Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathophysiologic Approach to Therapy in Patients With Newly Diagnosed Type 2 Diabetes

Two general approaches to the treatment of type 2 diabetes mellitus (T2DM) have been advocated. 1 ) A “guideline” approach that advocates sequential addition of antidiabetes agents with “more established use” (1); this approach more appropriately should be called the “treat to failure” approach, and deficiencies with this approach have been discussed (2). And 2 ) a “pathophysiologic” approach using initial combination therapy with agents known to correct established pathophysiologic defects in T2DM (3). Within the pathophysiologic approach, choice of antidiabetes agents should take into account the patient’s general health status and associated medical disorders. This individualized approach, which we refer to as the ABCD(E) of diabetes treatment (4), has been incorporated into the updated American Diabetes Association (ADA) guidelines (5). Even though physicians must be cognizant of these associated conditions (ABCDE) when initiating therapy in newly diagnosed T2DM patients, we believe that the most important consideration is to select antidiabetes agents that correct specific pathophysiologic disturbances present in T2DM and that have complementary mechanisms of action. Although it has been argued that the pathogenesis of T2DM differs in different ethnic groups (6), evidence to support this is weak. Although the relative contributions of β-cell failure and insulin resistance to development of glucose intolerance may differ in different ethnic groups (6), the core defects of insulin resistance in muscle/liver/adipocytes and progressive β-cell failure (3) are present in virtually all T2DM patients and must be treated aggressively to prevent the relentless rise in HbA1c that is characteristic of T2DM. In subsequent sections, we provide a review of the natural history of T2DM, specific pathophysiologic abnormalities responsible for T2DM, currently …
Preliminary Survey of Ectoparasites and Associated Pathogens from Norway Rats in New York City

ABSTRACT The Norway rat (Rattus norvegicus) is a reservoir of many zoonotic pathogens and lives in close proximity to humans in urban environments. Human infection with rodent-borne disease occurs either directly through contact with a rat or its excreta, or indirectly via arthropod vectors such as fleas and ticks. Here, we report on the diversity and abundance of ectoparasitic arthropod species and associated pathogenic bacteria from 133 Norway rats trapped over a 10-mo period in Manhattan, New York, NY. Norway rats were host to the tropical rat mite [Ornithonyssus bacoti (Hirst)], the spiny rat mite (Laelaps echidnina Berlese), Laelaps nuttalli Hirst, the spined rat louse [Polyplax spinulosa (Burmeister)], and the Oriental rat flea [(Xenopsylla cheopis) (Rothschild)], with an average of 1.7 species per individual. A flea index of 4.1 X. cheopis was determined, whereas previous studies in New York City reported 0.22 fleas per rat. Multiple species of pathogenic Bartonella were identified from Oriental rat fleas that were related to Bartonella tribocorum, Bartonella rochalimae, and Bartonella elizabethae. However, no evidence of Yersinia pestis or Rickettsia spp. infection was detected in fleas. The identification of multiple medically important ectoparasite species in New York City underscores the need for future efforts to fully characterize the diversity and distribution of ectoparasites on Norway rats, and assess the risk to humans of vector-borne disease transmission.
Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipita- tion forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolu- tions. The four models evaluated are the Australian Commu- nity Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies consid- erably between rain gauging stations. In general, high spa- tial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underes- timates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncer- tainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to di- urnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecast- ing. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.
Watt-I-See: A Tangible Visualization of Energy

This paper describes a tangible visualization that explores the link between the impact of energy feedback on household consumers and the resource demand impact on energy production. Specifically, it positions a novel perspective attempting to move beyond the known limitations of current eco-feedback systems and contributes to enhance our understanding of how consumers comprehend energy production. The work is informed by a comprehensive study of an installation that displays the ratio of current power generation sources and the percentage of grid renewables. The paper provides design insights for creating novel eco-feedback visualizations that leverage the balance between user lifestyles and the desire to influence consumption behaviors and practices. Evaluation results show an increase in energy literacy and awareness as well as identifies high consumer preferences towards simple, representative interfaces and ubiquitous immediate feedback. Our study shows potential in terms of future scenarios for eco-feedback in distributed energy micro-generation and other inevitable disruptive changes for the energy utility.
Algorithm 718: A FORTRAN subroutine to solve the eigenvalue allocation problem for single-input systems

