Acoustic Tomography for Decay Detection in Black Cherry Trees

This study investigated the potential of using acoustic tomography for detecting internal decay in high-value hardwood trees in the forest. Twelve black cherry (Prunus serotina) trees that had a wide range of physical characteristics were tested in a stand of second-growth hardwoods in Kane, PA, using a PiCUS® Sonic Tomograph tool. The trees were felled after the field test and a disc from each sampling height was cut and subjected to laboratory evaluations. It was found that acoustic tomography underestimates heartwood decay when it is the major structural defect in the trees. However, when an internal crack is present in the tree trunk, the acoustic tomography tends to overestimate the size of the defects. In the presence of ring shake in the cross-section, the acoustic shadows resemble the influence of both extensive heartwood decay and lateral cracks. These findings highlight the importance of determining the nature of structural defects when assessing hardwood trees using the acoustic tomography technique. Results from this study offer insights that may be used to improve the interpretation algorithm embedded in the tomography software

AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection

While DeepFake applications are becoming popular in recent years, their abuses pose a serious privacy threat. Unfortunately, most related detection algorithms to mitigate the abuse issues are inherently vulnerable to adversarial attacks because they are built atop DNN-based classification models, and the literature has demonstrated that they could be bypassed by introducing pixel-level perturbations. Though corresponding mitigation has been proposed, we have identified a new attribute-variation-based adversarial attack (AVA) that perturbs the latent space via a combination of Gaussian prior and semantic discriminator to bypass such mitigation. It perturbs the semantics in the attribute space of DeepFake images, which are inconspicuous to human beings (e.g., mouth open) but can result in substantial differences in DeepFake detection. We evaluate our proposed AVA attack on nine state-of-the-art DeepFake detection algorithms and applications. The empirical results demonstrate that AVA attack defeats the state-of-the-art black box attacks against DeepFake detectors and achieves more than a 95% success rate on two commercial DeepFake detectors. Moreover, our human study indicates that AVA-generated DeepFake images are often imperceptible to humans, which presents huge security and privacy concerns

Subgraph Networks Based Contrastive Learning

Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for downstream tasks. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. Graph augmentation produces augmented views by graph perturbations. These views preserve a locally similar structure and exploit explicit features. However, these methods have not considered the interaction existing in subgraphs. To explore the impact of substructure interactions on graph representations, we propose a novel framework called subgraph network-based contrastive learning (SGNCL). SGNCL applies a subgraph network generation strategy to produce augmented views. This strategy converts the original graph into an Edge-to-Node mapping network with both topological and attribute features. The single-shot augmented view is a first-order subgraph network that mines the interaction between nodes, node-edge, and edges. In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information. We compare SGNCL with classical and state-of-the-art graph contrastive learning methods on multiple benchmark datasets of different domains. Extensive experiments show that SGNCL achieves competitive or better performance (top three) on all datasets in unsupervised learning settings. Furthermore, SGNCL achieves the best average gain of 6.9\% in transfer learning compared to the best method. Finally, experiments also demonstrate that mining substructure interactions have positive implications for graph contrastive learning.Comment: 12 pages, 6 figure

Spatiotemporal distribution of malaria and the association between its epidemic and climate factors in Hainan, China

