Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression Signature Developed from a Complex Series of Mouse Models Accurately Predicts Human Breast Cancer Survival

Purpose: The capability of microarray platform to interrogate thousands of genes has led to the development of molecular diagnostic tools for cancer patients. Although large-scale comparative studies on clinical samples are often limited by the access of human tissues, expression profiling databases of various human cancer types are publicly available for researchers. Given that mouse models have been instrumental to our current understanding of cancer progression, we aimed to test the hypothesis that novel gene signatures possessing predictability in clinical outcome can be derived by coupling genomic analyses in mouse models of cancer with publicly available human cancer data sets. Experimental Design: We established a complex series of syngeneic metastatic animal models using a murine breast cancer cell line. Tumor RNA was hybridized on Affymetrix MouseGenome-430A2.0 GeneChips. With the use of Venn logic, gene signatures that represent metastatic competency were derived and tested against publicly available human breast and lung cancer data sets. Results: Survival analyses showed that the spontaneous metastasis gene signature was significantly associated with metastasis-free and overall survival (P < 0.0005). Consequently, the six-gene model was determined and showed statistical predictability in predicting survival in breast cancer patients. In addition, the model was able to stratify poor from good prognosis for lung cancer patients in most data sets analyzed. Conclusions: Together, our data support that novel gene signature derived from mouse models of cancer can be used for predicting human cancer outcome. Our approaches set precedence that similar strategies may be used to decipher novel gene signatures for clinical utility. Clin Cancer Res; 16(1); 249–59
Antibiotic resistance evolved via inactivation of a ribosomal RNA methylating enzyme

Modifications of the bacterial ribosome regulate the function of the ribosome and modulate its susceptibility to antibiotics. By modifying a highly conserved adenosine A2503 in 23S rRNA, methylating enzyme Cfr confers resistance to a range of ribosome-targeting antibiotics. The same adenosine is also methylated by RlmN, an enzyme widely distributed among bacteria. While RlmN modifies C2, Cfr modifies the C8 position of A2503. Shared nucleotide substrate and phylogenetic relationship between RlmN and Cfr prompted us to investigate evolutionary origin of antibiotic resistance in this enzyme family. Using directed evolution of RlmN under antibiotic selection, we obtained RlmN variants that mediate low-level resistance. Surprisingly, these variants confer resistance not through the Cfr-like C8 methylation, but via inhibition of the endogenous RlmN C2 methylation of A2503. Detection of RlmN inactivating mutations in clinical resistance isolates suggests that the mechanism used by the in vitro evolved variants is also relevant in a clinical setting. Additionally, as indicated by a phylogenetic analysis, it appears that Cfr did not diverge from the RlmN family but from another distinct family of predicted radical SAM methylating enzymes whose function remains unknown.
NKp46+ natural killer cells attenuate metabolism‐induced hepatic fibrosis by regulating macrophage activation in mice

Nonalcoholic steatohepatitis (NASH) affects 3%‐5% of the U.S. population, having severe clinical complications to the development of fibrosis and end‐stage liver diseases, such as cirrhosis and hepatocellular carcinoma. A critical cause of NASH is chronic systemic inflammation promoted by innate immune cells, such as liver macrophages (Mϕ) and natural killer (NK) cells. However, little is known about how the crosstalk between Mϕ and NK cells contributes to regulate NASH progression to fibrosis. In this report, we demonstrate that NKp46+ cells play an important role in preventing NASH progression to fibrosis by regulating M1/M2 polarization of liver Mϕ. Using a murine model of NASH, we demonstrate that DX5+NKp46+ NK cells are increased during disease and play a role in polarizing Mϕ toward M1‐like phenotypes. This NK's immunoregulatory function depends on the production of interferon‐gamma (IFN‐γ), but not by granzyme‐mediated cytolytic activity. Notably, depletion of NKp46+ cells promotes the development of fibrosis with increased expression of profibrogenic genes as well as skewed M2 Mϕ phenotypes in hepatic tissues. Conclusions: NK cell‐derived IFN‐γ may be essential for maintaining a balanced inflammatory environment that promotes tissue integrity and limiting NASH progression to fibrosis. (Hepatology 2016;63:799–812)
Estimation of quantitative genetic parameters

