Miracle-dz/UniFAR-FTUs huggingface dataset:
Extremely acidic proteomes and metabolic flexibility in bacteria and highly diversified archaea thriving in geothermal chaotropic brines

Few described archaeal, and fewer bacterial, lineages thrive at salt-saturating conditions, such as solar saltern crystallizers (salinity above 30%-w/v). They accumulate molar K+ cytoplasmic concentrations to maintain osmotic balance (‘salt-in’ strategy), and have proteins adaptively enriched in negatively charged, acidic amino acids. Here, we analyzed metagenomes and metagenome-assembled genomes (MAGs) from geothermally influenced hypersaline ecosystems with increasing chaotropicity in the Danakil Depression. Normalized abundances of universal single-copy genes confirmed that haloarchaea and Nanohaloarchaeota encompass 99% of microbial communities in the near life-limiting conditions of the Western-Canyon Lakes (WCLs). Danakil metagenome- and MAG-inferred proteomes, compared to those of freshwater, seawater and solar saltern ponds up to saturation (6-14-32% salinity), showed that WCL archaea encode the most acidic proteomes ever observed (median protein isoelectric points ≤4.4). We identified previously undescribed Halobacteria families as well as an Aenigmatarchaeota family and a bacterial phylum independently adapted to extreme halophily. Despite phylum-level diversity decreasing with increasing salinity-chaotropicity, and unlike in solar salterns, adapted archaea exceedingly diversified in Danakil ecosystems, challenging the notion of decreasing diversity under extreme conditions. Metabolic flexibility to utilize multiple energy and carbon resources generated by local hydrothermalism along feast-and-famine strategies seemingly shape microbial diversity in these ecosystems near life limits.
Longevity and environmental temperature modulate mitochondrial DNA evolution in fishes

The link between longevity and mitochondrial function has been documented for years. Since mitochondrial DNA (mtDNA) encodes for electron transport system (ETS) proteins, we could suspect that its evolution is linked with that of longevity. A negative relationship has been documented between the synonymous substitution rate and lifespan when analyzing the whole mitochondrial genome in animals. In this study, we aimed to confirm this negative correlation for each of the mitochondrial protein coding genes (mtPCGs) and explore potential relationships between adaptation to extreme temperatures and the evolution of mtDNA. To this end, we selected 112 species of fish with a wide range of longevity as well as divergences in environmental temperature, which is a good proxy for energy metabolism in these animals. Our results 1) challenge the “rate of living” theory by not showing any correlation between longevity and environmental temperature, 2) confirm the negative relationship between substitution rate and longevity for each of the 13 mtPCGs, and 3) highlight for the first time a link between high conservation of the three COX genes and adaptation to warmer temperatures in fish. By challenging a paradigm and extending the conclusions made for mtDNA to individual genes, our study opens a wide field to be explored concerning study of the aging process. Moreover, the specific link between the evolution of COX genes and temperature tolerance confirms the importance of complex IV in adaptation to extreme temperatures and, more generally, the importance of distinguishing gene families when studying mtDNA evolution in animals.
zIncubascope: long-term quantitative imaging of multi-cellular assemblies inside an incubator

Recent advances in bioengineering have made it possible to develop increasingly complex biological systems to recapitulate organ functions as closely as possible in vitro. Monitoring the assembly and growth of multi-cellular aggregates, micro-tissues or organoids and extracting quantitative information is a crucial but challenging task required to decipher the underlying morphogenetic mechanisms. We present here an imaging platform designed to be accommodated inside an incubator which provides high-throughput monitoring of cell assemblies over days and weeks. We exemplify the capabilities of our system by investigating human induced pluripotent stem cells (hiPSCs) enclosed in spherical capsules, hiPSCs in tubular capsules and yeast cells in spherical capsules. Combined with a customized pipeline of image analysis, our solution provides insight into the impact of confinement on the morphogenesis of these self-organized systems.
Role of the right middle occipital gyrus in egocentric spatial orientation in reference to gravitational information: Evidence from a pre-registered rTMS study

