Miracle-dz/UniFAR-FTUs huggingface dataset:
SplatArmor: Articulated Gaussian splatting for animatable humans from monocular RGB videos

We propose SplatArmor, a novel approach for recovering detailed and animatable human models by `armoring' a parameterized body model with 3D Gaussians. Our approach represents the human as a set of 3D Gaussians within a canonical space, whose articulation is defined by extending the skinning of the underlying SMPL geometry to arbitrary locations in the canonical space. To account for pose-dependent effects, we introduce a SE(3) field, which allows us to capture both the location and anisotropy of the Gaussians. Furthermore, we propose the use of a neural color field to provide color regularization and 3D supervision for the precise positioning of these Gaussians. We show that Gaussian splatting provides an interesting alternative to neural rendering based methods by leverging a rasterization primitive without facing any of the non-differentiability and optimization challenges typically faced in such approaches. The rasterization paradigms allows us to leverage forward skinning, and does not suffer from the ambiguities associated with inverse skinning and warping. We show compelling results on the ZJU MoCap and People Snapshot datasets, which underscore the effectiveness of our method for controllable human synthesis.
Unraveling oscillations at ferro(para)magnetic and non-collinear antiferromagnetic interfaces

Here, we show that the ferro(para)magnetic and non-collinear antiferromagnetic interfaces contribute to oscillating signals observed in the magnetoresistance. We associate the effect with the fact that the spins on the surface of IrMn3 produce instability in the surface magnetoresistance of the material, which is sensitive to the magnetic field. We carried out experiments using bilayers of IrMn3 under permalloy (Py) and IrMn3 under platinum (Pt). The oscillations were intensely evident at the Py/IrMn3 interface and less explicit at the Pt/IrMn3 interface. We carried out the experiments using pulsed current, with a square pulse width of 1{\mu}s and amplitudes of 20 {\mu}A to 20 mA. The oscillating signals are proportional to the crystallographic direction of the material, the ferromagnetism of the material adjacent to IrMn3, and sensitive to the amplitude of the pulsed current. We believe that observation is a way of transmitting encoded information through magnetoresistance.
Automatic Restoration of Diacritics for Speech Data Sets

Automatic text-based diacritic restoration models generally have high diacritic error rates when applied to speech transcripts as a result of domain and style shifts in spoken language. In this work, we explore the possibility of improving the performance of automatic diacritic restoration when applied to speech data by utilizing the parallel spoken utterances. In particular, we use the pre-trained Whisper ASR model fine-tuned on relatively small amounts of diacritized Arabic speech data to produce rough diacritized transcripts for the speech utterances, which we then use as an additional input for a transformer-based diacritic restoration model. The proposed model consistently improve diacritic restoration performance compared to an equivalent text-only model, with at least 5\% absolute reduction in diacritic error rate within the same domain and on two out-of-domain test sets. Our results underscore the inadequacy of current text-based diacritic restoration models for speech data sets and provide a new baseline for speech-based diacritic restoration.
Estimation of entropy-regularized optimal transport maps between non-compactly supported measures

This paper addresses the problem of estimating entropy-regularized optimal transport (EOT) maps with squared-Euclidean cost between source and target measures that are subGaussian. In the case that the target measure is compactly supported or strongly log-concave, we show that for a recently proposed in-sample estimator, the expected squared $L^2$-error decays at least as fast as $O(n^{-1/3})$ where $n$ is the sample size. For the general subGaussian case we show that the expected $L^1$-error decays at least as fast as $O(n^{-1/6})$, and in both cases we have polynomial dependence on the regularization parameter. While these results are suboptimal compared to known results in the case of compactness of both the source and target measures (squared $L^2$-error converging at a rate $O(n^{-1})$) and for when the source is subGaussian while the target is compactly supported (squared $L^2$-error converging at a rate $O(n^{-1/2})$), their importance lie in eliminating the compact support requirements. The proof technique makes use of a bias-variance decomposition where the variance is controlled using standard concentration of measure results and the bias is handled by T1-transport inequalities along with sample complexity results in estimation of EOT cost under subGaussian assumptions. Our experimental results point to a looseness in controlling the variance terms and we conclude by posing several open problems.
Data Equity: Foundational Concepts for Generative AI

