Miracle-dz/UniFAR-FTUs huggingface dataset:
Rheological study of a new semi-rigid giant polysaccharide for the mechanical reinforcement of hydrogels

We study the relationship structure/properties of rigid hydrogels composed of a new giant polysaccharide, sacran. This novel polysaccharide has a contour length larger than 10 μm and an intrinsic length of persistence of approximately 60 nm. By crosslinking the chains of this polysaccharide we expect prepare rigid networks, in which the mesh size is controlled by rigid chain segments. Physical (transient bonds) and chemical hydrogels of sacran have been prepared and their rheological properties have been studied. The formation of rigid networks of sacran has been demonstrated by using the non-conventional microrheology by DWS allowing to characterize the dynamics at single chain scale of the network. Different specific rheological signatures of these rigid networks have been highlighted and strain-hardening properties have been observed in the non-linear regime for the chemical gels, as well as for the physical gels. The strain-hardening observed for the chemical gels, which are large compared to those of other biopolymers reported in the literature, were modeled to obtain the structural parameters of these networks which lead to their hardening.
Predicting Non-response to Ranibizumab in Patients with Neovascular Age-related Macular Degeneration

Abstract Purpose: To validate known and determine new predictors of non-response to ranibizumab in patients with neovascular age-related macular degeneration (AMD) and to incorporate these factors into a prediction rule. Methods: This multicenter, observational cohort study included 391 patients treated with ranibizumab for neovascular AMD. We performed genetic analysis for single nucleotide polymorphisms in AMD-associated genes and collected questionnaires regarding environmental factors and disease history. The primary outcome was non-response to treatment, defined as a loss of visual acuity ≥30% of letters. Results: Of the 391 patients, 47 were classified as non-responsive. Independent predictors for non-response were age, baseline visual acuity, diabetes mellitus and accumulation of risk alleles in the CFH, ARMS2 and VEGF-A genes. The area under the receiver operating characteristic curve was 0.77 (95% confidence interval 0.70–0.84). We derived a clinical prediction rule, with possible total risk scores ranging from 0–19 points. The absolute risk of non-response varied from 3–52% between risk score groups. Conclusion: This is an important step towards a clinical prediction rule that can aid clinicians in identifying AMD patients with increased likelihood of non-response, and consequently contribute to making shared treatment decisions.
The thermodynamics of Pr55Gag-RNA interaction regulate the assembly of HIV

The interactions that occur during HIV Pr55Gag oligomerization and genomic RNA packaging are essential elements that facilitate HIV assembly. However, mechanistic details of these interactions are not clearly defined. Here, we overcome previous limitations in producing large quantities of full-length recombinant Pr55Gag that is required for isothermal titration calorimetry (ITC) studies, and we have revealed the thermodynamic properties of HIV assembly for the first time. Thermodynamic analysis showed that the binding between RNA and HIV Pr55Gag is an energetically favourable reaction (ΔG<0) that is further enhanced by the oligomerization of Pr55Gag. The change in enthalpy (ΔH) widens sequentially from: (1) Pr55Gag-Psi RNA binding during HIV genome selection; to (2) Pr55Gag-Guanosine Uridine (GU)-containing RNA binding in cytoplasm/plasma membrane; and then to (3) Pr55Gag-Adenosine(A)-containing RNA binding in immature HIV. These data imply the stepwise increments of heat being released during HIV biogenesis may help to facilitate the process of viral assembly. By mimicking the interactions between A-containing RNA and oligomeric Pr55Gag in immature HIV, it was noted that a p6 domain truncated Pr50Gag Δp6 is less efficient than full-length Pr55Gag in this thermodynamic process. These data suggest a potential unknown role of p6 in Pr55Gag-Pr55Gag oligomerization and/or Pr55Gag-RNA interaction during HIV assembly. Our data provide direct evidence on how nucleic acid sequences and the oligomeric state of Pr55Gag regulate HIV assembly.
Epistemic First-order Queries over Description Logic Knowledge Bases

Querying Description Logic knowledge bases has received great attention in the last years. The need of coping with incomplete information is the distinguishing feature with respect to querying databases. Due to this feature, we have to deal with two conflicting needs: on the one hand, we would like to query the knowledge base with sophisticated mechanisms provided by full first-order logic as in databases; on the other hand, the presence of incomplete information makes query answering a much more difficult task than in databases. In this paper we advocate the use of an epistemic first-order query language, which is able to incorporate closed-world reasoning on demand, as a means for expressing sophisticated queries over Description Logic knowledge bases. We show that through a subset of this language, called EQL-Lite, we are able to formulate full first-order queries over Description Logic knowledge bases, while keeping computational complexity of query answering under control. In particular, we show that EQL-Lite queries over DL-Lite knowledge bases are first-order reducible (i.e., can be compiled into SQL) and hence can be answered in LogSpace through standard database technologies.
Estimation of Optimized Energy and Latency Constraints for Task Allocation in 3d Network on Chip

