Response Surface Optimization of a Rapid Ultrasound-Assisted Extraction Method for Simultaneous Determination of Tetracycline Antibiotics in Manure

A rapid and cleanup-free ultrasound-assisted extraction method is proposed for the simultaneous extraction of oxytetracycline, tetracycline, chlortetracycline, and doxycycline in manure. The analytes were determined using high-performance liquid chromatography with ultraviolet detector. The influence of several variables on the efficiency of the extraction procedure was investigated by single-factor experiments. The temperature, pH, and amount of extraction solution were selected for optimization experiment using response surface methodology. The calibration curves showed good linearity (R2>0.99) for all analytes in the range of 0.1–20 μg/mL. The four antibiotics were successfully extracted from manure with recoveries ranging from 81.89 to 92.42% and good reproducibility (RSD, <4.06%) under optimal conditions, which include 50 mL of McIlvaine buffer extraction solution (pH 7.15) mixed with 1 g of manure sample, extraction temperature of 40°C, extraction time of 10 min, and three extraction cycles. Method quantification limits of 1.75–2.32 mg/kg were obtained for the studied compounds. The proposed procedure demonstrated clear reductions in extraction time and elimination of cleanup steps. Finally, the applicability to tetracyclines antibiotics determination in real samples was evaluated through the successful determination of four target analytes in swine, cow manure, and mixture of animal manure with inorganic fertilizer

G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection

In this paper, we focus on a realistic yet challenging task, Single Domain Generalization Object Detection (S-DGOD), where only one source domain's data can be used for training object detectors, but have to generalize multiple distinct target domains. In S-DGOD, both high-capacity fitting and generalization abilities are needed due to the task's complexity. Differentiable Neural Architecture Search (NAS) is known for its high capacity for complex data fitting and we propose to leverage Differentiable NAS to solve S-DGOD. However, it may confront severe over-fitting issues due to the feature imbalance phenomenon, where parameters optimized by gradient descent are biased to learn from the easy-to-learn features, which are usually non-causal and spuriously correlated to ground truth labels, such as the features of background in object detection data. Consequently, this leads to serious performance degradation, especially in generalizing to unseen target domains with huge domain gaps between the source domain and target domains. To address this issue, we propose the Generalizable loss (G-loss), which is an OoD-aware objective, preventing NAS from over-fitting by using gradient descent to optimize parameters not only on a subset of easy-to-learn features but also the remaining predictive features for generalization, and the overall framework is named G-NAS. Experimental results on the S-DGOD urban-scene datasets demonstrate that the proposed G-NAS achieves SOTA performance compared to baseline methods. Codes are available at https://github.com/wufan-cse/G-NAS.Comment: Accepted by AAAI2

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully exploit the 3D point-wise geometric correspondences, nor effectively tackle the ambiguities in the photometric warping caused by occlusions or illumination inconsistency. To address these problems, this work proposes Density Volume Construction Network (DevNet), a novel self-supervised monocular depth learning framework, that can consider 3D spatial information, and exploit stronger geometric constraints among adjacent camera frustums. Instead of directly regressing the pixel value from a single image, our DevNet divides the camera frustum into multiple parallel planes and predicts the pointwise occlusion probability density on each plane. The final depth map is generated by integrating the density along corresponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-2015 outdoor dataset and NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth estimation. Code is available at https://github.com/gitkaichenzhou/DevNet.Comment: Accepted by European Conference on Computer Vision 2022 (ECCV2022

Contextualizing Multiple Tasks via Learning to Decompose

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts. Those ambiguities increase the difficulty of learning a generalizable model when there exists one concept or mixed concepts in a task. We propose a general approach Learning to Decompose Network (LeadNet) for both two cases, which contextualizes a model through meta-learning multiple maps for concepts discovery -- the representations of instances are decomposed and adapted conditioned on the contexts. Through taking a holistic view over multiple latent components over instances in a sampled pseudo task, LeadNet learns to automatically select the right concept via incorporating those rich semantics inside and between objects. LeadNet demonstrates its superiority in various applications, including exploring multiple views of confusing tasks, out-of-distribution recognition, and few-shot image classification

DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation

While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework to a wide range of applications is challenging, mainly due to possible correlation shift and diversity shift in the real world. Most of the previous approaches can only solve one specific distribution shift, such as shift across domains or the extrapolation of correlation. To address that, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization. DecAug disentangles the category-related and context-related features. Category-related features contain causal information of the target object, while context-related features describe the attributes, styles, backgrounds, or scenes, causing distribution shifts between training and test data. The decomposition is achieved by orthogonalizing the two gradients (w.r.t. intermediate features) of losses for predicting category and context labels. Furthermore, we perform gradient-based augmentation on context-related features to improve the robustness of the learned representations. Experimental results show that DecAug outperforms other state-of-the-art methods on various OoD datasets, which is among the very few methods that can deal with different types of OoD generalization challenges.Comment: Accepted by AAAI202

Optical properties of MoSe2_2 monolayer implanted with ultra-low energy Cr ions

The paper explores the optical properties of an exfoliated MoSe$_2$ monolayer implanted with Cr$^+$ ions, accelerated to 25 eV. Photoluminescence of the implanted MoSe$_2$ reveals an emission line from Cr-related defects that is present only under weak electron doping. Unlike band-to-band transition, the Cr-introduced emission is characterised by non-zero activation energy, long lifetimes, and weak response to the magnetic field. To rationalise the experimental results and get insights into the atomic structure of the defects, we modelled the Cr-ion irradiation process using ab-initio molecular dynamics simulations followed by the electronic structure calculations of the system with defects. The experimental and theoretical results suggest that the recombination of electrons on the acceptors, which could be introduced by the Cr implantation-induced defects, with the valence band holes is the most likely origin of the low energy emission. Our results demonstrate the potential of low-energy ion implantation as a tool to tailor the properties of 2D materials by doping

Two New Compounds from the Fungus <i>Xylaria nigripes</i>

In the process of discovering more neural-system-related bioactive compounds from Xylaria nigripes, xylariamino acid A (1), a new amino acid derivative, and a new isovaleric acid phenethyl ester (2) were isolated and identified. Their structures and absolute configurations were determined by analyses of IR, HRESIMS, NMR spectroscopic data, and gauge-independent atomic orbital (GIAO) NMR calculation, as well as electronic circular dichroism (ECD) calculation. The isolated compounds were evaluated for their neuroprotective effects against damage to PC12 cells by oxygen and glucose deprivation (OGD). Compounds 1 and 2 can increase the viability of OGD-induced PC12 cells at all tested concentrations. Moreover, compound 2 (1 μmol L−1) can significantly reduce the percentage of apoptotic cells