Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Resistance to Wet Bubble Disease and Genetic Diversity in Wild and Cultivated Strains of Agaricus bisporus

Outbreaks of wet bubble disease (WBD) caused by Mycogone perniciosa are increasing across the world and seriously affecting the yield of Agaricus bisporus. However, highly WBD-resistant strains are rare. Here, we tested 28 A. bisporus strains for WBD resistance by inoculating M. perniciosa spore suspension on casing soil, and assessed genetic diversity of these strains using 17 new simple sequence repeat (SSR) markers developed in this study. We found that 10 wild strains originating from the Tibetan Plateau in China were highly WBD-resistant strains, and 13 cultivated strains from six countries were highly susceptible strains. A total of 88 alleles were detected in these 28 strains, and the observed number of alleles per locus ranged from 2 to 8. Cluster and genetic structure analysis results revealed the wild resources from China have a relatively high level of genetic diversity and occur at low level of gene flow and introgression with cultivated strains. Moreover, the wild strains from China potentially have the consensus ancestral genotypes different from the cultivated strains and evolved independently. Therefore, the highly WBD-resistant wild strains from China and newly developed SSR markers could be used as novel sources for WBD-resistant breeding and quantitative trait locus (QTL) mapping of WBD-resistant gene of A. bisporus.
Eurographics Symposium on Geometry Processing (2005) Atomic Volumes for Mesh Completion

The increased use of scanned geometry for applications in computer graphics and 3D hardcopy output has highlighted the need for general, robust algorithms for reconstruction of watertight 3D models given partial polygonal meshes as input. We present an algorithm for 3D hole filling based on a decomposition of space into atomic volumes , which are each determined to be either completely inside or completely outside the model. By defining the output model as the union of interior atomic volumes we guarantee that the resulting mesh is watertight. Individual volumes are labeled as " inside " or " outside " by computing a minimum-cost cut of a graph representation of the atomic volume structure, patching all the holes simultaneously in a globally sensitive manner. User control is provided to select between multiple topologically distinct, yet still valid, ways of filling holes. Finally, we use an octree decomposition of space to provide output-sensitive computation time. We demonstrate the ability of our algorithm to fill complex, non-planar holes in large meshes obtained from 3D scanning devices.
A 12b 200KS/s SAR ADC with novel capacitor switching procedure and digital background calibration

Modern biomedical application such as implanted systems calls for low power high performance analog-to-digital converters. The successive approximation register (SAR) architecture has been proved to be a best choice for its low power consumption and small die area, which benefits from CMOS technology. This work concentrates on a new capacitor array with a novel switching procedure to further improve power efficiency and area economy. The proposed SAR ADC is designed with a supply voltage of 1.8V and at a sampling rate of 200kS/s, and an SNDR of 67dB and power consumption of 6. 2μW is achieved. The chip is implemented in 0. 18μm SMIC CMOS technology and calibrated with LMS adaptive algorithm, leading to a 2~5dB increase in SNDR.
Entropic contributions to rate accelerations in enzymic and intramolecular reactions and the chelate effect.

It is pointed out that translational and (overall) rotational motions provide the important entropic driving force for enzymic and intramolecular rate accelerations and the chelate effect; internal rotations and unusually severe orientational requirements are generally of secondary importance. The loss of translational and (overall) rotational entropy for 2 --> 1 reactions in solution is ordinarily on the order of 45 entropy units (e.u.) (standard state 1 M, 25 degrees C); the translational entropy is much larger than 8 e.u. (corresponding to 55 M). Low-frequency motions in products and transition states, about 17 e.u. for cyclopentadiene dimerization, partially compensate for this loss, but "effective concentrations" on the order of 10(8) M may be accounted for without the introduction of new chemical concepts or terms.
Research on Methodology of Classification Mining for Tumor Markers

Reliability is one of the key issues in data mining. In the case of massive protein mass spectrum data from SELDI-TOF-MS, this paper proposes an effective and reliable method to extract tumor markers. First of all, an adaptive threshold approach based on wavelet transformation is put forward to eliminate the noise in raw data so as to furnish reliable foundation for tumor markers extraction. Then a kind of genetic algorithm based on SVM is designed to construct discriminating model in order to find the optimal combination of distinct protein peaks and obtain tumor markers. Finally, the method proposed in this paper is applied to extract tumor markers from the protein mass spectrum data that come from normal mouse serums and induced pancreatic cancer mouse serums to verify the feasibility and reliability of our method.
Stability scale and atomic solvation parameters extracted from 1023 mutation experiments

