Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Coding Networks for Temporal Prediction

One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of predictive coding. Although the original proposals of predictive coding have discussed temporal prediction, later work developing this theory mostly focused on static stimuli, and key questions on neural implementation and computational properties of temporal predictive coding networks remain open. Here, we address these questions and present a formulation of the temporal predictive coding model that can be naturally implemented in recurrent networks, in which activity dynamics rely only on local inputs to the neurons, and learning only utilises local Hebbian plasticity. Additionally, we show that temporal predictive coding networks can approximate the performance of the Kalman filter in predicting behaviour of linear systems, and behave as a variant of a Kalman filter which does not track its own subjective posterior variance. Importantly, temporal predictive coding networks can achieve similar accuracy as the Kalman filter without performing complex mathematical operations, but just employing simple computations that can be implemented by biological networks. Moreover, when trained with natural dynamic inputs, we found that temporal predictive coding can produce Gabor-like, motion-sensitive receptive fields resembling those observed in real neurons in visual areas. In addition, we demonstrate how the model can be effectively generalized to nonlinear systems. Overall, models presented in this paper show how biologically plausible circuits can predict future stimuli and may guide research on understanding specific neural circuits in brain areas involved in temporal prediction. Author summary While significant advances have been made in the neuroscience of how the brain processes static stimuli, the time dimension has often been relatively neglected. However, time is crucial since the stimuli perceived by our senses typically dynamically vary in time, and the cortex needs to make sense of these changing inputs. This paper describes a computational model of cortical networks processing temporal stimuli. This model is able to infer and track the state of the environment based on noisy inputs, and predict future sensory stimuli. By ensuring that these predictions match the incoming stimuli, the model is able to learn the structure and statistics of its temporal inputs and produces responses of neurons resembling those in the brain. The model may help in further understanding neural circuits in sensory cortical areas.
Lip movements and lexical features improve speech tracking differently for clear and multi-speaker speech

Visual speech plays a powerful role in facilitating auditory speech processing and has been a publicly noticed topic with the wide usage of face masks during the Covid-19 pandemic. In a previous magnetoencephalography (MEG) study we showed that occluding the mouth area significantly impairs neural speech tracking. To rule out the possibility that this deterioration is due to degraded sound quality, in the present follow-up study, we presented participants with audiovisual (AV) and audio-only (A) speech. We further independently manipulated the trials by adding a face mask and a distractor speaker. Our results clearly show that face masks only affect speech tracking in AV conditions, not in A conditions. This shows that face masks indeed primarily impact speech processing by blocking visual speech and not by acoustic degradation. Furthermore, we observe differences in the speech features that are used for visual speech processing. On the one hand, processing in clear speech, but not in noisy speech, is profiting more from lexical unit features (phonemes and word onsets) hinting at improved phoneme discrimination. On the other hand, we observe an improvement in speech tracking driven by the modulations of the lip area in clear speech and conditions with a distractor speaker, which might aid by providing temporal cues for subsequent auditory processing. With this work, we highlight the effects of face masks in AV speech tracking and show two separate ways how visual speech might support successful speech processing.
A new antibiotic from an uncultured bacterium binds to an immutable target

Antimicrobial resistance is a leading mortality factor worldwide. Here we report the discovery of clovibactin, a new antibiotic, isolated from uncultured soil bacteria. Clovibactin efficiently kills drug-resistant bacterial pathogens without detectable resistance. Using biochemical assays, solid-state NMR, and atomic force microscopy, we dissect its mode of action. Clovibactin blocks cell wall synthesis by targeting pyrophosphate of multiple essential peptidoglycan precursors (C55PP, Lipid II, LipidWTA). Clovibactin uses an unusual hydrophobic interface to tightly wrap around pyrophosphate, but bypasses the variable structural elements of precursors, accounting for the lack of resistance. Selective and efficient target binding is achieved by the irreversible sequestration of precursors into supramolecular fibrils that only form on bacterial membranes that contain lipid-anchored pyrophosphate groups. Uncultured bacteria offer a rich reservoir of antibiotics with new mechanisms of action that could replenish the antimicrobial discovery pipeline.
Microhabitat acclimatization shifts physiological baselines and thermal tolerance of the symbiotic anemone, Anthopleura elegantissima

