Efficient image copy detection using multi-scale fingerprints

Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods.Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods

Electrochemical Treatment of Synthetic Wastewaters Contaminated by Organic Pollutants at Ti4O7 Anode. Study of the Role of Operative Parameters by Experimental Results and Theoretical Modelling

In the last years, an increasing attention has been devoted to the utilization of anodic oxidation (AO) technologies for the treatment of wastewater polluted by recalcitrant organics. Recently, Ti4O7 was proposed as a promising anode for AO for the treatment of various organics. Here the potential utilization of commercial Ti4O7 anodes has been evaluated considering the electrochemical treatment of synthetic wastewater contaminated by three very different organic molecules (namely, oxalic acid, phenol and Acid Orange 7), all characterized by a very high resistance to AO. The performances of Ti4O7 were compared with that of two largely investigated anodes: Boron-doped diamond (BDD), which is probably the most effective electrode for AO, and an Ir-based anode which presents a relatively low cost. Moreover, the effect of various operative conditions (current density, mixing rate and initial concentration of the organic) was evaluated by both experimental studies and the adoption of a theoretical model previously developed for BDD anodes. It was shown that the performances of the process can be improved by a proper selection of operative conditions. Moreover, it was found that the proposed model can be effectively used to predict the effect of operative parameters at Ti4O7 anodes, thus helping the process optimization

Phenylpropanoid amides from Solanum rostratum and their phytotoxic activities against Arabidopsis thaliana

IntroductionSolanum rostratum, an annual malignant weed, has seriously damaged the ecological environment and biodiversity of invasion area. This alien plant gains a competitive advantage by producing some new phytotoxic substances to inhibit the growth of native plants, thus achieving successful invasion. However, the chemical structures, inhibitory functions and action mechanisms of phytotoxic substances of S. rostratum remain unclear.MethodsIn this study, to clarify the chemical structures of phytotoxic substances from S. rostratum, we isolated phenylpropanoid amides from the plant. Their structures were identified by comprehensive HR-ESIMS, NMR and ECD data. And the inhibitory functions of isolated phenylpropanoid amides on one model plant (Arabidopsis thaliana) were also investigated. In addition, the action mechanisms of active phenylpropanoid amides were revealed by antioxidant-related enzymes [Catalase (CAT), Peroxidase (POD), Superoxide dismutase (SOD)] activities and corresponding molecular docking analyses.Results and DiscussionPhytochemical research on the whole plant of S. rostratum led to the isolation and identification of four new phenylpropanoid amides (1−4), together with two known analogues (5−6). All the compounds showed phytotoxic effects with varying levels on the seed germination and root elongation of one model plant (Arabidopsis thaliana), especially compound 2 and 4. Likewise, compounds 2 and 4 displayed potent inhibitory effects on antioxidant-related enzyme (POD). In addition, compounds 2 and 4 formed common conventional hydrogen bonds with residues Ala34 and Ser35 in POD revealed by molecular docking analyses. These findings not only helped to reveal the invasion mechanism of S. rostratum from the perspective of “novel weapons hypothesis”, but also opened up new ways for the exploitation and utilization of S. rostratum

Comorbid depressive symptoms can aggravate the functional changes of the pain matrix in patients with chronic back pain: A resting-state fMRI study

ObjectiveThe purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function.MethodsThirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations.ResultCompared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = −2.175, p = 0.034), gamma (t = −2.332, p = 0.023), and local efficiency (t = −2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = −3.581, p = 0.0007), and the network efficiency values (t = −2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores.ConclusionOur study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP

Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting

Frequent outbreaks of agricultural pests can reduce crop production severely and restrict agricultural production. Therefore, automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. In recent years, pest recognition and detection have been rapidly improved with the development of deep learning-based methods. Although certain progress has been made in the research on pest detection and identification technology based on deep learning, there are still many problems in the production application in a field environment. This work presents a pest detector for multi-category dense and tiny pests named the Pest-YOLO. First, the idea of focal loss is introduced into the loss function using weight distribution to improve the attention of hard samples. In this way, the problems of hard samples arose from the uneven distribution of pest populations in a dataset and low discrimination features of small pests are relieved. Next, a non-Intersection over Union bounding box selection and suppression algorithm, the confluence strategy, is used. The confluence strategy can eliminate the errors and omissions of pest detection caused by occlusion, adhesion and unlabeling among tiny dense pest individuals to the greatest extent. The proposed Pest-YOLO model is verified on a large-scale pest image dataset, the Pest24, which includes more than 20k images with over 190k pests labeled by agricultural experts and categorized into 24 classes. Experimental results show that the Pest-YOLO can obtain 69.59% for mAP and 77.71% for mRecall on the 24-class pest dataset, which is 5.32% and 28.12% higher than the benchmark model YOLOv4. Meanwhile, our proposed model is superior to other several state-of-the-art methods, including the SSD, RetinaNet, Faster RCNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, YOLOX, DETR, TOOD, YOLOv3-W, and AF-RCNN detectors. The code of the proposed algorithm is available at: https://github.com/chr-secrect/Pest-YOLO

