Miracle-dz/UniFAR-FTUs huggingface dataset:
Analgesic targets identified in mouse sensory neuron somata and terminal pain translatomes

The relationship between transcription and protein expression is complex. We identified polysome-associated RNA transcripts in the somata and central terminals of mouse sensory neurons in control, painful (+ Nerve Growth Factor (NGF)) and pain-free conditions (Nav1.7 null mice). The majority (98%) of translated transcripts are shared between male and female mice in both the somata and terminals. Some transcripts are highly enriched in the somata or terminals. Changes in the translatome in painful and pain-free conditions include novel and known regulators of pain pathways. Antisense knockdown of selected somatic and terminal polysome-associated transcripts that correlate with pain states diminished pain behaviour. Terminal-enriched transcripts encoding synaptic proteins (e.g. Synaptotagmin), non-coding RNAs, transcription factors (e.g. Znf431), proteins associated with trans-synaptic trafficking (HoxC9), GABA generating enzymes (Gad1 and Gad2) and neuropeptides (Penk). Thus, central terminal translation may well be a significant regulatory locus for peripheral input from sensory neurons.
FOXO-regulated Deaf1 controls muscle regeneration through autophagy

The commonality between various muscle diseases is the loss of muscle mass, function, and regeneration, which severely restricts mobility and impairs the quality of life. With muscle stem cells (MuSCs) playing a key role in facilitating muscle repair, targeting regulators of muscle regeneration has been shown to be a promising therapeutic approach to repair muscles. However, the underlying molecular mechanisms driving muscle regeneration are complex and poorly understood. Here, we identified a new regulator of muscle regeneration, Deformed epidermal autoregulatory factor 1 (Deaf1) - a transcriptional factor downstream of FOXO signaling. We showed that Deaf1 is transcriptionally repressed by FOXOs and that Deaf1 targets to PI3KC3 and Atg16l1 promoter regions and suppresses their expressions. Deaf1 depletion therefore induces autophagy, which in turn blocks MuSC survival and differentiation. In contrast, Deaf1 overexpression inactivates autophagy in MuSCs, leading to increased protein aggregation and cell death. Interestingly, Deaf1 depletion and overexpression both lead to defects in muscle regeneration, highlighting the importance of fine tuning Deaf1-regulated autophagy during muscle regeneration. We further showed that Deaf1 expression is altered in aging and cachectic MuSCs. Remarkably, manipulation of Deaf1 expression can attenuate muscle atrophy and restore muscle regeneration in aged mice or mice with cachectic cancers. Together, our findings unveil an evolutionarily conserved role for Deaf1 in muscle regeneration, providing insights into the development of new therapeutic strategies against muscle atrophy.
Node centrality in MEG resting-state networks co-varies with neurotransmitter receptor and transporter density

Neuronal oscillations are central mechanisms in the regulation of neuronal processing and communication (Deco et al., 2011; Fries, 2015; S. Palva & Palva, 2012; Siegel et al., 2012; Singer, 1999), but the relationship between the emergent inter-areal synchronization of oscillations and their underlying synaptic and neuromodulatory mechanisms - the dynome - has remained poorly understood (Kopell et al., 2014). While oscillations are largely generated by fast synaptic neurotransmission among pyramidal cells and interneurons (Traub et al., 2004), these microcircuits are subject to slower neuromodulation in a frequency- and region-specific manner (Batista-Brito et al., 2018; Roopun et al., 2010). While the efferent connections, receptor densities, and neurotransmitter reuptake regulation of neuromodulatory systems are highly heterogeneous across the cortical mantle (Avery & Krichmar, 2017; Deco et al., 2017; Hansen et al., 2022), it has remained unresolved how this variability shapes inter-areal connectivity of neuronal oscillations. Here, we used source-reconstructed human magnetoencephalography (MEG) data to assess how the centrality of brain areas (nodes) in large-scale networks of phase synchrony and amplitude correlations covaries with neurotransmitter receptor and transporter densities. Node centrality strongly covaried with receptor and transporter densities in a coupling- and frequency-specific manner both at the level of individual receptors and transporters and that of principal components. In delta, theta, and gamma frequencies, node centrality in phase-synchronization networks covaried positively, and in high-alpha and beta bands negatively, with dopaminergic, GABA, NMDA, muscarinic, and most serotonergic receptor densities. In amplitude-correlation networks, node centrality in delta and gamma bands covaried positively, and in theta to beta bands negatively, with most receptor and transporter densities. These results establish the contribution of neurotransmitter receptor and transporter densities for shaping connectivity of neuronal oscillations in the human brain and demonstrate a link between coupling of neuronal oscillations with the underlying biological details.
Strategies to Improve Portfolio Management of New Products

