Miracle-dz/UniFAR-FTUs huggingface dataset:
Where to Look: Exploring Peripheral Cues for Shifting Attention to Spatially Distributed Out-of-View Objects

Knowing the locations of spatially distributed objects is important in many different scenarios (e.g., driving a car and being aware of other road users). In particular, it is critical for preventing accidents with objects that come too close (e.g., cyclists or pedestrians). In this paper, we explore how peripheral cues can shift a user's attention towards spatially distributed out-of-view objects. We identify a suitable technique for visualization of these out-of-view objects and explore different cue designs to advance this technique to shift the user's attention. In a controlled lab study, we investigate non-animated peripheral cues with audio stimuli and animated peripheral cues without audio stimuli. Further, we looked into how user's identify out-of-view objects. Our results show that shifting the user's attention only takes about 0.86 seconds on average when animated stimuli are used, while shifting the attention with non-animated stimuli takes an average of 1.10 seconds.
Exploring Social Desirability Bias in Perceptions of Dog Adoption: All’s Well that Ends Well? Or Does the Method of Adoption Matter?

Simple Summary Dog adoption is often cited as an ethical and popular method of acquisition but interpretation of the term ‘adoption’ may vary. In a nationally representative survey of the U.S., 767 respondents were asked questions regarding their opinions of dog acquisition including adoption. Within the sample, 45% had a dog; of those, 40% had adopted a dog and 47% visited a veterinarian once a year. Respondents’ preferences for the most ethical method of dog adoption were elicited using a survey instrument. Our results indicate that respondents had the largest preference share for adoption from a municipal animal shelter’ (56%) and the smallest preference share for adoption from a pet store (3%). Dog acquisition was further evaluated by creating an index of social desirability bias comparing how important respondents believed certain dog characteristics were and how important respondents believed others would rate/rank the same dog characteristics. The highest incidences of social desirability bias occurred for the dog characteristics of appearance and breed. Abstract Dogs are a popular companion animal in the United States; however, dog acquisition is often a contentious subject. Adoption is often cited as an ethical and popular method of acquisition but interpretation of the term ‘adoption’ may vary. In a nationally representative survey of the U.S., 767 respondents were asked questions regarding their opinions of dog acquisition and adoption. Within the sample, 45% had a dog; of those, 40% had adopted a dog, and 47% visited a veterinarian once a year. A best-worst choice experiment, where respondents were asked to choose the most ethical and least ethical method of acquiring a dog from a statistically determined set of choices, was used to elicit respondents’ preferences for the most ethical method of dog adoption. A random parameters logit and a latent class model were used to estimate relative rankings of dog adoption methods. In the random parameters logit model, the largest preference share was for adoption from a municipal animal shelter (56%) and the smallest preference share was for adoption from a pet store (3%). Dog acquisition was further evaluated by creating an index of social desirability bias using how important respondents believed certain dog characteristics were compared to how important respondents believed others would rate/rank the same dog characteristics. The highest incidences of social desirability bias occurred for the dog characteristics of appearance and breed.
Proteins that control the geometry of microtubules at the ends of cilia

Cilia, essential motile and sensory organelles, have several compartments: the basal body, transition zone, and the middle and distal axoneme segments. The distal segment accommodates key functions, including cilium assembly and sensory activities. While the middle segment contains doublet microtubules (incomplete B-tubules fused to complete A-tubules), the distal segment contains only A-tubule extensions, and its existence requires coordination of microtubule length at the nanometer scale. We show that three conserved proteins, two of which are mutated in the ciliopathy Joubert syndrome, determine the geometry of the distal segment, by controlling the positions of specific microtubule ends. FAP256/CEP104 promotes A-tubule elongation. CHE-12/Crescerin and ARMC9 act as positive and negative regulators of B-tubule length, respectively. We show that defects in the distal segment dimensions are associated with motile and sensory deficiencies of cilia. Our observations suggest that abnormalities in distal segment organization cause a subset of Joubert syndrome cases.
Let Me Finish before I Take Over: Towards Attention Aware Device Integration in Highly Automated Vehicles