This paper gives a short review of the development of genetic parameter estimation over the last 40 years. This shows the development of more statistically and computationally efficient methods that allow the fitting of more biologically appropriate models. Methods have evolved from direct methods based on covariances between relatives to methods based on individual animal models. Maximum-likelihood methods have a natural interpretation in terms of best linear unbiased predictors. Improvements in iterative schemes to give estimates are discussed. As an example, a recent estimation of genetic parameters for a British population of dairy cattle is discussed. The development makes a connection to relevant work by Bill Hill.
A methodology for joint stiffness identification of serial robots

This paper presents a new methodology for joint stiffness identification of serial robots. This methodology aims at evaluating all joint stiffness values responsible for both translational and rotational displacements of the robot end-effector subject to an external wrench (force and torque). The links of the robot are supposed to be quite stiffer than the joints and not known as it is usually the case with industrial serial robots. The robustness of the identification method and the sensitivity of the results to measurement errors and number of experimental tests are also analyzed. The Kuka KR240-2 robot is used as an illustrative example through the paper.
Analyzing Animal Movement Characteristics From Location Data

When individuals of a species utilize an environment, they generate movement patterns at a variety of spatial and temporal scales. Field observations coupled with location technologies (e.g. GPS tags) enable the capture of detailed spatio‐temporal data regarding these movement patterns. These patterns contain information about species‐specific preferences regarding individual decision‐making, locational choices and the characteristics of the habitat in which the animal resides. Spatial Data Mining approaches can be used to extract repeated spatio‐temporal patterns and additional habitat preferences hidden within large spatially explicit movement datasets. We describe a method to determine the periodicity and directionality in movement exhibited by a migratory bird species. Results using a High Arctic‐nesting Svalbard Barnacle Goose movement data yielded undetected patterns that were secondarily corroborated with expert field knowledge. Individual revisits by the geese to specific locations in the breeding and wintering grounds of Svalbard, Norway and Solway, Scotland, occurred with a periodicity of 334 days . Further, the orientation of this movement was detected to be mostly north‐south. During long‐range migration the geese use the north‐south oriented Norwegian islands as “stepping stones”, Short‐range movement between mudbank roosts to feeding fields in Solway also retained a north‐south orientation.
Reliability and validity of estimating the NIH stroke scale score from medical records.

BACKGROUND AND PURPOSE The aim of our study was to determine whether the National Institutes of Health Stroke Scale (NIHSS) can be estimated retrospectively from medical records. The NIHSS is a quantitative measure of stroke-related neurological deficit with established reliability and validity for use in prospective clinical research. Recently, retrospective observational studies have estimated NIHSS scores from medical records for quantitative outcome analysis. The reliability and validity of estimation based on chart review has not been determined. METHODS Thirty-nine patients were selected because their NIHSS scores were formally measured at admission and discharge. Handwritten notes from medical records were abstracted and NIHSS scores were estimated by 6 raters who were blinded to the actual scores. Estimated scores were compared among raters and with the actual measured scores. RESULTS Interrater reliability was excellent, with an intraclass correlation coefficient of 0.82. Scores were well calibrated among the 6 raters. Estimated NIHSS scores closely approximated the actual scores, with a probability of 0.86 of correctly ranking a set of patients according to 5-point interval categories (as determined by the area under the receiver-operator characteristic curve). Patients with excellent outcomes (NIHSS score of </=5) could be identified with sensitivity of 0.72 and specificity of 0.89. There were no significant differences between these parameters at admission and discharge. CONCLUSIONS For the purposes of retrospective studies of acute stroke outcome, the NIHSS can be abstracted from medical records with a high degree of reliability and validity.
Location Identification for Geographic Information Retrieval

