Miracle-dz/UniFAR-FTUs huggingface dataset:
Solution Structure of Synthetic Penaeidin-4 with Structural and Functional Comparisons with Penaeidin-3*

Antimicrobial peptide structure has direct implications for the complexity of functions and mechanisms of action. The penaeidin antimicrobial peptide family from shrimp is divided into multiple class designations based on primary structure. The penaeidin classes are not only characterized by variability in primary sequence but also by variation in target specificity and effectiveness. Whereas class 4 exhibits low isoform diversity within species and is highly conserved between species, the primary sequence of penaeidin class 3 is less conserved between species and exhibits considerable isoform diversity within species. All penaeidins, regardless of class or species, are composed of two dramatically different domains: an unconstrained proline-rich domain and a disulfide bond-stabilized cysteine-rich domain. The proline-rich domain varies in length and is generally less conserved, whereas the spacing and specific residue content of the cysteine-rich domain is more conserved. The structure of the synthetic penaeidin class 4 (PEN4-1) from Litopenaeus setiferus was analyzed using several approaches, including chemical mapping of disulfide bonds, circular dichroism analysis of secondary structural characteristics, and complete characterization of the solution structure of the peptide by proton NMR. L. setiferus PEN4-1 was then compared with the previously characterized structure of penaeidin class 3 from Litopenaeus vannamei. Moreover, the specificity of these antimicrobial peptides was examined through direct comparison of activity against a panel of microbes. The penaeidin classes differ in microbial target specificity, which correlates to variability in specific domain sequence. However, the tertiary structure of the cysteine-rich domain and indeed the overall structure of penaeidins are conserved across classes.
ec 2 01 0 Self-consistent method for density estimation

The estimation of a density profile from experimental data points is a challenging problem, usually tackled by plotting a histogram. Prior assumptions on the nature of the density, from its smoothness to the specification of its form, allow the design of more accurate estimation procedures, such as Maximum Likelihood. Our aim is to construct a procedure that makes no explicit assumptions, but still providing an accurate estimate of the density. We introduce the self-consistent estimate: the power spectrum of a candidate density is given, and an estimation procedure is constructed on the assumption, to be released a posteriori, that the candidate is correct. The self-consistent estimate is defined as a prior candidate density that precisely reproduces itself. Our main result is to derive the exact expression of the self-consistent estimate for any given dataset, and to study its properties. Applications of the method require neither priors on the form of the density nor the subjective choice of parameters. A cutoff frequency, akin to a bin size or a kernel bandwidth, emerges naturally from the derivation. We apply the self-consistent estimate to artificial data generated from various distributions and show that it reaches the theoretical limit for the scaling of the square error with the dataset size.
Web Search Personalization by User Profiling

The World Wide Web is growing at a rate of about a million pages per day, making it tougher for search engines to extract relevant information for its users. Earlier Search Engines used simple indexing techniques to search for keywords in websites and gave more weightage to pages with higher frequency of keyword occurrences. This technique was easy to trick by using meta-tags liberally, claiming that their page used popular search terms, thereby, made meta-tags useless for search engines. Another technique widely used was to repeatedly use popular search terms in invisible text (white text on a white background) to fool engines. These fallacies called for a set of algorithms which would sort the results using an unbiased parameter. The currently employed Link Analysis Algorithms make use of the structure present in 'hyperlinks', sorted and displayed depending on a 'popularity index' decided to pages linking to it. In this work, we have analyzed the mathematics behind these 'link analysis algorithms' and their effective use in ecommerce applications where they could be used for displaying 'personalized information'.
Multilingual OCR for Indic Scripts

