HyperFace: A Deep Fusion Model for Hyperspectral Face Recognition.

Face recognition has been well studied under visible light and infrared (IR) in both intra-spectral and cross-spectral cases. However, how to fuse different light bands for face recognition, i.e., hyperspectral face recognition, is still an open research problem, which has the advantages of richer information retention and all-weather functionality over single-band face recognition. Thus, in this research, we revisit the hyperspectral recognition problem and provide a deep learning-based approach. A new fusion model (named HyperFace) is proposed to address this problem. The proposed model features a pre-fusion scheme, a Siamese encoder with bi-scope residual dense learning, a feedback-style decoder, and a recognition-oriented composite loss function. Experiments demonstrate that our method yields a much higher recognition rate than face recognition using only visible light or IR data. Moreover, our fusion model is shown to be superior to other general-purpose image fusion methods that are either traditional or deep learning-based, including state-of-the-art methods, in terms of both image quality and recognition performance.

GMLM-CNN: A Hybrid Solution to SWIR-VIS Face Verification with Limited Imagery.

Cross-spectral face verification between short-wave infrared (SWIR) and visible light (VIS) face images poses a challenge, which is motivated by various real-world applications such as surveillance at night time or in harsh environments. This paper proposes a hybrid solution that takes advantage of both traditional feature engineering and modern deep learning techniques to overcome the issue of limited imagery as encountered in the SWIR band. Firstly, the paper revisits the theory of measurement levels. Then, two new operators are introduced which act at the nominal and interval levels of measurement and are named the Nominal Measurement Descriptor (NMD) and the Interval Measurement Descriptor (IMD), respectively. A composite operator Gabor Multiple-Level Measurement (GMLM) is further proposed which fuses multiple levels of measurement. Finally, the fused features of GMLM are passed through a succinct and efficient neural network based on PCA. The network selects informative features and also performs the recognition task. The overall framework is named GMLM-CNN. It is compared to both traditional hand-crafted operators as well as recent deep learning-based models that are state-of-the-art, in terms of cross-spectral verification performance. Experiments are conducted on a dataset which comprises frontal VIS and SWIR faces acquired at varying standoffs. Experimental results demonstrate that, in the presence of limited data, the proposed hybrid method GMLM-CNN outperforms all the other methods.

Tumor microenvironment responsive Mn3O4 nanoplatform for in vivo real-time monitoring of drug resistance and photothermal/chemodynamic synergistic therapy of gastric cancer.

BACKGROUND: Postoperative chemotherapy for gastric cancer often causes multidrug resistance (MDR), which has serious consequences for therapeutic effects. Individualized treatment based on accurate monitoring of MDR will greatly improve patient survival. RESULTS: In this article, a self-enhanced Mn3O4 nanoplatform (MPG NPs) was established, which can react with glutathione to produce Mn2+ to enhance T1-weighted magnetic resonance imaging (MRI) and mediate in vivo real-time MDR monitoring. In vitro MRI results showed that MRI signals could be enhanced in the presence of hydrogen peroxide and glutathione and at acidic pH. In vivo MRI results indicated that MPG NPs could specifically target MDR cells, thereby realizing real-time monitoring of MDR in gastric cancer. Furthermore, MPG NPs have good chemodynamic activity, which can convert the endogenous hydrogen peroxide of tumor cells into highly toxic hydroxyl radical through Fenton-like reaction at acidic pH to play the role of chemodynamic therapy. In addition, Mn3O4 can significantly enhance the chemodynamic therapy effect because of its good photothermal conversion effect. Furthermore, in situ photothermal/chemodynamic synergistic therapy obtained remarkable results, the tumors of the mice in the synergistic therapy group gradually became smaller or even disappeared. CONCLUSIONS: MPG NPs have good biocompatibility, providing a good nanoplatform for real-time monitoring and precise diagnosis and treatment of MDR in gastric cancer.

Balancing Heterogeneous Image Quality for Improved Cross-Spectral Face Recognition.

