HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures

Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures remains a significant challenge, making it a vibrant research area. In this paper, we introduce HydraProt (deep Hydration of Proteins), a novel methodology for predicting precise positions of water molecule oxygen atoms around protein structures, leveraging two interconnected deep learning architectures: a 3D U-net and a Multi-Layer Perceptron (MLP). Our approach starts by introducing a coarse voxel-based representation of the protein, which allows for rapid sampling of candidate water positions via the 3D U-net. These water positions are then assessed by embedding the water-protein relationship in the Euclidean space by means of an MLP. Finally, a postprocessing step is applied to further refine the MLP predictions. HydraProt surpasses existing state-of-the-art approaches in terms of precision and recall and has been validated on large data sets of protein structures. Notably, our method offers rapid inference runtime and should constitute the method of choice for protein structure studies and drug discovery applications. Our pretrained models, data, and the source code required to reproduce these results are accessible at https://github.com/azamanos/HydraProt.

Hydrogen-Assisted Thermal Treatment of Electrode Materials for Electrochemical Double-Layer Capacitors

The capacitance of electrode materials used in electrochemical double-layer capacitors (EDLCs) is currently limited by several factors, including inaccessible isolated micropores in high-surface area carbons, the finite density of states resulting in a quantum capacitance in series to Helmholtz double-layer capacitance, and the presence of surface impurities, such as functional groups and adsorbed species. To unlock the full potential of EDLC active materials and corresponding electrodes, several post-production treatments are commonly proposed to improve their capacitance and, thus, the energy density of the corresponding devices. In this work, we report a systematic study of the effect of a prototypical treatment, namely H2-assisted thermal treatment, on the chemical, structural, and thermal properties of activated carbon and corresponding electrodes. By combining multiple characterization techniques, we clarify the actual origins of the improvement of the performance (e.g., > +35% energy density for the investigated power densities in the 0.5-45 kW kg-1 range) of the EDLCs based on treated electrodes compared to the case based on the pristine electrodes. Contrary to previous works supporting a questionable graphitization of the activated carbon at temperatures <1000 °C, we found that a "surface graphitization" of the activated carbon, detected by spectroscopic analysis, is mainly associated with the desorption of surface contaminants. The elimination of surface impurities, including adsorbed species, improves the surface capacitance of the activated carbon (CsurfAC) by +37.1 and +36.3% at specific currents of 1 and 10 A g-1, respectively. Despite the presence of slight densification of the activated carbon upon the thermal treatment, the latter still improves the cell gravimetric capacitance normalized on the mass of the activated carbon only (CgAC), e.g., + 28% at 1 A g-1. Besides, our holistic approach identifies the change in the active material and binder contents as a concomitant cause of the increase of cell gravimetric capacitance (Cg), accounting for the mass of all of the electrode materials measured for treated electrodes compared to pristine ones. Overall, this study provides new insights into the relationship between the modifications of the electrode materials induced by H2-assisted thermal treatments and the performance of the resulting EDLCs.

Mid-infrared Ring Interband Cascade Laser: Operation at the Standard Quantum Limit

Many precision applications in the mid-infrared spectral range have strong constraints based on quantum effects that are expressed in particular noise characteristics. They limit, e.g., sensitivity and resolution of mid-infrared imaging and spectroscopic systems as well as the bit-error rate in optical free-space communication. Interband cascade lasers (ICLs) are a class of mid-infrared lasers exploiting interband transitions in type-II band alignment geometry. They are currently gaining significant importance for mid-infrared applications from < 3 to > 6 μm wavelength, enabled by novel types of high-performance ICLs such as ring-cavity devices. Their noise behavior is an important feature that still needs to be thoroughly analyzed, including its potential reduction with respect to the shot-noise limit. In this work, we provide a comprehensive characterization of λ = 3.8 μm-emitting, continuous-wave ring ICLs operating at room temperature. It is based on an in-depth study of their main physical intensity noise features such as their bias-dependent intensity noise power spectral density and relative intensity noise. We obtained shot-noise-limited statistics for Fourier frequencies above 100 kHz. This is an important result for precision applications, e.g., interferometry or advanced spectroscopy, which benefit from exploiting the advantage of using such a shot-noise-limited source, enhancing the setup sensitivity. Moreover, it is an important feature for novel quantum optics schemes, including testing specific light states below the shot-noise level, such as squeezed states.

Nonaqueous Oxidation in DNA Microarray Synthesis Improves the Oligonucleotide Quality and Preserves Surface Integrity on Gold and Indium Tin Oxide Substrates

Nucleic acids attached to electrically conductive surfaces are very frequently used platforms for sensing and analyte detection as well as for imaging. Synthesizing DNA on these uncommon substrates and preserving the conductive layer is challenging as this coating tends to be damaged by the repeated use of iodine and water, which is the standard oxidizing medium following phosphoramidite coupling. Here, we thoroughly investigate the use of camphorsulfonyl oxaziridine (CSO), a nonaqueous alternative to I2/H2O, for the synthesis of DNA microarrays in situ. We find that CSO performs equally well in producing high hybridization signals on glass microscope slides, and CSO also protects the conductive layer on gold and indium tin oxide (ITO)-coated slides. DNA synthesis on conductive substrates with CSO oxidation yields microarrays of quality approaching that of conventional glass with intact physicochemical properties.

