Experimental Generation of Spin-Photon Entanglement in Silicon Carbide.

A solid-state approach for quantum networks is advantageous, as it allows the integration of nanophotonics to enhance the photon emission and the utilization of weakly coupled nuclear spins for long-lived storage. Silicon carbide, specifically point defects within it, shows great promise in this regard due to the easy of availability and well-established nanofabrication techniques. Despite of remarkable progresses made, achieving spin-photon entanglement remains a crucial aspect to be realized. In this Letter, we experimentally generate entanglement between a silicon vacancy defect in silicon carbide and a scattered single photon in the zero-phonon line. The spin state is measured by detecting photons scattered in the phonon sideband. The photonic qubit is encoded in the time-bin degree of freedom and measured using an unbalanced Mach-Zehnder interferometer. Photonic correlations not only reveal the quality of the entanglement but also verify the deterministic nature of the entanglement creation process. By harnessing two pairs of such spin-photon entanglement, it becomes straightforward to entangle remote quantum nodes at long distance.

Retrieving profile of photoresist with high aspect ratio and subwavelength features using optical spectroscopy and artificial neural network.

Profile measurements of structures with a high aspect ratio and subwavelength features (HARSW) can be achieved using transmission electron microscopy and tilted scanning electron microscopy. Although electron microscopy can provide accurate HARSW measurements, it is laborious and destructive. In this paper, nondestructive and labor-saving methods were proposed to measure the dimensions of HARSW structures. The optical reflection spectrum, along with an artificial neural network (ANN) model, was adopted for interpolation with the simulation database to retrieve the dimensions of HARSW structures. To generate the ANN model, the experimental and simulated reflection spectra were adopted as the input and output variables for the training data, respectively. This ANN model can learn the discrepancy between simulation and experimental reflections. The finite-difference time-domain method was also adopted to calculate the simulated reflection spectra of HARSW structures with various dimensions, which can be used as a database. Once the experimental reflection of a HARSW structure with unknown dimensions was obtained, the ANN model could generate a simulation-like reflection spectrum. Linear regression was used to determine the correlation coefficients of the simulation-like reflection spectra in the database. The accurate dimensions of HARSW structures can be determined using a higher correlation coefficient. This methodology can be a prominent method for the process monitoring of HARSW structures.

Elucidating the impact of oxygen functional groups on the catalytic activity of M-N4-C catalysts for the oxygen reduction reaction: a density functional theory and machine learning approach.

Efforts to enhance the efficiency of electrocatalysts for the oxygen reduction reaction (ORR) in energy conversion and storage devices present formidable challenges. In this endeavor, M-N4-C single-atom catalysts (MN4) have emerged as promising candidates due to their precise atomic structure and adaptable electronic properties. However, MN4 catalysts inherently introduce oxygen functional groups (OGs), intricately influencing the catalytic process and complicating the identification of active sites. This study employs advanced density functional theory (DFT) calculations to investigate the profound influence of OGs on ORR catalysis within MN4 catalysts (referred to as OGs@MN4, where M represents Fe or Co). We established the following activity order for the 2eORR: for OGs@CoN4: OH@CoN4 > CoN4 > CHO@CoN4 > C-O-C@CoN4 > COC@CoN4 > COOH@CoN4 > CO@CoN4; for OGs@FeN4: COC@FeN4 > CO@FeN4 > OH@FeN4 > FeN4 > COOH@FeN4 > CHO@FeN4 > C-O-C@FeN4. Multiple oxygen combinations were constructed and found to be the true origin of MN4 activity (for instance, the overpotential of 2OH@CoN4 as low as 0.07 V). Furthermore, we explored the performance of the OGs@MN4 system through charge and d-band center analysis, revealing the limitations of previous electron-withdrawing/donating strategies. Machine learning analysis, including GBR, GPR, and LINER models, effectively guides the prediction of catalyst performance (with an R2 value of 0.93 for predicting ΔG*OOH_vac in the GBR model). The Eg descriptor was identified as the primary factor characterizing ΔG*OOH_vac (accounting for 62.8%; OGs@CoN4: R2 = 0.9077, OGs@FeN4: R2 = 0.7781). This study unveils the significant impact of OGs on MN4 catalysts and pioneers design and synthesis criteria rooted in Eg. These innovative findings provide valuable insights into understanding the origins of catalytic activity and guiding the design of carbon-based single-atom catalysts, appealing to a broad audience interested in energy conversion technologies and materials science.

