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Introduction The standard approach to thyroidectomy is a collar incision via the anterior neck, and the neck scar has always been a source of worry for patients. Acceptable wound cosmetology has become a focus for thyroid surgeons. Objective To verify the effectiveness and cosmetic results of thyroidectomy through a lateral supraclavicular incision. Methods 180 patients were randomly divided into two groups: a lateral supraclavicular approach and a conventional transcervical approach. The main outcomes included incision length, intraoperative blood loss, operative time, total drainage volume, hospitalization expense, early postoperative pain measured by visual analog scale, infection, and perceived cosmetic outcome. Results There were no statistical significances between the two groups in terms of age, gender, nodule size, intraoperative blood loss, operative time, total drainage volume, hospital expense and postoperative complications, whereas there were significant differences in terms of incision length (5.2 ± 1.04 cm vs. 6.9 ± 1.14 cm, p < 0.05). Conclusions The lateral supraclavicular incision is a safe and feasible approach for thyroidectomy. Compared with conventional approach, it provides a better cosmetic result. Resumo Introdução A abordagem padrão para tireoidectomia é uma incisão em colar na face anterior do pescoço; a cicatriz no pescoço sempre foi uma fonte de preocupação para os pacientes; consequentemente, a cosmetologia aceitável da ferida tornou‐se um foco de atenção para os cirurgiões de cabeça e pescoço. Objetivos Verificar a eficácia e os resultados cosméticos da tireoidectomia por meio de incisão supraclavicular lateral. Método Foram divididos aleatoriamente 180 pacientes em dois grupos: um grupo supraclavicular lateral (Grupo LS) e outro transcervical convencional (Grupo TC). Os desfechos principais incluíram comprimento da incisão, perda de sangue intraoperatória, tempo cirúrgico, volume total de drenagem, despesas hospitalares, dor no pós‐operatório imediato medida através de escala visual analógica, infecção e resultado cosmético percebido. Resultados Não houve significância estatística entre os dois grupos em termos de idade, sexo, tamanho do nódulo, perda sanguínea intraoperatória, tempo cirúrgico, volume total de drenagem, custo hospitalar e complicações pós‐operatórias, mas houve diferença significante em termos de comprimento da incisão (5,2 ± 1,04 cm vs. 6,9 ± 1,14 cm, p < 0,05). Conclusão A incisão supraclavicular lateral é uma abordagem segura e viável para tireoidectomia. Em comparação com a abordagem convencional, oferece um melhor resultado cosmético.

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

Gesture recognition has become increasingly important in human-computer interaction and can support different applications such as smart home, VR, and gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using commodity WiFi without requiring user to wear any sensors. Our system takes advantages of the fine-grained Channel State Information available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. We devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Lastly, we utilize multiple WiFi links and larger bandwidth at 5GHz to achieve finger gesture recognition under multi-user scenario. Our experimental evaluation in different environments demonstrates that our system can achieve over 90% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can provide accurate gesture recognition under different scenarios.

To compare safety and healthcare utilization after sleeve gastrectomy versus Roux-en-Y gastric bypass in a national Medicare cohort.Though bariatric surgery is increasing among Medicare beneficiaries, no long-term, national studies examining comparative effectiveness between procedures exist. Bariatric outcomes are needed for shared decision-making and coverage policy concerns identified by the cMS Medicare Evidence Development and Coverage Advisory Committee.Retrospective instrumental variable analysis of Medicare claims (2012-2017) for 30,105 bariatric surgery patients entitled due to disability or age. We examined clinical safety outcomes (mortality, complications, and reinterventions), healthcare utilization [Emergency Department (ED) visits, rehospitalizations, and expenditures], and heterogeneity of treatment effect. We compared all outcomes between sleeve and bypass for each entitlement group at 30 days, 1 year, and 3 years.Among the disabled (n = 21,595), sleeve was associated with lower 3-year mortality [2.1% vs 3.2%, absolute risk reduction (ARR) 95% confidence interval (CI): -2.2% to -0.03%], complications (22.2% vs 27.7%, ARR 95%CI: -8.5% to -2.6%), reinterventions (20.1% vs 27.7%, ARR 95%CI: -10.7% to -4.6%), ED utilization (71.6% vs 77.1%, ARR 95%CI: -8.5% to -2.4%), and rehospitalizations (47.4% vs 52.3%, ARR 95%Ci: -8.0% to -1.7%). Cumulative expenditures were $46,277 after sleeve and $48,211 after bypass (P = 0.22). Among the elderly (n = 8510), sleeve was associated with lower 3-year complications (20.1% vs 24.7%, ARR 95%CI: -7.6% to -1.7%), reinterventions (14.0% vs 21.9%, ARR 95%CI: -10.7% to -5.2%), ED utilization (51.7% vs 57.2%, ARR 95%CI: -9.1% to -1.9%), and rehospitalizations (41.8% vs 45.8%, ARR 95%Ci: -7.5% to -0.5%). Expenditures were $38,632 after sleeve and $39,270 after bypass (P = 0.60). Procedure treatment effect significantly differed by entitlement for mortality, revision, and paraesophageal hernia repair.Bariatric surgery is safe, and healthcare utilization benefits of sleeve over bypass are preserved across both Medicare elderly and disabled subpopulations.

To compare out-of-pocket (OOP) costs for patients up to 3 years after bariatric surgery in a large, commercially-insured population.More information on OOP costs following bariatric surgery may affect patients' procedure choice.Retrospective study using the IBM MarketScan commercial claims database, representing patients nationally who underwent laparoscopic sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) January 1, 2011 to December 31, 2017. We compared total OOP costs after the surgical episode between the 2 procedures using difference-in-differences analysis adjusting for demographics, comorbidities, operative year, and insurance type.Of 63,674 patients, 64% underwent SG and 36% underwent RYGB. Adjusted OOP costs after SG were $1083, $1236, and $1266 postoperative years 1, 2, and 3. For RYGB, adjusted OOP costs were $1228, $1377, and $1369. In our primary analysis, SG OOP costs were $122 (95%CI: -$155 to -$90) less than RYGB year 1. This difference remained consistent at -$119 (95%CI: -$158 to -$79) year 2 and -$80 (95%CI: -$127 to -$35) year 3. These amounts were equivalent to relative differences of -7%, -7%, and -5% years 1, 2, and 3. Plan features contributing the most to differences were co-insurance years 1, 2, and 3.. The largest clinical contributors to differences were endoscopy and outpatient care year 1, outpatient care year 2, and emergency department use year 3.Our study is the first to examine the association between bariatric surgery procedure and OOP costs. Differences between procedures were approximately $100 per year which may be an important factor for some patients deciding whether to pursue SG or gastric bypass.