Accurate perception of the orientation of external objects relative to the body, known as egocentric spatial orientation, is fundamental to action. Previously, we found via behavioral and magnetic resonance imaging voxel-based morphometry studies that egocentric spatial orientation is distorted when the whole body is tilted with respect to gravity, and that the magnitude of this perceptual distortion is correlated with grey matter volume in the right middle occipital gyrus (rMOG). In the present pre-registered study, we demonstrated that neural processing in the rMOG is indeed a cause of the perceptual distortion. We transiently suppressed neural activity in the rMOG by applying low-frequency repetitive transcranial magnetic stimulation (rTMS) and evaluated the consequent effect on perceptual distortion. Our results showed that while rTMS over the rMOG significantly reduced perceptual distortion when the body was tilted with respect to gravity, it did not affect egocentric spatial orientation when in the upright position. No changes in perceptual distortion were observed when rTMS was applied to a control site (right temporoparietal junction) or to air (sham TMS). These results indicate that neural processing in the rMOG during body tilt is an essential cause of perceptual distortion, suggesting that the rMOG is engaged in egocentric spatial orientation concerning gravitational information. Significance statement The findings of this pre-registered study support a causal role of neural activity in the right middle occipital gyrus (rMOG) in the perceptual distortion of egocentric spatial orientation induced by whole-body tilt relative to gravity. We suppressed neural activity in the rMOG using low-frequency repetitive transcranial magnetic stimulation (rTMS) and measured perceptual distortion. We observed a significant reduction in perceptual distortion after rTMS over the rMOG, but not after control or sham rTMS. These results provide, for the first time, direct evidence of the engagement of the rMOG in egocentric spatial orientation in reference to gravitational information.
DeST-OT: Alignment of Spatiotemporal Transcriptomics Data

Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We demonstrate the advantage of DeST-OT on simulated slices. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells. DeST-OT outperforms existing methods on these metrics in the alignment of spatiotemporal transcriptomics data from the development of axolotl brain. Code availability Software is available at https://github.com/raphael-group/DeST_OT
Multifocus Image Fusion Using Artificial Neural Networks

Due to the limited depth-of-focus of optical lenses (especially such with long focal lengths) it is often not possible to get an image which contains all relevant objects in focus. One possibility to overcome this problem is to take several pictures with different focus points and combine them together into a single frame, which contains the focused regions of all input images. This paper describes the application of artificial neural networks to pixel-level fusion of multifocus images taken from the same scene. The proposed fusion method exploits the pattern recognition capabilities of artificial neural networks. Moreover, the learning capability of neural networks makes it feasible to customize the image fusion process. The experimental results show that the proposed method can perform better than the wavelet transform based method in some situations.
Psicología Educativa

Learning potential can be used to assess and provide direction for action in diverse populations in modern educational settings. This study examined the relationship between the EHPAP dynamic evaluation scale and cognitive abilities (linguistic development, specific cognitive abilities, and executive functioning) in typically developing preschool children. Linear regression models indicated that children’s cognitive abilities formed part of the prediction of their independent performance in tasks before and after mediation. The abilities that formed part of the predictions were related to general linguistic development, specific cognitive functions, and executive functioning. Age as a predictive factor only contributed to verbal planning. This study examined the contribution of these variables in different EHPAP scales.
A Biomedical Imaging Analysis of the Prevalent Neuropsychiatric Disorders

The utilization of various neuroimaging modalities to illuminate the structural and functional abnormalities detected in neuropsychiatric brain disorders has reached an unprecedented stage of evolvement in the medical community. Advances in structural and functional brain imaging technologies enable the medical community to discover the neurobiological basis of neurological illness in a very precise and dependable manner. Neuroimaging tools such as PET, SPECT and MRI have been better developed in order to further the neuroanatomical and neurophysiological basis of mental illnesses and cognitive disorders. With the advent of radiological innovation in neuroimaging, the focus in the medical community has shifted from the examination and study of single brain regions perhaps responsible for a specific psychiatric illness, to the critical examination of integrated brain systems which may be responsible for the phenotypic expressions of these illnesses. Imaging techniques have now made it quite clear that we must begin to investigate the neural networks involved in the pathophysiology of neuropsychiatric disorders. This paradigm shift, due to the innovations in brain imaging technologies, will likely facilitate the diagnostic reclassification of these complex heterogeneous disorders, enhance our understanding of genetic and environmental causes of the disorders, and improve our ability to treat these patients.
Derivation of A1-type granites by partial melting of newly underplated rocks related with the Tarim mantle plume

