Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-cell-autonomous regulation of germline proteostasis by insulin/IGF-1 signaling via the intestinal peptide transporter PEPT-1

Gametogenesis involves active protein synthesis and heavily relies on proteostasis. How animals regulate germline proteostasis at the organismal level is poorly understood. Taking C. elegans as a model, we show that germline proteostasis requires coupled activities of HSF-1-dependent protein folding and insulin/IGF-1 signaling controlled protein synthesis. Depletion of HSF-1 from germ cells impairs chaperone gene expression, causing protein degradation and aggregation and, consequently, declines in fecundity and gamete quality. Reduced insulin/IGF-1 signaling confers germ cells’ resilience to limited protein folding capacity and proteotoxic stress by lowering ribosome biogenesis and the rate of translation. Interestingly, insulin/IGF-1 signaling promotes the expression of the evolutionarily conserved intestinal peptide transporter PEPT-1 via its downstream transcription factor FOXO/DAF-16, therefore allowing dietary proteins to be incorporated into an amino acid pool that fuels protein synthesis in the germline. We propose that this non-cell-autonomous pathway plays a critical role in regulating proteostasis in gametogenesis. Teaser Insulin/IGF-1 signaling regulates proteostasis in gametogenesis via the control of dietary protein absorption.
Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations

Large stochastic population abundance fluctuations are ubiquitous across the tree of life1–7, impacting the predictability of population dynamics and influencing eco-evolutionary outcomes. It has generally been thought that these large abundance fluctuations do not strongly impact evolution (in contrast to genetic drift), as the relative frequencies of alleles in the population will be unaffected if the abundance of all alleles fluctuate in unison. However, we argue that large abundance fluctuations can lead to significant genotype frequency fluctuations if different genotypes within a population experience these fluctuations asynchronously. By serially diluting mixtures of two closely related E. coli strains, we show that such asynchrony can occur, leading to giant frequency fluctuations that far exceed expectations from models of genetic drift. We develop a flexible, effective model that explains the abundance fluctuations as arising from correlated offspring numbers between individuals, and the large frequency fluctuations result from even slight decoupling in offspring numbers between genotypes. This model accurately describes the observed abundance and frequency fluctuation scaling behaviors. Our findings suggest chaotic dynamics underpin these giant fluctuations, causing initially similar trajectories to diverge exponentially; subtle environmental changes can be magnified, leading to batch correlations in identical growth conditions. Furthermore, we present evidence that such decoupling noise is also present in mixed-genotype S. cerevisiae populations. We demonstrate that such decoupling noise can strongly influence evolutionary outcomes, in a manner distinct from genetic drift. Given the generic nature of asynchronous fluctuations, we anticipate they are widespread in biological populations, significantly affecting evolutionary and ecological dynamics.
Svm Decision Boundary Based Discriminative Subspace Induction ଁ

We study the problem of linear dimension reduction for classification, with a focus on sufficient dimension reduction, i.e., finding subspaces without loss of discrimination power. First, we formulate the concept of sufficient subspace for classification in parallel terms as for regression. Then we present a new method to estimate the smallest sufficient subspace based on an improvement of decision boundary analysis (DBA). The main idea is to combine DBA with support vector machines (SVM) to overcome the inherent difficulty of DBA in small sample size situations while keeping DBA’s estimation simplicity. The compact representation of SVM boundary results in a significant gain in both speed and accuracy over previous DBA implementations. Alternatively, this technique can be viewed as a way to reduce the run-time complexity of SVM itself. Comparative experiments on one simulated and four real-world benchmark datasets highlight the superior performance of the proposed approach. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Robust watermarking of " ngerprint images

This paper introduces two spatial methods in order to embed watermark data into "ngerprint images, without corrupting their features. The "rst method inserts watermark data after feature extraction, thus preventing watermarking of regions used for "ngerprint classi"cation. The method utilizes an image adaptive strength adjustment technique which results in watermarks with low visibility. The second method introduces a feature adaptive watermarking technique for "ngerprints, thus applicable before feature extraction. For both of the methods, decoding does not require original "ngerprint image. Unlike most of the published spatial watermarking methods, the proposed methods provide high decoding accuracy for "ngerprint images. High data hiding and decoding performance for color images is also observed. 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
A Variational Image Segmentation Model Based on Normalized Cut with Adaptive Similarity and Spatial Regularization

