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Ubiquitous computing and artificial intelligence are widely used in the field of Wise Information Technology of Med. It promotes the development of intelligent diagnosis and treatment, as well as reducing the workload of doctors’ diagnosis and promoting the improvement of the diagnosis level of medical institutions in remote areas. The accurate detection of cervical cytopathy is related to the precise treatment and rehabilitation of patients. However, the low rate of accuracy in the existing cervical cytopathy image detection cannot satisfy the application needs of intelligent diagnosis. In this paper, a dual path network efficient detection method for cervical cytopathy (the proposed DSRNet50) is proposed, which is based on the deep learning method, and combines residual structure and dense connection. The proposed DSRNet50 is mainly based on residual structure, supplemented by dense connection paths, which improves the utilization of features, and maintains the ability of exploring new features by reducing feature redundancy. Meanwhile, the proposed DSRNet50 leverages packet convolution to reduce the computation burden of the network and eliminate the over fitting phenomenon of the network. The proposed DSRNet50 further exploits channel attention mechanism to recalibrate important features to suppress the propagation of irrelevant information. We use the Herlev dataset to verify the proposed DSRNet50 in the terms of the detection accuracy, parameter quantity, and computing complexity. The experiment results are presented to show the achievable performance of the proposed DSRNet50.

Projecting the original data into a low-dimensional target space for feature extraction is a common method. Recently, presentation-based approaches have been widely concerned and many feature extraction algorithms based on this have been proposed. However, in the process of acquiring real data, the pollution of complex noise cannot always be avoided, which will greatly increase the difficulty of feature extraction and even lead to failed feature extraction results. Thus, a robust image feature extraction model based on Orthogonal Low-Rank Projection Learning (OLRPL) is proposed, in which the introduction of orthogonal matrix can encourage the preservation of the main components of the sample. Particularly, the row sparsity constraint introduced on the projection matrix can encourage the features to be more compact, discriminative and interpretable. In particular, the Weighted Truncated Schatten \emph{p}-norm (WTSN) is proposed to better solve the optimization problem of low-rank constraints. At the same time, the correntropy is applied in OLRPL to suppress the complex noise in the data and thus improve the robustness of the model. Finally, we specially design a robust classification loss function so that our model can be fitted the supervised scene effectively. Experiments on five general databases have proved that OLRPL has better effectiveness and robustness than existing advanced methods.

The toxicity and incidence of fumonisin B

Because of diverse adverse effects of carbendazim (CBZ) upon the human body and environment, a simple method for the determination of this molecule is urgently needed. Therefore, a method based on ...

In this study, yolk-shell Fe

Zearalenone (ZEN) is a widespread nonsteroidal mycotoxin with estrogen-like activity. Sensitive and reliable quantification of ZEN in food is critical to ensure food safety and safeguard agricultural production. Herein, by combining the metal-organic frameworks-based nanozyme (MOFzyme) and hyaluronic acid (HA)-DNA hydrogel, a well-designed colorimetric aptasensor was developed. The HA-DNA hydrogel was deposited on the surface of the bimetallic MOFzyme via strand-induced hybridization chain reaction (HCR). Relying on the ZEN-specific aptamer integrated in hydrogel structure, the disintegration of the hydrogel network and the concomitant exposure of the encapsulated MOFzyme could be specifically triggered by the introduction of ZEN molecules. Moreover, the magnitude of hydrogel disintegration was positively correlated with the amount of ZEN, by which the quantification of ZEN can be effortlessly achieved. Benefiting from the delicate design, the satisfactory catalytic performance and stability of bimetallic MOFzyme and the appealing stimuli-responsiveness of DNA hydrogel, the developed aptasensor demonstrated superior analytical performance and ease of use. Under optimal conditions, the linear range of the aptasensor fell between 0.001 and 200 ng mL

Using multi-kernel learning to deal with the non-linear relationship of data has become a new research topic in the field of multi-view subspace clustering. However, the existing methods have the following three defects: 1) the simple consensus kernel weighting strategy cannot give full play to the advantages of multiple kernels; 2) they are sensitive to non-Gaussian noise and their learning affinity matrices cannot meet the block diagonal properties required by clustering, resulting in low clustering performance; 3) the complementary feature information between the data of each view cannot be fully mined. In this paper, a novel robust multi-view subspace clustering method is proposed based on weighted multi-kernel learning and co-regularization (WMKMSC). Based on the self-expression learning framework, block diagonal regularizer (BDR), multi-kernel learning strategy and co-regularization are integrated into the proposed model. Especially, as a robust learning method, the mixture correntropy is used to construct a robust multi-kernel weighting strategy, which is helpful to learn the best consensus kernel. Our method is more effective and robust than several of the most advanced methods on five commonly used datasets.