This paper presents a novel network, Contribution-Degree-based Spiking Neural Network (CDSNN), which combines ideas of spiking neural network (SNN) and fuzzy set theory. In this framework, two types of information, interval and instantaneous information conveyed by the membrane potential are described by two concepts such as area under membrane potential (AUM) and firing strength. Given that the neuron with large AUM or strong firing strength would enhance the frequency of action potentials of its postsynaptic neurons, the connection between the neuron and its postsynaptic neurons should be strengthened. Combined with an idea of membership function, three contribution degrees (E, S and ES) are defined to quantify the ability of a neuron to provide information for postsynaptic neurons. According to these three degrees, the corresponding SpikeProp learning algorithms, referred to as SPE, SPS and SPES, are developed. Experimental results obtained on ten benchmark datasets, one high-dimensional feature dataset, one big dataset and one time series dataset with some commonly used algorithms, networks and CDSNN demonstrate that CDSNN can achieve improved performance in terms of accuracy, generalization, precision, recall and F-measure. The study demonstrates that the mechanism by which interval-instantaneous information is simultaneously learned in a SNN is feasible.

Face anti-spoofing (FAS) plays a critical role in the face recognition community for securing the face presentation attacks. Many works have been proposed to regard FAS as a domain generalization problem for robust deployment in real-world scenarios. However, existing methods focus on extracting intrinsic spoofing cues to improve the generalization ability, yet neglect to train a robust classifier. In this paper, we propose a framework to improve the generalization ability of face anti-spoofing in two folds:) a generalized feature space is obtained via aggregation of all live faces while dispersing each domain’s spoof faces; and) a domain agnostic classifier is trained through low-rank decomposition. Specifically, a Common Specific Decomposition for Specific (CSD-S) layer is deployed in the last layer of the network to select common features while discarding domain-specific ones among multiple source domains. The above-mentioned two components are integrated into an end-to-end framework, ensuring the generalization ability to unseen scenarios. The extensive experiments demonstrate that the proposed method achieves state-of-the-art results on four public datasets, including CASIA-MFSD, MSU-MFSD, Replay-Attack, and OULU-NPU.

Nowadays, energy costs are the most important factor in cloud computing. Therefore, the implementation of energy-aware task scheduling methods is of utmost importance. A task scheduling framework considering deadlines, data locality and resource utilization is proposed to save on energy costs in heterogeneous clusters. The framework consists of task list construction, task scheduling and slot list updating. In terms of deadline constraints, number of job slots allocated and possible processing times of jobs, a new job sequence is proposed to construct an reasonable task list. Tasks are scheduled to promising slots from their rack-local servers, cluster-local servers and remote servers in the produced task scheduling, which greatly improves data locality. After the assignment among tasks and slots, an update of available slots in clusters is proposed not only to find available slots but also to improve server resource utilization using fuzzy logic with the available number of slots according to current CPU, memory and bandwidth utilization. Experimental results show that the proposed heuristic results in lower energy consumption than the adapted existing algorithms with a variable total number of slots.

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers' location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the "double count" and "data reliability" problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment., Paper accepted for publication in IEEE Transactions on Wireless Communications

Background The optimal cumulative cisplatin dose (CCD) during radiation therapy for locoregionally advanced nasopharyngeal carcinoma (LA‐NPC) patients receiving induction chemotherapy (IC) plus CCRT remains controversial. This study aimed to explore the treatment efficiency of CCD for high‐and low‐risk patients with LA‐NPC. Methods Data from 472 LA‐NPC patients diagnosed from 2014 to 2018 and treated with IC plus CCRT were reviewed. After propensity score matching, the therapeutic effects of a CCD > 200 and CCD ≤ 200 mg/m2 were evaluated comparatively. Five factors selected by multivariate analysis were incorporated to develop a nomogram. Subgroup analysis was conducted to explore the role of different CCDs in nomogram‐defined high‐ and low‐risk groups. Additionally, acute toxicities were evaluated comparatively between the high‐ and low‐CCD groups. Results After matching, there was no difference between different CCD groups for all patients in terms of 3‐year overall survival (OS), distant metastasis‐free survival (DMFS), locoregional recurrence‐free survival (LRRFS), or progression‐free survival (PFS). A nomogram was built by integrating pretreatment EBV DNA, clinical stage, and post‐IC EBV DNA, post‐IC primary gross tumor and lymph node volumes obtained a C‐index of 0.674. The high‐risk group determined by the nomogram had poorer 3‐year PFS, OS, DMFS, and LRRFS than the low‐risk group. A total of CCD > 200 mg/m2 increased the survival rates of 3‐year PFS and DMFS (PFS: 72.5% vs. 54.4%, p = 0.012; DMFS: 81.9% vs. 61.5%, p = 0.014) in the high‐risk group but not in the low‐risk group. Moreover, the high CCD increased treatment‐related acute toxicities. Conclusions A high CCD was associated with better 3‐year PFS and DMFS rates than a low dose for high‐risk patients but could not produce a survival benefit for low‐risk patients.

The deep convolutional neural network (CNN) has been extensively applied to polarimetric synthetic radar (PolSAR) imagery classification. However, its success is greatly dependent on numerous labeled samples for revealing and modeling the characteristics of different targets, thus remaining a challenge in maintaining high accuracy in limited sample cases. To address this issue, a deep reinforcement learning (RL)-based PolSAR image classification framework, named deep Q-fully CNN (DQFCN), is proposed in this article. In this framework, two ways are adopted to boost the classification performance while reducing training samples. On the information utilization hand, the spatial neighboring information and polarimetric decomposition information of PolSAR data are both extracted to enrich the feature representation of the sample. Meanwhile, the 3-D CNN architecture is adopted to learn the spatial-polarimetric jointed characteristics simultaneously. On the model learning hand, two RL learning strategies are employed to promote classification performance. The first one is learning from scratch, which does not use any label information as prior knowledge but learns from its self-generated experience. Learning from pretraining is the second strategy in which the networks are sequentially trained from labeled samples and experience data to reduce the time cost. As far as we know, it is the first time that an RL-based fully CNN has been proposed for PolSAR image classification. Experiments on three benchmark datasets prove the effectiveness of the proposed framework, suggesting that the two adopted strategies achieve boosted performance in all experiments, particularly in a limited sample size.