An efficient implementation of an algorithm for the eigenvalue allocation (pole placement) problem of single-input linear systems using state feedback is given in this paper. The implementation uses the BLAS level-1 [2] subroutines when possible for better performance. A brief description of the algorithm along with some computational details is also given.
Components in time-varying graphs

Real complex systems are inherently time-varying. Thanks to new communication systems and novel technologies, today it is possible to produce and analyze social and biological networks with detailed information on the time of occurrence and duration of each link. However, standard graph metrics introduced so far in complex network theory are mainly suited for static graphs, i.e., graphs in which the links do not change over time, or graphs built from time-varying systems by aggregating all the links as if they were concurrent in time. In this paper, we extend the notion of connectedness, and the definitions of node and graph components, to the case of time-varying graphs, which are represented as time-ordered sequences of graphs defined over a fixed set of nodes. We show that the problem of finding strongly connected components in a time-varying graph can be mapped into the problem of discovering the maximal-cliques in an opportunely constructed static graph, which we name the affine graph. It is, therefore, an NP-complete problem. As a practical example, we have performed a temporal component analysis of time-varying graphs constructed from three data sets of human interactions. The results show that taking time into account in the definition of graph components allows to capture important features of real systems. In particular, we observe a large variability in the size of node temporal in- and out-components. This is due to intrinsic fluctuations in the activity patterns of individuals, which cannot be detected by static graph analysis.
Non-Linear Dynamics of Geared Systems Using the Asymptotic Method, Part 3: Computation of Instability Frontiers

The increased interest for improved gear design has led to extensive research into the field of non-linear dynamics of such systems. The paper reveals a complex dynamic model to study the behavior in a gear-pair system taking into consideration backlash and time-dependent mesh stiffness and mesh damping. In many applications including turbo machinery, machine tools and diesel engines non-linearities are present due to tooth stiffness, damping and backlash that induced micro-vibrations of non-linear parametric type. In the mean time the input link of the driver ax and the output link of the driven ax induce non-linearities. The paper presents the use of asymptotic method in order to compute the frontiers of instability.
The effective evidence-based high school librarian: a journey to decision.

This thesis aims to establish how high school librarians in Scotland and America can become effective evidence-based decision-makers. Initial data collection consisted of written questionnaires to elicit background data from librarians on the extent of their evidence-based practice. Three main themes emerged: collaboration; interprofessional relationships and roles; and decision-making. These themes are discussed in more detail. A range of qualitative methodologies was designed and implemented to gain more in-depth information on practice and viewpoints, involving librarians and stakeholders. A draft model was created of the effective evidence-based practitioner in a high school library, based on an interpretation of findings and readings from the literature. After member-checking and validating by stakeholders, a final model was produced. This emergent model focuses not on the role of the school librarian in isolation, but stresses the importance of interrelationships involving the librarian. The study makes an original contribution to knowledge by giving a better understanding of the effective evidence-based high school librarian in the wider context of education. There is also a contribution to knowledge by adding to the general theory of workplace decision-making and evidence-based practice which is applicable outwith the school sector, and the pragmatic solution-driven model of decision-making is introduced. Key findings give an insight into the complexity of the school library situation, and highlights that ideally there will be positive relationships between school management, school librarian and collaboration. When these come together, it can mean an increase in student achievement, and more effective evidence-based decisions can be made. Findings also demonstrate links between these elements with evidence, national bodies and skills and qualities needed to be an effective evidence-based high school librarian, and identify how the new and complex expectations made of school librarians can be met. Recommendations are made to help stakeholders improve practice.
The Histone Methyltransferase Inhibitor A-366 Uncovers a Role for G9a/GLP in the Epigenetics of Leukemia

