Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration

Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Each weather type calls for a tailored degradation removal approach due to its unique impact on images. Conversely, content reconstruction can employ a uniform approach, as the underlying image content remains consistent. Although previous techniques can handle multiple weather types within a single network, they neglect the crucial distinction between these two processes, limiting the quality of restored images. This work introduces a novel adverse weather image restoration method, called DDCNet, which decouples the degradation removal and content reconstruction process at the feature level based on their channel statistics. Specifically, we exploit the unique advantages of the Fourier transform in both these two processes: (1) the degradation information is mainly located in the amplitude component of the Fourier domain, and (2) the Fourier domain contains global information. The former facilitates channel-dependent degradation removal operation, allowing the network to tailor responses to various adverse weather types; the latter, by integrating Fourier's global properties into channel-independent content features, enhances network capacity for consistent global content reconstruction. We further augment the degradation removal process with a degradation mapping loss function. Extensive experiments demonstrate our method achieves state-of-the-art performance in multiple adverse weather removal benchmarks.
Long-range near-side correlation in $e^+e^-$ Collisions at 183-209 GeV with ALEPH Archived Data

The first measurement of two-particle angular correlations for charged particles produced in $e^+e^-$ annihilation up to $\sqrt{s} = 209$ GeV with LEP-II data is presented. Hadronic $e^+e^-$ data, archived at center-of-mass energies ranging from 183 to 209 GeV, were collected using the ALEPH detector at LEP. The angular correlation functions have been measured across a wide range of pseudorapidities and the full azimuth in bins of charged particle multiplicity. Results for $e^+e^-$ data at high energies, which allow for higher event multiplicities reaching approximately 50 than LEP-I at Z pole energy, are presented for the first time. A long-range near-side excess in the correlation function has been identified in the analysis when calculating particle kinematic variables with respect to the thrust axis. Moreover, the two-particle correlation functions were decomposed using a Fourier series, and the resulting Fourier coefficients $v_n$ were compared with event generator outputs. In events with high multiplicity featuring more than 50 particles, the extracted $v_2$ magnitude from the data are compared to those from the Monte Carlo reference.
Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning

Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance with an average F1 score of 0.66 and demonstrated ensemble generalization to C-AFM images of WSe2 and STM images of MoSe2. Defect coordinates were automatically extracted from defect detections maps showing that STM image analysis enhanced by machine learning can be used to dramatically increase sample characterization throughput.
On the Inadequacy of Similarity-based Privacy Metrics: Reconstruction Attacks against "Truly Anonymous Synthetic Data"

Training generative models to produce synthetic data is meant to provide a privacy-friendly approach to data release. However, we get robust guarantees only when models are trained to satisfy Differential Privacy (DP). Alas, this is not the standard in industry as many companies use ad-hoc strategies to empirically evaluate privacy based on the statistical similarity between synthetic and real data. In this paper, we review the privacy metrics offered by leading companies in this space and shed light on a few critical flaws in reasoning about privacy entirely via empirical evaluations. We analyze the undesirable properties of the most popular metrics and filters and demonstrate their unreliability and inconsistency through counter-examples. We then present a reconstruction attack, ReconSyn, which successfully recovers (i.e., leaks all attributes of) at least 78% of the low-density train records (or outliers) with only black-box access to a single fitted generative model and the privacy metrics. Finally, we show that applying DP only to the model or using low-utility generators does not mitigate ReconSyn as the privacy leakage predominantly comes from the metrics. Overall, our work serves as a warning to practitioners not to deviate from established privacy-preserving mechanisms.
Interpretable Underwater Diver Gesture Recognition

In recent years, usage and applications of Autonomous Underwater Vehicles has grown rapidly. Interaction of divers with the AUVs remains an integral part of the usage of AUVs for various applications and makes building robust and efficient underwater gesture recognition systems extremely important. In this paper, we propose an Underwater Gesture Recognition system trained on the Cognitive Autonomous Diving Buddy Underwater gesture dataset using deep learning that achieves 98.01\% accuracy on the dataset, which to the best of our knowledge is the best performance achieved on this dataset at the time of writing this paper. We also improve the Gesture Recognition System Interpretability by using XAI techniques to visualize the model's predictions.
Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games

In this work, we introduce a self-supervised behavior cloning transformer for text games, which are challenging benchmarks for multi-step reasoning in virtual environments. Traditionally, Behavior Cloning Transformers excel in such tasks but rely on supervised training data. Our approach auto-generates training data by exploring trajectories (defined by common macro-action sequences) that lead to reward within the games, while determining the generality and utility of these trajectories by rapidly training small models then evaluating their performance on unseen development games. Through empirical analysis, we show our method consistently uncovers generalizable training data, achieving about 90\% performance of supervised systems across three benchmark text games.
Mathematical model of RNA-directed DNA methylation predicts siRNA production in bursts

