Miracle-dz/UniFAR-FTUs huggingface dataset:
Consensus and variations in opinions on delirium care: a survey of European delirium specialists

ABSTRACT Background: There are still substantial uncertainties over best practice in delirium care. The European Delirium Association (EDA) conducted a survey of its members and other interested parties on various aspects of delirium care. Methods: The invitation to participate in the online survey was distributed among the EDA membership. The survey covered assessment, treatment of hyperactive and hypoactive delirium, and organizational management. Results: A total of 200 responses were collected (United Kingdom 28.6%, Netherlands 25.3%, Italy 15%, Switzerland 9.7%, Germany 7.1%, Spain 3.8%, Portugal 2.5%, Ireland 2.5%, Sweden 0.6%, Denmark 0.6%, Austria 0.6%, and others 3.2%). Most of the responders were doctors (80%), working in geriatrics (45%) or internal medicine (14%). Ninety-two per cent of the responders assessed patients for delirium daily. The most commonly used assessment tools were the Confusion Assessment Method (52%) and the Delirium Observation Screening Scale (30%). The first-line choice in the management of hyperactive delirium was a combination of non-pharmacological and pharmacological approaches (61%). Conversely, non-pharmacological management was the first-line choice in hypoactive delirium (67%). Delirium awareness (34%), knowledge (33%), and lack of education (13%) were the most commonly reported barriers to improving the detection of delirium. Interestingly, 63% of the responders referred patients after an episode of delirium to a follow-up clinic. Conclusions: This is the first systematic survey involving an international group of specialists in delirium. Several areas of lack of consensus were found. These results emphasise the importance of further research to improve care of this major unmet medical need.
Molecular analysis of two ScrR repressors and of a ScrR–FruR hybrid repressor for sucrose and D‐fructose specific regulons from enteric bacteria

The scr regulon of pUR400 and the chromosomally encoded scr regulon of Klebsiella pneumoniae KAY2026 are both negatively controlled by a specific repressor (ScrR). As deduced from the nucleotide sequences, both scrR genes encode polypeptides of 334 residues (85.5% identical base pairs, 91.3% identical amino acids), containing an N‐terminal helix‐turn‐helix motif. Comparison with other regulatory proteins revealed 30.6% identical amino acids to FruR, 27.0% to Lacl and 28.1% to GaIR. Six scrRs super‐repressor mutations define the inducer‐binding domain. The scr operator sequences were identified by in vivo titration tests of the sucrose repressor and by in vitro electrophoretic mobility shift assays. D‐fructose, an intracellular product of sucrose transport and hydrolysis, and D‐fructose 1‐phosphate were shown to be molecular inducers of both scr regulons. An active ScrR–FruR hybrid repressor protein was constructed with the N‐terminal part of the sucrose repressor of K. pneumoniae and the C‐terminal part of the fructose repressor of Salmonella typhimurium, LT2. Gel retardation assays showed that the hybrid protein bound to scr‐specific operators, and that D‐fructose 1‐phosphate, the inducer for FruR, was the only inducer. In vivo, neither the operators of the fru operon nor of the pps, operon, the natural targets for FruR, were recognized, but the scr operators were. These data and the data obtained from the super‐repressor alleles confirm previous models on the binding of repressors of the Lacl family to their operators.
Application-Specific Cross-Layer Optimization Based on Predictive Variable-Latency VLSI Design