To address the problem of phase unwrapping for interferograms, a deep learning (DL) phase-unwrapping method based on adaptive noise evaluation is proposed to retrieve the unwrapped phase from the wrapped phase. First, this method uses a UNet3+ as the skeleton and combines with a residual neural network to build a network model suitable for unwrapping wrapped fringe patterns. Second, an adaptive noise level evaluation system for interferograms is designed to estimate the noise level of the interferograms by integrating phase quality maps and phase residues of the interferograms. Then, multiple training datasets with different noise levels are used to train the DL network to achieve the trained networks suitable for unwrapping interferograms with different noise levels. Finally, the interferograms are unwrapped by the trained networks with the same noise levels as the interferograms to be unwrapped. The results with simulated and experimental interferograms demonstrate that the proposed networks can obtain the popular unwrapped phase from the wrapped phase with different noise levels and show good robustness in the experiments of phase unwrapping for different types of fringe patterns.

Deoxynivalenol (DON) is a mycotoxin commonly found in cereals and animal feeds, which also is a carcinogen easily soluble in water. So it has drawn considerable interest in food safety. In this study, a specific aptamer named DON-A16 was developed from an immobilized ssDNA library by affinity chromatography column-based the SELEX. The complementary chain competition method for DON detection was established, and its limit of detection (LOD) was 488 ng/mL. Graphene oxide-based fluorescence quenching was developed using DON-A16 with fluorescein (FAM) label; its LOD was 200 ng/mL, and the detection time was 3 h. The recovery rate of fluorescence quenching was also detected in different samples (oat, wheat flour, corn), which showed the commercial DON ELISA kit led to an increase in repeatability, satisfactory recovery and consistency. The three-dimensional structure and the main binding site of of DON-A16 was ultimately obtained by molecular docking simulation.

How Can Internet Technologies Help Hospital to Curb COVID-19: PUMCH Experience from ChinaQianyu Zhuang1,2, Guoqiang Sun1, Feng Zhang1, Jianguo Zhang2, Liluan You1, Si Chen1, Ke Zeng1, Xiaoyang Meng1, Wen Li1, Yue Peng2, Peng Peng1,3, Weiguo Zhu1,4*, Mingwei Qin5, Mingyan Chen6, Bingshui Hu7, Hui Pan8Department of information managementDepartment of OrthopedicsDepartment of Obstetrics and GynaecologyDepartment of Primary Care and Family MedicineTelemedicine centerPublicity DepartmentOutpatient departmentMedical administration department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, P.R. China