RNA-directed DNA Methylation (RdDM) is a plant-specific de novo methylation pathway that is responsible for maintenance of asymmetric methylation (CHH, where H=A, T, or G) in euchromatin. Loci with CHH methylation are transcriptionally silent and produce 24-nucleotide (nt) short interfering (si) RNAs. These siRNAs direct additional CHH methylation to the locus, thereby maintaining methylation states through DNA replication. To understand the necessary conditions to produce stable CHH methylation, we developed a stochastic mathematical model of RdDM. The model describes DNA target search of DNA or RNA by siRNAs derived from CHH-methylated loci. When the siRNA (bound by an Argonaute protein) finds the matching locus, the model uses the dwell time of the matched complex to determine the degree of CHH reinforcing methylation. Reinforcing methylation occurs either throughout the cell cycle (steady reinforcement), or immediately following replication (bursty reinforcement). Each simulation occurs over 10 cell cycles, and for 7 bootstrapped replicates. We use nonparametric statistics to compare initial and final CHH methylation distributions to determine whether the simulation conditions produce stable maintenance. We apply this method to the low CHH methylation case, wherein the median is only 8%, and many loci have less than 8% methylation. The resulting model predicts that siRNA production must be linearly proportional to CHH methylation levels at each locus, that bursty reinforcement produces more stable systems, and that slightly higher levels of siRNA production are required for DNA target search, compared to RNA target search.
Dietary restriction of individual amino acids stimulates unique molecular responses in mouse liver

Dietary protein and essential amino acid (EAA) restriction promotes favorable metabolic reprogramming, ultimately resulting in improvements to both health and lifespan. However, as individual EAAs have distinct catabolites and engage diverse downstream signaling pathways, it remains unclear to what extent shared or AA-specific molecular mechanisms promote diet-associated phenotypes. Here, we investigated the physiological and molecular effects of restricting either dietary methionine, leucine, or isoleucine (Met-R, Leu-R, and Ile-R) for 3 weeks in C57BL/6J male mice. While all 3 AA-depleted diets promoted fat and lean mass loss and slightly improved glucose tolerance, the molecular responses were more diverse; while hepatic metabolites altered by Met-R and Leu-R were highly similar, Ile-R led to dramatic changes in metabolites, including a 3-fold reduction in the oncometabolite 2-hydroxyglutarate. Pathways regulated in an EAA-specific manner included glycolysis, the pentose phosphate pathway (PPP), nucleotide metabolism, the TCA cycle and amino acid metabolism. Transcriptiome analysis and global profiling of histone post-translational modifications (PTMs) revealed different patterns of responses to each diet, although Met-R and Leu-R again shared similar transcriptional responses. While the pattern of global histone PTMs were largely unique for each dietary intervention, Met-R and Ile-R had similar changes in histone-3 methylation/acetylation PTMs at lysine-9. Few similarities were observed between the physiological or molecular responses to EAA restriction and treatment with rapamycin, an inhibitor of the mTORC1 AA-responsive protein kinase, indicating the response to EAA restriction may be largely independent of mTORC1. Together, these results demonstrate that dietary restriction of individual EAAs has unique, EAA-specific effects on the hepatic metabolome, epigenome, and transcriptome, and suggests that the specific EAAs present in dietary protein may play a key role at regulating health at the molecular level.
A 3-component module maintains sepal flatness in Arabidopsis

As in origami, morphogenesis in living systems heavily relies on tissue curving and folding, through the interplay between biochemical and biomechanical cues. In contrast, certain organs maintain their flat posture over several days. Here we identified a pathway, which is required for the maintenance of organ flatness, taking the sepal, the outermost floral organ, in Arabidopsis as a model system. Through genetic, cellular and mechanical approaches, our results demonstrate that global gene expression regulator VERNALIZATION INDEPENDENCE 4 (VIP4) fine-tunes the mechanical properties of sepal cell walls and maintains balanced growth on both sides of the sepals, mainly by orchestrating the distribution pattern of AUXIN RESPONSE FACTOR 3 (ARF3). vip4 mutation results in softer cell walls and faster cell growth on the adaxial sepal side, which eventually cause sepals to bend outward. Downstream of VIP4, ARF3 works through modulating auxin signaling to down-regulate pectin methylesterase VANGUARD1, resulting in decreased cell wall stiffness. Our work unravels a 3-component module, which relates hormonal patterns to organ curvature, and actively maintains sepal flatness during its growth.
Exploratory study of pulsed electric field ablation on atherosclerotic plaque in a rabbit model

