Miracle-dz/UniFAR-FTUs huggingface dataset:
Valuing the Dental Caries Utility Index in Australia

Introduction The Dental Caries Utility Index (DCUI) is a new oral health–specific health state classification system for adolescents, consisting of 5 domains: pain/discomfort, difficulty eating food/drinking, worried, ability to participate in activities, and appearance. Each domain has 4 response levels. This study aims to generate an Australian-specific utility algorithm for the DCUI. Methods An online survey was conducted using a representative sample of the adult Australian general population. The discrete choice experiment (DCE) was used to elicit the preferences on 5 domains. Then, the latent utilities were anchored onto the full health-dead scale using the visual analogue scale (VAS). DCE data were modeled using conditional logit, and 2 anchoring procedures were considered: anchor based on the worst health state and a mapping approach. The optimal anchoring procedure was selected based on the model parsimony and the mean absolute error (MAE). Results A total of 995 adults from the Australian general population completed the survey. The conditional logit estimates on 5 dimensions and levels were monotonic and statistically significant, except for the second level of the “worried” and “appearance” domains. The mapping approach was selected based on a smaller MAE between the 2 anchoring procedures. The Australian-specific tariff of DCUI ranges from 0.1681 to 1. Conclusion This study developed a utility algorithm for the DCUI. This value set will facilitate utility value calculations from the participants’ responses for DCUI in economic evaluations of dental caries interventions targeted for adolescents. Highlights Preference-based quality-of-life measures (PBMs), which consist of a health state classification system and a set of utility values (a scoring algorithm), are used to generate utility weights for economic evaluations. This study is the first to develop an Australian utility value set for the Dental Caries Utility Index (DCUI), a new oral health–specific classification system for adolescents. The availability of a utility value set will enable using DCUI in economic evaluations of oral health interventions targeted for adolescents and may ultimately lead to more effective and efficient planning of oral health care services.
VirB, a key transcriptional regulator of Shigella virulence, requires a CTP ligand for its regulatory activities

The VirB protein, encoded by the large virulence plasmid of Shigella spp. , is a key transcriptional regulator of virulence genes. Without a functional virB gene, Shigella cells are avirulent. On the virulence plasmid, VirB functions to offset transcriptional silencing mediated by the nucleoid structuring protein, H-NS, which binds and sequesters AT-rich DNA, making it inaccessible for gene expression. Thus, gaining a mechanistic understanding of how VirB counters H-NS-mediated silencing is of considerable interest. VirB is unusual in that it does not resemble classic transcription factors. Instead, its closest relatives are found in the ParB superfamily, where the best-characterized members function in faithful DNA segregation before cell division. Here, we show that VirB is a fast-evolving member of this superfamily and report for the first time that the VirB protein binds a highly unusual ligand, CTP. VirB binds this nucleoside triphosphate preferentially and with specificity. Based on alignments with the best-characterized members of the ParB family, we identify amino acids of VirB likely to bind CTP. Substitutions in these residues disrupt several well-documented activities of VirB, including its anti-silencing activity at a VirB-dependent promoter, its role in generating a Congo red positive phenotype in Shigella, and the ability of the VirB protein to form foci in the bacterial cytoplasm when fused to GFP. Thus, this work is the first to show that VirB is a bona fide CTP-binding protein and links Shigella virulence phenotypes to the nucleoside triphosphate, CTP. Importance Shigella species cause bacillary dysentery (shigellosis), the second leading cause of diarrheal deaths worldwide. With growing antibiotic resistance, there is a pressing need to identify novel molecular drug targets. Shigella virulence phenotypes are controlled by the transcriptional regulator, VirB. We show that VirB belongs to a fast-evolving, primarily plasmid-borne clade of the ParB superfamily, which has diverged from versions that have a distinct cellular role – DNA partitioning. We are the first to report that, like classic members of the ParB family, VirB binds a highly unusual ligand, CTP. Mutants predicted to be defective in CTP binding are compromised in a variety of virulence attributes controlled by VirB. This study i) reveals that VirB binds CTP, ii) provides a link between VirB-CTP interactions and Shigella virulence phenotypes, and iii) broadens our understanding of the ParB superfamily, a group of bacterial proteins that play critical roles in many different bacteria.
NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites

Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.
An Evaluation of Representational Similarity Analysis for Model Selection and Assessment in Computational Neuroscience