In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation schemes which affect the performance of the system. In this paper we test the pre-existing proposed algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark and has been compared with the existing mapping algorithm spiral and crinkle and has shown better reduction in the communication energy consumption and shows improvement in the performance of the system. On performing experimental analysis of proposed algorithm results shows that average reduction in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%. Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.
Recovering inter-project dependencies in software ecosystems

In large software systems, knowing the dependencies between modules or components is critical to assess the impact of changes. To recover the dependencies, fact extractors analyze the system as a whole and build the dependency graph, parsing the system down to the statement level. At the level of software ecosystems, which are collections of software projects, the dependencies that need to be recovered reside not only within the individual systems, but also between the libraries, frameworks, and entire software systems that make up the complete ecosystem; scaling issues arise. In this paper we present and evaluate several variants of a lightweight and scalable approach to recover dependencies between the software projects of an ecosystem. We evaluate our recovery algorithms on the Squeak 3.10 Universe, an ecosystem containing more than 200 software projects.
On the Complexity of Learning Decision Trees

Various factors a ecting decision tree learning time are explored. The factors which consistently a ect accuracy are those which directly or indirectly (as in the handling of continuous attributes) allow a greater variety of potential trees to be explored. Other factors, e.g., pruning and choice of heuristics, generally have little e ect on accuracy, but signi cantly a ect learning time. We prove that the time complexity of induction and post-processing is exponential in tree height in the worst case and, under fairly general conditions, in the average case. This puts a premium on designs which produce shallower and more balanced trees. Simple pruning is linear in tree height, contrasted to the exponential growth of more complex operations. The key factor in uencing whether simple pruning will su ce is that the split selection and pruning heuristics should be the same and unbiased. The information gain and 2 tests are biased towards unbalanced splits, and neither is admissible for pruning. Empirical results show that the hypergeometric function can be used for both split selection and pruning, and that the resulting trees are simpler, more quickly learned, and no less accurate than trees resulting from other heuristics and more complex post-processing.
Reasoning over SPARQL

Until now, the sparql query language was restricted to simple entailment. Now sparql is being extended with more expressive entailment regimes. This allows to query over inferred, implicit knowledge. However, in this case the sparql endpoint provider decides which inference rules are used for its entailment regimes. In this paper, we propose an extension to the sparql query language to support remote reasoning, in which the data consumer can dene the inference rules. It will supplement the supported entailment regimes of the sparql endpoint provider with an additional reasoning step using the inference rules dened by the data consumer. At the same time, this solution oers possibilities to solve interoperability issues when querying remote sparql endpoints, which can support federated querying frameworks. These frameworks can then be extended to provide distributed, remote reasoning.
Space Syntax And Spatial Cognition

Space syntax research has found that spatial configuration alone explains a substantial proportion of the variance between aggregate human movement rates in different locations in both urban and building interior space. Although it seems possible to explain how people move on the basis of these analyses, the question of why they move this way has always seemed problematic because the analysis contains no explicit representations of either motivations or individual cognition. One possible explanation for the method’s predictive power is that some aspects of cognition are implicit in space syntax analysis. This article reviews the contribution made by syntax research to the understanding of environmental cognition. It proposes that cognitive space, defined as that space which supports our understanding of configurations more extensive than our current visual field, is not a metric space, but topological. A hypothetical process for deriving a nonmetric space from the metric visibility graph involving exploratory movement is developed. The resulting space is shown to closely resemble the axial graph.
Preimage attack on BioHashing

Biometric recognition is more and more employed in authentication and access control of various applications. Biometric data are strongly linked with the user and do not allow revocability nor diversity, without an adapted post-processing. Cancelable biometrics, including the very popular algorithm BioHashing, is used to cope with the underlying privacy and security issues. The principle is to transform a biometric template in a BioCode, in order to enhance user privacy and application security. These schemes are used for template protection of several biometric modalities, as fingerprints or face and the robustness is generally related to the hardness to recover the original biometric template by an impostor. In this paper, we propose to use genetic algorithms to approximate the original biometric feature and spoof the authentication system. We show through experimental results on fingerprints the efficiency of the proposed attack on the BioHashing algorithm, by approximating the original FingerCode, given the seed and the corresponding BioCode.
A statistical framework for artificial bandwidth extension exploiting speech waveform and phonetic transcription

