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Background Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community. Methods By elaborately designing crawlers, we retrieved a complete dataset from the “HIV bar,” the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure. Results In HIV communities, the average topic similarity among members is positively correlated to network efficiency ( r  = 0.70, p  < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size ( r  = 0.25, p  = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests. Conclusions Online communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.

Background In eudicots, germination begins with water uptake by the quiescent dry seed and is greatly related to the permeability of micropyle enriched callose layers. Once imbibition starts, seeds undergo a cascade of physiological, biochemical, and molecular events to initiate cellular activities. However, the effects of callose on water uptake and following seed metabolic events during germination are largely unknown. Cotton ( Gossypium hirsutum ) is a eudicot plant with natural fiber and edible oil production for humans. Here, we addressed this question by examining the role of GhGLU19 , a gene encoding β-1,3-glucanase, in cotton seed germination. Results GhGLU19 belongs to subfamily B and was expressed predominately in imbibed seeds and early seedlings. Compared to wild type, GhGLU19 -suppressing and GhGLU19 -overexpressing transgenic cotton lines showed the higher and lower seed germination percentage, respectively. Callose was enriched more at inner integument (ii) than that in embryo and seed coat in cotton seeds. In GhGLU19 -suppressing lines, callose at ii of cotton seeds was greatly increased and brought about a prolonged water uptake process during imbibition. Both proteomic and transcriptomic analysis revealed that contrary to GhGLU19 -overexpressing lines, the glycolysis and pyruvate metabolism was decreased, and abscisic acid (ABA) biosynthesis related genes were downregulated in imbibed seeds of GhGLU19 -suppressing lines. Also, endogenous ABA was significantly decreased in GhGLU19 -suppressing line while increased in GhGLU19 -overexpressing line. Conclusions Our results demonstrate that suppression of GhGLU19 improves cotton seed germination via accumulating callose of inner integument, modulating glycolysis and pyruvate metabolism, and decreasing ABA biosynthesis. This study provides a potential way for improving germination percentage in cotton seed production, and other eudicot crops.

Many etiologies of acute liver injury (ALI) include drug-induced liver injury (DILI), viral illness, and autoimmune disease. Acute pancreatitis is an uncommon though significant etiology of ALI caused by inflammation, fluid shifts, and ischemia secondary to microthrombi formation that can progress to liver failure if left untreated. We present a case of hypertriglyceridemia-induced pancreatitis resulting in liver injury-associated acute pancreatitis (LIAAP) and a concurrent consumptive coagulopathy consistent with an ischemic hepatopathy. Through treatment of her pancreatitis with intravenous insulin and plasmapheresis and subsequent transition to an oral regimen for her hypertriglyceridemia upon hospital discharge, the patient demonstrated full resolution of her ALI and coagulopathy. Through this case, we hope to highlight the importance of recognizing LIAAP and its underlying pathogenesis.

Middle-aged obesity, characterized by excessive fat accumulation and systemic energy imbalance, often precedes various health complications. Recent research has unveiled a surprising link between DNA damage response and energy metabolism. Here, we explore the role of Eepd1, a DNA repair enzyme, in regulating adipose tissue function and obesity onset. Eepd1 is primarily expressed in adipose tissue, where its downregulation or deletion accelerates obesity development. We show that Eepd1 ablation hinders PKA activation, thereby inhibiting lipolysis and thermogenesis in adipose tissue. Notably, cold exposure enhances Eepd1’s myristoylation, facilitating its anchorage to adipocyte membranes and subsequent activation of PKA, while a mutation at the myristoylation site of Eepd1 disrupts this process. Moreover, individuals with obesity exhibit reduced Eepd1 expression. Pharmacological restoration of Eepd1 with Retigabine dihydrochloride effectively mitigates obesity. This study reveals Eepd1’s unexpected role in promoting adipose lipolysis and thermogenesis, underscoring its potential as a promising therapeutic target to combat obesity. Middle-aged obesity, marked by excessive fat accumulation, is closely linked to DNA damage. Here, the authors show that the DNA repair enzyme Eepd1, primarily expressed in adipose tissue, in its myristoylated form, promotes lipolysis and thermogenesis by activating PKA to combat obesity.

