Paneth cells at the base of small intestinal crypts of Lieberkühn secrete high levels of α-defensins in response to cholinergic and microbial stimuli. Paneth cell α-defensins are broad spectrum ...microbicides that function in the extracellular environment of the intestinal lumen, and they are responsible for the majority of secreted bactericidal peptide activity. Paneth cell α-defensins confer immunity to oral infection by
Salmonella enterica
serovar Typhimurium, and they are major determinants of the composition of the small intestinal microbiome. In addition to host defense molecules such as α-defensins, lysozyme, and Pla2g2a, Paneth cells also produce and release proinflammatory mediators as components of secretory granules. Disruption of Paneth cell homeostasis, with subsequent induction of endoplasmic reticulum stress, autophagy, or apoptosis, contributes to inflammation in diverse genetic and experimental mouse models.
Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a major challenge in early stages of drug discovery. Using modular message passing graph neural networks ...describing both the ligand and the protein in their free and bound states, we unambiguously evidence that an explicit description of protein–ligand noncovalent interactions does not provide any advantage with respect to ligand or protein descriptors. Simple models, inferring binding affinities of test samples from that of the closest ligands or proteins in the training set, already exhibit good performances, suggesting that memorization largely dominates true learning in the deep neural networks. The current study suggests considering only noncovalent interactions while omitting their protein and ligand atomic environments. Removing all hidden biases probably requires much denser protein–ligand training matrices and a coordinated effort of the drug design community to solve the necessary protein–ligand structures.
This article considers a form of marketing strategy among upmarket food and beverage establishments in Hong Kong and Singapore involving the use of Chinese text in their decor. Although the two ...cities have a majority Chinese population, English is widely considered the language of social mobility and an unmarked language in the discursive construction of eliteness. In asking, “Why Chinese?” we consider how the indexical value of a vernacular language can be rescaled in upmarket commercial spaces for an emergent group of consumers known as “cultural omnivores.” Through the process of indexical selectivity, the invocation of Chinese in these establishments taps into the unique disposition of cultural omnivores by feeding their multilingual desires, and more specifically their desire to consume relatively more or less prestigious languages omnivorously in indexing social distinction. Such alternative readings of the prestige value of the vernacular by a privileged group of consumers point to the ambivalent indexicality of language.
Following my observation of an increased prevalence of neon or neon-esque signs within high-end spaces of consumption, this paper seeks to theorise how such signs can function as vehicles for the ...circulation of neoliberal ideas, a key concept structuring the capitalist economies, whilst having effects for individuals who are sold a brand of cool capitalist consumption. I demonstrates how neoliberalism is semiotically realised both online and offline through examples of “language objects” consisting of neon signs found in cafes located in Hong Kong and Singapore, and their Instagram resemiotizations, focusingon dimensions of precarity and the entrepreneurial self. In doing so, I suggest that these signs are not merely aphorismic decor but a means to reproduce neoliberal ideology in both their medium and message when they stylise concepts of individual agency, success and the turning of everyday activities into “projects”. Instead of the transformation and success promised by these stances, these signs further obscure the social embeddedness of neoliberalism and resulting inequality.
Older age, male sex, and non-white race have been repor ted to be risk factors for COVID-19 mor tality. Few studies have explored how these intersecting factors contribute to COVID-19 outcomes. This ...study aimed to compare demographic characteristics and trends in SARS-CoV-2 admissions and the health care they received. Hospital admission data were collected through DATCOV, an active national COVID-19 surveillance programme. Descriptive analysis was used to compare admissions and deaths by age, sex, race, and health sector as a proxy for socio-economic status. COVID-19 mor tality and healthcare utilisation were compared by race using random effect multivariable logistic regression models. On multivariable analysis, black African patients (adjusted OR aOR 1.3, 95% confidence interval CI 1.2, 1.3), coloured patients (aOR 1.2, 95% CI 1.1, 1.3), and patients of Indian descent (aOR 1.2, 95% CI 1.2, 1.3) had increased risk of in-hospital COVID-19 mor tality compared to white patients; and admission in the public health sector (aOR 1.5, 95% CI 1.5, 1.6) was associated with increased risk of mor tality compared to those in the private sector. There were higher percentages of COVID-19 hospitalised individuals treated in ICU, ventilated, and treated with supplemental oxygen in the private compared to the public sector. There were increased odds of non-white patients being treated in ICU or ventilated in the private sector, but decreased odds of black African patients being treated in ICU (aOR 0.5; 95% CI 0.4, 0.5) or ventilated (aOR 0.5; 95% CI 0.4, 0.6) compared to white patients in the public sector. These ifndings demonstrate the impor tance of collecting and analysing data on race and socio-economic status to ensure that disease control measures address the most vulnerable populations affected by COVID-19.Significance: • These findings demonstrate the importance of collecting data on socio-economic status and race alongside age and sex, to identify the populations most vulnerable to COVID-19. • This study allows a better understanding of the pre-existing inequalities that predispose some groups to poor disease outcomes and yet more limited access to health interventions. • Interventions adapted for the most vulnerable populations are likely to be more effective. • The national government must provide efficient and inclusive non-discriminatory health services, and urgently improve access to ICU, ventilation and oxygen in the public sector. • Transformation of the healthcare system is long overdue, including narrowing the gap in resources between the private and public sectors.
