Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation of MIC with Outcome for Candida Species Tested against Voriconazole: Analysis and Proposal for Interpretive Breakpoints

ABSTRACT Developing interpretive breakpoints for any given organism-drug combination requires integration of the MIC distribution, pharmacokinetic and pharmacodynamic parameters, and the relationship between the in vitro activity and outcome from both in vivo and clinical studies. Using data generated by standardized broth microdilution and disk diffusion test methods, the Antifungal Susceptibility Subcommittee of the Clinical and Laboratory Standards Institute has now proposed interpretive breakpoints for voriconazole and Candida species. The MIC distribution for voriconazole was determined using a collection of 8,702 clinical isolates. The overall MIC90 was 0.25 μg/ml and 99% of the isolates were inhibited at ≤1 μg/ml of voriconazole. Similar results were obtained for 1,681 Candida isolates (16 species) from the phase III clinical trials. Analysis of the available data for 249 patients from six phase III voriconazole clinical trials demonstrated a statistically significant correlation (P = 0.021) between MIC and investigator end-of-treatment assessment of outcome. Consistent with parallel pharmacodynamic analyses, these data support the following MIC breakpoints for voriconazole and Candida species: susceptible (S), ≤1 μg/ml; susceptible dose dependent (SDD), 2 μg/ml; and resistant (R), ≥4 μg/ml. The corresponding disk test breakpoints are as follows: S, ≥17 mm; SDD, 14 to 16 mm; and R, ≤13 mm.
Stereo- and neural network-based pedestrian detection

In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to identify pedestrians in various poses, shapes, sizes, clothing, occlusion status. The experiments on a large number of urban street scenes demonstrate the feasibility of the approach in terms of pedestrian detection rate and frame processing rate.
A construct on fostering unity through virtual community cohesion

This paper presents a research outcome that examines the construct that explains the cohesiveness of members of the social media in fostering unity.As the proliferation of the new media becomes rampant with the prevalence of the Internet, society tends to become individualistic and spends more time with their digital devices rather than interacting with family members, neighbors and colleagues.This phenomenon if left unchecked may contribute to disunity within the society. Hence, the objective of the study presented in this paper was to come up with a construct that can explain virtual community cohesion in fostering unity.The research approach includes consulting relevant materials on social cohesion and identifying dimensions that make up the social cohesion construct. These were then subjected to reviews by experts in the field of community cohesion. Meanwhile operational definitions of virtual community were examined and mapped against the traditional social cohesion. This results in a construct comprising of 8 components with 46 items for measuring unity through virtual community cohesion.
ERBB2, TBX2, RPS6KB1, and MYC alterations in breast tissues of BRCA1 and BRCA2 mutation carriers

Breast cancer risk is greatly increased in women who carry mutations in the BRCA1 or BRCA2 genes. Because breast cancer initiation is different between BRCA1/2 mutation carriers and women who do not carry mutations, it is possible that the mechanism of breast cancer progression is also different. Histopathologic and genetic studies have supported this hypothesis. To test this hypothesis further, we utilized a large cohort of women who underwent therapeutic mastectomy (TM) and contralateral prophylactic mastectomy (PM). From this cohort, we developed case groups of women with a family history of breast cancer with BRCA1/2 deleterious mutations, with unclassified variant alterations, and with no detected mutation and matched these cases with sporadic controls from the same TM and PM cohort. Fluorescence in situ hybridization was performed on paraffin sections by use of dual‐color probes for ERBB2/CEP17, MYC/CEP8, TBX2/CEP17, and RPS6KB1/CEP17. All malignant and benign lesions, including putative precursor lesions, were studied. The invasive cancers from deleterious mutation carriers had a higher prevalence of duplication of MYC (P = 0.006) and TBX2 (P = 0.0008) compared to controls and a lower prevalence of ERBB2 amplification (P = 0.011). Coduplication of MYC and TBX2 was common in the in situ and invasive lesions from the deleterious mutation carriers. The odds ratio of having a BRCA1/2 mutation is 31.4 (95% CI = 1.7–569) when MYC and TBX2 are coduplicated but ERBB2 is normal. Unclassified variant carriers/no mutation detected and sporadic controls had a similar prevalence of alterations, suggesting that hereditary patients with no deleterious mutations follow a progression pathway similar to that of sporadic cases. With the exception of one atypical ductal hyperplasia lesion, no putative precursor lesion showed any detectable alteration of the probes tested. There was no significant intratumoral heterogeneity of genetic alterations. Our data confirm that a specific pattern of genomic instability characterizes BRCA1/2‐related cancers and that this pattern has implications for the biology of these cancers. Moreover, our current and previous results emphasize the interaction between phenotype and genotype in BRCA1/2‐related breast cancers and that a combination of morphologic features and alterations of ERBB2, MYC, and TBX2 may better define mechanisms of tumor progression, as well as determine which patients are more likely to carry BRCA1/2 mutations. © 2004 Wiley‐Liss, Inc.
A Software Framework for Supporting General Purpose Applications on Raw Computation Fabrics