Traditional synchronous VLSI design requires that all computations in a logic stage complete in one clock cycle. This leads to increasingly pessimistic design as technology scaling introduces increasingly significant parametric variations that result in an increasing performance variability. Alternatively, by allowing computations in a logic stage to complete in a variable number of clock cycles, variable-latency design provides relaxed timing constraints for average performance, area, and power consumption optimization. In this article, we present improved variable-latency design techniques including: (1) a generic minimum-intrusion variable-latency VLSI design paradigm, (2) a signal probability-based approximate prediction logic construction method for minimum misprediction rate at minimum cost, and (3) an application-specific cross-layer analysis methodology. Our experiments show that the proposed variable-latency design methodology on average reduces the computation latency by 26.80%(14.65%) at cost of 0.08%(3.4%) area and 0.4%(2.2%) energy consumption increase for the interger (floating point) unit of an open-source SPARC V8 processor LEON2 synthesized with a clock-cycle time between 1.97ns(3.49ns) and 5.96ns(13.74ns) based on the 45nm Nangate open cell library, while an automotive application-specific design further achieves an average latency reduction of 41.8%.
Exploiting the synergy between gossiping and structured overlays

In this position paper we argue for exploiting the synergy between gossip-based algorithms and structured overlay networks (SON). These two strands of research have both aimed at building fault-tolerant, dynamic, self-managing, and large-scale distributed systems. Despite the common goals, the two areas have, however, been relatively isolated. We focus on three problem domains where there is an untapped potential of using gossiping combined with SONs. We argue for applying gossip-based membership for ring-based SONs---such as Chord and Bamboo---to make them handle partition mergers and loopy networks. We argue that small world SONs---such as Accordion and Mercury---are specifically well-suited for gossip-based membership management. The benefits would be better graph-theoretic properties. Finally, we argue that gossip-based algorithms could use the overlay constructed by SONs. For example, many unreliable broadcast algorithms for SONs could be augmented with anti-entropy protocols. Similarly, gossip-based aggregation could be used in SONs for network size estimation and load-balancing purposes.
Searching for Recursive Causal Structures in Multivariate Quantitative Genetics Mixed Models

Biology is characterized by complex interactions between phenotypes, such as recursive and simultaneous relationships between substrates and enzymes in biochemical systems. Structural equation models (SEMs) can be used to study such relationships in multivariate analyses, e.g., with multiple traits in a quantitative genetics context. Nonetheless, the number of different recursive causal structures that can be used for fitting a SEM to multivariate data can be huge, even when only a few traits are considered. In recent applications of SEMs in mixed-model quantitative genetics settings, causal structures were preselected on the basis of prior biological knowledge alone. Therefore, the wide range of possible causal structures has not been properly explored. Alternatively, causal structure spaces can be explored using algorithms that, using data-driven evidence, can search for structures that are compatible with the joint distribution of the variables under study. However, the search cannot be performed directly on the joint distribution of the phenotypes as it is possibly confounded by genetic covariance among traits. In this article we propose to search for recursive causal structures among phenotypes using the inductive causation (IC) algorithm after adjusting the data for genetic effects. A standard multiple-trait model is fitted using Bayesian methods to obtain a posterior covariance matrix of phenotypes conditional to unobservable additive genetic effects, which is then used as input for the IC algorithm. As an illustrative example, the proposed methodology was applied to simulated data related to multiple traits measured on a set of inbred lines.
Emotion based image musicalization

Playing appropriate music when watching images can make the images vivid and bring people into their intrinsic world. In this paper, we propose to musicalize images based on their emotions. Most of previous works on image emotion analysis mainly used elements-of-art based low-level visual features, which are vulnerable to the arrangements of elements. Here we propose to extract visual features, inspired by the concept of principles-of-art, to recognize image emotions. To enrich the descriptive power, a dimensional perspective is introduced to emotion modeling. Experiments on the IAPS dataset demonstrate the superiority of the proposed method in comparison to the state-of-the-art methods for emotion regression. The music in MST dataset with approximate emotions to the recognized image emotions is selected to musicalize these images. The user study results show its effectiveness and popularity of the image musicalization method.
Alzheimer's disease patients classification through EEG signals processing

Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must manually perform laborious interpretations. Computational methods may lead to a quantitative analysis of these signals and hence to characterize EEG time series. The aim of this work is to achieve an automatic patients classification from the EEG biomedical signals involved in AD and MCI in order to support medical doctors in the right diagnosis formulation. The analysis of the biological EEG signals requires effective and efficient computer science methods to extract relevant information. Data mining, which guides the automated knowledge discovery process, is a natural way to approach EEG data analysis. Specifically, in our work we apply the following analysis steps: (i) pre-processing of EEG data; (ii) processing of the EEG-signals by the application of time-frequency transforms; and (iii) classification by means of machine learning methods. We obtain promising results from the classification of AD, MCI, and control samples that can assist the medical doctors in identifying the pathology.
Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies

The quantitative analysis of pooled data from related functional magnetic resonance imaging (fMRI) experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results), mega-analysis (the pooled analysis of raw data) or multi-site studies, which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated.
A Flexible Multi-Channel Coordination MAC Protocol for Vehicular Ad Hoc Networks

The IEEE 802.11p/1609.4 standard supports both safety applications and non-safety services for vehicular ad hoc networks (VANETs) via multi-channel operations. In this letter, we establish an analytical model to calculate the optimal bandwidth resource allocation and propose a flexible multi-channel coordination MAC (FMC-MAC) protocol for VANETs. The FMC-MAC protocol allows safety messages broadcasted on service channel and non-safety data transmitted on control channel in a flexible way. It does not only guarantee the reliability of safety applications, but also improve the throughput of non-safety services. The simulation results validate the analytical model and the proposed FMC-MAC protocol.
Revealing Resources in Strategic Contexts

Identifying an optimal game strategy often involves estimating the strategies of other agents, which in turn depends on hidden parts of the game state. In this paper we focus on the win-lose game The Settlers of Catan (or Settlers ), in which players negotiate over limited resources. More precisely, our goal is to map each player’s utterances in such negotiations to a model of which resources they currently possess,or don’t possess. Our approach comprises three subtasks: (a) identify whether a given utterance (dialogue turn) reveals possession of a resource, or not; (b) determine the type of resource; and (c) determine the exact interval representing the quantity involved. This information can be exploited by a Settlers playing agent to identify his optimal strategy for winning.
Identification of a Novel Tandem Duplication in Exon 1 of the TNFRSF11A Gene in Two Unrelated Patients With Familial Expansile Osteolysis

Familial expansile osteolysis (FEO) is a rare autosomal dominant disorder characterized by striking focal expansile osteolytic bone lesions and generalized osteopenia, often accompanied by characteristic early hearing loss and dental disease. The TNFRSF11A gene encodes the receptor activator of nuclear factor‐κB (RANK), which has been demonstrated to be essential in bone remodeling and osteoclast differentiation. Identical insertional mutations in the first exon of RANK have been identified in all published FEO kindreds. The mutation is an 18 base pair tandem duplication in the sequence coding for the signal peptide of RANK, which causes an increase in NF‐κB signaling. We report the identification and mutational analysis of two unrelated FEO patients. One had no family history of FEO, but presented with bilateral hearing loss at an early age, deterioration of teeth, and severe pain and swelling in the distal tibia before the age of 20. The second patient had a family history of FEO and exhibited an extensive expansile tibial lesion and lesions in one humerus and a phalanx. She also had early hearing loss and dental disease. Mutational analysis of the TNFRSF11A gene in our patients demonstrated an 18 base pair tandem duplication, one base proximal to the duplications previously reported. This novel mutation results in addition of the same six amino acids to the RANK signal peptide that has been observed previously. Further analysis of the exon 1 sequence demonstrated that it has the ability to form a stable secondary structure that may facilitate the generation of tandem duplications.
Forward error correction for data dissemination in MANET