The stability scale of 20 amino acid residues is derived from a database of 1023 mutation experiments on 35 proteins. The resulting scale of hydrophobic residues has an excellent correlation with the octanol‐to‐water transfer free energy corrected with an additional Flory–Huggins molar‐volume term (correlation coefficient r = 0.95, slope = 1.05, and a near zero intercept). Thus, hydrophobic contribution to folding stability is characterized remarkably well by transfer experiments. However, no corresponding correlation is found for hydrophilic residues. Both the hydrophilic portion and the entire scale, however, correlate strongly with average burial accessible surface (r = 0.76 and 0.97, respectively). Such a strong correlation leads to a near uniform value of the atomic solvation parameters for atoms C, S, O/N, O−0.5, and N+0.5,1. All are in the range of 12–28 cal mol−1 Å−2, close to the original estimate of hydrophobic contribution of 25–30 cal mol−1 Å−2 to folding stability. Without any adjustable parameters, the new stability scale and new atomic solvation parameters yielded an accurate prediction of protein–protein binding free energy for a separate database of 21 protein–protein complexes (r = 0.80 and slope = 1.06, and r = 0.83 and slope = 0.93, respectively). Proteins 2002;49:483–492. © 2002 Wiley‐Liss, Inc.
Molecular Pattern Discovery Based on Penalized Matrix Decomposition

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.
Nyquist Sampling Requirements for Polar Grids in Bistatic Time-Domain Algorithms

The paper presents a derivation of Nyquist sampling requirements for the polar grids in some bistatic time-domain algorithms. The derivation is based on an airborne bistatic system with general bistatic geometry. The Nyquist sampling requirements are shown to be the functions of operating radar frequency, transmitter and receiver subaperture lengths, and bistatic geometry. How to decide the Nyquist sampling requirements for different bistatic geometries and the relationship between the Nyquist sampling requirements in the monostatic and bistatic cases are also addressed in the paper. The derived Nyquist sampling requirements is examined with the bistatic CARABAS-II like data.
A Hierarchical Stochastic Model for Automatic Prediction of Prosodic Boundary Location

Prosodic phrase structure provides important information for the understanding and naturalness of synthetic speech, and a good model of prosodic phrases has applications in both speech synthesis and speech understanding. This work describes a statistical model of an embedded hierarchy of prosodic phrase structure, motivated by results in linguistic theory. Each level of the hierarchy is modeled as a sequence of subunits at the next level, with the lowest level of the hierarchy representing factors such as syntactic branching and prosodic constituent length using a binary tree classification. A maximum likelihood solution for parameter estimation is presented, allowing automatic training of different speaking styles. For predicting prosodic phrase breaks from text, a dynamic programming algorithm is given for finding the maximum probability prosodic parse. Experimental results on a corpus of radio news demonstrate a high rate of success for predicting major and minor phrase boundaries from text without syntactic information (81% correct prediction with 4% false prediction).
Simulation of structural transformations in nanoparticles

Structural changes in solid and liquid like clusters and nanoparticles have been investigated by means of molecular dynamics simulations. While in the solid phases of the clusters the reactions leading to structural changes spontaneously occur upon heating, the reverse transition, when decreasing the temperature, is often hindered because of large undercooling effects and missing nucleation sites. Similar tendencies are observed when melting and freezing the clusters; while melting occurs without overheating, freezing is sterically hindered by the development of false symmetries. Both the austenitic transformation as well as the melting temperature scale with the inverse of the particle diameter.
The design of a PDL environment

The need to improve software productivity and reliability has become an area of increasing importance to the software engineering community. During the early processes of the software life cycle, there is a strong need to emphasize the use of tools which provide a representation of the program structure that can be easily understood and modified. Thus, a significant usage of Program Design Languages (PDLs) as a design tool in software engineering environments has surfaced in recent years. This paper discusses the desired characteristics of such a design tool and highlights the shortcomings of existing PDLs. Finally, we discuss the design of a PDL environment which overcomes many of the shortcomings associated with existing PDLs.
AN APPROACH FOR BREAST CANCER DIAGNOSIS CLASSIFICATION USING NEURAL NETWORK

Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets. Classification is one of the most active research and application areas of neural networks. Major disadvantages of artificial neural network (ANN) classifier are due to its sluggish convergence and always being trapped at the local minima. To overcome this problem, differential evolution algorithm (DE) has been used to determine optimal value or near optimal value for ANN parameters. DE has been applied successfully to improve ANN learning from previous studies. However, there are still some issues on DE approach such as longer training time and lower classification accuracy. To overcome these problems, island based model has been proposed in this system. The aim of our study is to propose an approach for breast cancer distinguishing between different classes of breast cancer. This approach is based on the Wisconsin Diagnostic and Prognostic Breast Cancer and the classification of different types of breast cancer datasets. The proposed system implements the island-based training method to be better accuracy and less training time by using and analysing between two different migration topologies.
Local Histogram of Figure/Ground Segmentations for Dynamic Background Subtraction

