Ivan Anishchenko, Yakov Kipnis, Indrek Kalvet, Guangfeng Zhou, Rohith Krishna, Samuel J. Pellock, Anna Lauko, Gyu Rie Lee, Linna An, Justas Dauparas, Frank DiMaio, David Baker <<<

AbstractModeling the conformational heterogeneity of protein-small molecule systems is an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called ChemNet trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. ChemNet accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, and builds up structures of small molecules and protein side chains for protein-small molecule docking. Because ChemNet is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using ChemNet to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with akcat/KMof 11000 M-1min- 1, considerably higher than any pre-deep learning design for this reaction. We anticipate that ChemNet will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes. <<<

Kimberly Siletti, Rebecca Hodge, Alejandro Mossi Albiach, Ka Wai Lee, Song-Lin Ding, Lijuan Hu, Peter Lönnerberg, Trygve Bakken, Tamara Casper, Michael Clark, Nick Dee, Jessica Gloe, Daniel Hirschstein, Nadiya V. Shapovalova, C. Dirk Keene, Julie Nyhus, Herman Tung, Anna Marie Yanny, Ernest Arenas, Ed S. Lein, Sten Linnarsson <<<

The human brain directs complex behaviors, ranging from fine motor skills to abstract intelligence, but the diversity of cell types that support these skills has not been fully described. In this work, we used single-nucleus RNA sequencing to systematically survey cells across the entire adult human brain. We sampled more than three million nuclei from approximately 100 dissections across the forebrain, midbrain, and hindbrain in three postmortem donors. Our analysis identified 461 clusters and 3313 subclusters organized largely according to developmental origins and revealing high diversity in midbrain and hindbrain neurons. Astrocytes and oligodendrocyte-lineage cells also exhibited regional diversity at multiple scales. The transcriptomic census of the entire human brain presented in this work provides a resource for understanding the molecular diversity of the human brain in health and disease. <<<

Transformer models have demonstrated impressive capabilities when trained atscale, excelling at difficult cognitive tasks requiring complex reasoning andrational decision-making. In this paper, we explore the application oftransformer models to chess, focusing on the critical role of the positionencoding within the attention mechanism. We show that in chess, transformersendowed with a sufficiently versatile position encoding can match existingchess-playing models at a fraction of the computational cost. Our architecturesignificantly outperforms AlphaZero at 8x fewer FLOPS and matches priorgrandmaster-level transformer-based agents at 30x fewer FLOPS. <<<

AbstractOlfaction is a fundamental sensory modality that guides animal and human behaviour1,2. However, the underlying neural processes of human olfaction are still poorly understood at the fundamental—that is, the single-neuron—level. Here we report recordings of single-neuron activity in the piriform cortex and medial temporal lobe in awake humans performing an odour rating and identification task. We identified odour-modulated neurons within the piriform cortex, amygdala, entorhinal cortex and hippocampus. In each of these regions, neuronal firing accurately encodes odour identity. Notably, repeated odour presentations reduce response firing rates, demonstrating central repetition suppression and habituation. Different medial temporal lobe regions have distinct roles in odour processing, with amygdala neurons encoding subjective odour valence, and hippocampal neurons predicting behavioural odour identification performance. Whereas piriform neurons preferably encode chemical odour identity, hippocampal activity reflects subjective odour perception. Critically, we identify that piriform cortex neurons reliably encode odour-related images, supporting a multimodal role of the human piriform cortex. We also observe marked cross-modal coding of both odours and images, especially in the amygdala and piriform cortex. Moreover, we identify neurons that respond to semantically coherent odour and image information, demonstrating conceptual coding schemes in olfaction. Our results bridge the long-standing gap between animal models and non-invasive human studies and advance our understanding of odour processing in the human brain by identifying neuronal odour-coding principles, regional functional differences and cross-modal integration. <<<

Anna Goldie, Azalia Mirhoseini, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nova, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean <<<

Nga Yan Tse, Aswin Ratheesh, Ye Ella Tian, Colm G. Connolly, Christopher G. Davey, Saampras Ganesan, Ian H. Gotlib, Ben J. Harrison, Laura K. M. Han, Tiffany C. Ho, Alec J. Jamieson, Jaclyn S. Kirshenbaum, Yong Liu, Xiaohong Ma, Amar Ojha, Jiang Qiu, Matthew D. Sacchet, Lianne Schmaal, Alan N. Simmons, John Suckling, Dongtao Wei, Xiao Yang, Tony T. Yang, Robin F. H. Cash, Andrew Zalesky <<<

