Cysteine palmitoylation (S-palmitoylation) is a reversible post-translational modification that is installed by the DHHC family of palmitoyltransferases and is reversed by several acyl protein thioesterases 1,2 . Although thousands of human proteins are known to undergo S-palmitoylation, how this modification is regulated to modulate specific biological functions is poorly understood. Here we report that the key T helper 17 (T H 17) cell differentiation stimulator, STAT3 3,4 , is subject to reversible S-palmitoylation on cysteine 108. DHHC7 palmitoylates STAT3 and promotes its membrane recruitment and phosphorylation. Acyl protein thioesterase 2 (APT2, also known as LYPLA2) depalmitoylates the phosphorylated STAT3 (p-STAT3) and enables it to translocate to the nucleus. This palmitoylation-depalmitoylation cycle enhances STAT3 activation and promotes T H 17 cell differentiation; perturbation of either palmitoylation or depalmitoylation negatively affects T H 17 cell differentiation. Overactivation of T H 17 cells is associated with several inflammatory diseases, including inflammatory bowel disease (IBD). In a mouse model, pharmacological inhibition of APT2 or knockout of Zdhhc7-which encodes DHHC7 [Author:OK?]-relieves the symptoms of IBD. Our study reveals not only a potential therapeutic strategy for the treatment of IBD but also a model through which S-palmitoylation regulates cell signalling, †

A new data set of seasonal stable water isotopes (δD and δ18O) and temperature‐salinity profiles was applied to improve our understanding of water mass distributions and their impact on the environment of the Beibu Gulf (BG). Our study revealed that the coastal current (CC), West‐Guangdong coastal current (WGCC), and South China Sea water (SCSW) were the three dominant water masses in the BG, and their influence was exhibited in seasonal variations. The CC was the dominant contributor to the BG water during summer (43%) and fall (45%), while it changed to the intrusion of SCSW with higher salinity in winter (57%). The contribution of WGCC to the BG was relatively stable during the three seasons (24%–31%). In addition, the nutrients in the BG were greatly affected by different water mixing occurring in the gulf. The nutrients mainly originated from the CC in summer (52%–68%) and fall (32%–69%), while the dominant source shifted to the WGCC in winter (36%–69%). Moreover, the contribution of SCSW to the nutrients loading (15%–49%) in the BG was relatively high due to its high contribution (57%) to the BG water during winter. These indicated that the BG has a stable input of external nutrients from different water masses to sustain primary production in the BG. Our study uses dual water isotopes to quantify the seasonal intrusion of water masses and their impact on nutrients, providing a new method to study the impact of the distribution of water masses on nutrients in the gulf.

Objectives This study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse. Methods Twitter data (N = 82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics. Results The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than 1 in 10 (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. Discussion Ageist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Public education on the risk of severe illness is needed to correct misperceptions.

The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering research. This article presents an empirical study of behavioral service profiles collected from millions of users whose devices are deployed with Wandoujia, a leading Android app-store service in China. The dataset of Wandoujia service profiles consists of two kinds of user behavioral data from using 0.28 million free Android apps, including (1) app management activities (i.e., downloading, updating, and uninstalling apps) from over 17 million unique users and (2) app network usage from over 6 million unique users. We explore multiple aspects of such behavioral data and present patterns of app usage. Based on the findings as well as derived knowledge, we also suggest some new open opportunities and challenges that can be explored by the research community, including app development, deployment, delivery, revenue, etc.Index Terms-mobile apps, app store, user behavior analysis !

Sirtuin 2 (SIRT2) is a protein lysine deacylase that has been indicated as a therapeutic target for cancer. To further establish the role of SIRT2 in cancers, it is necessary to develop selective and potent inhibitors. Here, we report the facile synthesis of novel lysine derived thioureas as mechanism-based SIRT2 inhibitors with anticancer activity. Compounds AF8, AF10, and AF12 selectively inhibited SIRT2 with IC 50 values of 0.06, 0.15, and 0.08 μM, respectively. Compounds AF8 and AF10 demonstrated broad cytotoxicity amongst cancer cell lines, but minimal toxicity in noncancerous cells. AF8 and AF10 inhibited the anchorage-independent growth of human colorectal cancer cell line HCT116 with GI 50 values of ~7 μM. Furthermore, AF8 potently inhibited tumor growth in a HCT116 xenograft murine model, supporting that SIRT2 is a viable therapeutic target for colorectal cancer.

We show that the radiation forces (RFs) on a Rayleigh dielectric sphere induced by a partially coherent light beam are greatly affected by the spatial coherence. We find that the magnitude of the RFs greatly decreases as the spatial coherence decreases and derive an inequality for the required correlation width sigma(0) (i.e., the spatial coherence of the beam) to stably trap the particles.

Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A forums. However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason. Then, researchers have to utilize labeled SE-related texts to customize sentiment analysis for SE tasks via a variety of algorithms. However, the scarce labeled data can cover only very limited expressions and thus cannot guarantee the analysis quality. To address such a problem, we turn to the easily available emoji usage data for help. More specifically, we employ emotional emojis as noisy labels of sentiments and propose a representation learning approach that uses both Tweets and GitHub posts containing emojis to learn sentiment-aware representations for SE-related texts. These emoji-labeled posts can not only supply the technical jargon, but also incorporate more general sentiment patterns shared across domains. They as well as labeled data are used to learn the final sentiment classifier. Compared to the existing sentiment analysis methods used in SE, the proposed approach can achieve significant improvement on representative benchmark datasets. By further contrast experiments, we find that the Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource, but try to transform knowledge from the open domain through ubiquitous signals such as emojis.

Typhoons (tropical storms or hurricanes) are among the most extreme weather events affecting open seas and coastal areas, and these storm systems play an important role in phytoplankton growth in oligotrophic oceanic waters, especially in tropical and subtropical zones (