On QBF Proofs and Preprocessing

QBFs (quantified boolean formulas), which are a superset of propositional formulas, provide a canonical representation for PSPACE problems. To overcome the inherent complexity of QBF, significant effort has been invested in developing QBF solvers as well as the underlying proof systems. At the same time, formula preprocessing is crucial for the application of QBF solvers. This paper focuses on a missing link in currently-available technology: How to obtain a certificate (e.g. proof) for a formula that had been preprocessed before it was given to a solver? The paper targets a suite of commonly-used preprocessing techniques and shows how to reconstruct certificates for them. On the negative side, the paper discusses certain limitations of the currently-used proof systems in the light of preprocessing. The presented techniques were implemented and evaluated in the state-of-the-art QBF preprocessor bloqqer.Comment: LPAR 201

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Tissue invasion and metastasis: molecular, biological and clinical perspectives

Cancer is a key health issue across the world, causing substantial patient morbidity and mortality. Patient prognosis is tightly linked with metastatic dissemination of the disease to distant sites, with metastatic diseases accounting for a vast percentage of cancer patient mortality. While advances in this area have been made, the process of cancer metastasis and the factors governing cancer spread and establishment at secondary locations is still poorly understood. The current article summarizes recent progress in this area of research, both in the understanding of the underlying biological processes and in the therapeutic strategies for the management of metastasis. This review lists the disruption of E-cadherin and tight junctions, key signaling pathways, including urokinase type plasminogen activator (uPA), phosphatidylinositol 3-kinase/v-akt murine thymoma viral oncogene (PI3K/AKT), focal adhesion kinase (FAK), β-catenin/zinc finger E-box binding homeobox 1 (ZEB-1) and transforming growth factor beta (TGF-β), together with inactivation of activator protein-1 (AP-1) and suppression of matrix metalloproteinase-9 (MMP-9) activity as key targets and the use of phytochemicals, or natural products, such as those from Agaricus blazei, Albatrellus confluens, Cordyceps militaris, Ganoderma lucidum, Poria cocos and Silybum marianum, together with diet derived fatty acids gamma linolenic acid (GLA) and eicosapentanoic acid (EPA) and inhibitory compounds as useful approaches to target tissue invasion and metastasis as well as other hallmark areas of cancer. Together, these strategies could represent new, inexpensive, low toxicity strategies to aid in the management of cancer metastasis as well as having holistic effects against other cancer hallmarks.W.G. Jiang ... S.K. Thompson ... et al

Estimating the Yield Strength of Thin Metal Films through Elastic-Plastic Buckling-Induced Debonding

In this paper, we propose a procedure to estimate the yield strength of thin films by debonding films from their substrate by elastic-plastic buckling under thermally-induced compressive loading. The out-of-plane displacement of the metal lines under conditions of elastic-plastic buckling is dependent on the yield strength of the film. Thus, an inverse estimate of the yield strength is made from measurements of the out-of-plane displacements of the buckled metal lines. The procedure is demonstrated to estimate the yield strength of aluminum lines consistent with measurements by other techniques

An Efficient Network Model for Determining the Effective Thermal Conductivity of Particulate Thermal Interface Materials

Particulate composites are commonly used in Microelectronics applications. One example of such materials is Thermal Interface Materials (TIMs) that are used to reduce the contact resistance between the chip and the heat sink. The existing analytical descriptions of thermal transport in particulate systems do not accurately account for the effect of inter-particle interactions, especially in the intermediate volume fractions of 30-80%. Another crucial drawback in the existing analytical as well as the network models is the inability to model size distributions (typically bimodal) of the filler material particles that are obtained as a result of the material manufacturing process. While full-field simulations (using, for instance, the finite element method) are possible for such systems, they are computationally expensive. In the present paper, we develop an efficient network model that captures the physics of inter-particle interactions and allows for random size distributions. Twenty random microstructural arrangements each of Alumina as well as Silver particles in Silicone and Epoxy matrices were generated using an algorithm implemented using a java language code. The microstructures were evaluated through both full-field simulations as well as the network model. The full-field simulations were carried out using a novel meshless analysis technique developed in the author’s (GS) research [26]. In all cases, it is shown that the random network models are accurate to within 5% of the full field simulations. The random network model simulations were efficient since they required two orders of magnitude smaller computation time to complete in comparison to the full field simulation

Multi-index based analysis of genotype × environment interaction and selection of superior maize (Zea mays L.) hybrids