In this paper we identify location names that appear in queries written in Indonesian using geographic gazeeter. We built the gazeeter by collecting geographic information from a number of geographic resources. We translated an Indonesian query set into English using a machine translation technique. We also made an attempt to improve the retrieval effectiveness using a query expansion technique. The result shows that identifying locations in the queries and applying the query expansion technique can help improve the retrieval effectiveness for certain queries.
Metrics for knowledge management process

In search of sustainable competitive advantages, several organizations are adopting initiatives to manage knowledge. Knowledge management can be defined as a set of processes that includes knowledge creation, storage, dissemination, application, and measurement within the organization. As any other organizational process, knowledge management should be evaluated in order to verify if it is achieving the expected results. The aim of this paper is to relate metrics with the knowledge management process phases. Based on a bibliography research, this paper suggests metrics for knowledge management process phases such as creation (number of discussion groups concerning process innovation), storage (number of documents), dissemination (number of communities of practice), application (number of users that access the system), and measurement (number of evaluations). The knowledge management process phases do not follow a linear sequence. Measurement, for instance, is developed from the beginning to the end of the process. Another aspect to be considered is the criteria used to select the metrics in order to evaluate the knowledge management process, in which there should be an alignment with business goals.
Resource Efficient Arithmetic Effects on RBM Neural Network Solution Quality Using MNIST

This paper presents a case study on the impact of using reduced precision arithmetic on learning in Restricted Boltzmann Machine (RBM) deep belief networks. FPGAs provide a hardware accelerator framework to speed up many algorithms, including the learning and recognition tasks of ever growing neural network topologies and problem complexities. Current FPGAs include DSP blocks - hard blocks that allow designers to roll in hardware otherwise built using significant quantity of reconfigurable logic (slices) and increase clock performance of arithmetic operations. Accelerators on FPGAs can take advantage of, in some products, thousands DSP blocks on a single chip to scale up the parallelism of designs. Conversely, IEEE floating point representation cannot be fully implemented in single DSP slices and requires a significant amount of general logic thus reducing the amount of resources available to breadth of parallelism in an accelerator design. Reduced precision fixed point format arithmetic can fit within a single DSP slice without external logic. It has been used successfully for training MLP-BP neural networks on small problems. The merit of reduced precision computation in RBM networks for sizable problems has not been evaluated. In this work, a three layer RBM network linked to one classification layer (1.6M weights) is used to learn the classic MNIST dataset over a set of common limited precisions used in FPGA designs. Issues of parameter saturation and a method to overcome inherent training difficulties is discussed. The results demonstrate that RBM can be trained successfully using resource-efficient fixed point formats commonly found in current FPGA devices.
Difluoro-and Trifluoromethylation of Electron-Deficient Alkenes in an Electrochemical Microreactor

Electrochemical microreactors, which have electrodes integrated into the flow path, can afford rapid and efficient electrochemical reactions without redox reagents due to the intrinsic properties of short diffusion distances. Taking advantage of electrochemical microreactors, Kolbe electrolysis of di-and trifluoroacetic acid in the presence of various electron-deficient alkenes was performed under constant current at continuous flow at room temperature. As a result, di-and trifluoromethylated compounds were effectively produced in either equal or higher yields than identical reactions under batch conditions previously reported by Uneyamas group. The strategy of using electrochemical microreactor technology is useful for an effective fluoromethylation of alkenes based on Kolbe electrolysis in significantly shortened reaction times.
Modelling and analysis of operation PMSG based WECS under different load conditions