This briefing paper focuses on data equity within foundation models, both in terms of the impact of Generative AI (genAI) on society and on the further development of genAI tools. GenAI promises immense potential to drive digital and social innovation, such as improving efficiency, enhancing creativity and augmenting existing data. GenAI has the potential to democratize access and usage of technologies. However, left unchecked, it could deepen inequities. With the advent of genAI significantly increasing the rate at which AI is deployed and developed, exploring frameworks for data equity is more urgent than ever. The goals of the briefing paper are threefold: to establish a shared vocabulary to facilitate collaboration and dialogue; to scope initial concerns to establish a framework for inquiry on which stakeholders can focus; and to shape future development of promising technologies. The paper represents a first step in exploring and promoting data equity in the context of genAI. The proposed definitions, framework and recommendations are intended to proactively shape the development of promising genAI technologies.
LPS-Induced Inflammation Reduces GABAergic Interneuron markers and Brain-derived Neurotrophic Factor in Mouse Prefrontal Cortex and Hippocampus

Inflammation, reduced gamma-aminobutyric acidergic (GABAergic) function and altered neuroplasticity are co-occurring pathophysiologies in major depressive disorder (MDD). However, the link between these biological changes remains unclear. We hypothesized that inflammation induces deficits in GABAergic interneuron markers and that this effect is mediated by brain-derived neurotrophic factor (BDNF). We report here that systemic inflammation induced by intraperitoneal injection of lipopolysaccharide (LPS) (0.125, 0.25, 0.5, 1, 2mg/kg) in the first cohort of C57BL/6 mice (n=72; 9-12 weeks; 50% female) resulted in increased interleukin 1-beta and interleukin-6 in prefrontal cortex (PFC) and hippocampus (HPC), as measured using enzyme-linked immunosorbent assay (ELISA). Quantitative real-time polymerase reaction (qPCR) was used to explore the effect of LPS on the expression of GABAergic interneuron markers. In the PFC of the second cohort (n=39; 9-12 weeks; 50% female), 2mg/kg of LPS decreased the expression of somatostatin (Sst) (p=0.0014), parvalbumin (Pv) (p=0.0257), cortistatin (Cort) (p=0.0003), neuropeptide Y (Npy) (p=0.0033) and cholecystokinin (Cck) (p=0.0041), and did not affect corticotropin-releasing hormone (Crh) and vasoactive intestinal peptide (Vip) expression. In the HPC, 2mg/kg of LPS decreased the expression of Sst (P=0.0543), Cort (p=0.0011), Npy (p=0.0001), and Cck (p<0.0001), and did not affect Crh, Pv, and Vip expression. LPS decreased the expression of Bdnf in the PFC (P<0.0001) and HPC (P=0.0003), which significantly correlated with affected markers (Sst, Pv, Cort, Cck, and Npy). Collectively, these results suggest that inflammation may causally contribute to cortical cell microcircuit GABAergic deficits observed in MDD.
Dynamic Functional Connectivity Encodes Generalizable Representations of Emotional Arousal But Not Valence

Human affective experience varies along the dimensions of valence (positivity or negativity) and arousal (high or low activation). It remains unclear how these dimensions are encoded in the brain and if the representations generalize across diverse situations. Here we utilized two publicly available functional MRI datasets of participants watching movies to build predictive models of moment-to-moment valence and arousal from functional correlations in brain activity. We tested the models both within and across datasets and identified a situation-general arousal representation characterized by the interaction between multiple large-scale functional networks. The arousal representation generalized to two additional datasets. Predictions based on multivariate patterns of activation underperformed connectome-based predictions and did not generalize. In contrast, we found no evidence of a situation-general valence representation. Together, our findings reveal a generalizable representation of arousal encoded in patterns of dynamic functional connectivity, revealing an underlying similarity in how arousal is encoded across affective contexts.
Autapses enable temporal pattern recognition in spiking neural networks

Most sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience. Although there is evidence that the precise timing of spikes represents information in spiking neuronal networks, information processing in spiking networks is still not fully understood. One feasible way to understand the working mechanism of a spiking network is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for a temporal pattern recognition task. The task is to recognise a specific order of the input signals so that the Out put neurone of the network spikes only for the correct placement and remains silent for all others. The minimal networks obtained for this task revealed the twofold importance of autapses in recognition; first, autapses simplify the switching among different network states. Second, autapses enable a network to maintain a network state, a form of memory. To show that the recognition task is accomplished by transitions between network states, we map the network states of a functional spiking neural network (SNN) onto the states of a finite-state transducer (FST, a formal model of computation that generates output symbols, here: spikes or no spikes at specific times, in response to input, here: a series of input signals). Finally, based on our understanding, we define rules for constructing the topology of a network handcrafted for recognising a subsequence of signals (pattern) in a particular order. The analysis of minimal networks recognising patterns of different lengths (two to six) revealed a positive correlation between the pattern length and the number of autaptic connections in the network. Furthermore, in agreement with the behaviour of neurones in the network, we were able to associate specific functional roles of ‘locking,’ ‘switching,’ and ‘accepting’ to neurones.
Pangenome- and genome-based taxonomic classification inference for the marine bacterial strain KMM 296 producing a highly active PhoA alkaline phosphatase and closely related Cobetia species