Construction of a High-Density Paulownia Genetic Map and QTL Mapping of Important Phenotypic Traits Based on Genome Assembly and Whole-Genome Resequencing.

Quantitative trait locus (QTL) mapping based on a genetic map is a very effective method of marker-assisted selection in breeding, and whole-genome resequencing is one of the useful methods to obtain high-density genetic maps. In this study, the hybrid assembly of Illumina, PacBio, and chromatin interaction mapping data was used to construct high-quality chromosomal genome sequences of Paulownia fortunei, with a size of 476.82 Mb, a heterozygosity of 0.52%, and a contig and scaffold N50s of 7.81 Mb and 21.81 Mb, respectively. Twenty scaffolds with a total length of 437.72 Mb were assembled into 20 pseudochromosomes. Repeat sequences with a total length of 243.96 Mb accounted for 51.16% of the entire genome. In all, 26,903 protein-coding gene loci were identified, and 26,008 (96.67%) genes had conserved functional motifs. Further comparative genomics analysis preliminarily showed that the split of P. fortunei with Tectona grandis likely occurred 38.8 (33.3-45.1) million years ago. Whole-genome resequencing was used to construct a merged genetic map of 20 linkage groups, with 2993 bin markers (3,312,780 SNPs), a total length of 1675.14 cm, and an average marker interval of 0.56 cm. In total, 73 QTLs for important phenotypic traits were identified (19 major QTLs with phenotypic variation explained ≥ 10%), including 10 for the diameter at breast height, 7 for the main trunk height, and 56 for branch-related traits. These results not only enrich P. fortunei genomic data but also form a solid foundation for fine QTL mapping and key marker/gene mining of Paulownia, which is of great significance for the directed genetic improvement of these species.

Early versus late surgical intervention for cervical spinal cord injury: A protocol for systematic review and meta-analysis.

BACKGROUND: Acute traumatic cervical spinal cord injury (SCI) is a catastrophic event with substantial physical, emotional, and economic burdens to patients, families, and society. Spinal cord decompression is recommended for the treatment of acute SCI. However, the optimal surgical timing remains controversial. Therefore, we perform a protocol for systematic review and meta-analysis to compare the efficacy of early and late surgical intervention for acute SCI. METHODS: This systematic review and meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols statement, which have been registered in advance in the International prospective register of systematic reviews (registration number: CRD42023397592). We will search the following databases for randomized controlled trials: the Cochrane Skin Group Trials Register, MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, Chinese Biomedical Literature Database, Chinese Medical Current Content, and China National Knowledge Infrastructure. The risk of bias of the included studies will be appraised using the Cochrane Collaboration tool for randomized controlled trials. Statistical analysis will be performed using IBM SPSS Statistics (Armonk, NY). RESULT: The results of this systematic review will be published in a peer-reviewed journal. CONCLUSION: This systematic review will provide evidence regarding the optimal timing for spinal cord decompression in patients with acute SCI.

Effective suppression of myocardial glucose uptake using predesigned low-carbohydrate boxed meals.

BACKGROUND: Dietary preparation protocols are an effective means to suppress physiological myocardial 18F-fluorodeoxyglucose (FDG) uptake. This study aimed to investigate the efficacy of various carbohydrate-restricted diets using predesigned boxed meals. METHODS: The patients were divided into four groups to undergo different preparatory protocols as follows: a minimum 15-hour fast alone, two meals of high-fat, low-carbohydrate diet (HFLCD), two meals of high-animal-protein, low-carbohydrate diet (HAPLCD), and two meals of high-plant-based-protein, low-carbohydrate diet (HPPLCD). Boxed meals were prepared to meet the required carbohydrate restrictions. Myocardial SUVmax and SUVmean were measured and the suppression rate was analyzed. RESULTS: The average myocardial SUVmax of fast alone, HFLCD, HAPLCD, and HPPLCD were 8.26 ± 5.85, 2.21 ± 1.50, 2.34 ± 1.88, and 4.10 ± 3.61, respectively, and the suppression rates were 36.6%, 93.3%, 93.3%, and 70%, respectively. The effectiveness of HFLCD, HAPLCD, and HPPLCD was all statistically superior to that of a 15-hour fast alone. SUVmax of HFLCD and HAPLCD showed no significant differences (p = 1), whereas HFLCD and HPPLCD had significant differences (p = .046). CONCLUSIONS: Using the predesigned boxed meals based on carbohydrate restriction, HFLCD, HAPLCD, and HPPLCD can be administered to patients with different dietary needs while providing a substantial reduction in physiological myocardial FDG uptake.

Allelopathic Toxicity of Cyanamide Could Control Amaranth (Amaranthus retroflexus L.) in Alfalfa (Medicago sativa L.) Field.

The inclination toward natural products has led to the onset of the discovery of new bioactive metabolites that could be targeted for specific therapeutic or agronomic applications. Despite increasing knowledge coming to light of allelochemicals as leads for new herbicides, relatively little is known about the mode of action of allelochemical-based herbicides on herbicide-resistant weeds. Cyanamide is an allelochemical produced by hairy vetch (Vicia villosa Roth.). This study aimed to detect the toxicity of cyanamide to alfalfa and amaranth. Seed germination experiments were carried out by the filter paper culture, and the seedling growth inhibition experiment was carried out by spraying alfalfa (Medicago sativa L.) and amaranth (Amaranthus retroflexus L.) seedlings with cyanamide. The results showed that when the concentration of cyanamide was 0.1 g·L-1, the germination of amaranth seeds could be completely inhibited without affecting the germination of alfalfa seeds. At the concentration of 0.5 g·L-1, cyanamide could significantly inhibit the growth of the root and stem of amaranth seedlings but did not affect the growth of alfalfa. This effect was associated with the induction of oxidative stress. The ascorbate peroxidase (APX) and catalase (CAT) activity of amaranth decreased by 6.828 U/g FW and 290.784 U/g FW, respectively. The malondialdehyde (MDA) content, peroxidase (POD), and superoxide dismutase (SOD) activity of amaranth firstly increased and then decreased with the increasing concentration of CA. These enzyme activities of amaranth changed more than that of alfalfa. Activities of the antioxidant enzymes APX, CAT, POD, and SOD and the content of MDA varied dramatically, thereby demonstrating the great influence of reactive oxygen species upon identified allelochemical exposure. In addition, cyanamide can also inhibit the production of chlorophyll, thereby affecting the growth of plants. From the above experiments, we know that cyanamide can inhibit the growth of amaranth in alfalfa fields. Thus, the changes caused by cyanamide described herein can contribute to a better understanding of the actions of allelochemical and the potential use of cyanamide in the production of bioherbicides.

Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans' sentiment toward vaccines was relatively lower than other races.

Botanical extracts in combination improve autosomal recessive woolly hair/hypotrichosis caused by LIPH mutations.

BACKGROUND: Mutation in the lipase H (LIPH) gene is a main reason for autosomal recessive woolly hair (ARWH)/hypotrichosis. Although some studies reported that topical minoxidil could improve ARWH, an effective treatment method for this disease is still lacking. AIM: We attempt to explore potential treatment options for ARWH. MATERIALS & METHODS: A female 6-year-old child was diagnosed with ARWH/hypotrichosis caused by LIPH mutations. And she was treated with combined treatment of botanical extracts. RESULTS: After 6 months of treatment, the patient's hair grew remarkably. After 4 years of treatment, the patient's hair remained dense. DISCUSSION: After the combination treatment, the patient saw a favorable clinical effect. However, the specific mechanisms of action for botanical extracts require further validation. In addition, some regenerative strategies may be considered as potential treatment options for ARWH. We should actively attempt treatment for ARWH patients and encourage prenatal diagnosis due to the great impact of hair loss. CONCLUSION: The combined therapy of botanical extracts may improve ARWH long-term with a sustainable therapeutic effect.