Interconnecting consumer electronic devices in an ad-hoc manner is considered one of the solutions to provide next generation services like “Ambient Intelligence” or the “Internet of Things”. Since the transmission range of a single device is limited, multi-hop techniques are often employed to provide a larger network coverage. In such a mobile ad-hoc network (MANET) broadcast data is often disseminated via a simple flooding algorithm. Due to the high mobility and changing link conditions data packets get lost on their way to the receiver. To achieve acceptable user experience for these applications, forward error correction (FEC) schemes can be used. In this work we evaluate some of these schemes for multi-level coded audio data, using a simulator. An overview of the recovery results and the additional costs for the data overhead is presented for each FEC scheme. Furthermore based on the simulation results we implemented an application-layer based Parity FEC in a testbed which is used to broadcast real-time voice data in a MANET. Measurements for the real world performance of single- and multi-hop testcases are presented. Additionally a simple adaptive scheme was developed to provide a good balance between loss rate and overhead.
TLR-Mediated Inflammatory Responses to Streptococcus pneumoniae Are Highly Dependent on Surface Expression of Bacterial Lipoproteins

Streptococcus pneumoniae infections induce inflammatory responses that contribute toward both disease pathogenesis and immunity, but the host–pathogen interactions that mediate these effects are poorly defined. We used the surface lipoprotein-deficient ∆lgt pneumococcal mutant strain to test the hypothesis that lipoproteins are key determinants of TLR-mediated immune responses to S. pneumoniae. We show using reporter assays that TLR2 signaling is dependent on pneumococcal lipoproteins, and that macrophage NF-κB activation and TNF-α release were reduced in response to the ∆lgt strain. Differences in TNF-α responses between Δlgt and wild-type bacteria were abrogated for macrophages from TLR2- but not TLR4-deficient mice. Transcriptional profiling of human macrophages revealed attenuated TLR2-associated responses to ∆lgt S. pneumoniae, comprising many NF-κB–regulated proinflammatory cytokine and chemokine genes. Importantly, non-TLR2–associated responses were preserved. Experiments using leukocytes from IL-1R–associated kinase-4–deficient patients and a mouse pneumonia model confirmed that proinflammatory responses were lipoprotein dependent. Our data suggest that leukocyte responses to bacterial lipoproteins are required for TLR2- and IL-1R–associated kinase-4–mediated inflammatory responses to S. pneumoniae.
Cords: keyframe control of curves with physical properties

Cords are curve primitives that allow an animator to create, with keyframe precision, complex animations of one-dimesional objects such as string or wire that bend and wrap around scene geometry. While physical simulations are commonly used to model the motion of physically-based curve primitives [Pai 2002], they remain difficult for an animator to manage when detailed control is required. We address this problem by using intuitive controls of length, stiffness, and elasticity (the latter two being resistance to bending and stretching, respectively), in conjunction with a parametric guide curve to analytically define a cord’s shape. This minimal interface allows animators to quickly develop complex, physically plausible motion, and cords have found extensive use in the creation of the rich, metaphorical world of the animated film Ryan (SIGGRAPH 2004 ET Jury Prize).
Tumor Necrosis Factor-α and Muc2 Mucin Play Major Roles in Disease Onset and Progression in Dextran Sodium Sulphate-Induced Colitis

The sequential events and the inflammatory mediators that characterize disease onset and progression of ulcerative colitis (UC) are not well known. In this study, we evaluated the early pathologic events in the pathogenesis of colonic ulcers in rats treated with dextran sodium sulfate (DSS). Following a lag phase, day 5 of DSS treatment was found clinically most critical as disease activity index (DAI) exhibited an exponential rise with severe weight loss and rectal bleeding. Surprisingly, on days 1-2, colonic TNF-α expression (70-80-fold) and tissue protein (50-fold) were increased, whereas IL-1β only increased on days 7-9 (60-90-fold). Days 3-6 of DSS treatment were characterized by a prominent down regulation in the expression of regulatory cytokines (40-fold for IL-10 and TGFβ) and mucin genes (15-18 fold for Muc2 and Muc3) concomitant with depletion of goblet cell and adherent mucin. Remarkably, treatment with TNF-α neutralizing antibody markedly altered DSS injury with reduced DAI, restoration of the adherent and goblet cell mucin and IL-1β and mucin gene expression. We conclude that early onset colitis is dependent on TNF-α that preceded depletion of adherent and goblet cell mucin prior to epithelial cell damage and these biomarkers can be used as therapeutic targets for UC.
Jointly Learning to Embed and Predict with Multiple Languages

