Experimentation with Proof Methods for Non-Horn Sets

Two Resolution Proof Strategies Developed by Peterson Are Implemented by Modifying Otter, an Existing Automated Theorem Prover. the Methods, Lock-T Refutation and LNL-T Refutation, Are Generalizations of Unit Refutation and Input Resolution, Respectively, to Non-Horn Sets and Represent Independent, Equivalent but Opposite Ways of Searching. the Algorithms Used Are based on a Corrected Version of the Foundational Work. the Strategies Have Been Tested on Various Non-Horn Challenge Problems from the Tarskian Geometry and the Non-Obvious Problem, with the Results Being in Some Cases Quite Favorable When Compared to Other Resolution Techniques

Osmotic Regulation of Rab-Mediated Organelle Docking

SummaryOsmotic gradients across organelle and plasma membranes modulate the rates of membrane fission and fusion; sufficiently large gradients can cause membrane rupture [1–6]. Hypotonic gradients applied to living yeast cells trigger prompt (within seconds) swelling and fusion of Saccharomyces cerevisiae vacuoles, whereas hypertonic gradients cause vacuoles to fragment on a slower time scale [7–11]. Here, we analyze the influence of osmotic strength on homotypic fusion of isolated yeast vacuoles. Consistent with previously reported in vivo results, we find that decreases in osmolyte concentration increase the rate and extent of vacuole fusion in vitro, whereas increases in osmolyte concentration prevent fusion. Unexpectedly, our results reveal that osmolytes regulate fusion by inhibiting early Rab-dependent docking or predocking events, not late events. Our experiments reveal an organelle-autonomous pathway that may control organelle surface-to-volume ratio, size, and copy number: Decreasing the osmolyte concentration in the cytoplasmic compartment accelerates Rab-mediated docking and fusion. By altering the relationship between the organelle surface and its enclosed volume, fusion in turn reduces the risk of membrane rupture

Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop

The Nynex Max expert system analyzes the result of an automated electric test on a telephone line and determines the type of problem. However, tuning the system\u27s parameter values can be difficult. The Opti-Max system can automatically set these parameters by analyzing decisions made by experts who troubleshoot problem

Intrinsic tethering activity of endosomal Rab proteins.

Rab small G proteins control membrane trafficking events required for many processes including secretion, lipid metabolism, antigen presentation and growth factor signaling. Rabs recruit effectors that mediate diverse functions including vesicle tethering and fusion. However, many mechanistic questions about Rab-regulated vesicle tethering are unresolved. Using chemically defined reaction systems, we discovered that Vps21, a Saccharomyces cerevisiae ortholog of mammalian endosomal Rab5, functions in trans with itself and with at least two other endosomal Rabs to directly mediate GTP-dependent tethering. Vps21-mediated tethering was stringently and reversibly regulated by an upstream activator, Vps9, and an inhibitor, Gyp1, which were sufficient to drive dynamic cycles of tethering and detethering. These experiments reveal a previously undescribed mode of tethering by endocytic Rabs. In our working model, the intrinsic tethering capacity Vps21 operates in concert with conventional effectors and SNAREs to drive efficient docking and fusion

SeMi-Supervised Adaptive Resonance Theory (SMART2)

Adaptive resonance theory (ART) algorithms represent a class of neural network architectures which self-organize stable recognition categories in response to arbitrary sequences of input patterns. The authors discuss incorporation of supervision into one of these architectures, ART2. Results of numerical experiments indicate that this new semi-supervised version of ART2 (SMART2) outperformed ART for classification problems. The results and analysis of runs on several data sets by SMART2, ART2, and backpropagation are analyzed. The test accuracy of SMART2 was similar to that of backpropagation. However, SMART2 network structures are easier to interpret than the corresponding structures produced by backpropagation

Hemoglobin level is an independent predictor for adverse cardiovascular outcomes in women undergoing evaluation for chest pain Results from the National Heart, Lung, and Blood Institute women's ischemia syndrome evaluation study

AbstractObjectivesThis study was designed to investigate the relationship between hemoglobin level (Hgb) and adverse cardiovascular outcomes in women with suspected ischemia.BackgroundLow Hgb levels correlate with increased cardiovascular morbidity and mortality in patients presenting with acute myocardial infarction (MI) or congestive heart failure (CHF). However, the prognostic significance of Hgb in women with suspected ischemia is unclear.MethodsAs part of the National Heart, Lung, and Blood Institute (NHLBI)-sponsored Women's Ischemia Syndrome Evaluation (WISE), we prospectively studied 936 women referred for coronary angiography to evaluate suspected ischemia. We compared Hgb levels with cardiovascular risk factors, core lab interpreted angiograms, inflammatory markers, and adverse cardiovascular outcomes.ResultsOf women enrolled, 864 (mean age 58.4 ±11.6 years) had complete Hgb, angiogram, and follow-up (mean 3.3 ± 1.7 years) data. The mean Hgb was 12.9 g/dl (range 7.7 to 16.4 g/dl) and 184 women (21%) were anemic (Hgb <12 g/dl). Anemic women had higher creatinine and were more likely to be nonwhite and have a history of diabetes, hypertension, and CHF (p < 0.05). However, we found no difference in EF or severity of coronary artery disease. Anemic women had a higher risk of death from any cause (10.3% vs. 5.4%; p = 0.02) and total adverse outcomes (26% vs. 16%, p < 0.01). In a multivariable model, decreasing Hgb was associated with significantly higher risk of adverse outcomes (hazard ratio = 1.20, p = 0.002). Also, anemic women had shorter survival time free of adverse outcome (p < 0.001).ConclusionsOur findings extend previous reports, linking lower hemoglobin levels with higher risk for adverse cardiovascular outcomes, to women evaluated for suspected ischemia in the absence of acute MI or CHF

MetaBags: Bagged Meta-Decision Trees for Regression

Ensembles are popular methods for solving practical supervised learning problems. They reduce the risk of having underperforming models in production-grade software. Although critical, methods for learning heterogeneous regression ensembles have not been proposed at large scale, whereas in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel, practically useful stacking framework for regression. MetaBags is a meta-learning algorithm that learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. A set of meta-decision trees are learned using different types of meta-features, specially created for this purpose - to then be bagged at meta-level. This procedure is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on synthetic, open and real-world application datasets. The obtained results show that our method significantly outperforms existing state-of-the-art approaches