The study aimed to compare the performance of human papillomavirus (HPV)-based strategies to cytology for detecting cervical intraepithelial neoplasia grade 3 or worse (CIN3+) in routine program in China. The study included 50,434 women ages 30–64 years from January 2015 to December 2019, to assess four strategies: cytology with HPV triage (strategy 1), primary HPV testing with reflex cytology (strategy 2), primary HPV testing with HPV-16/18 genotyping and reflex cytology for non-16–18 high-risk HPV genotypes (strategy 3), and co-testing (strategy 4). The main outcomes were sensitivity for CIN3+ and colposcopy referral rate. Overall, the rates of HPV positivity and cytologic abnormality were 7.0% [95% confidential interval (CI), 6.8–7.2] and 3.5% (95% CI, 3.3–3.6], respectively. The sensitivity for CIN3+ were 88.5% for strategy 4, 83.2% for strategy 3, 64.6% for strategy 1, and 60.1% for strategy 2. The relative sensitivity of strategy 4 and strategy 3 compared with strategy 1 for detecting CIN3+ were 1.38 (95% CI, 1.24–1.54) and 1.29 (95% CI, 1.14–1.46), respectively. The colposcopy referral rates of strategies 4 and 3 were significantly higher than that of strategy 1 (2.4% and 2.2% vs. 1.4%, P < 0.001). In conclusion, co-testing and primary HPV testing with HPV-16/18 genotyping and reflex cytology improved the sensitivity for CIN3+ compared with cytology but increased the colposcopy referral rate. Long-term negative predicted value for HPV-negative women should be studied to determine the screening interval. Our study provides further evidence to the introduction of HPV-based strategies in China. Prevention Relevance: Both co-testing and primary HPV testing with HPV-16/18 genotyping and reflex cytology triage provided higher sensitivity for detecting CIN3+; however, the number of colposcopy referrals also increased compared with cytology in a routine program. It has great public health implications for the introduction of HPV-based screening strategies in China.

Outfalls into rivers are the final gate of anthropogenic pollution flowing to receiving waters, which means that outfall surveys are significant to basin environmental protection and ecosystem health management. Unmanned aircraft systems (UAS) with high spatial resolution imagery have become important data for ongoing surveys of outfalls. However, outfalls retrieval from UAS imagery is inefficient to visual interpretation and a challenging task for traditional spectral-based and object-oriented classification methods given the problems of salt-and-pepper noise and scale selection. In this study, an improved geo-deep learning approach based on the faster region convolutional neural network (R-CNN) architecture (GDCNN-outfalls) is proposed for retrieving outfalls into rivers with UAS imagery. In the proposed method, three tactics--anchor size, region of interest (RoI), and hard negative mining--were adopted to optimize the benchmark Faster R-CNN application in outfalls retrieval. Meanwhile, a geo-classifier module with digital surface model (DSM) enhancement and a spatial activation function was integrated with the Faster R-CNN architecture to generate GDCNN-outfalls. The validation experiments indicated that GDCNN-outfalls improved the performance of Faster R-CNN in outfall retrieval by suppressing false positive (FPs) from 33.52% to 26.14% and increasing the F1 score from 0.72 to 0.75. The test results confirm the performance of GDCNN-outfalls with a recall of 79.3% and higher precision (48.4%) than that of Faster R-CNN (2.1%), also show the GDCNN-outfalls is ten times faster than visual interpretation. This study demonstrates that the combination of deep learning and UAS techniques can be a feasible solution to detect outfalls in outfall surveys.

Seizure prediction for drug-refractory epilepsy patients can improve their quality of life, reduce their anxiety, and help them take the necessary precautions. Nowadays, numerous deep learning algorithms have been proposed to predict seizure onset and obtain better performance than traditional machine learning methods. However, these methods require a large set of parameters and large hardware resources; they also have high energy consumption. Therefore, these methods cannot be implemented on compact, low-power wearable, or implantable medical devices. The devices should operate on a real-time basis to continually inform the epileptic patients. In this paper, we describe energy-efficient and hardware-friendly methods to predict the epileptic seizures. A model of only 45 kB was obtained by the neural architecture search and was evaluated across three datasets. The overall accuracy, sensitivity, false prediction rate, and area under receiver operating characteristic curve were 99.53%, 99.81%, 0.005/h, 1 and 93.60%, 93.48%, 0.063/h, 0.977 and 86.86%, 85.19%, 0.116/h, 0.933, respectively, for the CHB-MIT scalp, the AES and Melbourne University intracranial electroencephalography (EEG) datasets. This model was further reduced with network pruning, quantization, and compact neural networks. The performances for the model sizes less than 50 kB for scalp EEG data and less than 10 kB for intracranial EEG data outperformed all the other models of similar model sizes. In particular, the energy consumption estimation was less than 10 mJ per inference for scalp EEG signal and less than 0.5 mJ per inference for intracranial EEG signal, which meet the requirements for low-power wearable and implantable devices, respectively.

Pilot spoofing attack brings new challenges to physical layer secure transmission. However, this method will not work without any knowledge about the pilot sequence and active eavesdropping can be detected by constructing random pilot sequence. Intelligent reflecting surface (IRS), with the real-time programmable features for wireless channels, provides new possibilities for effective pilot spoofing. In this paper, the IRS is deployed near the legitimate users and the legitimate signal can always be passively reflected. Then the control strategy is embedded into the communication process under time-division duplex mode to assist eavesdroppers to conduct pilot spoofing. By setting different phase shifts at the IRS during the uplink phase and downlink phase, the channel reciprocity disappears, and thus secure beamforming vector is biased towards the eavesdropper. Furthermore, in order to overhear more information, the average secrecy rate minimization problem based on statistical channel state information is established by carefully designing the phase shifts, which is non-trivial to solve. With alternating optimization and Charnes-Cooper transformation technique, the original problem is transformed into convex form and a near optimal solution is achieved. Finally, simulation results show that our proposed scheme can pose serious secure threat without any energy footprint. What's more, if the IRS is not utilized by the internal users properly, it will bring more threat.

The purpose of this study was to explore the relationship between plaque characteristics and re-occlusion after surgical treatment of internal carotid artery occlusion (ICAO). From January 2015 to January 2021, 177 patients with ICAO underwent surgery. Eighty-five cases were included in the study, and in 13 of them, re-occlusion occurred within 6 mo after surgery treatment (13/85, 15.85%). The calcification at the base of the plaque was longer in the re-occlusion group than in the non-occlusion group (10.70 ± 4.22 mm vs. 7.15 ± 1.41 mm, p = 0.001). Multivariate regression analysis revealed that the length of calcification at the base of the plaque was an independent risk factor for postoperative re-occlusion (odds ratio [OR]: 1.414, 95% confidence interval [CI]: 1.078–1.855, p = 0.012). The cutoff value for the length of calcification at the base of the plaque predicting re-occlusion after ICAO was 8.5 mm (95% CI: 0.700–0.962, p = 0.001). The area under the receiver operating characteristic curve was 0.831. Sensitivity and specificity were 70% and 80.9%, respectively. These results indicate that pre-operative ultrasound examination of the length of calcification at the base of the plaque could predict re-occlusion after surgical treatment of ICAO.