An important goal in neuroscience is to determine what types of information are represented across brain regions. Often, a computational model is used to extract stimulus features that are hypothesized to be represented within a particular brain region. Any particular study tries to assess the relationship between the features extracted by the computational model and the measured activity from a brain region. In recent years, several approaches to studying this relationship have been developed in the field of cognitive neuroscience. A simple and widely used approach is representational similarity analysis (RSA). This approach attempts to quantify similarities between the representational space of a computational model and a set of brain responses. RSA begins with an estimate of the stimulus-by-stimulus representational similarity (or dissimilarity) matrix computed from a set of stimulus-evoked brain responses. Then, a stimulus-by-stimulus representational similarity matrix is obtained from a computational model. RSA computes the similarity of these similarity matrices. However, there exists little work assessing the validity of RSA. In this paper, we show that RSA actually makes very strong assumptions about the relationship between representational spaces and brain responses. When these assumptions are violated, RSA can fail to detect significant relationships. More worryingly, when used for model selection RSA can lead researchers to the wrong answer. In contrast, we show that standard encoding models that use regression methods perform better than RSA.
Non-pharmacological interventions to reduce psychological distress in patients undergoing diagnostic cardiac catheterization: a rapid review

Background: Cardiac catheterization is the standard procedure for the diagnosis of coronary heart disease. The threat physically and emotionally from this procedure can affect the patient’s perception of their health. The heightened psychological distress associated with this diagnostic procedure can cause adverse patient outcomes. Non-pharmacologic interventions have been implemented to reduce psychological distress associated with cardiac catheterization. Aims: The objective of this rapid review is to assess the efficacy of non-pharmacologic interventions (procedural education, relaxation techniques, psychological preparation) on psychological distress experienced by patients as they undergo a cardiac catheterization. Methods: Published, peer-reviewed, English-language intervention studies from 1981 to 2014 were identified in a search of CINAHL, Medline, and Cochrane Library. Eligible studies included adults undergoing cardiac catheterization. Studies included in this review used experimental and quasi-experimental designs and assessed at least one primary outcome: anxiety, depression, and pain to test non-pharmacologic interventions pre and post-cardiac catheterization. Researchers independently extracted data from included studies and completed a quality assessment using a published tool. Data were synthesized as a narrative. Results: There were 29 eligible experimental and quasi-experimental studies that tested the three interventions (n=2504). Findings suggest that non-pharmacologic interventions were able to effectively reduce psychological distress in some patients undergoing cardiac catheterization. Conclusion: Evidence is stronger in recent studies that non-pharmacologic interventions of procedural education and psychological preparation can reduce psychological distress in patients undergoing cardiac catheterization. Further research is needed to define the various relaxation techniques that can be effectively implemented for patients undergoing cardiac catheterization.
Early weakening processes inside thrust fault

Observations from deep boreholes at several locations worldwide, laboratory measurements of frictional strength on quartzo‐feldspathic materials, and earthquake focal mechanisms indicate that crustal faults are strong (apparent friction μ ≥ 0.6). However, friction experiments on phyllosilicate‐rich rocks and some geophysical data have demonstrated that some major faults are considerably weaker. This weakness is commonly considered to be characteristic of mature faults in which rocks are altered by prolonged deformation and fluid‐rock interaction (i.e., San Andreas, Zuccale, and Nankai Faults). In contrast, in this study we document fault weakening occurring along a marly shear zone in its infancy (<30 m displacement). Geochemical mass balance calculation and microstructural data show that a massive calcite departure (up to 50 vol %) from the fault rocks facilitated the concentration and reorganization of weak phyllosilicate minerals along the shear surfaces. Friction experiments carried out on intact foliated samples of host marls and fault rocks demonstrated that this structural reorganization lead to a significant fault weakening and that the incipient structure has strength and slip behavior comparable to that of the major weak faults previously documented. These results indicate that some faults, especially those nucleating in lithologies rich of both clays and high‐solubility minerals (such as calcite), might experience rapid mineralogical and structural alteration and become weak even in the early stages of their activity.
Proceedings of the Annual Meeting of the Cognitive Science Society

Compositionality has been argued to be both a desirable and perhaps even necessary component of interpreting language, yet there appear to be many linguistic phenomena that do not overtly exhibit semantic compositional behavior. One of the most interesting challenges involves the phenomena of contex- tual modulations referred to collectively as semantic coercion or logical metonymy. Some models of how we understand, for example, Alex enjoyed her coffee and Jen heard the train incor-porate mechanisms that provide “compositional flexibility”, to allow event-selecting and sound-selecting verbs, respectively, to combine with arguments that denote neither. In this paper, we present a computational model that provides for such flex-ibility in the interpretation of a verb with its arguments, for such coercive contexts in English. Specifically, we argue that such constructions typically have surface structural correlates in the form of dense paraphrases , and that these forms can be used to model the masked content in the coerced compositional context. We present preliminary results using a transformer architecture (BERT) on a masked completion task. This sug- gests that constructions involving “enriched composition” can in fact be computationally analyzed with attention-based archi- tectures. Our results show that modeling logical metonymy is a challenging task but can be substantially improved by fine- tuning through dense paraphrasing.
of the Annual Meeting of the Cognitive Science Society Title Contrast-Class Cues and Dual Goal Facilitation in Wason ' s 2-4-6 Task : Evidence for an Extended Iterative Counterfactual Model Permalink

