Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Amyloidogenic and Disordered Regions in Protein Chains

The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.
How does software project risk vary across projects? An exploratory study from India

Software development risk continues to be a major concern for software development organisations across the globe. Review of literature shows that while many studies on risk construct have been done, there is a scarcity of empirical studies on the possible variation in risk across project and organisational characteristics. This research, based on the data collected from 527 software development projects in India, checks how risk levels vary across different types of projects executed with different types of organisations. Major risk dimensions were identified and confirmed through a combination of exploratory factor analysis and confirmatory factor analysis. Possible variation in the risk scores across the sample was checked through a series of one way ANOVA tests. Risk levels were seen to be highly influenced by select project characteristics and organisational attributes.
Price Analysis of Export Behavior of Aquaculture Producers in Honduras and Peru

Many small-scale aquaculture producers in developing countries find it challenging to access international value chains due to barriers-to-entry. To shed light on this issue, this article presents three case studies where we analyze export behavior of aquaculture producers from Honduras and Peru based on price behavior in export markets (EU and USA) and in domestic wholesale markets. The price analysis suggests that in all three value chains that include shrimp, tilapia and trout there are competitive aquaculture producers that participate in international value chains and that are able to respond to profitable opportunities when relative prices change. While some small scale producers are able to enter international chains through larger exporters, the majority seem to serve domestic markets and often markets close to the production sites.
Prevention of insulin-dependent diabetes mellitus in nonobese diabetic mice by immunogenic but not by tolerated peptides

In the nonobese diabetic (NOD) mouse, susceptibility to insulin- dependent diabetes mellitus is in part controlled by a single expressed class II major histocompatibility complex (MHC) molecule, I-Ag7. This molecule probably exerts its control through the representation of a self-peptide, derived from an unknown beta cell antigen, leading to T cell activation and eventual islet destruction. In this paper, synthetic peptides have been used to compete for binding to the I-Ag7 molecule in an attempt to suppress the autoimmune response. The administration of an I-Ag7-binding immunogenic peptide, lambda repressor (cI) 12-26, in a water and oil emulsion (incomplete Freund's adjuvant) can prevent the transfer of IDDM into irradiated recipients by spleen cells from diabetic donors. Nonbinding, nonimmunogenic peptides have no effect in this situation. However, the immune response to the "blocking" peptide in these experiments was a complicating factor in interpreting the results. To establish that the effect was at the level of competition for MHC binding, two additional approaches were tried. First, tolerance was induced to the immunogenic peptide, cI 12-26, before using it to "block" disease. Tolerance abolished the effect on diabetes transfer. Second, an effort was made to identify peptides that were nonimmunogenic but that bound to I-Ag7. Such a peptide, mouse prostatic secretory glycoprotein precursor 63-76, had no effect on the incidence of transferred disease. We conclude that the "blocking" effects seen in initial experiments in the NOD mouse were not caused by blockade of MHC presentation, but by other unknown effects related to the immunogenicity of the "blocking" peptide.
A Wideband Linear I/Q-Interleaving DDRM

This paper presents a wideband linear direct-digital RF modulator (DDRM) in 40-nm CMOS technology. An innovative $I/Q$ -interleaving RF digital-to-analog converter (DAC) is proposed to enable the combination of in-phase ( $I$ ) and quadrature ( $Q$ ) signals in a more digital fashion, thereby improving the linearity performance at large bandwidths. The DDRM also features an advanced second-order hold digital interpolation filter to suppress the sampling spectral replicas in the presence of large bandwidth signals. Moreover, the harmonic rejection technique in the context of RF DAC operation is adopted and implemented. The $2 \times 9$ -bit DDRM core occupies 0.21 mm2 and consumes 110 and 146 mW at 1 and 3 GHz, respectively, with the peak power of +9.2 dBm. The error vector magnitude (EVM) and adjacent channel power ratio (ACPR) at 3 GHz for a 57-MHz 64-QAM signal are better than −30 and −45 dB, respectively, and ACPR remains as low as −44 dBc up to a wide bandwidth of 113 MHz.
Adoption as an offspring strategy to reduce ectoparasite exposure