We propose a novel feature, local histogram of figure/ground segmentations, for robust and efficient background subtraction (BGS) in dynamic scenes (e.g., waving trees, ripples in water, illumination changes, camera jitters, etc.). We represent each pixel as a local histogram of figure/ground segmentations, which aims at combining several candidate solutions that are produced by simple BGS algorithms to get a more reliable and robust feature for BGS. The background model of each pixel is constructed as a group of weighted adaptive local histograms of figure/ground segmentations, which describe the structure properties of the surrounding region. This is a natural fusion because multiple complementary BGS algorithms can be used to build background models for scenes. Moreover, the correlation of image variations at neighboring pixels is explicitly utilized to achieve robust detection performance since neighboring pixels tend to be similarly affected by environmental effects (e.g., dynamic scenes). Experimental results demonstrate the robustness and effectiveness of the proposed method by comparing with four representatives of the state of the art in BGS.
Variation of SEMG during sprint cycling

As a non-invasive technique of identifying muscle activity, the SEMG (surface electromyogram) may be very useful. SEMG amplitude and frequency have been investigated as an indicator of muscular fatigue and to estimate muscle force and torque for the purpose of training athletes and for machine control. Many authors in the past have observed a monotonically increasing relationship between the RMS of SEMG with the force of contraction developed by the muscles. When the muscle is contracting to produce motion, the strength of contraction is varying and the variation in SEMG is far greater during isometric contraction. The biological processes associated with the onset of muscle fatigue changes, due to these different causes, are believed to be different. And in turn, these can be different from person to person. This paper attempts to investigate such variations in RMS of the SEMG in VL and VM muscles and carry out an analysis of the results thus obtained.
DNS Privacy

KURZFASSUNG Im Domain Name System (DNS) existieren sowohl auf Seiten der Clients als auch auf Seiten der Betreiber erhebliche Sicherheitslücken im Bezug auf die Vertraulichkeit und Privatheit der jeweils eigenen Daten. Der Informationsfluss von Clients, die einen Domain Name auflösen wollen, findet unverschlüsselt statt und wird meist durch mehrere Server geleitet. Serverbetreiber und Angreifer, die die Nachrichten zwischen Client und primären DNS-Server mitschneiden können, haben so Zugriff auf Informationen zu den verwendeten Domain Names, sowie deren Häufigkeit. Auf Seiten der DNS Serverbetreiber öffnet die Verwendung der standardisierten DNS Sicherheitserweiterung DNSSec Angreifern die Möglichkeit die komplette Zone des Betreibers auszulesen. Mit Verschlüsselung der DNS Nachrichten können nur die auf dem Übertragungsweg auftretenden Probleme behoben werden. Weitere Lösungsansätze, die einen Umbau des Domain Name Systems erfordern, greifen auch diese Probleme auf, sind jedoch aufgrund ihrer schlechten Skalierbarkeit nicht realistisch einsetzbar.
The Concept of Digital Literacy from the Perspective of the Creative Multimedia Industry

It is pertinent to develop digital literacy skills that enable the effective and efficient use of Information Technology applications to access and use the digital information needed for work tasks (Coiro, Knoble, Lank shear & Leu, 2008) to meet the current needs of the creative multimedia industry. Even when graduates have access to computers and the Internet and use these tools on a daily basis, they may not have the digital literacy to fulfil the requirements as needed by employers. This preliminary study seeks to identify the digital literacy requirement of the creative multimedia industry. This is a small scale study that employs the interview method to obtain information from five leaders, selected through purposive sampling, of the creative multimedia industry. Findings indicate three aspect of digital literacy, namely research competence, communication competence and social competence, are needed to prepare students for employment in creative multimedia industries.
Design of V2X runtime emulation framework for evaluation of vehicle safety applications

Recently, interest for vehicle safety system and applications, which supports drivers can drive safely and easily via V2X Communications, is increasing. Generally, FOTs (field operational tests) method is performed in order to verify and evaluate vehicular safety system or applications and it requires driving test in real road environments. FOTs method, however, costs huge human and material resources due to building test-bed. Therefore, organizations such as school, companies and laboratories, which cannot afford to build test-bed, need reliable test-bed that show similar results compared to FOTs. Also, in many researches, evaluation and verification of basic performance of vehicular safety system and applications were performed using integrated VANET simulator which coupled traffic and network simulators before testing in real road environments. In this thesis, we developed V2XREF (V2X runtime emulation framework), which is efficient method of coupling network and traffic simulators, for building test-bed. Also, we applied characteristic of real road environment such as mobility and channel model. We adjusted network simulator communication stack according to IEEE 802.11p and WAVE (wireless access in vehicular environment) protocol stack. In addition to that, we analyzed work flow and performance of V2XREF with making performance evaluation safety scenarios.
Making Recommendations Better: The Role of User Online Purchase Intention Identification

