Elucidating the Effects of Cerium Oxide Nanoparticles and Zinc Oxide Nanoparticles on Arsenic Uptake by Rice (Oryza sativa) in a Hydroponic System

Arsenic (As) is a toxic element widely encountered in the environment and a food safety concern. The use of engineered nanoparticles (ENPs) has grown rapidly due to the unique properties that make them beneficial in a wide range of technologies. Studies abound concerning the phytotoxicity of ENPs and their accumulation in plant tissues. However, investigations on ENPs interactions with co-existing contaminants in a plant system, especially with redox sensitive heavy metals, are rare. Two ENPs of interest are cerium oxide nanoparticles (CeO₂ NPs) and zinc oxide nanoparticles (ZnO NPs). The goals of this study were to: (1) determine the impact of CeO₂ NPs and ZnO NPs on the As accumulation in rice, and (2) evaluate whether inorganic As species including both As(III) and As (V) may modify the plant uptake and accumulation of the metal elements of co-present CeO₂ NPs and ZnO NPs. This was done by administering either 1 mg/L of As(III) or As(V), or 100 mg/L of CeO₂ NPs or ZnO NPs or Zn²⁺, or different combinations of As and ENPs or ions at the same concentrations to rice plants. Rice (Oryza sativa) was utilized in this study as a model plant duo to its high propensity for As uptake, and its widespread consumption as a staple food around the world. A hydroponic system was used to avoid the compounding effects of soil and the microorganisms in soil. The results indicated that CeO₂NPs did not show significant effect on total As plant accumulation. The presence of ZnO NPs and Zn²⁺ significantly reduced total As in rice seedlings, except for the concentration of total As in rice shoots with the co-presence of ZnO NPs and As(III). The co-presence of As significantly increased Ce in rice shoots in the CeO₂ NPs + As(III) treatment but did not affect the plant uptake of Zn from ZnO NPs or Zn²⁺. The results confirmed the active interactions between ENPs and co-existing inorganic As species and the extent to which their interactions depend on the properties of ENPs as well as the initial oxidation state of As

ON BAYESIAN METHODS AND FUNCTIONAL REGISTRATION OF FMRI

The application of functional magnetic resonance imaging (fMRI) has greatly improved our comprehension of the human brain and behaviour. However, after anatomical alignment, there remains large inter-individual variability in brain anatomy and functional localization, which is one of the obstacles to conducting group studies and performing group-level inference. This major paper addresses this problem by applying a new method (Bayesian Functional Registration) to decrease misalignment in functional brain systems between people by spatially transforming each subject’s functional data into a common reference map. The proposed approach allows us to assess differences in brain function across subjects. It also creates a framework that integrates feature- and intensity-based data and enables inference of the transformation parameters using posterior samples. Next, we evaluate the method using the data from a study of the correspondence of categorical and feature-based representations of music in the human brain. Finally, the proposed approach shows an increased sensitivity for group-level inference compared with the standard method, which uses the registration estimation toolbox in Matlab

Laser Densification of Extruded Dental Porcelain Bodies in Multi-Material Laser Densification (MMLD) Process

In this study commercial dental porcelain powder was deposited via slurry extrusion and laser densified to fabricate dental restorations in a Multi-Material Laser Densification (MMLD) process. The processing conditions for laser densification of single lines and closed rings were investigated in order to avoid warping and cracking. Multi-layer rings were also investigated to study the dependence of bonding between layers on the laser densification conditions. The laser densified rings showed no warping, and good bonding between layers could be achieved when the laser densification condition was selected properly. The mechanism to achieve porcelain rings without warping and cracking is discussed. The understanding developed will pave the way for fabricating a physical dental restoration unit.Mechanical Engineerin

Learning under Label Proportions for Text Classification

We present one of the preliminary NLP works under the challenging setup of Learning from Label Proportions (LLP), where the data is provided in an aggregate form called bags and only the proportion of samples in each class as the ground truth. This setup is inline with the desired characteristics of training models under Privacy settings and Weakly supervision. By characterizing some irregularities of the most widely used baseline technique DLLP, we propose a novel formulation that is also robust. This is accompanied with a learnability result that provides a generalization bound under LLP. Combining this formulation with a self-supervised objective, our method achieves better results as compared to the baselines in almost 87% of the experimental configurations which include large scale models for both long and short range texts across multiple metrics.Comment: accepted as long paper in Findings of EMNLP 202

Defending Against Local Adversarial Attacks through Empirical Gradient Optimization