A major promise of automated vehicles is to render it possible for drivers to engage in nondriving related tasks, a setting where the execution pattern will switch from concurrent to sequential multitasking. To allow drivers to safely and efficiently switch between multiple activities (including vehicle control in case of Take-Over situations), we postulate that future vehicles should incorporate capabilities of attentive user interfaces, that precisely plan the timing of interruptions based on driver availability. We propose an attention aware system that issues Take-Over Requests (1) at emerging task boundaries and (2) directly on consumer devices such as smartphones or tablets. Results of a driving simulator study (N=18), where we evaluated objective, physiological, and subjective measurements, confirm our assumption: attention aware Take-Over Requests have the potential to reduce stress, increase Take-Over performance, and can further raise user acceptance/trust. Consequently, we emphasize to implement attentive user interfaces in future vehicles.
Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.
Identification of circular RNAs hsa_circ_0044235 in peripheral blood as novel biomarkers for rheumatoid arthritis

Circular RNAs (circRNAs) are a new class of RNAs that can be used as biomarkers in clinical blood samples. However, little is known about circRNAs' diagnostic values for rheumatoid arthritis (RA). In this study, the hsa_circ_0054189, hsa_circ_0008675, hsa_circ_0082689, hsa_circ_0082688, hsa_circ_0010932, hsa_circ_0002473 and hsa_circ_0044235 in peripheral blood were determined by quantitative reverse transcription–polymerase chain reaction (qRT–PCR). For hsa_circ_0044235, only one abnormal expression circRNAs in peripheral blood was selected as a targeted circRNA to explore the diagnostic value for RA. Our work demonstrated that the hsa_circ_0044235 in peripheral blood was decreased significantly in RA patients. The hsa_circ_0044235 in peripheral blood from RA patients did not correlate with C‐reactive protein (CRP), erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), anti‐citrullinated protein antibodies (ACPA) or disease activity score 28 (DAS28). Receiver operating characteristic (ROC) curve analysis suggested that the hsa_circ_0044235 in peripheral blood has significant value in the diagnosis of RA. The risk score based on hsa_circ_0044235 in peripheral blood also distinguished significantly the patients with RA from systemic lupus erythematosus (SLE). This study suggests that the hsa_circ_0044235 in peripheral blood may be a potential biomarker of patients with RA.
Phosphatase activity of small C-terminal domain phosphatase 1 (SCP1) controls the stability of the key neuronal regulator RE1-silencing transcription factor (REST)

The RE1-silencing transcription factor (REST) is the major scaffold protein for assembly of neuronal gene silencing complexes that suppress gene transcription through regulating the surrounding chromatin structure. REST represses neuronal gene expression in stem cells and non-neuronal cells, but it is minimally expressed in neuronal cells to ensure proper neuronal development. Dysregulation of REST function has been implicated in several cancers and neurological diseases. Modulating REST gene silencing is challenging because cellular and developmental differences can affect its activity. We therefore considered the possibility of modulating REST activity through its regulatory proteins. The human small C-terminal domain phosphatase 1 (SCP1) regulates the phosphorylation state of REST at sites that function as REST degradation checkpoints. Using kinetic analysis and direct visualization with X-ray crystallography, we show that SCP1 dephosphorylates two degron phosphosites of REST with a clear preference for phosphoserine 861 (pSer-861). Furthermore, we show that SCP1 stabilizes REST protein levels, which sustains REST's gene silencing function in HEK293 cells. In summary, our findings strongly suggest that REST is a bona fide substrate for SCP1 in vivo and that SCP1 phosphatase activity protects REST against degradation. These observations indicate that targeting REST via its regulatory protein SCP1 can modulate its activity and alter signaling in this essential developmental pathway.
Clinical Inquiries. How often does long-term PPI therapy cause clinically significant hypomagnesemia?

A Rarely. Proton pump inhibitors (PPIs) may be associated with decreases in serum magnesium laboratory values to below 1.6 to 1.8 mg/dL, especially when used concurrently with diuretics and loop diuretics (strength of recommendation [SOR]: C, disease-oriented outcomes based on cohort, case-control, and crosssectional studies). Clinically significant or symptomatic hypomagnesemia (below 1.2 mg/dL) appears to be quite rare, however.
Chromogenic Multiplex Immunohistochemistry Reveals Modulation of the Immune Microenvironment Associated with Survival in Elderly Patients with Lung Adenocarcinoma

