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China’s first autonomous Mars rover, Zhurong, has carried out an exploration mission on the Utopian Plain. As Martian rocks and their three-dimensional (3D) morphology are essential for exploring the geological evolution of Mars, we identified 6,185 centimeter-scale rocks from the Zhurong NaTeCams images using an automatic approach, and 3D morphological features are then calculated for each rock. The statistical result shows that small rocks in centimeters make up the majority. Morphological analyses of rocks in different areas show that landscape type affects the number of rocks but has no clear relation to the size-frequency distribution of rocks. The outcrop rocks, the predominate type of rocks in the Zhurong landing area, and the float rocks around a crater are distributed differently in proportions regarding all morphological features, suggesting different formation and fragmentation processes of these two types of rocks. Further comparative analysis with the rocks at the Spirit traverse is also conducted. Some commonalities between the Zhurong outcrop rocks and the Spirit volcanic rocks are found in 2D shape compactness and 3D sphericity. Meanwhile, the float rocks are distributed significantly differently, indicating their source is not the same with the outcrop rocks, but is highly likely an impact event. In general, this study provides new insight into the Martian surface characteristics based on the centimeter-scale 3D rocks in the Zhurong landing area., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Shihan Xiao,1,2 Jianping Guo,3,4 Wuming Zhang,1,2 Xianqin Hu,1,2 Ran Wang,1,2 Zhikang Chen,1,2,5,* Chen Lai1,2,5,* 1Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 2International Joint Research Center of Minimally Invasive Endoscopic Technology Equipment & Standardization, Xiangya Hospital, Central South University, Changsha, Hunan Province, People’s Republic of China; 3Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 4Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 5Hunan Key Laboratory of Precise Diagnosis and Treatment of Gastrointestinal Tumor, Xiangya Hospital Central South University, Changsha, Hunan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhikang Chen; Chen LaiDepartment of General Surgery, Xiangya Hospital, Central South University, 87th Xiangya Road, Kaifu District, Changsha, Hunan, People’s Republic of ChinaTel +86-13875982443Tel +86-13875982443Email chenzhikang1974@126.com; chenl_csu@126.comPurpose: Tumor deposits (TDs) are acknowledged negative prognostic factors in colorectal cancer (CRC), and their pathogenesis remains a puzzle. This study aimed to construct and validate a nomogram available for preoperative TDs prediction in CRC patients.Patients and Methods: Patients from the Surveillance, Epidemiology, and End Results (SEER) and the cancer genome atlas (TCGA) databases were randomly divided into training and validation sets according to the sample size ratio of 7:3. Univariate logistic regression was performed for identifying differentially expressed microRNAs between TDs and non-TDs. Nomograms for TDs prediction were developed from the multivariate logistic regression model with least absolute shrinkage and selection operator and were validated internally in terms of accuracy, calibration, and clinical utility. Based on the target genes, pathways tightly associated with TDs were selected using enrichment analysis.Results: Six clinicopathologic factors and expressions of six microRNAs (miR-614, miR-1197, miR-4770, miR-3136, miR-3173, and miR-4636) differed significantly between TDs and non-TDs CRC patients from the SEER and TCGA training sets. We compared potential prediction discrimination between two nomograms: a clinicopathologic nomogram and a six-microRNA signature nomogram. The six-microRNA signature nomogram revealed better accuracy than the clinicopathologic one for TDs prediction (AUC values of 0.96 and 0.93 in the validation cohort). The calibration plots and decision curve analysis demonstrated that the six-microRNA signature nomogram had better validity and a greater prognostic benefit versus the clinicopathologic one for TDs prediction. Calcium signaling pathways were closely associated with roles of the six microRNAs in TDs of CRC patients.Conclusion: The six-microRNA signature nomogram can be used as an efficient tool for preoperative TDs prediction in CRC patients.Keywords: tumor deposits, colorectal cancer, microRNA, prediction, nomogram

Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate inversion of forest parameters. However, forest occlusion poses challenges for marker-based registration methods, and some marker-free automated registration methods have low efficiency due to the process of object (e.g., tree, crown) segmentation. Therefore, we use a divide-and-conquer strategy and propose an automated and efficient method to register ULS and ground LiDAR point clouds in forests. Registration involves coarse alignment and fine registration, where the coarse alignment of point clouds is divided into vertical and horizontal alignment. The vertical alignment is achieved by ground alignment, which is achieved by the transformation relationship between normal vectors of the ground point cloud and the horizontal plane, and the horizontal alignment is achieved by canopy projection image matching. During image matching, vegetation points are first distinguished by the ground filtering algorithm, and then, vegetation points are projected onto the horizontal plane to obtain two binary images. To match the two images, a matching strategy is used based on canopy shape context features, which are described by a two-point congruent set and canopy overlap. Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration. Also, the effectiveness, accuracy, and efficiency of the proposed method are demonstrated by field measurements of forest plots. Experimental results show that the ULS and ground LiDAR data in different plots are registered, of which the horizontal alignment errors are less than 0.02 m, and the average runtime of the proposed method is less than 1 second.

