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Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra (Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.

In this paper, a visualization and modeling method for the internal fixation plate in personalized and minimally invasive long bone fracture surgery is presented. This framework mainly includes six sub-steps: firstly, the Principal Component Analysis (PCA) method is used to extract the 3D axis of the broken bone; secondly, the Gauss mapping method is utilized to segment the end surfaces of the broken bone; thirdly, the maximum common subgraph strategy is utilized to complete the end surface matching; fourthly, the OBB (Oriented Bounding Box) method is utilized to accomplish the pre-registration of the fracture surface; fifthly, the ICP (Iterative Closest Point) algorithm is utilized to achieve the fracture surface models’ accurate alignment; lastly, the personalized bent internal fixation plate model can be reconstructed based on anchor points. Via this method, doctors can obtain the accurate and personalized internal fixation plate model that has the best matching geometric form to the patient’s broken bones. This method can provide a well technique base and computational solution for the minimally invasive long bone fracture surgery.

A fluorescence microsphere suspension array technique has been developed as a promising tool for the molecular detection of Listeria monocytogene (LM), which is a dangerous food borne pathogen that can cause severe food intoxication in both humans and animals. In such a technique, fluorescent microspheres as the key matrix for detection are often decorated with neutral organic dyes, which usually suffer from instability, easy photobleaching and loss from the matrix. Herein, we design and synthesize a new organic–inorganic hybrid POM–pyrene compound [N(C4H9)4]2[V6O13{(OCH2)3CNH–CH2–C16H9}2] (1), which is employed as a negatively-charged fluorescent material to prepare a new class of fluorescent microspheres by a layer-by-layer electrostatic self-assembly method. As a result, such POM–pyrene-decorated fluorescent microspheres possess obvious luminescence properties, superior photo-stability and long-term stability. Therefore, the POM–pyrene-decorated fluorescent microspheres are conjugated by monoclonal anti-LM antibodies, and introduced into the fluorescence microsphere-based suspension immunoassay system for the detection of LM in food, which exhibits a high sensitivity. The lowest limit of detection (LOD) of LM in artificially contaminated meat is down to 6 CFU g−1 without cross-reactivity with other pathogens. Furthermore, the suspension immunoassay based on PS@POM–pyrene microspheres shows excellent specificity to LM, good repeatability and high efficiency in contrast to the conventional culture-based method.

It is challenging to achieve high implant accuracy in dental implant placement, because high risk tissues need to be avoided. In this study, we present an augmented reality (AR) surgical navigation with an accurate cone beam computed tomography (CBCT)-patient registration method to provide clinically desired dental implant accuracy. A registration device is used for registration between preoperative data and patient outside the patient's mouth. After registration, the registration device is worn on the patient's teeth for tracking the patient. Naked-eye 3D images of the planning path and the mandibular nerve are superimposed onto the patient in situ to form an AR scene. Simultaneously, a 3D image of the drill is overlaid accurately on the real one to guide the implant procedure. Finally, implant accuracy is evaluated postoperatively. A model experiment was performed by an experienced dentist. Totally, ten parallel pins were inserted into five 3D-printed mandible models guided by our AR navigation method and through the dentist's experience, respectively. AR-guided dental implant placement showed better results than the dentist's experience (mean target error = 1.25 mm vs. 1.63 mm; mean angle error = 4.03° vs. 6.10°). Experimental results indicate that the proposed method is expected to be applied in the clinic. Graphical abstract ᅟ.

We present a new quantitative method for detecting changes in 3D medical images. The dissimilarity between shapes is quantified as a measure of the effort it takes to deform one 3D region into another. Our main tool is an assessment of conformal and isometric distortions of mappings between volumes. Unlike most existing techniques for shape comparison, our algorithm operates both on triangular and tetrahedral meshes, and therefore can be applied both for closed simply connected surfaces, as well as for volumetric domains homeomorphic to a ball, with geometrically complicated boundaries. Furthermore we extend our main geometric distortion measure to higher dimensions, in a manner that allows for the dealing with spatial data at the maximal, as well as at all the lower dimensions.