Abstract Overview: This article presents the findings of an Innovation Research Interchange (IRI) study on practices and outcomes in new product (NP) portfolio management. Given the substantial investments in R&D and the consequential impact on the business’s future, effective portfolio management decisions play a pivotal role in achieving business success. Portfolio decisions influence R&D productivity, the success rates of new products, and several other performance metrics related to NPD.
Family and school factors related to internalizing and externalizing problems in adolescence

ABSTRACT Family and school factors are among the most important determinants of internalizing and externalizing problem behavior in adolescents. The aim of this study is to explore how certain family and school factors explain internalizing and externalizing problems after one year. In addition to individual-level variables, school level variables are taken into account. This multilevel study was conducted among 507 adolescents (mean age 13.44; 52.3% girls) who reported their internalizing problems (anxiety, depressive symptoms), externalizing problems (aggressive and norm-breaking behavior), parental knowledge of their whereabouts, parental rule setting, school connectedness and teacher support. At the school level, the aggregated scores for school connectedness and teacher support were calculated. The results show that the differences between schools explained 13.4% of the variance in externalizing problem behavior although it did not account for internalizing behavior. Parental knowledge and school connectedness were significant predictors of both internalizing and externalizing problems. At the school level, aggregated teacher support predicted fewer externalizing problems. While some of the results are partially in line with previous findings, others were found to be contradictory. The implications for practice are provided.
Cross-domain Semantic Decoupling for Weakly-Supervised Semantic Segmentation

Weakly-supervised semantic segmentation (WSSS) aims to obtain pixel-wise pseudo labels from image-level labels for segmentation supervision. However, due to the co-occurrence of multiple categories in an image, it is difficult to obtain accurate pseudo labels for supervision, leading to the unsatisfactory performances of current methods. In this paper, we observe that accurate pseudo labels are easier to obtain from images with only a single semantic object (i.e., single-label images) compared to those with multiple semantic objects (i.e., multi-label images). This inspires us to treat the localization maps from single-label images (referred to as the source domain) as good prior knowledge and transfer to multi-label images (referred to as the target domain). Specifically, we present a cross-domain semantic decoupling (CSD) method that first splits image data into source and target domains, and then utilizes the co-occurrence oriented copy-and-paste scheme to enforce pixel-wise consistency and regularize the network responses to the same objects in the two domains. Such a design reduces semantic ambiguity and generates more accurate class boundaries for the pseudo labels. Our method can be seamlessly incorporated into existing WSSS models. Extensive experiments on PASCAL-VOC 2012 demonstrate that the proposed CSD can significantly improve the quality of pseudo labels and final segmentation results.
Distillation for High-Quality Knowledge Extraction via Explainable Oracle Approach

Recent successes suggest that knowledge distillation techniques can usefully transfer knowledge between deep neural networks as compression and acceleration techniques, e.g., effectively and reliably compress a large teacher model into a smaller student model with limited resources. However, knowledge distillation performance is degraded when the model compression rate becomes excessively high due to the size of the teacher model. To address this, we advocate for improving the teacher-to-student knowledge transfer by identifying and reinforcing input-level signals of substantial contributions for a final verdict, e.g., signals for a long trunk of elephants are strengthened and transferred to the student model. To this end, we adopt gradient-based explainable AI techniques for extracting output-relevant input-level features. Then, we strengthen and transfer these signals to improve the knowledge distillation performance. Our experiments on public datasets (i.e., CIFAR-10, CIFAR-100, Tiny-ImageNet, and Ima-geNet) show that our method clearly outperforms existing knowledge distillation approaches, especially in the case of using a small teacher model. Our code is available at https://github.com/myunghakLee/Distillation-for-High-Quality-Knowledge-Extraction.
Color Constancy: How to Deal with Camera Bias?

In color constancy, we seek to estimate and remove the color of the illuminating light from the captured raw RGB images. While the learning-based methods perform better than the traditional statistics-based methods, they are typically tuned to perform well on a particular camera, i.e., their performance on images captured by other cameras is subject to their cross-camera generalizability. This problem was partially addressed, in various prior works, using deep learning architectures. In this paper, we examine how these cross-camera models perform compared to where we “pre-calibrate” the camera biases in the training and/or testing images using a simple homographic color correction procedure which can be easily done on the camera manufacturer’s part. And further, with pre-calibrated data we examine by how much we could simplify the original cross-camera models. We show that cross-camera color constancy with a simple pre-calibration process yields up to 36% performance boost, which in turn indicates that the original cross-camera methods have only limited ability to compensate for the cross-camera prediction bias. Surprisingly, with this newly proposed evaluation protocol, we also found that some single-camera color constancy algorithms already possess cross-camera ability similar to adopting a camera bias pre-calibration.
Comprehensive Quantitative Quality Assessment of Thermal Cut Sheet Edges using Convolutional Neural Networks

