Miracle-dz/UniFAR-FTUs huggingface dataset:
Modification of Acute Stroke Pathway in Korea After the Coronavirus Disease 2019 Outbreak

Background: Since the global pandemic of coronavirus disease 2019 (COVID-19), the process of emergency medical services has been modified to ensure the safety of healthcare professionals as well as patients, possibly leading to a negative impact on the timely delivery of acute stroke care. This study aimed to assess the impact of the COVID-19 pandemic on the acute stroke care processes and outcomes in tertiary COVID-19-dedicated centers in South Korea. Methods: We included 1,213 patients with acute stroke admitted to three centers in three cities (Seoul, Seongnam, and Daegu) through the stroke critical pathway between September 2019 and May 2020 (before and during the COVID-19 pandemic). In all three centers, we collected baseline characteristics and parameters regarding the stroke critical pathway, including the number of admitted patients diagnosed with acute stroke through the stroke critical pathway, door to brain imaging time, door to intravenous recombinant tissue plasminogen activator time, door to groin puncture time, and door to admission time. We performed an interrupted time series analysis to determine the impact of the COVID-19 outbreak on outcomes and critical pathway parameters. Results: Three centers modified the protocol of the stroke critical pathway during the COVID-19 pandemic. There was an immediate decrease in the number of patients admitted with acute ischemic stroke after the outbreak of COVID-19 in Korea, especially in the center of Daegu, an epicenter of the COVID-19 outbreak. However, the number of patients with stroke soon increased to equal that before the Covid-19 outbreak. In several critical pathway parameters, door to imaging time showed a temporary increase, and door to admission was transiently decreased after the COVID-19 outbreak. However, there was no significant effect on the timely trend. Moreover, there was no significant difference in the baseline characteristics and clinical outcomes between the periods before and during the COVID-19 pandemic. Conclusion: This study demonstrated that the COVID-19 outbreak immediately affected the management process. However, it did not have a significant overall impact on the trends of stroke treatment processes and outcomes. The stroke management process should be modified according to changing situations for optimal acute management.
Mannan-MOG35-55 Reverses Experimental Autoimmune Encephalomyelitis, Inducing a Peripheral Type 2 Myeloid Response, Reducing CNS Inflammation, and Preserving Axons in Spinal Cord Lesions

CNS autoantigens conjugated to oxidized mannan (OM) induce antigen-specific T cell tolerance and protect mice against autoimmune encephalomyelitis (EAE). To investigate whether OM-peptides treat EAE initiated by human MHC class II molecules, we administered OM-conjugated murine myelin oligodendrocyte glycoprotein peptide 35-55 (OM-MOG) to humanized HLA-DR2b transgenic mice (DR2b.Ab°), which are susceptible to MOG-EAE. OM-MOG protected DR2b.Ab° mice against MOG-EAE by both prophylactic and therapeutic applications. OM-MOG reversed clinical symptoms, reduced spinal cord inflammation, demyelination, and neuronal damage in DR2b.Ab° mice, while preserving axons within lesions and inducing the expression of genes associated with myelin (Mbp) and neuron (Snap25) recovery in B6 mice. OM-MOG-induced tolerance was peptide-specific, not affecting PLP178-191-induced EAE or polyclonal T cell proliferation responses. OM-MOG-induced immune tolerance involved rapid induction of PD-L1- and IL-10-producing myeloid cells, increased expression of Chi3l3 (Ym1) in secondary lymphoid organs and characteristics of anergy in MOG-specific CD4+ T cells. The results show that OM-MOG treats MOG-EAE in a peptide-specific manner, across mouse/human MHC class II barriers, through induction of a peripheral type 2 myeloid cell response and T cell anergy, and suggest that OM-peptides might be useful for suppressing antigen-specific CD4+ T cell responses in the context of human autoimmune CNS demyelination.
Irc20 Regulates the Yeast Endogenous 2-μm Plasmid Levels by Controlling Flp1

The endogenous yeast 2-μm plasmid while innocuous to the host, needs to be properly regulated to avoid a toxic increase in copy number. The plasmid copy number control system is under the control of the plasmid encoded recombinase, Flp1. In case of a drop in 2-μm plasmid levels due to rare plasmid mis-segregation events, the Flp1 recombinase together with the cell’s homologous recombination machinery, produce multiple copies of the 2-μm plasmid that are spooled during DNA replication. The 2-μm plasmid copy number is tightly regulated by controlled expression of Flp1 as well as its ubiquitin and SUMO modification. Here, we identify a novel regulator of the 2-μm plasmid, the ATPase, ubiquitin ligase, Irc20. Irc20 was initially identified as a homologous recombination regulator, and here we uncover a new role for Irc20 in maintaining the 2-μm plasmid copy number and segregation through regulating Flp1 protein levels in the cell.
Development of Site-Specific PEGylated Granulocyte Colony Stimulating Factor With Prolonged Biological Activity