In recent years, biometrics have received substantial attention due to the evergrowing need for automatic individual authentication. Among various physiological biometric traits, face offers unmatched advantages over the others, such as fingerprints and iris, because it is natural, non-intrusive and easily understandable by humans. Nowadays conventional face recognition techniques have attained quasi-perfect performance in a highly constrained environment wherein poses, illuminations, expressions and other sources of variations are strictly controlled. However these approaches are always confined to restricted application fields because non-ideal imaging environments are frequently encountered in practical cases. To adaptively address these challenges, this dissertation focuses on this unconstrained face recognition problem, where face images exhibit more variability in illumination. Moreover, another major question is how to leverage limited 3D shape information to jointly work with 2D based techniques in a heterogeneous face recognition system. To deal with the problem of varying illuminations, we explicitly build the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborate the formation of face color. With this physics-based image formation model involved, an illumination-robust representation, namely Chromaticity Invariant Image (CII), is proposed which can subsequently help reconstruct shadow-free and photo-realistic color face images. Due to the fact that this shadow removal process is achieved in color space, this approach could thus be combined with existing gray-scale level lighting normalization techniques to further improve face recognition performance. The experimental results on two benchmark databases, CMU-PIE and FRGC Ver2.0, demonstrate the generalization ability and robustness of our approach to lighting variations. We further explore the effective and creative use of 3D data in heterogeneous face recognition. In such a scenario, 3D face is merely available in the gallery set and not in the probe set, which one would encounter in real-world applications. Two Convolutional Neural Networks (CNN) are constructed for this purpose. The first CNN is trained to extract discriminative features of 2D/3D face images for direct heterogeneous comparison, while the second CNN combines an encoder-decoder structure, namely U-Net, and Conditional Generative Adversarial Network (CGAN) to reconstruct depth face image from its counterpart in 2D. Specifically, the recovered depth face images can be fed to the first CNN as well for 3D face recognition, leading to a fusion scheme which achieves gains in recognition performance. We have evaluated our approach extensively on the challenging FRGC 2D/3D benchmark database. The proposed method compares favorably to the state-of-the-art and show significant improvement with the fusion scheme., Ces dernières années, la biométrie a fait l’objet d’une grande attention en raison du besoin sans cesse croissant d’authentification d’identité, notamment pour sécuriser de plus en plus d’applications enlignes. Parmi divers traits biométriques, le visage offre des avantages compétitifs sur les autres, e.g., les empreintes digitales ou l’iris, car il est naturel, non-intrusif et facilement acceptable par les humains. Aujourd’hui, les techniques conventionnelles de reconnaissance faciale ont atteint une performance quasi-parfaite dans un environnement fortement contraint où la pose, l’éclairage, l’expression faciale et d’autres sources de variation sont sévèrement contrôlées. Cependant, ces approches sont souvent confinées aux domaines d’application limités parce que les environnements d’imagerie non-idéaux sont très fréquents dans les cas pratiques. Pour relever ces défis d’une manière adaptative, cette thèse porte sur le problème de reconnaissance faciale non contrôlée, dans lequel les images faciales présentent plus de variabilités sur les éclairages. Par ailleurs, une autre question essentielle vise à profiter des informations limitées de 3D pour collaborer avec les techniques basées sur 2D dans un système de reconnaissance faciale hétérogène. Pour traiter les diverses conditions d’éclairage, nous construisons explicitement un modèle de réflectance en caractérisant l’interaction entre la surface de la peau, les sources d’éclairage et le capteur de la caméra pour élaborer une explication de la couleur du visage. A partir de ce modèle basé sur la physique, une représentation robuste aux variations d’éclairage, à savoir Chromaticity Invariant Image (CII), est proposée pour la reconstruction des images faciales couleurs réalistes et sans ombre. De plus, ce processus de la suppression de l’ombre en niveaux de couleur peut être combiné avec les techniques existantes sur la normalisation d’éclairage en niveaux de gris pour améliorer davantage la performance de reconnaissance faciale. Les résultats expérimentaux sur les bases de données de test standard, CMU-PIE et FRGC Ver2.0, démontrent la capacité de généralisation et la robustesse de notre approche contre les variations d’éclairage. En outre, nous étudions l’usage efficace et créatif des données 3D pour la reconnaissance faciale hétérogène. Dans un tel scénario asymétrique, un enrôlement combiné est réalisé en 2D et 3D alors que les images de requête pour la reconnaissance sont toujours les images faciales en 2D. A cette fin, deux Réseaux de Neurones Convolutifs (Convolutional Neural Networks, CNN) sont construits. Le premier CNN est formé pour extraire les descripteurs discriminants d’images 2D/3D pour un appariement hétérogène. Le deuxième CNN combine une structure codeur-décodeur, à savoir U-Net, et Conditional Generative Adversarial Network (CGAN), pour reconstruire l’image faciale en profondeur à partir de son homologue dans l’espace 2D. Plus particulièrement, les images reconstruites en profondeur peuvent être également transmise au premier CNN pour la reconnaissance faciale en 3D, apportant un schéma de fusion qui est bénéfique pour la performance en reconnaissance. Notre approche a été évaluée sur la base de données 2D/3D de FRGC. Les expérimentations ont démontré que notre approche permet d’obtenir des résultats comparables à ceux de l’état de l’art et qu’une amélioration significative a pu être obtenue à l’aide du schéma de fusion.

