Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis

The Accounting Network: how financial institutions react to systemic crisis

The role of Network Theory in the study of the financial crisis has been widely spotted in the latest years. It has been shown how the network topology and the dynamics running on top of it can trigger the outbreak of large systemic crisis. Following this methodological perspective we introduce here the Accounting Network, i.e. the network we can extract through vector similarities techniques from companies' financial statements. We build the Accounting Network on a large database of worldwide banks in the period 2001-2013, covering the onset of the global financial crisis of mid-2007. After a careful data cleaning, we apply a quality check in the construction of the network, introducing a parameter (the Quality Ratio) capable of trading off the size of the sample (coverage) and the representativeness of the financial statements (accuracy). We compute several basic network statistics and check, with the Louvain community detection algorithm, for emerging communities of banks. Remarkably enough sensible regional aggregations show up with the Japanese and the US clusters dominating the community structure, although the presence of a geographically mixed community points to a gradual convergence of banks into similar supranational practices. Finally, a Principal Component Analysis procedure reveals the main economic components that influence communities' heterogeneity. Even using the most basic vector similarity hypotheses on the composition of the financial statements, the signature of the financial crisis clearly arises across the years around 2008. We finally discuss how the Accounting Networks can be improved to reflect the best practices in the financial statement analysis

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

Strategic Groups and Banks’ Performance

The theory of strategic groups predicts the existence of stable groups of companies that adopt similar business strategies. The theory also predicts that groups will differ in performance and in their reaction to external shocks. We use cluster analysis to identify strategic groups in the Polish banking sector. We find stable groups in the Polish banking sector constituted after the year 2000 following the major privatisation and ownership changes connected with transition to the mostly-privately-owned banking sector in the late 90s. Using panel regression methods we show that the allocation of banks to groups is statistically significant in explaining the profitability of banks. Thus, breaking down the banks into strategic groups and allowing for the different reaction of the groups to external shocks helps in a more accurate explanation of profits of the banking sector as a whole. Therefore, a more precise ex ante assessment of the loss absorption capabilities of banks is possible, which is crucial for an analysis of banking sector stability. However, we did not find evidence of the usefulness of strategic groups in explaining the quality of bank portfolios as measured by irregular loans over total loans, which is a more direct way to assess risks to financial stability.strategic groups, financial stability, clustering, Ward algorithm, panel regression

Fine-grained Search Space Classification for Hard Enumeration Variants of Subset Problems

We propose a simple, powerful, and flexible machine learning framework for (i) reducing the search space of computationally difficult enumeration variants of subset problems and (ii) augmenting existing state-of-the-art solvers with informative cues arising from the input distribution. We instantiate our framework for the problem of listing all maximum cliques in a graph, a central problem in network analysis, data mining, and computational biology. We demonstrate the practicality of our approach on real-world networks with millions of vertices and edges by not only retaining all optimal solutions, but also aggressively pruning the input instance size resulting in several fold speedups of state-of-the-art algorithms. Finally, we explore the limits of scalability and robustness of our proposed framework, suggesting that supervised learning is viable for tackling NP-hard problems in practice.Comment: AAAI 201

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

Resistance to autosomal dominant Alzheimer's disease in an APOE3 Christchurch homozygote: a case report.

We identified a PSEN1 (presenilin 1) mutation carrier from the world's largest autosomal dominant Alzheimer's disease kindred, who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset. The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements. Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer's disease

How High Performance Human Resource Practices and Workforce Unionization Affect Managerial Pay

Using data from a nationally representative sample of telecommunications establishments, this study finds that HR practices and workforce unionization influence managerial pay levels and the ratio of manager-to-worker pay. High performance HR practices, including investment in the skills of the workforce, in computer-based technologies, and in performance-based worker pay practices, are all positively related to managerial pay; but the use of workforce teams, which shift some managerial responsibilities to workers, has the opposite association. High performance HR practices also are associated with lower manager to- worker pay differentials. In addition, workforce unionization is positively associated with managerial pay levels, with worker base pay mediating the relationship between managers\u27 pay and unionization

Microbial communities and arsenic biogeochemistry at the outflow of an alkaline sulfide-rich hot spring.

Alkaline sulfide-rich hot springs provide a unique environment for microbial community and arsenic (As) biogeochemistry. In this study, a representative alkaline sulfide-rich hot spring, Zimeiquan in the Tengchong geothermal area, was chosen to study arsenic geochemistry and microbial community using Illumina MiSeq sequencing. Over 0.26 million 16S rRNA sequence reads were obtained from 5-paired parallel water and sediment samples along the hot spring's outflow channel. High ratios of As(V)/AsSum (total combined arsenate and arsenite concentrations) (0.59-0.78), coupled with high sulfide (up to 5.87 mg/L), were present in the hot spring's pools, which suggested As(III) oxidation occurred. Along the outflow channel, AsSum increased from 5.45 to 13.86 μmol/L, and the combined sulfide and sulfate concentrations increased from 292.02 to 364.28 μmol/L. These increases were primarily attributed to thioarsenic transformation. Temperature, sulfide, As and dissolved oxygen significantly shaped the microbial communities between not only the pools and downstream samples, but also water and sediment samples. Results implied that the upstream Thermocrinis was responsible for the transformation of thioarsenic to As(III) and the downstream Thermus contributed to derived As(III) oxidation. This study improves our understanding of microbially-mediated As transformation in alkaline sulfide-rich hot springs