Deep neural networks (DNNs) are susceptible to adversarial attacks, including the recently introduced locally visible adversarial patch attack, which achieves a success rate exceeding 96%. These attacks pose significant challenges to DNN security. Various defense methods, such as adversarial training, robust attention modules, watermarking, and gradient smoothing, have been proposed to enhance empirical robustness against patch attacks. However, these methods often have limitations concerning patch location requirements, randomness, and their impact on recognition accuracy for clean images.To address these challenges, we propose a novel defense algorithm called Local Adversarial Attack Empirical Defense using Gradient Optimization (LAAGO). The algorithm incorporates a low-pass filter before noise suppression to effectively mitigate the interference of high-frequency noise on the classifier while preserving the low-frequency areas of the images. Additionally, it emphasizes the original target features by enhancing the image gradients. Extensive experimental results demonstrate that the proposed method improves defense performance by 3.69% for 80 × 80 noise patches (representing approximately 4% of the images), while experiencing only a negligible 0.3% accuracy drop on clean images. The LAAGO algorithm provides a robust defense mechanism against local adversarial attacks, overcoming the limitations of previous methods. Our approach leverages gradient optimization, noise suppression, and feature enhancement, resulting in significant improvements in defense performance while maintaining high accuracy for clean images. This work contributes to the advancement of defense strategies against emerging adversarial attacks, thereby enhancing the security and reliability of deep neural networks

Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN

In the sea-land clutter classification of sky-wave over-the-horizon-radar (OTHR), the imbalanced and scarce data leads to a poor performance of the deep learning-based classification model. To solve this problem, this paper proposes an improved auxiliary classifier generative adversarial network~(AC-GAN) architecture, namely auxiliary classifier variational autoencoder generative adversarial network (AC-VAEGAN). AC-VAEGAN can synthesize higher quality sea-land clutter samples than AC-GAN and serve as an effective tool for data augmentation. Specifically, a 1-dimensional convolutional AC-VAEGAN architecture is designed to synthesize sea-land clutter samples. Additionally, an evaluation method combining both traditional evaluation of GAN domain and statistical evaluation of signal domain is proposed to evaluate the quality of synthetic samples. Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified. Further, the effect of AC-VAEGAN as a data augmentation method on the classification performance of imbalanced and scarce sea-land clutter samples is validated. The experiment results show that the quality of samples synthesized by AC-VAEGAN is better than that of AC-GAN, and the data augmentation method with AC-VAEGAN is able to improve the classification performance in the case of imbalanced and scarce sea-land clutter samples.Comment: 13 pages, 16 figure

Flavonoid Apigenin Inhibits Lipopolysaccharide-Induced Inflammatory Response through Multiple Mechanisms in Macrophages

Background Apigenin is a non-toxic natural flavonoid that is abundantly present in common fruits and vegetables. It has been reported that apigenin has various beneficial health effects such as anti-inflammation and chemoprevention. Multiple studies have shown that inflammation is an important risk factor for atherosclerosis, diabetes, sepsis, various liver diseases, and other metabolic diseases. Although it has been long realized that apigenin has anti-inflammatory activities, the underlying functional mechanisms are still not fully understood. Methodology and Principal Findings In the present study, we examined the effect of apigenin on LPS-induced inflammatory response and further elucidated the potential underlying mechanisms in human THP-1-induced macrophages and mouse J774A.1 macrophages. By using the PrimePCR array, we were able to identify the major target genes regulated by apigenin in LPS-mediated immune response. The results indicated that apigenin significantly inhibited LPS-induced production of pro-inflammatory cytokines, such as IL-6, IL-1β, and TNF-α through modulating multiple intracellular signaling pathways in macrophages. Apigenin inhibited LPS-induced IL-1β production by inhibiting caspase-1 activation through the disruption of the NLRP3 inflammasome assembly. Apigenin also prevented LPS-induced IL-6 and IL-1β production by reducing the mRNA stability via inhibiting ERK1/2 activation. In addition, apigenin significantly inhibited TNF-α and IL-1β-induced activation of NF-κB. Conclusion and Significance Apigenin Inhibits LPS-induced Inflammatory Response through multiple mechanisms in macrophages. These results provided important scientific evidences for the potential application of apigenin as a therapeutic agent for inflammatory diseases

Investments In Information Technology, Organizational Slack, And Economic Productivity

From a resource-based view (RBV), information technology (IT) investments affect organizational slack resources and therefore influence firm economic productivity. In this study, we develop a framework and test the relationship between economic productivity and organizational slack through an examination of 9 years financial data of 106 U.S. listed companies. Each variable has been tested for three stages of IT investments. Our results suggest that organizational slack resources increase after IT investments which later are consumed and converted into economic productivity