Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulin resistance induces earlier initiation of cognitive dysfunction mediated by cholinergic deregulation in a mouse model of Alzheimer's disease

Although insulin resistance increases the risk of Alzheimer's disease (AD), the mechanisms remain unclear, partly because no animal model exhibits the insulin‐resistant phenotype without persistent hyperglycemia. Here we established an AD model with whole‐body insulin resistance without persistent hyperglycemia (APP/IR‐dKI mice) by crossbreeding constitutive knock‐in mice with P1195L‐mutated insulin receptor (IR‐KI mice) and those with mutated amyloid precursor protein (AppNL‐G‐F mice: APP‐KI mice). APP/IR‐dKI mice exhibited cognitive impairment at an earlier age than APP‐KI mice. Since cholinergic dysfunction is a major characteristic of AD, pharmacological interventions on the cholinergic system were performed to investigate the mechanism. Antagonism to a nicotinic acetylcholine receptor α7 (nAChRα7) suppressed cognitive function and cortical blood flow (CBF) response to cholinergic‐regulated peripheral stimulation in APP‐KI mice but not APP/IR‐dKI mice. Cortical expression of Chrna7, encoding nAChRα7, was downregulated in APP/IR‐dKI mice compared with APP‐KI. Amyloid β burden did not differ between APP‐KI and APP/IR‐dKI mice. Therefore, insulin resistance, not persistent hyperglycemia, induces the earlier onset of cognitive dysfunction and CBF deregulation mediated by nAChRα7 downregulation. Our mouse model will help clarify the association between type 2 diabetes mellitus and AD.
Red Blood Cell Transfusion: 2023 AABB International Guidelines.

Importance Red blood cell transfusion is a common medical intervention with benefits and harms. Objective To provide recommendations for use of red blood cell transfusion in adults and children. Evidence Review Standards for trustworthy guidelines were followed, including using Grading of Recommendations Assessment, Development and Evaluation methods, managing conflicts of interest, and making values and preferences explicit. Evidence from systematic reviews of randomized controlled trials was reviewed. Findings For adults, 45 randomized controlled trials with 20 599 participants compared restrictive hemoglobin-based transfusion thresholds, typically 7 to 8 g/dL, with liberal transfusion thresholds of 9 to 10 g/dL. For pediatric patients, 7 randomized controlled trials with 2730 participants compared a variety of restrictive and liberal transfusion thresholds. For most patient populations, results provided moderate quality evidence that restrictive transfusion thresholds did not adversely affect patient-important outcomes. Recommendation 1: for hospitalized adult patients who are hemodynamically stable, the international panel recommends a restrictive transfusion strategy considering transfusion when the hemoglobin concentration is less than 7 g/dL (strong recommendation, moderate certainty evidence). In accordance with the restrictive strategy threshold used in most trials, clinicians may choose a threshold of 7.5 g/dL for patients undergoing cardiac surgery and 8 g/dL for those undergoing orthopedic surgery or those with preexisting cardiovascular disease. Recommendation 2: for hospitalized adult patients with hematologic and oncologic disorders, the panel suggests a restrictive transfusion strategy considering transfusion when the hemoglobin concentration is less than 7 g/dL (conditional recommendations, low certainty evidence). Recommendation 3: for critically ill children and those at risk of critical illness who are hemodynamically stable and without a hemoglobinopathy, cyanotic cardiac condition, or severe hypoxemia, the international panel recommends a restrictive transfusion strategy considering transfusion when the hemoglobin concentration is less than 7 g/dL (strong recommendation, moderate certainty evidence). Recommendation 4: for hemodynamically stable children with congenital heart disease, the international panel suggests a transfusion threshold that is based on the cardiac abnormality and stage of surgical repair: 7 g/dL (biventricular repair), 9 g/dL (single-ventricle palliation), or 7 to 9 g/dL (uncorrected congenital heart disease) (conditional recommendation, low certainty evidence). Conclusions and Relevance It is good practice to consider overall clinical context and alternative therapies to transfusion when making transfusion decisions about an individual patient.
Inter-individual variation in human cortical cell type abundance and expression

