Miracle-dz/UniFAR-FTUs huggingface dataset:
The involvement of the human prefrontal cortex in the emergence of visual awareness

Exploring the neural mechanisms of awareness is a fundamental task of cognitive neuroscience. There is an ongoing dispute regarding the role of the prefrontal cortex (PFC) in the emergence of awareness, which is partially raised by the confound between report- and awareness-related activity. To address this problem, we designed a visual awareness task that can minimize report-related motor confounding. Our results show that saccadic latency is significantly shorter in the aware trials than in the unaware trials. Local field potential (LFP) data from 6 patients consistently show early (200-300 ms) awareness-related activity in the PFC, including event-related potential and high-gamma activity. Moreover, the awareness state can be reliably decoded by the neural activity in the PFC since the early stage, and the neural pattern is dynamically changed rather than being stable during the representation of awareness. Furthermore, the enhancement of dynamic functional connectivity, through the phase modulation at low frequency, between the PFC and other brain regions in the early stage of the awareness trials may explain the mechanism of conscious access. These results indicate that the PFC is critically involved in the emergence of awareness.
Transcription dosage compensation does not occur in Down syndrome

Background: Trisomy 21, also known as Down syndrome, describes the genetic condition of having an extra copy of chromosome 21. The increase in DNA copy number has led to the "DNA dosage hypothesis", which claims that the level of gene transcription is proportional to the gene’s DNA copy number. Yet many reports have suggested that a proportion of chromosome 21 genes are dosage compensated back towards typical expression levels (1.0x). In contrast, other reports suggest that dosage compensation is not a common mechanism of gene regulation in Trisomy 21, providing support to the DNA dosage hypothesis. Results In our work, we use both simulated and real data to dissect the elements of differential expression analysis that can lead to the appearance of dosage compensation even when compensation is demonstrably absent. Using lymphoblastoid cell lines derived from a family of an individual with Down syndrome, we demonstrate that dosage compensation is nearly absent at both nascent transcription (GRO-seq) and steady-state RNA (RNA-seq) levels. Conclusions Transcriptional dosage compensation does not occur in Down syndrome. Simulated data containing no dosage compensation can appear to have dosage compensation when analyzed via standard methods. Moreover, some chromosome 21 genes that appear to be dosage compensated are consistent with allele specific expression.
Sex specific role of RNA-binding protein, AUF1, on prolonged hypersensitivity after repetitive ischemia with reperfusion injury

Repetitive ischemia with reperfusion (I/R) injury is a common cause of myalgia. I/R injuries occur in many conditions that differentially affect males and females including complex regional pain syndrome and fibromyalgia. Our preclinical studies have indicated that primary afferent sensitization and behavioral hypersensitivity due to I/R may be due to sex specific gene expression in the DRGs and distinct upregulation of growth factors and cytokines in the affected muscles. In order to determine how these unique gene expression programs may be established in a sex dependent manner in a model that more closely mimics clinical scenarios, we utilized a newly developed prolonged ischemic myalgia model in mice whereby animals experience repeated I/R injuries to the forelimb and compared behavioral results to unbiased and targeted screening strategies in male and female DRGs. Several distinct proteins were found to be differentially expressed in male and female DRGs, including AU-rich element RNA binding protein (AUF1), which is known to regulate gene expression. Nerve specific siRNA-mediated knockdown of AUF1 inhibited prolonged hypersensitivity in females only, while overexpression of AUF1 in male DRG neurons increased some pain-like responses. Further, AUF1 knockdown was able to specifically inhibit repeated I/R induced gene expression in females but not males. Data suggests that RNA binding proteins like AUF1 may underlie the sex specific effects on DRG gene expression that modulate behavioral hypersensitivity after repeated I/R injury. This study may aid in finding distinct receptor differences related to the evolution of acute to chronic ischemic muscle pain development between sexes.
On small-depth Frege proofs for PHP