This paper presents SUDS (Software Un-Do System), a data speculation system for Raw processors. SUDS manages speculation in software. The key to managing speculation in software is to use the compiler to minimize the number of data items that need to be managed at runtime. Managing speculation in software enables Raw processors to achieve good performance on integer applications without sacrificing chip area for speculation hardware. This additional area can instead be devoted to additional compute resources, improving the performance of dense matrix and media applications.
Business Intelligence for Improving Supply Chain Risk Management

The risk management over a supply chain has to be founded on the risk management in each of partner companies in the chain. The business relationship and operations dependence inevitably bind the management control efforts of partner companies together. This proposes challenges for supply chain risk management and at the same time for the BI application. In this paper we analyzed the management control situations where business intelligence technology can be applied and describe the concepts of systematic risk analysis to improve the management controls, based on causal analysis of business exceptions. A “risk template” is provide to assist analysts to fully comprehend the risk scenario in the practical business setting for evaluate and re-design the existing controls for management improvement.
Europe in the shadow of financial crisis: Policy Making via Stance Classification

Since 2009, the European Union (EU) is phasing a multi–year financial crisis affecting the stability of its involved countries. Our goal is to gain useful insights on the societal impact of such a strong political issue through the exploitation of topic modeling and stance classification techniques. To perform this, we unravel public’s stance towards this event and empower citizens’ participation in the decision making process, taking policy’s life cycle as a baseline. The paper introduces and evaluates a bilingual stance classification architecture, enabling a deeper understanding of how citizens’ sentiment polarity changes based on the critical political decisions taken among European countries. Through three novel empirical studies, we aim to explore and answer whether stance classification can be used to: i) determine citizens’ sentiment polarity for a series of political events by observing the diversity of opinion among European citizens, ii) predict political decisions outcome made by citizens such as a referendum call, ii) examine whether citizens’ sentiments agree with governmental decisions during each stage of a policy life cycle.
Effective Design of Products/Services: An Approach Based on Integration of Marketing and Operations Management Decisions

This paper presents an integrated framework for designing profit-maximizing products/ services, which can also be produced at reasonable operating difficulty levels. Operating difficulty is represented as a function of product and process attributes, and measures a firm's relative ease or difficulty in meeting customer demand patterns under specified operating conditions. Earlier optimum product design procedures have not considered. operational difficulty. We show that optimum profit, market share, cost, and product profiles are dependent on operating difficulty level. Empirical results from the pizza delivery industry demonstrate the value of the proposed Effective Product/Service Design approach.
Developmental learning for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system tries to propose to the student the activity which makes him progress best. We introduce two algorithms that rely on the empirical estimation of the learning progress, one that uses information about the difficulty of each exercise RiARiT and another that does not use any knowledge about the problem ZPDES. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge.
Face recognition using extended Fisherface with 3D morphable model

In this paper, Fisherface is extended for face recognition from one example image per person. Fisherface is one of the most successful face recognition methods. However, Fisherface requires several training images for each face, so it cannot be applied to face recognition applications where only one example image per person is available for training. To tackle this problem, Fisherface method is extended by utilizing 3D morphable model to derive multiple images of a face from one single image. Experimental results on ORL face database and UMIST face database show that face recognition method proposed in this paper makes impressive performance improvement compared with conventional eigenface methods.
Large-Scale Multi-label Ensemble Learning on Spark