The permanent magnet synchronous generator (PMSG) is widely used in wind power generation due to its high energy efficiency and controllability. Unbalance load conditions or different load conditions effects PMSG operation system. Furthermore, these conditions occur in problematic operation and distorted converter currents. The operation condition of the PMSG system to the effects of variation load is observed in this study and the performances of the PMSG based on wind energy conversion system (WECS) are analyzed. Simulation of the whole system using a 850-kW PMSG based on WECS system is built in the Matlab Simulink. The power flow efficiency of a PMSG is implemented to this study and control of the system for WECS. The proposed model is proven to verify under different operation conditions.
A mean-semivariance model for stock portfolio selection in fuzzy random environment

This paper discusses stock portfolio selection problem in fuzzy random environment. In the paper, the returns of each security are assumed to be fuzzy random variables, then following the ideas of mean-semivariance model in a fuzzy random environment is proposed. Based on the concept of semivariance of fuzzy random variable, a mean-semivariance model in is proposed. To solve the new model in general cases, a fuzzy random simulation based genetic algorithm is presented in the paper. In addition, a numerical example is presented to illustrate the proposed stock portfolio selection model and the effectiveness of the designed algorithm.
Readers of poly(ADP-ribose): designed to be fit for purpose

Post-translational modifications (PTMs) regulate many aspects of protein function and are indispensable for the spatio-temporal regulation of cellular processes. The proteome-wide identification of PTM targets has made significant progress in recent years, as has the characterization of their writers, readers, modifiers and erasers. One of the most elusive PTMs is poly(ADP-ribosyl)ation (PARylation), a nucleic acid-like PTM involved in chromatin dynamics, genome stability maintenance, transcription, cell metabolism and development. In this article, we provide an overview on our current understanding of the writers of this modification and their targets, as well as the enzymes that degrade and thereby modify and erase poly(ADP-ribose) (PAR). Since many cellular functions of PARylation are exerted through dynamic interactions of PAR-binding proteins with PAR, we discuss the readers of this modification and provide a synthesis of recent findings, which suggest that multiple structurally highly diverse reader modules, ranging from completely folded PAR-binding domains to intrinsically disordered sequence stretches, evolved as PAR effectors to carry out specific cellular functions.
Computed tomography--an increasing source of radiation exposure.

The number of computed tomographic (CT) studies performed is increasing rapidly. Because CT scans involve much higher doses of radiation than plain films, we are seeing a marked increase in radiation exposure in the general population. Epidemiologic studies indicate that the radiation dose from even two or three CT scans results in a detectable increase in the risk of cancer, especially in children. This article summarizes the facts about this form of radiation exposure and the implications for public health.
Characterization and thermoelectric properties of Si-Ge nanocomposite

Submitted for the MAR06 Meeting of The American Physical Society Characterization and thermoelectric properties of Si-Ge nanocomposite1 DEZHI WANG, WENZHONG WANG, SHUO CHEN, JIANYU HUANG, ZHIFENG REN, Boston College, Dept. of Physics, HOHYUN LEE, GANG CHEN, MIT, Dept. of mechanical Engineering, MING TANG, M. S. DRESSELHAUS, MIT, Dept. of Electrical Engineering, PAWAN GOGNA, JEAN-PIERRE FLEURIAL, Jet Propulsion Laboratory, BRADLEY KLOTZ, Dynamic Science, Inc. — Low dimension is one of the most promising directions to search for high-ZT thermoelectric materials. It has been predicted by theory and proved by experiments that structures such as quantum well and superlattice can increase ZT by several times vs the corresponding bulk materials. However, it is very difficult to manufacture those low dimensional structures in large scale for bulk applications. To realize those principles, we have designed and synthesized successfully a new structure so called Si-Ge nanocomposite—nano Silicon particles in SiGe alloy matrix. The Si-Ge nanocomposite was made of nano silicon and germanium particles by a unique hot-press procedure. The samples are being characterized by TEM, SEM, XRD and thermoelectric property measurements. The results will be reported in detail.
Training Wheels: A New Approach to Teaching Mobile Device Security