In the past, artificial bandwidth extension (ABWE) has primarily been investigated to enhance transmitted narrowband speech signals at the receiving side. State-of-the-art schemes show improved quality versus narrowband speech; however, a clear gap to wideband speech is still reported. This is largely due to the insufficient ABWE performance on fricatives, particularly /s/. We asked ourselves to what extent the speech quality could be improved, if we knew the currently spoken phoneme. In this paper we present a framework using phonetic transcriptions as a-priori knowledge besides the speech waveform. Possible applications are high-quality offline ABWE of telephone, pilot, or historic speech recordings, memory efficient narrowband speech synthesis followed by ABWE, and extension of narrowband telephone databases to train wideband acoustic models for automatic speech recognition. For the classical conversational telephony application, an improved ABWE scheme is also proposed making use of transcription information only during training.
New constraints from PAMELA anti-proton data on annihilating and decaying dark matter

Recently the PAMELA experiment has released its updated anti-proton flux and anti-proton to proton flux ratio data up to energies of ≈ 200GeV. With no clear excess of cosmic ray anti-protons at high energies, one can extend constraints on the production of anti-protons from dark matter. In this letter, we consider both the cases of dark matter annihilating and decaying into standard model particles that produce significant numbers of anti-protons. We provide two sets of constraints on the annihilation cross-sections/decay lifetimes. In the one set of constraints we ignore any source of anti-protons other than dark matter, which give the highest allowed cross-sections/inverse lifetimes. In the other set we include also anti-protons produced in collisions of cosmic rays with interstellar medium nuclei, getting tighter but more realistic constraints on the annihilation cross-sections/decay lifetimes.
Hybrid ORC waste heat recovery system and solar thermal plant in Morocco

This document presents the heat recovery system of a cement plant in Morocco. The particularity of the plant is that it utilizes a hybrid solution with an Organic Rankine Cycle (ORC) turbogenerator. The heat used in the ORC unit to be transformed into electricity comes from both heat recovery from the cement production and from solar collectors through a thermal oil loop.
Adaptive design of a global opacity transfer function for direct volume rendering of ultrasound data

While there are a couple of transfer function design approaches for CT and MRI (magnetic resonance imaging) data, direct volume rendering of ultrasound data still relies on manual adjustment of an inflexible piecewise linear opacity transfer function (OTF) on a trial-and-error basis. The main challenge of automatically designing an OTF for visualization of sonographic data is the low signal-to-noise ratio in combination with real time data acquisition at frame rates up to 25 volumes per second. In this paper, we present an efficient solution of this task. Our approach is based on the evaluation of tube cores, i.e., collections of voxels gathered by traversing the volume in rendering directions. We use information about the probable position of an interface between tissues of different echogenicity to adaptively design an OTF in a multiplicative way. We show the appropriateness of our approach by examples, deliberately on data sets of moderate quality arising frequently in clinical settings.
Next-Generation Delta-Sigma Converters: Trends and Perspectives

This paper presents an overview of emerging circuits and systems techniques which are at the forefront of the state of the art in ΔΣ modulators, pushing their performance forward and giving rise to new generations of data converters. Among others, those strategies involving the development of new applications and paradigms-like RF/GHz-range ΔΣ digitisation, digital-assisted embedded loop filters, time-to-digital conversion and hybrid ΔΣ/Nyquist-rate architectures-are discussed, as well as the implications and design challenges derived from their integration in deep nanometer CMOS technologies. The envisioned ΔΣ techniques are presented in a systematic way around the main analog building blocks embedded in a ΔΣ modulator, i.e., the loop filter and the quantizer. Analysing the trends in the design of these blocks allows us to offer perspectives on how ΔΣ converters will evolve in the next years.
Efficacy of dronabinol alone and in combination with ondansetron versus ondansetron alone for delayed chemotherapy-induced nausea and vomiting*