Matching infrared (IR) facial probes against a gallery of visible light faces remains a challenge, especially when combined with cross-distance due to deteriorated quality of the IR data. In this paper, we study the scenario where visible light faces are acquired at a short standoff, while IR faces are long-range data. To address the issue of quality imbalance between the heterogeneous imagery, we propose to compensate it by upgrading the lower-quality IR faces. Specifically, this is realized through cascaded face enhancement that combines an existing denoising algorithm (BM3D) with a new deep-learning-based deblurring model we propose (named SVDFace). Different IR bands, short-wave infrared (SWIR) and near-infrared (NIR), as well as different standoffs, are involved in the experiments. Results show that, in all cases, our proposed approach for quality balancing yields improved recognition performance, which is especially effective when involving SWIR images at a longer standoff. Our approach outperforms another easy and straightforward downgrading approach. The cascaded face enhancement structure is also shown to be beneficial and necessary. Finally, inspired by the singular value decomposition (SVD) theory, the proposed deblurring model of SVDFace is succinct, efficient and interpretable in structure. It is proven to be advantageous over traditional deblurring algorithms as well as state-of-the-art deep-learning-based deblurring algorithms.

Hyaluronic acid functionalized biodegradable mesoporous silica nanocomposites for efficient photothermal and chemotherapy in breast cancer.

Chemotherapy is one of conventional treatment methods for breast cancer, but drug toxicity and side effects have severely limited its clinical applications. Photothermal therapy has emerged as a promising method that, upon combination with chemotherapy, can better treat breast cancer. In this context, a biodegradable mesoporous silica nanoparticle (bMSN NPs) system was developed for loading doxorubicin (DOX) and IR780, to be potentially applied in the treatment of breast cancer. IR780 is encapsulated in the pores of bMSN NPs by hydrophobic adsorption, while DOX is adsorbed on the surface of the bMSN NPs by hyaluronic acid electrostatically, to form the bMID NPs. Transmission electron microscopy, fluorescence spectrum and UV absorption spectrum are used to prove the successful encapsulation of IR780 and the loading of DOX. In vitro experiments have shown bMID NPs present an excellent therapeutic effect on breast cancer cells. In vivo fluorescence imaging results have indicated that bMID NPs can accumulate in tumor sites gradually and achieve in vivo long-term circulation and continuous drug release. Furthermore, bMID NPs have provided obvious antitumor effects in breast cancer mouse models, thus evolving as an efficient platform for breast cancer therapy.

The Antitumor Effects of Britanin on Hepatocellular Carcinoma Cells and its Real-Time Evaluation by In Vivo Bioluminescence Imaging.

BACKGROUND: Hepatocellular carcinoma is cancer with many new cases and the highest mortality rate. Chemotherapy is the most commonly used method for the clinical treatment of hepatocellular carcinoma. Natural products have become clinically important chemotherapeutic drugs due to their great potential for pharmacological development. Many sesquiterpene lactone compounds have been proven to have antitumor effects on hepatocellular carcinoma. OBJECTIVE: Britanin is a sesquiterpene lactone compound that can be considered for the treatment of hepatocellular carcinoma. The present study aimed to investigate the antitumor effect of britanin. METHODS: BEL 7402 and HepG2 cells were used to study the cytotoxicity and antitumor effects of britanin. Preliminary studies on the nuclear factor kappa B pathway were conducted by western blot analysis. A BEL 7402-luc subcutaneous tumor model was established for the in vivo antitumor studies of britanin. In vivo bioluminescence imaging was conducted to monitor changes in tumor size. RESULTS: The results of the cytotoxicity analysis showed that the IC50 values for britanin in BEL 7402 and HepG2 cells were 2.702µM and 6.006µM, respectively. The results of the colony formation demonstrated that the number of cells in a colony was reduced significantly after britanin treatment. And the results of transwell migration assays showed that the migration ability of tumor cells was significantly weakened after treatment with britanin. Tumor size measurements and staining results showed that tumor size was inhibited after britanin treatment. The western blot analysis results showed the inhibition of p65 protein expression and reduced the ratio of Bcl-2/Bax after treatment. CONCLUSION: A series of in vitro and in vivo experiments demonstrated that britanin had good antitumor effects and provided an option for hepatocellular carcinoma treatment.