Despite massive investments in cyber security education, training, and awareness programs, most people retain unsafe mobile computing habits. They not only jeopardize their own data, but also risk the security of their associated organizations. It appears that conventional training programs are not ingraining sound security practices on trainees. This research questions the efficacy of legacy SETA frameworks and proposes a new cyber training tool for mobile devices. The tool is called Training Wheels. Training Wheels stands a number of cyber security training practices on their heads: instead of using punitive methods of reinforcement it provides rewards to encourage good behavior, instead of summary measures of security compliance it gives real-time feedback, and instead of isolating participants it displays participants’ performance relative to their peers. These changes are grounded in established psychological theory. They are incorporated as key features of Training Wheels. Besides introducing the new training tool, this study also provides recommendations for its usage and implications for research. Disciplines Information Security | Management Information Systems | Technology and Innovation This event is available at DigitalCommons@Kennesaw State University: http://digitalcommons.kennesaw. edu/ccerp/2016/ Academic/2
Intra-class Recognition of Fruits using Color and Texture Features with Neural Classifiers

Intra-class recognition of fruits using image processing and pattern recognition techniques, is a challenging task mainly because sub-types of the same fruit show a large amount of similarities between each other and hence more difficult to distinguish than when different types of fruits are involved (inter-class). The problem becomes more acute when the camera viewpoint also changes which tend to change the known characteristics of the fruits like contour shape. To solve this problem, this paper proposes a view point invariant solution for intra-class recognition of fruits by combining color and texture features and using a Neural Network (NN) classifier. Experimentations done on a dataset of 270 fruit images show satisfactory performance across different fruit types and sub-types. General Terms Pattern Recognition, Image Processing
M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework

Airplane detection is a challenging problem in complex remote sensing imaging. In this letter, an effective airplane detection framework called Markov random field-fully convolutional network (M-FCN) is proposed. The M-FCN uses a cascade strategy that consists of an FCN-based coarse candidate extraction stage, a multi-Markov random field (multi-MRF)-based region proposal (RP) generation stage, and a final classification stage. In the first stage, the FCN model is trained to be sensitive to airplanes, and a coarse candidate map is generated. This model is scale-, direction-, and color-invariant and does not require many training examples. After the first stage, the coarse candidate map is used as the initial labeling field for a multi-MRF algorithm, and RPs are generated according to the multi-MRF output. This RP-generating strategy can yield more accurate locations with fewer RPs. In the last stage, a convolutional neural network-based classifier is used to improve the precision of the entire framework. Experiments show that the M-FCN has high precision, recall, and location accuracy.
Coding Theorems for Reversible Embedding

We consider embedding of messages (data-hiding) into i.i. d. host sequences. As in Fridrich et al. [2002] we focus on the case where reconstruction of the host sequence from the composite sequence is required. We study the balance between embedding rate and embedding distortion. First we determine the distortion-rate region corresponding to this setup. Then we generalize this result in two directions. (A) The reversible embedding setup is not robust. Therefore we also consider reconstruction based on the output sequence of a discrete memoryless channel whose input is the composite sequence. We also determine the distortion-rate region for this setup. (B) Then we consider the case where only partial reconstruction of the host sequence is required. We determine the possible trade-offs here between embedding rate, distortion between source sequence and composite sequence (embedding distortion), and distortion between source sequence and restoration sequence (restoration distortion), i.e. the distortion-rate region. All achievability proofs in this paper are based on the Gelfand-Pinsker [1980] achievability proof.
Subcarrier based resource allocation

Normally resource allocation is considered from the view point of users and the algorithms are designed so as to maximize total data rate supported while maintaining some constraints on quality of service and fairness. In this paper we approach the problem from the subcarrier's perspective. We propose a novel subcarrier based resource allocation scheme that assigns a subcarrier either to a single user or to more than one user on time shared basis according to the users' signal to noise ratios. This approach offers extra degree of freedom because unlike in user based schemes, in subcarrier based approach each user in principle can contend for any subcarrier unless it has already fulfilled its rate demand. Our simulation results demonstrate improved capacity without degradation of fairness.