With underrepresentation of elderly patients with lung adenocarcinoma (LADC) in anti-PD-1/PD-L1 clinical trials, better understanding of the interplay of PD-L1 and tumor-associated immune cells (TAICs) could assist clinicians in stratifying these patients for immunotherapy. One hundred and one patients with LADCs, stratified by age, were included for analysis of PD-L1 expression and density of TAICs expressing CD4, CD8, and CD33, by using multiplex chromogenic immunohistochemistry (IHC) assays and automated digital quantification. The CD4+/CD8+ ratio was significantly higher in elderly patients. In patients <75 years, the density of CD4+, CD8+, and PD-L1 in TAICs showed a positive significant correlation with PD-L1 expression in tumor cells (TCs), while a lower correlation was observed in the elderly population. In the latter, a high CD4+/CD8+ ratio, and combined PD-L1 expression ≥1% TCs with a low CD8+ density, low CD33+ density, and a high CD4+ density correlated to worse overall survival. We identified differences according to age in the CD4+/CD8+ ratio and in correlation between PD-L1 expression and the density of TAICs in LADC patients. Distinct groups of tumor microenvironments had an impact on the OS of elderly patients with LADC.
A sensitive and specific assay for the serological diagnosis of antilaminin 332 mucous membrane pemphigoid

Antilaminin 332 mucous membrane pemphigoid (MMP) is an autoimmune subepidermal blistering disease with predominant mucosal involvement and autoantibodies against laminin 332. Malignancies have been associated with this disease; however, no standardized detection system for antilaminin 332 serum antibodies is widely available.
Deep Reinforcement Learning with Sarsa and Q-Learning: A Hybrid Approach

The commonly used Deep Q Networks is known to overestimate action values under certain conditions. It’s also proved that overestimations do harm to performance, which might cause instability and divergence of learning. In this paper, we present the Deep Sarsa and Q Networks (DSQN) algorithm, which can considered as an enhancement to the Deep Q Networks algorithm. First, DSQN algorithm takes advantage of the experience replay and target network techniques in Deep Q Networks to improve the stability of neural networks. Second, double estimator is utilized for Qlearning to reduce overestimations. Especially, we introduce Sarsa learning to Deep Q Networks for removing overestimations further. Finally, DSQN algorithm is evaluated on cart-pole balancing, mountain car and lunarlander control task from the OpenAI Gym. The empirical evaluation results show that the proposed method leads to reduced overestimations, more stable learning process and improved performance. key words: deep reinforcement learning, Deep Q Network, overestimation, double estimator, Sarsa
A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction

The automatic extraction of building outlines from aerial imagery for the purposes of navigation and urban planning is a long-standing problem in the field of remote sensing. Currently, most methods utilize variants of fully convolutional networks (FCNs), which have significantly improved model performance for this task. However, pursuing more accurate segmentation results is still critical for additional applications, such as automatic mapping and building change detection. In this study, we propose a boundary regulated network called BR-Net, which utilizes both local and global information, to perform roof segmentation and outline extraction. The BR-Net method consists of a shared backend utilizing a modified U-Net and a multitask framework to generate predictions for segmentation maps and building outlines based on a consistent feature representation from the shared backend. Because of the restriction and regulation of additional boundary information, the proposed model can achieve superior performance compared to existing methods. Experiments on an aerial image dataset covering 32 km2 and containing more than 58,000 buildings indicate that our method performs well at both roof segmentation and outline extraction. The proposed BR-Net method significantly outperforms the classic FCN8s model. Compared to the state-of-the-art U-Net model, our BR-Net achieves 6.2% (0.869 vs. 0.818), 10.6% (0.772 vs. 0.698), and 8.7% (0.840 vs. 0.773) improvements in F1 score, Jaccard index, and kappa coefficient, respectively.
Randomised Trial of HPV Self-sampling Among Non-attenders in the Slovenian Cervical Screening Programme ZORA: Comparing Three Different Screening Approaches