Heterogeneous face recognition between color image and depth image is a much desired capacity for real world applications where shape information is looked upon as merely involved in gallery. In this paper, we propose a cross-modal deep learning method as an effective and efficient workaround for this challenge. Specifically, we begin with learning two convolutional neural networks (CNNs) to extract 2D and 2.5D face features individually. Once trained, they can serve as pre-trained models for another two-way CNN which explores the correlated part between color and depth for heterogeneous matching. Compared with most conventional cross-modal approaches, our method additionally conducts accurate depth image reconstruction from single color image with Conditional Generative Adversarial Nets (cGAN), and further enhances the recognition performance by fusing multi-modal matching results. Through both qualitative and quantitative experiments on benchmark FRGC 2D/3D face database, we demonstrate that the proposed pipeline outperforms state-of-the-art performance on heterogeneous face recognition and ensures a drastically efficient on-line stage.

Terrestrial LiDAR systems have received lots of attention on three-dimensional (3D) structure reconstruction for trees, especially on the branches skeleton generation. On this basis, a method is proposed to add leaves structures based on point density by dividing small cube in the canopy to reduce the influence of uneven distribution of point cloud, combining gap fraction model to retrieve leaf area of a tree using terrestrial LiDAR data. It is successfully applied to reconstruct 3D trees using points data simulated by ray tracing algorithm as well as field measured points data. The relative error of leaf area between reconstructed and real structure is less than 0.9%. Meanwhile, the most relative error of directional gap fraction is also less than 4.1%. The experimental results prove that the method has gotten a satisfied consistency on visual sense and quantitative evaluation between the 3D structure reconstructed and real structure.

Determination of isocarbophos by cathodically sweeping oscilliopolarography is described. In a 1.0×10−5 mol/L sodium dodecylbenzene sulfonate (SDS)+0.1 mol/L HAC-NaAC (pH=4.0) buffer medium, isocarbophos and its alkaline hydrolysate exhibited sensitive second derivative wave at −0.50 V and −0.48 V respectively. The peak current was linearly proportional to the concentration of isocarbophos in the range of 5.40×10−6−1.05×10−4 mol/L by detecting isocarbophos directly. The detection limit was 3.60×10−6 mol/L with the relative standard derivation (RSD) of 7.3%. By employing an alkaline hydrolysis, the peak current was linearly proportional to the concentration of isocarbophos in the range from 7.70×10−7 mol/L to 1.24×10−4 mol/L, and the detection limit was 5.80×10−7 mol/L with RSD of 3.1%. The hydrolysis procedure and the electrode reaction were studied by voltammetry.

Nitric oxide can be oxidized to NO − 2 (then to NO − 3 ) and reduced to N 2 O at a glassy carbon electrode modified with the film of poly(neutral red). When the modified electrode is further coated with a thin Nafion film, the interference of NO − 2 can be almost thoroughly eliminated, and the electrode can be employed to detect nitric oxide.