Deterministic Time-Bin Entanglement between a Single Photon and an Atomic Ensemble.

Hybrid matter-photon entanglement is the building block for quantum networks. It is very favorable if the entanglement can be prepared with a high probability. In this Letter, we report the deterministic creation of entanglement between an atomic ensemble and a single photon by harnessing the Rydberg blockade. We design a scheme that creates entanglement between a single photon's temporal modes and the Rydberg levels that host a collective excitation, using a process of cyclical retrieving and patching. The hybrid entanglement is tested via retrieving the atomic excitation as a second photon and performing correlation measurements, which suggest an entanglement fidelity of 87.8%. Our source of matter-photon entanglement will enable the entangling of remote quantum memories with much higher efficiency.

Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective.

The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records.

In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus's spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John's Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

New Metrics for Assessing the State Performance in Combating the COVID-19 Pandemic.

Previous research has noted that many factors greatly influence the spread of COVID-19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID-19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision-making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county-level covariates and unobservable state-level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model-based metrics-the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID-19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.

Phased Implementation of COVID-19 Vaccination: Rapid Assessment of Policy Adoption, Reach and Effectiveness to Protect the Most Vulnerable in the US.

The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate yet became economically and administratively difficult to enforce as the pandemic continued. In late December 2020, COVID-19 vaccines were first approved in the US and states began a phased implementation of COVID-19 vaccination. However, there is limited quantitative evidence regarding the effectiveness of the phased COVID-19 vaccination. This study aims to provide a rapid assessment of the adoption, reach, and effectiveness of the phased implementation of COVID-19 vaccination. We utilize an event-study analysis to evaluate the effect of vaccination on the state-level daily COVID-19 case growth rate. Through this analysis, we assert that vaccination was effective in reducing the spread of COVID-19 shortly after the first shots were given. Specifically, the case growth rate declined by 0.124, 0.347, 0.345, 0.464, 0.490, and 0.756 percentage points corresponding to the 1-5, 6-10, 11-15, 16-20, 21-25, and 26 or more day periods after the initial shots. The findings could be insightful for policymakers as they work to optimize vaccine distribution in later phases, and also for the public as the COVID-19 related health risk is a contentious issue.

Full-length SMRT transcriptome sequencing and microsatellite characterization in Paulownia catalpifolia.

Paulownia catalpifolia is an important, fast-growing timber species known for its high density, color and texture. However, few transcriptomic and genetic studies have been conducted in P. catalpifolia. In this study, single-molecule real-time sequencing technology was applied to obtain the full-length transcriptome of P. catalpifolia leaves treated with varying degrees of drought stress. The sequencing data were then used to search for microsatellites, or simple sequence repeats (SSRs). A total of 28.83 Gb data were generated, 25,969 high-quality (HQ) transcripts with an average length of 1624 bp were acquired after removing the redundant reads, and 25,602 HQ transcripts (98.59%) were annotated using public databases. Among the HQ transcripts, 16,722 intact coding sequences, 149 long non-coding RNAs and 179 alternative splicing events were predicted, respectively. A total of 7367 SSR loci were distributed throughout 6293 HQ transcripts, of which 763 complex SSRs and 6604 complete SSRs. The SSR appearance frequency was 28.37%, and the average distribution distance was 5.59 kb. Among the 6604 complete SSR loci, 1-3 nucleotide repeats were dominant, occupying 97.85% of the total SSR loci, of which mono-, di- and tri-nucleotide repeats were 44.68%, 33.86% and 19.31%, respectively. We detected 112 repeat motifs, of which A/T (42.64%), AG/CT (12.22%), GA/TC (9.63%), GAA/TTC (1.57%) and CCA/TGG (1.54%) were most common in mono-, di- and tri-nucleotide repeats, respectively. The length of the repeat SSR motifs was 10-88 bp, and 4997 (75.67%) were ≤ 20 bp. This study provides a novel full-length transcriptome reference for P. catalpifolia and will facilitate the identification of germplasm resources and breeding of new drought-resistant P. catalpifolia varieties.