Escherichia coli O157: H7 (E. coli O157: H7) has become one of the most dangerous foodborne pathogenic bacteria around the world. Currently, because of the tedious, high-cost and stringent laboratory conditions required, the conventional E. coli O157: H7 detection methods, such as culture-based methods and polymerase chain reaction (PCR), have much limitation. Thus, we developed a novel paper-based enzyme-linked immunosorbent assay (p-ELISA) with shorter operation duration, lower cost, relatively higher sensitivity and wider application. This method required less than 3 h and 5 μL of sample to complete the detection. The limit of detection (LOD) for E. coli O157: H7 reached 1 × 104 CFU/mL with high specificity. To be more suitable for on-site testing, the readout could be rapidly obtained without any expensive instruments. In this study, we chose E. coli O157:H7 as the representative, and our method could provide a platform for determination of other pathogenic bacteria.

Biometric applications have been used globally in everyday life. However, conventional biometrics is created and optimized for high-security scenarios. Being used in daily life by ordinary untrained people is a new challenge. Facing this challenge, designing a biometric system with prior constraints of ergonomics, we propose ergonomic biometrics design model, which attains the physiological factors, the psychological factors, and the conventional security characteristics. With this model, a novel hand-based biometric system, door knob hand recognition system (DKHRS), is proposed. DKHRS has the identical appearance of a conventional door knob, which is an optimum solution in both physiological factors and psychological factors. In this system, a hand image is captured by door knob imaging scheme, which is a tailored omnivision imaging structure and is optimized for this predetermined door knob appearance. Then features are extracted by local Gabor binary pattern histogram sequence method and classified by projective dictionary pair learning. In the experiment on a large data set including 12 000 images from 200 people, the proposed system achieves competitive recognition performance comparing with conventional biometrics like face and fingerprint recognition systems, with an equal error rate of 0.091%. This paper shows that a biometric system could be built with a reliable recognition performance under the ergonomic constraints.

Biometric recognition systems have been widely used globally. However, one effective and highly accurate biometric authentication method, palmprint recognition, has not been popularly applied as it should have been, which could be due to the lack of small, flexible and user-friendly acquisition systems. To expand the use of palmprint biometrics, we propose a novel palmprint acquisition system based on the line-scan image sensor. The proposed system consists of a customized and highly integrated line-scan sensor, a self-adaptive synchronizing unit, and a field-programmable gate array controller with a cross-platform interface. The volume of the proposed system is over 94% smaller than the volume of existing palmprint systems, without compromising its verification performance. The verification performance of the proposed system was tested on a database of 8000 samples collected from 250 people, and the equal error rate is 0.048%, which is comparable to the best area camera-based systems.

Identity authentication based on face recognition has been significantly improved due to the outstanding ability of face detection, thus it plays an important role in society. However, face recognition system might be deceived by malicious face spoof attacks raising risk from both safety and property. The algorithm to accurately detect face anti-spoofing in identity authentication system is becoming crucial. In this paper, a shallow convolutional neural network with laplacian embedding (shallowCNN-LE) is proposed for face anti-spoofing. Two different types of features are concatenated to accurately detect the face liveness, including depth features and dynamic texture features. First, the developed shallow CNN model contains four layers which make the model faster. Second, we integrate dynamic texture features extracted by using the dual tree complex wavelet transform (DT-CWT) with the depth features as input features to feed into the proposed model. Finally, we propose a laplacian embedding algorithm, which can maintain the inter-class discrimination and penalize the distance of intra-class. When embedding the laplacian loss with the softmax loss, the proposed method can obtain much more discriminative features, which is helpful to detect face anti-spoofing. Experimental results on public databases of CASIA FASD, Replay attack and MSU USSA database demonstrate that our proposed method outperforms the state-of-the-art methods for face anti-spoofing detection.