Image segmentation is a fundamental research topic in image processing and computer vision. In the last decades, researchers developed a large number of segmentation algorithms for various applications. Amongst these algorithms, the Normalized cut (Ncut) segmentation method is widely applied due to its good performance. The Ncut segmentation model is an optimization problem whose energy is defined on a specifically designed graph. Thus, the segmentation results of the existing Ncut method are largely dependent on a pre-constructed similarity measure on the graph since this measure is usually given empirically by users. This flaw will lead to some undesirable segmentation results. In this paper, we propose a Ncut-based segmentation algorithm by integrating an adaptive similarity measure and spatial regularization. The proposed model combines the Parzen-Rosenblatt window method, non-local weights entropy, Ncut energy, and regularizer of phase field in a variational framework. Our method can adaptively update the similarity measure function by estimating some parameters. This adaptive procedure enables the proposed algorithm finding a better similarity measure for classification than the Ncut method. We provide some mathematical interpretation of the proposed adaptive similarity from multi-viewpoints such as statistics and convex optimization. In addition, the regularizer of phase field can guarantee that the proposed algorithm has a robust performance in the presence of noise, and it can also rectify the similarity measure with a spatial priori. The well-posed theory such as the existence of the minimizer for the proposed model is given in the paper. Compared with some existing segmentation methods such as the traditional Ncut-based model and the classical Chan-Vese model, the numerical experiments show that our method can provide promising segmentation results.
Adaptive Active Anti-Vibration Control for a 3-D Helicopter Flexible Slung-Load System With Input Saturations and Backlash

This article investigates active anti-vibration control for a 3-D helicopter flexible slung-load system (HFSLS) subject to input saturations and backlash. The first target of the study is to establish a model for a 3-D HFSLS. The second target is to develop an adaptive control law for an HFSLS by analyzing its ability to compensate for the effects of input saturations, input backlash, and external disturbances, while achieving the goal of vibration reduction. Simulation results of the numerical model show that the proposed adaptive active control technology is effective in solving the oscillation suppression problem for the 3-D HFSLS with input saturations and backlash.
The Effect of Indian Residential Schools on Height and Body Mass Post-1930

We study the effects of Canadian Indian residential schooling on two anthropometric measures of health during childhood: adult height and body weight. We use repeated cross-sectional data from the 1991 and 2001 Aboriginal Peoples Surveys and detailed historical data on school closures and location to identify the effect of residential schooling on these outcomes. We find some evidence that on average residential schooling increases adult height and decreases adult body weight of those that attended. However, these effects are concentrated after the 1950s when the schools were subject to tighter health regulations and students were selected to attend residential school based partly on their need for medical care that was otherwise unavailable. Residential schooling is only one policy in Canada that impacted Status Indian peoples’ health, so our results must be understood in the broader social context. We also document significant increases in height and body weight for Status Indian people born after the 1960s which is suggestive of non-trivial changes in diet and living conditions during this time period.
An Exploratory Study and Thematic Analysis of Responses to Understand the Perspectives and the Level of Support Offered to Healthcare Workers During the Pandemic in Ireland

The COVID-19 pandemic has placed exceptional demands on the healthcare workforce around the world (Vindrola-Padros et al., 2020). The aim of this study was to undertake an exploration of the experiences of healthcare workers (HCWs) who worked during the pandemic and their views about the support they were offered during the pandemic in Dublin, Ireland. Understanding the experiences of HCWs can help identify gaps in healthcare systems and inform efforts to strengthen them. This primary qualitative study was conducted using semi-structured interviews with six healthcare professionals. Participants were recruited via snowballing sampling technique and included nurses and healthcare assistants. The interview discussion guide consisted of questions on COVID-19-related challenges such as the demands at the workplace, the level of stress and uncertainty to HCWs, availability and quality of personal protective equipment (PPE) and the support as well as coping strategies they received, from the management to handle the pandemic. Braun and Clarke’s (2021) reflexive thematic analysis generated two themes with eight sub-themes. The two major themes were emotional exhaustion and inconsistent guidelines. The findings from the study indicated that healthcare workers were practising and carrying out duties outside their usual roles and reported very high levels of stress and anxiety. The second theme discusses the lack of consistency, which leads to a number of challenges for HCWs while implementing standardised practices. Understanding the perspectives of healthcare workers would facilitate the hospital administrations as well as managements in Ireland to proactively support healthcare providers during future pandemics by ensuring access to mental health programs, standardising communication
Microsecond dynamics control the HIV-1 Envelope conformation

The HIV-1 Envelope (Env) glycoprotein facilitates host cell fusion through a complex series of receptor-induced structural changes. Although remarkable progress has been made in understanding the structures of various Env conformations, microsecond timescale dynamics have not been studied experimentally. Here, we used time-resolved, temperature-jump small-angle x-ray scattering to monitor structural rearrangements in an HIV-1 Env SOSIP ectodomain construct with microsecond precision. In two distinct Env variants, we detected a transition that correlated with known Env structure rearrangements with a time constant in the hundreds of microseconds range. A previously unknown structural transition was also observed, which occurred with a time constant below 10 μs, and involved an order-to-disorder transition in the trimer apex. Using this information, we engineered an Env SOSIP construct that locks the trimer in the prefusion closed state by connecting adjacent protomers via disulfides. Our findings show that the microsecond timescale structural dynamics play an essential role in controlling the Env conformation with impacts on vaccine design.
Ocean access beneath the southwest tributary of Pine Island Glacier, West Antarctica