Histone methyltransferases are epigenetic regulators that modify key lysine and arginine residues on histones and are believed to play an important role in cancer development and maintenance. These epigenetic modifications are potentially reversible and as a result this class of enzymes has drawn great interest as potential therapeutic targets of small molecule inhibitors. Previous studies have suggested that the histone lysine methyltransferase G9a (EHMT2) is required to perpetuate malignant phenotypes through multiple mechanisms in a variety of cancer types. To further elucidate the enzymatic role of G9a in cancer, we describe herein the biological activities of a novel peptide-competitive histone methyltransferase inhibitor, A-366, that selectively inhibits G9a and the closely related GLP (EHMT1), but not other histone methyltransferases. A-366 has significantly less cytotoxic effects on the growth of tumor cell lines compared to other known G9a/GLP small molecule inhibitors despite equivalent cellular activity on methylation of H3K9me2. Additionally, the selectivity profile of A-366 has aided in the discovery of a potentially important role for G9a/GLP in maintenance of leukemia. Treatment of various leukemia cell lines in vitro resulted in marked differentiation and morphological changes of these tumor cell lines. Furthermore, treatment of a flank xenograft leukemia model with A-366 resulted in growth inhibition in vivo consistent with the profile of H3K9me2 reduction observed. In summary, A-366 is a novel and highly selective inhibitor of G9a/GLP that has enabled the discovery of a role for G9a/GLP enzymatic activity in the growth and differentiation status of leukemia cells.
A variational (E)(M) algorithm for large-scale mixture modeling

Mixture densities constitute a rich family of models that can be used in several data mining and machine learning applications, for instance, clustering. Although practical algorithms exist for learning such models from data, these algorithms typically do not scale very well with large datasets. Our approach, which builds on previous work by other authors, offers an acceleration of the EM algorithm for Gaussian mixtures by precomputing and storing sufficient statistics of the data in the nodes of a kd-tree. Contrary to other works, we obtain algorithms that strictly increase a lower bound on the data log-likelihood in every learning step. Experimental results illustrate the validity of our approach.
Novel similarity measure for document clustering based on topic phrases

Document clustering is a subset of the data clustering field which categorizes large set of documents into similar and related groups. In the traditional Vector Space Model (VSM) researchers have considered the unique word which occurs in the document set as the candidate feature. Recently a new trend which considered the phrase to be a more informative feature has taken place; the matter which contributes in improving the document clustering accuracy and effectiveness. This paper proposes a new approach for computing the similarity measure of the traditional VSM by considering the topic phrases of the document as the constituting terms for the VSM instead of the traditional term “word” and applying the new approach to the Buckshot method, which is a mix of the Hierarchical Agglomerative Clustering (HAC) algorithm and the K-means partitioning algorithm. Such a mechanism may raise the effectiveness of the clustering by increasing the evaluation metrics values.
How Algorithmic Trading Undermines Efficiency in Capital Markets

This Article argues that the rise of algorithmic trading profoundly challenges the foundation on which much of today’s securities regulation framework rests: the understanding that securities’ prices objectively reflect available information in the market. The Efficient Capital Markets Hypothesis (ECMH) has long provided the theoretical touchstone undergirding central pillars of securities regulation. Mandatory disclosure, evidentiary presumptions in anti-fraud litigation and regulation driving the design of modern exchanges all look to the ECMH for theoretical validation. It is easy to understand why. Laws that make markets better at interpreting information can also improve their ability to allocate capital across the real economy.Theory and regulation have failed to keep pace with markets where traders rely on pre-programmed algorithms to execute trades. This Article makes two claims. First, complex algorithms foster a separation between the trader and her ability to fully control the operation of the algorithm. Algorithms can execute many thousands of trades in milliseconds, crunching vast quantities of data and dynamically interacting with other traders in the process. This intelligence makes it difficult for a trader to fully predict how an algorithm might behave ex ante and near-impossible for her to track and control its activities in real-time. Secondly, though markets have traditionally relied on informed fundamental traders to decode complexity, these actors now possess reduced incentives to perform this function in algorithmic markets. Fundamental traders routinely see their gains diminished by faster, automated counterparts, able to front-run trades and to derive maximal benefit from the research of others. In arguing that algorithmic trading is transforming how markets process and interpret information, this Article shows that conventional assumptions in securities law doctrine and policy also break down. With these insights, this Article, offers a new framework to thoroughly reevaluate the centrality of efficiency economics in regulatory design.
Notice of RetractionResearch on the Evolution Mechanism of Enterprise Strategic Alliances Based on Resources Perspective

