Ioannis Sarropoulos, Mari Sepp, Robert Frömel, Kevin Leiss, Nils Trost, Evgeny Leushkin, Konstantin Okonechnikov, Piyush Joshi, Peter Giere, Lena M Kutscher, Margarida Cardoso-Moreira, Stefan M Pfister, Henrik Kaessmann <<<

Organ development is orchestrated by cell- and time-specific gene regulatory networks. In this study, we investigated the regulatory basis of mouse cerebellum development from early neurogenesis to adulthood. By acquiring snATAC-seq (single-nucleus assay for transposase accessible chromatin using sequencing) profiles for ~90,000 cells spanning 11 stages, we mapped cerebellar cell types and identified candidate cisregulatory elements (CREs). We detected extensive spatiotemporal heterogeneity among progenitor cells and a gradual divergence in the regulatory programs of cerebellar neurons during differentiation. Comparisons to vertebrate genomes and snATAC-seq profiles for ∼20,000 cerebellar cells from the marsupial opossum revealed a shared decrease in CRE conservation during development and differentiation as well as differences in constraint between cell types. Our work delineates the developmental and evolutionary dynamics of gene regulation in cerebellar cells and provides insights into mammalian organ development. <<<

This paper aims to generate materials for 3D meshes from text descriptions.Unlike existing methods that synthesize texture maps, we propose to generatesegment-wise procedural material graphs as the appearance representation, whichsupports high-quality rendering and provides substantial flexibility inediting. Instead of relying on extensive paired data, i.e., 3D meshes withmaterial graphs and corresponding text descriptions, to train a material graphgenerative model, we propose to leverage the pre-trained 2D diffusion model asa bridge to connect the text and material graphs. Specifically, our approachdecomposes a shape into a set of segments and designs a segment-controlleddiffusion model to synthesize 2D images that are aligned with mesh parts. Basedon generated images, we initialize parameters of material graphs and fine-tunethem through the differentiable rendering module to produce materials inaccordance with the textual description. Extensive experiments demonstrate thesuperior performance of our framework in photorealism, resolution, andeditability over existing methods. Project page: https://zju3dv.github.io/MaPa <<<

Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M Olmsted, Emily S Zhai, Esther T Anene, Megan Chesnut, Bailey Holt-Gosselin, Sarah Chang, Patrick C Stetz, Carolina A Ramirez, Laura M Hack, Mayuresh S Korgaonkar, Max Wintermark, Ian H Gotlib, Jun Ma, Leanne M Williams <<<

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry. <<<

Sankaraleengam Alagapan, Ki Sueng Choi, Stephen Heisig, Patricio Riva-Posse, Andrea Crowell, Vineet Tiruvadi, Mosadoluwa Obatusin, Ashan Veerakumar, Allison C Waters, Robert E Gross, Sinead Quinn, Lydia Denison, Matthew O'Shaughnessy, Marissa Connor, Gregory Canal, Jungho Cha, Rachel Hershenberg, Tanya Nauvel, Faical Isbaine, Muhammad Furqan Afzal, Martijn Figee, Brian H Kopell, Robert Butera, Helen S Mayberg, Christopher J Rozell <<<

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD). However, achieving stable recovery is unpredictable, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment. <<<

Conventional deformable registration methods aim at solving an optimizationmodel carefully designed on image pairs and their computational costs areexceptionally high. In contrast, recent deep learning based approaches canprovide fast deformation estimation. These heuristic network architectures arefully data-driven and thus lack explicit geometric constraints, e.g.,topology-preserving, which are indispensable to generate plausibledeformations. We design a new deep learning based framework to optimize adiffeomorphic model via multi-scale propagation in order to integrateadvantages and avoid limitations of these two categories of approaches.Specifically, we introduce a generic optimization model to formulatediffeomorphic registration and develop a series of learnable architectures toobtain propagative updating in the coarse-to-fine feature space. Moreover, wepropose a novel bilevel self-tuned training strategy, allowing efficient searchof task-specific hyper-parameters. This training strategy increases theflexibility to various types of data while reduces computational and humanburdens. We conduct two groups of image registration experiments on 3D volumedatasets including image-to-atlas registration on brain MRI data andimage-to-image registration on liver CT data. Extensive results demonstrate thestate-of-the-art performance of the proposed method with diffeomorphicguarantee and extreme efficiency. We also apply our framework to challengingmulti-modal image registration, and investigate how our registration to supportthe down-streaming tasks for medical image analysis including multi-modalfusion and image segmentation. <<<