Purpose The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises. Design/methodology/approach In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result. Findings The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963. Originality/value A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

International audience; This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay. The gated recurrent unit (GRU) neural network is adopted to formulate the forecast model and learn the potential rules in the field observations using the Nesterov-accelerated Adam (Nadam) algorithm. In the proposed procedure, the GRU-based forecast model is first trained based on the field data of previous and current stages. Then, the field data of the current stage are used as input to predict the deformation response of the next stage via the previously trained GRU-based forecast model. This updating process will loop up till the end of the excavation. This procedure has the advantage of directly predicting the deformation response of unexcavated stages based on the monitoring data. The proposed intelligent procedure is verified on two well-documented cases in terms of accuracy and reliability. The results indicate that both wall deflection and ground settlement are accurately predicted as the excavation proceeds. Furthermore, the advantages of the proposed intelligent procedure compared with the Bayesian/optimization updating are illustrated.

Background : Neuroimaging studies have revealed abnormal cortical folding pattern and disruptive functional connectivity in major depressive disorder (MDD). Combining structure and function in the same population may further our understanding of the neuropathological mechanisms of MDD. Method : Sixty-two patients with MDD and 61 healthy controls (HCs) underwent structural and resting-state functional magnetic resonance imaging (MRI). Group differences in the cortical folding (measured by local gyrification index (LGI)) were analyzed in FreeSurfer. Taking the brain regions with significant group differences in LGI as seed regions, the resting-state functional connectivity analysis was further conducted to explore the corresponding functional connectivity alterations. Results : Comparing with HCs, patients with MDD showed significantly decreased LGI in the right fusiform gyrus (cohen's d = 0.70). In the seed-based functional connectivity analysis, we found that compared with HCs, patients with MDD showed decreased functional connections between the right fusiform gyrus with sensorimotor areas (precentral and postcentral gyrus) (cohen's d = 1.32) and right superior temporal gyrus (cohen's d = 0.94). Limitations : Main limitations are the relatively small sample size and the cross-sectional study design. Conclusion : Decreased LGI in the right fusiform gyrus, as well as decreased functional connectivity between the right fusiform gyrus and the sensorimotor area and right superior temporal gyrus, appears to play a role in the pathophysiology of MDD.

Background Primary cutaneous gamma/delta T-cell lymphoma is aggressive, frequently presenting as multiple plaques, tumors, and/or subcutaneous nodules. Methods In this study, we conducted a retrospective study in a tertiary center in Taiwan to characterize this rare tumor. Results We identified six patients. Five presented with a solitary lesion, including two with clinical impression of epidermal inclusion cyst or lipoma. Two of four evaluable cases exhibited epidermotropism, with one mimicking Pautrier's microabscess. The neoplastic cells were pleomorphic and mostly medium to large-sized. In all cases, the neoplastic cells expressed TCR-γ and/or TCR-δ, with four coexpressing βF1. Two of these βF1+ cases co-expressed TCR-γ, but not TCR-δ (two different clones). All were negative for EBV, low stage, and treated with radiotherapy alone or combined chemotherapy and radiotherapy. In two patients, lymphoma relapsed in 3 and 7 months, respectively, with one patient died of the disease in 7 months. Four other patients were free of disease for 6-126 months. Conclusion PCGD-TCL cases in Taiwan are more commonly solitary, frequently with indolent courses. The two currently available TCR-δ clones alone might be insufficient to detect all tumors. This article is protected by copyright. All rights reserved.

Context Limited studies have focused on the impact of subclinical hyperthyroidism (SHyper) on poor prognosis in patients with known coronary artery disease (CAD). Objective We implemented the present study to explore the association between SHyper and adverse cardiovascular events in CAD patients who underwent drug-eluting stent implantation. Methods We consecutively recruited 8283 CAD patients undergoing percutaneous coronary intervention (PCI). All subjects were divided into 2 groups according to their thyroid function: group 1 (euthyroidism group, n = 7942) and group 2 (SHyper group, n = 341). After 1:4 propensity score (PS) matching, 1603 patients (332 SHyper group and 1271 euthyroidism group) were selected. The primary endpoint was major adverse cardiovascular events (MACEs), a composite of cardiac mortality, nonfatal myocardial infarction (MI), and target vessel revascularization (TVR). Results Kaplan–Meier (K-M) survival analyses suggested that there was no significant difference in the primary endpoint and secondary endpoints (MACE: 11.4% vs 8.8%, log-rank P = .124; cardiac death: 1.2% vs 0.9%, log-rank P = .540; nonfatal MI: 5.7% vs 4%, log-rank P = .177; and TVR: 6% vs 4.7%, log-rank P = .303) in the PS-matched population. Cox regression analysis indicated that SHyper was not an independent risk factor for MACEs (HR 1.33, 95% CI 0.92-1.92, P = .127). Conclusion SHyper is not independently associated with adverse cardiovascular events in CAD patients undergoing PCI. More studies should be implemented in the future to assess the long-term predictive value of SHyper with thyrotropin levels

PURPOSE High mobility group box 1 (HMGB1) and homocysteine (Hcy) play important roles in contrast-induced acute kidney injury (CI-AKI). We compared HMGB1 to Hcy as preprocedural predictors for CI-AKI in coronary artery disease (CAD) patients after percutaneous coronary artery intervention (PCI). METHODS We included 257 eligible patients who were categorized into CI-AKI ( +) and CI-AKI ( -) group. The differences in clinical characteristics and biochemical indexes between two groups were analyzed. RESULTS We observed that thirty-eight (14.8%) of 257 eligible CAD patients developed CI-AKI. HMGB1 (14.65 [11.13-24.89] vs 10.88 [7.94-13.23], p

