Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the CRSwNP Patient as Whole

Background Chronic rhinosinusitis with nasal polyps (CRSwNP) is a distinct inflammatory disease of the upper airways with a significant impact on the health and quality of life of affected patients. Several comorbid conditions such as allergic rhinitis, asthma, sleep disorders, and gastroesophageal reflux disease are commonly reported in patients with CRSwNP. Objective In this article, we intended to review the UpToDate information on how these comorbidities can impact CRSwNP patients’ health and well-being. Methods A PUBMED search was performed to review relevant recent article on the topic. Results While there have been significant advances in the knowledge and management options for CRSwNP in the past few years, additional studies are needed to understand the underlying pathophysiologic mechanisms of these associations. In addition, awareness of the impact of CRSwNP on mental health, quality of life, and cognition is paramount to treating this condition. Conclusion Recognition and addressing CRSwNP comorbidities such as allergic rhinitis, asthma, sleep disorders, gastroesophageal reflux disease, and cognitive function impairment are important to optimally understand and manage the patient with CRSwNP as a whole.
Thermal decay in high density disk media

An experimental technique has been developed to accurately measure the time dependence of playback voltage due to the decay of recorded magnetization patterns. A "calibration" procedure is utilized to minimize the effect of magnetoresistive head sensitivity variations. This technique is applied to a systematic study of the dependence of the signal decay on recording bit density and magnetic layer thickness in longitudinal thin film media. A Neel-Arrhenius type model is introduced to calculate the degradation of a square wave magnetization pattern subject to thermal agitation. Because the equivalent external field which reduces the energy barrier is the time and spatially varying magnetostatic field produced by magnetic transitions, an iterative procedure is utilized to obtain the time dependent magnetization. The model takes into account distributions of grain volume and anisotropy axis. The calculation results are compared with the experimental data and good agreement is obtained.
A systematic review and meta-analysis of self-guided online acceptance and commitment therapy as a transdiagnostic self-help intervention

ABSTRACT Online Acceptance and Commitment Therapy (ACT) interventions use websites and smartphone apps to deliver ACT exercises and skills. The present meta-analysis provides a comprehensive review of online ACT self-help interventions, characterizing the programs that have been studied (e.g. platform, length, content) and analyzing their efficacy. A transdiagnostic approach was taken, including studies that addressed a range of targeted problems and populations. Multi-level meta-analyses were used to nest multiple measures of a single construct within their respective studies. A total of 53 randomized controlled trials were included (n = 10,730). Online ACT produced significantly greater outcomes than waitlist controls at post-treatment for anxiety, depression, quality of life, psychological flexibility, and all assessed outcomes (i.e. omnibus effect), which were generally maintained at follow-up. However, only psychological flexibility and all assessed outcomes at post-treatment were found to be significantly greater for online ACT when compared to active controls, with no significant follow-up effects. Overall, these results further clarify that ACT can be effectively delivered in an online format to target a wide range of mental health concerns, although it is less clear if and when online ACT is more efficacious than other online interventions.
The effects of beetroot and nitrate supplementation on body composition: a GRADE-assessed systematic review and meta-analysis

Abstract This systematic review and meta-analysis aimed to investigate the effects of beetroot (BR) or nitrate supplements on body composition indices. A systematic search was conducted for randomised controlled trials (RCT) published up to August 2022 among online databases including Scopus, PubMed/Medline, Web of Science and Embase. Meta-analyses were carried out using a random-effects model. The I2 index was used to assess the heterogeneity of RCT. A total of twelve RCT met the inclusion criteria for this meta-analysis. The pooled effect size of included studies indicated that BR or nitrate supplementation did not change body weight (weighted mean differences (WMD): –0·14 kg, 95 % CI –1·22, 1·51; P = 0·836; I2 = 0 %), BMI (WMD: −0·07 kg/m2, 95 % CI −0·19,0·03; P = 0·174, I2 = 0 %), fat mass (WMD: –0·26 kg, 95 % CI –1·51, 0·98; P = 0·677, I2 = 0 %), waist circumference (WMD: –0·28 cm, 95 % CI –2·30, 1·74; P = 0·786, I2 = 0 %), body fat percentage (WMD: 0·18 %, 95 % CI –0·62, 0·99; P = 0·651, I2 = 0 %), fat-free mass (WMD: 0·31 kg, 95 % CI –0·31, 1·94; P = 0·703, I2 = 0 %) and waist-to-hip ratio (WMD: 0, 95 % CI –0·01, 0·02; P = 0·676, I2 = 0 %). Subgroup analyses based on trial duration, BR or nitrate dose, study design, baseline BMI and athletic status (athlete v. non-athlete) demonstrated similar results. Certainty of evidence across outcomes ranged from low to moderate. This meta-analysis study suggests that BR or nitrate supplements cannot efficiently ameliorate body composition indices regardless of supplement dosage, trial duration and athletic status.
Rapid CRISPR-Cas13a genetic identification enables new opportunities for listed Chinook salmon management.