AbstractIn this article, we will introduce the basic concept and the quantum feature of a novel computing system, coherent Ising machines, and describe their theoretical and experimental performance. We start with the discussion how to construct such physical devices as the quantum analog of classical neuron and synapse, and end with the performance comparison against various classical neural networks implemented in CPU and supercomputers. <<<

The fetal fraction (FF) is a function of both biological factors and bioinformatics algorithms used to interpret DNA sequencing results. It is an essential quality control component of noninvasive prenatal testing (NIPT) results. Clinicians need to understand the biological influences on FF to be able to provide optimal post-test counseling and clinical management. There are many different technologies available for the measurement of FF. Clinicians do not need to know the details behind the bioinformatics algorithms of FF measurements, but they do need to appreciate the significant variations between the different sequencing technologies used by different laboratories. There is no universal FF threshold that is applicable across all platforms and there have not been any differences demonstrated in NIPT performance by sequencing platform or method of FF calculation. Importantly, while FF should be routinely measured, there is not yet a consensus as to whether it should be routinely reported to the clinician. The clinician should know what to expect from a standard test report and whether reasons for failed NIPT results are revealed. Emerging solutions to the challenges of samples with low FF should reduce rates of failed NIPT in the future. In the meantime, having a "plan B" prepared for those patients for whom NIPT is unsuccessful is essential in today's clinical practice. <<<

Jinge Tian, Chenglong Wang, Fengyi Chen, Wenchao Qin, Hong Yang, Sihang Zhao, Jinliang Xia, Xian Du, Yifan Zhu, Lishuan Wu, Yan Cao, Hong Li, Junhong Zhuang, Shaojiang Chen, Huayuan Zhang, Qiuyue Chen, Mingcai Zhang, Xing Wang Deng, Dezhi Deng, Jigang Li, Feng Tian <<<

Increasing planting density is a key strategy to enhance maize yields. An ideotype for dense planting requires a 'smart canopy' with leaf angles at different canopy layers differentially optimized to maximize light interception and photosynthesis, amongst other features. Here, we identified leaf angle architecture of smart canopy 1 (lac1), a natural mutant possessing upright upper leaves, less erect middle leaves and relatively flat lower leaves. lac1 has improved photosynthetic capacity and weakened shade-avoidance responses under dense planting. lac1 encodes a brassinosteroid C-22 hydroxylase that predominantly regulates upper leaf angle. Phytochrome A photoreceptors accumulate in shade and interact with the transcription factor RAVL1 to promote its degradation via the 26S proteasome, thereby attenuating RAVL1 activation of lac1 and reducing brassinosteroid levels. This ultimately decreases upper leaf angle in dense fields. Large-scale field trials demonstrate lac1 boosts maize yields under high densities. To quickly introduce lac1 into breeding germplasm, we transformed a haploid inducer and recovered homozygous lac1 edits from 20 diverse inbred lines. The tested doubled haploids uniformly acquired smart-canopy-like plant architecture. We provide an important target and an accelerated strategy for developing high-density-tolerant cultivars, with lac1 serving as a genetic chassis for further engineering of a smart canopy in maize. <<<

The epigenome involves a complex set of cellular processes governing genomic activity. Dissecting this complexity necessitates the development of tools capable of specifically manipulating these processes. The repurposing of prokaryotic CRISPR systems has allowed for the development of diverse technologies for epigenome engineering. Here, we review the state of currently achievable epigenetic manipulations along with corresponding applications. With future optimization, CRISPR-based epigenomic editing stands as a set of powerful tools for understanding and controlling biological function. <<<

