Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Identification of a chaperone-code responsible for Rad51-mediated genome repair.

Posttranslational modifications of Hsp90 are known to regulate its in vivo chaperone functions. Here, we demonstrate that the lysine acetylation-deacetylation dynamics of Hsp82 is a major determinant in DNA repair mediated by Rad51. We uncover that the deacetylated lysine 27 in Hsp82 dictates the formation of the Hsp82-Aha1-Rad51 complex, which is crucial for client maturation. Intriguingly, Aha1-Rad51 complex formation is not dependent on Hsp82 or its acetylation status; implying that Aha1-Rad51 association precedes the interaction with Hsp82. The DNA damage sensitivity of Hsp82 (K27Q/K27R) mutants are epistatic to the loss of the (de)acetylase hda1Δ; reinforcing the importance of the reversible acetylation of Hsp82 at the K27 position. These findings underscore the significance of the cross talk between a specific Hsp82 chaperone modification code and the cognate cochaperones in a client-specific manner. Given the pivotal role that Rad51 plays during DNA repair in eukaryotes and particularly in cancer cells, targeting the Hda1-Hsp90 axis could be explored as a new therapeutic approach against cancer.

Deep learning based decoding of single local field potential events.

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST.

As the development of new drugs reaches its physical and financial limits, drug repurposing has become more important than ever. For mechanistically grounded drug repurposing, it is crucial to uncover the disease mechanisms and to detect clusters of mechanistically related diseases. Various methods for computing candidate disease mechanisms and disease clusters exist. However, in the absence of ground truth, in silico validation is challenging. This constitutes a major hurdle toward the adoption of in silico prediction tools by experimentalists who are often hesitant to carry out wet-lab validations for predicted candidate mechanisms without clearly quantified initial plausibility. To address this problem, we present DIGEST (in silico validation of disease and gene sets, clusterings or subnetworks), a Python-based validation tool available as a web interface (https://digest-validation.net), as a stand-alone package or over a REST API. DIGEST greatly facilitates in silico validation of gene and disease sets, clusterings or subnetworks via fully automated pipelines comprising disease and gene ID mapping, enrichment analysis, comparisons of shared genes and variants and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines. DIGEST hence allows the user to assess the statistical significance of candidate mechanisms with regard to functional and genetic coherence and enables the computation of empirical $P$-values with just a few mouse clicks.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

MOTIVATION: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. RESULTS: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. AVAILABILITY AND IMPLEMENTATION: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

Online bias-aware disease module mining with ROBUST-Web.

SUMMARY: We present ROBUST-Web which implements our recently presented ROBUST disease module mining algorithm in a user-friendly web application. ROBUST-Web features seamless downstream disease module exploration via integrated gene set enrichment analysis, tissue expression annotation, and visualization of drug-protein and disease-gene links. Moreover, ROBUST-Web includes bias-aware edge costs for the underlying Steiner tree model as a new algorithmic feature, which allow to correct for study bias in protein-protein interaction networks and further improves the robustness of the computed modules. AVAILABILITY AND IMPLEMENTATION: Web application: https://robust-web.net. Source code of web application and Python package with new bias-aware edge costs: https://github.com/bionetslab/robust-web, https://github.com/bionetslab/robust_bias_aware.

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception.

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.

Consequences of intense drought on CO2 and CH4 fluxes of the reed ecosystem at Lake Neusiedl.

Reed (Phragmites australis) dominated wetlands are commonly known as strong carbon (C) sinks due to the high productivity of the reed plant and C fixation in the wetland soil. However, little is known about the effects of drought on reed-dominated wetlands and the possibility of Pannonian reed ecosystems being a source of greenhouse gases (GHG). The drought at Lake Neusiedl had a particular impact on the water level, but also had consequences for the reed belt. Therefore, we investigated the drought-influenced C fluxes and their drivers in the reed ecosystem of this subsaline lake over a period of 4.5 years (mid-2018 to 2022). We applied eddy covariance technique to continuously quantify the vertical turbulent GHG exchange between reed belt & atmosphere and used vegetation indices to account for reed growth. Methane emissions decreased by 76% from 9.2 g CH4-C m-2a-1 (2019) to 2.2 g CH4-C m-2 a-1 (2022), which can be explained by the falling water level, the associated drying out of the reed belt and its consequences. Carbon dioxide emissions initially decreased by 85% from 181 g CO2-C m-2 a-1 (2019) to 27 g CO2-C m-2 a-1 (2021), but then increased to twice the 2019 level in 2022 (391 g CO2-C m-2 a-1). Due to the drying reed belt, the reed initially grew into formerly water-covered areas within the reed belt, especially in 2021, leading to higher photosynthesis through 2021. This development stopped and even reversed in 2022 as a consequence of the sharp decrease in sediment water content from about 65 to 32 Vol-% in mid-2022. Overall, drought led to a decoupling of the reed ecosystem from the open lake area and developed the wetland into a strong C source.

Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study.

OBJECTIVES: Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a "CLE map" of physiological mucosa as baseline for further application of this powerful technology. MATERIALS AND METHODS: The CLE database consisted of 27 patients. The following spots were examined: (1) upper lip (intraoral) (2) alveolar ridge (3) lateral tongue (4) floor of the mouth (5) hard palate (6) intercalary line. All sequences were examined by two CLE experts for morphological differences and video quality. RESULTS: Analysis revealed clear differences in image quality and possibility of depicting tissue morphologies between the various localizations of oral mucosa: imaging of the alveolar ridge and hard palate showed visually most discriminative tissue morphology. Labial mucosa was also visualized well using CLE. Here, typical morphological features such as uniform cells with regular intercellular gaps and vessels could be clearly depicted. Image generation and evaluation was particularly difficult in the area of the buccal mucosa, the lateral tongue and the floor of the mouth. CONCLUSION: A physiological "CLE map" for the entire oral cavity could be created for the first time. CLINICAL RELEVANCE: This will make it possible to take into account the existing physiological morphological features when differentiating between normal mucosa and oral squamous cell carcinoma in future work.

A Systematic Review of Features Forecasting Patient Arrival Numbers.

Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area.

MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T2 -induced blurring in spin echo sequences.

PURPOSE: An end-to-end differentiable 2D Bloch simulation is used to reduce T2 induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network. METHODS: Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard. RESULTS: The optimized RARE was able to successfully lower T2 -induced blurring for single-shot RARE sequences in proton density-weighted and T2 -weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance. CONCLUSIONS: This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T2 -induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.

The effects of RF coils and SAR supervision strategies for clinically applicable nonselective parallel-transmit pulses at 7 T.

PURPOSE: To investigate the effects of using different parallel-transmit (pTx) head coils and specific absorption rate (SAR) supervision strategies on pTx pulse design for ultrahigh-field MRI using a 3D-MPRAGE sequence. METHODS: The PTx universal pulses (UPs) and fast online-customized (FOCUS) pulses were designed with pre-acquired data sets (B0 , B1 + maps, specific absorption rate [SAR] supervision data) from two different 8 transmit/32 receive head coils on two 7T whole-body MR systems. For one coil, the SAR supervision model consisted of per-channel RF power limits. In the other coil, SAR estimations were done with both per-channel RF power limits as well as virtual observation points (VOPs) derived from electromagnetic field (EMF) simulations using three virtual human body models at three different positions. All pulses were made for nonselective excitation and inversion and evaluated on 132 B0 , B1 + , and SAR supervision datasets obtained with one coil and 12 from the other. At both sites, 3 subjects were examined using MPRAGE sequences that used UP/FOCUS pulses generated for both coils. RESULTS: For some subjects, the UPs underperformed when simulated on a different coil from which they were derived, whereas FOCUS pulses still showed acceptable performance in that case. FOCUS inversion pulses outperformed adiabatic pulses when scaled to the same local SAR level. For the self-built coil, the use of VOPs showed reliable overestimation compared with the ground-truth EMF simulations, predicting about 52% lower local SAR for inversion pulses compared with per-channel power limits. CONCLUSION: FOCUS inversion pulses offer a low-SAR alternative to adiabatic pulses and benefit from using EMF-based VOPs for SAR estimation.

DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification.

PURPOSE: In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. METHODS: We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B1 level, and a B1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B0 - and B1 -corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. RESULTS: The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. CONCLUSION: The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B0 - and B1 -corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.

Evolutionary optimization and long-term stabilization of a white-light seeded two-stage OPCPA seed laser.