Accurate taxonomic identification is foundational for effective species monitoring and management. When visual identifications are infeasible or inaccurate, genetic approaches provide a reliable alternative. However, these approaches are sometimes less viable (e.g., need for near real-time results, remote locations, funding concerns, molecular inexperience). In these situations, CRISPR-based genetic tools can fill an unoccupied niche between real-time, inexpensive, but error-prone visual identification and more expensive or time-consuming, but accurate genetic identification for taxonomic units that are difficult or impossible to visually identify. Herein, we use genomic data to develop CRISPR-based SHERLOCK assays capable of rapidly (<1 hour), accurately (94 - 98% concordance between phenotypic and genotypic assignments), and sensitively (detects 1 - 10 DNA copies/reaction) distinguishing ESA-listed Chinook salmon runs (winter- and spring-run) from each other and from unlisted runs (fall- and late fall-run) in California's Central Valley. The assays can be field deployable with minimally invasive mucus swabbing negating the need for DNA extraction (decreasing costs and labor), minimal and inexpensive equipment needs, and minimal training to conduct following assay development. This study provides a powerful genetic approach for a species of conservation concern that benefits from near real-time management decision-making but also serves as a precedent for transforming how conservation scientists and managers view genetic identification going forward. Once developed, CRISPR-based tools can provide accurate, sensitive, and rapid results, potentially without the prohibitive need for expensive specialty equipment or extensive molecular training. Further adoption of this technology will have widespread value for the monitoring and protection of our natural resources.
Patients’ preferences in dental care: A discrete-choice experiment and an analysis of willingness-to-pay

Introduction Dental diseases are a major problem worldwide. Costs are a burden on healthcare systems and patients. Missed treatments can have health and financial consequences. Compared to other health services, dental treatments are only covered in parts by statutory health insurance (SHI). Using the example of dental crowns for a cost-intensive treatment, our study aims to investigate whether (1) certain treatment attributes determine patients’ treatment choice, and (2) out-of-pocket payments represent a barrier to access dental care. Methods We conducted a discrete-choice-experiment by mailing questionnaires to 10,752 people in Germany. In presented scenarios the participants could choose between treatment options (A, B, or none) composed of treatment attribute levels (e.g., color of teeth) for posterior (PT) and anterior teeth (AT). Considering interaction effects, we used a D-efficient fractional factorial design. Choice analysis was performed using different models. Furthermore, we analyzed willingness-to-pay (WTP), preference of choosing no and SHI standard care treatment, and influence of socioeconomic characteristics on individual WTP. Results Out of n = 762 returned questionnaires (response rate of r = 7.1), n = 380 were included in the analysis. Most of the participants are in age group "50 to 59 years" (n = 103, 27.1%) and female (n = 249, 65.5%). The participants’ benefit allocations varied across treatment attributes. Aesthetics and durability of dental crowns play most important roles in decision-making. WTP regarding natural color teeth is higher than standard SHI out-of-pocket payment. Estimations for AT dominate. For both tooth areas, "no treatment" was a frequent choice (PT: 25.7%, AT: 37.2%). Especially for AT, treatment beyond SHI standard care was often chosen (49.8%, PT: 31.3%). Age, gender, and incentive measures (bonus booklet) influenced WTP per participant. Conclusion This study provides important insights into patient preferences for dental crown treatment in Germany. For our participants, aesthetic for AT and PT as well as out-of-pocket payments for PT play an important role in decision-making. Overall, they are willing to pay more than the current out-of-pockt payments for what they consider to be better crown treatments. Findings may be valuable for policy makers in developing measures that better match patient preferences.
Influenza A Virus-Induced circRNA circMerTK Negatively Regulates Innate Antiviral Responses