Strategic alliance has become an important way for enterprises to gain competitive advantage, thus lots of theories to explain strategic alliance appear. Resource-based view starts with the angle of discussing the way to acquire internal heterogeneous resources in enterprises. While enterprises are short of some kind of resources, they are able to acquire external resources in the form of strategic alliance in a network of heterogeneous resources, according to resources leverage principle. While resources are gradually internalized, their utilization effectiveness disappears, thus strategic alliances disband The process of outsourcing and internalization consists in the dynamic process of strategic alliances' evolution correspondingly. This paper presents an explanation of evolution mechanism of strategic alliances based on resources perspective.
Three Essays on Misintermediation

This dissertation investigates the role of misintermediation in macroeconomic fluctuations. A perennial problem of financial markets is that of maturity mismatching, or misintermediation, a situation in which financial intermediaries fund long-term, illiquid loans with short-term liabilities. McCulloch (1981) concludes that misintermediation can be responsible for business cycles and predicts pro-cyclic behavior of surprises in real interest rates over business cycles. In my dissertation, I study the issue from both theoretical and empirical perspectives. An interpolation method for yield curve fitting is also specified to enable the empirical study of misintermediation. My dissertation consists of four chapters. In Chapter 1 and 2, I theoretically investigate the mechanism for generating a disequilibrium boom or recession in a finite horizon structural model. In each period, agents decide how much to consume and how much to invest in heterogeneous capital for subsequent periods. If demand and supply happen to coincide in each period, the model will have a unique equilibrium term structure of interest rates. Otherwise, unexpected changes in real interest rates will occur accompanied by the realization of a recession or boom because previous plans cannot be completely corrected as a new period starts. The model is then extended to study the changes of real wage rates and heterogeneous capital prices under either a recession or boom regime. It reveals that misintermediation would not only bring about unexpected
Training a Multilayer Perceptron to predict the final selling price of an apartment in co-operative housing society sold in Stockholm city with features stemming from open data

The need for a robust model for predicting the value of condominiums and houses are becoming more apparent as further evidence of systematic errors in existing models are presented. Traditional valuation methods fail to produce good predictions of condominium sales prices and systematic patterns in the errors linked to for example the repeat sales methodology and the hedonic pricing model have been pointed out by papers referenced in this thesis. This inability can lead to monetary problems for individuals and in worst-case economic crises for whole societies.In this master thesis paper we present how a predictive model constructed from a multilayer perceptron can predict the price of a condominium in the centre of Stockholm using objective data from sources publicly available. The value produced by the model is enriched with a predictive interval using the Inductive Conformal Prediction algorithm to give a clear view of the quality of the prediction. In addition, the Multilayer Perceptron is compared with the commonly used Support Vector Regression algorithm to underline the hallmark of neural networks handling of a broad spectrum of features.The features used to construct the Multilayer Perceptron model are gathered from multiple “Open Data” sources and includes data as: 5,990 apartment sales prices from 2011- 2013, interest rates for condominium loans from two major banks, national election results from 2010, geographic information and nineteen local features. Several well-known techniques of improving performance of Multilayer Perceptrons are applied and evaluated. A Genetic Algorithm is deployed to facilitate the process of determine appropriate parameters used by the backpropagation algorithm.Finally, we conclude that the model created as a Multilayer Perceptron using backpropagation can produce good predictions and outperforms the results from the Support Vector Regression models and the studies in the referenced papers.
Ordering Slots of Semantically Related Schemata of Polish Verbs

In this paper a method of ordering slots of verb schemata related by diathesis alternations is presented. Such schemata are grouped together and represent a single meaning of a verb. The schema dominating in a group is found and its slots are numbered w.r. t. their obliqueness hierarchy. These numbers are propagated to other schemata accordingly to alternations linking slots of schemata related by them.
Initial Results from an IPv6 Darknet13

A darknet is an advertised and routed portion of Internet address space that contains no advertised services. Any traffic observed on a darknet is therefore illegitimate and darknets are useful tools for observing the level of background 'noise' on a larger network. Darknets have been used in existing IPv4 networks to help to identify malicious traffic, malware trends, or the consequences of misconfiguration. We have created what may be the world's first IPv6 darknet to help us observe the 'noise' present on the IPv6 Internet and to see how this differs from the IPv4 Internet. Initial results suggest that the level of undirected malicious software active on the IPv6 Internet is currently minimal and there is no apparent undirected port-scanning activity. We suspect this is partially a (predicted) consequence of the larger IPv6 address space and also an indication of the immaturity of the IPv6 Internet at the present time