Single cell transcriptomic studies have identified a conserved set of neocortical cell types from small post-mortem cohorts. We extend these efforts by assessing cell type variation across 75 adult individuals undergoing epilepsy and tumor surgeries. Nearly all nuclei map to one of 125 robust cell types identified in middle temporal gyrus, but with varied abundances and gene expression signatures across donors, particularly in deep layer glutamatergic neurons. A minority of variance is explainable by known factors including donor identity and small contributions from age, sex, ancestry, and disease state. Genomic variation was significantly associated with variable expression of 150-250 genes for most cell types. Thus, human individuals display a highly consistent cellular makeup, but with significant variation reflecting donor characteristics, disease condition, and genetic regulation. One-Sentence Summary Inter-individual variation in human cortex is greatest for deep layer excitatory neurons and largely unexplainable by known factors.
Asymptotics for power posterior mean estimation

Power posteriors "robustify" standard Bayesian inference by raising the likelihood to a constant fractional power, effectively downweighting its influence in the calculation of the posterior. Power posteriors have been shown to be more robust to model misspecification than standard posteriors in many settings. Previous work has shown that power posteriors derived from low-dimensional, parametric locally asymptotically normal models are asymptotically normal (Bernstein-von Mises) even under model misspecification. We extend these results to show that the power posterior moments converge to those of the limiting normal distribution suggested by the Bernstein-von Mises theorem. We then use this result to show that the mean of the power posterior, a point estimator, is asymptotically equivalent to the maximum likelihood estimator.
New aerogeophysical view of the Antarctic Peninsula: More pieces, less puzzle

New airborne geophysical data reveal subglacial imprints of crustal growth of the Antarctic Peninsula by Mesozoic arc magmatism and terrane accretion along the paleo‐Pacific margin of Gondwana. Potential field signatures indicate that the Antarctic Peninsula batholith is a composite magmatic arc terrane comprising two distinct arcs, separated by a >1500 km‐long suture zone, similar to the Peninsular Ranges batholith in southern and Baja California. Aeromagnetic, aerogravity and geological data suggest that a mafic Early Cretaceous western arc was juxtaposed against a more felsic eastern arc which, in mid‐Cretaceous times, was intruded by highly magnetic tonalitic/granodioritic plutons of island arc affinity. Suturing of the two arcs against the Gondwana margin caused the mid‐Cretaceous Palmer Land orogenic event. Convergence and suturing may have been driven by two subduction zones or, alternatively, by a decrease in slab dip, leading to an inboard migration of the arc, as in California.
Palaeomagnetism of the Borrowdale and Eycott volcanic groups, English Lake District: primary and secondary magnetization during a single late Ordovician polarity chron

Late Ordovician volcanic rocks of the English Lake District typically have a magnetic remanence dominated by a single characteristic component. Previous investigations have interpreted this remanence as both of primary (pre-folding) and secondary origin. Palaeomagnetic field tests have been conducted on (a) andesite blocks from an autobrecciated lava top, (b) andesite blocks in mass-flow breccias, and (c) fault-blocks tilted during Ordovician caldera collapse to establish the time of remanence acquisition. All three tests show that the lavas retain a magnetization acquired during initial cooling: magnetizations of the breccias are coherent within clasts and random between clasts, whilst magnetizations of the tilted fault blocks converge with better that 95% confidence when corrected for the effects of caldera collapse. In contrast, the volcaniclastic sedimentary and pyroclastic rocks possess an Ordovician secondary remanence acquired after strata had been tilted by volcano-tectonic subsidence. A distributed sample of 65 andesite and basalt sheets through the Borrowdale Volcanic Group has a mean remanence direction D/I=341.9/−48.9° (α95=4.0°) yielding a positive fold test and a palaeomagnetic pole at 12.7°E, 4.3°S (dp/dm=3.5/5.3°). A progressive steepening of the palaeofield direction is recorded during emplacement of the Borrowdale Volcanic Group (∼I=−39° to I=−51°) which continued into the interval of volcanotectonic overprinting (I=−62°); the equivalent motion of Eastern Avalonia is ∼20° into higher southerly latitudes. Both the Eycott and Borrowdale volcanic groups exhibit uniform normal polarity throughout. Correlation with the geomagnetic time scale for the Ordovician restores the broad correlation between the two groups by constraining their emplacement and partial overprinting to a single long normal polarity chron occupying the Nemagraptus gracilis and earlier part of the Diplograptus multidens biozones (late Llandeilo and early Caradoc). All the volcanism, therefore, occurred within a period of no more than ∼5 Ma. The palaeomagnetic evidence confirms that the Borrowdale Volcanic Group was affected by both syn-volcanic deformation (caldera collapse) and regional compressive deformation prior to deposition of the (late Ordovician–Silurian) Windermere Supergroup. The succession of primary and secondary Ordovician palaeomagnetic poles from the Lake District inlier defines an anticlockwise apparent polar wander (APW) loop with the apex correlating with ‘soft’ closure of the Iapetus Ocean and late Ordovician deformation. The APW paths from Avalonia and Baltica converge at this point as subduction ceased and the arc subsided beneath the sea after mid-Caradoc times.
Large Language Models Are Zero-Shot Time Series Forecasters

