Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding. We provide (i)a comprehensive overview of the DL techniques that have been applied in PAI, (ii)references for designing DL models for various PAI tasks, and (iii)a summary of the future challenges and opportunities. Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types image understanding, reconstruction of the initial pressure distribution, and QPAI. When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.Force transmission throughout a monolayer is the result of complex interactions between cells. Monolayer adaptation to force imbalances such as singular stiffened cells provides insight into the initiation of disease and fibrosis. Here, NRK-52E cells transfected with ∆50LA, which significantly stiffens the nucleus. These stiffened cells were sparsely placed in a monolayer of normal NRK-52E cells. Through morphometric analysis and temporal tracking, the impact of the singular stiffened cells shows a pivotal role in mechanoresponse of the monolayer. A method for a detailed analysis of the spatial aspect and temporal progression of the nuclear boundary was developed and used to achieve a full description of the phenotype and dynamics of the monolayers under study. Our findings reveal that cells are highly sensitive to the presence of mechanically impaired neighbors, leading to generalized loss of coordination in collective cell migration, but without seemingly affecting the potential for nuclear lamina fluctuations of neighboring cells. ABT-199 mw Reduced translocation in neighboring cells appears to be compensated by an increase in nuclear rotation and dynamic variation of shape, suggesting a "frustration" of cells and maintenance of motor activity. Interestingly, some characteristics of the behavior of these cells appear to be dependent on the distance to a ∆50LA cell, pointing to compensatory behavior in response to force transmission imbalances in a monolayer. These insights may suggest the long-range impacts of single cell defects related to tissue dysfunction.Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed, and also developed a forecasting model using long short-term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID-19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID-19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short-term (1 day lead) forecast of COVID-19 cases (relative error less then 20%). Moreover, the multivariate LSTM model improved the medium-range forecast skill (1-7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India. Hypotrichosis with juvenile macular dystrophy (HJMD) is a rare autosomal recessive inherited disorder caused by biallelic variants in the CDH3 gene encoding P-cadherin. Here, we report two Japanese sibling patients with HJMD. Whole-exome sequencing (WES) was performed to identify disease-causing variants. In addition, ophthalmic and dermatological examinations were performed to classify the phenotype of each patient. The WES analysis revealed novel compound heterozygous CDH3 variants [c.123_129dupAGGCGCG (p.Glu44fsX26) and c.2280+1G>T] in both patients; the unaffected, nonconsanguineous parents each exhibited one of the variants. Both patients showed the same clinical findings. Ophthalmologically, they exhibited progressive loss of visual acuity and chorioretinal macular atrophy, as examined with fundoscopy, fundus autofluorescence imaging, and optical coherence tomography. Full-field electroretinography, assessing generalized retinal function, revealed nearly normal amplitudes of both rod- and cone-mediated responses. Multifocal electroretinography, reflecting macular function, showed extremely decreased responses in the central area, corresponding to the chorioretinal atrophy. Dermatological examination revealed diffuse thinning of the scalp hair, which was sparse and fragile. This is the first report of Japanese patients with HJMD and novel compound heterozygous truncating variants in CDH3. Our findings can expand the knowledge and understanding of CDH3-related HJMD, which could be helpful to ophthalmologists and dermatologists.This is the first report of Japanese patients with HJMD and novel compound heterozygous truncating variants in CDH3. Our findings can expand the knowledge and understanding of CDH3-related HJMD, which could be helpful to ophthalmologists and dermatologists.