Implicit Decomposition for Write-Efficient Connectivity Algorithms

The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an $o(n)$-sized "implicit decomposition" of a bounded-degree graph $G$ on $n$ nodes, which combined with read-only access to $G$ enables fast answers to connectivity and biconnectivity queries on $G$. The construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on $m$ edges, we also provide the first $o(m)$ writes and $O(m)$ operations parallel algorithms for connectivity and biconnectivity. These algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry

Efficient Algorithms with Asymmetric Read and Write Costs

In several emerging technologies for computer memory (main memory), the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fundamentally different model from the RAM for algorithm design. In this paper we study lower and upper bounds for various problems under such asymmetric read and write costs. We consider both the case in which all but O(1) memory has asymmetric cost, and the case of a small cache of symmetric memory. We model both cases using the (M,omega)-ARAM, in which there is a small (symmetric) memory of size M and a large unbounded (asymmetric) memory, both random access, and where reading from the large memory has unit cost, but writing has cost omega >> 1. For FFT and sorting networks we show a lower bound cost of Omega(omega*n*log_{omega*M}(n)), which indicates that it is not possible to achieve asymptotic improvements with cheaper reads when omega is bounded by a polynomial in M. Moreover, there is an asymptotic gap (of min(omega,log(n)/log(omega*M)) between the cost of sorting networks and comparison sorting in the model. This contrasts with the RAM, and most other models, in which the asymptotic costs are the same. We also show a lower bound for computations on an n*n diamond DAG of Omega(omega*n^2/M) cost, which indicates no asymptotic improvement is achievable with fast reads. However, we show that for the minimum edit distance problem (and related problems), which would seem to be a diamond DAG, we can beat this lower bound with an algorithm with only O(omega*n^2/(M*min(omega^{1/3},M^{1/2}))) cost. To achieve this we make use of a "path sketch" technique that is forbidden in a strict DAG computation. Finally, we show several interesting upper bounds for shortest path problems, minimum spanning trees, and other problems. A common theme in many of the upper bounds is that they require redundant computation and a tradeoff between reads and writes

THE PROBLEM OF THE RELATIONSHIP OF SCIENCE AND RELIGION IN THE SYSTEM OF SECULAR GENERAL EDUCATION

The relationship between science and religion throughout history ranged from opposition to their unity. The long-standing prevalence of theology over science has caused a counter-atheistic reaction, which has grown into the absolutization of science in all spheres of life. The introduction to the secular general education of the course “Fundamentals of Religious Cultures and Secular Ethics” was ambiguously accepted by society. The author substantiates the position that science and religion are not mutually exclusive, but rather can complement each other, forming a unified picture of the world among the younger generation.Взаимоотношения науки и религии на протяжении всей истории выстраивались от противопоставления до их единства. Многолетнее преобладание богословия над наукой вызвало встречную атеистическую реакцию, переросшую в абсолютизацию науки во всех сферах бытия. Введение в светское общее образование курса «Основы религиозных культур и светской этики» было неоднозначно воспринято обществом. Автор обосновывает позицию, что наука и религия не взаимно исключают друг друга, а напротив, могут дополнять друг друга, формируя у подрастающего поколения единую картину мира

Prevalence of programmed death ligand-1 (PD-L1) by demographic, disease and sample characteristics in unresectable, stage III NSCLC (PACIFIC)

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3D time series analysis of cell shape using Laplacian approaches

Background: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations

Biological activities of fusarochromanone: a potent anti-cancer agent

Background Fusarochromanone (FC101) is a small molecule fungal metabolite with a host of interesting biological functions, including very potent anti-angiogenic and direct anti-cancer activity. Results Herein, we report that FC101 exhibits very potent in-vitro growth inhibitory effects (IC50 ranging from 10nM-2.5 μM) against HaCat (pre-malignant skin), P9-WT (malignant skin), MCF-7 (low malignant breast), MDA-231 (malignant breast), SV-HUC (premalignant bladder), UM-UC14 (malignant bladder), and PC3 (malignant prostate) in a time-course and dose-dependent manner, with the UM-UC14 cells being the most sensitive. FC101 induces apoptosis and an increase in proportion of cells in the sub-G1 phase in both HaCat and P9-WT cell lines as evidenced by cell cycle profile analysis. In a mouse xenograft SCC tumor model, FC101 was well tolerated, non-toxic, and achieved a 30% reduction in tumor size at a dose of 8 mg/kg/day. FC101 is also a potent anti-angiogenenic agent. At nanomolar doses, FC101 inhibits the vascular endothelial growth factor-A (VEGF-A)-mediated proliferation of endothelial cells. Conclusions Our data presented here indicates that FC101 is an excellent lead candidate for a small molecule anti-cancer agent that simultaneously affects angiogenesis signaling, cancer signal transduction, and apoptosis. Further understanding of the underlying FC101’s molecular mechanism may lead to the design of novel targeted and selective therapeutics, both of which are pursued targets in cancer drug discovery

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes