Lrig1 and Wnt dependent niches dictate segregation of resident immune cells and melanocytes in murine tail epidermis

The barrier-forming, self-renewing mammalian epidermis comprises keratinocytes, pigment-producing melanocytes, and resident immune cells as first-line host defense. In murine tail skin, interfollicular epidermis patterns into pigmented ′scale′ and non-pigmented ′interscale′ epidermis. Why and how mature melanocytes confine to scale epidermis is unresolved. Here, we delineate a cellular hierarchy among epidermal cell types that determines skin patterning. Already during postnatal development, melanocytes co-segregate with newly forming scale compartments. Intriguingly, this process coincides with partitioning of both Langerhans cells and dendritic epidermal T-cells to interscale epidermis, suggesting functional segregation of pigmentation and immune surveillance. Analysis of non-pigmented mice and of mice lacking melanocytes or resident immune cells revealed that immunocyte patterning is melanocyte- and melanin-independent, and, vice versa, immune cells do not control melanocyte localization. Instead, genetically enforced progressive scale fusion upon Lrig1 deletion showed that melanocytes and immune cells dynamically follow epithelial scale:interscale patterns. Importantly, disrupting Wnt-Lef1 function in keratinocytes caused melanocyte mislocalization to interscale epidermis, implicating canonical Wnt signaling in organizing the pigmentation pattern. Together, this work uncovered cellular and molecular principles underlying the compartmentalization of tissue functions in skin.

Video Scene Information Detection Based on Entity Recognition

Video situational information detection is widely used in the fields of video query, character anomaly detection, surveillance analysis, and so on. However, most of the existing researches pay much attention to the subject or video backgrounds, but little attention to the recognition of situational information. What is more, because there is no strong relation between the pixel information and the scene information of video data, it is difficult for computers to obtain corresponding high-level scene information through the low-level pixel information of video data. Video scene information detection is mainly to detect and analyze the multiple features in the video and mark the scenes in the video. It is aimed at automatically extracting video scene information from all kinds of original video data and realizing the recognition of scene information through “comprehensive consideration of pixel information and spatiotemporal continuity.” In order to solve the problem of transforming pixel information into scene information, this paper proposes a video scene information detection method based on entity recognition. This model integrates the spatiotemporal relationship between the video subject and object on the basis of entity recognition, so as to realize the recognition of scene information by establishing mapping relation. The effectiveness and accuracy of the model are verified by simulation experiments with the TV series as experimental data. The accuracy of this model in the simulation experiment can reach more than 85%.

African Swine Fever in Wild Boar (Poland 2020): Passive and Active Surveillance Analysis and Further Perspectives

African swine fever (ASF) is a fatal hemorrhagic disease of wild boar and domestic pigs which has been present in Poland since 2014. By 2020, the ASF virus (ASFV) spread across Central, Eastern and Western Europe (including Germany), and Asian countries (including China, Vietnam, and South Korea). The national ASF eradication and prevention program includes continuous passive (wild boar found dead and road-killed wild boar) and active (hunted wild boar) surveillance. The main goal of this study was to analyze the dynamic of the spread of ASF in the wild boar population across the territory of Poland in 2020. In that year in Poland, in total 6191 ASF-positive wild boar were declared. Most of them were confirmed in a group of animals found dead. The conducted statistical analysis indicates that the highest chance of obtaining an ASF-positive result in wild boar was during the winter months, from January to March, and in December 2020. Despite the biosecurity measures implemented by holdings of domestic pigs, the disease also occurred in 109 pig farms. The role of ASF surveillance in the wild boar population is crucial to apply more effective and tailored measures of disease control and eradication. The most essential measures to maintain sustainable production of domestic pigs in Poland include effective management of the wild boar population, along with strict implementation of biosecurity measures by domestic pig producers.