A strictly aerobic, Gram-stain-negative, rod-shaped and motile bacterium, designated strain KMM 296, isolated from the coelomic fluid of mussel Crenomytilus grayanus, was investigated in details due to its ability to produce a highly active alkaline phosphatase of the structural family PhoA. A previous taxonomic study placed the strain to the species Cobetia marina, a member of the family Halomonadaceae of the class Gammaproteobacteria. However, the comprehensive phylogenetic analysis based on 16S rRNA gene sequencing revealed that the strain KMM 296 is most closely related to Cobetia amphilecti NRIC 815T with the 16S rRNA gene sequence similarity of 100%. The mussel isolate grew with 0.5-19% NaCl and at 4 - 42°C and hydrolysed Tweens 20 and 40, and L-tyrosine. The DNA G+C content was 62.5 mol%. The prevalent fatty acids were C18:1 ω7c, C12:0 3-OH, C18:1 ω7c, C12:0 and C17:0 cyclo. The polar lipid profile was characterized by the presence of phosphatidylethanolamine, phosphatidylglycerol, phosphatidic acid, and unidentified aminolipid, phospholipid, and lipids. The major respiratory quinone was Q-8. According to phylogenetic evidence and similarity in the chemotaxonomic and genotypic properties of the mussel isolate and its nearest neighbors, the strain KMM 296 represents a member of the species C. amphilecti. A comparative analysis of the type strains genomes of the species C. amphilecti and C. litoralis showed that they belong to a single species. In addition, a high similarity of the genome sequences of C. pacifica NRIC 813T and C. marina LMG 2217T also allows suggesting the affiliation of these two species to one species. Based on the rules of priority, C. litoralis should be reclassified as a later heterotypic synonym of C. amphilecti, and C. pacifica is a later heterotypic synonym of C. marina. The emended descriptions of the species C. amphilecti and C. marina are also proposed.
Peripheral nerve injury results in a biased loss of sensory neuron sub-populations

There is a rich literature describing loss of dorsal root ganglion (DRG) neurons following peripheral axotomy, but the vulnerability of discrete subpopulations has not yet been characterised. Furthermore, the extent and even presence of neuron loss following injury has recently been challenged. In this study, we have used a range of transgenic recombinase driver mouse lines to genetically label molecularly defined subpopulations of DRG neurons and track their survival following traumatic nerve injury. We find that spared nerve injury (SNI) leads to a marked loss of cell containing DRG-volume and a concomitant loss of small diameter DRG neurons. Neuron loss occurs unequally across subpopulations and is particularly prevalent in non-peptidergic nociceptors, marked by expression of Mrgprd. We show that this subpopulation is almost entirely lost following SNI and severely depleted (by roughly 50%) following sciatic nerve crush. Finally, we used an in vitro model of DRG neuron survival to demonstrate that non-peptidergic nociceptor loss is likely dependent on the absence of neurotrophic support. Together, these results profile the extent to which DRG neuron subpopulations can survive axotomy, with implications for our understanding of nerve injury-induced plasticity and pain.
oncotree2vec – A method for embedding and clustering of tumor phylogenetic trees

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient’s tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor sub-types, for more accurate clinical diagnosis and prognosis. Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies, and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.
Distinct roles of monkey OFC-subcortical pathways in adaptive behavior

To be the most successful, primates must adapt to changing environments and optimize their behavior by making the most beneficial choices. At the core of adaptive behavior is the orbitofrontal cortex (OFC) of the brain, which updates choice value through direct experience or knowledge-based inference. Here, we identify distinct neural circuitry underlying these two separate abilities. We designed two behavioral tasks in which macaque monkeys updated the values of certain items, either by directly experiencing changes in stimulus-reward associations, or by inferring the value of unexperienced items based on the task’s rules. Chemogenetic silencing of bilateral OFC combined with mathematical model-fitting analysis revealed that monkey OFC is involved in updating item value based on both experience and inference. In vivo imaging of chemogenetic receptors by positron emission tomography allowed us to map projections from the OFC to the rostromedial caudate nucleus (rmCD) and the medial part of the mediodorsal thalamus (MDm). Chemogenetic silencing of the OFC-rmCD pathway impaired experience-based value updating, while silencing the OFC-MDm pathway impaired inference-based value updating. Our results thus demonstrate a dissociable contribution of distinct OFC projections to different behavioral strategies, and provide new insights into the neural basis of value-based adaptive decision-making in primates.
Bacterial assembly in the switchgrass rhizosphere is shaped by phylogeny, host genotype, and growing site

