Identification of Campylobacter jejuni and Campylobacter coli genes contributing to oxidative stress response using TraDIS analysis.

BACKGROUND: Campylobacter jejuni and Campylobacter coli are the major causative agents of bacterial gastroenteritis worldwide and are known obligate microaerophiles. Despite being sensitive to oxygen and its reduction products, both species are readily isolated from animal food products kept under atmospheric conditions where they face high oxygen tension levels. RESULTS: In this study, Transposon Directed Insertion-site Sequencing (TraDIS) was used to investigate the ability of one C. jejuni strain and two C. coli strains to overcome oxidative stress, using H2O2 to mimic oxidative stress. Genes were identified that were required for oxidative stress resistance for each individual strain but also allowed a comparison across the three strains. Mutations in the perR and ahpC genes were found to increase Campylobacter tolerance to H2O2. The roles of these proteins in oxidative stress were previously known in C. jejuni, but this data indicates that they most likely play a similar role in C. coli. Mutation of czcD decreased Campylobacter tolerance to H2O2. The role of CzcD, which functions as a zinc exporter, has not previously been linked to oxidative stress. The TraDIS data was confirmed using defined deletions of perR and czcD in C. coli 15-537360. CONCLUSIONS: This is the first study to investigate gene fitness in both C. jejuni and C. coli under oxidative stress conditions and highlights both similar roles for certain genes for both species and highlights other genes that have a role under oxidative stress.

Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection.

To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms.

Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning.

Glaucoma diagnosis often suffers from two types of data imbalances: 1) class imbalance, i.e., the non-glaucoma majority cases occupy most of the data; 2) rare cases, i.e., few cases present the uncommon retinopathy e.g., bayoneting or physiologic cupping. This dual-imbalances make glaucoma diagnosis model easy to be dominated by the majority cases but cannot correctly classify the minority and/or rare ones. In this paper, we propose an adaptive re-balancing strategy in the feature space, Self-Ensemble Dual-Curriculum learning (SEDC), to improve the glaucoma diagnosis on imbalanced data by augmenting feature distribution with feature distilling and feature re-weighting. Firstly, the self-ensembling (SEL) is developed to reinforce the discriminative ability of feature representations for the minority class and rare cases by distilling the features learned from the abundant majority cases. Secondly, the dual-curriculum (DCL) is designed to adaptively re-weight the imbalanced data in the feature space to learn a balanced decision function for accurate glaucoma diagnosis. Benefiting from feature distilling and re-weighting, the proposed SEDC fairly represents fundus images, regardless of the majority or rare cases, by augmenting the feature distribution to obtains the optimal decision boundary for accurate glaucoma diagnosis on the imbalanced dataset. Experimental results on three challenging glaucoma datasets show that our SEDC successfully delivers accurate glaucoma diagnosis by the adaptive re-balancing strategy, with the average mean value of Accuracy 0.9712, Sensitivity 0.9520, Specificity 0.9816, AUC 0.9928, F2-score 0.9547. Ablation and comparison studies demonstrate that our method outperforms state-of-the-art methods and traditional re-balancing strategies. The experiment also shows that the adaptive re-balancing strategy proposed in our method provides a more effective training approach with optimal convergence performance. It endows our SEDC a great advantage to handle the disease diagnosis on imbalanced data distribution.

Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-Supervised Learning.

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. In this paper, a direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in which CDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled. The method directly regresses CDR value based on the feature representation of optic nerve head via deep learning technique while bypassing intermediate segmentation. The scheme is a two-stage cascaded approach comprised of two phases: unsupervised feature representation of fundus image with a convolutional neural networks (MFPPNet) and CDR value regression by random forest regressor. The proposed scheme is validated on the challenging glaucoma dataset Direct-CSU and public ORIGA, and the experimental results demonstrate that our method can achieve a lower average CDR error of 0.0563 and a higher correlation of around 0.726 with measurement before manual segmentation of optic disc/cup by human experts. Our estimated CDR values are also tested for glaucoma screening, which achieves the areas under curve of 0.905 on dataset of 421 fundus images. The experiments show that the proposed method is capable of state-of-the-art CDR estimation and satisfactory glaucoma screening with calculated CDR value.

Impact assessment of high soil CO2 on plant growth and soil environment: a greenhouse study.

To ensure the safety of carbon capture and storage (CCS) technology, insight into the potential impacts of CO2 leakage on the ecosystem is necessary. We conducted a greenhouse experiment to investigate the effects of high soil CO2 on plant growth and the soil environment. Treatments comprised 99.99% CO2 injection (CG), 99.99% N2injection (NG), and no injection (BG). NG treatment was employed to differentiate the effects of O2 depletion from those of CO2 enrichment. Soil CO2 and O2 concentrations were maintained at an average of 53% and 11%, respectively, under CG treatment. We verified that high soil CO2 had negative effects on root water absorption, chlorophyll, starch content and total biomass. Soil microbial acid phosphatase activity was affected by CG treatment. These negative effects were attributed to high soil CO2 instead of low O2 or low pH. Our results indicate that high soil CO2 affected the root system, which in turn triggered further changes in aboveground plant tissues and rhizospheric soil water conditions. A conceptual diagram of CO2 toxicity to plants and soil is suggested to act as a useful guideline for impact assessment of CCS technology.

Experimental and numerical observation of dark and bright breathers in the band gap of a diatomic electrical lattice.

We observe dark and bright intrinsic localized modes (ILMs), also known as discrete breathers, experimentally and numerically in a diatomic-like electrical lattice. The experimental generation of dark ILMs by driving a dissipative lattice with spatially homogenous amplitude is, to our knowledge, unprecedented. In addition, the experimental manifestation of bright breathers within the band gap is also novel in this system. In experimental measurements the dark modes appear just below the bottom of the top branch in frequency. As the frequency is then lowered further into the band gap, the dark ILMs persist, until the nonlinear localization pattern reverses and bright ILMs appear on top of the finite background. Deep into the band gap, only a single bright structure survives in a lattice of 32 nodes. The vicinity of the bottom band also features bright and dark self-localized excitations. These results pave the way for a more systematic study of dark breathers and their bifurcations in diatomic-like chains.

Charge and Energy Transfer Between CdSe Quantum Dots and Polyaniline.

CdSe quantum dots (QDs) and polyaniline (PAni) were mixed to prepare CdSe QDs/PAni complex. PAni can quench the fluorescence of CdSe QDs. Fluorescence intensity of CdSe QDs/PAni complex is related to the size of CdSe QDs and the concentration of PAni. UV-Vis absorption spectra, fluorescence spectra, time-resolved fluorescence spectroscopy were used to analys the quenching phenomenon. The mechanism of fluorescence quenching is dependent on two factors: on one hand, the Förster resonance energy transfer from CdSe to PAni; on the other hand, PAni can intercept the charge relaxation process of CdSe and lead to the interruption of radiative recombination.