Lawrence Berkeley National Laboratory

The insights into interactions between host genetics and gut microbiome (GM) in colorectal tumor susceptibility (CTS) remains lacking. We used Collaborative Cross mouse population model to identify genetic and microbial determinants of Azoxymethane-induced CTS. We identified 4417 CTS-associated single nucleotide polymorphisms (SNPs) containing 334 genes that were transcriptionally altered in human colorectal cancers (CRCs) and consistently clustered independent human CRC cohorts into two subgroups with different prognosis. We discovered a set of genera in early-life associated with CTS and defined a 16-genus signature that accurately predicted CTS, the majority of which were correlated with human CRCs. We identified 547 SNPs associated with abundances of these genera. Mediation analysis revealed GM as mediators partially exerting the effect of SNP UNC3869242 within Duox2 on CTS. Intestine cell-specific depletion of Duox2 altered GM composition and contribution of Duox2 depletion to CTS was significantly influenced by GM. Our findings provide potential novel targets for personalized CRC prevention and treatment.

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

The decrease in sequencing expenses has facilitated the creation of reference genomes and proteomes for an expanding array of organisms. Nevertheless, no established repository that details organism-specific genomic and proteomic sequences of specific lengths, referred to as kmers, exists to our knowledge. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer-based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 54,039 and 21,865 reference genomes and proteomes, respectively, as well as 6,905,362 and 149,305,183 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O3. Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Water structure near electrode interfaces may play an important role in controlling CO2 electroreduction. Using plasmon-enhanced vibrational sum frequency generation spectroscopy, we demonstrate the emergence of an interfacial water subpopulation with large electric fields along their OH bonds, when Na2CO3 ions are present near the electrode under applied potential. With molecular dynamics simulations, we show that the approach of aqueous Na2CO3 to electrodes is coupled to the formation of structured and oriented ion complexes, and that the emergent water population is associated with the first solvation shell of these complexes. This water subpopulation is seen even when the sole source of CO32− is its in-situ generation from CO2, indicating that the interfacial species investigated here are likely ubiquitous in CO2 electroreduction contexts.

Human African trypanosomiasis, or sleeping sickness, is a neglected tropical disease caused by Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense and is invariably fatal unless treated. Current therapies present limitations in their application, parasite resistance, or require further clinical investigation for wider use. Our work, informed by previous findings, presents novel 4-[4-(4-methylpiperazin-1-yl)phenyl]-6-arylpyrimidine derivatives with promising antitrypanosomal activity. In particular, 32 exhibits an in vitro EC₅₀ value of 0.5 µM against Trypanosoma brucei rhodesiense, and analogues 29, 30 and 33 show antitrypanosomal activities in the <1 µM range. We have demonstrated that substituted 4-[4-(4-methylpiperazin-1-yl)phenyl]-6-arylpyrimidines present promising antitrypanosomal hit molecules with potential for further preclinical development.

Dehydration has been a key limiting factor for the operation of conductive hydrogels in practical application. Here, we report self-healable ionic skins that can self-regulate their internal moisture level by capturing extenral moistures via hygroscopic ion-coordinated polymer backbones through antipolyelectrolyte effect. Results show the ionic skin can maintain its mechanical and electrical functions over 16 months in the ambient environment with high stretchability (fracture stretch ∼2216 %) and conductivity (23.5 mS/cm). The moisture self-regulating capability is further demonstrated by repeated exposures to harsh environments such as 200°C heating, freezing, and vacuum drying with recovered conductivity and stretchability. Their reversible ionic and hydrogen bonds also enable self-healing feature as a sample with the fully cut-through damage can restore its conductivity after 24 h at 40 % relative humidity. Utilizing the ionic skin as a building block, self-healing flexible piezoelecret sensors have been constructed to monitor physiological signals. Together with a facile transfer-printing process, a self-powered sensing system with a self-healable supercapacitor and humidity sensor has been successfully demonstrated. These results illustrate broad-ranging possibilities for the ionic skins in applications such as energy storage, wearable sensors, and human-machine interfaces.