Currently, amino-terminal PEGylated human granulocyte colony stimulating factor (huG-CSF) is used to prevent and treat neutropenia. Although huG-CSF has been used as a drug for more than 20 years, it has three significant drawbacks: (i) it relies on PEG aldehyde for PEGylation of the alpha-amino group of the first amino acid, and this leads to non-specific PEGylation of the epsilon amino group of lysine residues within the G-CSF; (ii) longer-acting G-CSF variants are desirable to reduce the risk of chemotherapy-associated neutropenia; and (iii) G-CSF cannot be administered on the day of chemotherapy. In an attempt to overcome the above drawbacks, we engineered cysteine variants of G-CSF to facilitate the maleimide PEG-based site-specific PEGylation that leads to a highly homogenous PEGylated product. Importantly, we have demonstrated that 20 kDa thiol-reactive PEG conjugated by maleimide chemistry to the Cys2 G-CSF variant exhibits leukocyte proliferative activity similar to that of the commercially available G-CSF conjugated with aldehyde PEG in a neutropenia mice model. Moreover, we have demonstrated that PEGylation of the cysteine variant of huG-CSF with higher molecular weight PEGs, such as 30 kDa PEG and 40 kDa PEG, leads to significantly prolonged leukocyte proliferation activity compared to the variant conjugated with 20 kDa PEG. Importantly, even a half-dose of the engineered variant conjugated with 40 kDa PEG exhibited significantly longer biological activity than the commercially available 20 kDa PEGylated huG-CSF. Finally, we have demonstrated that administration of the engineered variant conjugated with 40 kDa PEG on the day of administration of cyclophosphamide for inducing neutropenia in mice can alleviate neutropenia through leukocyte proliferation. In summary, this study provides the design of site-specific PEGylated huG-CSF variants with improved therapeutic potential. It opens the possibility of long-acting and same-day prophylactic administration of G-CSF after chemotherapy drug regimens. These results may pave the way for the development of potential G-CSF derivatives possessing longer half-lives and favorable clinical attributes.
A Critical Review of Ultra-Short-Term Heart Rate Variability Norms Research

Heart rate variability (HRV) is the fluctuation in time between successive heartbeats and is defined by interbeat intervals. Researchers have shown that short-term (∼5-min) and long-term (≥24-h) HRV measurements are associated with adaptability, health, mobilization, and use of limited regulatory resources, and performance. Long-term HRV recordings predict health outcomes heart attack, stroke, and all-cause mortality. Despite the prognostic value of long-term HRV assessment, it has not been broadly integrated into mainstream medical care or personal health monitoring. Although short-term HRV measurement does not require ambulatory monitoring and the cost of long-term assessment, it is underutilized in medical care. Among the diverse reasons for the slow adoption of short-term HRV measurement is its prohibitive time cost (∼5 min). Researchers have addressed this issue by investigating the criterion validity of ultra-short-term (UST) HRV measurements of less than 5-min duration compared with short-term recordings. The criterion validity of a method indicates that a novel measurement procedure produces comparable results to a currently validated measurement tool. We evaluated 28 studies that reported UST HRV features with a minimum of 20 participants; of these 17 did not investigate criterion validity and 8 primarily used correlational and/or group difference criteria. The correlational and group difference criteria were insufficient because they did not control for measurement bias. Only three studies used a limits of agreement (LOA) criterion that specified a priori an acceptable difference between novel and validated values in absolute units. Whereas the selection of rigorous criterion validity methods is essential, researchers also need to address such issues as acceptable measurement bias and control of artifacts. UST measurements are proxies of proxies. They seek to replace short-term values which, in turn, attempt to estimate long-term metrics. Further adoption of UST HRV measurements requires compelling evidence that these metrics can forecast real-world health or performance outcomes. Furthermore, a single false heartbeat can dramatically alter HRV metrics. UST measurement solutions must automatically edit artifactual interbeat interval values otherwise HRV measurements will be invalid. These are the formidable challenges that must be addressed before HRV monitoring can be accepted for widespread use in medicine and personal health care.
Worry, Risk Perception, and Controllability Predict Intentions Toward COVID-19 Preventive Behaviors