Genotype-environment interaction (GEI) plays a critical role in genotype adaptation, making it essential for selecting stable, widely adapted genotypes for cultivation. GEI estimation enables the identification of genotypes that perform consistently across diverse conditions. Models and stability indices derived from fixed-effect and/or mixed-effect models are frequently utilized for analyzing GEI and selecting genotypes. In this study, thirty hybrids developed through a diallele fashion, along with two checks, were grown across three environments during kharif 2023. Analysis of variance revealed significant contributions from the environment and GEI, alongside genotypic effects for eight traits studied, covering flowering, plant architecture and yield. Plot yield (t/ha) was subjected to additive main effects and multiplicative interaction effects (AMMI) analysis to study the stability and genotype interactions with the environment. The first two principal components (PCs) of AMMI analysis explained 69.1% and 30.9% of the total variation, respectively, identifying stable hybrids such as MH-TN-15 and MH-TN-30. The Genotype-genotype×environment (GGE) biplot further highlighted the adaptability and stability of all the genotypes, with the first two PCs explaining 86.11% of the G+GE variation. A multi-trait stability index (MTSI) was employed to select stable and high-performing genotypes across multiple traits. A comprehensive analysis of all the genotypes through various indices showed that hybrids MH-TN-15 and MH-TN-30 were consistently selected as stable and high-yielding genotypes across all indices, demonstrating higher yields than check hybrids and being identified for cultivation. These methods underscore the importance of combining yield and stability metrics for effective genotype selection in varied environments

Designing a broad-spectrum integrative approach for cancer prevention and treatment

Targeted therapies and the consequent adoption of "personalized" oncology have achieved notablesuccesses in some cancers; however, significant problems remain with this approach. Many targetedtherapies are highly toxic, costs are extremely high, and most patients experience relapse after a fewdisease-free months. Relapses arise from genetic heterogeneity in tumors, which harbor therapy-resistantimmortalized cells that have adopted alternate and compensatory pathways (i.e., pathways that are notreliant upon the same mechanisms as those which have been targeted). To address these limitations, aninternational task force of 180 scientists was assembled to explore the concept of a low-toxicity "broad-spectrum" therapeutic approach that could simultaneously target many key pathways and mechanisms. Using cancer hallmark phenotypes and the tumor microenvironment to account for the various aspectsof relevant cancer biology, interdisciplinary teams reviewed each hallmark area and nominated a widerange of high-priority targets (74 in total) that could be modified to improve patient outcomes. For thesetargets, corresponding low-toxicity therapeutic approaches were then suggested, many of which werephytochemicals. Proposed actions on each target and all of the approaches were further reviewed forknown effects on other hallmark areas and the tumor microenvironment. Potential contrary or procar-cinogenic effects were found for 3.9% of the relationships between targets and hallmarks, and mixedevidence of complementary and contrary relationships was found for 7.1%. Approximately 67% of therelationships revealed potentially complementary effects, and the remainder had no known relationship. Among the approaches, 1.1% had contrary, 2.8% had mixed and 62.1% had complementary relationships. These results suggest that a broad-spectrum approach should be feasible from a safety standpoint. Thisnovel approach has potential to be relatively inexpensive, it should help us address stages and types ofcancer that lack conventional treatment, and it may reduce relapse risks. A proposed agenda for futureresearch is offered

Upregulation of ASCL1 and inhibition of Notch signaling pathway characterize progressive astrocytoma

Astrocytoma is the most common type of brain cancer constituting more than half of all brain tumors. With an aim to identify markers describing astrocytoma progression, we have carried out microarray analysis of astrocytoma samples of different grades using cDNA microarray containing 1152 cancer-specific genes. Data analysis identified several differentially regulated genes between normal brain tissue and astrocytoma as well as between grades II/III astrocytoma and glioblastoma multiforme (GBM; grade IV). We found several genes known to be involved in malignancy including Achaete-scute complex-like 1 (Drosophila) (ASCL1; Hash 1). As ASCL has been implicated in neuroendocrine, medullary thyroid and small-cell lung cancers, we chose to examine the role of ASCL1 in the astrocytoma development. Our data revealed that ASCL1 is overexpressed in progressive astrocytoma as evidenced by increased levels of ASCL1 transcripts in 85.71% (6/7) of grade II diffuse astrocytoma (DA), 90% (9/10) of grade III anaplastic astrocytoma (AA) and 87.5% (7/8) of secondary GBMs, while the majority of primary de novo GBMs expressed similar to or less than normal brain levels (66.67%; 8/12). ASCL1 upregulation in progressive astrocytoma is accompanied by inhibition of Notch signaling as seen by uninduced levels of HES1, a transcriptional target of Notch1, increased levels of HES6, a dominant-negative inhibitor of HES1-mediated repression of ASCL1, and increased levels of Notch ligand Delta1, which is capable of inhibiting Notch signaling by forming intracellular Notch ligand autonomous complexes. Our results imply that inhibition of Notch signaling may be an important early event in the development of grade II DA and subsequent progression to grade III AA and secondary GBM. Furthermore, ASCL1 appears to be a putative marker to distinguish primary GBM from secondary GBM