Adoption occurs frequently in colonial species where both the cost of parasitism and the opportunity for dependent young to find a foster family are typically high. Because ectoparasites show highly aggregated distributions among colony members, we tested two central predictions of the novel hypothesis that adoption is driven by selection on young to reduce ectoparasite load: first, that nest-based ectoparasites cause offspring to seek adoption, and second, that an individual's parasite load will be reduced after it has been adopted. In agreement with these predictions, experimentally infested Alpine swift Apus melba offspring sought adoption significantly more often and at an earlier stage than young kept free of ectoparasitic louse-flies. Second, the parasite load of experimentally infested young was reduced after adoption via a redistribution of ectoparasites among the foster family members. Our findings emphasize what we believe to be a novel role for parasites in the evolution of adoption and, by extension, in the emergence of social interactions.
Combination method of bone-conduction speech and air-conduction speech for speaker recognition

Recently, some new sensors, such as bone-conductive micro-phones, throat microphones, and non-audible murmur (NAM) microphones, besides conventional condenser microphones have been developed for collecting speech data. Accordingly, some researchers began to study speaker and speech recognition using speech data collected by these new sensors. We focus on bone-conduction speech data collected by the bone-conductive microphone. This paper proposes a novel speaker identiﬁcation method which combines “bone-conduction speech” and “air-conduction speech”. The proposed method conducts speaker identiﬁcation by integrating the similarity calculated by air-conduction speech model and similarity calculated by bone-conduction speech model. For evaluating the proposed method, we conduct the speaker identiﬁcation experiment using part of a large bone-conduction speech corpus constructed by National Research Institute of Police Science, Japan (NRIPS). Experimental results show that the proposed method can reduce a identiﬁcation error rate of air-conduction speech and bone-conduction speech. Especially, the proposed method achieves that the average error reduction rate from air-conduction speech to the proposed method is 35.8%.
Evaluating the Effect of Huge Page on Large Scale Applications

The ever-increasing memory footprint in large scale applications not only requires efficient computation, but also needs efficient communication. Traditionally huge page is used to reduce CPU’s TLB miss, but how huge page impacts communication performance, and how HPC applications in large scale benefit from huge page are still not clear. In this paper, we support huge page in our D5000A platform and micro-benchmark the communication impacts of huge page in detail, also we evaluate the huge page impact on large scale HPC applications. Results show that huge page improves communication bandwidth by 40MB/s in large message sizes, greatly benefits communication performance when multiple connections share the same NIC. Applications such as NPB, Sweep3D, HPL show good performance improvement and good scalability in large scale. With huge page support, HPL achieves 2.76% improvement in 16384 cores.
Document Images Retrieval Based on Multiple Features Combination

Retrieving the relevant document images from a great number of digitized pages with different kinds of artificial variations and documents quality deteriorations caused by scanning and printing is a meaningful and challenging problem. We attempt to deal with this problem by combining up multiple different kinds of document features in a hybrid way. Firstly, two new kinds of document image features based on the projection histograms and crossings number histograms of an image are proposed. Secondly, the proposed two features, together with density distribution feature and local binary pattern feature, are combined in a multistage structure to develop a novel document image retrieval system. Experimental results show that the proposed novel system is very efficient and robust for retrieving different kinds of document images, even if some of them are severely degraded.
An abstract authorization system for the Internet

Most of the work on Internet security focuses on cryptographic approaches. While valuable, this is not a feasible way to control access to documents. Cryptography can only control secrecy and authentication aspects, but cannot handle different types of access by different users, access to portions of documents, and other content restrictions. A higher-level approach is needed. We present here an authorization model for hypertext documents based on the access matrix. We classify different types of documents, we model these using object-oriented approaches, and we define access policies that specify access to those types of documents. Authorization restrictions can be superimposed on the document class model and on its dynamic model. These authorizations are based on a mandatory version of the access matrix, implementing role-based access control. We consider possible implementation architectures, involving servers and databases.
A Hybrid Model to Predict Electron and Ion Distributions in Entire Interelectrode Space of a Negative Corona Discharge

