Vipul Singhal

Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY's spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.

RNA regulators are emerging as powerful tools to engineer synthetic genetic networks or rewire existing ones. A potential strength of RNA networks is that they may be able to propagate signals on time scales that are set by the fast degradation rates of RNAs. However, a current bottleneck to verifying this potential is the slow design-build-test cycle of evaluating these networks in vivo. Here, we adapt an Escherichia coli-based cell-free transcription-translation (TX-TL) system for rapidly prototyping RNA networks. We used this system to measure the response time of an RNA transcription cascade to be approximately five minutes per step of the cascade. We also show that this response time can be adjusted with temperature and regulator threshold tuning. Finally, we use TX-TL to prototype a new RNA network, an RNA single input module, and show that this network temporally stages the expression of two genes in vivo.

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

The full-wave rectifier is the most straightforward way of extracting energy from a piezoelectric source. Unfortunately, the inherent capacitance of the piezoelement significantly limits the efficiency of extraction. The bias-flip rectifier, which aims to mitigate this problem, not only needs a large inductor for efficient operation, but also needs the generation of pulses with a precisely defined ontime. A large inductor increases the overall volume of the system. We present the multi-stage bias-flip rectifier, which is a technique that achieves a high voltage-flip efficiency using a much smaller inductor, and relaxes timing-accuracy requirements. The rectifier, implemented in a 130-nm CMOS process, dissipates about 2 μW and achieves a voltage-flip efficiency of 89.5% while using only a 47 μH inductor.

Script-based text document classification is an important field of research in the context of multilingual textual document processing. But, all script identification techniques available in the literature so far do not consider handwritten documents. Variations in the writing style, character size, inter-line and inter-word spacings, etc. make the recognition process difficult and unreliable when these script identification algorithms, more specifically visual appearance based approaches, are applied directly on hand-written documents. Therefore, in this paper, we propose to preprocess the input document images so as to compensate for the variations due to writing style and thereby making them suitable for analysis on the basis of their visual appearances. Accordingly, we apply denoising, thinning, pruning, m-connectivity and text size normalization in sequence. Multi-channel Gabor filtering is used to extract texture features that characterize the visual appearances of the document images. Experimental result proves the potentiality of our proposed method of script identification for hand-written text document classification.