In Indian scenario, a document analysis system has to support multiple languages at the same time. With emerging multilingualism in urban India, often bilingual, trilingual or even more languages need to be supported. This demands development of a multilingual OCR system which can work seamlessly across Indic scripts. In our approach the script is identified at word level, prior to the recognition of the word. An end-to-end RNN based architecture which can detect the script and recognize the text in a segmentation-free manner is proposed for this purpose. We demonstrate the approach for 12 Indian languages and English. It is observed that, even with the similar architecture, performance on Indian languages are poorer compared to English. We investigate this further. Our approach is evaluated on a large corpus comprising of thousands of pages. The Hindi OCR is compared with other popular OCRs for the language, as a further testimony for the efficacy of our method.
A Comparative Evaluation of Trust Models in P 2 P Networks

Peer to Peer (P2P) networks have been widely used in recent years in various of applications such as file sharing, content distribution, and e-commerce. At the same time, there were a number of attacks on the reputation mechanisms in these P2P networks. These attacks intend to manipulate or misuse the reputation systems so that ratings on certain peers are biased or ignored. Many approaches in the academia have tried to defend against these attacks. This paper first provides a comparative study on three major trust models: Eigentrust, PeerTrust, and RTrust. We then propose a generalized trust model, show its parameters, and present a detailed analysis how existing approaches can be encompassed in this model. In addition we discuss issues related to existing models, and show a few potential areas for future research.
Prevalence and phenology of mycorrhizal colonization across populations of Lycopodiella inundata

Mycorrhizal fungi are critical components of terrestrial habitats and agroecosystems. Recently, Mucoromycotina fine root endophyte (MucFRE) fungi were found to engage in nutritional mutualism with the rare plant Lycopodiella inundata (‘marsh clubmoss’), one of the earliest vascular plant lineages known to associate with MucFRE. The extent to which this mutualism plays a role in resilient plant populations can only be understood by examining its occurrence rate and phenological patterns. To test for prevalence and seasonality in colonization, we examined 1,297 individual L. inundata roots collected during spring and autumn 2019 from 11 semi-natural heathlands in Britain and the Netherlands. We quantified presence/absence of MucFRE-like hyphae and vesicles and explored possible relationships between temperature and precipitation in the months immediately before sampling. MucFRE-like hyphae were the dominant mycorrhizal fungi observed in all of the examined heathlands. However, we found significant differences in colonization between the two seasons at every site. Overall, 14% of L. inundata roots were colonised in spring (2.4% with vesicles) compared with 86% in autumn (7.6% with vesicles). Colonization levels between populations were also significantly different, and correlated with temperature and precipitation, suggesting some local environments may be more conducive to hyphal growth. These marked seasonal differences in host-plant colonization suggest that results about mycorrhizal status - typically drawn from single time point collections - should be carefully interpreted. Our findings are directly relevant to habitat restoration, species conservation plans, agricultural bio-inoculation nutrient enhancement treatments, microbial diversity and functional studies of host plants and symbionts.
Identification of OmpR: a positive regulatory protein controlling expression of the major outer membrane matrix porin proteins of Escherichia coli K-12

We report here on the cloning of a gene located within the ompB locus of E. coli K-12. This gene, designated ompR, resides on a 10.9-kilobase EcoRI fragment that we cloned, using a lambda vector and in vitro packaging techniques. By subcloning portions of this fragment into the high-copy-number plasmid pBR322, we have isolated the ompR gene on a 1.4-kilobase EcoRI-AvaI fragment. This fragment has been characterized physically and will facilitate a more detailed study of the role and mechanism of porin regulation by the ompB locus.
Design of a Module for Cultivation of Tuberous Plants in Microgravity: The ESA Project “Precursor of Food Production Unit” (PFPU)

