Cdc4p, a contractile ring protein essential for cytokinesis in Schizosaccharomyces pombe, interacts with a phosphatidylinositol 4-kinase.

The proposed function of Cdc4p, an essential contractile ring protein in Schizosaccharomyces pombe, is that of a myosin essential light chain. However, five conditionally lethal cdc4 alleles exhibit complementation in diploids. Such interallelic complementation is not readily explained if the sole function of Cdc4p is that of a myosin essential light chain. Complementation of cdc4 alleles could occur only if different mutant forms can assemble into an active oligomeric complex or if Cdc4p has more than one essential function. To search for other proteins that may interact with Cdc4p, we performed a two-hybrid screen and identified two such candidates: one similar to Saccharomyces cerevisiae Vps27p and the other a putative phosphatidylinositol (PI) 4-kinase. Binding of Cdc4p to the latter and to myosin heavy chain (Myo2p) was confirmed by immunosorbent assays. Deletion studies demonstrated interaction between the Cdc4p C-terminal domain and the PI 4-kinase C-terminal domain. Furthermore, interaction was abolished by the Cdc4p C-terminal domain point mutation, Gly107 to Ser. This allele also causes failure of cytokinesis. Ectopic expression of the PI 4-kinase C-terminal domain caused cytokinesis defects that were most extreme in cells carrying the G107S allele. We suggest that Cdc4p plays multiple roles in cytokinesis and that interaction with a PI 4-kinase may be important for contractile ring assembly and/or function

Essential Role for Schizosaccharomyces pombe pik1 in Septation

Background: Schizosaccharomyces pombe pik1 encodes a phosphatidylinositol 4-kinase, reported to bind Cdc4, but no

Detection of microcalcifications in mammograms using error of prediction and statistical measures

A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50- m resolution, and the results are evaluated using a freeresponse receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per imag

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Structure of Cdc4p, a Contractile Ring Protein Essential for Cytokinesis in Schizosaccharomyces pombe

The Schizosaccharomyces pombe Cdc4 protein is required for the formation and function of the contractile ring, presumably acting as a myosin light chain. By using NMR spectroscopy, we demonstrate that purified Cdc4p is a monomeric protein with two structurally independent domains, each exhibiting a fold reminiscent of the EF-hand class of calcium-binding proteins. Although Cdc4p has one potentially functional calcium-binding site, it does not bind calcium in vitro. Three variants of Cdc4p containing single point mutations responsible for temperature-sensitive arrest of the cell cycle at cytokinesis (Gly-19 to Glu, Gly-82 to Asp, and Gly-107 to Ser) were also characterized by NMR and circular dichroism spectroscopy. In each case, the amino acid substitution only leads to small perturbations in the conformation of the protein. Furthermore, thermal unfolding studies indicate that, like wild-type Cdc4p, the three mutant forms are all extremely stable, remaining completely folded at temperatures significantly above those causing failure of cytokinesis in intact cells. Therefore, the altered phenotype must arise directly from a disruption of the function of Cdc4p rather than indirectly through a disruption of its overall structure. Several mutant alleles of Cdc4p also show interallelic complementation in diploid cells. This phenomenon can be explained if Cdcp4 has more than one essential function or, alternatively, if two mutant proteins assemble to form a functional complex. Based on the structure of Cdc4p, possible models for interallelic complementation including interactions with partner proteins and the formation of a myosin complex with Cdc4p fulfilling the role of both an essential and regulatory light chain are proposed

Comparison of bioinspired algorithms applied to cancer database

Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database

Development of a DNA probe colony hybridization hydrophobic grid membrane filter method for detection of Escherichia coli isolates carrying virulence and antimicrobial resistance genes in the pig intestinal microflora

The objective of this study was to develop a DNA probe hybridization procedure using hydrophobic-grid membrane filters (HGMFs) for the detection of E. coli isolates carrying specific virulence or antimicrobial resistance genes. Genes were detected using digoxigenin-labelled probes and an alkaline-phosphatase substrate. Specifically, the eft, estB, and faeG genes encoding the enterotoxins LT, STb, and fimbrial adhesin F4 were targeted. The hybridization procedure was optimized using E. coli strain ECL 7805 which possesses the three targeted genes, and rectal swabs from weaned pigs excreting E. coli positive for these genes, as demonstrated by enrichment PCR

Prediction of early unplanned intensive care unit readmission in a UK tertiary-care hospital: A cross-sectional machine learning approach

Objectives: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult, and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. Setting: A single academic, tertiary care hospital in the United Kingdom. Participants: A set of 3,326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who: were ≤ 16 years of age; visited intensive care units other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2,018 outcome-labeled episodes remained. Primary and Secondary Outcome Measures: Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge. Results: In ten-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the MIMIC-III database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built SWIFT score (AUROC = 0.6082, SE 0.0249; p = 0.014, pairwise t-test). Conclusions: Despite the inherent difficulties, we demonstrate that a novel ML algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.esearch reported in this publication was supported by the National Institute of Nursing Research, of the National Institutes of Health, under award number R43NR015945

Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography

High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro- and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart. The arcuate, inferior longitudinal, inferior fronto-occipital, and uncinate fasciculi were reconstructed in the left and right hemispheres and the following diffusion measures were extracted along each tract: fractional anisotropy, mean, axial, and radial diffusivity, number of fiber orientations, mean length of streamlines, and volume. All fiber bundles showed good morphological overlap between the two scanning timepoints and the test-retest reliability of all diffusion measures in most fiber bundles was good to excellent. We thus propose a fairly simple, but robust, HARDI-based tractography pipeline reliable for the longitudinal study of white matter language fiber bundles, which increases its potential applicability to research on the neurobiological mechanisms supporting language