Background Accumulating evidence suggests a critical role of intestinal dysbacteriosis in surgical site infections and anastomotic leakage after abdominal surgery. However, a direct correlation between pre-existing dysbacteriosis and postoperative infectious complications has not yet been established clinically. Methods A total of 353 consecutive patients who underwent colorectal cancer (CRC) surgery were enrolled. Gram-stained faecal smears were tested at admission and the first defecation after surgery. Intestinal dysbacteriosis was graded into three groups: normal or slightly decreased intestinal microflora (grade 1), moderate dysbacteriosis (grade 2), and severe dysbacteriosis (grade 3). Clinical outcomes were postoperative infections and anastomotic leakage within 30 days after surgery. Results At the preoperative assessment, 268 (75.9%) patients had normal or slightly decreased intestinal microflora, 58 (16.4%) patients had moderate dysbacteriosis, and 27 (7.6%) patients had severe dysbacteriosis. The patients with preoperative dysbacteriosis had a higher rate of early postoperative diarrhoea (grade 2: OR = 4.53, 95% CI 2.28–9.00, grade 3: OR = 4.52, 95% CI 1.81–11.31), total complications (grade 3 40.7% vs. grade 2 25.9% vs. grade 1 12.7%, P . grade 1 1.5%, P = 0.002). An interaction effect among preoperative dysbacteriosis and early postoperative diarrhoea on total complications was observed in rectal cancer patients (P for interaction = 0.007). Conclusions An imbalance of the intestinal microbiome exists in a considerable proportion of CRC patients before surgery. Preoperative dysbacteriosis is associated with higher rates of early postoperative diarrhoea, which further correlates with infectious complications and anastomotic leakage. However, the contribution of preoperative dysbacteriosis to the occurrence of anastomotic leakage needs to be clarified in further studies. Trial registration ChiCTR, ChiCTR1800018755. Registered 8 October 2018—Retrospectively registered, http://www.chictr.org.cn/ChiCTR1800018755.

Presently Si-based field-effect transistors (FETs) are approaching their physical limit, and further scaling their gate length down to the sub-10 nm region is becoming extremely difficult. Benefitting from the atomic-scale thickness and dangling-bond-free flat surface, two-dimensional semiconductors (2DSCs) have good electrostatics and carrier transportability. The FETs based on the 2DSC channel have the potential to scale the FETs’ gate length down to the sub-10 nm region while avoiding apparent degradation of the device performance. In this review, we introduce the recent experimental and ab initio quantum transport simulation progress in the 2D FETs with a gate length less than 10 nm. Remarkably, in the extremely optimistic condition, many 2D FETs (i.e phosphorene, silicane, arsenene, tellurene, WSe2, InSe, Bi2O2Se, GeSe, etc.) show excellent device performance for the high performance and/or low power applications and indeed can extend Moore’s law down to 1 ∼ 2-nm gate length in terms of the ab initio quantum transport simulation. The sub-10 nm 2D tunneling FETs are predicted to generally have smaller energy-delay products compared with the 2D metal–oxide–semiconductor FETs and appear more competitive for the low power application. The carrier effective mass plays a key role in determining the device performance. Via negative capacitance techniques, the device performance can be further improved. Finally, we outline the challenges and outlook on the future development directions in the sub-10 nm 2D FETs.

Background To date, there is no approved blood‐based biomarker for breast cancer detection. Herein, we aimed to assess semaphorin 4C (SEMA4C), a pivotal protein involved in breast cancer progression, as a serum diagnostic biomarker. Methods We included 6,213 consecutive inpatients from Tongji Hospital, Qilu Hospital, and Hubei Cancer Hospital. Training cohort and two validation cohorts were introduced for diagnostic exploration and validation. A pan‐cancer cohort was used to independently explore the diagnostic potential of SEMA4C among solid tumors. Breast cancer patients who underwent mass excision prior to modified radical mastectomy were also analyzed. We hypothesized that increased pre‐treatment serum SEMA4C levels, measured using optimized in‐house enzyme‐linked immunosorbent assay kits, could detect breast cancer. The endpoints were diagnostic performance, including area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Post‐surgery pathological diagnosis was the reference standard and breast cancer staging followed the TNM classification. There was no restriction on disease stage for eligibilities. Results We included 2667 inpatients with breast lesions, 2378 patients with other solid tumors, and 1168 healthy participants. Specifically, 118 patients with breast cancer were diagnosed with stage 0 (5.71%), 620 with stage I (30.00%), 966 with stage II (46.73%), 217 with stage III (10.50%), and 8 with stage IV (0.39%). Patients with breast cancer had significantly higher serum SEMA4C levels than benign breast tumor patients and normal controls (P < 0.001). Elevated serum SEMA4C levels had AUC of 0.920 (95% confidence interval [CI]: 0.900–0.941) and 0.932 (95%CI: 0.911–0.953) for breast cancer detection in the two validation cohorts. The AUCs for detecting early‐stage breast cancer (n = 366) and ductal carcinoma in situ (n = 85) were 0.931 (95%CI: 0.916–0.946) and 0.879 (95%CI: 0.832–0.925), respectively. Serum SEMA4C levels significantly decreased after surgery, and the reduction was more striking after modified radical mastectomy, compared with mass excision (P < 0.001). The positive rate of enhanced serum SEMA4C levels was 84.77% for breast cancer and below 20.75% for the other 14 solid tumors. Conclusions Serum SEMA4C demonstrated promising potential as a candidate biomarker for breast cancer diagnosis. However, validation in prospective settings and by other study groups is warranted., Breast cancer patients had significantly higher serum SEMA4C levels than patients with benign breast tumor and normal controls. Serum SEMA4C is considered as a candidate diagnostic biomarker for the early detection and diagnosis of breast cancer.

Introduction Spinal subarachnoid haemorrhage is extremely rare in cases of subarachnoid haemorrhage and possesses servere characteristics. Additionally, spinal rheumatoid vasculitis is rare for spinal subarachnoid haemorrhage. The pathogenesis is unknown. Case presentation A 52-year-old woman with a 10-year history of seropositive rheumatoid arthritis was managed with leflunomide and celecoxib, and stable low disease activity was achieved. The patient had also been diagnosed with spinal subarachnoid haemorrhage secondary to isolated spinal rheumatoid vasculitis and obtained good therapeutic effects. Conclusion This is the first case to describe spinal subarachnoid haemorrhage secondary to isolated spinal vasculitis in a patient with rheumatoid arthritis, which provides more proof of anomalous neovascularization in the central nervous system in rheumatoid arthritis.