We propose a joint formulation for learning task-specific cross-lingual word embeddings, along with classifiers for that task. Unlike prior work, which first learns the embeddings from parallel data and then plugs them in a supervised learning problem, our approach is oneshot: a single optimization problem combines a co-regularizer for the multilingual embeddings with a task-specific loss. We present theoretical results showing the limitation of Euclidean co-regularizers to increase the embedding dimension, a limitation which does not exist for other co-regularizers (such as the ‘1distance). Despite its simplicity, our method achieves state-of-the-art accuracies on the RCV1/RCV2 dataset when transferring from English to German, with training times below 1 minute. On the TED Corpus, we obtain the highest reported scores on 10 out of 11 languages.
A single-trim CMOS bandgap reference with a 3σ inaccuracy of ±0.15% from -40°C to 125°C

This paper describes a CMOS bandgap reference with an inaccuracy of ±0.15% (3σ) from −40°C to 125°C. This level of performance, previously only achieved by trimming at two or more temperatures [1, 2], was obtained by using chopping and curvature correction to minimize non-PTAT (proportional-to-absolute-temperature) errors, and canceling residual PTAT errors with a single room-temperature trim.
Targeting of the Small GTPase Rab6A′ by the Legionella pneumophila Effector LidA

ABSTRACT When the bacterium Legionella pneumophila, the causative agent of Legionnaires' disease, is phagocytosed by alveolar macrophages, it delivers a large number of effector proteins through its Dot/Icm type IV secretion system into the host cell cytosol. Among those proteins is LidA, an effector that interacts with several host GTPases of the Rab family, including Rab6A′, a regulator of retrograde vesicle trafficking within eukaryotic cells. The effect of LidA on Rab6A′ function and the role of Rab6A′ for L. pneumophila growth within host cells has been unclear. Here, we show that LidA preferentially binds Rab6A′ in the active GTP-bound conformation. Rab6 binding occurred through the central region of LidA and followed a stoichiometry for LidA and Rab6A′ of 1:2. LidA maintained Rab6A′ in the active conformation by efficiently blocking the hydrolysis of GTP by Rab6A′, even in the presence of cellular GTPase-activating proteins, suggesting that the function of Rab6A′ must be important for efficient intracellular replication of L. pneumophila. Accordingly, we found that production of constitutively inactive Rab6A′(T27N) but not constitutively active Rab6A′(Q72L) significantly reduced the ability of L. pneumophila to initiate intracellular replication in human macrophages. Thus, the presence of an active pool of Rab6 within host cells early during infection is required to support efficient intracellular growth of L. pneumophila.
Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets With Application to UXO Sensing

To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper, we propose a method to relax this requirement in the context of logistic regression. Assuming <i>D</i> <sup>p</sup> and <i>D</i> <sup>a</sup> are two sets of examples drawn from two different distributions <i>T</i> and <i>A</i> (called concepts, borrowing a term from psychology), where <i>D</i> <sup>a</sup> are fully labeled and <i>D</i> <sup>p</sup> partially labeled, our objective is to complete the labels of <i>D</i> <sup>p</sup>. We introduce an auxiliary variable mu for each example in <i>D</i> <sup>a</sup> to reflect its mismatch with <i>D</i> <sup>p</sup>. Under an appropriate constraint the mus are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in <i>D</i> <sup>p</sup>. The proposed algorithm, called <i>migratory</i> <i>logistic</i> <i>regression</i>, is demonstrated successfully on simulated data as well as on real measured data of interest for unexploded ordnance cleanup.