The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective.

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space-time disaggregation.

Analysis of hydrochemical evolution in main discharge aquifers under mining disturbance and water source identification.

To discuss the hydrochemical evolution characteristics of the mining process of Peigou Coal Mine, based on the test results of 43 water samples collected at different times from three main discharge aquifers, namely, Carboniferous Taiyuan Formation limestone water (L7-8 + L5-6 water), Ordovician limestone water (including Taiyuan Formation L1-4), and Permian main mining coal seam roof and floor sandstone water (roof and floor water), a hydrochemical evolution model of the mining disturbances since 2003 has been established. The carbonate and sulphate dissolution and pyrite oxidation in Ordovician limestone water significantly decreased and then increased in 2006, and silicate weathering was weak. The carbonate and sulphate dissolution, silicate weathering and pyrite oxidation of roof and floor sandstone water increased. At the same time, a water source identification model suitable for the Peigou Coal Mine was developed by comparing the Fisher discriminant and the BP (back propagation) neural network discriminant. The accuracy rates of Fisher discriminant and BP neural network discriminant are 81.40% and 83.72% respectively, which indicates that BP neural network is more accurate. Finally, the evolution of hydraulic connection between aquifers is analysed. We speculate that there is a fracture development channel between Ordovician limestone water and roof and floor water aquifers that is affected in 2005 by the mining disturbance. This study has significance for examining the hydrochemical evolution of groundwater in mines and acting as a guideline to prevent and control water inrushes.

Spatiotemporal impacts of COVID-19 on air pollution in California, USA.

Various recent studies have shown that societal efforts to mitigate (e.g. "lockdown") the outbreak of the 2019 coronavirus disease (COVID-19) caused non-negligible impacts on the environment, especially air quality. To examine if interventional policies due to COVID-19 have had a similar impact in the US state of California, this paper investigates the spatiotemporal patterns and changes in air pollution before, during and after the lockdown of the state, comparing the air quality measurements in 2020 with historical averages from 2015 to 2019. Through time series analysis, a sudden drop and uptick of air pollution are found around the dates when shutdown and reopening were ordered, respectively. The spatial patterns of nitrogen dioxide (NO2) tropospheric vertical column density (TVCD) show a decreasing trend over the locations of major powerplants and an increasing trend over residential areas near interactions of national highways. Ground-based observations around California show a 38%, 49%, and 31% drop in the concentration of NO2, carbon monoxide (CO) and particulate matter 2.5 (PM2.5) during the lockdown (March 19-May 7) compared to before (January 26-March 18) in 2020. These are 16%, 25% and 19% sharper than the means of the previous five years in the same periods, respectively. Our study offers evidence of the environmental impact introduced by COVID-19, and insight into related economic influences.

Innovative use of concentrated growth factors combined with corticosteroids to treat discoid lupus erythematosus alopecia: A case report.

Alopecia for patients with discoid lupus erythematosus can sometimes be a refractory condition, where mixed infiltrates of T lymphocytes and histiocytes leads to destruction of hair follicles, which might cause permanent scarring. Early diagnosis and timely treatment can achieve hair regeneration and prevent further disease progression. Concentrated growth factor, a novel autologous plasma extract, contains various growth factors that could promote tissue regeneration. In this article, we report a case of cell growth factor combined with corticosteroids for the treatment of discoid lupus erythematosus alopecia. This case study concludes with satisfactory clinical effect.

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.