CircRNAs are an important class of noncoding RNAs characterized by a covalently closed circular structure. CircRNAs have been proven to impact numerous cellular processes, where they conduct specialized biological activities. ABSTRACT Circular RNAs (circRNAs) are an important subclass of noncoding RNAs implicated in the regulation of multiple biological processes. However, the functional involvement of circRNAs in the pathogenesis of influenza A viruses (IAVs) remains largely unknown. Here, we employed RNA sequencing (RNA-Seq) to examine the differentially expressed circRNAs in mouse lung tissues challenged or not challenged with IAV to evaluate the impact of viral infection on circRNAs in vivo. We observed that 413 circRNAs exhibited significantly altered levels following IAV infection. Among these, circMerTK, the derivative of myeloid-epithelial-reproductive tyrosine kinase (MerTK) pre-mRNA, was highly induced by IAV. Interestingly, circMerTK expression was also increased upon infection with multiple DNA and RNA viruses in human and animal cell lines, and thus it was selected for further studies. Poly(I:C) and interferon β (IFN-β) stimulated circMerTK expression, while RIG-I knockout and IFNAR1 knockout cell lines failed to elevate circMerTK levels after IAV infection, demonstrating that circMerTK is regulated by IFN signaling. Furthermore, circMerTK overexpression or silencing accelerated or impeded IAV and Sendai virus replication, respectively. Silencing circMerTK enhanced the production of type I IFNs and interferon-stimulating genes (ISGs), whereas circMerTK overexpression suppressed their expression at both the mRNA and protein levels. Notably, altering circMerTK expression had no effect on the MerTK mRNA level in cells infected or not infected with IAV, and vice versa. In addition, human circMerTK and mouse homologs functioned similarly in antiviral responses. Together, these results identify circMerTK as an enhancer of IAV replication through suppression of antiviral immunity. IMPORTANCE CircRNAs are an important class of noncoding RNAs characterized by a covalently closed circular structure. CircRNAs have been proven to impact numerous cellular processes, where they conduct specialized biological activities. In addition, circRNAs are believed to play a crucial role in regulating immune responses. Nevertheless, the functions of circRNAs in the innate immunity against IAV infection remain obscure. In this study, we employed transcriptomic analysis to investigate the alterations in circRNAs expression following IAV infection in vivo. It was found that expression of 413 circRNAs was significantly altered, of which 171 were upregulated, and 242 were downregulated following the IAV infection. Interestingly, circMerTK was identified as a positive regulator of IAV replication in both human and mouse hosts. CircMerTK was shown to influence IFN-β production and its downstream signaling, enhancing IAV replication. This finding provides new insights into the critical roles of circRNAs in regulating antiviral immunity.
Pressure‐based adaption indicator for compressible euler equations

We consider the Euler equations of gas dynamics and develop a new adaption indicator, which is based on the weak local residual measured for the nonconservative pressure variable. We demonstrate that the proposed indicator is capable of automatically detecting discontinuities and distinguishing between the shock and contact waves when they are isolated from each other. We then use the developed indicator to design a scheme adaption algorithm, according to which nonlinear limiters are used only in the vicinity of shocks. The new adaption algorithm is realized using a second‐order limited and a high‐order nonlimited central‐upwind scheme. We demonstrate robustness and high resolution of the designed method on a number of one‐ and two‐dimensional numerical examples. © 2015 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 31: 1844–1874, 2015
On-Device Voice Authentication with Paralinguistic Privacy

Using our voices to access, and interact with, online services raises concerns about the trade-offs between convenience, privacy, and security. The conflict between maintaining privacy and ensuring input authenticity has often been hindered by the need to share raw data, which contains all the paralinguistic information required to infer a variety of sensitive characteristics. Users of voice assistants put their trust in service providers; however, this trust is potentially misplaced considering the emergence of first-party 'honest-but-curious' or 'semi-honest' threats. A further security risk is presented by imposters gaining access to systems by pretending to be the user leveraging replay or 'deepfake' attacks. Our objective is to design and develop a new voice input-based system that offers the following specifications: local authentication to reduce the need for sharing raw voice data, local privacy preservation based on user preferences, allowing more flexibility in integrating such a system given target applications privacy constraints, and achieving good performance in these targeted applications. The key idea is to locally derive token-based credentials based on unique-identifying attributes obtained from the user's voice and offer selective sensitive information filtering before transmitting raw data. Our system consists of (i) 'VoiceID', boosted with a liveness detection technology to thwart replay attacks; (ii) a flexible privacy filter that allows users to select the level of privacy protection they prefer for their data. The system yields 98.68% accuracy in verifying legitimate users with cross-validation and runs in tens of milliseconds on a CPU and single-core ARM processor without specialized hardware. Our system demonstrates the feasibility of filtering raw voice input closer to users, in accordance with their privacy preferences, while maintaining their authenticity.
Fair Short Paths in Vertex-Colored Graphs

