Deep learning in crowd counting: A survey

Counting high-density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three-tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small-scale, large-scale and hyper-scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data-driven perspective: multi-scale networks, single-column networks, multi-column networks, multi-task networks, attention networks and weak-supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three-tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small-scale datasets has slowed down. There are few new datasets and algorithms on small-scale datasets. The studies focused on large or hyper-scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real-world scenarios and processing large crowds in real-time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.BHF, AA/18/3/34220Hope Foundation for Cancer Research, RM60G0680GCRF, P202PF11;Sino‐UK Industrial Fund, RP202G0289LIAS, P202ED10, P202RE969Data Science Enhancement Fund, P202RE237Sino‐UK Education Fund, OP202006Fight for Sight, 24NN201Royal Society International Exchanges Cost Share Award, RP202G0230MRC, MC_PC_17171BBSRC, RM32G0178B

High Viral Load of Human Bocavirus Correlates with Duration of Wheezing in Children with Severe Lower Respiratory Tract Infection

Background: Human bocavirus (HBoV) is a newly discovered parvovirus and increasing evidences are available to support its role as an etiologic agent in lower respiratory tract infection (LRTI). The objective of this study is to assess the impact of HBoV viral load on clinical characteristics in children who were HBoV positive and suffered severe LRTI. Methods: Lower respiratory tract aspirates from 186 hospitalized children with severe LRTI were obtained by bronchoscopy. HBoVs were detected by real-time PCR and other 10 infectious agents were examined using PCR and/or direct fluorescent assay. Results: Thirty-one patients (24.6%) were tested positive for HBoV in the respiratory tract aspirates. Fifteen samples had a high viral load (.10 4 copies/mL) and the other sixteen samples had a low viral load (,10 4 copies/mL). The duration of presented wheezing and hospitalization was longer in children with high viral load of HBoV than that in children with low viral load. The days of wheezing showed a correlation with viral load of HBoV. Conclusion: We confirmed that HBoV was frequently detected in patients with severe LRTI. Wheezing was one of the most common symptoms presented by patients with positive HBoV. A high HBoV viral load could be an etiologic agent for LRTI

Hydrogels for Oral Tissue Engineering: Challenges and Opportunities

Oral health is crucial to daily life, yet many people worldwide suffer from oral diseases. With the development of oral tissue engineering, there is a growing demand for dental biomaterials. Addressing oral diseases often requires a two-fold approach: fighting bacterial infections and promoting tissue growth. Hydrogels are promising tissue engineering biomaterials that show great potential for oral tissue regeneration and drug delivery. In this review, we present a classification of hydrogels commonly used in dental research, including natural and synthetic hydrogels. Furthermore, recent applications of these hydrogels in endodontic restorations, periodontal tissues, mandibular and oral soft tissue restorations, and related clinical studies are also discussed, including various antimicrobial and tissue growth promotion strategies used in the dental applications of hydrogels. While hydrogels have been increasingly studied in oral tissue engineering, there are still some challenges that need to be addressed for satisfactory clinical outcomes. This paper summarizes the current issues in the abovementioned application areas and discusses possible future developments

Investigating Dynamic Molecular Events in Melanoma Cell Nucleus During Photodynamic Therapy by SERS

Photodynamic therapy (PDT) involves the uptake of photosensitizers by cancer cells and the irradiation of a light with a specific wavelength to trigger a series of photochemical reactions based on the generation of reactive oxygen, leading to cancer cell death. PDT has been widely used in various fields of biomedicine. However, the molecular events of the cancer cell nucleus during the PDT process are still unclear. In this work, a nuclear-targeted gold nanorod Raman nanoprobe combined with surface-enhanced Raman scattering spectroscopy (SERS) was exploited to investigate the dynamic intranuclear molecular changes of B16 cells (a murine melanoma cell line) treated with a photosensitizer (Chlorin e6) and the specific light (650 nm). The SERS spectra of the cell nucleus during the PDT treatment were recorded in situ and the spectroscopic analysis of the dynamics of the nucleus uncovered two main events in the therapeutic process: the protein degradation and the DNA fragmentation. We expect that these findings are of vital significance in having a better understanding of the PDT mechanism acting on the cancer cell nucleus and can further help us to design and develop more effective therapeutic platforms and methods