With the rapid advancement in artificial intelligence, autonomous driving has emerged as a prominent research frontier. Autonomous vehicles rely on high-precision high-definition map data, necessitating timely map updates by map companies to accurately reflect road conditions. This paper proposes an efficient and fair map-data-sharing mechanism for vehicular networks. To encourage vehicles to share data, we introduce a reputation unit to resolve the cold-start issue for new vehicles, effectively distinguishing legitimate new vehicles from malicious attackers. Considering both the budget constraints of map companies and heterogeneous data collection capabilities of vehicles, we design a fair incentive mechanism based on the proposed reputation unit and a reverse auction algorithm, achieving an optimal balance between data quality and procurement costs. Furthermore, the scheme has been developed to facilitate mutual authentication between vehicles and Roadside Unit(RSU), thereby ensuring the security of shared data. In order to address the issue of redundant authentication in overlapping RSU coverage areas, we construct a Merkle hash tree structure using a set of anonymous certificates, enabling single-round identity verification to enhance authentication efficiency. A security analysis demonstrates the robustness of the scheme, while performance evaluations and the experimental results validate its effectiveness and practicality.

Viruses, such as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infect hosts and take advantage of host cellular machinery for genome replication and new virion production. Identifying and elucidating host pathways for viral infection is critical for understanding the development of the viral life cycle and novel therapeutics. The SARS-CoV-2 N protein is critical for viral RNA (vRNA) genome packaging in new virion formation. Using our quantitative Förster energy transfer/Mass spectrometry (qFRET/MS) coupled method and immunofluorescence imaging, we identified three SUMOylation sites of the SARS-CoV-2 N protein. We found that (1) Small Ubiquitin-like modifier (SUMO) modification in Nucleocapsid (N) protein interaction affinity increased, leading to enhanced oligomerization of the N protein; (2) one of the identified SUMOylation sites, K65, is critical for its nuclear translocation. These results suggest that the host human SUMOylation pathway may be critical for N protein functions in viral replication and pathology in vivo. Thus, blocking essential host pathways could provide a novel strategy for future anti-viral therapeutics development, such as for SARS-CoV-2 and other viruses.

Background Understanding the geographic distribution of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequate deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level. Methods We collect a comprehensive dataset from the largest sub-community related to MSM topics in Baidu Tieba, covering 628,360 MSM-related users. Based on users’ publicly available posts, we evaluate and compare the performances of mainstream location inference algorithms on the online locating problem of Chinese MSM population. To improve the inference accuracy, other approaches in natural language processing are introduced into the location extraction, such as context analysis and pattern recognition. In addition, we develop a hybrid voting algorithm (HVA-LI) by allowing different approaches to vote to determine the best inference results, which guarantees a more effective way on location inference for hidden population. Results By comparing the performances of popular inference algorithms, we find that the classic gazetteer-based algorithm has achieved better results. And in the HVA-LI algorithms, the hybrid algorithm consisting of the simple gazetteer-based method and named entity recognition (NER) is proven to be the best to deal with inferring users’ locations disclosed in short texts on online communities, improving the inferring accuracy from 50.3 to 71.3% on the MSM-related dataset. Conclusions In this study, we have explored the possibility of location inferring by analyzing textual content posted by online users. A more effective hybrid algorithm, i.e., the Gazetteer & NER algorithm is proposed, which is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.

In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.

According to official statistics, cancer remains the main reason of death and over 50% of patients with cancer receive radiotherapy. However, adverse consequences after radiation exposure like radiation-induced skin reactions (RISR) have negative or even fatal impact on patients’ quality of life (QoL). In this review we summarize the mechanisms and managements of RISRs, a process that involve a variety of extracellular and intracellular signals, among which oxidative stress (OS) are now commonly believed to be the initial part of the occurrence of all types of RISRs. As for the management of RISRs, traditional treatments have been widely used but without satisfying outcomes while some promising therapeutic strategies related to OS still need further researches. In the context we discuss how OS leads to the happening of RISRs of different types, hoping it can shed some light on the exploration of new countermeasures.