We study Frege proofs for the one-to-one graph Pigeon Hole Principle defined on the $n \times n$ grid where n is odd. We are interested in the case where each formula in the proof is a depth d formula in the basis given by $\wedge, \vee$, and $\neg$. We prove that in this situation the proof needs to be of size exponential in $n^{\Omega(1 / d)}$. If we restrict the size of each line in the proof to be of size M then the number of lines needed is exponential in $n /(\log M)^{O(d)}$. The main technical component of the proofs is to design a new family of random restrictions and to prove the appropriate switching lemmas.
Euler-Lagrange Analysis of Generative Adversarial Networks

We consider Generative Adversarial Networks (GANs) and address the underlying functional optimization problem ab initio within a variational setting. Strictly speaking, the optimization of the generator and discriminator functions must be carried out in accordance with the Euler-Lagrange conditions, which become particularly relevant in scenarios where the optimization cost involves regularizers comprising the derivatives of these functions. Considering Wasserstein GANs (WGAN) with a gradient-norm penalty, we show that the optimal discriminator is the solution to a Poisson differential equation. In principle, the optimal discriminator can be obtained in closed form without having to train a neural network. We illustrate this by employing a Fourier-series approximation to solve the Poisson differential equation. Experimental results based on synthesized Gaussian data demonstrate superior convergence behavior of the proposed approach in comparison with the baseline WGAN variants that employ weight-clipping, gradient or Lipschitz penalties on the discriminator on low-dimensional data. We also analyze the truncation error of the Fourier-series approximation and the estimation error of the Fourier coefficients in a high-dimensional setting. We demonstrate applications to real-world images considering latent-space prior matching in Wasserstein autoencoders and present performance comparisons on benchmark datasets such as MNIST, SVHN, CelebA, CIFAR-10, and Ukiyo-E. We demonstrate that the proposed approach achieves comparable reconstruction error and Fréchet inception distance with faster convergence and up to two-fold improvement in image sharpness.
Robust H_∞ Control for a Kind of Singular Networked Control System with Short Time-Delay

H∞ control was determined for a kind of networked control system(NCS) based on a regular,impulse-free,and linear time-invariant singular control system.For the NCS with clock-driven sensors,actuators, and event-driven controllers,given an uncertain time-delay with no more than one sampling period and a limited external disturbance,the sufficient condition of the existence of dynamic output feedback H∞ control law was derived through Lyapunov theory and linear matrix inequality.A corresponding design method is also presented.Validity of the proposed approach is demonstrated by a simulation example.
Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling

The framework of optimal transport has been leveraged to extend the notion of rank to the multivariate setting as corresponding to an optimal transport map, while preserving desirable properties of the resulting goodness-of-fit (GoF) statistics. In particular, the rank energy (RE) and rank maximum mean discrepancy (RMMD) are distribution-free under the null, exhibit high power in statistical testing, and are robust to outliers. In this paper, we point to and alleviate some of the shortcomings of these GoF statistics that are of practical significance, namely high computational cost, curse of dimensionality in statistical sample complexity, and lack of differentiability with respect to the data. We show that all these issues are addressed by defining multivariate rank as an entropic transport map derived from the entropic regularization of the optimal transport problem, which we refer to as the soft rank. We consequently propose two new statistics, the soft rank energy (sRE) and soft rank maximum mean discrepancy (sRMMD). Given n sample data points, we provide non-asymptotic convergence rates for the sample estimate of the entropic transport map to its population version that are essentially of the order n−1/2 when the source measure is subgaussian and the target measure has compact support. This result is novel compared to existing results which achieve a rate of n−1 but crucially rely on both measures having compact support. In contrast, the corresponding convergence rate of estimating an optimal transport map, and hence the rank map, is exponential in the data dimension. We leverage these fast convergence rates to show that the sample estimates of sRE and sRMMD converge rapidly to their population versions. Combined with the ∗. Equal contribution c ©2023 Shoaib Bin Masud, Mathew Werenski, James M Murphy, and Shuchin Aeron. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v24/22-0616.html. Masud, Werenski, Murphy, and Aeron computational efficiency of methods in solving the entropy-regularized optimal transport problem, these results enable efficient rank-based GoF statistical computation, even in high dimensions. Furthermore, the sample estimates of sRE and sRMMD are differentiable with respect to the data and amenable to popular machine learning frameworks that rely on gradient methods. We leverage these properties towards showcasing their utility for generative modeling on two important problems: image generation and generating valid knockoffs for controlled feature selection.
Bounded Relativization