Abstract The granitic rocks of the Tarim large igneous province (LIP) are temporally and spatially related to mafic intrusions. However, their tectonic setting and genetic relationship are debated. Here, we report geochemical, and zircon U–Pb–Hf isotopic results for three alkali feldspar granitic plutons in the Halajun area, western margin of the Tarim Block. Zircon U–Pb ages suggest these plutons were emplaced at 268–275 Ma, coeval with the neighbouring mafic–ultramafic complexes and syenitic rocks. These granitic rocks have high contents of SiO2, alkalis, Rb, Th, Zr and REE (except Eu), and high ratios of FeO*/MgO and Ga/Al, and show strong depletions in Ba, Sr, Eu, which are commonly observed in the A1-type granites. Zircon Hf isotopes reveal a limited range of εHf(t) values from −1.0 to +3.5 for different samples from three granitic plutons, obviously higher than those (mostly <0) of the mafic rocks. This distinct difference, along with a Daly gap and small volume of mafic rocks, argues against extreme fractionation of mafic magma as the main origin of the A1-type granites. Instead the A1-type granites were most likely derived from partial melting of newly underplated rocks triggered by the upwelling asthenosphere, followed by extensive fractionation. These A1-type granites were emplaced within an anorogenic setting during the late stage of the Tarim LIP, which possibly lasts for more than 30 Ma. The Piqiang mafic–ultramafic complex directly stemmed from asthenospheric mantle and Halajun A1-type granites represent two manners of vertical crustal growth.
Security Issuance with Undersubscription Risk

A successful security offering raises more revenue for investments that will be used to pay distributions on the securities. This can give rise to a coordination problem among investors, which the issuer can overcome by underpricing the security. Debt minimizes the required underpricing and is thus optimal. Underpricing of equity can be mitigated by share rationing or a minimum sales requirement. The theory also yields several testable predictions and explains why the inclusion of secondary shares lessens underpricing.
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields

Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity. However, the presence of motion blur, resulting from slight camera movements during extended shutter exposures, poses a significant challenge, potentially compromising the quality of the reconstructed 3D scenes. While recent studies have addressed this issue, they do not consider the continuous dynamics of camera movements during image acquisition, leading to inaccurate scene reconstruction. Additionally, these methods are plagued by slow training and rendering speed. To effectively handle these issues, we propose sequential motion understanding radiance fields (SMURF), a novel approach that employs neural ordinary differential equation (Neural-ODE) to model continuous camera motion and leverages the explicit volumetric representation method for faster training and robustness to motion-blurred input images. The core idea of the SMURF is continuous motion blurring kernel (CMBK), a unique module designed to model a continuous camera movements for processing blurry inputs. Our model, rigorously evaluated against benchmark datasets, demonstrates state-of-the-art performance both quantitatively and qualitatively.
Success functions in large contests

We consider contests with a large set (continuum) of participants and axiomatize contest success functions that arise when performance is composed of both effort and a random element, and when winners are those whose performance exceeds a cutoff determined by a market clearing condition. A co-monotonicity property is essentially all that is needed for a representation in the general case, but significantly stronger conditions must hold to obtain an additive structure. We illustrate the usefulness of this framework by revisiting some of the classic questions in the contests literature.
ORPO: Monolithic Preference Optimization without Reference Model

While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT) remains imperative for achieving successful convergence. In this paper, we study the crucial role of SFT within the context of preference alignment, emphasizing that a minor penalty for the disfavored generation style is sufficient for preference-aligned SFT. Building on this foundation, we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the necessity for an additional preference alignment phase. We demonstrate, both empirically and theoretically, that the odds ratio is a sensible choice for contrasting favored and disfavored styles during SFT across the diverse sizes from 125M to 7B. Specifically, fine-tuning Phi-2 (2.7B), Llama-2 (7B), and Mistral (7B) with ORPO on the UltraFeedback alone surpasses the performance of state-of-the-art language models with more than 7B and 13B parameters: achieving up to 12.20% on $\text{AlpacaEval}_{2.0}$ (Figure 1), 66.19% on IFEval (instruction-level loose, Table 6), and 7.32 in MT-Bench (Figure 12). We release code and model checkpoints for Mistral-ORPO-$\alpha$ (7B) and Mistral-ORPO-$\beta$ (7B).
Fusing Climate Data Products using a Spatially Varying Autoencoder

Autoencoders are powerful machine learning models used to compress information from multiple data sources. However, autoencoders, like all artificial neural networks, are often unidentifiable and uninterpretable. This research focuses on creating an identifiable and interpretable autoencoder that can be used to meld and combine climate data products. The proposed autoencoder utilizes a Bayesian statistical framework, allowing for probabilistic interpretations while also varying spatially to capture useful spatial patterns across the various data products. Constraints are placed on the autoencoder as it learns patterns in the data, creating an interpretable consensus that includes the important features from each input. We demonstrate the utility of the autoencoder by combining information from multiple precipitation products in High Mountain Asia.
Last Iterate Convergence of Incremental Methods and Applications in Continual Learning

