AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

Superconducting crossed correlations in ferromagnets: implications for thermodynamics and quantum transport

It is demonstrated that non local Cooper pairs can propagate in ferromagnetic electrodes having an opposite spin orientation. In the presence of such crossed correlations, the superconducting gap is found to depend explicitly on the relative orientation of the ferromagnetic electrodes. Non local Cooper pairs can in principle be probed with dc-transport. With two ferromagnetic electrodes, we propose a ``quantum switch'' that can be used to detect correlated pairs of electrons. With three or more ferromagnetic electrodes, the Cooper pair-like state is a linear superposition of Cooper pairs which could be detected in dc-transport. The effect also induces an enhancement of the ferromagnetic proximity effect on the basis of crossed superconducting correlations propagating along domain walls.Comment: 4 pages, RevTe

Smoke gets in your eyes:what is sociological about cigarettes?

Contemporary public health approaches increasingly draw attention to the unequal social distribution of cigarette smoking. In contrast, critical accounts emphasize the importance of smokers’ situated agency, the relevance of embodiment and how public health measures against smoking potentially play upon and exacerbate social divisions and inequality. Nevertheless, if the social context of cigarettes is worthy of such attention, and sociology lays a distinct claim to understanding the social, we need to articulate a distinct, positive and systematic claim for smoking as an object of sociological enquiry. This article attempts to address this by situating smoking across three main dimensions of sociological thinking: history and social change; individual agency and experience; and social structures and power. It locates the emergence and development of cigarettes in everyday life within the project of modernity of the nineteenth and twentieth centuries. It goes on to assess the habituated, temporal and experiential aspects of individual smoking practices in everyday lifeworlds. Finally, it argues that smoking, while distributed in important ways by social class, also works relationally to render and inscribe it

Biological activity differences between TGF-β1 and TGF-β3 correlate with differences in the rigidity and arrangement of their component monomers

[Image: see text] TGF-β1, -β2, and -β3 are small, secreted signaling proteins. They share 71–80% sequence identity and signal through the same receptors, yet the isoform-specific null mice have distinctive phenotypes and are inviable. The replacement of the coding sequence of TGF-β1 with TGF-β3 and TGF-β3 with TGF-β1 led to only partial rescue of the mutant phenotypes, suggesting that intrinsic differences between them contribute to the requirement of each in vivo. Here, we investigated whether the previously reported differences in the flexibility of the interfacial helix and arrangement of monomers was responsible for the differences in activity by generating two chimeric proteins in which residues 54–75 in the homodimer interface were swapped. Structural analysis of these using NMR and functional analysis using a dermal fibroblast migration assay showed that swapping the interfacial region swapped both the conformational preferences and activity. Conformational and activity differences were also observed between TGF-β3 and a variant with four helix-stabilizing residues from TGF-β1, suggesting that the observed changes were due to increased helical stability and the altered conformation, as proposed. Surface plasmon resonance analysis showed that TGF-β1, TGF-β3, and variants bound the type II signaling receptor, TβRII, nearly identically, but had small differences in the dissociation rate constant for recruitment of the type I signaling receptor, TβRI. However, the latter did not correlate with conformational preference or activity. Hence, the difference in activity arises from differences in their conformations, not their manner of receptor binding, suggesting that a matrix protein that differentially binds them might determine their distinct activities

Complex exon-intron marking by histone modifications is not determined solely by nucleosome distribution

