PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning.

One of the challenges in a viral pandemic is the emergence of novel variants with different phenotypical characteristics. An ability to forecast future viral individuals at the sequence level enables advance preparation by characterizing the sequences and closing vulnerabilities in current preventative and therapeutic methods. In this article, we explore, in the context of a viral pandemic, the problem of generating complete instances of undiscovered viral protein sequences, which have a high likelihood of being discovered in the future using protein language models.

HELLO: improved neural network architectures and methodologies for small variant calling.

Background: Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy.

Multimode smartphone biosensing: the transmission, reflection, and intensity spectral (TRI)-analyzer.

We demonstrate a smartphone-integrated handheld detection instrument capable of utilizing the internal rear-facing camera as a high-resolution spectrometer for measuring the colorimetric absorption spectrum, fluorescence emission spectrum, and resonant reflection spectrum from a microfluidic cartridge inserted into the measurement light path. Under user selection, the instrument gathers light from either the white "flash" LED of the smartphone or an integrated green laser diode to direct illumination into a liquid test sample or onto a photonic crystal biosensor. Light emerging from each type of assay is gathered via optical fiber and passed through a diffraction grating placed directly over the smartphone camera to generate spectra from the assay when an image is collected.

Characterization of drug authenticity using thin-layer chromatography imaging with a mobile phone.

Thin-layer chromatography (TLC) has a myriad of separation applications in chemistry, biology, and pharmacology due to its simplicity and low cost. While benchtop laboratory sample application and detection systems for TLC provide accurate quantitation of TLC spot positions and densities, there are many applications where inexpensive and portable instruments would greatly expand the applicability of the technology. In this work, we demonstrate identity verification and concentration determination of pharmaceutical compounds via TLC using a custom 3D-printed cradle that interfaces with an ordinary mobile phone.