New understanding of the pathogenesis of atherosclerotic diseases has led to the emergence of new therapeutic approaches. We explored the potential therapeutic effects of pulsed field potential ablation (PFA), a non-thermal ablation technique with high tissue selectivity, on atherosclerotic plaques. Carotid arteries of 30 high-fat rabbits were dilated with a balloon to obtain atherosclerotic plaques. PFA was administered on the carotid atherosclerotic plaques with 1000V/cm, 2000V/cm, and 1000V/cm ablation followed by rapamycin infusion. There were no visible changes in blood vessels after acute ablation, but apoptosis and polarity of cells were observed in atherosclerotic plaques. At 7 and 30 days after ablation, the density of lipid deposition in the plaque was significantly reduced, and multiple layers of new arranged anterograde smooth muscle cells appeared, replacing the original atherosclerotic plaque. The residual atherosclerotic structure is sandwiched between the new smooth muscle layer and the original smooth muscle layer, which makes vascular wall thicker and makes vascular wall elasticity increased. Rapamycin delays the vascular remodeling process. Conclusion: PFA ablation can reduce lipid deposition in atherosclerotic plaques, cause vascular remodeling, and enhance vascular elasticity. We believe that it may be a potential method for the treatment of atherosclerotic plaques.
Selective lipid recruitment by an archaeal DPANN symbiont from its host

The symbiont Ca. Nanohaloarchaeum antarcticus is obligately dependent on its host Halorubrum lacusprofundi for lipids and other metabolites due to its lack of certain biosynthetic genes. However, it remains unclear which specific lipids or metabolites are acquired from its host, and how the host responds to infection. Here, we explored the lipidome dynamics of the Ca. Nha. antarcticus – Hrr. lacusprofundi symbiotic relationship during co-cultivation. By using a comprehensive untargeted lipidomic methodology, our study reveals that Ca. Nha. antarcticus selectively recruits 110 lipid species from its host, i.e. nearly two-thirds of the total number of host lipids. Lipid profiles of co-cultures displayed shifts in abundances of bacterioruberins and menaquinones and changes in degree of bilayer-forming glycerolipid unsaturation. This likely results in increased membrane fluidity and improved resistance to membrane disruptions, consistent with compensation for higher metabolic load and mechanical stress on host membranes when in contact with Ca. Nha. antarcticus cells. Notably, our findings differ from previous observations of other DPANN symbiont-host systems, where no differences in lipidome composition were reported. Altogether, our work emphasizes the strength of employing untargeted lipidomics approaches to provide details into the dynamics underlying a DPANN symbiont-host system.
Human Risk Assessment and Mitigation Using Bow Tie Strategy

Human reliability assessment (HRA) techniques are used for quantifying human error probability for the purpose of providing feedback regarding the overall performance, and most importantly, safety of the system. Performing HRA involves various activities, including task analysis, conducting experiments, which have been found generally difficult, time-consuming and costly. In this project, the whole process will be based on HRA methodology. For problem identification, a survey and interview are conducted. The task analysis of the finding was then constructed and the cause of human error is identified using human HAZOP and to be considered in Fault Tree Analysis (FTA) while all the controls will be developed. The Event Tree Analysis then developed based on consequences of the human error and all the control will be developed as well. The analysis then combined and Bow-Tie analysis is developed.
Iceberg and ship detection and classification in single, dual and quad polarized synthetic aperture radar

Iceberg and ship identification in satellite synthetic aperture radar (SAR) data is an essential part of offering an operational iceberg surveillance program. Identification here refers to detection of ocean SAR targets and then classification of these targets as iceberg, ship, or unknown. Maximizing the detection and minimizing incorrect classification of iceberg and ship targets are required. Because coarser resolution satellite SAR data is at times not as intuitive as satellite optical data for manual human interpreted target classification, this process can be labor intensive, subjective, and error prone. Therefore, it is desired that an automated method for iceberg or ship identification be implemented. The methodology investigated here follows a well known standard in supervised pattern recognition, the maximum likelihood-quadratic discriminant function. The goal here in this thesis is to build class models from known iceberg and ship targets. Each class model is based on features that describe targets such as brightness, texture, and shape. Based on these descriptors as training input into the discriminant functions, future unknown targets can be compared with the class model for best fit. The best fit (or minimum distance) is used to assign class status for these unknown targets. One major consideration when using this type of pattern recognition approach is feature selection. Feature selection is based on the notion that some subset (subspace) of the descriptive metrics will lead to improved classification accuracy when comparing discriminant functions. Sequential forward selection and variants of exhaustive search algorithms are implemented and compared. RADARSAT-1, ENVSIAT AP (HH/HV), and EMISAR SAR iceberg and ship targets are used for algorithm training, feature selection, and performance estimation.
Influence of the addition of different origin sources of protein on meat products sensory acceptance