Abstract Background To overcome obstacles within the Slovenian organised cervical cancer screening programme, a randomised pilot study of human papillomavirus (HPV) self-sampling among non-attenders was performed, aiming to assess three different screening approaches. Participants and methods Non-attenders aged 30–64 years from two Slovenian regions were randomised to two HPV self-sampling groups–the opt-in (I1, n = 14.400) and the opt-out (I2, n = 9.556), with a control group (P, n = 2.600). Self-collected samples were analysed using the Hybrid Capture 2 assay. HPV-positive women were invited to a colposcopy. The overall and type-specific intention-to-screen response rates and histological outcomes with a positive predictive value (PPV) according to the women’s age, the screening approach, the level of protection resulting from previous screening history, and the region of residence were assessed. Results Of the 26.556 women enrolled, 8.972 (33.8%) responded with self-sample for HPV testing and/or traditional cytology within one year of enrolment. Response rates were 37.7%, 34.0% and 18.4% (p < 0.050) for opt-out, opt-in and control groups. Cervical intraepithelial neoplasia (CIN)2+ was diagnosed in 3.9/1.000, 3.4/1.000, and 3.1/1.000 women (p > 0.050), respectively. PPV of the HPV self-sampling was 12.0% and 9.6% for CIN2+ and CIN3+. The highest PPV was obtained in non-attenders in screening programme for more than 10-years and concordant results of HPV testing with 40.8% for CIN2+ and 38.8% for CIN3+. Conclusions The results of our study show that a high response to HPV self-sampling can be achieved also in an opt-in approach, if women are encouraged to choose between self-sampling at home and screening with gynaecologist. In addition, clinically important risk difference for a high-grade cervical lesion exists in the case of a positive result of HPV testing on self-collected samples, depending on the length of the interval since last screening. Stratified management of these women should be strongly considered. Women who were not screened with cytology for at least 10 years should be referred to immediate colposcopy for histology verification instead to delayed re-testing.
Pharmacokinetic modeling and simulation of etodolac following single oral administration in dogs

Abstract Etodolac is a nonsteroidal anti-inflammatory drug with selective cyclooxygenase-2 inhibition to treat pain and inflammation associated with osteoarthritis in humans and dogs. The aim of the study was to investigate the pharmacokinetics of etodolac following single oral administration of 200 mg to 10 healthy beagle dogs. The plasma concentrations of etodolac were detected using liquid chromatography-tandem mass spectrometry. Pharmacokinetic analysis was conducted using the noncompartmental method and modeling approaches. Etodolac was rapidly absorbed (Tmax = 0.85 h, Ka = 1.49 h−1) and slowly eliminated (T1/2 = 39.55 h) following oral administration to the dogs. A two-compartment pharmacokinetic model with first-order absorption and elimination rate constants was successfully explained for the pharmacokinetic aspects of etodolac in dogs. From a Monte Carlo simulation (1000 repetitions), the accumulation index and AUCτ at steady state were predicted as 1.60 [90% confidence intervals (CI), 1.24–2.81] and 408.18 ng·hr/mL [90% CI, 271.26–590.58 ng·hr/mL], respectively. This study will help to enact a more accurate optimal dosing regimen of etodolac in dogs with osteoarthritis, and may be useful in developing a novel formulation of etodolac for human in the future.
Non-size increasing graph rewriting for natural language processing

A very large amount of work in Natural Language Processing (NLP) use tree structure as the first class citizen mathematical structures to represent linguistic structures, such as parsed sentences or feature structures. However, some linguistic phenomena do not cope properly with trees; for instance, in the sentence ‘Max decides to leave,’ ‘Max’ is the subject of the both predicates ‘to_decide’ and ‘to_leave’. Tree-based linguistic formalisms generally use some encoding to manage sentences like the previous example. In former papers (Bonfante et al. 2011; Guillaume and Perrier 2012), we discussed the interest to use graphs rather than trees to deal with linguistic structures, and we have shown how Graph Rewriting could be used for their processing, for instance in the transformation of the sentence syntax into its semantics. Our experiments have shown that Graph Rewriting applications to NLP do not require the full computational power of the general Graph Rewriting setting. The most important observation is that all graph vertices in the final structures are in some sense ‘predictable’ from the input data, and so we can consider the framework of Non-size increasing Graph Rewriting. In our previous papers, we have formally described the Graph Rewriting calculus we used and our purpose here is to study the theoretical aspect of termination with respect to this calculus. Given that termination is undecidable in general, we define termination criterions based on weight, we prove the termination of weighted rewriting systems, and we give complexity bounds on derivation lengths for these rewriting systems.
Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.
Variability of Botulinum Toxins: Challenges and Opportunities for the Future

Botulinum neurotoxins (BoNTs) are the most potent known toxins, and are therefore classified as extremely harmful biological weapons. However, BoNTs are therapeutic drugs that are widely used and have an increasing number of applications. BoNTs show a high diversity and are divided into multiple types and subtypes. Better understanding of the activity at the molecular and clinical levels of the natural BoNT variants as well as the development of BoNT-based chimeric molecules opens the door to novel medical applications such as silencing the sensory neurons at targeted areas and dermal restoration. This short review is focused on BoNTs’ variability and the opportunities or challenges posed for future clinical applications.