Multi-label learning is a challenging problem which has received growing attention in the research community over the last years. Hence, there is a growing demand of effective and scalable multi-label learning methods for larger datasets both in terms of number of instances and numbers of output labels. The use of ensemble classifiers is a popular approach for improving multi-label model accuracy, especially for datasets with high-dimensional label spaces. However, the increasing computational complexity of the algorithms in such ever-growing high-dimensional label spaces, requires new approaches to manage data effectively and efficiently in distributed computing environments. Spark is a framework based on MapReduce, a distributed programming model that offers a robust paradigm to handle large-scale datasets in a cluster of nodes. This paper focuses on multi-label ensembles and proposes a number of implementations through the use of parallel and distributed computing using Spark. Additionally, five different implementations are proposed and the impact on the performance of the ensemble is analyzed. The experimental study shows the benefits of using distributed implementations over the traditional single-node single-thread execution, in terms of performance over multiple metrics as well as significant speedup tested on 29 benchmark datasets.
Intersession Compensation and Scoring Methods in the i-vectors Space for Speaker Recognition

The total variability factor space in speaker verification system architecture based on Factor Analysis (FA) has greatly improved speaker recognition performances. Carrying out channel compensation in a low dimensional total factor space, rather than in the GMM supervector space, allows for the application of new techniques. We propose here new intersession compensation and scoring methods. Furthermore, this new approach contributes to a better understanding of the session variability characteristics in the total factor space.
Maximizing the lifetime of wireless ad hoc networks using Minimizing the Maximum used Power Routing

In this paper, we present minimizing the maximum used power routing algorithm (MIVIPR) in mobile ad hoc network (MANET). Most previous energy aware routing protocols usually optimize just one optimization criterion, which minimizes the total used energy or evenly distributes power consumption in each node to prevent node from being over exhausted. MMPR achieves two optimization objectives using a single route cost equation, which is based on the similar optimization, minimizing the maximum link utilization in LPF-Based-Re-Routing (LPF-RR), of traffic engineering (TE). MMPR can be implemented by modifying a route selection procedure in well-known routing protocol of MANET. We validate MMPR by performing a simulation based on a modification of DSR, and the simulation results showed improved performance in the maximum used energy and the number of dead nodes by time than the conditional max-min battery routing (CMMBCR).
A calmodulin-target peptide hybrid molecule with unique calcium-binding properties.

This paper describes the production and properties of a hybrid protein comprising the full length of the Xenopus laevis calmodulin (CaM) sequence, followed, through a glycylglycine linker, by the 26-residue CaM-binding region of myosin light-chain kinase (M13). This hybrid molecule appears to have high thermal stability (Tm > 75 degrees C in the presence of Ca2+) as well as unusual Ca(2+)-binding properties: (i) a wide-range biphasic Ca(2+)-binding response (extending over pCa 4.8-7.4) and (ii) a high apparent binding constant (pCa50% = 6.3, a 10-fold increase from that of wild-type CaM). NMR and CD data indicate that the CaM-M13 hybrid molecule exists in equilibrium in an approximate 1:1 ratio between two major conformations, one of which is similar to the compact globular structure of the CaM-M13 complex [M.Ikura, G.M. Clore, A.M. Gronenborn, G. Zhu, C.B. Klee and A. Bax (1992) Science, 256, 632-638] and the other to the dumb-bell-like structure of the wild type CaM [Y.S. Babu, C.E. Bugg and W.J. Cook (1988) J. Mol. Biol., 204, 191-204]. The biphasic Ca(2+)-binding curve can be interpreted using a linear combination of two Hill binding curves with significantly different dissociation constants (2 x 10(-9) M and 8 x 10(-8) M), which can be attributed to the two conformations in equilibrium. The present study has opened an avenue to engineer proteins with higher Ca(2+)-binding affinities using the known CaM structures as a template.
Supplementary document : Learning hash functions using column generation ∗

We provide more details in this supplementary document on (1) learning hash functions with the hinge loss; (2) learning hash functions with general convex loss. Those information is omitted in our paper [3] due to space limit. More specifically, the optimization problems in (1) are regularized by two types of norms: l1 norm and l∞ norm. (2) We provide the details of deriving the optimization formulation using squared hinge loss and logistic loss regularized by the l1 and l∞ norms.
Request: a natural language question-answering system

REQUEST [1,2,3] is an experimental Restricted English Question-answering system that has been implemented in LISP 1.5 and runs under an interactive operating system in one million bytes of virtual storage. It is currently capable of analyzing and answering a variety of English questions, spanning a significant range of syntactic complexity, with respect to a small Fortune-500-type data base.
ICES REPORT 16-26 December 2016 An ultraweak DPG method for viscoelastic fluids

