Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic architecture and genomic predictive ability of apple quantitative traits across environments

Abstract Implementation of genomic tools is desirable to increase the efficiency of apple breeding. Recently, the multi-environment apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic predictive ability, and studying genotype by environment interactions (G × E). So far, only two phenological traits were investigated using the apple REFPOP, although the population may be valuable when dissecting genetic architecture and reporting predictive abilities for additional key traits in apple breeding. Here we show contrasting genetic architecture and genomic predictive abilities for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic predictive abilities of 0.18–0.88 were estimated using main-effect univariate, main-effect multivariate, multi-environment univariate, and multi-environment multivariate models. The G × E accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.
Evolution of monovarietal virgin olive oils as a function of chemical composition and oxidation status

Abstract Virgin Olive Oil (VOO) shelf life is determined by the varietal-specific chemical composition and principally by the of phenolic composition. The aim of this study was to investigate the changes in fatty acid profile, phenolic composition, and quality parameters of nine Italian monovarietal VOOs obtained under the same pedoclimatic, agronomic and technological conditions and stored for 12 months at 15 °C in the dark. The varieties with medium-high concentrations of secoiridoids and balanced values between the individual molecules were those with the highest stability. Orthogonal Projections to Latent Structures (OPLS) regression revealed that oleuropein derivatives and phenolic alcohols had the highest antioxidant activity. OPLS discriminant analysis separated well fresh and stored oils. PV, K270, tyrosol, hydroxytyrosol, and oxidated oleacein were the most effective indicators of VOO ageing. Oleacein and oleocanthal decreased after storage, phenolic alcohols, oleacein and ligstroside aglycon increased. Graphical Abstract
Unconscious associations between stressor type and ability to cope: An experimental approach using ancient and modern sources of stress

Objectives Work has emerged that suggests it is salient and feasible to include a chronological approach to the taxonomy of stress. The ability to make an explicit distinction between ancient stressors (AS) and modern stressors (MS) has been reported in young and older adults; AS have been associated with greater ability to cope and MS with poorer health outcomes. Whether these explicit distinctions exist at an implicit, unconscious level, has yet to be determined. Design A quantitative design employed a computer‐based Implicit Association Test (IAT) to examine implicit associations between AS/MS and coping appraisal. Methods One hundred adults (75 females) aged 18–58 years (M = 28.27 years, SD = 10.02) completed the AS/MS IAT, to compare reaction time (RT) and accuracy between consistent pairs (AS/ability to cope; MS/inability to cope) and inconsistent pair responses (AS/inability to cope; MS/ability to cope); followed by an explicit self‐report questionnaire. Results Repeated measures ANCOVAs, controlling for sex and age, revealed significant main effects of faster RT and higher accuracy in responses for consistent than inconsistent pairs. Adult participants made implicit associations indicating an unconscious AS and MS distinction. Using the D algorithm, a univariate ANCOVA and independent t‐tests found that males, compared to females, showed a stronger implicit preference for consistent than inconsistent pairs. Conclusions Findings suggest an implicit association between ancient and modern stressors and perceived coping ability. Utilizing a chronological taxonomy for understanding evolutionary origins that drive individual’s responses to stress has implications for developing effective coping strategies to improve health outcomes.
Mesoporous silica nanoparticles incorporated with zinc oxide as a novel antifungal agent against toxigenic fungi strains

Abstract Developing environmentally friendly alternative strategies to reduce the damage caused by fungi in agriculture has been widely investigated. In this study, we evaluated using mesoporous silica nanoparticles (MSNs) incorporated with zinc oxide (MSNs–ZnO) as a potential antifungal agent against Fusarium graminearum and Aspergillus flavus strains, as well as their antimycotoxin properties. The MSNs that synthesized and characterized could release abundant ZnO in the first 24 h. Subsequently, the ZnO release became slower, providing greater durability of the antifungal effect. Significant (P < 0.001) growth reductions in F. graminearum (81%) and A. flavus (65%) compared to the control were obtained at a high concentration of the MSNs–ZnO (1.0 mg mL−1). Moreover, the MSNs–ZnO treatment at a high concentration (1.0 mg mL−1) caused morphology alteration in both fungi, showing ruptures and deformations in the fungal hyphae, affecting their growth and toxin production. A significant reduction (P < 0.001) in the productions of deoxynivalenol (89%) and aflatoxin B1 (58%) by F. graminearum and A. flavus were also observed. These findings imply that using MSNs as the carriers of zinc compounds, such as ZnO, could be investigated as a safe alternative for effectively controlling toxigenic fungi in agriculture.
Anti‐KIT monoclonal antibody CDX‐0159 induces profound and durable mast cell suppression in a healthy volunteer study

