Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.
Surface plasmon resonance biochips for tuberculosis bacillus detection

Tuberculosis is one of notifiable infectious diseases which may cause serious epidemic problems. Traditional diagnostic techniques include acid-fast stain, bacteria culture, metabolic monitoring have some disadvantages such as low sensitivity, time consumption and less specificity. In this study we proposed a new diagnostic technique based on the protein chips concept which detected by SPR phenomena. It was found that the resonance angle (/spl sim/51.86/spl deg/) shifted slightly toward right with antibody concentration of 10X, 30X, 100X, and 300X dilution under optimal concentration of immobilized TB antigen W38 (MW 41.5 kDa, 50 /spl mu/g/ml). While for the W06 (MW 14 kDa, 50 /spl mu/g/ml), the resonance angle was around 50.13/spl deg/ and shifted with the same trend as W38 did.
Trends of Adherence to the Mediterranean Dietary Pattern in Northern Italy from 2010 to 2016

Little information is available on the trends of adherence to the Mediterranean dietary pattern (MDP). This study investigates food consumption trends from 2010 to 2016 in subjects living in Northern Italy. A cross-sectional study of 8584 subjects enrolled between January 2010 and December 2016 was conducted. Socio-demographic, nutrition and lifestyle characteristics were collected. A 14-item questionnaire was used to evaluate adherence to MDP. Multivariable Poisson regression was used to evaluate the trends of and the determinants for the adherence to MDP. The overall prevalence of adherence to MDP was 14% and the trend remained constant over the six years. However, there was a marked increase in nuts consumption and a slight one in white meat consumption. Furthermore, we observed a decrease in the consumption of fruit, red meat, sweets and sugar-sweetened beverages and in the use of soffritto. Finally, higher education, being older, married, physically active, and ex-smoker was associated with greater adherence to MDP, whereas the prevalence of adherence was lower in the obese. In conclusion, the consumption of some Mediterranean and non-Mediterranean food groups changed over the six years. However, overall, the prevalence of adherence to MDP did not change. Additional strategies promoting healthy dietary habits are needed.
BULK-LOADING R-TREES BASED ON PARTITION TECHNOLOGY OF DATABASE

Bulk-loading of spatial data is time-consuming and can not satisfy the desire of the applications dealing with massive spatial data. In the article, the TGS-based (Top-Down Greedy Split) parallel technique is made to accelerate the processing of spatial data bulkloading, adopting the DCSO (DecomposeConquerStitch Output) strategy to build the R-tree in parallel. In order to manage and access the spatial data more efficiently, partition technology is applied to the physical storage of spatial data. This study accelerates the spatial data bulk loading efficient and makes the management and maintain of the spatial index easier and more flexible. The study also proves the R-tree constructed by parallel partitions performs better than serially strategy from the theoretical and experimental points of view as long as the decomposition of the study area is reasonable. Finally, an experiment is made to demonstrate the rationality of the proposed method and gains a perfect result.
Development and Validation of a Discharge Planning Index for Achieving Home Discharge After Hospitalization for Acute Stroke Among Those Who Received Rehabilitation Services

ObjectiveThe aim of this study was to develop an index for establishing the probability of being discharged home after hospitalization for acute stroke using information about previous living circumstances, comorbidities, hospital course, and the physical grades and cognitive stages of independence achieved. DesignThis is a longitudinal observational population-based study. All 6515 persons treated for acute stroke who received rehabilitation services in 110 Veterans Affairs facilities within a 2-yr period were included. ResultsThere were eight independent predictors of home discharge identified, and points were assigned through logistic regression: married (2 points); location before hospitalization (extended care = 0 points, other hospital = 9 points, home = 11 points); discharge physical grade (grade I, II, or III = 0 points; grade IV or V = 3 points; grade VI or VII = 5 points); discharge cognitive stage (stage I = 0 points; stage II, III, IV, or V = 3 points; stage VI or VII = 5 points); and absence of liver disease (2 points), mechanical ventilation (3 points), nonoral feeding (2 points), and intensive care unit admission (1 point). The points were added for all present factors to calculate scores. The probabilities of home discharge ranged from 65.03% in the least likely (⩽21 points) to 98.24% in the most likely group (≥27 points). ConclusionsThe treatment team might apply prognostic estimates from this index in discharge planning and functional goal setting after initial physical medicine and rehabilitation assessment.
Heart Rate Variability Classification and Feature Extraction Using Support Vector Machine and PCA : An Overview