In this study, we present a novel holistic approach to assess the quality of thermal cut edges using images of the cut edges. Using deep learning techniques, we estimate quality criteria such as roughness, edge slope tolerance, groove tracking, and burr height. Our approach significantly surpasses the current state of the art in evaluating thermal cut edges using 2D images. To the best of our knowledge, this study presents the first image-based groove tracking evaluation for thermal cut edges. Our results show that a comprehensive, accurate, and fast prediction of edge quality can be effectively achieved by implementing a simple image acquisition system combined with a convolutional neural network (CNN).
RUPQ: Improving low-bit quantization by equalizing relative updates of quantization parameters

Neural network quantization is a model compression technique using low-bit width representation of floating-point weights and activations. Although quantization can significantly reduce power consumption and inference time, it often leads to worse target metrics, such as accuracy or peak signal-to-noise ratio. To improve upon the target metrics, particularly at low-bit width computations, we propose a new Relative Update-Preserving Quantizer. This quantizer stabilizes relative updates for all parameters during training. Our experimental results show that the proposed quantizer performs consistently better than LSQ+ and, compared to full-precision models, significantly reduces the gap in quality for low-bit SRResNet, EDSR and YOLO-v3 models. The method is easy to implement and use in practice. The code is available at https: //github.com/Valentin-Buchnev/RUPQ .
Self-Supervised Adversarial Training for Robust Face Forgery Detection

With the advancement of face forgery technologies, the high-fidelity generation and substitution of human faces have become increasingly prevalent, leading to an emerging research topic of face forgery detection. Despite the outstanding performance of current face forgery detectors in benchmark datasets, their real-life application is fraught with challenges due to complex scenarios. Therefore, we propose a self-supervised adversarial training network to enhance the robustness of face forgery detection, promoting their applicability in real-life scenarios. We generate multiple adversarial examples using a pool of attack strategies and strengthen the sensitivity to perturbations by compelling the model to predict these attack strategies. Additionally, we employ an adversarial training strategy to dynamically generate the most challenging adversarial examples for the current model. A fast training strategy is proposed to reduce the computation cost of adversarial training. Through extensive experiments, we demonstrate that our approach significantly outperforms the baseline and state-of-the-art methods in terms of robustness to perturbations.
Unifying the Harmonic Analysis of Adversarial Attacks and Robustness

Adversarial examples pose a unique challenge for deep learning systems. Despite recent advances in both attacks and defenses, a consensus on their true nature eludes the community. A deep understanding of these can provide new insights towards the development of more effective attacks and defenses. Driven by the common misconception that adversarial examples are high-frequency noise, we present a frequency-based understanding of adversarial examples, supported by theoretical and empirical findings. Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent. Particularly, we highlight the glaring disparities between models trained on CIFAR-10 and ImageNet-derived datasets. Utilizing this framework, we analyze many intriguing properties of training robust models with frequency constraints, and propose a frequency-based explanation for the commonly observed accuracy vs robustness trade-off.
EyeGuide - From Gaze Data to Instance Segmentation

Obtaining precise instance-level segmentations is a challenging task in machine learning. Especially for objects with complex and non-convex geometries or with partial occlusions scribble, bounding boxes or user clicks are often provided to guide the segmentation. In this paper, we explore the usage of a remote eye tracking system to generate gaze data as an additional input for object segmentation models (called EyeGuide). The gaze data is recorded during routine image inspections (i.e. without giving a particular task) and is used as an additional input to train neural networks. Our results indicate that the acquisition of gaze data is faster and more convenient than providing explicit user input, less annotations are necessary to generate equal or better segmentation results and also overall better generalisation capabilities on unseen classes compared to state-of-the-art techniques. In summary, EyeGuide is a simple yet powerful guidance strategy that can directly be integrated in image inspection routines and neural network architectures.
Train ViT on Small Dataset With Translation Perceptibility

The Vision Transformer (ViT) has emerged as a prominent model in the domain of computer vision in recent years, effectively supplanting traditional Convolutional Neural Network (CNN) models. However, due to the absence of certain properties intrinsic to CNNs, such as parameter sharing and translation invariance, ViTs tend to necessitate larger quantities of training data. To circumvent this limitation, a multitude of methods have been proposed by the academic community to optimize ViT’s performance when dealing with smaller datasets. In this paper, we introduce a novel self-supervised auxiliary task designed to guide ViTs in acquiring the capability of translation perceptibil-ity. This strategy facilitates the models in obtaining inductive bias more efficiently from smaller datasets, obviating the need for pre-training on larger datasets or modifications to the network architecture. The efficacy of our approach is corroborated through its application to multiple small datasets, demonstrating impressive scale perceptibility. Furthermore, when utilized in tandem with current state-of-the-art methods, our approach yields significant performance enhancements.
Exploring how Transit Practitioners Navigate Nervousness