Contemporary organisms in extreme environments can give insight into how species will respond to environmental change. The intertidal forms an environmental gradient where stress increases with tidal height. Here, we explore the contribution of fixed genotypic and plastic environmental effects on thermal tolerance of the intertidal anemone Anthopleura elegantissima and its algal symbionts using a laboratory-based tank experiment. High intertidal anemones had lower baseline symbiont-to-host cell ratios under control conditions, but their symbionts had higher baseline maximum quantum yield compared to low intertidal anemone symbionts, despite identical symbiont communities. High intertidal anemones maintained greater maximum quantum yield and symbiont-to-host cell ratios under heat stress compared to low intertidal anemones, suggesting that high intertidal holobionts have greater thermal tolerance. However, thermal tolerance of clonal anemones acclimatized to different zones was not explained by tidal height alone, indicating emersion duration is not the sole environmental driver of physiological variation. Fixed genotypic effects also influenced physiological baselines, but did not modulate thermal tolerance, demonstrating thermal tolerance is largely driven by environmental history. These results indicate that this symbiosis is highly plastic and may be able to rapidly acclimatize to climate change, defying the convention that symbiotic organisms are more susceptible to environmental stress.
Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration

Personalizing treatments for patients often involves a period of trial-and-error search until an optimal choice is found. To minimize su � ering and other costs, it is critical to make this process as short as possible. When treatments have primarily short-term e � ects, search can be performed with multi-armed bandits (MAB), but these typically require long exploration periods to guarantee optimality. In this work, we design MAB algorithms which provably identify optimal treatments quickly by leveraging prior knowledge of the types of decision processes (patients) we can encounter, in the form of a latent variable model. We present two algorithms, the Latent LP-based Track and Stop (LLPT) explorer and the Divergence Explorer for this setting: ﬁxed-conﬁdence pure-exploration latent bandits. We give a lower bound on the stopping time of any algorithm which is correct at a given certainty level, and prove that the expected stopping time of the LLPT Explorer matches the lower bound in the high-certainty limit. Finally, we present results from an experimental study based on realistic simulation data for Alzheimer’s disease, demonstrating that our formulation and algorithms lead to a signiﬁcantly reduced stopping time.
Group Fairness in Reinforcement Learning

We pose and study the problem of satisfying fairness in the online Reinforcement Learning (RL) setting. We focus on the group notions of fairness, according to which agents belonging to different groups should have similar performance based on some given measure. We consider the setting of maximizing return in an unknown environment (unknown transition and reward function) and show that it is possible to have RL algorithms that learn the best fair policies without violating the fairness requirements at any point in time during the learning process. In the tabular finite-horizon episodic setting, we provide an algorithm that combines the principle of optimism and pessimism under uncertainty to achieve zero fairness violation with arbitrarily high probability while also maintaining sub-linear regret guarantees. For the high-dimensional Deep-RL setting, we present algorithms based on the performance-difference style approximate policy improvement update step and we report encouraging empirical results on various traditional RL-inspired benchmarks showing that our algorithms display the desired behavior of learning the optimal policy while performing a fair learning process.
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection

Developing new methods for detecting anomalies in time series is of great practical significance, but progress is hindered by the difficulty of assessing the benefit of new methods, for the following reasons. (1) Public benchmarks are flawed (e.g., due to potentially erroneous anomaly labels), (2) there is no widely accepted standard evaluation metric, and (3) evaluation protocols are mostly inconsistent. In this work, we address all three issues: (1) We critically analyze several of the most widely-used multivariate datasets, identify a number of significant issues, and select the best candidates for evaluation. (2) We introduce a new evaluation metric for time-series anomaly detection, which—in contrast to previous metrics—is recall consistent and takes temporal correlations into account. (3) We analyze and overhaul existing evaluation protocols and provide the largest benchmark of deep multivariate time-series anomaly detection methods to date. We focus on deep-learning based methods and multivariate data, a common setting in modern anomaly detection. We provide all implementations and analysis tools in a new comprehensive library for Time Se ries A nomaly D etection, called TimeSeAD 1 .
Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction

A common step in differentially private (DP) Riemannian optimization is sampling from the (tangent) Gaussian distribution as noise needs to be generated in the tangent space to perturb the gradient. In this regard, existing works either use the Markov chain Monte Carlo (MCMC) sampling or explicit basis construction based sampling methods on the tangent space. This becomes a computational bottleneck in the practical use of DP Riemannian optimization, especially when performing stochastic optimization. In this paper, we discuss different sampling strategies and develop efficient sampling procedures by exploiting linear isometry between tangent spaces and show them to be orders of magnitude faster than both the MCMC and sampling using explicit basis construction. Furthermore, we develop the DP Riemannian stochastic variance reduced gradient algorithm and compare it with DP Riemannian gradient descent and stochastic gradient descent algorithms on various problems.
Bayesian Transformed Gaussian Processes

The Bayesian transformed Gaussian (BTG) model, proposed by Kedem and Oliviera in 1997, was developed as a Bayesian approach to trans-Kriging in the spatial statistics community. In this paper, we revisit BTG in the context of modern Gaussian process literature by framing it as a fully Bayesian counterpart to the Warped Gaussian process that marginalizes out a joint prior over input warping and kernel hyperparameters. As with any other fully Bayesian approach, this treatment introduces prohibitively expensive computational overhead; unsurprisingly, the BTG posterior predictive distribution, itself estimated through high-dimensional integration, must be inverted in order to perform model prediction. To address these challenges, we introduce principled numerical techniques for computing with BTG efficiently using a combination of doubly sparse quadrature rules, tight quantile bounds, and rank-one matrix algebra to enable both fast model prediction and model selection. These efficient methods allow us to compute with higher-dimensional datasets and apply BTG with layered transformations that greatly improve its expressibility. We demonstrate that BTG achieves superior empirical performance over MLE-based models in the low-data regime —situations in which MLE tends to overfit.
Case report: Hepatectomy with Rex bypass for a child with hepatoblastoma and portal vein thrombosis

Pediatric liver tumors with portal vein obstruction are often candidates for liver transplantation. However, lifelong use of immunosuppressants and invasiveness to healthy donors in the case of living-donor liver transplantation is inevitable. Moreover, when lung metastasis is involved, the lung recurrence rate after liver transplantation is still high. Therefore, transplantation should be avoided as much as possible. In cases of tumors in the right lobe of the liver, complete resection of the portal vein trunk may be possible by creating a Rex bypass, but with the original method, end-to-side anastomosis to the umbilical portal vein is difficult in small children. We report a case of a 2-year-old girl with hepatoblastoma in whom a Rex shunt was created by end-to-end anastomosing the recanalized umbilical vein to the portal vein stump with interposing a vein graft, and the right lobe was successfully resected along with the tumor.
New index using triglyceride glucose-body mass index for predicting mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis

Background This study aimed to investigate whether triglyceride glucose-body mass index (TyG-BMI) and a new index using TyG-BMI (NITGB) could predict all-cause mortality in non-obese patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Methods The medical records of 78 non-obese AAV patients (BMI < 23.0 kg/m2 for Asians) were retrospectively reviewed. TyG-BMI was calculated by the equation: Ln (triglyceride × fasting glucose/2) × BMI. To develop NITGB, we assigned a weight of a number close to an 0.1 decimal integer to each variable according to the slopes for independent variables with P-value < 0.1 in the multivariable Cox analysis. Results The median age was 54.3 years and five patients died. When non-obese AAV patients were divided into two groups based on TyG-BMI ≥ 187.74, those with TyG-BMI ≥ 187.74 exhibited a significantly higher risk for all-cause mortality than those without (RR 9.450). Since age (HR 1.324), Birmingham vasculitis activity score (BVAS; HR 1.212), and TyG-BMI ≥ 187.74 (HR 12.168) were independently associated with all-cause mortality, NITGB was developed as follows: age + 0.2 × BVAS + 2.5 × TyG-BMI ≥ 187.74. When non-obese AAV patients were divided into two groups based on NITGB ≥ 27.36, those with NITGB ≥ 27.36 showed a significantly higher risk for all-cause mortality than those without (RR 284.000). Both non-obese AAV patients with TyG-BMI ≥ 187.74 and those with NITGB ≥ 27.36 exhibited significantly higher cumulative rates of all-cause mortality than those without. Conclusion NITGB along with TyG-BMI could predict all-cause mortality in non-obese AAV patients.
Ubiquitination-related biomarkers in metastatic melanoma patients and their roles in tumor microenvironment

Background Skin cutaneous melanoma (SKCM) is the deadliest type of cutaneous malignancy. Ubiquitination is a process of protein sorting and degradation that exhibits multiple functions in the progression of various tumors. This study aimed to characterize a set of genes for ubiquitination in SKCM. Methods The expression patterns of ubiquitin-associated genes (URGs) and the corresponding clinical information in SKCM tissues were comprehensively analyzed based on The Cancer Genome Atlas (TCGA) database. We performed univariate and multivariate Cox proportional regression models to characterize the risk scores and identify four critical genes related to prognostic ubiquitination (HCLS1, CORO1A, NCF1 and CCRL2), which were used to construct the prognostic signatures. We also studied the effects of HCLS1, CORO1A and CCRL2 on tumor metastasis-related indicators at the cellular level through in vitro experiments. Results SKCM patients in the low-risk group showing a longer survival than those in the high-risk group. Characteristic risk scores correlated with several clinicopathological variables and reflected the infiltration of multiple immune cells. In addition, the knockdown of CLS1, CORO1A and CCRL2 affected cellular malignant biological behavior through the EMT signaling pathway. Conclusion This study provides a novel and prospective strategy to improve the clinical survival of SKCM patients.
Snow and meteorological conditions at Villum Research Station, Northeast Greenland: on the adequacy of using atmospheric reanalysis for detailed snow simulations

Reliable and detailed measurements of atmospheric and snow conditions in the Arctic are limited. While modern atmospheric reanalyses could potentially replace the former, the latter can be principally simulated by dedicated snow modelling. However, because the uncertainties of reanalyses and modelling are still exceptionally large at high latitudes, a thorough analysis of the performance of atmospheric reanalyses and the snow model simulations are required. Specifically, we aim to answer the following questions for Villum Research Station (VRS), northeast Greenland: (1) What are the predominant snow and meteorological conditions? (2) What are systematic differences between the modern atmospheric reanalysis ERA5 and in situ measurements? (3) Can the snow model Crocus simulate reliably snow depth and stratigraphy? We systematically compare atmospheric in situ measurements and ERA5 reanalysis (November 2015–August 2018) and evaluate simulated and measured snow depth (October 2014–September 2018). Moreover, modelled and measured vertical profiles of snow density and snow specific surface area (SSA) are analysed for two days where a survey had taken place. We found good agreement between in situ and ERA5 atmospheric variables with correlation coefficients >0.84 except for precipitation, wind speed, and wind direction. ERA5’s resolution is too coarse to resolve the topography in the study area adequately, leading presumably to the detected biases. Crocus can simulate satisfactorily the evolution of snow depth, but simulations of SSA and density profiles, whether driven by ERA5 or in situ measurements are biased compared to measurements. Unexpectedly, measured snow depth agrees better with ERA5 driven simulation than with simulation forced with in situ measurements (explained variance: 0.73 versus 0.23). This is due to differences in snowfall, humidity and air temperature between both forcing datasets. In conclusion, ERA5 has great potential to force snow models but the use of Crocus in the Arctic is affected by limitations such as inappropriate parametrisations for Arctic snowpack evolution, but also by lack of process formulations such as vertical water vapour transport. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.
Influence of solar forcing on multidecadal variability in the Atlantic meridional overturning circulation (AMOC)