Since microbial traits are conserved at different taxonomic levels, plant hosts may influence microbiome composition differently at different levels to broadly promote or resist microbiota with traits that impact host fitness. We tested this hypothesis by assessing signals of host genetic influence on bacterial composition in the switchgrass rhizosphere using 128 genotypes in dissimilar growing sites. We employed three common gardens, combined with host genetic mapping, 16S rRNA gene sequence analysis, hierarchical modeling, tests of phylogenetic conservation of host influence, and genome-wide association analyses to determine the contributions of host genetics in shaping rhizosphere bacterial composition at different taxonomic levels. Modeling bacterial assembly showed that growing site was a strong factor shaping bacterial composition in the rhizosphere, though host genetic influence played a significant role. The heritability of bacterial abundance was strongest at the genus level. Phylogenetic signal for heritability was detected within the bacterial phylogeny but conserved clades differed between common gardens. We identified shared host genetic variants associated with bacterial abundance and host traits related to plant metabolism. Our results suggest further investigation is required regarding the genotype-by-environment-by-microbiome relationship to elucidate the factors shaping rhizosphere microbiome composition and the agroecological dynamics shaping plant phenotype.
The embryonic DNA methylation program modulates the cis-regulatory landscape via CTCF antagonism

During mammalian embryogenesis, both the 5-cytosine DNA methylation (5meC) landscape and three-dimensional (3D) chromatin architecture are profoundly remodeled during a process known as “epigenetic reprogramming. ” An understudied aspect of epigenetic reprogramming is how the 5meC flux, per se, affects the 3D genome. This is pertinent given the 5meC-sensitivity of DNA binding for a key regulator of chromosome folding: CTCF. We profiled the CTCF binding landscape using a mouse embryonic stem cell (ESC) differentiation protocol that models the exit of naïve pluripotency, wherein global DNA methylation levels start low and increase to somatic levels within four days. We took advantage of the fact that mouse ESCs lacking DNA methylation machinery exhibit globally similar differentiation dynamics, thus allowing for dissection of more subtle effects of CTCF misregulation on gene expression. We carried this out by performing CTCF HiChIP in both wild-type and mutant conditions to assess aberrant CTCF-CTCF contacts in the absence of 5meC. We went on to assess the impact that misregulated CTCF binding has on cis-regulatory contacts by performing H3K27ac HiChIP, given that H3K27ac is enriched on active promoters and enhancers. Using DNA methylation epigenome editing, we were able to directly demonstrate that the DNA methyl-mark is able to impact CTCF binding. Finally, a detailed dissection of the imprinted Zdbf2 gene showed how 5meC-antagonism of CTCF allows for proper gene regulation during differentiation. This work provides a comprehensive overview of how DNA methylation impacts the 3D genome in a relevant model for early embryonic events.
An ensemble pneumonia prediction and classification model including InceptionNeXtPneumonia prediction and classification

Pneumonia is an important threat to human health, and different types of pneumonia have different treatment options, so the prediction and classification of pneumonia are important health issues. In this paper, chest X-ray images are used as data, and the final ensemble model can achieve excellent performance on these two tasks. In addition, this paper introduces the InceptionNeXt model to pneumonia prediction and classification problems for the first time, and finds that different model convolution kernels and perception fields may be more suitable for different medical image research tasks.
Advancing Pulmonary Nodule Classification: A Novel Multi-Scale Fusion and Joint Upsampling Strategy using 3D Convolutional Neural Networks

Lung cancer, renowned for having the highest global incidence and mortality rates among all cancers, presents a promising avenue for improving survival rates through early detection and precise diagnosis. However, current diagnostic methods relying on the manual interpretation of CT images are susceptible to subjectivity and potential errors. To address this challenge, we introduce an innovative fully convolutional neural network that synergistically integrates multi-scale feature fusion and joint upsampling modules. Our model aims to enhance the precision of lung cancer diagnosis by effectively categorizing benign and malignant pulmonary nodules within CT images. Leveraging a comprehensive dataset comprising 1012 pulmonary nodule samples sourced from LIDC-IDRI, our evaluation reveals exceptional performance metrics. Notably, the model achieves an impressive area under the ROC curve of 97.35%, along with a high accuracy of 94.21%, sensitivity of 93.79%, and specificity of 94.91%.