Plant cultivation systems for Bioregenerative Life-Support Systems in Space developed on Earth need to be tested in space, where reduced gravity alters the liquid and gas behavior both within the plant and between the plant and its surrounding environment, making the distribution of water and nutrients a critical issue. The ESA project “Precursor of Food Production Unit” (PFPU) aims to design a modular cultivation system for edible tuberous plants (such as potato and sweet potato) in microgravity, to be preliminary tested in ground conditions in the view of successive space application. Among the different modules of the PFPU demonstrator, the Root Module (RM) is the component physically hosting the plant and accommodating tubers and roots. This paper describes the step-by-step procedure adopted to realize the RM, including the design, the building, and the ground testing of its prototype. Specifically, the hydrological characterization of possible cultivation substrates, the set-up of the water distribution system, and the validation test of the assembled prototype in a tuber-to-tuber growing cycle of potato plants are described. Among six substrates tested, including three organic materials and three synthetic materials, cellulosic sponge was selected as the best one, based on the hydrological behavior in terms of air and water transport and water retention capacity. The water sensor WaterScout was successfully calibrated to monitor the water status in cellulosic sponge and to drive irrigation and fertigation management. The designed porous tubes-based distribution system, integrated with water sensors, was able to provide water or nutrient solution in a timely and uniform way in cellulosic sponge.
Robust image watermarking based on quantization index modulation

A robust image watermarking based on the quantization index modulation (QIM) method is proposed. Conventional QIM methods employ a xed quantization step-size that results in poor robustness of the algorithm. In this paper, the quantization step-size in the QIM method is adaptively selected using a power-law function. To keep the watermark imperceptible and increase its robustness, the low frequency components of high entropy image blocks are used for data hiding. The analytical error probability and embedding distortion are derived and assessed by simulations on artificial signals. The optimum parameter in the power-law function is obtained based on minimizing the error probability. Experimental results con rm the superiority of the proposed technique against common attacks in comparison with the recently proposed methods.
Recognition of typhus group rickettsia-infected targets by human lymphokine-activated killer cells

Lymphokine-activated killer cells caused significant lysis of target cells infected with typhus group rickettsiae (TGR) but not cells infected with Rickettsia tsutsugamushi. Monoclonal and polyclonal antibodies against TGR or TGR-infected cells failed to bind specifically to infected targets, as determined by flow cytometry or by immunoprecipitation of radiolabeled cell surface proteins. Major unaltered antigenic determinants of TGR therefore cannot be detected on the surface of infected targets.
Cross-media retrieval: state-of-the-art and open issues

Cross-media retrieval is able to provide retrieval results with similar semantics but different media to the query. Since cross-media retrieval complements currently popular text/content-based retrieval which provides retrieval results with the same media to the query, it becomes an emerging research topic. In this paper, we give an overview of recent advances and research challenges in cross-media retrieval. Such retrieval paradigm has been driven by the wide availability of large multimedia resources on the web, innovative approaches to semantically understanding various multimedia objects, and novel machine learning techniques for the interactive mappings among heterogeneous feature spaces. Emphasising on these respects, we review the state-of-the-art approaches and achieved progresses in cross-media retrieval. According to the current technologies used in cross-media retrieval and the demand from real-world applications, open research issues and future research opportunities are identified.
Modèles semi-Markoviens : Application à l'analyse de l'évolution de pathologies chroniques

L'etude de l'evolution du pronostic de sante d'un patient constitue un domaine important en recherche clinique. Recemment, le developpement des modeles multi-etats a permis d'etudier cette dynamique en prenant en compte plusieurs etats de sante. Dans ce manuscrit, nous utilisons plus particulierement les modeles semi-markoviens. Ce type de processus distingue les temps de sejour dans les etats et les trajectoires des transitions, contrairement a l'approche markovienne classique. Nous avons propose plusieurs adaptations pour pouvoir appliquer ce type de modele : la censure par intervalle, le choix des distributions des temps d'attente et l'introduction des covariables. Un test d'adequation est aussi propose pour verifier l'hypothese de stationnarite. Enfin, une methode originale, incluant la theorie des courbes ROC, est presentee pour definir des etats de sante pertinents au regard du pronostic. Ces developpements sont principalement appliques a une cohorte de patients greffes renaux (base de donnees DIVAT).
A New Technique to Exploit Instruction-Level Parallelism for Reducing Microprocessor Power Consumption