There is a growing debate regarding the influence of solar activity on climate change as the solar forcing signal on decadal/multidecadal timescales is not robust in long-term reconstructed climate data or numerical simulations. However, solar forcing could be amplified by ocean–atmosphere coupling in sensitive regions, including the North Atlantic Ocean (N.A.). This study assessed the influence of varied total solar irradiance (TSI) due to the effects of solar activity on Atlantic Meridional Overturning Circulation (AMOC) based on an Earth System model with intermediate complexity (PLASIM-GENIE). Three groups of experiments with different TSI series; i.e., constant (NS), decadal varied (DS), and reconstructed whole (AS) for 1610–2000, were conducted and the AMOC response was investigated. The results showed that the internal forcing of the climate system led to quasi-35-year and quasi-65-year AMOC cycles and a significant and stable negative correlation between TSI and AMOC on a multidecadal timescale. The period was significantly extended due to solar forcing. The declining AMOC trend occurred in simulations after 1800. Thus, solar forcing contributed to a weakening AMOC at a rate of 0.41 Sv per century. The decadal variation in TSI was the main contributor to this decline due to solar forcing.
Transnasal targeted delivery of therapeutics in central nervous system diseases: a narrative review

Currently, neurointervention, surgery, medication, and central nervous system (CNS) stimulation are the main treatments used in CNS diseases. These approaches are used to overcome the blood brain barrier (BBB), but they have limitations that necessitate the development of targeted delivery methods. Thus, recent research has focused on spatiotemporally direct and indirect targeted delivery methods because they decrease the effect on nontarget cells, thus minimizing side effects and increasing the patient’s quality of life. Methods that enable therapeutics to be directly passed through the BBB to facilitate delivery to target cells include the use of nanomedicine (nanoparticles and extracellular vesicles), and magnetic field-mediated delivery. Nanoparticles are divided into organic, inorganic types depending on their outer shell composition. Extracellular vesicles consist of apoptotic bodies, microvesicles, and exosomes. Magnetic field-mediated delivery methods include magnetic field-mediated passive/actively-assisted navigation, magnetotactic bacteria, magnetic resonance navigation, and magnetic nanobots—in developmental chronological order of when they were developed. Indirect methods increase the BBB permeability, allowing therapeutics to reach the CNS, and include chemical delivery and mechanical delivery (focused ultrasound and LASER therapy). Chemical methods (chemical permeation enhancers) include mannitol, a prevalent BBB permeabilizer, and other chemicals—bradykinin and 1-O-pentylglycerol—to resolve the limitations of mannitol. Focused ultrasound is in either high intensity or low intensity. LASER therapies includes three types: laser interstitial therapy, photodynamic therapy, and photobiomodulation therapy. The combination of direct and indirect methods is not as common as their individual use but represents an area for further research in the field. This review aims to analyze the advantages and disadvantages of these methods, describe the combined use of direct and indirect deliveries, and provide the future prospects of each targeted delivery method. We conclude that the most promising method is the nose-to-CNS delivery of hybrid nanomedicine, multiple combination of organic, inorganic nanoparticles and exosomes, via magnetic resonance navigation following preconditioning treatment with photobiomodulation therapy or focused ultrasound in low intensity as a strategy for differentiating this review from others on targeted CNS delivery; however, additional studies are needed to demonstrate the application of this approach in more complex in vivo pathways.