This study explores how practitioners navigate feelings of nervousness associated with racial and economic inequities connected to public transit systems. It utilizes Gooden's (2014) 10 principles for conquering nervousness, which have received little attention in the literature to date. This preliminary investigation thus examines the extent to which the principles are adopted – or not – by transit practitioners across four diverse metropolitan areas. A purposive sample of transit equity experts led to 12 in-depth interviews. The analysis illustrates varying degrees of principle adoption amongst the participants. Findings suggest that external legal triggers (such as Title VI of the Civil Rights Act of 1964) most often move the needle on racial equity in public transit. The least adopted principles indicate that agencies are less likely to adopt a race-conscious approach, limiting acknowledgement of historical inequities and structural barriers, which allows nervousness to linger and inequities to persist on a societal level.
More Than Half of Statistically Significant Research Findings in the Environmental Sciences are Actually Not

Researchers have incentives to search for and selectively report findings that appear to be statistically significant and/or conform to prior beliefs. Such selective reporting practices, including p -hacking and publication bias, can lead to a distorted set of results being published, potentially undermining the process of knowledge accumulation and evidence-based decision making. We take stock of the state of empirical research in the environmental sciences using 67,947 statistical tests obtained from 547 meta-analyses. We find that 59% of the p -values that were reported as significant are not actually expected to be statistically significant. The median power of these tests is between 6% to 12%, which is the lowest yet identified for any discipline. Only 8% of tests are adequately powered with statistical power of 80% or more. Exploratory regressions suggest that increased statistical power and the use of experimental research designs reduce the extent of selective reporting. Differences between subfields can be mostly explained by methodological differences. To improve the environmental sciences evidence base, researchers should pay more attention to statistical power, but incentives for selective reporting may remain even with adequate statistical power. Ultimately, a paradigm shift towards open science is needed to ensure the reliability of published empirical research.
Identifying the Biological Potential of Western Balkan Polypore Mushroom Species to Mitigate the Negative Effects of Global Mushroom Cultivation

: Growing mushrooms is meeting challenges while aiming for sustainability and circularity. Wherever the producer is located, commercial strains are the same originating from several producers. Following the harvest, enormous quantities of spent mushroom substrate containing spores are disposed presenting a type of foreign material pressure on the ecosystem in the form of the loss of genetic diversity in wild mushroom populations. This challenge can be mitigated by bioprospecting local strains and using them to generate commercial inoculum. Accordingly, the mycoceutical potential of five polypore mushroom species from Serbia was evaluated: secondary metabolite composition, oxidative damage prevention, anti-tyrosinase, and anti-angiotensin converting enzyme (ACE) properties. The phenolic pattern was comparable in all samples, but the amounts of specific chemicals varied. Hydroxybenzoic acids were the primary components. All samples had varying quantities of ascorbic acid, carotene, and lycopene, and showed a pronounced inhibition of lipid peroxidation (LPx) and ability to scavenge HO • . Extracts were more potent tyrosinase inhibitors but unsuccessful when faced with ACE. Fomitopsis pinicola had the strongest anti-tumor efficacy while Ganoderma lucidum demonstrated strong selectivity in anti-tumor effect in comparison to healthy cells. The evaluated species provided a solid foundation for commercial development while keeping the local ecology in mind.
Thermal boundary layer of laminar ﬂow of dilute polymer solution

The thermal boundary layer ﬂow is a canonical ﬂow with characteristics that are present in most natural and industrial convection ﬂows. An approximate self-similar solution is proposed for the ﬁrst time for the thermal boundary layer of steady laminar ﬂow of viscoelastic ﬂuids, described by the ﬁnitely extensible nonlinear elastic constitutive equation with Peterlin’s closure (FENE-P model). This semi-analytical thermal solution is obtained by performing an order of magnitude analysis and ensuing simpliﬁcations of the governing equations by assuming that the ﬂuid properties are independent of temperature therefore de-coupling the ﬂow governing equations from the energy equation. The effects of viscoelasticity quantiﬁed with the Weissenberg number based on the streamwise coordinate ( x ) ( W i x ) up to W i x = 1 and viscous dissipation (results are presented for Brinkman numbers between -40 and + 40) on thermal boundary layer characteristics are investigated comprehensively for both constant wall temperature and constant wall heat ﬂux. At low elasticity levels ( W i x < 0 . 01 ) the solution exhibits a global self-similar behavior in which ﬂow and thermal quantities collapse on the corresponding Newtonian curves, and the polymer characteristics show a unique behavior if adequately normalized. However, by increasing ﬂow elasticity the unique self-similar behavior of the approximate solution is lost, with the elasticity dependent results exhibiting local variations. In addition, the effects of elasticity are intensiﬁed by viscous dissipation. For the present study cases, it is observed that elasticity may change Nusselt numbers by more than 8%, and the thermal boundary layer thickens by up to 10%.