Incremental gradient methods and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, when it comes to their convergence guarantees, nonasymptotic (first-order or proximal) oracle complexity bounds have been obtained fairly recently, almost exclusively applying to the average iterate. Motivated by applications in continual learning, we obtain the first convergence guarantees for the last iterate of both incremental gradient and incremental proximal methods, in general convex smooth (for both) and convex Lipschitz (for the proximal variants) settings. Our oracle complexity bounds for the last iterate nearly match (i.e., match up to a square-root-log or a log factor) the best known oracle complexity bounds for the average iterate, for both classes of methods. We further obtain generalizations of our results to weighted averaging of the iterates with increasing weights, which can be seen as interpolating between the last iterate and the average iterate guarantees. Additionally, we discuss how our results can be generalized to variants of studied incremental methods with permuted ordering of updates. Our results generalize last iterate guarantees for incremental methods compared to state of the art, as such results were previously known only for overparameterized linear models, which correspond to convex quadratic problems with infinitely many solutions.
Reinforced Sequential Decision-Making for Sepsis Treatment: The PosNegDM Framework with Mortality Classifier and Transformer

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. This paper introduces the POSNEGDM -- ``Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making"framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7\% accuracy guides treatment decisions towards positive outcomes. The POSNEGDM framework significantly improves patient survival, saving 97.39% of patients, outperforming established machine learning algorithms (Decision Transformer and Behavioral Cloning) with survival rates of 33.4% and 43.5%, respectively. Additionally, ablation studies underscore the critical role of the transformer-based decision maker and the integration of a mortality classifier in enhancing overall survival rates. In summary, our proposed approach presents a promising avenue for enhancing sepsis treatment outcomes, contributing to improved patient care and reduced healthcare costs.
Infinite tower of higher-curvature corrections: Quasinormal modes and late-time behavior of D-dimensional regular black holes

Recently, Bueno, Cano, and Hennigar [arXiv: 2403.04827] proposed a generic approach for incorporating an infinite tower of higher curvature corrections into the Einstein theory. In this study, we compute quasinormal modes for certain regular D-dimensional black holes resulting from this infinite series of higher curvature corrections, specifically focusing on the D-dimensional extensions of the Bardeen and Hayward black holes. We demonstrate that while the fundamental mode is minimally affected by moderate coupling constants, the higher overtones exhibit significant sensitivity even to small coupling values, yielding unconventional modes characterized by vanishing real oscillation frequencies. When comparing the frequencies derived from the metric truncated at several orders of higher curvature corrections with those resulting from the infinite series of terms, we observe a rapid convergence of the frequencies to their limit for the complete regular black hole. This validates the extensive research conducted on specific theories with a finite number of higher curvature corrections, such as the Lovelock theory.
Holistic numerical simulation of a quenching process on a real-size multifilamentary superconducting coil

Superconductors play a crucial role in the advancement of high-field electromagnets. Unfortunately, their performance can be compromised by thermomagnetic instabilities, wherein the interplay of rapid magnetic and slow heat diffusion can result in catastrophic flux jumps eventually leading to irreversible damage. This issue has long plagued high-$J_c$ Nb$_3$Sn wires at the core of high-field magnets. In this study, we introduce a groundbreaking large-scale GPU-optimized algorithm aimed at tackling the complex intertwined effects of electromagnetism, heating, and strain acting concomitantly during the quenching process of superconducting coils. We validate our model by conducting comparisons with magnetization measurements obtained from short multifilamentary Nb$_3$Sn wires and further experimental tests conducted on solenoid coils while subject to ramping transport currents. Furthermore, leveraging our developed numerical algorithm, we unveil the dynamic propagation mechanisms underlying thermomagnetic instabilities (including flux jumps and quenches) within the coils. Remarkably, our findings reveal that the velocity field of flux jumps and quenches within the coil is correlated with the amount of Joule heating experienced by each wire over a specific time interval, rather than solely being dependent on instantaneous Joule heating or maximum temperature. These insights have the potential to pave the way for optimizing the design of next-generation superconducting magnets, thereby directly influencing a wide array of technologically relevant and multidisciplinary applications.