ABSTRACT The catchments of Pine Island Glacier and Thwaites Glacier in the Amundsen Sea Embayment are two of the largest, most rapidly changing, and potentially unstable sectors of the West Antarctic Ice Sheet. They are also neighboring outlets, separated by the topographically unconfined eastern shear margin of Thwaites Glacier and the southwest tributary of Pine Island Glacier. This tributary begins just downstream of the eastern shear margin and flows into the Pine Island ice shelf. As a result, it is a potential locus of interaction between the two glaciers and could result in cross-catchment feedback during the retreat of either. Here, we analyze relative basal reflectivity profiles from three radar sounding survey lines collected using the UTIG HiCARS radar system in 2004 and CReSIS MCoRDS radar system in 2012 and 2014 to investigate the extent and character of ocean access beneath the southwest tributary. These profiles provide evidence of ocean access ~12 km inland of the 1992–2011 InSAR-derived grounding line by 2014, suggesting either retreat since 2011 or the intrusion of ocean water kilometers inland of the grounding line.
Shifts in the Community Dynamics and Activity of Ammonia‐Oxidizing Prokaryotes Along the Yangtze Estuarine Salinity Gradient

Ammonia oxidation, the first and rate‐limiting step in nitrification, plays a critical role in the nitrogen cycle. However, the links between the dynamics of ammonia‐oxidizing communities and ecosystem processes along the estuarine salinity gradient remain uncertain. In this study, we examined the diversity, abundance, and community structure of ammonia‐oxidizing prokaryotes, and the potential nitrification rates along the Yangtze estuarine salinity gradient. Phylogenetic analysis showed that the predominant ammonia‐oxidizing bacteria (AOB) and ammonia‐oxidizing archaea (AOA) fell within the Nitrosospira and Nitrosopumilus clusters, respectively. The AOB amoA gene abundance (4.67 × 105 to 3.90 × 107 copies per gram of dry sediment) outnumbered AOA (5.14 × 104 to 8.88 × 106 copies per gram of dry sediment). The potential nitrification rates varied between 0.13 and 0.63 μg N·g−1·day−1 and related only to AOA amoA gene abundance. Salinity had significant effects on AOA amoA gene abundance, nitrification rates, and the community structure of ammonia‐oxidizing prokaryotes. Principal coordinate analysis showed that the AOB amoA gene clones derived from the middle‐ and high‐salinity regions behaved as a cohesive group, while all the low‐salinity clone libraries were grouped together. Moreover, the distribution of AOA communities showed a distinct salinity differentiation. Overall, this study improves the understanding of the dynamic shifts in ammonia‐oxidizing microorganisms in the Yangtze Estuary.
On the Use of eXplainable Artiﬁcial Intelligence to Evaluate School Dropout

: The school dropout problem has been recurrent in different educational areas, which has reinforced important challenges when pursuing education objectives. In this scenario, technical schools have also suffered from considerable dropout levels, even when considering a still increasing need for professionals in areas associated to computing and engineering. Actually, the dropout phenomenon may be not uniform and thus it has become urgent the identiﬁcation of the proﬁle of those students, putting in evidence techniques such as eXplainable Artiﬁcial Intelligence (XAI) that can ensure more ethical, transparent, and auditable use of educational data. Therefore, this article applies and evaluates XAI methods to predict students in school dropout situation, considering a database of students from the Federal Institute of Rio Grande do Norte (IFRN), a Brazilian technical school. For that, a checklist was created comprising explanatory evaluation metrics according to a broad literature review, resulting in the proposal of a new explainability index to evaluate XAI frameworks. Doing so, we expect to support the adoption of XAI models to better understand school-related data, supporting important research efforts in this area.
Financial Turmoil and Earnings Mobility

We analyze how earnings dynamics changed in the US after the ﬁnancial crisis of 2007-2009. Differently from most models for earnings mobility, we allow persistence patters to depend semi-nonparametrically on both the past individual position in the distribution and on a set of individual-level covariates. Allowing for more ﬂexibility in the model yields a better ﬁt to the data and permits us to uncover changes in earnings mobility patterns that would otherwise remain hidden. Indeed, at the aggregate level, we ﬁnd no evidence of changes in individual positional persistence in any part of the earnings distribution after the crisis, both with the parametric and with the semi-nonparametric model. However, the semi-nonparametric copula allows us to uncover an increase in earnings mobility for 45-year-old workers with college degree after the crisis
Label Augmentation as Inter-class Data Augmentation for Conditional Image Synthesis with Imbalanced Data

