Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

Analysis of Lower-Limb Ulcers in Participants with Leprosy Sequelae Using Metabolomics and 16S Ribosomal DNA Sequencing.

Purpose: This study investigated microbiome and metabolome differences between ulcerated tissues and normal skin from the lower limbs of participants with leprosy. Patients and Methods: Ulcerated tissues and surrounding normal skin were collected from the lower limbs of 28 participants with leprosy who had been cured. The 16S ribosomal DNA sequencing analysis of the samples was conducted with the Illumina NovaSeq platform to analyze the community structure and diversity of microorganisms on the skin surface, followed by non-targeted metabolomic analysis with LC-MS technology. Next, differential metabolites were statistically screened, followed by metabolic pathway analysis. The Spearman method was used to analyze the correlation between differential microbiota and differential metabolites. Results: Compared to normal skin, ulcerated tissues showed a decrease in microbial α diversity (species richness, homogeneity, and sequencing depth), without significant differences (observed species, Chao1, Shannon, Simpson, and Pielou's evenness index; P > 0.05). Conversely, Jaccard distance demonstrated that sample ß-diversity exhibited a certain degree of clustering (P < 0.05), with significant differences between the two groups. The results of LEfSe analysis revealed that compared to the normal skin, the ulcerated tissues had significantly decreased microbial abundance of Flavobacteriaceae, Flavobacteriales, Lachnospiraceae, Lachnospirales, Enterobacterales, Acinetobacter, and Moraxellaceae, which might be associated with the ulcerative state. The Spearman correlation analysis suggested a strong correlation between skin metabolome and skin microbiome. Conclusion: For participants with leprosy sequelae, skin microecology and metabolites are disturbed and species diversity and homogeneity are reduced in lower-limb ulcers, and the types of skin metabolites are dependent on the microbiota.

Keloids After Herpes Zoster: Report of Wolf's Isotopic Phenomenon and Literature Review.

Wolf's isotopic response (WIR) refers to the appearance of a new skin disorder at the exact site of an unrelated skin disorder that had previously been cured. The most common primary skin disease in which WIR occurs is herpes zoster. Despite numerous theories being put forward to explain this occurrence, including viral, vascular, immunological, and neurological hypotheses, the pathogenesis of WIR after herpes zoster remains unclear. We report the case of a 76-year-old patient who presented with keloids at the site of the original herpes zoster. Based on this observation and a review of the literature, The clinical characteristics and possible theoretical reasons for keloids after herpes zoster will be discussed.

Successful Treatment of Multiple Plantar and Periungual Warts by Local Hyperthermia Treatment: A Case Report.

Local hyperthermia treatment, a common immunoadjuvant therapy, has been used in treating various human papillomavirus (HPV) infections. For multiple warts, it can clear off all warts of the body through targeted irradiation on one wart. We reported a case of a 22-year-old patient with multiple plantar warts combined with periungual warts, in which previous treatments were ineffective. Therefore, we utilized local hyperthermia treatment at 44°C using the largest fused-wart plaque on the right plantar as the treatment target for 30-minute irradiation each time. The hyperthermia induction therapy was performed on day 1, 2, 3, 12 and 13, followed by weekly maintenance therapy for 6 times. After 2 months of treatment, both irradiated and non-irradiated lesions disappeared, and no recurrence occurred during the follow-up period. This case indicates that local hyperthermia treatment may be a safe and effective approach for patients with multiple, recalcitrant, and site-specific viral warts due to the ineffectiveness of conventional treatment.

Guided Colorization Using Mono-Color Image Pairs.

Compared to color images captured by conventional RGB cameras, monochrome (mono) images usually have higher signal-to-noise ratios (SNR) and richer textures due to the lack of color filter arrays in mono cameras. Therefore, using a mono-color stereo dual-camera system, we can integrate the lightness information of target monochrome images with the color information of guidance RGB images to accomplish image enhancement in a colorization manner. In this work, based on two assumptions, we introduce a novel probabilistic-concept guided colorization framework. First, adjacent contents with similar luminance are likely to have similar colors. By lightness matching, we can utilize colors of the matched pixels to estimate the target color value. Second, by matching multiple pixels from the guidance image, if more of these matched pixels have similar luminance values to the target one, we can estimate colors with more confidence. Based on the statistical distribution of multiple matching results, we retain the reliable color estimates as initial dense scribbles and then propagate them to the rest of the mono image. However, for a target pixel, the color information provided by its matching results is quite redundant. Hence, we introduce a patch sampling strategy to accelerate the colorization process. Based on the analysis of the posteriori probability distribution of the sampling results, we can use much fewer matches for color estimation and reliability assessment. To alleviate incorrect color propagation in the sparsely scribbled regions, we generate extra color seeds according to the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently and effectively restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.