In today’s era Heart Rate Variability becomes an important characteristic to determine the condition of heart. That’s why the calculation of HRV and classification to generate rules is necessary. Human Heart Generates the electrical signal. ECG is used to detect the heart beat. ECG signal contains lots of noise. To classify the signals first to decompose the signals using wavelet transform. Many Mother wavelet are used to denoise the signals. Support Vector Machine is used to classify the denoise signal and recognize pattern for better classification of ECG signal. Various methods have been done using different classification tools like Neural Network, Support Vector Machine, and Wavelet transform. Among them Support Vector Machine is very successful in many research areas such as pattern recognition, bioinformatics, etc. This paper gives Brief Survey on Support Vector Machine and Combination of Wavelet Transform & PCA for better Feature Extraction of ECG signals Keyword Classification, ECG, HRV, Kernel SVM, PCA, SVM, Wavelet Transform
First response communication sandbox

First response communication is tackled by several independent research groups. While there are existing prototypes and simulated results, comparison of first response solutions is hardly possible so far. We have built an universal XML based description format to handle all relevant settings and actions typical for first response scenarios. In addition we implemented a user-friendly movement and environment simulator which interacts with the network simulation on top of the simulated movement. The chosen data structure have proven to be well suited for describing settings and actions found in a first response scenario. The simulator combines movement and network simulation and therefore enables both, fine grained movement models and location aware network models with reciprocal interdependencies. The simulation results of the chosen communication approach are therefore finer grained than using a network or movement simulator separately.
Comparative allergenicity studies of native and recombinant Blomia tropicalis Paramyosin  (Blo t 11)

Background: The complementary DNA (cDNA) encoding for Blo t 11, a 102 kD allergen from Blomia tropicalis (Bt) was isolated, expressed and characterized previously. This study aimed to isolate the native Blo t 11 allergen and compare its allergenicity with the recombinant forms.
Disability, Health, and Multiple Chronic Conditions Among People Eligible for Both Medicare and Medicaid, 2005–2010

Introduction People who are eligible for both Medicare and Medicaid (dual eligibles) and who have disabilities and multiple chronic conditions (MCC) present challenges for treatment, preventive services, and cost-effective access to care within the US health system. We sought to better understand dual eligibles and their association with MCC, accounting for sociodemographic factors inclusive of functional disability category. Methods Medical Expenditure Panel Survey (MEPS) data for 2005 through 2010 were stratified by ages 18 to 64 and 65 or older to account for unique subsets of dual eligibles. Prevalence of MCC was calculated for those with physical disabilities, physical plus cognitive disabilities, and all others, accounting for sociodemographic and health-related factors. Adjusted odds for having MCC were calculated by using logistic regression. Results Of dual eligibles aged 18 to 64, 53% had MCC compared with 73.5% of those aged 65 or older. Sixty-five percent of all dual eligibles had 2 or more chronic conditions, and among dual eligibles aged 65 or older with physical disabilities and cognitive limitations, 35% had 4 or more, with hypertension and arthritis the most common conditions. Dual eligibles aged 18 to 64 who had a usual source of medical care had a 127% increased likelihood of having MCC compared with those who did not have a usual source of care. Conclusion Attention to disability can be a component to helping further understand the relationship between health and chronic conditions for dual eligible populations and other segments of our society with complex health and medical needs.
Entropy-based pruning of hidden units to reduce DNN parameters

For acoustic modeling, the use of DNN has become popular due to its superior performance improvements observed in many automatic speech recognition (ASR) tasks. Typically, DNNs with deep (many layers) and wide (many hidden units per layer) architectures are chosen in order to achieve good gains. An issue with such approaches is that there is an explosion in the number of learnable parameters. Thus, it is often difficult to build models in cases where there is no sufficient amount of training data (or data for adaptation), and also limits the usage of ASR systems on hand-held devices such as mobile phones. A method to overcome this issue is to reduce the number of parameters. In this work, we provide a framework to effectively reduce the number of parameters by removing the hidden units. Each hidden unit is represented by an activity vector associated with speech attributes such as phones. A normalized entropy-based measure is computed from these activity vectors which reflects the significance of these units in the DNN model. For comparison we also use low-rank matrix factorization to reduce the number of parameters. We show that low-rank matrix factorization can reduce the number of parameters only to a certain extent. Thus, we extend the pruning technique in combination with low-rank matrix factorization to further reduce the model. In this work, we provide detailed experimental results on the Aurora-4 and TEDLIUM databases and show that the models can be reduced to approximately 20 – 30% of its initial size without much loss in the ASR performance.
WIM at TREC 2007

