Deep learning method for pinhole array color image reconstruction.

The following paper proposes a combination of a supervised encoder-decoder neural network with coded apertures. Coded apertures provide improved sensitivity and signal-to-noise ratio (SNR) in planar images. The unique array design of this method overcomes the spatial frequency cutoff found in standard multi-pinhole arrays. In this design, the pinholes were positioned to minimize loss in spatial frequencies. The large number of pinholes results in significant overlapping on the detector. To overcome the overlapping issue, reconstruction of the object from the obtained image is done using inverse filtering methods. However, traces of duplications remain leading to a decline in SNR, contrast, and resolution. The proposed technique addresses the challenge of image distortion caused by the lack of accuracy in the inverse filter methods, by using a deep neural network. In this work, the coded aperture is combined with a deep convolutional neural network (CNN) to remove noise caused by pinhole imaging and inverse filter limitations. Compared to only using Wiener filtering, the proposed method delivers higher SNR, contrast, and resolution. The imaging system is presented in detail with experimental results that illustrate its efficiency.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.

BACKGROUND: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. METHODS: In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. RESULTS: Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). CONCLUSIONS: We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

An open-source computational tool for measuring bacterial biofilm morphology and growth kinetics upon one-sided exposure to an antimicrobial source.

Bacillus subtilis biofilms are well known for their complex and highly adaptive morphology. Indeed, their phenotypical diversity and intra-biofilm heterogeneity make this gram-positive bacterium the subject of many scientific papers on the structure of biofilms. The "robustness" of biofilms is a term often used to describe their level of susceptibility to antimicrobial agents and various mechanical and molecular inhibition/eradication methods. In this paper, we use computational analytics to quantify Bacillus subtilis morphological response to proximity to an antimicrobial source, in the form of the antiseptic chlorhexidine. Chlorhexidine droplets, placed in proximity to Bacillus subtilis macrocolonies at different distances result in morphological changes, quantified using Python-based code, which we have made publicly available. Our results quantify peripheral and inner core deformation as well as differences in cellular viability of the two regions. The results reveal that the inner core, which is often characterized by the presence of wrinkled formations in the macrocolony, is more preserved than the periphery. Furthermore, the paper describes a crescent-shaped colony morphology which occurs when the distance from the chlorhexidine source is 0.5 cm, as well as changes observed in the growth substrate of macrocolonies exposed to chlorhexidine.

Assessing predictions of the impact of variants on splicing in CAGI5.

Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.

The role of reverse transcriptase in intron gain and loss mechanisms.

Intron density is highly variable across eukaryotic species. It seems that different lineages have experienced considerably different levels of intron gain and loss events, but the reasons for this are not well known. A large number of mechanisms for intron loss and gain have been suggested, and most of them have at least some level of indirect support. We therefore figured out that the variability in intron density can be a reflection of the fact that different mechanisms are active in different lineages. Quite a number of these putative mechanisms, both for intron loss and for intron gain, postulate that the enzyme reverse transcriptase (RT) has a key role in the process. In this paper, we lay out three predictions whose approval or falsification gives indication for the involvement of RT in intron gain and loss processes. Testing these predictions requires data on the intron gain and loss rates of individual genes along different branches of the eukaryotic phylogenetic tree. So far, such rates could not be computed, and hence, these predictions could not be rigorously evaluated. Here, we use a maximum likelihood algorithm that we have devised in the past, Evolutionary Reconstruction by Expectation Maximization, which allows the estimation of such rates. Using this algorithm, we computed the intron loss and gain rates of more than 300 genes in each branch of the phylogenetic tree of 19 eukaryotic species. Based on that we found only little support for RT activity in intron gain. In contrast, we suggest that RT-mediated intron loss is a mechanism that is very efficient in removing introns, and thus, its levels of activity may be a major determinant of intron number. Moreover, we found that intron gain and loss rates are negatively correlated in intron-poor species but are positively correlated for intron-rich species. One explanation to this is that intron gain and loss mechanisms in intron-rich species (like metazoans) share a common mechanistic component, albeit not a RT.