AbstractExtracting the underlying temporal structure of experience is a fundamental aspect of learning and memory that allows us to predict what is likely to happen next. Current knowledge about the neural underpinnings of this cognitive process in humans stems from functional neuroimaging research1–5. As these methods lack direct access to the neuronal level, it remains unknown how this process is computed by neurons in the human brain. Here we record from single neurons in individuals who have been implanted with intracranial electrodes for clinical reasons, and show that human hippocampal and entorhinal neurons gradually modify their activity to encode the temporal structure of a complex image presentation sequence. This representation was formed rapidly, without providing specific instructions to the participants, and persisted when the prescribed experience was no longer present. Furthermore, the structure recovered from the population activity of hippocampal–entorhinal neurons closely resembled the structural graph defining the sequence, but at the same time, also reflected the probability of upcoming stimuli. Finally, learning of the sequence graph was related to spontaneous, time-compressed replay of individual neurons’ activity corresponding to previously experienced graph trajectories. These findings demonstrate that neurons in the hippocampus and entorhinal cortex integrate the ‘what’ and ‘when’ information to extract durable and predictive representations of the temporal structure of human experience. <<<

AbstractSeveral computational methods have recently been developed for characterizing molecular tissue regions in spatially resolved transcriptomics (SRT) data. However, each method fundamentally relies on spatially smoothing transcriptomic features across neighboring cells. Here, we demonstrate that smoothing increases autocorrelation between neighboring cells, causing latent space to encode physical adjacency rather than spatial transcriptomic patterns. We find that randomly sub-sampling neighbors before smoothing mitigates autocorrelation, improving the performance of existing methods and further enabling a simpler, more efficient approach that we callspatialintegration (SPIN). SPIN leverages the conventional single-cell toolkit, yielding spatial analogies to each tool: clustering identifies molecular tissue regions; differentially expressed gene analysis calculates region marker genes; trajectory inference reveals continuous, molecularly defined ana tomical axes; and integration allows joint analysis across multiple SRT datasets, regardless of tissue morphology, spatial resolution, or experimental technology. We apply SPIN to SRT datasets from mouse and marmoset brains to calculate shared and species-specific region marker genes as well as a molecularly defined neocortical depth axis along which several genes and cell types differ across species. <<<

Recent advancements in quantum computing have opened new avenues for tacklinglong-standing complex combinatorial optimization problems that are intractablefor classical computers. Predicting secondary structure of mRNA is one suchnotoriously difficult problem that can benefit from the ever-increasingmaturity of quantum computing technology. Accurate prediction of mRNA secondarystructure is critical in designing RNA-based therapeutics as it dictatesvarious steps of an mRNA life cycle, including transcription, translation, anddecay. The current generation of quantum computers have reached utility-scale,allowing us to explore relatively large problem sizes. In this paper, weexamine the feasibility of solving mRNA secondary structures on a quantumcomputer with sequence length up to 60 nucleotides representing problems in thequbit range of 10 to 80. We use Conditional Value at Risk (CVaR)-based VQEalgorithm to solve the optimization problems, originating from the mRNAstructure prediction problem, on the IBM Eagle and Heron quantum processors. Toour encouragement, even with ``minimal'' error mitigation and fixed-depthcircuits, our hardware runs yield accurate predictions of minimum free energy(MFE) structures that match the results of the classical solver CPLEX. Ourresults provide sufficient evidence for the viability of solving mRNA structureprediction problems on a quantum computer and motivate continued research inthis direction. <<<

AbstractBackgroundNeurofilament light protein (NfL) has been proven to be a sensitive biomarker for Huntington's disease (HD). However, these studies did not include HD patients at advanced stages or with larger CAG repeats (>50), leading to a knowledge gap of the characteristics of NfL.MethodsSerum NfL (sNfL) levels were quantified using an ultrasensitive immunoassay. Participants were assessed by clinical scales and 7.0 T magnetic resonance imaging. Longitudinal samples and clinical data were obtained.ResultsBaseline samples were available from 110 controls, 90 premanifest HD (pre‐HD) and 137 HD individuals. We found levels of sNfL significantly increased in HD compared to pre‐HD and controls (both P < 0.0001). The increase rates of sNfL were differed by CAG repeat lengths. However, there was no difference in sNfL levels in manifest HD from early to late stages. In addition, sNfL levels were associated with cognitive measures in pre‐HD and manifest HD group, respectively. The increased levels of sNfL were also closely related to microstructural changes in white matter. In the longitudinal analysis, baseline sNfL did not correlate with subsequent clinical function decline. Random forest analysis revealed that sNfL had good power for predicting disease onset.ConclusionsAlthough sNfL levels are independent of disease stages in manifest HD, it is still an optimal indicator for predicting disease onset and has potential use as a surrogate biomarker of treatment effect in clinical trials. © 2023 International Parkinson and Movement Disorder Society. <<<