Relativization is one of the most fundamental concepts in complexity theory, which explains the difficulty of resolving major open problems. In this paper, we propose a weaker notion of relativization called bounded relativization . For a complexity class C , we say that a statement is C -relativizing if the statement holds relative to every oracle O ∈ C . It is easy to see that every result that relativizes also C -relativizes for every complexity class C . On the other hand, we observe that many non-relativizing results, such as IP = PSPACE , are in fact PSPACE -relativizing. First, we use the idea of bounded relativization to obtain new lower bound results, including the following nearly maximum circuit lower bound: for every constant ε > 0,
DART: Distance Assisted Recursive Testing

Multiple testing is a commonly used tool in modern data science. Sometimes, the hypotheses are embedded in a space; the distances between the hypotheses reflect their co-null/co-alternative patterns. Properly incorporating the distance information in testing will boost testing power. Hence, we developed a new multiple testing framework named Distance Assisted Recursive Testing (DART). DART features in joint artificial intelligence (AI) and statistics modeling. It has two stages. The first stage uses AI models to construct an aggregation tree that reflects the distance information. The second stage uses statistical models to embed the testing on the tree and control the false discovery rate. Theoretical analysis and numerical experiments demonstrated that DART generates valid, robust, and powerful results. We applied DART to a clinical trial in the allogeneic stem cell transplantation study to identify the gut microbiota whose abundance was impacted by post-transplant care.
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in treatment. Second, they yield doubly-local treatment effect estimates, and fail to provide more general causal effect estimates away from the discontinuity. To address these limitations, we introduce a novel method for automatically detecting RDs at scale, integrating information from multiple discovered discontinuities with an observational estimator, and extrapolating away from discovered, local RDs. We demonstrate the performance of our method on two synthetic datasets, showing improved performance compared to direct use of an observational estimator, direct extrapolation of RD estimates, and existing methods for combining multiple causal effect estimates. Finally, we apply our novel method to estimate spatially heterogeneous treatment effects in the context of a recent economic development problem.
Spatial and temporal variations in sea surface pCO2 and air-sea flux of CO2 in the Bering Sea revealed by satellite-based data during 2003–2019

The understanding of long-time-series variations in air-sea CO2 flux in the Bering Sea is critical, as it is the passage area from the North Pacific Ocean water to the Arctic. Here, a data-driven remote sensing retrieval method is constructed based on a large amount of underway partial pressure of CO2 (pCO2) data in the Bering Sea. After several experiments, a Gaussian process regression model with input parameters of sea surface temperature, sea surface height, mixed-layer depth, chlorophyll a concentration, dry air mole fractions of CO2, and bathymetry was selected. After validation with independent data, the root mean square error of pCO2 was< 24 μatm (R2 = 0.94) with satisfactory performance. Then, we reconstructed the sea surface pCO2 in the Bering Sea from 2003 to 2019 and estimated the corresponding air-sea CO2 fluxes. Significant seasonal variations were identified, with higher sea surface pCO2 in winter/spring than in summer/autumn in both the basin and shelf area. Semiquantitative analysis reveals that the Bering Sea is a non-temperature-dominated area with a mean temperature effect on pCO2 of 12.7 μatm and a mean non-temperature effect of −51.8 μatm. From 2003 to 2019, atmospheric pCO2 increased at a rate of 2.1 μatm yr−1, while sea surface pCO2 in the basin increased rapidly (2.8 μatm yr−1); thus, the CO2 emissions from the basin increased. However, the carbon sink in the continental shelf still continuously increased. The whole Bering Sea exhibited an increasing carbon sink with the area integral of air-sea CO2 fluxes increasing from 6 to 19 TgC over 17 years. Meanwhile, the seasonal amplitudes in pCO2 in the shelf area also increased, approaching 14 μatm per decade. The reaction of the continuously added CO2 in continental seawater reduced the ocean CO2 system capacity. This is the first study to present long-time-series satellite data with high resolution in the Bering Sea, which is beneficial for studying the changes in ocean ecosystems and carbon sink capacity.
Sensory processing sensitivity and social pain: a hypothesis and theory