The computation of short paths in graphs with arc lengths is a pillar of graph algorithmics and network science. In a more diverse world, however, not every short path is equally valuable. For the setting where each vertex is assigned to a group (color), we provide a framework to model multiple natural fairness aspects. We seek to find short paths in which the number of occurrences of each color is within some given lower and upper bounds. Among other results, we prove the introduced problems to be computationally intractable (NP-hard and parameterized hard with respect to the number of colors) even in very restricted settings (such as each color should appear with exactly the same frequency), while also presenting an encouraging algorithmic result ("fixed-parameter tractability") related to the length of the sought solution path for the general problem.
Let's have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations

The advent of AI-empowered chatbots capable of constructing human-like sentences and articulating cohesive essays has captivated global interest. This paper provides a historical perspective on chatbots, focusing on the technology underpinning the Chat Generative Pre-trained Transformer, better known as ChatGPT. We underscore the potential utility of ChatGPT across a multitude of fields, including healthcare, education, and research. To the best of our knowledge, this is the first review that not only highlights the applications of ChatGPT in multiple domains, but also analyzes its performance on examinations across various disciplines. Despite its promising capabilities, ChatGPT raises numerous ethical and privacy concerns that are meticulously explored in this paper. Acknowledging the current limitations of ChatGPT is crucial in understanding its potential for growth. We also ask ChatGPT to provide its point of view and present its responses to several questions we attempt to answer.
Efficient Hardware Primitives for Immediate Memory Reclamation in Optimistic Data Structures

Safe memory reclamation (SMR) algorithms are crucial for preventing use-after-free errors in optimistic data structures. SMR algorithms typically delay reclamation for safety and reclaim objects in batches for efficiency. It is difficult to strike a balance between performance and space efficiency. Small batch sizes and frequent reclamation attempts lead to high overhead, while freeing large batches can lead to long program interruptions and high memory footprints. An ideal SMR algorithm would forgo batching, and reclaim memory immediately, without suffering high reclamation overheads.To this end, we propose Conditional Access: a set of hardware instructions that offer immediate reclamation and low overhead in optimistic data structures. Conditional Access harnesses cache coherence to enable threads to efficiently detect potential use-after-free errors without explicit shared memory communication, and without introducing additional coherence traffic.We implement and evaluate Conditional Access in Graphite, a multicore simulator. Our experiments show that Conditional Access can rival the performance of highly optimized and carefully tuned SMR algorithms while simultaneously allowing immediate reclamation. This results in concurrent data structures with similar memory footprints to their sequential counterparts.
Transport properties of polycrystalline silicon films

The transport properties of polycrystalline silicon films are examined and interpreted in terms of a modified grain‐boundary trapping model. The theory has been developed on the assumption of both a δ‐shaped and a uniform energy distribution of interface states. A comparison with experiments indicates that the interface states are nearly monovalent and peaked at midgap. Their density is 3.8×1012 cm−2, in accordance with carrier‐lifetime measurements performed on CVD films.
Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients

Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions. Among unsupervised methods, patch-based auto-encoders with their efficient representation power provided by their latent space, have shown good results for visible lesion detection. However, the commonly used reconstruction error criterion may limit their performance when facing less obvious lesions. In this work, we design two alternative detection criteria. They are derived from multivariate analysis and can more directly capture information from latent space representations. Their performance compares favorably with two additional supervised learning methods, on a difficult de novo Parkinson Disease (PD) classification task.
Real-time Reconstruction of Markov Sources and Remote Actuation over Wireless Channels

In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely an $N$-state Markov chain and an $N$-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance with respect to a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose a \emph{wait-then-generate} sampling policy which is simple to implement.