The past few years has witnessed the great success of recommender systems, which can significantly help users find relevant and interesting items from the vast array of online products. Recently, a vast class of researches in this area mainly focus on making recommendations by designing effective algorithms. Comparatively, the user intention, especially the role of purchase intention in recommender systems is relatively lack of study. In this paper, with real e-commercial data from Tmall.com, we firstly analyze users' online behaviors and propose a scenario-based identification approach to classify users into two groups: one with obvious purchase intention, and another without such motivation. We then use Random Forest classification method to validate its usefulness. Subsequently, we implement an online demo to visually detect the real-time purchase intention. Finally, we employ the classical item-based collaborative filtering framework to provide recommendations to those two group users. Experimental results show that recommendation performance indeed can be enhanced by identifying online purchase intention.
Modeling Hierarchical User Interests Based on HowNet and Concept Mapping

Modeling user interests plays an important role in personalized service on the Internet. Many systems use classification methods to construct the user profile to represent user interests. However, it is difficult to cover all individual interests and capture the changes of user profiles. This paper introduces a method with two steps to solve the problem. In the first step it clusters the viewed web pages to extract individual interests by extending the vector of VSM with sememes from the HowNet. The second step is to map each interest cluster to the reference taxonomy to model hierarchical user interests based on similarity calculation of keywords and the extended vector. Experiments show that the method can model hierarchical user interests with a promising result. When a new interest emerges, it does not need any adjustment like collecting new training data or rebuilding the classifier. It can capture the diversified user interests and map to an interests taxonomy.
Non-visual interfaces for wearable computers

INTRODUCTION As computing devices drop in size and rise in power/bandwidth, novel interface techniques are needed to keep pace. Interfaces which rely on screens and keyboards/handwriting are not always effective in mobile computing scenarios. Visual displays are wasted on eyes engaged in navigational tasks such as walking or driving. Similarly, keyboards and handwriting-based interfaces are difficult to use whilst the arms and hands are involved in realworld tasks (including posture control). Furthermore, these input techniques typically require a dedicated visual resource in the interaction loop.
High dynamic range fusion of magnetic resonance flow imaging data

Time-resolved, volumetric phase-contrast magnetic resonance imaging (also known as 4D flow MRI) is a comprehensive, diagnostic tool used by radiologists to simultaneously assess cardiovascular anatomy, flow, and function. Its ability to resolve both slow and fast hemodynamics (dynamic range) is limited, however. To address this, postprocessing techniques such as phase unwrapping have been used to extend the dynamic range, but are highly variable with respect to patient anatomy and physiology. Recently, a new technique called multi-venc 4D flow has gained traction due to its ability to simultaneously and robustly measure slow and fast flow. However, its use of the massive amount of data that it collects (10-20 GB) is inefficient. In this paper, I propose a high dynamic range (HDR) postprocessing technique for reconstructing and compressing multi-venc data down into digestible images that allow for more precise measurement of typical hemodynamic parameters measured from this data such as peak flow. I applied this technique to both simulated 4D flow MRI data, and invivo data. This technique shows promise for allowing comprehensive abdominal and neurovascular imaging, in which there is a large dynamic range of blood velocity.
Testing Implications for Vaccine Design and Complex Class I Molecule Mamu-A * 01 : Bound by the Major Histocompatibility Macaques Recognize 14 Different Epitopes Immunodeficiency Virus-Infected Rhesus Lymphocytes from Simian + CD 8

2001, 75(2):738. DOI: 10.1128/JVI.75. 2.738-749.2001. J. Virol. Sette Alessandro Thomson, John D. Altman, David I. Watkins and H. O'Connor, Xiaochi Wang, Michael C. Wussow, James A. Jing, John L. Dzuris, Max E. Liebl, Thorsten U. Vogel, David Todd M. Allen, Bianca R. Mothé, John Sidney, Peicheng Testing Implications for Vaccine Design and Complex Class I Molecule Mamu-A*01: Bound by the Major Histocompatibility Macaques Recognize 14 Different Epitopes Immunodeficiency Virus-Infected Rhesus Lymphocytes from Simian + CD8