A large portion of the power consumed by a high-performance out-of order microprocessor is attributed to units within the instruction window pipeline stages, from dispatch to commit. Power consumed within the window, and by the overall processor, is reduced by dynamic size adjustment of those units based on program needs. In this article, we introduce a new metric for estimating the instruction level parallelism (ILP) for a group of instructions using a simple ratio of the longest data dependence path's length to the total number of instructions in the group, hence referred to as the longest dependence path ratio (LDPR). We couple this concept with a design where units in the instruction window are divided into segments that are dynamically disabled for power savings when they are unused. The maximum number of enabled segments that a program can utilize is limited based on the amount of ILP estimated using the LDPR. We also show how measuring the LDPR is implemented by a simple extension to the dependence-check-logic at dispatch stage. Results using the LDPR show that power is saved with a minimal impact on performance, by reducing the number of available segments, not only in low ILP periods, but also over those having very high ILP. Using the Wattch power models and the MinneSPEC benchmarks, the LDPR implementation achieves an average of 36.5% of window power savings and 11% overall processor power savings, and with an average performance loss of a very modest 1.9%
Reinforcement learning based throttle and brake control for autonomous vehicle following

In this paper, we focus on the basic form of autonomous follow driving problem with one leader and one follower. A reinforcement learning based throttle and brake control approach is developed for the follower vehicle. Near optimal control law is directly learned by “trial and error” with the neural dynamic programming algorithm. According to the timely updated following state, the learned control policy can deliver appropriate throttle and brake control commands for the follower. Simulation tests to illustrate the effectiveness of the presented method are carried out with the highly recognized vehicle dynamic simulator CarSim.
Composite MRI measures and short-term disability in patients with clinically isolated syndrome suggestive of MS

Background: The use of composite magnetic resonance imaging (MRI) measures has been suggested to better explain disability in patients with multiple sclerosis (MS). However, little is known about the utility of composite scores at the earliest stages of the disease. Objective: To investigate whether, in patients with clinically isolated syndrome (CIS), a composite MRI measure, rather than the single metrics, would explain conversion to MS and would better correlate with disability at baseline and at 1 year of follow-up. Methods: Corticospinal tract (CST), corpus callosum (CC) and optic radiation (OR) volume, fractional anisotropy (FA), and mean diffusivity (MD) values were measured in 27 CIS patients and 24 healthy controls (HCs). Z-scores of FA, MD, and tract volume measures were calculated in patients, based on the corresponding measures obtained from HCs, and then combined in a composite score for each tract. Correlations between Z-scores at baseline and both the Expanded Disability Status Scale (EDSS) at baseline and at follow-up (FU-EDSS) were investigated. Results: Only CST, CC, and OR composite scores as well as the CST volume were significantly associated with FU-EDSS (p = 0.005, p = 0.007, p = 0.020, and p = 0.010, respectively). Conclusion: The combination of MRI measures rather than the individual metrics better captured the association between tissue damage in both the CC, OR and CST and short-term follow-up disability.
Test Set Generation with a Large Number of Unspecified Bits Using Static and Dynamic Techniques

This work presents two new methods for the generation of test sets with a small number of specified bits. Such type of test sets have been proven beneficial to a large number of test-related applications such as deterministic BIST, low power testing and test set enrichment. The first technique is static, since it considers an initial test set which attempts to relax via test replacement with tests of similar coverage but with fewer specified bits. The second technique is dynamic; it generates a test set from a zero base using a hierarchical fault-compatibility algorithm. Both methods are applicable to any enumerative fault method (linear to the circuit size). The experiments performed using the stuck-at fault model demonstrate the superiority of the proposed methods over comparable existing techniques, in reducing the total number of specified bits per generated test set. The applicability of the generated relaxed test sets is demonstrated for one, out of the many, possible applications, that of deterministic test set embedding. A general framework that integrates the proposed relaxation methods in two popular LFSR-based test set embedding schemes (full and partial reseeding), along with a systematic exploration of related parameters, is proposed. The obtained results show significant reductions in seed storage requirements.
Collagen induced arthritis as an experimental model for rheumatoid arthritis

