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A fluorinated distyrylanthracene (DSA) derivative, 9, 10‐bis((E)‐4‐(trifluoromethyl)styryl)anthracene abbreviated as 4FDSA with two crystalline polymorphs (4FDSA‐G (green emission) and 4FDSA‐O (orange emission)), showing remarkable aggregation‐induced enhanced emission and mechanofluorochromic characteristics, was developed. One of the polymorphs in its crystalline arrangement exhibits the hardly seen F...F interactions. It questions the conventional belief of the non‐polarisable nature of fluorine atoms in forming the halogen bond. The twisted molecular conformation facilitated by the various supramolecular interactions resulted in the formation of another intensely emissive bluer nanocrystal (4FDSA‐NC) at aggregating conditions. Even though, the both polymorphs show distinct tricolor luminescence switching on the action of mechanical force, fumigation of ground crystals with solvent vapor resulted in the formation of a more thermodynamically favorable 4FDSA‐NC form. The work demonstrates the effect of supramolecular interactions assisted conformational changes in tuning the unique mechanofluorochromic characteristics of the polymorphic crystals. [ABSTRACT FROM AUTHOR]

ABSTRACTPatients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus (FLUAV) coinfections were associated with severe respiratory failure and more deaths. Here, we developed a model for studying SARS-CoV-2 and FLUAV coinfection using human pluripotent stem cell-induced alveolar type II organoids (hiAT2). hiAT2 organoids were susceptible to infection by both viruses and had features of severe lung damage. A single virus markedly enhanced the susceptibility to other virus infections. SARS-CoV-2 delta variants upregulated α-2-3-linked sialic acid, while FLUAV upregulated angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). Moreover, coinfection by SARS-CoV-2 and FLUAV caused hyperactivation of proinflammatory and immune-related signaling pathways and cellular damage compared to a respective single virus in hiAT2 organoids. This study provides insight into molecular mechanisms underlying enhanced infectivity and severity in patients with co-infection of SARS-CoV-2 and FLUAV, which may aid in the development of therapeutics for such co-infection cases.

Abstract The early detection of initial dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior initial caries. In medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched, with promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for caries detection on bitewing radiographs and investigated whether this model can improve clinicians’ performance. The research complied with relevant ethical regulations. In total, 304 bitewing radiographs were used to train the CNN model and 50 radiographs for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased sensitivity ratio (D1, 85.34%; D1′, 92.15%; D2, 85.86%; D2′, 93.72%; D3, 69.11%; D3′, 79.06%; p

Global energy demand has unquestionably increased significantly in recent years. Nowadays, industries are very aware of global warming, and to save the environment, they produce green products with energy consumption. Day by day, energy use is increasing due to population, end-use markets of construction, transportation, industry, etc. But the energy limit is finite, whereas the daily use is rising, so the price is increasing. In this study, two situations have been shown in two models with renewable energy consumption. Model 1 analyzes the manufacturer and retailer’s optimal green quality and sales price in two-echelon supply chain systems with centralized and decentralized cases. In this case, the retailer sells their products through three different channels: online, offline, and buy-online-pickup-in store, with three different selling prices. In Model 2, Manufacturer 1 and Manufacturer 2 produce green and regular products with renewable energy consumption. In this case, both manufacturers sell their products through three different channels: online, offline, and buy-online-pickup-in store, with three different selling prices. There is competition between substitutable products with respect to green quality and the selling price of the products. A hybrid channel policy is studied here to maximize the total profit with considering corporate social responsibility under renewable energy consumption. The study has been analyzed mathematically. The classical optimization approach and game theory are applied here to find the optimal values of procurement cost, selling price, and green quality development cost. A numerical study shows that the centralized system gives a better result to the manufacturer than the decentralized system. When the demand is a power function of the selling price, the manufacturer producing eco-friendly products gains 0.99% more profit than the conventional product. This result shows that manufacturers creating eco-friendly products motivate other manufacturers to make eco-friendly products.

The central symptom of autism spectrum disorder (ASD) is deficiency in social communication, which is generally viewed as being caused by pragmatic impairment (PI). PI is difficulty in using language appropriately in social situations. Studies have confirmed that PI is the result of neurological, cognitive, linguistic, and sensorimotor dysfunctions involving intricately intertwined factors. To elucidate the whole picture of this impairment, an approach from a multifaceted perspective fusing those factors is necessary. To this end, comprehensive PI mapping is a must, since no comprehensive mapping has yet been developed. The aim of this research is to present a model of annotation scheme development and corpus construction to efficiently visualize and quantify for statistical investigation occurrences of PI, which enables comprehensive mapping of PI in the spoken language of Japanese ASD individuals. We constructed system networks (lexicogrammatical option systems speakers make choices from) in the theoretical framework of Systemic Functional Linguistics, from which we developed an annotation scheme to comprehensively cover PI. Since system network covers all possible lexicogrammatical choices in linguistic interaction, it enables a comprehensive view of where and in what lexicogrammar PI occurs. Based on this annotation scheme, we successfully developed the Corpus of ASD + Typically Developed Spoken Language consisting of texts from 1,187 audiotaped tasks performed by 186 ASD and 106 typically developed subjects, accommodating approximately 1.07 million morphemes. Moreover, we were successful in the automatization of the annotation process by machine learning, accomplishing a 90 percent precision rate. We exemplified the mapping procedure with a focus on the spoken use of negotiating particles. Our model corpus is applicable to any language by incorporating our method of constructing the annotation scheme, and would give impetus to defining PI from a cross-linguistic point of view, which is needed because PI of ASD reflects cross-linguistic differences.

Abstract Backgrounds Whether there is a true increase in autism spectrum disorder (ASD) frequency or not remains unclear. Additionally, the rates of co-existing neurodevelopmental disorders (NDD) in a total population sample has not been fully examined before. Therefore, using a total population sample in Japan, we aimed to estimate the prevalence and cumulative incidence of autism spectrum disorder (ASD) annually, to determine whether there is a true increase in ASD prevalence by estimating the cumulative incidence of ASD annually, and to examine the rates of co-existing neurodevelopmental disorders (NDD). Method In this cross-sectional sequential design study, all 5-year-old children in the catchment area underwent the screening annually from the year 2013–2016. Screen-positive children were invited to participate in a comprehensive assessment, including child and parent interview, behavioral observation, and cognitive and motor function testing. All cases were reviewed by a multidisciplinary research team. Results Caregivers of 3954 children returned the screening, among which 559 children underwent the assessment with 87 children receiving an ASD diagnosis. Adjusted ASD prevalence was 3.22% (95% confidence interval (CI) 2.66–3.76%). The male to female ratio of the crude prevalence was 2.2:1. The cumulative incidence of ASD up to 5 years of age for the total study years was 1.31% (95% CI 1.00–1.62%). A generalized linear model revealed no significant linear trends in 5-year cumulative incidence over the study years. Only 11.5% of children had ASD alone; the remaining 88.5% were found to have at least one co-existing NDD. Limitations Modest sample size for a total population study. Conclusions Our findings demonstrate the stability of the 5-year cumulative incidence of ASD, implying no true rise in ASD incident cases over the 4-year study period in the study catchment area. High rates of co-existing NDDs reflect the importance of investigating broad developmental challenges in children with ASD.