Mast cells (MC) are powerful inflammatory immune sentinel cells that drive numerous allergic, inflammatory, and pruritic disorders when activated. MC‐targeted therapies are approved in several disorders, yet many patients have limited benefit suggesting the need for approaches that more broadly inhibit MC activity. MCs require the KIT receptor and its ligand stem cell factor (SCF) for differentiation, maturation, and survival. Here we describe CDX‐0159, an anti‐KIT monoclonal antibody that potently suppresses MCs in human healthy volunteers.
Dissociating Statistically Determined Normal Cognitive Abilities and Mild Cognitive Impairment Subtypes with DCTclock

Abstract Objective: To determine whether the DCTclock can detect differences across groups of patients seen in the memory clinic for suspected dementia. Method: Patients (n = 123) were classified into the following groups: cognitively normal (CN), subtle cognitive impairment (SbCI), amnestic cognitive impairment (aMCI), and mixed/dysexecutive cognitive impairment (mx/dysMCI). Nine outcome variables included a combined command/copy total score and four command and four copy indices measuring drawing efficiency, simple/complex motor operations, information processing speed, and spatial reasoning. Results: Total combined command/copy score distinguished between groups in all comparisons with medium to large effects. The mx/dysMCI group had the lowest total combined command/copy scores out of all groups. The mx/dysMCI group scored lower than the CN group on all command indices (p < .050, all analyses); and lower than the SbCI group on drawing efficiency (p = .011). The aMCI group scored lower than the CN group on spatial reasoning (p = .019). Smaller effect sizes were obtained for the four copy indices. Conclusions: These results suggest that DCTclock command/copy parameters can dissociate CN, SbCI, and MCI subtypes. The larger effect sizes for command clock indices suggest these metrics are sensitive in detecting early cognitive decline. Additional research with a larger sample is warranted.
Fertility preservation in women with haematological malignancies

Abstract The incidence of haematological malignancies is increasing in women of childbearing age. Survival rates accompany this increase, making it essential to assess the impact of treatments on their future quality of life, evaluate the impact of each treatment on ovarian reserve and define the fertility preservation techniques used by women with haematologic malignancies. A retrospective study was conducted after data collection from 61 women diagnosed with haematological malignancies and followed-up in a fertility preservation centre between January 2008 and June 2019. Cancer treatments caused a decrease in ovarian reserve, demonstrated by an increase in FSH levels and a decrease in AMH levels. When assessing which treatments have the greatest impact on AMH levels, we found that the BEACOPP regimen, and the agents vincristine, etoposide, procarbazine, prednisone and the haematopoietic stem cell transplantation were mainly responsible. Regarding pregnancy after oncological treatments, of the eleven women who became pregnant, ten did so spontaneously. This study reinforces the importance of referring patients to a fertility preservation consultation before starting oncological treatment, as most of them opt to preserve fertility. This work also helps to clarify the impact of each chemotherapeutic agent on the ovarian reserve.
Functional data analysis for longitudinal data with informative observation times

In functional data analysis for longitudinal data, the observation process is typically assumed to be noninformative, which is often violated in real applications. Thus, methods that fail to account for the dependence between observation times and longitudinal outcomes may result in biased estimation. For longitudinal data with informative observation times, we find that under a general class of shared random effect models, a commonly used functional data method may lead to inconsistent model estimation while another functional data method results in consistent and even rate‐optimal estimation. Indeed, we show that the mean function can be estimated appropriately via penalized splines and that the covariance function can be estimated appropriately via penalized tensor‐product splines, both with specific choices of parameters. For the proposed method, theoretical results are provided, and simulation studies and a real data analysis are conducted to demonstrate its performance.
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy

Differentially private GNNs (Graph Neural Networks) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In particular, a recent study proposes an algorithm to protect each user's feature vector in an attributed graph with LDP (Local Differential Privacy), a strong privacy notion without a trusted third party. However, this algorithm does not protect edges (friendships) in a social graph, hence cannot protect user privacy in unattributed graphs. How to provide strong privacy with high accuracy in unattributed graphs remains open. In this paper, we propose a novel LDP algorithm called the DPRR (Degree-Preserving Randomized Response) to provide LDP for edges in GNNs. Our DPRR preserves each user's degree hence a graph structure while providing edge LDP. Technically, our DPRR uses Warner's RR (Randomized Response) and strategic edge sampling, where each user's sampling probability is automatically tuned using the Laplacian mechanism to preserve the degree information under edge LDP. We also propose a privacy budget allocation method to make the noise in both Warner's RR and the Laplacian mechanism small. We focus on graph classification as a task of GNNs and evaluate the DPRR using three social graph datasets. Our experimental results show that the DPRR significantly outperforms three baselines and provides accuracy close to a non-private algorithm in all datasets with a reasonable privacy budget, e.g., epsilon=1.
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks

We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is capable of accomplishing both at the same time. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We use a custom loss function for learning a PI range around optimized mean estimation with a desired coverage of a proportion of the target labels within the PI range. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We also examine the quality of our predictive uncertainty by evaluating on Active Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.
Detection of Stealthy Adversaries for Networked Unmanned Aerial Vehicles

A network of unmanned aerial vehicles (UAVs) provides distributed coverage, reconfigurability, and maneuverability in performing complex cooperative tasks. However, it relies on wireless communications that can be susceptible to cyber adversaries and intrusions, disrupting the entire network’s operation. This paper develops model-based centralized and decentralized observer techniques for detecting a class of stealthy intrusions, namely zero-dynamics and covert attacks, on networked UAVs in formation control settings. The centralized observer that runs in a control center leverages switching in the UAVs’ communication topology for attack detection, and the decentralized observers, implemented onboard each UAV in the network, use the model of networked UAVs and locally available measurements. Experimental results are provided to show the effectiveness of the proposed detection schemes in different case studies.
Exploring the Effects of AI-assisted Emotional Support Processes in Online Mental Health Community

Social support in online mental health communities (OMHCs) is an effective and accessible way of managing mental wellbeing. In this process, sharing emotional supports is considered crucial to the thriving social supports in OMHCs, yet often difficult for both seekers and providers. To support empathetic interactions, we design an AI-infused workflow that allows users to write emotional supporting messages to other users’ posts based on the elicitation of the seeker’s emotion and contextual keywords from writing. Based on a preliminary user study (N = 10), we identified that the system helped seekers to clarify emotion and describe text concretely while writing a post. Providers could also learn how to react empathetically to the post. Based on these results, we suggest design implications for our proposed system.
DFT-Spread Orthogonal Time Frequency Space System With Superimposed Pilots for Terahertz Integrated Sensing and Communication

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects, which are especially severe in the THz band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system with superimposed pilots is proposed to improve the robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase sensing parameter estimation algorithm is developed to integrate sensing functionality into the DFT-s-OTFS waveform. Meanwhile, a low-complexity iterative channel estimation and data detection method with a conjugate gradient based equalizer is proposed to recover the data symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA efficiency by 10% on average compared to OTFS. Simulation results demonstrate that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS systems is able to realize millimeter-level range estimation accuracy and decimeter-per-second-level velocity estimation accuracy. Moreover, the effectiveness of the iterative method for data detection aided by superimposed pilots in DFT-s-OTFS systems is validated by the simulations and the bit error rate performance is not degraded by the Doppler effects.
convex characteristics of quaternionic positive definite functions on abelian groups

This paper is concerned with the topological space of normalized quaternion-valued positive definite functions on an arbitrary abelian group G, especially its convex characteristics. There are two main results. Firstly, we prove that the extreme elements in the family of such functions are exactly the homomorphisms from G to the sphere group S, i.e., the unit 3-sphere in the quaternion algebra. Secondly, we reveal a phenomenon which does not exist in the complex setting: The compact convex set of such functions is not a Bauer simplex except when G is of exponent ≤ 2. In contrast, its complex counterpart is always a Bauer simplex, as is well known. We also present an integral representation for such functions as an application and some other minor interesting results.
A higher Hodge extension of the Feigin-Tsygan Theorem

We show that the extended noncommutative de Rham complex of a cofibrant resolution, when completed at a certain Hodge filtration, is (reduced) quasi-isomorphic to the periodic cyclic complex, while each of its filtration piece is quasi-isomorphic to the negative cyclic complex. This extends a classical result of Feigin and Tsygan, which corresponds to the Hodge degree 0 part of our quasiisomorphism. This result is applied to the study of Calabi-Yau categories in [14, 15].
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer—the “head”). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR → STL, CIFAR10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2% higher accuracy ID but 7% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head—this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, 10% better OOD than full fine-tuning).
Diffusion Causal Models for Counterfactual Estimation

We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for high-dimensional data with neural networks remains an open challenge. Herein we propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models. In our setting, inference is performed by iteratively sampling gradients of the marginal and conditional distributions entailed by the causal model. Counterfactual estimation is achieved by firstly inferring latent variables with deterministic forward diffusion, then intervening on a reverse diffusion process using the gradients of an anti-causal predictor w.r. t the input. Furthermore, we propose a metric for evaluating the generated counterfactuals. We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data. Code is available https://github.com/vios-s/Diff-SCM.
Theoretical Analysis of Deep Neural Networks in Physical Layer Communication

Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the “black box” of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.
On Matrix Method of Symmetric Games

This paper provides a new version of matrix semi-tensor product method based on adjacent transpositions to test symmetric games. The advantage of using adjacent transpositions lies in the great simplification of analysis of symmetric games. By using the new method, new necessary and sufficient conditions for symmetric games are proposed, and a group of bases of a symmetric game space can be easily calculated. Moreover, the testing equations with the minimum number can be concretely determined. Finally, two examples are displayed to show the effectiveness of the proposed method.