Nowadays, good medical services are located in the high-level hospitals. The uneven regional distribution and the unbalanced medical quality between different hospitals bring obstacles to patients for better healthcare. In the community hospital, surgeons may lack advanced surgical skills. Considering the clinical needs, we present an augmented-reality (AR) navigation system using 3-D image overlay technology for better healthcare visualization. Autostereoscopic 3-D images of anatomic structures, which are reproduced by employing integral videography technology, are superimposed on the patient via a half-silvered mirror to form an AR navigation scene. The proposed system can generate the vivid autostereoscopic 3-D medical images and realize in situ image overlay, which eliminates the hand-eye coordination problem. By using our system, surgeons can intuitively observe the operative area, the marked high-risky tissue, surgical tools and surgical planning pathway, which make the surgical operation more convenient and intuitive. To consider the demands of clinical applications, we design a new semi-streamlined prototype combining adjustment mechanism and servomotors, which can broaden the visual field. Results illustrate that the navigation system can enhance the surgeons’ operation of the facilitation, as well as the design is more integrated and commercialized, which are more suitable for clinical applications. It is advanced to realize remote visual display and intuitive guidance to solve the uneven distribution of medical resources. What’s more, the system can be used for surgical skill training under the guidance of the 3-D images. Thus, the AR system is beneficial to healthcare, especially in the resource-scare communities.

Background Cs2K4Na [SiW9Nb3O40] (POM93) is a novel broad-spectrum antiviral agent with high activity, high stability, and low toxicity in vitro. Most toxicity studies for POM93 have been performed in cultured cell lines rather than in animals. Like other POMs, there is a lack of evidence for in vivo toxicity limits, oral bioavailability, and therapeutic applications. Methods The toxic properties of POM93 were evaluated comprehensively in vivo, including the acute and subchronic oral toxicity studies and genotoxicity tests. Results The acute toxicity study showed no abnormal changes or mortality in rats treated with POM93 even at the single high dose of 5000 mg/kg body weight. In the subchronic toxicity study, regardless of the body weight, the organ weight, and the hematological parameters, similar results were observed between the control group and the experimental groups. POM93 produced mild changes in rare hematological parameters in the liver and kidneys, but did not induce the clinical symptoms of liver or kidneys injury in rats as confirmed by histopathological analysis. Moreover, neither mutagenicity nor clastogenicity was caused by POM93 treatment in vitro and in vivo. Conclusions The present study demonstrates that the oral administration of POM93 is presumed safe and poses a low risk of potential health risks. Electronic supplementary material The online version of this article (doi:10.1186/s40360-017-0133-x) contains supplementary material, which is available to authorized users.

By analyzing the difference of facial texture features between living face and photo, we propose a novel face anti-spoofing algorithm based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet tree (DT-CWT). Firstly, inspired by the co-occurrence matrix, we extract five texture features including angle second moment, entropy, contrast, correlation and local uniformity to represent the gray direction, interval and amplitude information for the face texture information. Secondly, DT-CWT has the advantages of approximate translation invariance and good direction selectivity. Therefore, the coefficients of DT-CWT can enhance the texture information and edge information in the frequency domain. At last, the SVM classification is used to distinguish between true and fake face. Our algorithm is demonstrated on the published NUAA database. Compared with the existing methods, the feature dimension is reduced. The experimental results show that the proposed algorithm improves the detection accuracy.

Brucella spp. are facultative intracellular bacteria that cause zoonotic disease of brucellosis worldwide. Traditional methods for detection of Brucella spp. take 48–72 h that does not meet the need of rapid detection. Herein, a new rapid detection method of Brucella was developed based on polyclonal antibody-conjugating quantum dots and antibody-modified magnetic beads. First, polyclonal antibodies IgG and IgY were prepared and then the antibody conjugated with quantum dots (QDs) and immunomagnetic beads (IMB), respectively, which were activated by N-(3-dimethylaminopropyl)-N’-ethylcar-bodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS) to form probes. We used the IMB probe to separate the Brucella and labeled by the QD probe, and then detected the fluorescence intensity with a fluorescence spectrometer. The detection method takes 105 min with a limit of detection of 103 CFU/mL and ranges from 10 to 105 CFU/mL (R 2 = 0.9983), and it can be well used in real samples.