Ultrafast laser systems, such as optical parametric chirped pulse amplifiers (OPCPA), are complex tools. Optimizing laser performance for a given application is often plagued by intricate couplings between different output parameters, making simultaneous control of multiple pulse properties difficult. Here, we experimentally demonstrate an autonomous tuning procedure of a white-light seeded two-stage OPCPA using an evolutionary strategy to reliably reach an optimized working point. We use the data collected during the tuning procedure to calibrate a performance model of the laser system, which we then apply to stabilize the intricately coupled laser output energy and spectrum simultaneously. Our approach ensures reliable day-to-day operation at optimized working points without manual tuning. We demonstrate shot-to-shot energy stability of <0.18 % rms, in combination with <25 pm rms wavelength stability and <0.2 % rms bandwidth stability during multi-day operation.

Out-of-plane multilayer-dielectric-grating compressor for ultrafast Ti:sapphire pulses.

Extreme heat loads on optics, in particular the final pulse compression gratings, are a major hurdle to overcome in the ongoing push towards high average power (kW) and high repetition rate (kHz) operation of terawatt-class Ti:sapphire lasers. Multilayer dielectric (MLD) diffraction gratings have been suggested as a potential alternative to traditionally gold-coated compressor gratings, which are plagued by high energy absorption in the top gold layer. However, to support the required bandwidth (and ultimately the desired pulse duration) with MLD gratings, the gratings have to be operated in an out-of-plane geometry near the Littrow angle. Here, we report on the design of an MLD-based out-of-plane test compressor and a matching custom stretcher. We present a full characterization of the MLD compressor, focusing on its spectral transmission and the significance of laser pulse polarization in the out-of-plane geometry. To demonstrate compression of 40 µJ pulses centered at 800 nm wavelength to 26 fs pulse duration, we use the compressor with an MLD and gold grating configuration, and fully characterize the compressed pulses. Extrapolating our results indicates that MLD-grating-based out-of-plane compressors can support near-transform-limited pulses with sub-30 fs duration and good quality, demonstrating the viability of this concept for kW-level ultrafast Ti:sapphire laser systems.

Nonlinear pulse compression of a 200 mJ and 1†kW ultrafast thin-disk amplifier.

We present a high-energy laser source consisting of an ultrafast thin-disk amplifier followed by a nonlinear compression stage. At a repetition rate of 5 kHz, the drive laser provides a pulse energy of up to 200 mJ with a pulse duration below 500 fs. Nonlinear broadening is implemented inside a Herriott-type multipass cell purged with noble gas, allowing us to operate under different seeding conditions. Firstly, the nonlinear broadening of 64 mJ pulses is demonstrated in an argon-filled cell, showing a compressibility down to 32 fs. Finally, we employ helium as a nonlinear medium to increase the energy up to 200 mJ while maintaining compressibility below 50 fs. Such high-energy pulses with sub-50 fs duration hold great promise as drivers of secondary sources.

Frequency-doubled Q-switched 4 × 4 multicore fiber laser system.

Frequency doubling of a Q-switched Yb-doped rod-type 4 × 4 multicore fiber (MCF) laser system is reported. A second harmonic generation (SHG) efficiency of up to 52% was achieved with type I non-critically phase-matched lithium triborate (LBO), with a total SHG pulse energy of up to 17 mJ obtained at 1 kHz repetition rate. The dense parallel arrangement of amplifying cores into a shared pump cladding enables a significant increase in the energy capacity of active fibers. The frequency-doubled MCF architecture is compatible with high-repetition-rate and high-average-power operation and may provide an efficient alternative to bulk solid-state systems as pump sources for high-energy titanium-doped sapphire lasers.

Classical and quantum trial wave functions in auxiliary-field quantum Monte Carlo applied to oxygen allotropes and a CuBr2 model system.

In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representative of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and the relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which is industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow it to generate energies close to chemical accuracy compared to full configuration interaction or experimental results. In the field of material science, we study the electronic structure properties of cuprates through the quasi-1D Fermi-Hubbard model derived from CuBr2, where we find that trial wave functions with both significantly larger fidelities and lower energies over a mean-field solution do not necessarily lead to AFQMC results closer to the exact ground state energy.

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.