Atmospheric direct current (dc) corona discharge from thin wires or sharp needles has been widely used as an ion source in many devices such as photocopiers, laser printers, and electronic air cleaners. Existing numerical models to predict the electron distribution in the corona plasma are based on charge continuity equations and the simplified Boltzmann equation. In this paper, negative dc corona discharges produced from a thin wire in dry air are modeled using a hybrid model of modified particle-in-cell plus Monte Carlo collision (PIC-MCC) and a continuum approach. The PIC-MCC model predicts densities of charge carriers and electron kinetic energy distributions in the plasma region, while the continuum model predicts the densities of charge carriers in the unipolar ion region. Results from the hybrid model are compared with those from prior continuum models. Superior to the prior continuum model, the hybrid model is able to predict the voltage-current curve of corona discharges. The PIC-MCC simulation results also suggest the validity of the local approximation used to solve the Boltzmann equation in the prior continuum model.
Beamforming techniques for wireless communications in low-rank channels: analytical models and synthesis algorithms.

The objective of this thesis is discussing the application of multiple antenna technology in some selected areas of wireless networks and fourth-generation tele-communication systems. The original contributions of this study involve, mainly, two research ﬁelds in the context of the emerging solutions for high-speed digital communications: the mathematical modeling of distributed wireless networks adopting advanced antenna techniques and the development of iterative algorithms for antenna array pattern synthesis. The material presented in this dissertation is the result of three-year studies performed within the Telecommunication Group of the
Characterization of functional polymorphisms and glucocorticoid-responsive elements in the promoter of TDO2, a candidate gene for ethanol-induced behavioural disorders.

AIMS In response to acute ethanol consumption, tryptophan 2,3-dioxygenase (TDO) induces the kynurenine pathway (KP) through a glucocorticoid-mediated mechanism, which could lead to a dramatic accumulation of neurotoxic metabolites in association with serotonin depletion. As a result, interindividual variability in ethanol-induced behavioural disorders, such as black-outs and violent impulsive behaviours (BOVIBs) following binge drinking, could be partly explained by genetic polymorphisms affecting the KP. The aim of this study was to identify polymorphisms on the promoter of the TDO2 gene that could affect expression and/or activity of TDO through glucocorticoid induction. METHODS Polymorphisms were screened using a PCR-sequencing strategy applied to 31 alcohol-dependent patients and 49 unrelated healthy volunteers, and functionally analysed with bioinformatic prediction tools and gene reporter assays in HepG2 and A549 cell lines. RESULTS We identified 12 polymorphisms in the human TDO2 promoter region, 2 of them corresponding to previously unknown single-nucleotide polymorphisms (SNPs) and 3 of them located in putative glucocorticoid-responsive elements (GREs). Gene reporter assays using HepG2 and A549 cell lines confirmed the presence of several functional GREs in the promoter region of TDO2 and revealed that some of the identified polymorphisms affect the promoter activity under glucocorticoid receptor over-expression and dexamethasone exposure conditions. CONCLUSIONS Correlational studies in larger samples could help to determine whether these polymorphisms are responsible for variations of expression and/or activity of TDO, in particular under conditions where release of glucocorticoids is increased, such as acute ethanol intake. If confirmed, such results would be of major interest in explaining part of the interindividual variability observed in behavioural responses to acute ethanol consumption.
The Significance of the Probabilistic Voting Theorem

Public decision-making by majority rule is open to the danger of exploitation of minorities by majorities. Since any majority can employ the vote to expropriate the corresponding minority, it would seem that there can be no electoral equilibrium allocation of income or transfers in a democratic society and that democracy itself might be unstable. Much of democratic theory is devoted to the study of how this danger can be averted. The probabilistic voting theorem establishes that a degree of voter insensitivity to offers of rival political parties imparts an electoral equilibrium where it would not otherwise exits. The theorem is valid on its assumptions, but those assumptions are considerably stronger, and the theorem is less comforting about the prospects for the stability of democratic government, than one might at first suppose.
Moving object tracking using particle filter and observational model based on multi-feature composition