This paper presents a prototype of a low-cost two-phase axial-gap transverse flux generator, in which the magnetic and electric circuits have been made of reused materials, and the stator housing has been manufactured by 3D printing of plastic. Therefore, this work presents as a novelty the combination of the novel transverse flux topology and two challenging trends in electrical machines manufacturing, such as reusing of components and additive manufacturing. Axial-gap transverse flux machines potentially enable the combination of two of the main advantages of axial flux machines and transverse flux machines, i.e., short axial length and a high number of poles. The two-phase arrangement with shared air gap is of great interest in order to reduce further the axial length while avoiding the use of magnetic materials in the rotor, such as iron or soft magnetic composites. However, the equivalent air gap might be large, with significant leakage and fringing effects as the magnetic flux closes through the air. Therefore, in this paper the accuracy of the analytical equations and the magnetic equivalent circuit is firstly investigated. The two-phase axial-gap transverse flux machine is prone to misalignment between phases and rotor imbalances that alter the air gap length, so these effects have been included in the simulations with the finite element method. Experimental tests have been conducted throughout the investigation, from the prototype characterization to the steady-state operation, both with no load and with resistive loads. <<<

Qiang Ning, Yinan Jian, Yanfang Du, Yunfu Li, Xiaomeng Shen, Haitao Jia, Ran Zhao, Jimin Zhan, Fang Yang, David Jackson, Lei Liu, Zuxin Zhang <<<

AbstractMaize ear size and kernel number differ among lines, however, little is known about the molecular basis of ear length and its impact on kernel number. Here, we characterize a quantitative trait locus, qEL7, to identify a maize gene controlling ear length, flower number and fertility. qEL7 encodes 1-aminocyclopropane-1- carboxylate oxidase2 (ACO2), a gene that functions in the final step of ethylene biosynthesis and is expressed in specific domains in developing inflorescences. Confirmation of qEL7 by gene editing of ZmACO2 leads to a reduction in ethylene production in developing ears, and promotes meristem and flower development, resulting in a ~13.4% increase in grain yield per ear in hybrids lines. Our findings suggest that ethylene serves as a key signal in inflorescence development, affecting spikelet number, floral fertility, ear length and kernel number, and also provide a tool to improve grain productivity by optimizing ethylene levels in maize or in other cereals. <<<

Fine-tuning large pre-trained models is an effective transfer mechanism inNLP. However, in the presence of many downstream tasks, fine-tuning isparameter inefficient: an entire new model is required for every task. As analternative, we propose transfer with adapter modules. Adapter modules yield acompact and extensible model; they add only a few trainable parameters pertask, and new tasks can be added without revisiting previous ones. Theparameters of the original network remain fixed, yielding a high degree ofparameter sharing. To demonstrate adapter's effectiveness, we transfer therecently proposed BERT Transformer model to 26 diverse text classificationtasks, including the GLUE benchmark. Adapters attain near state-of-the-artperformance, whilst adding only a few parameters per task. On GLUE, we attainwithin 0.4% of the performance of full fine-tuning, adding only 3.6% parametersper task. By contrast, fine-tuning trains 100% of the parameters per task. <<<

Abstract Background A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. Results We characterize and describe systematic errors using overlapping paired reads from high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that they are highly replicable across experiments. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq), and can be used with single-end datasets. Conclusions Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments. <<<

Rachael B. Chanin, Patrick T. West, Jakob Wirbel, Matthew O. Gill, Gabriella Z. M. Green, Ryan M. Park, Nora Enright, Arjun M. Miklos, Angela S. Hickey, Erin F. Brooks, Krystal K. Lum, Ileana M. Cristea, Ami S. Bhatt <<<