A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer's disease by using a predictive nomogram.

Predicting the probability of converting from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is still a challenging task. This study aims at providing a personalized MCI-to-AD conversion estimation by using a multipredictor nomogram that integrates neuroimaging features, cerebrospinal fluid (CSF) biomarker, and clinical assessments. To do so, 290 MCI patients were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of whom 76 has converted to AD and 214 remained with MCI. All subjects were randomly divided into a primary and validation cohort. Radiomics signature (Rad-sig) was obtained based on 17 cerebral cortex features selected by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Clinical factors and amyloid-beta peptide (Aß) concentration were selected by using Spearman correlation between the converted and not-converted patients. Then, a nomogram that combines image features, clinical factor, and Aß concentration was constructed and validated. Furthermore, we explored the associations between various predictors from the macro- to the microperspective by assessing gene expression patterns. Our results showed that the multipredictor nomogram (C-index 0.978 and 0.956 in both cohorts, respectively) outperformed the nomogram using either Rad-sig or Aß concentration as individual predictors. Significant associations were found between neuropsychological scores, cerebral cortex features, Aß levels, and underlying gene pathways. Our study may have a clinical impact as a powerful predictive tool for predicting the conversion probability of MCI and providing associations between cognitive impairment, structural changes, Aß levels, and underlying biological patterns from the macro- to the microperspective.

Novel vinyl-modified RGD conjugated silica nanoparticles based on photo click chemistry for in vivo prostate cancer targeted fluorescence imaging.

Molecular imaging is a powerful tool for non-invasive visualization of tumors that plays an important role in their diagnosis and treatment. The specificity of molecular imaging probes for cancer cells is important for accurate tumor visualization, with antibody and polypeptide nanoprobe conjugates having often been used as targeting agents for tumor detection. However, many traditional chemical conjugation methods employ complex conjugation reactions that result in poor efficiency and poor bioactivity. Herein, we describe the use of photo click methodology for the rapid synthesis of nanoprobes comprised of silica nanoparticles functionalized with RGD targeting units (SiO2@T1-RGDk NPs) (∼80 nm) for in vivo prostate cancer fluorescent imaging applications. These SiO2@T1-RGDk NPs exhibit a maximum absorption wavelength of 380 nm in their UV absorption spectra with a maximum fluorescence emission wavelength of 550 nm. Confocal immunofluorescent imaging reveal that SiO2@T1-RGDk NPs exhibit excellent targeting ability for visualizing cancer cells, with in vivo fluorescence imaging intensity in a subcutaneous tumor model of prostate cancer reaching a maxima after 4 h. Biosafety assessments showed that SiO2@T1-RGDk NPs demonstrate no obvious toxicity in mice, thus demonstrated that these novel NPs may prove to be promising fluorescent imaging agents for the accurate detection and treatment of tumors.

In vivo near infrared fluorescence imaging and dynamic quantification of pancreatic metastatic tumors using folic acid conjugated biodegradable mesoporous silica nanoparticles.

Cancer metastasis is one of the biggest challenges in cancer treatments since it increases the likelihood that a patient will die from the disease. Therefore, the availability of techniques for the early detection and quantification of tumors is very important. We have prepared cyanine 7.5 NHS ester (Cy7.5) and folic acid (FA) conjugated biodegradable mesoporous silica nanoparticles (bMSN@Cy7.5-FA NPs) (~100 nm) for visualizing tumors in vivo. The fluorescence spectra revealed that the emission peak of bMSN@Cy7.5-FA NPs had a red-shift of 1 nm. Confocal immunofluorescent images showed that bMSN@Cy7.5-FA NPs had an excellent targeting ability for visualizing cancer cells. In vivo fluorescence imaging has been conducted using an orthotopic model for pancreatic cancer within 48 h, and the fluorescence intensity reached a maximum at a post injection time-point of 12 h, which demonstrated that the use of bMSN@Cy7.5-FA NPs provides an excellent imaging platform for tumor precision therapy in mice.

