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We introduce the UConn Bubbles with Swatches dataset. This dataset contains images of voting bubbles, scanned from Connecticut ballots, either captured as grayscale (8 bpp) or color (RGB, 24 bpp) artifacts, and extracted through segmentation using ballot geometry. These images are organized into 4 groups of datasets. The stored file contains all data together in color and we manually convert to greyscale. Each image of a bubble is 40x50 pixels. The labels are produced from an optical lens scanner.  The first dataset, Gray-B (Bubbles), uses 42,679 images (40x50, 8 bpp) with blank (35,429 images) and filled (7,250 images) bubbles filled in by humans, but no marginal marks. There are two classes, mark and nonmark. The second dataset, RGB-B, is a 24 bpp color (RGB) version of Bubbles-Gray.  The third dataset, Gray-C (Combined), augments Gray-B with a collection of marginal marks called “swatches”, which are synthetic images that vary the position of signal to create samples close to the boundary of an optical lens scanner. The 423,703 randomly generated swatches place equal amounts of random noise throughout each image such that the amount of light is the same. This yields 466,382 labeled images. The fourth dataset, RGB-C, is a 24bpp color (RGB) version of Gray-C. The empty bubbles are bubbles that were printed by a commercial vendor. They have undergone registration and segmentation using predetermined coordinates. Marks are on paper printed by the same vendor. These datasets can be used for classification training. The .h5 has many levels of datasets as shown below.  The main dataset used for training is positional.  This is only separated into blank (non-mark) and vote (mark).  Whether the example is a bubble or a swatch is indicated by batch number.  See https://github.com/VoterCenter/Busting-the-Ballot/blob/main/Utilities/LoadVoterData.py for code that creates torch arrays for RGB-B and RGB-C. See the linked Github repo (https://github.com/VoterCenter/Busting-the-Ballot/blob/main/Utilities/VoterLab_Classifier_Functions.py) for grayscale conversion functions and other utilities.   Dataset structure: COLOR - POSITIONAL - INFORMATION / / / B/V/Q B/V/Q COLOR/POSITIONAL / / / IMAGE IMAGE B/V/Q / BACKGROUND RGB VALUES Images divided into 'batches' not all of which have dataInformation contains labels for all images. Q is the swatch data, while B and V are non-mark and mark respectively. 

Epigenetic mechanisms enable cells to develop novel adaptive phenotypes without altering their genetic blueprint. Recent studies show histone modifications, such as heterochromatin-defining H3K9 methylation (H3K9me), can be redistributed to establish adaptive phenotypes. We developed a precision-engineered genetic approach to trigger heterochromatin misregulation on-demand in fission yeast. This enabled us to trace genome-scale RNA and H3K9me changes over time in long-term, continuous cultures. Adaptive H3K9me establishes over remarkably slow timescales relative to the initiating stress. We captured dynamic H3K9me redistribution events which depend on an RNA binding complex MTREC, ultimately leading to cells converging on an optimal adaptive solution. Upon stress removal, cells relax to new transcriptional and chromatin states, establishing memory that is tunable and primed for future adaptive epigenetic responses. Collectively, we identify the slow kinetics of epigenetic adaptation that allow cells to discover and heritably encode novel adaptive solutions, with implications for drug resistance and response to infection. 

Abstract Microbial production of cannabinoids promises to provide a consistent, cheaper, and more sustainable supply of these important therapeutic molecules. However, scaling production to compete with traditional plant-based sources is challenging. Our ability to make strain variants greatly exceeds our capacity to screen and identify high producers, creating a bottleneck in metabolic engineering efforts. Here, we present a yeast-based biosensor for detecting microbially produced Δ⁹-tetrahydrocannabinol (THC) to increase throughput and lower the cost of screening. We port five human cannabinoid G protein-coupled receptors (GPCRs) into yeast, showing the cannabinoid type 2 receptor, CB2R, can couple to the yeast pheromone response pathway and report on the concentration of a variety of cannabinoids over a wide dynamic and operational range. We demonstrate that our cannabinoid biosensor can detect THC from microbial cell culture and use this as a tool for measuring relative production yields from a library of Δ⁹-tetrahydrocannabinol acid synthase (THCAS) mutants. 

Abstract Despite the availability of Cas9 variants with varied protospacer-adjacent motif (PAM) compatibilities, some genomic loci—especially those with pyrimidine-rich PAM sequences—remain inaccessible by high-activity Cas9 proteins. Moreover, broadening PAM sequence compatibility through engineering can increase off-target activity. With directed evolution, we generated four Cas9 variants that together enable targeting of most pyrimidine-rich PAM sequences in the human genome. Using phage-assisted noncontinuous evolution and eVOLVER-supported phage-assisted continuous evolution, we evolved Nme2Cas9, a compact Cas9 variant, into variants that recognize single-nucleotide pyrimidine-PAM sequences. We developed a general selection strategy that requires functional editing with fully specified target protospacers and PAMs. We applied this selection to evolve high-activity variants eNme2-T.1, eNme2-T.2, eNme2-C and eNme2-C.NR. Variants eNme2-T.1 and eNme2-T.2 offer access to N₄TN PAM sequences with comparable editing efficiencies as existing variants, while eNme2-C and eNme2-C.NR offer less restrictive PAM requirements, comparable or higher activity in a variety of human cell types and lower off-target activity at N₄CN PAM sequences.