Nitric oxide-induced lipophagic defects contribute to testosterone deficiency in rats with spinal cord injury

IntroductionMales with acute spinal cord injury (SCI) frequently exhibit testosterone deficiency and reproductive dysfunction. While such incidence rates are high in chronic patients, the underlying mechanisms remain elusive.Methods and resultsHerein, we generated a rat SCI model, which recapitulated complications in human males, including low testosterone levels and spermatogenic disorders. Proteomics analyses showed that the differentially expressed proteins were mostly enriched in lipid metabolism and steroid metabolism and biosynthesis. In SCI rats, we observed that testicular nitric oxide (NO) levels were elevated and lipid droplet-autophagosome co-localization in testicular interstitial cells was decreased. We hypothesized that NO impaired lipophagy in Leydig cells (LCs) to disrupt testosterone biosynthesis and spermatogenesis. As postulated, exogenous NO donor (S-nitroso-N-acetylpenicillamine (SNAP)) treatment markedly raised NO levels and disturbed lipophagy via the AMPK/mTOR/ULK1 pathway, and ultimately impaired testosterone production in mouse LCs. However, such alterations were not fully observed when cells were treated with an endogenous NO donor (L-arginine), suggesting that mouse LCs were devoid of an endogenous NO-production system. Alternatively, activated (M1) macrophages were predominant NO sources, as inducible NO synthase inhibition attenuated lipophagic defects and testosterone insufficiency in LCs in a macrophage-LC co-culture system. In scavenging NO (2-4-carboxyphenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (CPTIO)) we effectively restored lipophagy and testosterone levels both in vitro and in vivo, and importantly, spermatogenesis in vivo. Autophagy activation by LYN-1604 also promoted lipid degradation and testosterone synthesis.DiscussionIn summary, we showed that NO-disrupted-lipophagy caused testosterone deficiency following SCI, and NO clearance or autophagy activation could be effective in preventing reproductive dysfunction in males with SCI

Optical diagnostics and combustion analysis in a gasoline direct injection engine

Gasoline Direct Injection (GDI) engines work with stratified charge at part load and burn with lean mixtures in order to save fuel, whilst at full load, the fuel and air mix homogeneously for maximum power output. The higher compression ratio and the absence of throttling are two of the most significant benefits of GDI engines. The key issues facing GDI combustion include in-cylinder mixture preparation and post-combustion soot formation. This work was intended to investigate these aspects and was undertaken on a dedicated Jaguar single-cylinder optical GDI engine with a spray-guided combustion system. The spray-guided concept does not rely as much on charge motion or piston design, and can avoid wall-wetting effects so as to reduce engine emissions. Relevant engine control hardware and data acquisition equipment were commissioned. Data/image processing software was also developed to suit the measurements. A data-processing case study with data from a small two-stroke glow ignition engine has been conducted to develop a method to combine the burn rate and heat release analyses in the study of engines with premixed charge but compression ignition. Difficulties such as unknown ignition timing and polytropic index have been addressed. Results for all operating conditions have shown good correlations between the two methods. The technique of quantitative planar laser-induced fluorescence is useful for measuring 2-D fuel distribution in GDI engines. The relevant physics and literature were reviewed in depth. A multi-component fuel was designed to give reasonable co-evaporation characteristics with tracers matching different fuel fractions. The absorption and fluorescence features of each fuel component and tracer were characterised. Optimisation of hardware and signal-to-noise ratio was performed. A recirculating loop was set up for the calibration of the technique. The technique of colour-ratio pyrometry (CRP) for estimating the temperature and loading of soot was applied on the GDI engine. Critical features of the candidate CCD colour camera including its spectral response and noise behaviours were fully studied. Validation tests with reference sources together with an error analysis suggested an accuracy of ±50K within the combustion temperature range. Engine combustion images were then taken under various operating conditions. Temperature estimates were shown to be insensitive to the concentration of soot. Simulation with a thermodynamic modelling package, ISIS, was introduced for comparison with the experimental data. With careful tuning, ISIS gave outputs comparable to the CRP and proved to be a cost-effective tool to study GDI engines. High-speed combustion imaging was carried out using a CMOS camera, allowing the study of flame properties as well as crank-angle resolved CRP. By using a lens in the piston crown to give full bore optical access and appropriate image processing, the flame front could be detected reliably throughout the main combustion process.</p

Electrochemical treatment of real wastewater. Part 1: Effluents with low conductivity

The treatment of a real wastewater characterized by low conductivity was performed by anodic oxidation at boron doped diamond (BDD) in both conventional and microfluidic cells. The electrolyses carried out in conventional cells without supporting electrolyte were characterized by very high TOC removals but excessively high energetic consumptions and operating costs. The addition of sodium sulphate, as supporting electrolyte, allowed to strongly reduce the cell potentials and consequently the energetic consumptions and the operating costs. However, under various operating conditions, the addition of Na2SO4caused a lower removal of the TOC. The best results in terms of both TOC removal, energetic consumptions and operating costs (about 1 â¬/m3) were obtained using a cell with a very low inter-electrode distance (50 µm) with no addition of a supporting electrolyte