The effect of the addition of different protein sources on the sensory properties of meat products was evaluated. Pork liver pâte, turkey patties, cooked turkey breast, fresh pork sausages, and marinated turkey breast were elaborated with soybean (control), and other protein sources, such as aspea, lentil, bean, Chlorella, Spirulina, and dairy protein. Physicochemical parameters (pH, color, and hardness), taste profile, acceptance, and preference were assessed. The incorporation of different protein sources in meat products modified the sensorial characteristics, which differentially influenced the acceptance of panelists, generating panelist groups with different preference profiles. Meat products elaborated with Chlorella and Spirulina protein presented a good acceptance in pork liver pâte. Our data showed greater acceptability for the meat products supplemented with bean and lentil protein. Therefore, bean and lentil protein could be considered as suitable alternatives for the replacement of soy protein in meat products. PRACTICAL APPLICATIONS: A new food product can be designed by improving or modifying meat products and they will have the characteristics that suit customer’s needs. Enrichment of meat products with protein leads to further enhancement in nutritional profile and market opportunity but their sensory profile could modify their sensorial attributes. Therefore, the impact of these changes should be studied in order to predict the consumer acceptance of the new developed products.
Uterine immune function in the dairy cow: immunomodulation/immunosuppression

Reproductive performance in dairy cows and farm economic efficiency are related to uterine health status at the end of the voluntary waiting period (VWP). Uterine diseases affect about half of all dairy cows in the postpartum period, causing infertility by disrupting uterine and ovarian functions. The reproductive cycle of dairy cows has several critical checkpoints where interactions with the host immune response are necessary, and where deregulation can lead to reproductive failure. As immunity of the reproductive tract appears more and more as a major player in uterine health during the periparturient period in dairy cows, a better understanding of the complex immunological processes at play in the periparturient cow is essential to design animal and herd health management strategies to prevent postpartum uterine diseases in dairy cows and to reduce the carryover negative effect on reproductive performance. The objective of the present review is to focus on the immunological processes involved in pregnancy and the periparturient period in the dairy cow that could be associated with uterine diseases, as they relate to reproductive failure after the VWP.
Journal of Human Performance in Extreme Environments

Objective: Endothelial function, the ability of cells of the vascular endothelial wall to secrete compounds, is linked with metabolic and cardiovascular disease risks. One of the most well-known noninvasive tests used to assess skin vascular reactivity as a measure of endothelial function is the reactive hyperemic response test (RHRT). However, there is lack of consensus regarding the impact of thermoregulation on endothelial (dys)function and the results from the RHRT. Thus, the aim of the present study was to investigate the impact of core temperature on cutaneous vascular reactivity, as assessed via the finger RHRT. Approach: Following a 15-minute baseline period, seven adults entered a water tank maintained at 42 ˚ C and passively rested in a semi-supine position. Thereafter, they entered a water tank maintained at 12 ˚ C. They were immersed until their rectal temperature ( T re ) increased or decreased about 0.5 ˚ C above and below the baseline T re respectively. This procedure was repeated twice and an occlusion was conducted during the baseline period and at the second repetition of water immersions. Main results: During the post-occlusion phase, skin blood flow (SkBF) was greater, comparing to pre-and occlusion phases, across all T re levels (five levels: baseline, mild hyperthermia 1, mild hypothermia 1, mild hyperthermia 2, mild hypothermia 2). Also, SkBF throughout pre-occlusion, occlusion, and post-occlusion was greater during mild hyperthermia 2. Significance: We found a significant impact of core temperature on SkBF and cutaneous vascular reactivity which affects the diagnostic indicators obtained from the RHRT and can impact the final outcome.
The Gaia reference frame for bright sources examined using VLBI observations of radio stars (Corrigendum)

This is a corrigendum to Lindegren (2020). Owing to a coding error in the implementation of the analysis method described in the paper, the results obtained from its application to Gaia DR2 data were significantly wrong. Although the main conclusions of the original paper remain unchanged, the results presented in Tables 2 and 3, Figs. 3–5, and Sects. 3.3, 3.4, 4, and 5 are substantially affected. The revised tables, figures, and portions of the text are given below.
Epigenetics and fetal programming: what do we know?

A multitude of factors, including undernutrition, overnutrition, uterine capacity, as well as heat and other environmental stressors, can strongly influence the uterine environment during gestation. A rapidly growing body of evidence is uncovering a correlation between a suboptimal uterine environment during gestation and the incidence of metabolic diseases in the adult offspring . A variety of animal models, including sheep, cattle, rats, mice, and non-human primates, have been developed for investigation of this link between in utero environment and development of disease in response to a variety of environmental stressors. Maternal malnutrition and uterine capacity are perhaps the two more common factors influencing in utero development in horses. However, there is still a lack of experimental evidence regarding the developmental origins of disease in the horse. Given the observed incidence of equine metabolic disorders in veterinary practices, it is probable that similar mechanisms of fetal programming exist in the horse. The aim of this review is to provide a broad understanding of how fetal and neonatal environmental factors affect fetal programming, which may be applicable across of variety of species, including the horse.