Nuclear compartmentalization of active and inactive chromatin is thought to occur through microphase separation mediated by interactions between loci of similar type. The nature and dynamics of these interactions are not known. We developed liquid chromatin Hi-C to map the stability of associations between loci. Before fixation and Hi-C, chromosomes are fragmented, which removes strong polymeric constraint, enabling detection of intrinsic locus-locus interaction stabilities. Compartmentalization is stable when fragments are larger than 10-25 kb. Fragmentation of chromatin into pieces smaller than 6 kb leads to gradual loss of genome organization. Lamin-associated domains are most stable, whereas interactions for speckle- and polycomb-associated loci are more dynamic. Cohesin-mediated loops dissolve after fragmentation. Liquid chromatin Hi-C provides a genome-wide view of chromosome interaction dynamics. <<<

Pretraining Large Language Models (LLMs) on large corpora of textual data isnow a standard paradigm. When using these LLMs for many downstreamapplications, it is common to additionally bake in new knowledge (e.g.,time-critical news, or private domain knowledge) into the pretrained modeleither through RAG-based-prompting, or fine-tuning. However, the optimalmethodology for the model to gain such new knowledge remains an open question.In this paper, we present Retrieval Augmented FineTuning (RAFT), a trainingrecipe that improves the model's ability to answer questions in a "open-book"in-domain settings. In RAFT, given a question, and a set of retrieveddocuments, we train the model to ignore those documents that don't help inanswering the question, which we call, distractor documents. RAFT accomplishesthis by citing verbatim the right sequence from the relevant document thatwould help answer the question. This coupled with RAFT's chain-of-thought-styleresponse helps improve the model's ability to reason. In domain-specific RAG,RAFT consistently improves the model's performance across PubMed, HotpotQA, andGorilla datasets, presenting a post-training recipe to improve pre-trained LLMsto in-domain RAG. RAFT's code and demo are open-sourced atgithub.com/ShishirPatil/gorilla. <<<

INTRODUCTION: KRAS is a critical oncogenic protein intricately involved in tumor progression, and the difficulty in targeting KRAS has led it to be classified as an 'undruggable target.' Among the various KRAS mutations, KRASG12D is highly prevalent and represents a promising therapeutic target, yet there are currently no approved inhibitors for it.AREA COVERED: This review summarizes numerous patents and literature featuring inhibitors or degraders of KRASG12D through searching relevant information in PubMed, SciFinder and Web of Science databases from 2021 to February 2024, providing an overview of the research progress on inhibiting KRASG12D in terms of design strategies, chemical structures, biological activities, and clinical advancements.EXPERT OPINION: Since the approval of AMG510 (Sotorasib), there has been an increasing focus on the inhibition of KRASG12D, leading to numerous reports of related inhibitors and degraders. Among them, MRTX1133, as the first KRASG12D inhibitor to enter clinical trials, has demonstrated excellent tumor suppression in various KRASG12D-bearing human tumor xenograft models. It is important to note, however, that understanding the mechanisms of acquired resistance caused by KRAS inhibition and developing additional combination therapies is crucial. Moreover, seeking covalent inhibition of KRASG12D also holds significant potential. <<<

Personal names are a universal feature of human language, yet few analogues exist in other species. While dolphins and parrots address conspecifics by imitating the calls of the addressee, human names are not imitations of the sounds typically made by the named individual. Labelling objects or individuals without relying on imitation of the sounds made by the referent radically expands the expressive power of language. Thus, if non-imitative name analogues were found in other species, this could have important implications for our understanding of language evolution. Here we present evidence that wild African elephants address one another with individually specific calls, probably without relying on imitation of the receiver. We used machine learning to demonstrate that the receiver of a call could be predicted from the call's acoustic structure, regardless of how similar the call was to the receiver's vocalizations. Moreover, elephants differentially responded to playbacks of calls originally addressed to them relative to calls addressed to a different individual. Our findings offer evidence for individual addressing of conspecifics in elephants. They further suggest that, unlike other non-human animals, elephants probably do not rely on imitation of the receiver's calls to address one another. <<<

Path integration, the ability to sense self-motion for keeping track of changes in orientation and position, constitutes a fundamental mechanism of spatial navigation and a keystone for the development of cognitive maps. Whereas animal path integration is predominantly supported by the head-direction, grid, and place cell systems, the neural foundations are not well understood in humans. Here we used functional magnetic resonance imaging and a virtual rendition of a triangle completion paradigm to test whether human path integration recruits a cortical system similar to that of rodents and nonhuman primates. Participants traveled along two legs of a triangle before pointing toward the starting location. In accordance with animal models, stronger right hippocampal activation predicted more accurate updating of the starting location on a trial-by-trial basis. Moreover, between-subjects fluctuations in response consistency were negatively correlated with bilateral hippocampal and medial prefrontal activation, and bilateral recruitment of the human motion complex (hMT+) covaried with individual path integration capability. Given that these effects were absent in a perceptual control task, the present study provides the first evidence that visual path integration is related to the dynamic interplay of self-motion processing in hMT+, higher-level spatial processes in the hippocampus, and spatial working memory in medial prefrontal cortex. <<<