It has recently been shown that nucleosome distribution, histone modifications and RNA polymerase II (Pol II) occupancy show preferential association with exons (“exon-intron marking”), linking chromatin structure and function to co-transcriptional splicing in a variety of eukaryotes. Previous ChIP-sequencing studies suggested that these marking patterns reflect the nucleosomal landscape. By analyzing ChIP-chip datasets across the human genome in three cell types, we have found that this marking system is far more complex than previously observed. We show here that a range of histone modifications and Pol II are preferentially associated with exons. However, there is noticeable cell-type specificity in the degree of exon marking by histone modifications and, surprisingly, this is also reflected in some histone modifications patterns showing biases towards introns. Exon-intron marking is laid down in the absence of transcription on silent genes, with some marking biases changing or becoming reversed for genes expressed at different levels. Furthermore, the relationship of this marking system with splicing is not simple, with only some histone modifications reflecting exon usage/inclusion, while others mirror patterns of exon exclusion. By examining nucleosomal distributions in all three cell types, we demonstrate that these histone modification patterns cannot solely be accounted for by differences in nucleosome levels between exons and introns. In addition, because of inherent differences between ChIP-chip array and ChIP-sequencing approaches, these platforms report different nucleosome distribution patterns across the human genome. Our findings confound existing views and point to active cellular mechanisms which dynamically regulate histone modification levels and account for exon-intron marking. We believe that these histone modification patterns provide links between chromatin accessibility, Pol II movement and co-transcriptional splicing

Performing Amateurism: A Study of Camgirls’ Work

International audienc

Resistance of Renal Cell Carcinoma to Sorafenib Is Mediated by Potentially Reversible Gene Expression

Purpose: Resistance to antiangiogenic therapy is an important clinical problem. We examined whether resistance occurs at least in part via reversible, physiologic changes in the tumor, or results solely from stable genetic changes in resistant tumor cells. Experimental Design: Mice bearing two human RCC xenografts were treated with sorafenib until they acquired resistance. Resistant 786-O cells were harvested and reimplanted into naïve mice. Mice bearing resistant A498 cells were subjected to a 1 week treatment break. Sorafenib was then again administered to both sets of mice. Tumor growth patterns, gene expression, viability, blood vessel density, and perfusion were serially assessed in treated vs control mice. Results: Despite prior resistance, reimplanted 786-O tumors maintained their ability to stabilize on sorafenib in sequential reimplantation steps. A transcriptome profile of the tumors revealed that the gene expression profile of tumors upon reimplantation reapproximated that of the untreated tumors and was distinct from tumors exhibiting resistance to sorafenib. In A498 tumors, revascularization was noted with resistance and cessation of sorafenib therapy and tumor perfusion was reduced and tumor cell necrosis enhanced with re-exposure to sorafenib. Conclusions: In two RCC cell lines, resistance to sorafenib appears to be reversible. These results support the hypothesis that resistance to VEGF pathway therapy is not solely the result of a permanent genetic change in the tumor or selection of resistant clones, but rather is due to a great extent to reversible changes that likely occur in the tumor and/or its microenvironment

Effectiveness of a computer assisted learning (CAL) package to raise awareness of autism

<p>Abstract</p> <p>Background</p> <p>Promoting awareness of autism in populations who work with children may result in an earlier diagnosis of the condition. In this study, a computer assisted learning (CAL) package, containing educationally appropriate knowledge about autism was developed; and the effectiveness of this CAL package was evaluated.</p> <p>Methods</p> <p>The CAL package was developed using computer software, "Xerte" and "Flash Macromedia". The effectiveness of the CAL package was evaluated in 32 childcare students in the UK, who were randomised to watch the CAL package or to read the information leaflet containing the same information (n = 16 in each group). Retention performance, level of enjoyment, and level of confidence to identify a child with autism, after the interventions, were evaluated. The data obtained from two studied groups was analysed using unpaired Student's t-test, 95% confidence interval, and effect size.</p> <p>Results</p> <p>Students who watched the CAL package had superior retention performance percentage scores (p = 0.02, 95% CI = 0.83–12.19, effect size = 0.8) and level of enjoyment (p = 0.04, 95% CI = 0.03–2.75, effect size = 0.7) compared with students who read the information leaflet. However, there was no significant difference in level of confidence to identify a child with autism (p = 0.39, 95% CI = -1.80–0.72, effect size = -0.3).</p> <p>Conclusion</p> <p>The CAL package developed was an effective method of educating people who work with children about autism.</p