Person Re-Identification Based on Attention Mechanism and Context Information Fusion

Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

Exploring the implications of the new ICD-10-CM classification system for injury surveillance: analysis of dually coded data from two medical centres

IntroductionExternal cause of injury matrices is used to classify mechanisms/causes of injuries for surveillance and research. Little is known about the performance of the Centers for Disease Control and Prevention’s new external cause of injury matrix for Clinical Modification of the 10th Revision of the International Classification of Diseases (ICD-10-CM), compared with the ICD-9-CM version.MethodsDually coded (ICD-9-CM and ICD-10-CM) administrative data were obtained from two major academic trauma centres. Injury-related cases were identified and categorised by mechanism/cause and manner/intent. Comparability ratios (CR) were used to estimate the net impact of changing from ICD-9-CM to ICD-10-CM on the number of cases classified to each mechanism/cause category. Chamberlain’s percent positive agreements (PPA) were calculated and McNemar’s test was used to assess the significance of observed classification differences.ResultsOf 4832 and 5211 dual-coded records from the two centres, 632 and 520 with injury-related principal diagnoses and external cause codes in both ICD-9-CM and ICD-10-CM were identified. CRs for the mechanisms/causes with at least 20 records ranged from 0.85 to 1.9 at one centre and from 0.97 to 1.07 at the other. Among these mechanisms/causes, PPAs ranged from 33% for ‘other transport’ to 94% for poisoning at one centre, and from 75% for ‘other transport’ to 100% for fires/burns at the other centre. Case assignment differed significantly for falls, motor vehicle traffic, other transport, and ‘struck by/against’ injuries at one centre, and for ‘other pedal cyclist’ at the other centre.ConclusionSwitching to ICD-10-CM and the new external cause of injury matrix may affect injury surveillance and research, especially for certain mechanisms/causes.

Catheter Ablation of Life-Threatening Ventricular Arrhythmias in Athletes

A recent surveillance analysis indicates that cardiac arrest/death occurs in ≈1:50,000 professional or semi-professional athletes, and the most common cause is attributable to life-threatening ventricular arrhythmias (VAs). It is critically important to diagnose any inherited/acquired cardiac disease, including coronary artery disease, since it frequently represents the arrhythmogenic substrate in a substantial part of the athletes presenting with major VAs. New insights indicate that athletes develop a specific electro-anatomical remodeling, with peculiar anatomic distribution and VAs patterns. However, because of the scarcity of clinical data concerning the natural history of VAs in sports performers, there are no dedicated recommendations for VA ablation. The treatment remains at the mercy of several individual factors, including the type of VA, the athlete’s age, and the operator’s expertise. With the present review, we aimed to illustrate the prevalence, electrocardiographic (ECG) features, and imaging correlations of the most common VAs in athletes, focusing on etiology, outcomes, and sports eligibility after catheter ablation.

Vape shop and consumer activity during COVID-19 non-essential business closures in the USA

IntroductionVaping and vape shops pose risk for COVID-19 and its transmission.ObjectivesWe examined vape shop non-compliance with state-ordered business closures during COVID-19, changes in their marketing and experiences among consumers.MethodsAs part of a longitudinal study of vape retail in six metropolitan statistical areas (MSAs; Atlanta, Boston, Minneapolis, Oklahoma City, San Diego and Seattle), we conducted: (1) legal research to determine whether statewide COVID-19 orders required vape shops to close; (2) phone-based and web-based surveillance to assess vape shop activity in March–June 2020 during shelter-in-place periods; and (3) a concurrent online survey of e-cigarette users about their experiences with vape retail.ResultsNon-essential business closure varied in timing/duration across states and applied to vape shops in California, Massachusetts, Minnesota, Oklahoma (for a brief period) and Washington (Georgia’s orders were ambiguous). Surveillance analysis focused on the five MSAs in these states. Of 156 vape shops, 53.2% were open as usual, 11.5% permanently closed and 3.8% temporarily closed; 31.4% offered pick-up/delivery services. Among survey respondents (n=354, Mage=23.9±4.6; 46.9% male, 71.8% white, 13.0% Hispanic), 27.4% worried their vape shop would close/go out of business during COVID-19; 7.3% said their vape shop did so. Few noticed increases in vape product delivery options (7.3%), discounts/price promotions (9.9%) and/or prices (9.3%). While 20.3% stockpiled vape products, 20.3% tried to reduce use and 15.8% tried to quit.ConclusionsMany vape shops were non-compliant with state COVID-19 orders. E-cigarette users were as likely to stockpile vape products as to attempt to reduce or quit using e-cigarettes.