By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large language models (LLMs) such as GPT-3 and LLaMA-2 can surprisingly zero-shot extrapolate time series at a level comparable to or exceeding the performance of purpose-built time series models trained on the downstream tasks. To facilitate this performance, we propose procedures for effectively tokenizing time series data and converting discrete distributions over tokens into highly flexible densities over continuous values. We argue the success of LLMs for time series stems from their ability to naturally represent multimodal distributions, in conjunction with biases for simplicity, and repetition, which align with the salient features in many time series, such as repeated seasonal trends. We also show how LLMs can naturally handle missing data without imputation through non-numerical text, accommodate textual side information, and answer questions to help explain predictions. While we find that increasing model size generally improves performance on time series, we show GPT-4 can perform worse than GPT-3 because of how it tokenizes numbers, and poor uncertainty calibration, which is likely the result of alignment interventions such as RLHF.
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

Representing and rendering dynamic scenes has been an important but challenging task. Especially, to accurately model complex motions, high efficiency is usually hard to guarantee. To achieve real-time dynamic scene rendering while also enjoying high training and storage efficiency, we propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame. In 4D-GS, a novel explicit representation containing both 3D Gaussians and 4D neural voxels is proposed. A decomposed neural voxel encoding algorithm inspired by HexPlane is proposed to efficiently build Gaussian features from 4D neural voxels and then a lightweight MLP is applied to predict Gaussian deformations at novel timestamps. Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$\times$800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state-of-the-art methods. More demos and code are available at https://guanjunwu.github. io/4dgs/.
Multiple tropopauses in the tropics: A cold point approach

[1] New criteria for the identification of multiple tropopauses (MTs) in the tropics are proposed using COSMIC GPS RO data during 2006–2008 in the tropical belt (±30° latitude) and radiosonde data from 5 tropical stations during 1999 to 2008 in the ±15° latitude. In the present study, we emphasized that many times the WMO definition is not able to identify all the complex structures, such as MTs, and developed alternate criteria to delineate the MTs over the tropics effectively. The new criteria are based on the cold point tropopause (CPT) and its neighborhood points of inflection in the temperature profile rather than on lapse rate, which is used in the WMO definition. The method using the new criteria (called the cold point method) identifies MTs that occur below (referred to as Lower Tropopause, LT) and above (referred to as Second Tropopause, ST; and Third Tropopause, TT) the CPT. The percentage occurrences of MTs determined using the cold point method differ from those found using the WMO definition. The percentage occurrence of MTs within the tropics maximizes at the equator, reaching higher values in the northern hemisphere (NH) summer and lower values in the NH winter. Among the MTs, the percentage occurrence of the ST is the highest, followed by that of the LT. The percentage occurrence of MTs within the tropics also shows large longitudinal variability.
Tropical Andean forest derives calcium and magnesium from Saharan dust

We quantified base metal deposition to Amazonian montane rain forest in Ecuador between May 1998 and April 2003 and assessed the response of the base metal budget of three forested microcatchments (8–13 ha). There was a strong interannual variation in deposition of Ca [4.4–29 kg ha−1 a−1], Mg [1.6–12], and K [9.8–30]). High deposition changed the Ca and Mg budgets of the catchments from loss to retention, suggesting that the additionally available Ca and Mg was used by the ecosystem. Increased base metal deposition was related to dust outbursts of the Sahara and an Amazonian precipitation pattern with trans‐regional dry spells allowing for dust transport to the Andes. The increased base metal deposition coincided with a strong La Niña event in 1999/2000.
Social Approval and Network Homophily as Motivators of Online Toxicity

Online hate messaging is a pervasive issue plaguing the well-being of social media users. This research empirically investigates a novel theory positing that online hate may be driven primarily by the pursuit of social approval rather than a direct desire to harm the targets. Results show that toxicity is homophilous in users' social networks and that a user's propensity for hostility can be predicted by their social networks. We also illustrate how receiving greater or fewer social engagements in the form of likes, retweets, quotes, and replies affects a user's subsequent toxicity. We establish a clear connection between receiving social approval signals and increases in subsequent toxicity. Being retweeted plays a particularly prominent role in escalating toxicity. Results also show that not receiving expected levels of social approval leads to decreased toxicity. We discuss the important implications of our research and opportunities to combat online hate.
What Matters to You? Towards Visual Representation Alignment for Robot Learning