In this paper, we present a novel optical microelectromechanical systems (MEMS) accelerometer sensor dedicated to space applications. An in-plane Fabry-Pérot (FP) microcavity (FPM) with two ...distributed Bragg reflectors (DBRs) is used to detect the acceleration. One of the DBR mirrors is attached to two suspended proof masses, allowing the FP gap to change while proof masses experience acceleration. Acceleration is then detected by measuring the spectral shift of the FPM. The optical accelerometer presented here uses silicon strip waveguides integrated with MEMS on a single silicon-on-insulator wafer, making it compact and robust. All of the device components are fabricated using one single fabrication step. Immunity to electromagnetic interference, high sensitivity and resolution capability, integrability, reliability, low cross-sensitivity, simple fabrication, and possibility of having two- and three-axis sensitivities are numerous advantages of our sensor compared to the conventional ones. The sensor performance demonstrated a 90-nm/g sensitivity and 111-μg resolution and better than 250-mg dynamic range.
The following terminology was erroneously reported: “non-white race” should be “people of colour”, or “black African, coloured and people of Indian descent”.
The dynamics of microtubules is essential for many microtubule-dependent cellular functions such as the mitosis. It has been recognized for a long time that GTP hydrolysis in
αβ
-tubulin polymers ...plays a critical role in this dynamics. However, the effects of the changes in the nature of the guanosine nucleotide at the E-site in
β
-tubulin on microtubule structure and stability are still not well understood. In the present work, we performed all-atom molecular dynamics simulations of a
αβα
-tubulin heterotrimer harboring a guanosine nucleotide in three different states at the E-site: GTP, GDP-Pi and GDP. We found that changes in the nucleotide state is associated with significant conformational variations at the
α
-tubulin N- and
β
-tubulin M-loops which impact the interactions between tubulin protofilaments. The results also show that GTP hydrolysis reduces
αβ
-tubulin interdimer contacts in favor of intradimer interface. From an atomistic point view, we propose a role for
α
-tubulin glutamate residue 254 in catalytic magnesium coordination and identified a water molecule in the nucleotide binding pocket which is most probably required for nucleotide hydrolysis. Finally, the results are discussed with reference to the role of taxol in microtubule stability and the recent tubulin-sT2R crystal structures.
Generative models are being increasingly used in drug discovery, very often coupled with absorption, distribution, metabolism, and excretion (ADME) bioassays or quantitative structure–activity ...relationship (QSAR) models to optimize a given set of properties. The molecules proposed by these algorithms are often revealed to be false positives; that is, they are predicted to be active and turn out to be inactive after synthesis and testing, mostly due to overoptimization of the predicted scores, which leads to an actual decrease or stagnation of the real scores. This behavior is also known as the “hacking” of the predictive models by the generative model during the optimization step. This issue is reminiscent of adversarial examples in machine learning and it can be seen as enunciated by Goodhart’s law: “when a measure becomes a target, it ceases to be a good measure.” This issue is even more apparent in a multiparameter optimization (MPO) case, where the models need to extrapolate outside the training set distribution because there are no known molecules satisfying all the objectives simultaneously in the initial training set. Experimental evaluation of this problem is a hard and expensive task since it requires synthesis and testing of the generated molecules. Thus, efforts have been made to develop in silico “oracles”real-valued functions used as proxies for molecular propertiesto help with the evaluation of these generative-model-based pipelines. However, these oracles have had a limited value so far because they are often too easy to model in comparison with biological assays and are usually limited to mono-objective cases. In this work, we introduce a simulator of multitarget assays using a smartly initialized neural network (NN) that returns continuous values for any input molecule. We use this oracle to replicate a real-world prospective lead optimization (LO) scenario. First, we trained predictive models on an initial small sample of molecules aimed at predicting their oracle values. Afterward, we generated new optimized molecules using the open-source GuacaMol package coupled with the previously built predictive models. Finally, we selected compounds matching the candidate drug target profile (CDTP) according to the predicted values and evaluated them by computing the true oracle values. We observed that even when the predictive models had excellent estimated performance metrics, the final selection still contained multiple false positives according to the NN-based oracle. Then, we evaluated the optimization behavior in mono- and bi-objective scenarios using either a logistic regression or a random forest predictive model. We also propose and evaluate several methods to help mitigate the hacking issue.
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