A Novel Multi-Receiver Signcryption Scheme with Complete Anonymity.

Anonymity, which is more and more important to multi-receiver schemes, has been taken into consideration by many researchers recently. To protect the receiver anonymity, in 2010, the first multi-receiver scheme based on the Lagrange interpolating polynomial was proposed. To ensure the sender's anonymity, the concept of the ring signature was proposed in 2005, but afterwards, this scheme was proven to has some weakness and at the same time, a completely anonymous multi-receiver signcryption scheme is proposed. In this completely anonymous scheme, the sender anonymity is achieved by improving the ring signature, and the receiver anonymity is achieved by also using the Lagrange interpolating polynomial. Unfortunately, the Lagrange interpolation method was proven a failure to protect the anonymity of receivers, because each authorized receiver could judge whether anyone else is authorized or not. Therefore, the completely anonymous multi-receiver signcryption mentioned above can only protect the sender anonymity. In this paper, we propose a new completely anonymous multi-receiver signcryption scheme with a new polynomial technology used to replace the Lagrange interpolating polynomial, which can mix the identity information of receivers to save it as a ciphertext element and prevent the authorized receivers from verifying others. With the receiver anonymity, the proposed scheme also owns the anonymity of the sender at the same time. Meanwhile, the decryption fairness and public verification are also provided.

Improved Secret Image Sharing Scheme in Embedding Capacity without Underflow and Overflow.

Computational secret image sharing (CSIS) is an effective way to protect a secret image during its transmission and storage, and thus it has attracted lots of attentions since its appearance. Nowadays, it has become a hot topic for researchers to improve the embedding capacity and eliminate the underflow and overflow situations, which is embarrassing and difficult to deal with. The scheme, which has the highest embedding capacity among the existing schemes, has the underflow and overflow problems. Although the underflow and overflow situations have been well dealt with by different methods, the embedding capacities of these methods are reduced more or less. Motivated by these concerns, we propose a novel scheme, in which we take the differential coding, Huffman coding, and data converting to compress the secret image before embedding it to further improve the embedding capacity, and the pixel mapping matrix embedding method with a newly designed matrix is used to embed secret image data into the cover image to avoid the underflow and overflow situations. Experiment results show that our scheme can improve the embedding capacity further and eliminate the underflow and overflow situations at the same time.

A survey of research progress and development tendency of attribute-based encryption.

With the development of cryptography, the attribute-based encryption (ABE) draws widespread attention of the researchers in recent years. The ABE scheme, which belongs to the public key encryption mechanism, takes attributes as public key and associates them with the ciphertext or the user's secret key. It is an efficient way to solve open problems in access control scenarios, for example, how to provide data confidentiality and expressive access control at the same time. In this paper, we survey the basic ABE scheme and its two variants: the key-policy ABE (KP-ABE) scheme and the ciphertext-policy ABE (CP-ABE) scheme. We also pay attention to other researches relating to the ABE schemes, including multiauthority, user/attribute revocation, accountability, and proxy reencryption, with an extensive comparison of their functionality and performance. Finally, possible future works and some conclusions are pointed out.

Two-level incremental checkpoint recovery scheme for reducing system total overheads.

Long-running applications are often subject to failures. Once failures occur, it will lead to unacceptable system overheads. The checkpoint technology is used to reduce the losses in the event of a failure. For the two-level checkpoint recovery scheme used in the long-running tasks, it is unavoidable for the system to periodically transfer huge memory context to a remote stable storage. Therefore, the overheads of setting checkpoints and the re-computing time become a critical issue which directly impacts the system total overheads. Motivated by these concerns, this paper presents a new model by introducing i-checkpoints into the existing two-level checkpoint recovery scheme to deal with the more probable failures with the smaller cost and the faster speed. The proposed scheme is independent of the specific failure distribution type and can be applied to different failure distribution types. We respectively make analyses between the two-level incremental and two-level checkpoint recovery schemes with the Weibull distribution and exponential distribution, both of which fit with the actual failure distribution best. The comparison results show that the total overheads of setting checkpoints, the total re-computing time and the system total overheads in the two-level incremental checkpoint recovery scheme are all significantly smaller than those in the two-level checkpoint recovery scheme. At last, limitations of our study are discussed, and at the same time, open questions and possible future work are given.