Sensory-processing sensitivity (SPS) defined, as a personality trait, seems to be characterized by emotional sensitivity, and stronger reactivity to both external and internal stimuli. SPS can represent a risk factor for developing clinical conditions during childhood and adolescence. This personality trait is not to be considered a pathological clinical condition, however, can expose to greater environmental vulnerability. In particular, the recent studies about SPS can be contextualized to social situations that evoke traumatic and stressful emotional responses such as social exclusion. We hypothesize that highly sensitive people (HSP) are more vulnerable to social exclusion and social pain. This hypothesis could help structure new educational and intervention models designed to improve coping strategies and promote HSP’s psychophysical and social well-being.
Psychedelic unselfing: self-transcendence and change of values in psychedelic experiences

Psychedelic experiences have been shown to both facilitate (re)connection to one’s values and change values, including enhancing aesthetic appreciation, promoting pro-environmental attitudes, and encouraging prosocial behavior. This article presents an empirically informed framework of philosophical psychology to understand how self-transcendence relates to psychedelic value changes. Most of the observed psychedelic value changes are toward the self-transcendent values of Schwartz’s value theory. As psychedelics also reliably cause various self-transcendent experiences (STEs), a parsimonious hypothesis is that STEs change values toward self-transcendent values. I argue that STEs indeed can lead to value changes, and discuss the morally relevant process of self-transcendence through Iris Murdoch’s concept of “unselfing”. I argue that overt egocentric concerns easily bias one’s valuations. Unselfing reduces egocentric attributions of salience and enhances non-egocentric attention to the world, widening one’s perspective and shifting evaluation toward self-transcendent modes. Values are inherently tied to various evaluative contexts, and unselfing can attune the individual to evaluative contexts and accompanying values beyond the self. Understood this way, psychedelics can provide temporarily enhanced access to self-transcendent values and function as sources of aspiration and value change. However, contextual factors can complicate whether STEs lead to long-term changes in values. The framework is supported by various research strands establishing empirical and conceptual connections between long-term differences in egocentricity, STEs, and self-transcendent values. Furthermore, the link between unselfing and value changes is supported by phenomenological and theoretical analysis of psychedelic experiences, as well as empirical findings on their long-term effects. This article furthers understanding of psychedelic value changes and contributes to discussions on whether value changes are justified, whether they result from cultural context, and whether psychedelics could function as tools of moral neuroenhancement.
A group of three miRNAs can act as candidate circulating biomarkers in liquid biopsies from melanoma patients