The type II collagen (CII) induced arthritis animal model (CIA) provides opportunities to study the nature of autoimmune reactions leading to arthritis and may be used as a model for rheumatoid arthritis (RA). Thus, in similarity with RA, the CIA model, when induced with autologous CII, shows a chronic and progressive disease course. The susceptibility to both RA and CIA are correlated to the expression of certain MHC class II allotype genes. In both diseases are autoantibodies to CII and rheumatoid factors produced. Immunohistopathology of affected joints show in both diseases a dominance of activated macrophages/fibroblasts with a significant infiltration of activated T cells and an infiltration of granulocytes. We do here suggest that both RA and CIA are dependent on a synergy between delayed type hypersensitivity and immune complex mediated inflammarory mechanisems and that CIA provides opportunities for studies of immunspecific reactions leading to arthritis.
Creating Chinese-English Comparable Corpora

SUMMARY Comparable Corpora are valuable resources for many NLP applications, and extensive research has been done on information mining based on comparable corpora in recent years. While there are not enough large-scale available public comparable corpora at present, this paper presents a bi-directional CLIR-based method for creating comparable corpora from two independent news collections in di ﬀ erent languages. The original Chinese document collections and English documents collections are crawled from XinHuaNet respectively and formatted in a consistent manner. For each document from the two collections, the best query key-words are extracted to represent the essential content of the document, and then the keywords are translated into the language of the other collection. The translated queries are run against the collection in the same language to pick up the candidate documents in the other language and candidates are aligned based on their publication dates and the similarity scores. Results show that our approach signiﬁcantly outperforms previous approaches to the construction of Chinese-English comparable corpora.
Peripheral nerves responsible for sensory innervation of the hand

Neuropathic hand complaints—for which patients typically seek medical attention when the pain or paresthesia starts to interfere with their daily routine—are common and diverse. The ability to assess and accurately diagnose upper extremity compression neuropathies is critical for physicians in primary care. Assessment starts, of course, with a thorough history of the present illness and past medical history, which helps define a broad differential diagnosis and identify comorbidities. Physical examination, including judicious use of provocative testing, allows you to objectively identify the pathologic deficit, evaluate function and coordination of multiple organ systems, and detect nerve dysfunction. The results determine whether additional tools, such as electrodiagnostic testing, are needed. We’ve created this guide, detailed in the text, tables, and figures that follow, to help you hone your ability to accurately diagnose patients who present with compression neuropathies of the hand.
Robot Assisted Thoracic Surgery (RATS)

Robotic surgery using the da Vinci® Surgical System (dVS; Intuitive Surgical, Inc., Sunnyvale, CA, U.S.A) has been approved in many countries for cardiovascular surgery, urology, and gynecology. However, unlike mediastinal tumors, which can be located in many different sites, in other organs treated with the dVS (heart, bladder, prostate, and uterus), the targets are generally found in similar locations. Moreover, the anatomical structures surrounding mediastinal tumors are vital (heart, aorta, lungs, pulmonary vessels, esophagus, and vertebrae), and it is crucial to pay careful attention in performing operations. Video-assisted thoracic surgery (VATS) is less invasive than standard thoracotomy, but there is the problem of postoperative pain caused by the inevitable leverage of instruments on the chest wall during the procedure, and the difficulty of manipulation in the mediastinum. The most crucial aspect is the limited movement of long rigid instruments (stick surgery). The instrument arms used in the dVS inflict less stress on the chest wall than conventional VATS, especially when highly skilled techniques are needed to operate on lesions in difficult-to-access areas [1].