This paper introduced the four tracks that WIM-Lab Fudan University had taken part in at TREC 2007. For spam track, a multi-centre model was proposed considering the characteristics of spam mails in contrast of traditional 2-class classification methodology, and the incremental clustering and closeness-based classification methods were applied this year. For enterprise track, our research was mainly focused on ranking functions of experts and selecting correct supporting documents regarding to a given topic. For legal track, the effects of word distribution model in query expansion and various corpus pre-processing methods were mainly evaluated. For genomics track, three score methods were proposed to find the most relevant text snippets to a given topic. This paper gives an overview of the methods employed for each sub tasks, and compares the results of each track.
Visually exploring multi-dimensional code couplings

Dependencies and coupling relationships between code entities can be manifold. They form a graph structure with several different types of edges. Visualizing these graphs presents two challenges: the often large size of the graphs and the readable representation of the different edge types. In this paper we present a new node-link graph visualization technique addressing these challenges. Different types of edges are represented in multiple, small diagrams. These diagrams are placed side-by-side like the pictures of a comic strip. Among other things, edge bundling is applied to improve the scalability of the visualization. Elaborate interaction techniques with respect to focusing and merging data aim at providing help for comparing different edge types. A case study demonstrates how the visualization can be used to analyze a mid-size software project in practice.
Cohesive fracture modelling of overload e®ects in fatigue

A cohesive fracture model is used to capture the e®ect of a single peak overload in a ductile 316L steel alloy under plane stress conditions, viz. crack retardation. Previously, the model was used to capture the fatigue life at di®erent loading ratios. The model follows a bi-linear traction-displacement relationship coupled with a nonlinear damage evolution equation. The rate of damage evolution is characterized by three material parameters corresponding to damage accumulation, crack closure and stress threshold. The results indicate that a higher peak load results in higher fatigue crack retardation. The results also agree with experiments that suggest that strain hardening, not crack closure, is the leading mechanism for the overload e®ect.
An Attempt to Use the KEEL Tool to Evaluate Fuzzy Models for Real Estate Appraisal

Fuzzy models to assist with real estate appraisals are described and previous experiments on optimizing them with evolutionary algorithms implemented in MATLAB are summarized. An approach was made to use the KEEL Tool, developed in Java by a group of Spanish research centres, to investigate the models. Five fuzzy models comprising 3 or 4 input variables referring to the attributes of a property were learned and evaluated using six regression algorithms for fuzzy rule based systems implemented in the KEEL Tool. The experiments were conducted using a data set prepared on the basis of actual 134 sales transactions made in one of Polish cities and located in a residential section. The results were encouraging. The KEEL Tool has proved to be very useful and effective research tool, especially thanks to its 10-fold cross validation mechanism and relatively short time of data processing.
CHAPTER 8 Toward Collaborative SoftwareEngineering Leveraging the Crowd

Short, unpredictable business cycles and fluctuations, rapidly emerging technologies and trends, globalization, and the global interconnectedness provided by the Internet have increased the world’s economical clock speed and competition among companies. IT has always been a spearhead industry and is influenced by changing circumstances sooner than other industries. Competition in software engineering is high because of low market-entry barriers. IT companies, start-ups, and freelancers are competing against each other for market share. The professional development of software requires technology expertise and knowledge about the application domain, and must be inexpensive and agile to bring the company in a strong position. As technologies evolve constantly and new trends appear in very short time frames, the knowledge and competence of development teams have to be kept up to date accordingly. Time-consuming staffing periods causing long development cycles are not acceptable. However, many applications must follow strict guidelines to ensure both security and high quality. In the event specific competences are not available within a team, external knowledge has to be included. The hiring of new employees with the right skill set is the traditional instrument to enhance the development unit’s competence and capacity. However, it is too slow and inflexible for modern rapidly developed projects, as traditional hiring binds additional resources for assessing the job applicants. Not only Human Resources personnel are involved, but also domain experts to test the technical know-how of job applications. All of this is a rather time-consuming process. Recent developments in IT, like the rise of social networks, Cloud computing, global software development, and the emergence of crowdsourcing services, promise to help companies to cope with the new requirements they are facing. Social networks can be leveraged to support people in loosely coupled and open team structures to efficiently collaborate; Web-based crowdsourcing allows outsourcing tasks by broadcasting to a large network of people, the crowd, via an open call. The IT industry giants also are considering this trend; for example, Microsoft outsources key parts of its IT operations for cost minimization and business simplification (Computerworld, 2012).
Selection of Amplify-and-Forward Mobile Relay under Cascaded Rayleigh Fading