Background Staging of melanoma and follow up after melanoma diagnosis aims at predicting risk and detecting progression or recurrence at early stage, respectively in order to timely start and/or change treatment. Tumor thickness according to Breslow, status of the sentinel node and value of the lactate dehydrogenase (LDH) are well-established prognostic markers for metastatic risk, but reliable biomarkers identifying early recurrence or candidates who may benefit best from medical treatment are still warranted. Liquid biopsy has emerged to be a suitable method for identifying biomarkers for early cancer diagnosis, prognosis, therapeutic response prediction, and patient follow-up. Liquid biopsy is a blood-based non-invasive procedure that allows analyzing circulating analytes, including extracellular vesicles. Methods In this study we have explored the use of 7 miRNAs, namely hsa-miR-149-3p, hsa-miR-150-5p, hsa-miR-21-5p, hsa-miR-200c-3p, hsa-miR-134-5p, hsa-miR-144-3p and hsa-miR-221-3p in plasma exosomes to discriminate melanoma patients from controls without melanoma in a cohort of 92 individuals. Results and discussion Our results showed that three out seven miRNAs, namely hsa-miR-200c-3p, hsa-miR-144-3p and hsa-miR-221-3p were differentially expressed in plasma-derived exosomes from melanoma patients and controls. Furthermore, the expression of the three miRNAs may be a promising ancillary tool as a melanoma biomarker, even for discriminating between nevi and melanoma.
Differentiation of human induced pluripotent stem cells into functional lung alveolar epithelial cells in 3D dynamic culture

Introduction: Understanding lung epithelium cell development from human induced pluripotent stem cells (IPSCs) in vitro can lead to an individualized model for lung engineering, therapy, and drug testing. Method: We developed a protocol to produce lung mature type I pneumocytes using encapsulation of human IPSCs in 1.1% (w/v) alginate solution within a rotating wall bioreactor system in only 20 days without using feeder cells. The aim was to reduce exposure to animal products and laborious interventions in the future. Results: The three-dimensional (3D) bioprocess allowed cell derivation into endoderm, and subsequently into type II alveolar epithelial cells within a very short period. Cells successfully expressed surfactant proteins C and B associated with type II alveolar epithelial cells, and the key structure of lamellar bodies and microvilli was shown by transmission electron microscopy. The survival rate was the highest under dynamic conditions, which reveal the possibility of adapting this integration for large-scale cell production of alveolar epithelial cells from human IPSCs. Discussion: We were able to develop a strategy for the culture and differentiation of human IPSCs into alveolar type II cells using an in vitro system that mimics the in vivo environment. Hydrogel beads would offer a suitable matrix for 3D cultures and that the high-aspect-ratio vessel bioreactor can be used to increase the differentiation of human IPSCs relative to the results obtained with traditional monolayer cultures.
Differential regulation of Shigella Spa47 ATPase activity by a native C-terminal product of Spa33

Shigella is a Gram-negative bacterial pathogen that relies on a single type three secretion system (T3SS) as its primary virulence factor. The T3SS includes a highly conserved needle-like apparatus that directly injects bacterial effector proteins into host cells, subverting host cell function, initiating infection, and circumventing resulting host immune responses. Recent findings have located the T3SS ATPase Spa47 to the base of the Shigella T3SS apparatus and have correlated its catalytic function to apparatus formation, protein effector secretion, and overall pathogen virulence. This critical correlation makes Spa47 ATPase activity regulation a likely point of native control over Shigella virulence and a high interest target for non-antibiotic- based therapeutics. Here, we provide a detailed characterization of the natural 11.6 kDa C-terminal translation product of the Shigella T3SS protein Spa33 (Spa33C), showing that it is required for proper virulence and that it pulls down with several known T3SS proteins, consistent with a structural role within the sorting platform of the T3SS apparatus. In vitro binding assays and detailed kinetic analyses suggest an additional role, however, as Spa33C differentially regulates Spa47 ATPase activity based on Spa47s oligomeric state, downregulating Spa47 monomer activity and upregulating activity of both homo-oligomeric Spa47 and the hetero-oligomeric MxiN2Spa47 complex. These findings identify Spa33C as only the second known differential T3SS ATPase regulator to date, with the Shigella protein MxiN representing the other. Describing this differential regulatory protein pair begins to close an important gap in understanding of how Shigella may modulate virulence through Spa47 activity and T3SS function.