Intraoperative radiotherapy; Overall survival; Progression-free survival Radioterapia intraoperatoria; Supervivencia global; Supervivencia libre de progresión Radioteràpia intraoperatòria; Supervivència global; Supervivència lliure de progressió Objective To report survival outcomes in patients with locally recurrent gynecologic cancers managed with curative-intent radical extirpation, perioperative external beam radiotherapy, and intraoperative radiotherapy (IORT). Patients and Methods We conducted a retrospective cohort analysis of 44 patients with locally recurrent gynecologic cancer treated at a single tertiary-care center (Mayo Clinic in Arizona) over a 15-year period (January 1, 2004, to July 31, 2019). This cohort included patients with uterine (n=21, 47.7%), ovarian (n=3, 6.8%), cervical (n=11, 25.0%), vaginal (n=2, 4.5%), vulvar (n=1, 2.3%), and unknown primary (n=6, 13.6%) cancer. Curative-intent radical extirpation included pelvic exenteration (n=13, 29.5%), laterally extended endopelvic resection (n=22, 50.0%), excision of para-aortic lymph node metastasis (n=8, 18.2%), and radical vaginectomy (n=1, 2.3%). Of the 44 patients in our cohort, 37 (84.1%) received IORT and 7 (15.9%) had intended to receive IORT but did not receive it. Results The median follow-up for the 44 patients was 12 months (range, 1 to 161 months). For patients who received IORT, the median progression-free survival (PFS) and overall survival (OS) were 13 and 21 months, respectively, and the 3-year cumulative incidence of central, locoregional, and distant recurrence was 27.0% (10 of 37), 40.5% (15 of 37), and 37.8% (14 of 37), respectively. Surgical margins were classified as negative (28 of 44, 63.6%), microscopic (11 of 44, 25.0%), or macroscopic (5 of 44, 11.4%). Negative, microscopic, and macroscopic surgical margins resulted in 3-year PFS of 51.8%, 20.5%, and 0%, respectively (P=.01) and 3-year OS of 62.9%, 20.0%, and 0%, respectively (P=.035). Progression-free survival (P=.69) and OS (P=.88) were not different between patients with negative surgical margins who received (n=21) and did not receive (n=7) IORT. Ten of 37 patients (27.0%) had development of grade 3 or higher toxicities, with 1 death due to sepsis. Conclusion Complete tumor resection at the time of curative-intent radical extirpation achieved higher rates of PFS and OS regardless of IORT administration.

BackgroundThe purpose of this study was to examine the correlation between fasting blood glucose and new-onset hypertension and examine any synergistically effect modification with multiple risk factors.MethodsWe conducted post-hoc analyses of repeated-measures data in the original Dongzhi osteoporosis cohort study. In total, 3985 participants without hypertension aged 25–64 years were included in the current analyses. Generalized estimating equation models were used to assess the relationship between fasting blood glucose and risk of new-onset hypertension after adjusting for pertinent covariates and autocorrelations among siblings.Results393 men (19.4%) and 398 women (20.3%) without hypertension at the baseline developed hypertension by the end of the study period. Compared to lower baseline fasting blood glucose levels (Q1–Q3: ConclusionsHigh fasting blood glucose may be significantly associated with risk of new-onset hypertension in Chinese women, especially in women with higher total cholesterol. Further randomized studies are needed to confirm our findings.

Rapid adaptation to a hypoxic environment is an unanswered question that we are committed to exploring. At present, there is no suitable strategy to achieve rapid hypoxic adaptation. Here, we demonstrate that fasting preconditioning for 72 h reduces tissue injuries and maintains cardiac function, consequently significantly improving the survival rates of rats under extreme hypoxia, and this strategy can be used for rapid hypoxic adaptation. Mechanistically, fasting reduces blood glucose and further suppresses tissue mTOR activity. On the one hand, fasting-induced mTOR inhibition reduces unnecessary ATP consumption and increases ATP reserves under acute hypoxia as a result of decreased protein synthesis and lipogenesis; on the other hand, fasting-induced mTOR inhibition improves mitochondrial oxygen utilization efficiency to ensure ATP production under acute hypoxia, which is due to the significant decrease in ROS generation induced by enhanced mitophagy. Our findings highlight the important role of mTOR in acute hypoxic adaptation, and targeted regulation of mTOR could be a new strategy to improve acute hypoxic tolerance in the body.

The adverse effects of hypercortisolism on the human brain have been highlighted in previous studies of Cushing’s disease (CD). However, the relative alterations in regional hypercortisolism in the brain remain unclear. Thus, we investigated regional volumetric alterations in CD patients. We also analyzed the associations between these volumetric changes and clinical characteristics. The study participants comprised of active CD (n = 60), short-term-remitted CD (n = 28), and long-term-remitted CD (n = 32) patients as well as healthy control subjects (n = 66). Gray matter volumes (GMVs) were measured via voxel-based morphometry. The GMVs of substructures were defined using the automated anatomical labeling (AAL) atlas. Trends toward normalization in GMV were found in most brain substructures of CD patients. Different trends, including enlarged, irreversible, and unaffected, were observed in the other subregions, such as the amygdala, thalamus, and caudate. Morphological changes in GMVs after the resolution of hypercortisolism are a complex phenomenon; the characteristics of these changes significantly differ within the brain substructures.

Aiming at the problems of low fire detection accuracy and high false alarm rate of the current intelligent camera fire accident alarm system, a fire accident alarm system based on fuzzy recognition algorithm is designed. By analyzing the structural principle of the fire detection and alarm system, selecting the CO gas, temperature and smoke sensor selection, designing the corresponding fire signal detection circuit, and designing the single-chip system circuit, including the single-chip clock circuit, reset circuit, power supply circuit and A/D conversion circuit design, on the basis of in-depth study of the Bluetooth communication protocol structure, the hardware design of the serial interface circuit of the single-chip microcomputer, PC and Bluetooth module has been completed. The fuzzy recognition algorithm is used to set the input and output, establish the control rule table and reasoning relationship, generate the input and output rule table, preprocess the sensor signal, and finally output the fire alarm model through the fuzzy inference system, so as to realize the fire accident alarm of the intelligent camera. The experimental results show that the fire detection accuracy of the proposed method is high, and can effectively reduce the false alarm rate and false alarm rate of the system.

Objective To explore the effects and value of establishing a multi-target nursing group (MTNG) for facilitating goal-oriented enhanced recovery after surgery (ERAS) using the LEER (“less pain”, “early movement”, “early return to a normal diet” and “reassurance”) model. Methods The clinical data of 198 patients with hepatobiliary and pancreatic malignancies were retrospectively analyzed. The patients were divided into two groups: 91 cases were collected in a traditional group, which adopted traditional perioperative care, and 107 cases were collected in an MTNG group, which adopted MTNG measures. The differences in the clinical data including postoperative recovery, unplanned readmission rate, the implementation rate of nursing measures, the degree of a patient’s understanding of the disease, and patient compliance and satisfaction with nursing care during hospitalization were compared and analyzed between the two groups. Results The MTNG group reflected a lower pain degree and hospitalization expenses (P < 0.05), earlier postoperative flatulence, earlier recommencing of a normal diet, and earlier postoperative ambulation (P < 0.05), together with a shorter postoperative indwelling catheter duration and length of hospital stay (P < 0.05). There were no significant differences in the incidence of postoperative complications and unplanned postoperative readmission rates between the two groups (P > 0.05). The implementation rate of nursing measures and the degree of patients understanding the disease, and patient compliance and satisfaction with nursing care were higher in the MTNG group (P < 0.05). Conclusion The MTNG approach, based on ERAS with the LEER model, was conducive to the safe and rapid postoperative recovery of patients, the precise and efficient implementation of ERAS measures, the improvement of medical treatment satisfaction among patients.