COMIRE: A Consistence-Based Mislabeled Instances Removal Method.

Training neural network classifiers (NNCs) usually requires all instances to be correctly labeled, which is difficult and/or expensive to satisfy in some practical applications. When label noise is present, mislabeled data will severely mislead the training of NNCs, resulting in poor generalization performance. In this work, we address the label noise issue by removing mislabeled instances from the training data. A COnsistence-based Mislabeled Instances REmoval (COMIRE) method is proposed. The main idea is based on the observation that during the training of the NNC, the training loss and the model's prediction uncertainty of correctly labeled instances show similar trends, while those of mislabeled instances have quite different trends. Thus, the consistency between the two trends can be used to distinguish correctly labeled instances from mislabeled ones. On this basis, an iteration scheme is introduced to further increase the separability between the two types of data. Experimental results show that COMIRE can effectively identify the mislabeled instances. Moreover, the classification performance is significantly improved after removing the identified instances from the noisy training data.

Induction of lncRNA MALAT1 by hypoxia promotes bone formation by regulating the miR-22-3p/CEBPD axis.

Adaptation to hypoxia promotes fracture healing. However, the underlying molecular mechanism remains unknown. Increasing evidence has indicated that long non-coding RNAs (lncRNAs) play crucial roles in several diseases, including fracture healing. In the present study, lncRNA microarray analysis was performed to assess the expression levels of different lncRNAs in MC3T3-E1 cells cultured under hypoxic conditions. A total of 42 lncRNAs exhibited significant differences in their expression, including metastasis associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed 3, AK046686, AK033442, small nucleolar RNA host gene 2 and distal-less homeobox 1 splice variant 2. Furthermore, overexpression of MALAT1 promoted osteoblast differentiation, alkaline phosphatase (ALP) activity and matrix mineralization of MC3T3-E1 cells, whereas its knockdown diminished hypoxia-induced cell differentiation, ALP activity and matrix mineralization in these cells. Moreover, functional analysis indicated that MALAT1 regulated the mRNA and protein expression levels of CCAAT/enhancer binding protein δ by competitively binding to microRNA-22-3p. Adenoviral-mediated MALAT1 knockdown inhibited fracture healing in a mouse model. Taken together, the results indicated that MALAT1 may serve a role in hypoxia-mediated osteogenesis and bone formation.

Robustness improvement for cyber physical system based on an optimization model of interdependent constraints.

With the rapid development of information technology, traditional infrastructure networks have evolved into cyber physical systems (CPSs). However, this evolution has brought along with it cyber failures, in addition to physical failures, which can affect the safe and stable operation of the whole system. In light of this, in this paper, we propose an interdependence-constrained optimization model to improve the robustness of the cyber physical system. The proposed model includes not only the realistic physical law but also the interdependence between the physical network and the cyber network. However, this model is highly nonlinear and cannot be solved directly. Therefore, we transform the model into a bi-level mixed integer linear programming problem, which can be easily and effectively solved in polynomial time. We conduct the simulation based on standard Institute of Electrical and Electronics Engineers test cases and study the impact of the disaster level and coupling strength on the robustness of the whole system. The simulation results show that our proposed model can effectively improve the robustness of the cyber physical system. Moreover, we compare the performance of the power supply in different CPSs, which have different network structures of the cyber network. Our work can provide useful instructions for system operators to improve the robustness of CPSs after extreme events happen in them.

Equivalent Continuous Formulation of General Hashing Problem.