This paper assumes a practical channel model, called a cascaded Rayleigh fading channel, for a noncooperative distributed mobile relay network, which consists of one mobile source and one mobile destination, and multiple mobile amplify-and-forward (AF) relays. Then, an optimum AF relay amplifying vector (or matrix) is analytically designed using the minimum mean square error (MMSE) criteria and uses it for an efficient mobile relay selection. Power is intentionally not constrained. Instead, this paper presents a scaling factor scheme to meet a target signal-to-noise ratio (SNRTGT) at the destination. This SNRTGT strategy can implicitly embrace the power constraint problems and be a more practical implementation.
Inter-procedural data-flow analysis with IFDS/IDE and Soot

The IFDS and IDE frameworks by Reps, Horwitz and Sagiv are two general frameworks for the inter-procedural analysis of data-flow problems with distributive flow functions over finite domains. Many data-flow problems do have distributive flow functions and are thus expressible as IFDS or IDE problems, reaching from basic analyses like truly-live variables to complex analyses for problems from the current literature such as typestate and secure information-flow. In this work we describe our implementation of a generic IFDS/IDE solver on top of Soot and contrast it with an IFDS implementation in the Watson Libraries for Analysis (WALA), both from a user's perspective and in terms of the implementation. While WALA's implementation is geared much towards memory efficiency, ours is currently geared more towards extensibility and ease of use and we focus on efficiency as a secondary goal. We further discuss possible extensions to our IFDS/IDE implementation that may be useful to support a wider range of analyses.
Pentaeuropium dicadmium pentaantimonide oxide, Eu5Cd2Sb5O

The title compound, Eu5Cd2Sb5O adopts the Ba5Cd2Sb5F-type structure (Pearson symbol oC52), which contains nine crystallographically unique sites in the asymmetric unit, all on special positions. One Eu, two Sb, and the Cd atom have site symmetry m. ; two other Eu, the third Sb and the O atom have site symmetry m2m; the remaining Eu atom has 2/m. symmetry. Eu atoms fill pentagonal channels built from corner-sharing CdSb4 tetrahedra. The isolated O atom, i.e., an oxide ion O2−, is located in a distorted tetrahedral cavity formed by four Eu cations.
Two-Level Popularity-Oriented Cache Replacement Policy for Video Delivery over CCN

We introduce a novel cache replacement policy to improve the entire network performance of video delivery over content-centric networking (CCN). In the case of the CCN structure, we argue that: 1) for video multiplexing scenario, general cache strategies that ignore the intrinsic linear time characteristic of video requests are unable to make better use of the cache resources, and 2) it is inadequate to simply extend the existing research conclusions of file-oriented popularity to chunk-by-chunk popularity, which are widely used in CCN. Unlike previous works in this field, the proposed policy in this study, named two-level popularity-oriented time-to-hold cache replacement policy (TLP-TTH), is designed on the basis of the following principles. Firstly, the proposed cache replacement strategy is customized for video delivery by carefully considering the essential auto-correlated request feature of video chunks within a video file. Furthermore, the popularity in video delivery is subdivided into two levels, namely chunk-level access probability and file-level popularity, in order to efficiently utilize cache resources. We evaluated the proposed policy in both a hierarchical topology and a real network based hybrid topology, and took viewers departure into consideration as well. The results validate that for video delivery over CCN, TLP-TTH policy improves the network performance from several aspects. In particular, we observed that the proposed policy not only increases the cache hit ratio at the edge of the network but the cache utilization at the intermediate routers is also improved markedly. Further, with respect to the video popularity variation scenario, the cache hit ratio of TLP-TTH policy responds sensitively to maintain efficient cache utilization. key words: content-centric networking, in-network caching, TLP-TTH, popularity
Wood Blockboards Fabricated by Rotational Dowel Welding

Holding wood pieces together by rotationally welded dowels can be used to make blockboard panels with the dowels inserted in the side of the wood substrate slats. The average results for both tensile and three-point bending tests indicate that a 20° dowel insertion angle yields strength results better than 10° and 0° insertion angles. Applied load vs deformation in the two types of tests showed that blockboard panel stiffness was greater for the panels with 20° dowel insertion angle.