This study assesses the feasibility of real-time telephotocoagulation treatment for diabetic retinopathy., Key Points Question How feasible is fifth-generation (5G) real-time telemedicine-mediated laser photocoagulation as a treatment for diabetic retinopathy? Findings In this study, a retinal specialist in Beijing, China, performed an online 5G real-time navigated retinal laser photocoagulation procedure on 6 participants (9 eyes) with diabetic retinopathy located in Huzhou, China. All procedures were able to be completed without noticeable delay, and no safety issues were identified. Meaning The combination of a 5G high-speed network and navigated retinal laser photocoagulation may enable a novel teleophthalmology paradigm that can provide essential remote health care to patients with diabetic retinopathy., Importance Interest in teleophthalmology has been growing, especially during the COVID-19 pandemic. The advent of fifth-generation (5G) wireless systems has the potential to revolutionize teleophthalmology, but these systems have not previously been leveraged to conduct therapeutic telemedicine in the ophthalmology field. Objective To assess the feasibility of 5G real-time laser photocoagulation as a telemedicine-based treatment for diabetic retinopathy (DR). Design, Setting, and Participants This was a prospective study involving a retinal specialist from the Peking Union Medical College Hospital in Beijing, China, who performed online 5G real-time navigated retinal laser photocoagulation to treat participants with proliferative or severe nonproliferative DR who had been recruited in the Huzhou First People’s Hospital in Zhejiang Province, China, located 1200 km from Beijing from October 2019 to July 2020. Interventions These teleretinal DR and laser management procedures were conducted using a teleophthalmology platform that used the videoconference platform for teleconsultation, after which telelaser planning and intervention were conducted with a laser system and a platform for remote computer control, which were connected via 5G networks. Main Outcomes and Measures Diabetic eye prognosis and the real-time laser therapy transmission speed were evaluated. Results A total of 6 participants (9 eyes) were included. Six eyes were treated via panretinal photocoagulation alone, while 1 eye underwent focal/grid photocoagulation and 2 eyes underwent both panretinal photocoagulation and focal/grid photocoagulation. The mean (SD) age was 53.7 (13.6) years (range, 32-67 years). The mean (SD) duration of diabetes was 14.3 (6.4) years (range, 3-20 years). The mean (SD) logMAR at baseline was 0.32 (0.20) (20/30 Snellen equivalent). Retinal telephotocoagulation operations were performed on all eyes without any noticeable delay during treatment. The mean (SD) number of panretinal photocoagulation laser spots per eye in 1 session was 913 (243). Conclusions and Relevance This study introduces a novel teleophthalmology paradigm to treat DR at a distance. Applying novel technologies may continue to ensure that remote patients with DR and other conditions have access to essential health care. Further studies will be needed to compare this approach with the current standard of care to determine whether visual acuity or safety outcomes differ.

In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources.

Borax is considered highly toxic and a strong carcinogen, but it is widely used as a toughening and brightening additive in food processing. At present, there is no definitive standard assay for the direct identification of borax in foods in most countries. In this study, borax-specific aptamers with high affinity and specificity were selected using Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high throughput sequencing. Next, a colorimetric detection method was proved to be feasible by nuclear magnetic resonance (NMR), using a selected aptamer, Bor-A01, with gold nanoparticles (AuNPs) for the direct detection of borax. Due to the matrix effect of real foods, the actual results of most detection methods may have large deviation or error. After optimizing detection conditions and pretreating food samples, the colorimetric detection assay allowed the specific recognition of borax within 1 h, with the visual limit of detection (LOD) range for borax of 0.30−0.50 μg/mL in different food samples. Overall, this colorimetric method based on aptamer-AuNPs with high sensitivity, simple pre-treatment, short operation time, and low cost will be applicable as a novel detection strategy to prohibit the use of borax in foods.

This paper proposes a novel design method of parallel Finite Impulse Response (FIR) filter, which structure is parallel transposed. It can increase the running speed by M times compared with the serial FIR filter, where the M is the number of sub-filters, and the parallel FIR filter only introduces very small delay. Firstly the theoretical foundation of parallel FIR filters is analyzed. An example of the floating point parallel transposed FIR band-pass filter is given to verify the algorithm. Then a parallel transposed FIR band-pass filter is designed, which has optimum fixed point coefficients. Finally the fixed point of this filter is implemented in Xilinx's Virtex-6 Field Programmable Gate Array (FPGA). According to the simulation results, this filter has smaller resource consumption and its sampling rate up to 1.2GHz.

