Synergizing quantum techniques with machine learning for advancing drug discovery challenge.

The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the use of quantum computing for drug discovery. The challenge centered on designing algorithms to accurately estimate the ground state energy of molecules, specifically OH+, using quantum computing techniques.

Machine learning-based prediction of compost maturity and identification of key parameters during manure composting.

Evaluating compost maturity, e.g. via manual seed germination index (GI) measurement, is both time-consuming and costly during composting.

The role of biodegradable plastics in lignite anaerobic digestion: Changes of organics transformation and metabolic pathway.

Biodegradable plastics (BPs) and lignite, both rich in organic matter, present significant challenges for efficient conversion into clean energy. This study examined the anaerobic co-digestion of BPs and lignite under controlled laboratory conditions. The results demonstrated that the co-digestion of polylactic acid (PLA) and lignite (at a 1:2 mass ratio, with 5 g PLA and 10 g lignite as the model system) rapidly acclimated to the anaerobic environment, enhancing cumulative biogas production by 57 % compared to the mono-digestion of lignite alone.

Ginsenoside Ro prevents endothelial injury via promoting Epac1/AMPK- mediated mitochondria protection in early diabetic retinopathy.

Diabetic retinopathy (DR) is a blinding complication of microangiopathy. First-line therapeutic drugs are all focused on late-stage DR and have several side effects, which could not meet clinical needs. The plant-derived ginsenoside Ro (Ro) has a variety of effective anti-inflammatory, immune-regulating, and cardiovascular protective effects, but its microvascular protective effects are rarely studied.

Regboost: Enhancing mouse brain image registration using geometric priors and Laplacian interpolation.

We show in this work that incorporating geometric features and geometry processing algorithms for mouse brain image registration broadens the applicability of registration algorithms and improves the registration accuracy of existing methods. We introduce the preprocessing and postprocessing steps in our proposed framework as RegBoost. We develop a method to align the axis of 3D image stacks by detecting the central planes that pass symmetrically through the image volumes.