ABSTRACT Objective: To compare the efficacy and tolerability of dronabinol, ondansetron, or the combination for delayed chemotherapy-induced nausea and vomiting (CINV) in a 5-day, double-blind, placebo-controlled study. Research design and methods: Patients receiving moderately to highly emetogenic chemotherapy received dexamethasone (20 mg PO), ondansetron (16 mg IV) and either placebo or dronabinol (2.5 mg) prechemotherapy on day 1. Patients randomized to active treatment (dronabinol and/or ondansetron) also received dronabinol (2.5 mg) after chemotherapy on day 1. On day 2, fixed doses of placebo, dronabinol (10 mg), ondansetron (16 mg), or combination therapy were administered. On days 3–5, patients received placebo, flexible doses of dronabinol (10–20 mg), ondansetron (8–16 mg), or dronabinol and ondansetron (10–20 mg dronabinol, 8–16 mg ondansetron). Main outcome measures: Total response (TR = nausea intensity < 5 mm on visual analog scale, no vomiting/retching, no rescue antiemetic), nausea (occurrence and intensity) and vomiting/retching episodes. Results: Sixty-four patients were randomized; 61 analyzed for efficacy. TR was similar with dronabinol (54%), ondansetron (58%), and combination therapy (47%) versus placebo (20%). Nausea absence was significantly greater in active treatment groups (dronabinol, 71%; ondansetron, 64%; combination therapy, 53%) versus placebo (15%; p < 0.05 vs. placebo for all). Nausea intensity and vomiting/retching were lowest in patients treated with dronabinol. Active treatments were well tolerated. The low number of patients due to slow enrollment limits the interpretation of these data. Conclusions: Dronabinol or ondansetron was similarly effective for the treatment of CINV. Combination therapy with dronabinol and ondansetron was not more effective than either agent alone. Active treatments were well tolerated.
Finding the right consumer: optimizing for conversion in display advertising campaigns

The ultimate goal of advertisers are conversions representing desired user actions on the advertisers' websites in the form of purchases and product information request. In this paper we address the problem of finding the right audience for display campaigns by finding the users that are most likely to convert. This challenging problem is at the heart of display campaign optimization and has to deal with several issues such as very small percentage of converters in the general population, high-dimensional representation of the user profiles, large churning rate of users and advertisers. To overcome these difficulties, in our approach we use two sources of information: a seed set of users that have converted for a campaign in the past; and a description of the campaign based on the advertiser's website. We explore the importance of the information provided by each of these two sources in a principled manner and then combine them to propose models for predicting converters. In particular, we show how seed set can be used to capture the campaign-specific targeting constraints, while the campaign metadata allows to share targeting knowledge across campaigns. We give methods for learning these models and perform experiments on real-world advertising campaigns. Our findings show that the seed set and the campaign metadata are complimentary to each other and both sources provide valuable information for conversion optimization.
A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks

Computational prediction of interactions between drugs and their target proteins is of great importance for drug discovery and design. The difficulties of developing computational methods for the prediction of such potential interactions lie in the rarity of known drug-protein interactions and no experimentally verified negative drug-target interaction sample. Furthermore, target proteins need also to be predicted for some new drugs without any known target interaction information. In this paper, a semi-supervised learning method NetCBP is presented to address this problem by using labeled and unlabeled interaction information. Assuming coherent interactions between the drugs ranked by their relevance to a query drug, and the target proteins ranked by their relevance to the hidden target proteins of the query drug, we formulate a learning framework maximizing the rank coherence with respect to the known drug-target interactions. When applied to four classes of important drug-target interaction networks, our method improves previous methods in terms of cross-validation and some strongly predicted interactions are confirmed by the publicly accessible drug target databases, which indicates the usefulness of our method. Finally, a comprehensive prediction of drug–target interactions enables us to suggest many new potential drug–target interactions for further studies.
Enhancing SART Validity by Statistically Controlling Speed-Accuracy Trade-Offs

Numerous studies focused on elucidating the correlates, causes, and consequences of inattention/attention-lapses employ the Sustained Attention to Response Task (SART), a GO-NOGO task with infrequent withholds. Although the SART has become popular among inattention researchers, recent work has demonstrated its susceptibility to speed-accuracy trade-offs (SATOs), rendering its assessment of inattention problematic. Here, we propose and illustrate methods to statistically control for the occurrence of SATOs during SART performance. The statistical solutions presented here can be used to correct standard SART-error scores, including those of already-published data, thereby allowing researchers to re-examine existing data, and to more sensitively evaluate the validity of earlier conclusions.
Externalities in Rural Development: Evidence for China

The author tests for external effects of local economic activity on consumption and income growth at the farm-household level using panel data from four provinces of post-reform rural China. The tests allow for non-stationary fixed effects in the consumption growth process. Evidence is found of geographic externalities, stemming from spillover effects of the level and composition of local economic activity and private returns to local human and physical infrastructure endowments. The results suggest an explanation for rural underdevelopment arising from under-investment in certain externality-generating activities, of which agricultural development emerges as the most important.