Conditional image synthesis performs admirably when trained on well-constructed and balanced datasets. However, in practice, training datasets frequently contain minorities (i.e., a class with a few samples), known as imbalanced data , which causes difficulties in learning generative models. To address conditional image synthesis with imbalanced data, we analyze a diversity issue of label-preserving data augmentation and an affinity issue of non-label-preserving data augmentation. From this observation, we present label augmentation , which works as inter-class data augmentation that effectively augments data by predicting a new label for a given image using the prediction of a pretrained image classification model (i.e., probabilities for each class). We incorporate our label augmentation into the discriminator of a seminal conditional generative adversarial network (GAN) model, proposing Softlabel-GAN . Using class probabilities extracts class-invariant and shared features between similar classes, achieving data augmentation with high affinity and diversity. Our experiments on imbalanced datasets show that Softlabel-GAN produces images with high quality and diversity while being hardly affected by the number of samples in each class.
WIP: Adversarial Retroreflective Patches: A Novel Stealthy Attack on Traffic Sign Recognition at Night

—Traffic signs, essential for communicating critical rules to ensure safe and efficient traffic for entities such as pedestrians and motor vehicles, must be reliably recognized, especially in the realm of autonomous driving. However, recent studies have revealed vulnerabilities in vision-based traffic sign recognition systems to adversarial attacks, typically involving small stickers or laser projections. Our work advances this frontier by exploring a novel attack vector, the Adversarial Retroreflective Patch (ARP) attack. This method is stealthy and particularly effective at night by exploiting the optical properties of retroreflective materials, which reflect light back to its source. By applying retroreflective patches to traffic signs, the reflected light from the vehicle’s headlights interferes with the camera, causing perturbations that hinder the traffic sign recognition model’s ability to correctly detect the signs. In our preliminary study, we conducted a feasibility study of ARP attacks and observed that while a 100% attack success rate is achievable in digital simulations, it decreases to less than or equal to 90% in physical experiments. Finally, we discuss the current challenges and outline our future plans. This research gains significance in the context of autonomous vehicles’ 24/7 operation, emphasizing the critical need to assess sensor and AI vulnerabilities, especially in low-light nighttime environments, to ensure the continued safety and reliability of self-driving technologies.
Optimization of Injection Moulding Parameters for Constructing Motorcycle Footstep Cover Using an Environmentally friendly Composite

– Agra-forestry waste and recycled polypropylene have been combined to create sustainable composite materials for use in automotive applications in response to increased environmental concern over the depletion of petroleum-based resources. In this study, an injection molded motorcycle footstep cover was manufactured from recycled polypropylene (r-PP) composite reinforced with ironwood powder. It aims to optimize the manufacturing process parameters of barrel temperature, mould temperature, and holding time in minimizing shrinkage and maximizing product weight. This current research employed the Taguchi L4 orthogonal array experimental design followed by the multi-objective grey relation analysis (GRA). In this study, the composite material consisted of 30% ironwood powder, 65% r-PP, and 5% coupling agent. The specimens were processed using injection molding with two different levels for each parameter of barrel temperature, moulding temperature, and holding time. The injection moulded products were then tested for shrinkage and product weight. The results showed that moulding temperature was the most influential parameter in getting a minimum shrinkage percentage, contributing 60.89%. Meanwhile, holding time contributes the most to achieving a maximum product weight (90.65%). After conducting a grey relation analysis and a confirmation test, the optimal parameters for shrinkage minimization and product weight maximization of motorcycle footstep cover are 210 ℃ of barrel temperature, 45 ℃ of moulding temperature, and 5 seconds of holding time. This research highlights the prospective applications of recycled polypropylene composite reinforced with ironwood powder for application as automotive parts.
Unleashing the Potential of Fractional Calculus in Graph Neural Networks

We introduce the FRactional-Order graph Neural Dynamical network (FROND), a learning framework that augments traditional graph neural ordinary differential equation (ODE) models by integrating the time-fractional Caputo derivative. Thanks to its non-local characteristic, fractional calculus enables our framework to encapsulate long-term memories during the feature-updating process, diverging from the Markovian updates inherent in conventional graph neural ODE models. This capability could substantially enhance graph representation learning by introducing more nuanced feature updating dynamics. Analytically, we exhibit that the over-smoothing issue is mitigated when feature updating is regulated by a fractional diffusion process. Additionally, our framework affords a fresh dynamical system perspective to comprehend various skip or dense connections situated between GNN layers in existing literature.