Parkinson's disease (PD) is a common neurodegenerative disease. Here, the purpose of the study was to explore the function of long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) in PD and its underlying mechanism. An in vivo 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-hydrochloride (MPTP)-induced mouse model of PD was generated and the SH-SY5Y cells were treated with MPP to induce neuronal damage in vitro. Quantitative real-time polymerase chain reaction (QRT-PCR) and Western blot were used to detect the expression of HOTAIR, miR-221-3p, α-synuclein and apoptosis-related genes. MTT, flow cytometry and TUNEL assay was used to detect cell viability and apoptosis, respectively. The levels of inflammatory cytokines TNF-α,IL-1β and IL-6 were detected by ELISA assay. The levels of lactate dehydrogenase (LDH), reactive oxygen species (ROS), and superoxide dismutase (SOD) were determined using the appropriate assay kits. The interactions between miR-221-3p and HOTAIR or α-synuclein were determined by dual luciferase assay and RNA binding protein immunoprecipitation (RIP). Co-localization of HOTAIR and miR-221-3p was also proved by immunofluorescence staining. The results showed that HOTAIR was highly expressed, while miR-221-3p expression was decreased in PD model in vivo and in vitro. In SH-SY5Y cells treated with MPP, the knockdown of HOTAIR increased cell viability and reduced cell apoptosis, the secretion of inflammatory cytokines and oxidative stress reaction, while HOTAIR overexpression led to opposite effects. Furthermore, HOTAIR sponged miR-221-3p which directly targeted α-synuclein and thus regulated the expression of α-synuclein. Meanwhile, inhibiting miR-221-3p could partially reverse the neuroprotective effects of HOTAIR knockdown. In conclusion, HOTAIR attenuated the injury of SH-SY5Y cells induced by MPP via miR-221-3p/α-synuclein axis, suggesting the potential therapeutic value of HOTAIR in PD. <<<

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh, and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NLP models that solve simple sentence classification tasks on quantum hardware. We conduct quantum simulations that compare the syntax-sensitive model of Coecke et al. with two baselines that use less or no syntax; specifically, we implement the quantum analogues of a “bag-of-words” model, where syntax is not taken into account at all, and of a word-sequence model, where only word order is respected. We demonstrate that all models converge smoothly both in simulations and when run on quantum hardware, and that the results are the expected ones based on the nature of the tasks and the datasets used. Another important goal of this paper is to describe in a way accessible to AI and NLP researchers the main principles, process and challenges of experiments on quantum hardware. Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing. <<<

Kimberly Martin, Pe’er Dar, Cora MacPherson, Melissa Egbert, Zachary Demko, Sheetal Parmar, Katelyn Hashimoto, Sina Haeri, Fergal Malone, Ronald J. Wapner, Ashley S. Roman, Asma Khalil, Revital Faro, Rajeevi Madankumar, Noel Strong, Robert M. Silver, Nidhi Vohra, Jon Hyett, Matt Rabinowitz, Charlly Kao, Hakon Hakonarson, Bo Jacobsson, Mary E. Norton <<<

Human social networks are overwhelmingly homophilous: individuals tend to befriend others who are similar to them in terms of a range of physical attributes (e.g., age, gender). Do similarities among friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To test whether friendship, and more generally, social network proximity, is associated with increased similarity of real-time mental responding, we used functional magnetic resonance imaging to scan subjects' brains during free viewing of naturalistic movies. Here we show evidence for neural homophily: neural responses when viewing audiovisual movies are exceptionally similar among friends, and that similarity decreases with increasing distance in a real-world social network. These results suggest that we are exceptionally similar to our friends in how we perceive and respond to the world around us, which has implications for interpersonal influence and attraction. <<<