<p>Abstract</p> <p>Background</p> <p>Hainan is one of the provinces most severely affected by malaria epidemics in China. The distribution pattern and major determinant climate factors of malaria in this region have remained obscure, making it difficult to target countermeasures for malaria surveillance and control. This study detected the spatiotemporal distribution of malaria and explored the association between malaria epidemics and climate factors in Hainan.</p> <p>Methods</p> <p>The cumulative and annual malaria incidences of each county were calculated and mapped from 1995 to 2008 to show the spatial distribution of malaria in Hainan. The annual and monthly cumulative malaria incidences of the province between 1995 and 2008 were calculated and plotted to observe the annual and seasonal fluctuation. The Cochran-Armitage trend test was employed to explore the temporal trends in the annual malaria incidences. Cross correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on malaria transmission and the auto correlation of malaria incidence. A multivariate time series analysis was conducted to construct a model of climate factors to explore the association between malaria epidemics and climate factors.</p> <p>Results</p> <p>The highest malaria incidences were mainly distributed in the central-south counties of the province. A fluctuating but distinctly declining temporal trend of annual malaria incidences was identified (Cochran-Armitage trend test <it>Z </it>= -25.14, <it>P </it>< 0.05). The peak incidence period was May to October when nearly 70% of annual malaria cases were reported. The mean temperature of the previous month, of the previous two months and the number of cases during the previous month were included in the model. The model effectively explained the association between malaria epidemics and climate factors (<it>F </it>= 85.06, <it>P </it>< 0.05, adjusted <it>R </it>²= 0.81). The autocorrelations of the fitting residuals were not significant (<it>P </it>> 0.05), indicating that the model extracted information sufficiently. There was no significant difference between the monthly predicted value and the actual value (<it>t </it>= -1.91, <it>P </it>= 0.08). The <it>R </it>²for predicting was 0.70, and the autocorrelations of the predictive residuals were not significant (<it>P </it>> 0.05), indicating that the model had a good predictive ability.</p> <p>Discussion</p> <p>Public health resource allocations should focus on the areas and months with the highest malaria risk in Hainan. Malaria epidemics can be accurately predicted by monitoring the fluctuations of the mean temperature of the previous month and of the previous two months in the area. Therefore, targeted countermeasures can be taken ahead of time, which will make malaria surveillance and control in Hainan more effective and simpler. This model was constructed using relatively long-term data and had a good fit and predictive validity, making the results more reliable than the previous report.</p> <p>Conclusions</p> <p>The spatiotemporal distribution of malaria in Hainan varied in different areas and during different years. The monthly trends in the malaria epidemics in Hainan could be predicted effectively by using the multivariate time series model. This model will make malaria surveillance simpler and the control of malaria more targeted in Hainan.</p

Deformation and failure mechanisms of electrochemically lithiated silicon thin films

A fundamental understanding of mechanical behavior of a Li–Si system is necessary to address the poor mechanical integrity of amorphous silicon (a-Si) electrodes, in order to utilize their enormous capacity in Li-ion batteries. In this work, deformation and failure mechanisms of electrochemically lithiated a-Si thin films were investigated using nanoindentation and molecular dynamics simulation techniques. The cracking observed in the a-Si thin films after the initial lithiation–delithiation cycle is associated with the tension stress developed when constrained by the substrates. The MD simulations provide an atomistic insight on the origin of plasticity and transition of fracture mechanisms with increasing lithium concentration in the electrode. Both experiment and the MD simulations indicate reduced strength, elastic modulus but increased ductility in the a-Si films after the full lithiation–delithiation cycle, as a result of increased disorder in the microstructures. Also, the mapping of void nucleation and growth indicates different failure modes in pristine and delithiated a-Si

Dual modification of TiO2 nanorods for selective photoelectrochemical detection of organic compounds

Selective detection of organic compounds in water body is both desirable and challenging for photoelectrocatalytic (PEC) sensors. In this work, tunable oxidation capability is designed and achieved by modifying titanium dioxide nanorod arrays (TiO) photoelectrodes with nano-sized plasmonic gold (Au) particle deposition and subsequent hydrogenation treatment (i.e. Au@H-TiO). The effective incorporation of Au nanoparticles onto the TiO nanorods induces a plasmonic effect and extends light absorption from ultraviolet (UV) to the visible light range while the hydrogenation process dramatically improves PEC oxidation activity. Under visible light, the Au@H-TiO electrode exhibits selective detection capability to labile organic compounds. This excellent selectivity is demonstrated by a wide linear relationship between photocurrent and the concentration of different types of sugars, including glucose, fructose, sucrose and lactose in the presence of various concentrations of the aromatic compound potassium hydrogen phthalate (KHP). Furthermore, the modified electrode can also undiscriminately detect all kinds of organic compounds in a rapid manner under UV irradiation due to the strong oxidation capability. Such a unique feature of the tunable oxidation capability bestows the Au@H-TiO photoelectrodes a new generation of the PEC sensors for selective and collective degradation of organic compounds

Discovery of a Unique Structural Motif in Lanthipeptide Synthetases for Substrate Binding and Interdomain Interactions

Class III lanthipeptide synthetases catalyze the formation of lanthionine/methyllanthionine and labionin crosslinks. We present here the 2.40 Å resolution structure of the kinase domain of a class III lanthipeptide synthetase CurKC from the biosynthesis of curvopeptin. A unique structural subunit for leader binding, named leader recognition domain (LRD), was identified. The LRD of CurKC is responsible for the recognition of the leader peptide and for mediating interactions between the lyase and kinase domains. LRDs are highly conserved among the kinase domains of class III and class IV lanthipeptide synthetases. The discovery of LRDs provides insight into the substrate recognition and domain organization in multidomain lanthipeptide synthetases