Online Change Point Detection in Molecular Dynamics With Optical Random Features

Proteins are made of atoms constantly fluctuating, but can occasionally undergo large-scale changes. Such transitions are of biological interest, linking the structure of a protein to its function with a cell. Atomic-level simulations, such as Molecular Dynamics (MD), are used to study these events. However, molecular dynamics simulations produce time series with multiple observables, while changes often only affect a few of them. Therefore, detecting conformational changes has proven to be challenging for most change-point detection algorithms. In this work, we focus on the identification of such events given many noisy observables. In particular, we show that the No-prior-Knowledge Exponential Weighted Moving Average (NEWMA) algorithm can be used along optical hardware to successfully identify these changes in real-time. Our method does not need to distinguish between the background of a protein and the protein itself. For larger simulations, it is faster than using traditional silicon hardware and has a lower memory footprint. This technique may enhance the sampling of the conformational space of molecules. It may also be used to detect change-points in other sequential data with a large number of features.Comment: 15 pages, 12 figure

Confirmation of the Prognostic Value of Foxp3+ Cells in Canine Mammary Tumors

Foxp3+ cell counts were evaluated by immunohistochemistry in 59 canine mammary tumors, 20 adenomas, and 39 carcinomas in three different compartments: intratumoral, within the adjacent stroma, and in the distant stroma. Foxp3+ lymphocyte counts were compared with histotype, grading, presence of lymphatic invasion, immunohistochemical expression of estrogen and progesterone receptors, expression of c-erbB-2, and the overall survival (OS). Our findings confirmed that Foxp3+ cells were significantly higher in canine mammary carcinomas compared to adenomas. A significantly higher number of Foxp3+ cells were detected in grade III carcinomas compared to grade II carcinomas, as well as in tumors with lymphatic invasion and loss of ER-expression. Finally, a high number of Foxp3+ cells was associated with poor prognosis. In conclusion, our findings highlighted the association of Foxp3+ lymphocytes with negative clinicopathological features and shorter overall survival (OS), thus confirming the role of Tregs as a negative prognostic marker in canine mammary carcinomas

RITA: a Study on Scaling Up Generative Protein Sequence Models

In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first systematic study of how capabilities evolve with model size for autoregressive transformers in the protein domain: we evaluate RITA models in next amino acid prediction, zero-shot fitness, and enzyme function prediction, showing benefits from increased scale. We release the RITA models openly, to the benefit of the research community

Co-localization of PTEN and E-cadherin in canine mammary hyperplasias and benign and malignant mammary tumours

Fifty-four canine mammary lesions (15 hyperplasias, 7 adenomas and 32 carcinomas) were submitted to immunohistochemical analysis for the evaluation of PTEN and E-cadherin co-expression. Subjects bearing mammary carcinomas were also submitted to a 2-year follow-up study to compare immunohistochemical results with overall survival All the hyperplastic samples stained positive for both markers, 100% of adenomas were positive for PTEN and 86% for E-cadherin, and 69% and 34% of carcinomas were positive for PTEN and E-cadherin, respectively. Statistical analysis showed a positive correlation between these two proteins both considering all (p <0.01) or malignant tumours (p <0.05). The female dogs bearing tumours positively-stained for both markers had a longer overall survival (p <0.05) and absence of lymphatics invasion (p <0.05). Simultaneous double immunofluorescence confirmed the co-localization of the two proteins in neoplastic cells. Results reported in this study confirm the tumor suppressor effect of these two molecules

Learning Binary Data Representation for Optical Processing Units

Optical Processing Units (OPUs) are computing devices that perform random projections of input data by exploiting the physical phenomenon of scattering a light source through a diffusive medium. Random projections calculated by OPUs have been used successfully for approximating kernel ridge regression for large datasets with low power consumption and at high speed. However, OPUs require the input data to be binary. In this paper, we propose to use shallow and deep neural networks (NN) as binary encoders to perform input data binarization. The difficulty in developing a binarization strategy which is learned in an end-to-end fashion along with kernel ridge regression parameters, is due to the non-differentiability of the operation performed by the OPU. We overcome this difficulty by considering OPUs as a black-box and by employing the REINFORCE gradient estimator, which allows us to calculate the gradient of the loss function with respect to the weights of the binarization encoder and to optimize these together with the parameters of kernel ridge regression with gradient- based optimization. Through our experimental campaign on a variety of tasks and datasets, we show that our method outperforms alternative unsupervised and supervised binarization techniques