Infection is the leading cause of admission and mortality in neonatal intensive care units. Immature immune function and antibiotic resistance make the treatment more difficult. However, there is no effective prevention for it. Recently, more and more researches are focusing on stem cell therapy, especially mesenchymal stem cells (MSCs); their potential paracrine effect confer MSCs with a major advantage to treat the immune and inflammatory disorders associated with neonatal infection. In this review, we summarize the basal properties and preclinical evidence of MSCs and explore the potential mechanisms of paracrine factors of MSCs for neonatal infection.

Trends in the incidence of endometriosis in China remain unknown. The purpose of this study was to examine the trends in the incidence of endometriosis and the effects of age, period, and cohort on them.Trends in endometriosis incidence were estimated using joinpoint regression. Age-period-cohort analysis was used to analyze the effects of age, period, and cohort on these trends. Endometriosis incidences in China (1990-2019) were retrieved from the Global Burden of Disease Study 2019. Annual percentage change and average annual percent change (AAPC) were analyzed by joinpoint regression, and relative risks were analyzed using an age-period-cohort model.Age-standardized incidence rates (ASIRs) declined between 1990 and 2019 in China, with an overall AAPC of -1.2% (95% CI: -1.20, -1.10). Compared to 1990, the ASIR in 2019 decreased by almost 30%. Moreover, the joinpoint regression analysis revealed that endometriosis ASIRs showed a downward trend across all age groups. A significant age-related effect was seen for endometriosis incidence among young women aged 15-24 years, which then decreased with advancing age. Consistently, the effect of the period on endometriosis incidence showed a declining trend, and the effect of birth cohort decreased by 0.53 (42.7%) from 1938-1942 to 1998-2002.Endometriosis ASIRs declined from 1990 to 2019. The effects of period and birth cohort on endometriosis incidence exhibited a declining trend across all age groups. The effect of age on endometriosis incidence showed an increasing trend before the age of 24, followed by a decreasing trend with subsequent advancing age.

User relations and information propagation on social networks can reflect events in real society. Online detection of bursty events is of great significance to studying the evolution of social networks and cyberspace security. Current research works focus on building an event recognition model based on text information and then utilizing clustering or topic model methods to extract features from the data stream and then detect events that have not existed before. The text-based method is designed for specific content, and it does not consider the features of network dynamic evolution. It is restricted by the type and quality of text in social networks, limiting its practical application scenarios. However, there exist remarkable correlations between the occurrence of events and the evolution of the network. In this paper, we consider mining the network structure changes to identify bursty events, the superiority which is that it is sensitive and widely used. We integrate snapshot network topology indexes to quantify its structural features. Then we can judge whether there is a burst event by investigating the change degree of the network structure features of the adjacent snapshots. The effectiveness and efficiency of our approach are further confirmed by experimental studies on four real social network data sets. In addition, we also discuss the salient features of the bursty events and compare the impact of the bursty events on the network structure with that of the scheduled events.

A series of bio-based comb-like polymers with various lengths of alkyl chains were synthesized through combining the ring-opening copolymerization of sustainable CO2 with bio-renewable castor oil-d...

Objective To obtain evidence-based conclusions about the effect of acupuncture on pain relief in women undergoing oocyte retrieval, the results of randomized controlled trials (RCTs) that met the criteria were assessed on the Pain Assessment Scale and pregnancy indicators. Search Methods References were retrieved in MEDLINE, EMBASE, CNKI database, CBM database, VIP database, and Wanfang database from inception to June 26, 2021. Unpublished ongoing trials were searched in the Clinical Trials Registries. This review included RCTs that investigated the acupuncture analgesic effects during oocyte retrieval in women undergoing in vitro fertilization. Results Fourteen RCTs (2503 women in total) with six types of comparisons were finally included. The quality of concluding evidence was generally low or very low. Performance bias and outcome assessment bias was the main risk of bias of the included studies. Acupuncture combined with conscious sedation and analgesia (CSA) was associated with less intraoperative (SMD=−1.03; 95% CI: −1.71 to −0.36) and postoperative (SMD = −1.11; 95% CI: −1.51 to −0.71) pain compared to receive CSA alone in oocyte retrieval. Acupuncture with non-steroidal anti-inflammatory drugs (NSAIDs) was more effective than using NSAIDs alone for postoperative analgesia (MD = −1.76; 95% CI: −2.08 to −1.44). Conclusion Acupuncture complex analgesic therapy is more effective than utilizing CSA or NSAIDs alone. Furthermore, there is no significant consensus on whether there is an analgesic effect of applying acupuncture alone during oocyte retrievals, which needs further research. The overall results should be interpreted with caution due to the high risk of bias/low-GRADE scores among these studies. Protocol and Registration PROSPERO registration number: CRD42020170095.

Tremendous success of machine learning (ML) and the unabated growth in model complexity motivated many ML-specific designs in hardware architectures to speed up the model inference. While these architectures are diverse, highly optimized low-precision arithmetic is a component shared by most. Nevertheless, recommender systems important to Facebook’s personalization services are demanding and complex: They must serve billions of users per month responsively with low latency while maintaining high prediction accuracy. Do these low-precision architectures work well with our production recommendation systems? They do. But not without significant effort. In this article, we share our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the tool chain to maintain our models’ accuracy throughout their lifespan. We believe our lessons from the trenches can promote better codesign between hardware architecture and software engineering, and advance the state of the art of ML in industry.

Yuan, Fangzhengyuan, Zhexue Qin, Chuan Liu, Shiyong Yu, Jie Yang, Jun Jin, Shizhu Bian, Xubin Gao, Jihang Zhang, Chen Zhang, Mingdong Hu, Jingbin Ke, Yuanqi Yang, Jingdu Tian, Chunyan He, Wenzhu Gu, Chun Li, Rongsheng Rao, and Lan Huang. Echocardiographic right ventricular outflow track notch formation and the incidence of acute mountain sickness.