When operating in service of people, robots need to optimize rewards aligned with end-user preferences. Since robots will rely on raw perceptual inputs like RGB images, their rewards will inevitably use visual representations. Recently there has been excitement in using representations from pre-trained visual models, but key to making these work in robotics is fine-tuning, which is typically done via proxy tasks like dynamics prediction or enforcing temporal cycle-consistency. However, all these proxy tasks bypass the human's input on what matters to them, exacerbating spurious correlations and ultimately leading to robot behaviors that are misaligned with user preferences. In this work, we propose that robots should leverage human feedback to align their visual representations with the end-user and disentangle what matters for the task. We propose Representation-Aligned Preference-based Learning (RAPL), a method for solving the visual representation alignment problem and visual reward learning problem through the lens of preference-based learning and optimal transport. Across experiments in X-MAGICAL and in robotic manipulation, we find that RAPL's reward consistently generates preferred robot behaviors with high sample efficiency, and shows strong zero-shot generalization when the visual representation is learned from a different embodiment than the robot's.
Feature Learning and Generalization in Deep Networks with Orthogonal Weights

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of networks with width comparable to depth. We show analytically that rectangular networks with tanh activations and weights initialized from the ensemble of orthogonal matrices have corresponding preactivation fluctuations which are independent of depth, to leading order in inverse width. Moreover, we demonstrate numerically that, at initialization, all correlators involving the neural tangent kernel (NTK) and its descendants at leading order in inverse width -- which govern the evolution of observables during training -- saturate at a depth of $\sim 20$, rather than growing without bound as in the case of Gaussian initializations. We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed. We provide some experimental justification by relating empirical measurements of the NTK to the superior performance of deep nonlinear orthogonal networks trained under full-batch gradient descent on the MNIST and CIFAR-10 classification tasks.
Multimodal Variational Auto-encoder based Audio-Visual Segmentation

We propose an Explicit Conditional Multimodal Variational Auto-Encoder (ECMVAE) for audio-visual segmentation (AVS), aiming to segment sound sources in the video sequence. Existing AVS methods focus on implicit feature fusion strategies, where models are trained to fit the discrete samples in the dataset. With a limited and less diverse dataset, the resulting performance is usually unsatisfactory. In contrast, we address this problem from an effective representation learning perspective, aiming to model the contribution of each modality explicitly. Specifically, we find that audio contains critical category information of the sound producers, and visual data provides candidate sound producer(s). Their shared information corresponds to the target sound producer(s) shown in the visual data. In this case, cross-modal shared representation learning is especially important for AVS. To achieve this, our ECMVAE factorizes the representations of each modality with a modality-shared representation and a modality-specific representation. An orthogonality constraint is applied between the shared and specific representations to maintain the exclusive attribute of the factorized latent code. Further, a mutual information maximization regularizer is introduced to achieve extensive exploration of each modality. Quantitative and qualitative evaluations on the AVSBench demonstrate the effectiveness of our approach, leading to a new state-of-the-art for AVS, with a 3.84 mIOU performance leap on the challenging MS3 subset for multiple sound source segmentation.
TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD.
Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions

We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localization And Mapping (SLAM) algorithms against gyroscope saturations induced by aggressive motions. Field robotics expose robots to various hazards, including steep terrains, landslides, and staircases, where substantial accelerations and angular velocities can occur if the robot loses stability and tumbles. These extreme motions can saturate sensor measurements, especially gyroscopes, which are the first sensors to become inoperative. While the structural integrity of the robot is at risk, the resilience of the SLAM framework is oftentimes given little consideration. Consequently, even if the robot is physically capable of continuing the mission, its operation will be compromised due to a corrupted representation of the world. Regarding this problem, we propose a way to estimate the angular velocity using accelerometers during extreme rotations caused by tumbling. We show that our method reduces the median localization error by 71.5 % in translation and 65.5 % in rotation and reduces the number of SLAM failures by 73.3 % on the collected data. We also propose the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of outdoor experiments recording the motion of a lidar subject to angular velocities four times higher than other available datasets. The dataset is available online at https://github.com/norlab-ulaval/Norlab_wiki/wiki/TIGS-Dataset.