyses and evaluated the anti-HIV-1 activity for four DING protein homologues isolated from different species. Our data show that bacterial PfluDING, plant p38SJ (pDING), human phosphate binding protein (HPBP) and human extracellular DING from CD4 T cells (X-DING-CD4) share high degrees of structure and sequence homology. According to earlier reports on the anti-HIV-1 activity of pDING and X-DING-CD4, other members of this protein family from bacteria and humans were able to block transcription of HIV-1 and replication of virus in cell based assays. The efficacy studies for DING-mediated HIV-1 LTR and HIV-1 replication blocking activity showed that the LTR transcription inhibitory concentration 50 (IC50) values ranged from 0.052–0.449 ng/ml; and the HIV-1 replication IC50 values ranged from 0.075–0.311 ng/ml. Treatment of cells with DING protein alters the interaction between p65-NF-κB and HIV-1 LTR. Our data suggest that DING proteins may be part of an innate immunity defense against pathogen invasion; the conserved structure and activity makes them appealing candidates for development of a novel therapeutics targeting HIV-1 transcription

DMA, a Bisbenzimidazole, Offers Radioprotection by Promoting NFκB Transactivation through NIK/IKK in Human Glioma Cells

BACKGROUND: Ionizing radiation (IR) exposure often occurs for human beings through occupational, medical, environmental, accidental and/or other sources. Thus, the role of radioprotector is essential to overcome the complex series of overlapping responses to radiation induced DNA damage. METHODS AND RESULTS: Treatment of human glioma U87 cells with DMA (5- {4-methylpiperazin-1-yl}-2-[2'-(3, 4-dimethoxyphenyl)-5'-benzimidazolyl] in the presence or absence of radiation uncovered differential regulation of an array of genes and proteins using microarray and 2D PAGE techniques. Pathway construction followed by relative quantitation of gene expression of the identified proteins and their interacting partners led to the identification of MAP3K14 (NFκB inducing kinase, NIK) as the candidate gene affected in response to DMA. Subsequently, over expression and knock down of NIK suggested that DMA affects NFκB inducing kinase mediated phosphorylation of IKKα and IKKβ both alone and in the presence of ionizing radiation (IR). The TNF-α induced NFκB dependent luciferase reporter assay demonstrated 1.65, 2.26 and 3.62 fold increase in NFκB activation at 10, 25 and 50 µM DMA concentrations respectively, compared to control cells. This activation was further increased by 5.8 fold in drug + radiation (50 µM +8.5 Gy) treated cells in comparison to control. We observed 51% radioprotection in control siRNA transfected cells that attenuated to 15% in siRNA NIK treated U87 cells, irradiated in presence of DMA at 24 h. CONCLUSIONS: Our studies show that NIK/IKK mediated NFκB activation is more intensified in cells over expressing NIK and treated with DMA, alone or in combination with ionizing radiation, indicating that DMA promotes NIK mediated NFκB signaling. This subsequently leads to the radioprotective effect exhibited by DMA

Identification and Classification of Conserved RNA Secondary Structures in the Human Genome

The discoveries of microRNAs and riboswitches, among others, have shown functional RNAs to be biologically more important and genomically more prevalent than previously anticipated. We have developed a general comparative genomics method based on phylogenetic stochastic context-free grammars for identifying functional RNAs encoded in the human genome and used it to survey an eight-way genome-wide alignment of the human, chimpanzee, mouse, rat, dog, chicken, zebra-fish, and puffer-fish genomes for deeply conserved functional RNAs. At a loose threshold for acceptance, this search resulted in a set of 48,479 candidate RNA structures. This screen finds a large number of known functional RNAs, including 195 miRNAs, 62 histone 3′UTR stem loops, and various types of known genetic recoding elements. Among the highest-scoring new predictions are 169 new miRNA candidates, as well as new candidate selenocysteine insertion sites, RNA editing hairpins, RNAs involved in transcript auto regulation, and many folds that form singletons or small functional RNA families of completely unknown function. While the rate of false positives in the overall set is difficult to estimate and is likely to be substantial, the results nevertheless provide evidence for many new human functional RNAs and present specific predictions to facilitate their further characterization

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

A portfolio approach to analyzing complex human-environment interactions:Institutions and land change

The challenge confronting those seeking to understand the institutional dimensions of global environmental change and patterns of land-use and land-cover change is to find effective methods for analyzing the dynamics of socio-ecological systems. Such systems exhibit a number of characteristics that pose problems for the most commonly used statistical techniques and may require additional and innovative analytic tools. This article explores options available to researchers working in this field and recommends a strategy for achieving scientific progress. Statistical procedures developed in other fields of study are often helpful in addressing challenges arising in research into global change. Accordingly, we start with an assessment of some of the enhanced statistical techniques that are available for the study of socio-ecological systems. By themselves, however, even the most advanced statistical models cannot solve all the problems that arise in efforts to explain institutional effectiveness and patterns of land-use and land-cover change. We therefore proceed to an exploration of additional analytic techniques, including configurational comparisons and meta-analyses; case studies, counterfactuals, and narratives; and systems analysis and simulations. Our goal is to create a portfolio of complementary methods or, in other words, a tool kit for understanding complex human-environment interactions. When the results obtained through the use of two or more techniques converge, confidence in the robustness of key findings rises. Contradictory results, on the other hand, signal a need for additional analysis