Chromosome analysis plays an important role in detecting genetic disorders. However, it is time-consuming when it is done manually. The first step for an automated solution is removing the background noise in the chromosome images. Denoising is studied by many researchers; however, it is still a challenging task due to contrast issues, blotches, and non-chromosome objects. In this paper, we proposed a cascaded neural network architecture for denoising G-banding chromosomes images. The proposed method consists of two steps. The first step is the initial segmentation network which combines the capabilities of U-net and residual units. The second step is the classification block, which is implemented in order to automate the denoising process and reduce the pixel losses on the chromosomes. The results showed that the proposed segmentation network achieves a higher dice score compared to state-of-the-art semantic segmentation neural networks, and the classification block greatly reduces the pixel losses on the chromosomes. We tested the proposed denoising method on 84 G-banding chromosome images and achieved a 98.74% dice score. Our automated denoising method outperformed the methods presented in previous studies and state-of-the-art methods. It can help cytogeneticists with repetitive work and provide them more accurate chromosomes for further evaluation.

The rational optimization of plasmonic property of metal nanocrystals by manipulating the structure and morphology is crucial for the plasmon-enhanced application and has always been an urgent issue. Herein, Au nanorods with tunable surface roughness are prepared by growing PbS, overgrowing Au, and dissolving PbS nanoparticles on the basis of smooth Au nanorods. The transverse plasmon resonance of Au nanorods is notably improved due to plasmon coupling between Au nanorods and the surface-modified Au nanoparticles, resulting in the strong and full-spectrum light absorption. Numerical simulations demonstrate that the surface-rough Au nanorods have abundant and full-surround hotspots coming from surface particle-particle plasmon coupling between ultrasmall nanogaps, sharp tips, and uneven areas on Au nanorods. With these characters, the surface-roughness-adjustable Au nanorods possess high tunability and enhancement of surface-enhanced Raman scattering (SERS) detection of Rhodamine B and significantly improved photothermal conversion efficiency. Au nanorods with the largest surface roughness have the highest Raman enhancement factor both at 532 and 785 nm laser excitation. Meanwhile, photothermal conversion experiments under near-infrared (808 nm) and simulated sunlight irradiation confirm that the Au nanorods with rough surface have prominent photothermal conversion efficiency and can be regarded as promising candidates for photothermal therapy and solar-driven water evaporation.

The interaction between a moving submarine and seawater generates characteristic wakes on the sea surface, enabling indirect detection of undersea objects via airborne remote sensing. Here, we demonstrate the feasibility of using visible-light polarization imaging to observe submarine wakes. The key links in the imagine chain are considered separately. These include the polarization patterns of skylight, the elevations and slopes of submarine wakes and sea waves, and the changes in the sea surface polarized bidirectional reflectance characteristics due to modulation of gravity–capillary waves by the wake’s velocity field. A complete model of the airborne optical polarization imaging process is constructed and images are simulated via ray tracing. All theories proposed are verified by a series of terrestrial observation experiments. The results show that both the sea surface roughness modulation by the wake’s velocity field and the sea surface slope formed by wake elevation play significant roles in the imaging process. The wake features in the Stokes vector linear polarization component (Q, U) images are effectively enhanced, and the environmental adverse effect on these images is smaller than that on the intensity images. The degree of linear polarization (DoLP) and angle of polarization (AoP) images exhibit acceptable contrast under certain zenith and azimuth angles. Thus, our analysis confirms that airborne optical polarization imaging has considerable potential for observing wakes and other small- and medium-scale ocean dynamic processes.

Automatic modulation classification (AMC) is one of the most important technologies of cognitive radios and ubiquitous internet of things (IoT) cyber–physical systems, and it can be adopted to recognize unknown signals. Recently, deep learning (DL) has been applied into AMC for the advanced classification performance. However, DL-based AMC methods generally have high computation complexity and large model sizes, which means that these methods can be rarely implemented into some IoT devices. In this paper, inspired by ShuffleNet, we design a lightweight convolutional neural network (CNN), which is named as ShuffleCNN, and a ShuffleCNN-based AMC (ShffuleAMC) method is proposed for the ubiquitous IoT cyber–physical systems with orthogonal frequency division multiplexing (OFDM). Besides, we also introduce fast Fourier transform (FFT) to pre-process the OFDM signals for the classification performance improvement, and apply l 2 regularization to avoid overfitting. It is demonstrated by simulation results that our proposed ShuffleAMC method has little performance loss, when compared with the common CNN-based AMC methods. More importantly, our proposed ShuffleAMC method also has the strengths of low computation complexity and few model sizes.

Background Among recurrent implantation failure (RIF) patients, the rate of successful implantation remains relatively low due to the complex etiology of the condition, including maternal, embryo and immune factors. Effective treatments are urgently needed to improve the outcomes of embryo transfer for RIF patients. In recent years, many researchers have focused on immunotherapy using granulocyte colony-stimulating factor (G-CSF) to regulate the immune environment. However, the study of the G-CSF for RIF patients has reached conflicting conclusions. The aim of this systematic review and meta-analysis was performed to further explore the effects of G-CSF according to embryo transfer cycle (fresh or frozen) and administration route (subcutaneous injection or intrauterine infusion) among RIF patients. Method The PubMed, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched for literature published from the initial to October 2020. The meta-analysis, random-effects model and heterogeneity of the studies with I2 index were analyzed. Stata 15 was used for statistical analysis. Results A total of 684 studies were obtained through the databases mentioned above. Nine RCTs included 976 RIF patients were enrolled in this meta-analysis. Subgroup analysis indicated that G-CSF improved the clinical pregnancy rate for both the fresh and frozen embryo transfer cycles (fresh RR: 1.74, 95% CI: 1.27–2.37, I2 = 0.0%, n = 410; frozen RR: 1.44, 95% CI: 1.14–1.81, I2 = 0.0.%, n = 366), and for both subcutaneous injection and intrauterine infusion (subcutaneous RR: 1.73, 95% CI: 1.33–2.23, I2 = 0.0%, n = 497; intrauterine RR: 1.39, 95% CI: 1.09–1.78, I2 = 0.0%, n = 479), but the biochemical pregnancy rate of the RIF group was also higher than that of the control group (RR: 1.85, 95% CI: 1.28–2.68; I2 = 20.1%, n = 469). There were no significant differences in the miscarriage rate (RR: 1.13, 95% CI: 0.25–5.21: I2 = 63.2%, n = 472) and live birth rate (RR: 1.43, 95% CI: 0.86–2.36; I2 = 52.5%; n = 372) when a random-effects model was employed. Conclusion The administration of G-CSF via either subcutaneous injection or intrauterine infusion and during both the fresh and frozen embryo transfer cycles for RIF patients can improve the clinical pregnancy rate. However, whether G-CSF is effective in improving livebirth rates of RIF patients is still uncertain, continued research on the utilization and effectiveness of G-CSF is recommended before G-CSF can be considered mainstream treatment for RIF patients.