Background Studies have shown that nonspecific interstitial pneumonitis (NSIP), even when initially diagnosed as an idiopathic form of the disease, might be associated with an autoimmune background that later reveals itself as an organ-specific or a systemic autoimmune disease. Methods NSIP patients were divided into three groups. The NSIP patients who met the criteria for having a systemic autoimmune disease (SAD) were defined as the systemic autoimmune disease-associated NSIP (SAD-NSIP) group. The NSIP patients who did not meet the criteria for a systemic autoimmune disease were defined as an antibody-positive group (i-NSIP-Ab + group) if their sera were positive for autoantibodies. The NSIP patients with negative serologic tests for auto-antibodies were defined as the antibody-negative group (i-NSIP-Ab- group). The clinical characteristics were analyzed and compared among the three groups. Results Ninety-seven NSIP patients were included. The mean age of the study population was 48 ± 11 years. The mean follow-up time was 54 ± 34 months. At the time of the surgical lung biopsies, 23/97 (23.7%) of the patients were classified as SAD-NSIP; 30/97 (30.9%) were in the i-NSIP-Ab + group; and 44/97 (45.4%) were in the i-NSIP-Ab- group. At the end of the follow-up period, three cases were diagnosed with polymyositis (one case from the i-NSIP-Ab + group, two cases from the i-NSIP-Ab- group), one with scleroderma (from the i-NSIP-Ab + group, scl-70 positive and skin biopsy) and another one with microscopic polyarteritis (from the i-NSIP-AB-group, p-ANCA and MPO positive, renal biopsy). Three cases in the i-NSIP-Ab- group were later found to be positive for autoantibodies. Due to these changes in classification, at the end of the follow-up period, the SAD-NSIP group consisted of 28/97 patients (28.9%), the i-NSIP-Ab + group of 31/97 (32.0%) and the i-NSIP-Ab- group of 38/97(39.1%). There were no significant differences in clinical manifestations, radiographic findings or pulmonary function tests among the three groups at the time of surgical lung biopsy or after reclassification after the follow-up period. SAD was an independent risk factor for the survival of the patients with NSIP after follow-up. Conclusion Follow-up is recommended because idiopathic NSIP may be the first manifestation of a systemic autoimmune disease.

Littoral cell angioma is a rare vascular tumor of the spleen that was described by Falk et al. in 1991. Because of the limited number, untypical imaging manifestations, and lack of knowledge on this tumor type, these tumors are often misdiagnosed. In most cases, the tumor presents with multiple small hypoattenuating nodules in the spleen with delayed enhancement. However, solitary littoral cell angiomas have not been well described. We present the CT features of an unusual littoral cell angioma mimicking hepatic tumor.

To identify the optimal dosage of 17beta-estradiol gel + oral progestin for preventing bone loss in postmenopausal Chinese women.A 3-year open label, randomized, prospective clinical trial was conducted. Sixty healthy women who had been postmenopausal for 1 to 5 years were recruited and divided into following 4 groups: group 1, percutaneous gel 17beta-estradiol (E(2)) 1.5 mg/d plus micronized progesterone (MP) 100 mg/d; group 2, percutaneous gel 17beta-estradiol (E(2)) 1.5 mg/d plus medroxyprogesterone acetate (MPA) 2 mg/d; group 3, percutaneous gel 17beta-estradiol (E(2)) 0.75 mg/d plus micronized progesterone (MP) 100 mg/d; and group 4, percutaneous gel 17beta-estradiol (E(2)) 0.75 mg/d plus medroxyprogesterone acetate (MPA) 2 mg/d. Estrogen and progestin were given continuously for 25 days per month. Bone mineral density (BMD) was measured using quantitative computed tomography (QCT) for trabecular bone of L2-5 and dual energy X-ray absorptiometry (DEXA) for L2-4 and hip 5 times during the trial at baseline and at the 6-, 12-, 18-, 24- and 36-month visits.Fifty-nine patients (98.3%, 59/60) stayed in the study for 1 year, 56 patients (93.3%, 56/60) for 2 years, and 51 (85%, 51/50) for 3 years. On average, menopausal symptoms were relieved by 80% after 6 months of treatment. By the 24th month, the mean increase in BMD ranged from 4.3% to 7.5% in trabecular bone; and by the 36th month, it ranged from 4.2% to 6.2% in L2-4 and 1.61% to 3.77% in the neck. There were significant difference after treatment (P0.05). Among the four groups, no significant difference (P0.05) was found in improvement of symptoms, levels of bone markers or BMD.A daily dose of estradiol gel, either 0.75 mg or 1.5 mg, is effective in preventing early postmenopausal bone loss and relieving menopausal symptoms. After 3-year treatment, spinal BMD could increase steadily, so does hip BMD, especially in the first 2 years.