GJB2 mutation spectrum in 2063 Chinese patients with nonsyndromic hearing impairment

Background: Mutations in GJB2 are the most common molecular defects responsible for autosomal recessive nonsyndromic hearing impairment (NSHI). The mutation spectra of this gene vary among different ethnic groups. Methods: In order to understand the spectrum and frequency of GJB2 mutations in the Chinese population, the coding region of the GJB2 gene from 2063 unrelated patients with NSHI was PCR amplified and sequenced. Results: A total of 23 pathogenic mutations were identified. Among them, five (p.W3X, c.99delT, c.155_c.158delTCTG, c.512_c.513insAACG, and p.Y152X) are novel. Three hundred and seven patients carry two confirmed pathogenic mutations, including 178 homozygotes and 129 compound heterozygotes. One hundred twenty five patients carry only one mutant allele. Thus, GJB2 mutations account for 17.9% of the mutant alleles in 2063 NSHI patients. Overall, 92.6% (684/739) of the pathogenic mutations are frame-shift truncation or nonsense mutations. The four prevalent mutations; c.235delC, c.299_c.300delAT, c.176_c.191del16, and c.35delG, account for 88.0% of all mutantalleles identified. The frequency of GJB2 mutations (alleles) varies from 4% to 30.4% among different regions of China. It also varies among different sub-ethnic groups. Conclusion: In some regions of China, testing of the three most common mutations can identify at least one GJB2 mutant allele in all patients. In other regions such as Tibet, the three most common mutations account for only 16% the GJB2 mutant alleles. Thus, in this region, sequencing of GJB2 would be recommended. In addition, the etiology of more than 80% of the mutant alleles for NSHI in China remains to be identified. Analysis of other NSHI related genes will be necessary

Does EID1 aid the fine-tuning of phytochrome A signal transduction in Arabidopsis?

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000171200600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biochemistry & Molecular BiologyPlant SciencesCell BiologySCI(E)EI191983-19861

Management Responses to Online Hotel Reviews: Text Mining to Lift Sales

In the era of electronic word-of-mouth, hotels are under the pressure to respond to online reviews more effectively and strategically, to maintain and enhance hotel reputation and financial viability. Little research is done to guide organizations to effectively operate management response strategies. Using data mining techniques such as text mining, sentiment analysis, and Latent Dirichlet Allocation, guided by the affect theory, this study develops and evaluates an “AAAA” framework that classifies management responses to online hotel reviews into four categories: acknowledgment, account, action, and affect. A training sample of 29,606 review responses collected from three U.S cities on TripAdvisor.com are mined and analyzed. The results will be further tested by a longitudinal, econometric sales model, to identify the effectiveness of management response strategies for hotels. This research-in- progress will contribute to emerging research in management responses to online hotel reviews and provide managerial implications for managing responses to enhance hotel performance

A Fractional Brownian Motion Model for Forecasting Lost Load and Time Interval Between Power Outages

International audienceA novel idea is proposed to forecast lost load and time interval between power outages based on the similarity between the series generated by a stochastic model and the actual series. The lost load obeys a power-law distribution, which can be described as a stochastic process with long-range dependence (LRD). Fractional Brownian motion (fBm) is a stochastic model with LRD, we discretize the stochastic differential equation (SDE) driven by fBm and the difference iterative equation is constructed for forecasting. By calculating the Hurst exponent of lost load series proves that it has LRD characteristics. The stochastics series produced by fBm has a strong similarity with the actual series when the Hurst exponent is taken into fBm model. Moreover, the forecasting accuracy is enriched by considering the appropriate sample size and forecasting step size. The same process for the analysis of lost load can be applied to forecast the time interval between power outages. The efficiency of the proposed model is demonstrated by a case study of the medium voltage power grid in Shanghai compared with other approaches. Finally, the value at risk (VaR) and the conditional value at risk (CVaR) as two system-level indices for assessment of future power outages