Quantum attack-resistent certificateless multi-receiver signcryption scheme.

The existing certificateless signcryption schemes were designed mainly based on the traditional public key cryptography, in which the security relies on the hard problems, such as factor decomposition and discrete logarithm. However, these problems will be easily solved by the quantum computing. So the existing certificateless signcryption schemes are vulnerable to the quantum attack. Multivariate public key cryptography (MPKC), which can resist the quantum attack, is one of the alternative solutions to guarantee the security of communications in the post-quantum age. Motivated by these concerns, we proposed a new construction of the certificateless multi-receiver signcryption scheme (CLMSC) based on MPKC. The new scheme inherits the security of MPKC, which can withstand the quantum attack. Multivariate quadratic polynomial operations, which have lower computation complexity than bilinear pairing operations, are employed in signcrypting a message for a certain number of receivers in our scheme. Security analysis shows that our scheme is a secure MPKC-based scheme. We proved its security under the hardness of the Multivariate Quadratic (MQ) problem and its unforgeability under the Isomorphism of Polynomials (IP) assumption in the random oracle model. The analysis results show that our scheme also has the security properties of non-repudiation, perfect forward secrecy, perfect backward secrecy and public verifiability. Compared with the existing schemes in terms of computation complexity and ciphertext length, our scheme is more efficient, which makes it suitable for terminals with low computation capacity like smart cards.

Completely anonymous multi-recipient signcryption scheme with public verification.

Most of the existing multi-recipient signcryption schemes do not take the anonymity of recipients into consideration because the list of the identities of all recipients must be included in the ciphertext as a necessary element for decryption. Although the signer's anonymity has been taken into account in several alternative schemes, these schemes often suffer from the cross-comparison attack and joint conspiracy attack. That is to say, there are few schemes that can achieve complete anonymity for both the signer and the recipient. However, in many practical applications, such as network conference, both the signer's and the recipient's anonymity should be considered carefully. Motivated by these concerns, we propose a novel multi-recipient signcryption scheme with complete anonymity. The new scheme can achieve both the signer's and the recipient's anonymity at the same time. Each recipient can easily judge whether the received ciphertext is from an authorized source, but cannot determine the real identity of the sender, and at the same time, each participant can easily check decryption permission, but cannot determine the identity of any other recipient. The scheme also provides a public verification method which enables anyone to publicly verify the validity of the ciphertext. Analyses show that the proposed scheme is more efficient in terms of computation complexity and ciphertext length and possesses more advantages than existing schemes, which makes it suitable for practical applications. The proposed scheme could be used for network conferences, paid-TV or DVD broadcasting applications to solve the secure communication problem without violating the privacy of each participant.

Determining scientific impact using a collaboration index.

Researchers collaborate on scientific projects that are often measured by both the quantity and the quality of the resultant peer-reviewed publications. However, not all collaborators contribute to these publications equally, making metrics such as the total number of publications and the H-index insufficient measurements of individual scientific impact. To remedy this, we use an axiomatic approach to assign relative credits to the coauthors of a given paper, referred to as the A-index for its axiomatic foundation. In this paper, we use the A-index to compute the weighted sums of peer-reviewed publications and journal impact factors, denoted as the C- and P-indexes for collaboration and productivity, respectively. We perform an in-depth analysis of bibliometric data for 186 biomedical engineering faculty members and from extensive simulation. It is found that these axiomatically weighted indexes better capture a researcher's scientific caliber than do the total number of publications and the H-index, allowing for fairer and sharper evaluation of researchers with diverse collaborative behaviors.