The electron density: a fidelity witness for quantum computation

There is currently no combination of quantum hardware and algorithms that can provide an advantage over conventional calculations of molecules or materials. However, if or when such a point is reached, new strategies will be needed to verify predictions made using quantum devices. We propose that the electron density, obtained through experimental or computational means, can serve as a robust benchmark for validating the accuracy of quantum computation of chemistry. An initial exploration into topological features of electron densities, facilitated by quantum computation, is presented here as a proof of concept. Additionally, we examine the effects of constraining and symmetrizing measured one-particle reduced density matrices on noise-driven errors in the electron density distribution. We emphasize the potential benefits and future need for high-quality electron densities derived from diffraction experiments for validating classically intractable quantum computations of materials.

Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm

A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "Tiki-Taka" algorithm was developed to facilitate NN training in the presence of device nonidealities. However, so far, a resistive switching device exhibiting all the fundamental Tiki-Taka requirements, which are many programmable states, a centered symmetry point, and low programming noise, was not yet demonstrated. Here, a complementary metal-oxide semiconductor (CMOS)-compatible resistive random access memory (RRAM), showing more than 30 programmable states with low noise and a symmetry point with only 5% skew from the center, is presented for the first time. These results enable generalization of Tiki-Taka training from small fully connected networks to larger long-/short-term-memory types of NN.

CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery

AIM

Treatment pathways for significant rectal polyps differ depending on the underlying pathology, but pre-excision profiling is imperfect. It has been demonstrated that differences in fluorescence perfusion signals following injection of indocyanine green (ICG) can be analysed mathematically and, with the assistance of artificial intelligence (AI), used to classify tumours endoscopically as benign or malignant. This study aims to validate this method of characterization across multiple clinical sites regarding its generalizability, usability and accuracy while developing clinical-grade software to enable it to become a useful method.

METHODS

The CLASSICA study is a prospective, unblinded multicentre European observational study aimed to validate the use of AI analysis of ICG fluorescence for intra-operative tissue characterization. Six hundred patients undergoing transanal endoscopic evaluation of significant rectal polyps and tumours will be enrolled in at least five clinical sites across the European Union over a 4-year period. Video recordings will be analysed regarding dynamic fluorescence patterns centrally as software is developed to enable analysis with automatic classification to happen locally. AI-based classification and subsequently guided intervention will be compared with the current standard of care including biopsies, final specimen pathology and patient outcomes.

DISCUSSION

CLASSICA will validate the use of AI in the analysis of ICG fluorescence for the purposes of classifying significant rectal polyps and tumours endoscopically. Follow-on studies will compare AI-guided targeted biopsy or, indeed, AI characterization alone with traditional biopsy and AI-guided local excision versus traditional excision with regard to marginal clearance and recurrence.

Simple Adjustment of Intranucleotide Base-Phosphate Interaction in the OL3 AMBER Force Field Improves RNA Simulations

Molecular dynamics (MD) simulations represent an established tool to study RNA molecules. The outcome of MD studies depends, however, on the quality of the force field (ff). Here we suggest a correction for the widely used AMBER OL3 ff by adding a simple adjustment of the nonbonded parameters. The reparameterization of the Lennard-Jones potential for the -H8···O5'- and -H6···O5'- atom pairs addresses an intranucleotide steric clash occurring in the type 0 base-phosphate interaction (0BPh). The nonbonded fix (NBfix) modification of 0BPh interactions (NBfix0BPh modification) was tuned via a reweighting approach and subsequently tested using an extensive set of standard and enhanced sampling simulations of both unstructured and folded RNA motifs. The modification corrects minor but visible intranucleotide clash for the anti nucleobase conformation. We observed that structural ensembles of small RNA benchmark motifs simulated with the NBfix0BPh modification provide better agreement with experiments. No side effects of the modification were observed in standard simulations of larger structured RNA motifs. We suggest that the combination of OL3 RNA ff and NBfix0BPh modification is a viable option to improve RNA MD simulations.

Quantum ESPRESSO: One Further Step toward the Exascale

We review the status of the Quantum ESPRESSO software suite for electronic-structure calculations based on plane waves, pseudopotentials, and density-functional theory. We highlight the recent developments in the porting to GPUs of the main codes, using an approach based on OpenACC and CUDA Fortran offloading. We describe, in particular, the results achieved on linear-response codes, which are one of the distinctive features of the Quantum ESPRESSO suite. We also present extensive performance benchmarks on different GPU-accelerated architectures for the main codes of the suite.