Hashing-based approximate nearest neighbors search has attracted broad research interest, due to its low computational cost and fast retrieval speed. The hashing technique maps the data points into binary codes and, meanwhile, preserves the similarity in the original space. Generally, we need to solve a discrete optimization problem to learn the binary codes and hash functions, which is NP-hard. In the literature, most hashing methods choose to solve a relaxed problem by discarding the discrete constraints. However, such a relaxation scheme will cause large quantization error, which makes the learned binary codes less effective. In this paper, we present an equivalent continuous formulation of the discrete hashing problem. Specifically, we show that the discrete hashing problem can be transformed into a continuous optimization problem without any relaxations, while the transformed continuous optimization problem has the same optimal solutions and the same optimal value as the original discrete hashing problem. After transformation, the continuous optimization methods can be applied. We devise the algorithms based on the idea of DC (difference of convex functions) programming to solve this problem. The proposed continuous hashing scheme can be easily applied to the existing hashing models, including both supervised and unsupervised hashing. We evaluate the proposed method on several benchmarks and the results show the superiority of the proposed method compared with the state-of-the-art hashing methods.

Wavefront reconstruction based on deep transfer learning for microscopy.

The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.

An asymmetric interdependent networks model for cyber-physical systems.

Cyber-physical systems (CPSs) are integrations of information technology and physical systems, which are more and more significant in society. As a typical example of CPSs, smart grids integrate many advanced devices and information technologies to form a safer and more efficient power system. However, interconnection with the cyber network makes the system more complex, so that the robustness assessment of CPSs becomes more difficult. This paper proposes a new CPS model from a complex network perspective. We try to consider the real dynamics of cyber and physical parts and the asymmetric interdependency between them. Simulation results show that coupling with the communication network makes better robustness of power system. But since the influences between the power and communication networks are asymmetric, the system parameters play an important role to determine the robustness of the whole system.

Lighting Deviation Correction for Integrating-Sphere Multispectral Imaging Systems.

In an integrating sphere multispectral imaging system, measurement inconsistency can arise when acquiring the spectral reflectances of samples. This is because the lighting condition can be changed by the measured samples, due to the multiple light reflections inside the integrating sphere. Besides, owing to non-uniform light transmission of the lens and narrow-band filters, the measured reflectance is spatially dependent. To deal with these problems, we propose a correction method that consists of two stages. The first stage employs a white board to correct non-uniformity and a small white patch to correct lighting deviation, both under the assumption of ideal Lambertian reflection. The second stage uses a polynomial regression model to further remove the lighting inconsistency when measuring non-Lambertian samples. The method is evaluated on image data acquired in a real multispectral imaging system. Experimental results illustrate that our method eliminates the measurement inconsistency considerably. This consequently improves the spectral and colorimetric accuracy in color measurement, which is crucial to practical applications.

Inhibition of HIF-1α restrains fracture healing via regulation of autophagy in a rat model.

It has been demonstrated that bone fracture is associated with the activation of autophagy, and upregulation of autophagy could promote fracture healing. Previous study by our group demonstrated that activating the HIF-1α pathway via administration of cobalt (II) chloride (CoCl2) could promote fracture healing in vivo. However, the role of hypoxia-inducible factor-1α (HIF-1α) in autophagy remains unknown. In the current study, rats were divided into two groups following tibial fracture and treated with echinomycin or dimethyl sulfoxide (DMSO). Rats were sacrificed at 7, 14, 28 and 42 days after fracture. The evaluation of fracture healing was performed by micro-computed tomography. In addition, the effects of echinomycin on microtubule-associated protein 1 light chain 3 (LC3 II), runt-related transcription factor 2 (Runx2), alkaline phosphatase (ALP), Unc-51-like autophagy activating kinase 1 (ULK1) and P62 were detected at the mRNA and protein levels by reverse transcription-quantitative polymerase chain reaction, western blotting and immunohistochemistry. The results demonstrated that the expression of LC3 II was markedly decreased following systemic administration of echinomycin (0.05 mg/kg every other day for 42 days, intraperitoneally). Furthermore, the levels of Runx2, ALP and ULK1 were decreased, while those of P62 were increased, at the mRNA and protein levels in rats treated with echinomycin in vivo. In summary, the current study suggested that HIF-1α may serve an important role in fracture healing via the downregulation of autophagy.

Long non-coding RNA XIST predicts poor prognosis and promotes malignant phenotypes in osteosarcoma.