Successful performance on Wason’s (1960) 2-4-6 task is typically poor at approximately 20%. One reliable way to enhance solution rates is to use Tweney, Doherty, Worner, Pliske, Mynatt, Gross, and Arkkelin’s (1980) logicallyidentical Dual Goal (DG) version of the task, where participants are requested to discover two complementary rules, one labeled ‘Dax’ (the standard ‘ascending numbers’ rule) and the other labeled ‘Med’ (i.e., any other number triple). Despite the robustness of the DG effect, the mechanism by which the DG paradigm facilitates performance has remained obscure. The present studies assessed various theoretical proposals by providing participants examples of Med triples of varying ‘usefulness’, as indexed by the cues that they provided for establishing a relevant contrast class to the Dax rule. Results showed that the usefulness of the Med exemplar had a significant effect on successful discovery of the Dax rule. We propose that the present DG results can best be accommodated by extending Oaksford and Chater’s (1994) Iterative Counterfactual Model beyond its current focus on the standard single goal 2-4-6
SNEAK-PEEK: HIGH SPEED COVERT CHANNELS IN DATA CENTER NETWORKS BY RASHID TAHIR

With the advent of big data, modern businesses face an increasing need to store and process large volumes of sensitive customer information on the cloud. In these environments, resources are shared across a multitude of mutually untrusting tenants increasing propensity for data leakage. With the recent spate of high-profile data exfiltration attacks and the emergence of critical vulnerabilities such as Heartbleed and Shellshock, coupled with increasing use of clouds in all aspects of our daily lives, this problem stands to grow further in severity. In this thesis, we present a novel network-based covert channel that can arise in the context of shared network resources in data-center environments even in the presence of network monitors regulating flow destinations with NAC policies and VLAN-based isolation mechanisms. Through a series of experiments on diverse network hardware (including SDNs) and commercial clouds such as EC2 and Azure, we demonstrate that our network-based channel achieves orders of magnitude greater bit rates than reported in any recent literature. Furthermore, we present an information-theoretic framework to model and study the channel. Using this model we derive an upper bound on the information rate of the channel and propose a coding scheme that nearly achieves this upper bound. Additionally we introduce some techniques to make the covert channel robust to noise, and empirically study its performance in the presence of realistic crosstraffic. Finally, we discuss several avenues for mitigation, and demonstrate the effectiveness of our schemes both empirically and mathematically.
Predevelopment water-level map of the Santa Fe Group aquifer system in the middle Rio Grande basin between Cochiti Lake and San Acacia, New Mexico

Base compiled from U.S. Geological Survey digital data, 1:100,000, 1977, 1978; and City of Albuquerque digital data, 1:2,400, 1994. Faults modified from Mark Hudson and Scott Minor, U.S. Geological Survey, written commun. , 1999 U.S. DEPARTMENT OF THE INTERIOR U.S. GEOLOGICAL SURVEY Prepared in cooperation with the CITY OF ALBUQUERQUE WATER-RESOURCES INVESTIGATIONS REPORT 00-4249 Predevelopment Water-Level Map of the Santa Fe Group Aquifer System in the Middle Rio Grande Basin between Cochiti Lake and San Acacia, New Mexico
Aldehyde Dehydrogenase Genes as Prospective Actionable Targets in Acute Myeloid Leukemia

It has been previously shown that the aldehyde dehydrogenase (ALDH) family member ALDH1A1 has a significant association with acute myeloid leukemia (AML) patient risk group classification and that AML cells lacking ALDH1A1 expression can be readily killed via chemotherapy. In the past, however, a redundancy between the activities of subgroup members of the ALDH family has hampered the search for conclusive evidence to address the role of specific ALDH genes. Here, we describe the bioinformatics evaluation of all nineteen member genes of the ALDH family as prospective actionable targets for the development of methods aimed to improve AML treatment. We implicate ALDH1A1 in the development of recurrent AML, and we show that from the nineteen members of the ALDH family, ALDH1A1 and ALDH2 have the strongest association with AML patient risk group classification. Furthermore, we discover that the sum of the expression values for RNA from the genes, ALDH1A1 and ALDH2, has a stronger association with AML patient risk group classification and survival than either one gene alone does. In conclusion, we identify ALDH1A1 and ALDH2 as prospective actionable targets for the treatment of AML in high-risk patients. Substances that inhibit both enzymatic activities constitute potentially effective pharmaceutics.
A Dynamic Event-Triggered Transmission Strategy for Distributed Convex Optimization