The ongoing pandemic of COVID-19 has already had serious worldwide health, socio-economic, political, and educational consequences. In the present study, we investigated what factors can motivate young adults to comply with the recommended preventive measures against coronavirus infection. Even though young people are less likely to suffer severe medical consequences from the virus, they can still transmit it to more vulnerable individuals. Surprisingly, we found no significant effects of previously successful experimental manipulations (e.g., enhancing self-efficacy, and visual aids) that aimed to improve risk understanding and impact COVID-19 related behavioral intentions. Instead, intentions toward preventive behaviors were predicted by self-reported worry, perceived controllability of the pandemic, and risk perception. Interestingly, worry about health, and worry about restricting personal freedom predicted behavioral intentions in diverging directions. In particular, participants who were worried about health, were more willing to obey strict hygiene and social distancing restrictions. In contrast, participants who were worried about personal restrictions, were less ready to adopt these preventive actions.
Measuring Psychosocial Reactions to COVID-19: The COVID Reaction Scales (COVID-RS) as a New Assessment Tool

Knowing and measuring the psychosocial reactions of people to the coronavirus crisis could be useful for predicting citizen responsibility and psychological well-being in the general population. In this research, we present the COVID Reaction Scales (COVID-RS), a new tool that can measure and quantify the psychopathological reactions of the population to the COVID-19 crisis. The sample consisted of 667 subjects. Explorative and confirmative factor analyses were applied to examine the validity and reliability of the COVID-RS. Five dimensions were extracted that predicted 35.08% of the variance of the psychopathological reactions: (1) disorganized behaviors, (2) avoidant behaviors, (3) maladaptive information consumption, (4) herd behaviors and (5) loneliness. The results indicated that social quarantine induces and increases psychopathological reactions. However, emotional loneliness is reduced for each person with whom the respective subject lives during the quarantine. Finally, we can conclude that the COVID-RS has satisfactory validity and reliability. Measuring dysfunctional reactions to COVID-19 can enable the prediction of citizen responsibility.
Prospects of Directly Reprogrammed Adult Human Neurons for Neurodegenerative Disease Modeling and Drug Discovery: iN vs. iPSCs Models

A reliable disease model is critical to the study of specific disease mechanisms as well as for the discovery and development of new drugs. Despite providing crucial insights into the mechanisms of neurodegenerative diseases, translation of this information to develop therapeutics in clinical trials have been unsuccessful. Reprogramming technology to convert adult somatic cells to induced Pluripotent Stem Cells (iPSCs) or directly reprogramming adult somatic cells to induced Neurons (iN), has allowed for the creation of better models to understand the molecular mechanisms and design of new drugs. In recent times, iPSC technology has been commonly used for modeling neurodegenerative diseases and drug discovery. However, several technological challenges have limited the application of iN. As evidence suggests, iN for the modeling of neurodegenerative disorders is advantageous compared to those derived from iPSCs. In this review, we will compare iPSCs and iN models for neurodegenerative diseases and their potential applications in the future.
Decline in Soil Microbial Abundance When Camelina Introduced Into a Monoculture Wheat System

Camelina [Camelina sativa (L.) Crantz] of the Brassicaceae family is a potential alternative and oilseed biofuel crop for wheat (Triticum aestivum L.)-based cropping systems of the Inland Pacific Northwest (PNW) of the United States. We investigated the effect of this relatively new rotational crop on soil microbial communities. An 8-year cropping systems experiment was initiated in 2007 at Lind, WA, to compare a 3-year rotation of winter wheat (WW)-camelina (C)-fallow (F) to the typical 2-year WW-F rotation. All phases of both rotations (total = 20 plots) were present every year to allow valid statistical analysis and data interpretations. Monoculture WW-F is the dominant system practiced by the vast majority of farmers on 1.56 million ha of cropland in the PNW drylands that receive <300 mm average annual precipitation. Microbial abundance and community composition were determined using phospholipid fatty acid analysis (PLFA) from soil samples collected during 3 consecutive years beginning in 2010. The abundance of fungi, mycorrhizae, Gram positive and negative bacteria, and total microbial abundance all declined over the 3-year period in the WW-C-F rotation compared to the WW-F rotation. All microbial lipid biomarkers were significantly less in fallow compared to WW of the WW-C-F rotation. The 2-year WW-F rotation demonstrated few differences in microbial lipid abundance and community structure between the rotation phases. Microbial abundance declined and community structure shifted in the 3-year WW-C-F rotation likely due to the combination of a Brassica crop followed by a 13-month-long fallow. The results of this study suggest that camelina in combination with a fallow period may disrupt microbial communities that have become stable under historical WW-F monocropping. Such disturbances have the potential to affect soil processes that have been provided by wheat-adapted microbial communities. However, the disruption appears to be short-lived with the microbial abundance of WW in the WW-C-F rotation, returning to similar levels observed in the WW-F rotation.
A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches

This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, which allows the instance segmentation of objects after an initial training on manually labeled data. The algorithm is capable of identifying and classifying objects present in an image at the pixel scale, without human intervention, in a matter of seconds. This work demonstrates that it is possible to train the model to detect non-overlapping coarse sediments on scaled images, in order to extract their individual size and morphological characteristics with high efficiency (R2 = 0.98; Root Mean Square Error (RMSE) = 3.9 mm). It is then possible to measure element size profiles over a sedimentary body, as it was done on the pebble beach of Etretat (Normandy, France) in order to monitor the granulometric spatial variability before and after a storm. Applied at a larger scale using Unmanned Aerial Vehicle (UAV) derived ortho-images, the method allows the accurate characterization and high-resolution mapping of the surface coarse sediment size, as it was performed on the two pebble beaches of Etretat (D50 = 5.99 cm) and Hautot-sur-Mer (D50 = 7.44 cm) (Normandy, France). Validation results show a very satisfying overall representativity (R2 = 0.45 and 0.75; RMSE = 6.8 mm and 9.3 mm at Etretat and Hautot-sur-Mer, respectively), while the method remains fast, easy to apply and low-cost, although the method remains limited by the image resolution (objects need to be longer than 4 cm), and could still be improved in several ways, for instance by adding more manually labeled data to the training dataset, and by considering more accurate methods than the ellipse fitting for measuring the particle sizes.
Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.
Drivers of Supplier Participation in Ride-Hailing Platforms

ABSTRACT Ride-hailing platforms such as Uber offer service suppliers more flexible work conditions but lower security than permanent jobs. This study examines the preferences of ride-hailing drivers when they are offered contract and platform design options related to flexibility, financial security, and information features. The sample design and the discrete choice experiment approach we use permit the exploration of differences in the preferences of sub-groups of suppliers, including active and inactive drivers, drivers with and without other jobs, and those who multi-home. We find that the utility and willingness of drivers to work for ride-hailing platforms generally increase when the ride-hailing company provides a minimum wage guarantee, a company-sponsored benefit plan, and information features that protect drivers’ privacy and allow them to screen for undesired passengers. These attributes are important enough for drivers so that they are willing to sacrifice scheduling flexibility by committing to minimum working hours in exchange for those attributes. We identified significant heterogeneity in driver preferences. For drivers with a primary job or jobs other than ride-hailing, the perception of working conditions in the primary job affects their willingness to restart ride-hailing services. Preferences also vary depending on whether a driver participates in one or more platforms. These results suggest that offering a menu of contract options could provide novel tools for ride-hailing companies to improve the stability and predictability of supply. It may also alleviate some of the concerns about driver working conditions that have led to increasing calls for the regulation of ride-hailing services to protect drivers.
ChiTeSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset

Due to the lack of labeled data, previous research on text-to-SQL parsing mainly focuses on English. Representative English datasets include ATIS, WikiSQL, Spider, etc. This paper presents DuSQL, a larges-scale and pragmatic Chinese dataset for the cross-domain text-to-SQL task, containing 200 databases, 813 tables, and 23,797 question/SQL pairs. Our new dataset has three major characteristics. First, by manually analyzing questions from several representative applications, we try to figure out the true distribution of SQL queries in real-life needs. Second, DuSQL contains a considerable proportion of SQL queries involving row or column calculations, motivated by our analysis on the SQL query distributions. Finally, we adopt an effective data construction framework via human-computer collaboration. The basic idea is automatically generating SQL queries based on the SQL grammar and constrained by the given database. This paper describes in detail the construction process and data statistics of DuSQL. Moreover, we present and compare performance of several open-source text-to-SQL parsers with minor modification to accommodate Chinese, including a simple yet effective extension to IRNet for handling calculation SQL queries.