Increasing evidence indicates that long non-coding RNA (lncRNA) is involved in a number of types of human cancer and functions as an oncogene or tumor suppressor. However, little is known about the role of lncRNA X-inactive specific transcript (XIST) in osteosarcoma. In the present study, the expression of lncRNA XIST was analyzed in 56 osteosarcoma tissues and their corresponding normal bone tissues by reverse transcription-quantitative polymerase chain reaction. Increased lncRNA XIST expression was observed in osteosarcoma tissues compared with the normal tissues and was positively associated with advanced Enneking stage. In addition, lncRNA XIST may be used as an independent risk factor for the overall survival of patients with osteosarcoma. Knockdown of lncRNA XIST inhibited cell proliferation, induced cell apoptosis and arrested the cell cycle. The inhibition of lncRNA XIST may also suppress cell migration and invasion in vitro. The present study suggested that lncRNA XIST acted as an oncogene in osteosarcoma, which may make it a novel therapeutic target for the treatment of osteosarcoma.

Distributed Graph Hashing.

Recently, hashing-based approximate nearest neighbors search has attracted considerable attention, especially in big data applications, due to its low computation cost and fast retrieval speed. In the literature, most of the existing hashing algorithms are centralized. However, in many large-scale applications, the data are often stored or collected in a distributed manner. In this situation, the centralized hashing methods are not suitable for learning hash functions. In this paper, we consider the distributed learning to hash problem. We propose a novel distributed graph hashing model for learning efficient hash functions based on the data distributed across multiple agents over network. The graph hashing model involves a graph matrix, which contains the similarity information in the original space. We show that the graph matrix in the proposed distributed hashing model can be decomposed into multiple local graph matrices, and each local graph matrix can be constructed by a specific agent independently, with moderate communication and computation cost. Then, the whole objective function of the distributed hashing model can be represented by the sum of local objective functions of multiple agents, and the hashing problem can be formulated as a nonconvex constrained distributed optimization problem. For tractability, we transform the nonconvex constrained distributed optimization problem into an equivalent bi-convex distributed optimization problem. Then we propose two algorithms based on the idea of alternating direction method of multipliers to solve this problem in a distributed manner. We show that the proposed two algorithms have moderate communication and computational complexities, and both of them are scalable. Experiments on benchmark datasets are given to demonstrate the effectiveness of the proposed methods.

Multispectral Image Super-Resolution via RGB Image Fusion and Radiometric Calibration.

Multispectral imaging is of wide application for its capability in acquiring the spectral information of scenes. Due to hardware limitation, multispectral imaging device usually cannot achieve high-spatial resolution. To address the issue, this paper proposes a multispectral image super-resolution algorithm, referred as SRIF, by fusing the low-resolution multispectral image and the high-resolution (HR) RGB image. It deals with the general circumstance that image intensity is linear to scene radiance for multispectral imaging devices while is nonlinear and unknown for most RGB cameras. The SRIF algorithm first solves the inverse camera response function and a spectral sensitivity function of RGB camera, and establishes the linear relationship between multispectral and RGB images. Then the unknown HR multispectral image is efficiently reconstructed according to the linear image degradation models. Meanwhile, the edge structure of the reconstructed HR multispectral image is kept in accordance with that of the RGB image using a weighted total variation regularizer. The effectiveness of the SRIF algorithm is evaluated on both public datasets and our image set. Experimental results validate that the SRIF algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.

Comparison of remote ischemic preconditioning and intermittent hypoxia training in fracture healing.

Fracture healing in elderly patients is an emerging public health concern. As non­drug treatments, intermittent hypoxia training (IHT) and remote ischemic preconditioning (RIPC) are considered to have substantial advantages and to aid fracture healing in elderly patients. The purpose of the present study was to evaluate and compare the effects of IHT and RIPC on fracture healing. Micro­computed tomography (micro­CT) and biomechanical testing were used to assess the morphology and structural properties of bone callus dissected from aged rats with tibial fractures. In addition, hypoxia­inducible factor­1α (HIF­1α) and its target gene, associated with the healing process, were investigated by reverse transcription­quantitative polymerase chain reaction and western blot analyses. The micro­CT­based parameters, including bone mineral density and trabecular number, were measured, and significant differences were identified between the experimental and control groups. The IHT group exhibited superior bone formation and mineralization rates compared with the RIPC group. The biomechanical testing revealed that the ultimate loading and stiffness values were significantly higher in the IHT group compared with those in the RIPC group. In accordance with previous studies, RIPC exerted a similar effect in increasing the expression of HIF­1α, and its downstream genes, throughout the course of healing. In addition, the IHT group exhibited increased expression levels of HIF­1α compared with the RIPC group. Taken together, the results suggested that IHT and RIPC significantly enhanced fracture healing; however, IHT exhibited superior bone formation and healing effects compared with RIPC.