A dynamic event-triggered transmission strategy is considered for the continuous-time distributed convex optimization algorithm over a strongly connected and weight-balanced directed graph. An auxiliary variable is introduced in the dynamic event-triggered control (DETC) that is free of Zeno phenomenon. We model the DETC system into a hybrid system. Using Lyapunov stability theory, we give a detailed proof for convergence of the continuous-time distributed convex optimization algorithm. Finally, a numerical simulation shows that the DETC can effectively save network communication resources compared with a static event-triggered control (SETC).
A Closed Loop Stimuli-Responsive Concanavalin A-Loaded Chitosan–Pluronic Hydrogel for Glucose-Responsive Delivery of Short-Acting Insulin Prototyped in RIN-5F Pancreatic Cells

The optimal treatment of diabetes (in particular, type 1 diabetes—T1D) remains a challenge. Closed-loop systems (implants/inserts) provide significant advantages for glucose responsivity and providing real-time sustained release of rapid-acting insulin. Concanavalin A (ConA), a glucose affinity agent, has been used to design closed-loop insulin delivery systems but not without significant risk of leakage of ConA from the matrices and poor mechanical strength of the hydrogels impacting longevity and control of insulin release. Therefore, this work focused on employing a thermoresponsive co-forming matrix between Pluronic F-127 (PL) and structurally robust chitosan (CHT) via EDC/NHS coupling (i.e., covalent linkage of -NH2 from CHT and ConA to the -COOH of PL). The system was characterized for its chemical structure stability and integrity (FTIR, XRD and TGA), injectability, rheological parameters and hydrogel morphology (Texture Analysis, Elastosens TM Bio2 and SEM). The prepared hydrogels demonstrated shear-thinning for injectability with a maximum force of 4.9 ± 8.3 N in a 26G needle with sol–gel transitioning from 25 to 38 °C. The apparent yield stress value of the hydrogel was determined to be 67.47 Pa. The insulin loading efficiency within the hydrogel matrix was calculated to be 46.8%. Insulin release studies revealed glucose responsiveness in simulated glycemic media (4 and 10 mg/mL) over 7 days (97%) (305 nm via fluorescence spectrophotometry). The MTT studies were performed over 72 h on RIN-5F pancreatic cells with viability results >80%. Results revealed that the thermoresponsive hydrogel is a promising alternative to current closed-loop insulin delivery systems.
Serum Pentraxin 3 as Promising Biomarker for the Long-Lasting Inflammatory Response of COVID-19

Currently, biological markers for COVID-19 disease severity still constitute the main goal of enhancing an efficient treatment to reduce critical consequences such as an abnormal systemic inflammatory response. In this regard, the latest research has shown that Pentraxin 3 (PTX3), a highly conserved innate immunity protein, may serve as a valuable biochemical marker. Based on this evidence, we conducted a case–control study to compare the PTX3 serum levels and several immune-inflammatory mediators of 80 healthcare workers who were subdivided into subjects who were previously infected with SARS-CoV-2 (n = 40) and individuals who were never infected (n = 40). Using a commercially available Enzyme-Linked Immunosorbent Assay (ELISA), PTX3 and various immune-inflammatory protein levels were assessed in serum samples, while also considering possible variables (e.g., gender-related differences). We have shown elevated levels of PTX3 and other inflammatory proteins in previously infected COVID-19-positive subjects (p < 0.001). Moreover, the obtained data also indicate a degree of severity influenced by gender, as shown by the subgroup analysis, in which PTX3 expression was more pronounced in previously COVID-19-positive males (p < 0.001) than in females (p < 0.05) compared to the respective controls. In addition, our data further validate, through a direct comparison of previously COVID-19-positive subjects, greater pro-inflammatory levels in males than in females. Overall, our results may support the validity of PTX3 as a systemic biomarker in prolonged systemic inflammatory responses in the context of COVID-19. Thus, PTX3 modulation could constitute an effective therapeutic strategy for improving the recovery from COVID-19 and its systemic long-term consequences.