Determination of omethoate by cathodically sweeping oscillopolarography is described. The product of the alkaline hydrolysis exhibited a sensitive second derivative wave at −0.50 V (vs. SCE) in a 1.0 ×10−5mol/L sodium dodecylbenzene sulfonate (SDBS)+0.1 mol/L HAc-NaAc (pH 4.0) buffer. The peak current was linearly proportional to the concentration of omethoate in the range from 6.4×10−7 to 5.1×10−5 mol/L. The detection limit is 2.0×10−7 mol/L with the relative standard derivation (RSD) of 3.5%. The hydrolysis procedure and the electrode reaction were studied by voltammetry.

Electrochemical redox of hemoglobin at poly(o-phenylenediamine) coated glass carbon electrodes or at bare glass carbon electrodes promoted byo-phenylenediamine have been investigated. The electrochemical characters of poly(o-phenylenediamine) films have also been studied by using cyclic voltammetry and potential-step chronoamperometry as well as current-step chronocoulometry techniques. A possible catalyzing mechanism for hemoglobin is proposed.

The voltammetric behavior of concanavalin A at the bare silver electrode is described. Cyclic voltammograms show a pair of well-defined redox peaks for concanavalin A in the medium of phosphate buffer(pH=7.0) with electrochemical reaction rate constant ofK 2=0.053 s−1 and electron transfer number ofn=2. Moreover, both the oxidation currents and the reduction currents have linear relationship with concanavalin A concentrations in the range of 1.0×10−6∼2.0×10−5 mol/L, which can be used as an analytical method for determining concanavalin A concentration.

A novel electrophoresis technique, in which a strip of perflurosulfonic-acid (nafion 117) membrane was used to replace the conventional separation column and liquid buffer solution within, was developed and employed to separate the mixture of dopamine and epinephrine under a low separation voltage of 100 V with quadruple pulses amperometry detection. It was showed that the so-called Nafion membrane electrophoresis could be one of very simple and easy method and has the potentiality to be used to separate and analyze some small organic biologic molecules.

A re-utilizable piezoelectric crystal (PC) immunosensor for thyroidal triiodothyronine (T3) has been developed. The crystal was coated with protein A and then reacted with T3 antibody. T3 could be detected in the range of 0.5×10−6 g/L to 10.0×10−6g/L by the, system. Crystal was regenerated by saturating with T3 and subsequent binding of a new anti-T3 antibody layer. The T3 assay could be repeated up to 5 times using each crystal.

A surface acoustic wave (SAW) piezoelectric device with a dual-path SAW delay line oscillator configuration has been developed to detect nitrogen dioxide (NO 2 ) gas. One delay line is coated with a Langmuir-Blodgett (LB) film of copper tetrasulphonated phthalocyanine (CuTsPc), while the other is uncoated. NO 2 gas can be selectively abosorbed by the CuTsPc LB film and then alters the SAW characteristics, which in turn causes the oscillation frequency to change. The concentration of NO 2 can be detected by measuring the relative change in the frequency of the two oscillators. The CuTsPc LB film-coated SAW device has been demonstrated to be sensitive to low concentrations of NO 2 gas. There is a linear relationship between NO 2 concentration and frequency change under 12 ppm NO 2 concentration. The sensitivity of the device is about 128 Hz/ppm NO 2 . The response time is 10 min, and the recovery is 40 min. It can be applied as a small, inexpensive and sensitive NO 2 gas sensor.

The finite element method (FEM) employed to model the temperature field in a heated silicon plane was discussed, since optimum designing of the micro-silicon structure would be the basis of a micro-machined silicon-based thermal biosensor. In this research, the heat flow originally generated by the enzymatic reaction was conducted in the anisotropically etched silicon and collected by a thermopile array. Fourier's law and Laplace's equation were utilized to build the temperature field equations under appropriate boundary conditions in two-dimensions Cartesian coordinates, while FEM was used to calculate the temperature field and model conduction. Meanwhile, several available results for optimum design are reported.

Determination of omethoate by modified PQC (piezoelectric quartz crystal) sensor is described. Several modified films were studied and compared. Omethoate of 0.1-10 ppm can be determined directly and sensitively with PVC-dioctyl sebacate film modified PQC. The modified crystal has good reversibility and reproductivity. The lifetime is about 1 month.