Femoral artery injury during total hip arthroplasty.

There are an increasing number of vascular complications after hip replacement, some of which can be life-threatening. However, there are few reports of lower limb ischemic symptoms after undergoing an otherwise uncomplicated classic total hip replacement. We report a patient with low weight who developed postoperative limb ischemia resulting from blood clots caused by insertion of a Hohmann retractor close to small anterior acetabular osteophytes. Ultrasonography and angiography revealed her symptoms to be the result of femoral artery intimal injury with lower extremity arterial thrombosis, which led to pain, numbness, and decreased skin temperature. The patient underwent timely percutaneous intervention with a femoral artery stent, which relieved her symptoms. The discussion reviews femoral artery injury during total hip arthroplasty.

Machine learning guided rapid focusing with sensor-less aberration corrections.

Non-invasive, real-time imaging and deep focus into tissue are in high demand in biomedical research. However, the aberration that is introduced by the refractive index inhomogeneity of biological tissue hinders the way forward. A rapid focusing with sensor-less aberration corrections, based on machine learning, is demonstrated in this paper. The proposed method applies the Convolutional Neural Network (CNN), which can rapidly calculate the low-order aberrations from the point spread function images with Zernike modes after training. The results show that approximately 90 percent correction accuracy can be achieved. The average mean square error of each Zernike coefficient in 200 repetitions is 0.06. Furthermore, the aberration induced by 1-mm-thick phantom samples and 300-µm-thick mouse brain slices can be efficiently compensated through loading a compensation phase on an adaptive element placed at the back-pupil plane. The phase reconstruction requires less than 0.2 s. Therefore, this method offers great potential for in vivo real-time imaging in biological science.

Gait analysis of patients with continuous proximal sciatic nerve blockade in flexion contractures after primary total knee arthroplasty.

BACKGROUND: The main objective of total knee arthroplasty is to relieve pain, restore normal knee function, and improve gait stability. Significant flexion contractures can severely impair function after surgery. The purpose of this study is to evaluate the efficacy of implementing a continuous proximal sciatic nerve block in conjunction with aggressive physical therapy to treat patients with persistent flexion contractures that were recalcitrant to rehabilitation efforts following primary total knee arthroplasty (TKA). METHODS: From December 2012 to January 2016, the following subjects were enrolled in this study: 20 patients (15 females and 5 males aged between 62 and 78 years old; median age = 65.7 y) with flexion contractures ranging from 15° to 25° (19.2°±5.6°) that persisted for at least 1.5 months following total knee arthroplasty and showed no significant improvement in response to conventional therapeutic methods. Demographic data, the passive range of motion, flexion contracture, pain score during stretching, and the Hospital for Special Surgery knee score were recorded. A portable motion analyzer was used to obtain the corresponding gait parameters from the flexion contractures group and control group. Repeated measurement ANOVA was used to compare the clinical results. RESULTS: In combination with 2 to 4 (2.5 ± 1.3) months of aggressive knee stretching exercises, 16 out of 18 knees achieved full extension, and 2 out of 18 improved to within 5° of the full extension. An average of the 12 to 48 (26.6 ± 10.7) month follow-up showed that this improved range of motion was maintained for all the corresponding patients, and that there were no reoccurrences of deformity. The mean Hospital for Special Surgery knee scores improved from 61.2 to 93.2 points (p < 0.001). After six months of continuous proximal sciatic nerve blockage, all gait parameters for the flexion contractures group exhibited significant improvement. CONCLUSION: A continuous proximal sciatic nerve block could be a useful adjunct to a physical therapy regimen for patients with knee flexion contractures, especially for patients with difficult-to-treat cases of knee flexion contracture that are recalcitrant to conservative therapy.