Moving object tracking in a sequence of an image is one of the favorable issues in machine vision. Recently, particle filter have based developed as a powerful method in this field. Particle filter is a following method which estimates a target route in video image sequences by probability approaches. In many cases, the follower encounters with problems such as: local lighting variations or abrupt movements. In most of these cases, target tracking is missing, so an appropriate filter coupled observational model is required to improve follower performance and increase the efficiency. An observational model is used in order to improve filter performance. However, this color feature based model contains less computational volume and is more rapid but does not provide good performance at the presence of background color or some objects with similar color. In this paper, an observational model is proposed that performs using particle filter accompanied by mean shift algorithm based on incorporated color and edge features. The results show that the introduced method is not sensitive to color and intensity change while it also has a good performance.
Productivity of Indian Commercial Banks in the Pre- and Post-Liberalization Periods

The present study examines the changes in the Total Factor Productivity (TFP) of Indian commercial banks for the period 1985-2004, when TFP indices are estimated using Malmquist productivity index approach. Further, the TFP indices are decomposed into efficiency change and technical change to see what drives the TFP change. The results show that TFP has improved significantly after liberalization across bank groups. Foreign banks have experienced the highest TFP growth for the total study period. The results also suggest that, on an average, the TFP growth is more due to technological change than efficiency change. However, this does not apply to all years. For TFP, growth is due to efficiency change during some years, due to technical change during some years and due to both during some years. This shows that Indian banking sector has experienced efficiency change as well as technical change.
Economic growth and carbon emission in Mauritius: an econometric analysis in the context of the Maurice Ile Durable project

Using data over the period 1966-2008, this paper investigates the environmental Kuznets curve (EKC) hypothesis for Mauritius. The conventional inverted-U EKC is not found, instead a long-run quadratic relation implying higher carbon emission as a result of economic growth since the early 1980s is uncovered. The estimated cointegrating equation is used to derive projections for carbon emission up to 2014. These projections may potentially act as benchmarks in assessing the near-term effectiveness of the MID project in reducing carbon emission in Mauritius. In the longer-term however, the MID project should reverse the rising trend in emission without compromising output growth to be considered effective. For this to happen, the authorities should provide incentives to economic agents for the adoption of green technologies. If incentives do not prove effective, the legislation should be amended and toughened accordingly.
Topics and topical phases in German social media communication during a disaster*

Abstract Social media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field of natural language processing and unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.
SEER: Auto-Generating Information Extraction Rules from User-Specified Examples

Time-consuming and complicated best describe the current state of the Information Extraction (IE) field. Machine learning approaches to IE require large collections of labeled datasets that are difficult to create and use obscure mathematical models, occasionally returning unwanted results that are unexplainable. Rule-based approaches, while resulting in easy-to-understand IE rules, are still time-consuming and labor-intensive. SEER combines the best of these two approaches: a learning model for IE rules based on a small number of user-specified examples. In this paper, we explain the design behind SEER and present a user study comparing our system against a commercially available tool in which users create IE rules manually. Our results show that SEER helps users complete text extraction tasks more quickly, as well as more accurately.
The Adoption and Impact of Soil and Water Conservation Technology: An Endogenous Switching Regression Application

This paper identifies the factors that affect farmers’ decisions to adopt soil and water conservation technology in Africa and how this technology impacts farm yields and net returns. This technology is important because it improves efficiency of water use from rainfall—a critical issue in water-deficient Sub-Saharan Africa. An analysis of new data from a survey of 342 rice farmers in northern Ghana shows that farmers’ education, capital and labor constraints, social networks and extension contacts, and farm soil conditions mainly determine adoption of field ridging, and the adoption of this technology increases rice yields and net returns significantly. (JEL Q15, Q24)
Simulation and synthesis of networks-on-chip by using NoCSimp HDL library

This paper proposes an approach to the synthesis and modeling of networks-on-chip (NoCs) by using the NoCSimp library based on a simplified wormhole router with central buffer and without virtual channels. The analysis of the results of simulation and synthesis of NoCs with regular and quasi-optimal topologies with number of nodes 8 and 9 is given. It is shown that in the case where the number of NoC nodes is not a power of 2 the use of quasi-optimal topologies offers a significant advantage.