We explore a vexing benchmark problem for viscoelastic fluid flows with the discontinuous Petrov-Galerkin (DPG) finite element method of Demkowicz and Gopalakrishnan [1, 2]. In our analysis, we develop an intrinsic a posteriori error indicator which we use for adaptive mesh generation. The DPG method is useful for the problem we consider because the method is inherently stable—requiring no stabilization of the linearized discretization in order to handle the advective terms in the model. Because stabilization is a pressing issue in these models, this happens to become a very useful property of the method which simplifies our analysis. This built-in stability at all length scales and the a posteriori error indicator additionally allows for the generation of parameter-specific meshes starting from a common coarse initial mesh. A DPG discretization always produces a symmetric positive definite stiffness matrix. This feature allows us to use the most efficient direct solvers for all of our computations. We use the Camellia finite element software package [3, 4] for all of our analysis.
"Gangnam Mom": A Qualitative Study on the Information Behaviors of Korean Helicopter Mothers

This study investigates information seeking, sharing, and managing behaviors of “Gangnam mothers,” a group of dedicated Korean mothers who invest significant time and effort to micro-manage their child’s academic needs. These mothers’ vibrant and sophisticated information seeking and managing loads of education-related information sources is worthy of attention from information behavior research. To learn about their information behavior, field observations and interviews with mothers of school-aged children in Gangnam, the southern part of Seoul, have been conducted. The findings show that Gangnam mothers are personal information experts who heavily utilize human channels of information and employ local, group and personal filtering strategies. The fascinating information ecology of mothers in their diverse strategies for navigating and filtering information, coupled with the unique information environment in Gangnam, makes the flood of education-related information surprisingly manageable.
Using pseudo-recurrent connectionist networks to solve the problem of sequential learning

A major problem facing connectionist models of memory is how to make them simultaneously sensitive to, but not disrupted by, new input. This paper describes one solution to this problem. The resulting connectionist architecture is capable of sequential learning and exhibits gradual forgetting where standard connectionist architectures may forget catastrophically. The proposed architecture relies on separating previously learned representations from those that are currently being learned. Crucially, a method is described in which approximations of the previously learned data, called pseudopatterns, will be extracted from the network and mixed in with the new patterns to be learned, thereby alleviating sudden forgetting caused by new learning and allowing the network to forget gracefully.
Hexane Extracts of Calophyllum brasiliense Inhibit the Development of Gastric Preneoplasia in Helicobacter felis Infected INS-Gas Mice

Objectives: Indigenous Latin American populations have used extracts from Calophyllum brasiliense, a native hardwood, to treat gastrointestinal symptoms for generations. The hexane extract of Calophyllum brasiliense stem bark (HECb) protects against ethanol-mediated gastric ulceration in Swiss–Webster mice. We investigated whether HECb inhibits the development of gastric epithelial pathology following Helicobacter felis infection of INS-Gas mice. Materials and Methods: Groups of five male, 6-week-old INS-Gas mice were colonized with H. felis by gavage. From 2 weeks after colonization their drinking water was supplemented with 2% Tween20 (vehicle), low dose HECb (33 mg/L, lHECb) or high dose HECb (133 mg/L, hHECb). Equivalent uninfected groups were studied. Animals were culled 6 weeks after H. felis colonization. Preneoplastic pathology was quantified using established histological criteria. Gastric epithelial cell turnover was quantified by immunohistochemistry for Ki67 and active-caspase 3. Cytokines were quantified using an electrochemiluminescence assay. Results: Vehicle-treated H. felis infected mice exhibited higher gastric atrophy scores than similarly treated uninfected mice (mean atrophy score 5.6 ± 0.87 SEM vs. 2.2 ± 0.58, p < 0.01). The same pattern was observed following lHECb. Following hHECb treatment, H. felis status did not significantly alter atrophy scores. Gastric epithelial apoptosis was not altered by H. felis or HECb administration. Amongst vehicle-treated mice, gastric epithelial cell proliferation was increased 2.8-fold in infected compared to uninfected animals (p < 0.01). Administration of either lHECb or hHECb reduced proliferation in infected mice to levels similar to uninfected mice. A Th17 polarized response to H. felis infection was observed in all infected groups. hHECb attenuated IFN-γ, IL-6, and TNF production following H. felis infection [70% (p < 0.01), 67% (p < 0.01), and 41% (p < 0.05) reduction vs. vehicle, respectively]. Conclusion: HECb modulates gastric epithelial pathology following H. felis infection of INS-Gas mice. Further studies are indicated to confirm the mechanisms underlying these observations.