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Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

The paramount focus in the construction of lithium-ion capacitors (LICs) is the development of anode materials with high reversible capacity and fast kinetics to overcome the mismatch of kinetics and capacity between the anode and cathode. Herein, a strategy is presented for the controllable synthesis of cobalt-based phosphides with various morphologies by adjusting the time of the phosphidation process, including 3D hierarchical needle-stacked diabolo-shaped CoP nanorods, 3D hierarchical stick-stacked diabolo-shaped Co2P nanorods, and 3D hierarchical heterostructure CoP@Co2P nanorods. 3D hierarchical nanostructures and a highly conductive project to accommodate volume changes are rational designs to achieve a robust construction, effective electron-ion transportation, and rapid kinetics characteristics, thus leading to excellent cycling stability and rate performance. Owing to these merits, the 3D hierarchical CoP, Co2P, and CoP@Co2P nanorods demonstrate prominent specific capacities of 573, 609, and 621 mA h g-1 at 0.1 A g-1 over 300 cycles, respectively. In addition, a high-performance CoP@Co2P//AC LIC is successfully constructed, which can achieve high energy densities of 166.2 and 36 W h kg-1 at power densities of 175 and 17524 W kg-1 (83.7% capacity retention after 12000 cycles). Therefore, the controllable synthesis of various simultaneously constructed crystalline phases and morphologies can be used to fabricate other advanced energy storage devices.

Sarcomatoid malignant mesothelioma (SMM) tends to occur in the pleura and is morphologically similar to lung sarcomatoid carcinoma (LSC) and organizing pleuritis (OP). Because SMM often does not express mesothelial markers, it is very difficult to distinguish from LSC and OP. GATA-binding protein 3 (GATA3) is a specific immunohistochemical (IHC) marker of breast and urothelial carcinoma. We routinely find that GATA is expressed in MM; however, GATA3 expression in SMM and its reference value for distinguishing SMM from LSC and OP remain unclear. Here, we used IHC methods to detect the expression of GATA3 and classic mesothelial markers in 17 SMM, 12 LSC, and 7 OP cases. We detected the following expression rates in SMM versus LSC cases: GATA3 (70.6% vs. 16.7%, p = 0.008), calretinin (52.9% vs. 8.3%, p = 0.019), Wilms tumor (WT)-1 (64.7% vs. 0%, p = 0.000), D2-40 (47.1% vs. 16.7%, p = 0.126), CK5/6 (35.3% vs. 25.0%, p = 0.694), and pan-cytokeratin (CKpan) (88.2% vs. 100.0%, p = 0.498). The specificities of calretinin, WT-1, and GATA3 in distinguishing SMM from LSC were 91.7%, 100%, and 83.3%, respectively, and combinations of any two of these three markers exhibited 100% specificity for SMM. Notably, the sensitivity of calretinin+/WT1+ staining for SMM was only 23.5%, which increased to 64.7% after including GATA3. Furthermore, all OP cases showed partial or diffuse expression of CKpan, WT-1, and D2-40 but no GATA3 and calretinin expression. In conclusion, GATA3 is an IHC marker with excellent sensitivity and specificity for SMM, and the combined consideration of GATA3, calretinin, and WT-1 was best for distinguishing SMM from LSC. Moreover, CKpan, WT-1, and D2-40 had no value for distinguishing SMM from OP, and GATA3 and calretinin were the most specific markers for distinguishing these two lesions.

The discharge from wastewater treatment plants (WWTPs) into the receiving river is of concern for the health of the ecosystem. In this study, the microbial diversity impact of Fuxin Meng (FM) WWTP was completed with three pairs of primers. At the time of impact assessment, biochemical tests were carried out as well to investigate the water quality downstream and upstream FMWWTP. The general results showed this plant increased downstream biochemical oxygen demand (BOD) and chemical oxygen demand (COD) by 4.3- and 8.1-fold respectively, in comparison with the upstream. The microbial diversity impact from the plant was also investigated with three pairs of universal primers targeting rRNA genes and the internal transcribed spacer (ITS) by high-throughput sequencing. The downstream diversity of the bacterial and fungal communities was generally higher than upstream, with the exception of the eukaryotic community. Both biochemical and microbial assessments suggested that FMWWTP deteriorate Xi River water quality, potentially acting as a pollutive source. Of the receiving water. Accordingly, it was necessary to improve the operation of WWTP to live up to the environmental quality standards.

Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a holographic image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments., Comment: arXiv admin note: text overlap with arXiv:2006.06563

Increasing renewable carbon incorporation into conventional fuels through coprocessing with vacuum gas oil (VGO, a petroleum refining feedstock) is a critical step in biofuel development, scaling-u...

Background To determine the safety and efficacy of different doses of tolvaptan for treating Chinese cirrhotic patients with or without hyponatraemia who still had ascites after routine therapy with diuretics. Methods In the present placebo-controlled, randomized, double-blinded, multicentre clinical trial, patients with cirrhotic ascites who failed to adequately respond to a combination of an aldosterone antagonist plus an orally administered loop diuretic were randomly placed at a 4:2:1 ratio into 3 groups [the 15 mg/day tolvaptan group (N = 301), 7.5 mg/day tolvaptan group (N = 153) and placebo group (N = 76)] for 7 days of treatment. The effects and safety were evaluated on days 4 and 7. A change in body weight from baseline on day 7 of treatment was the primary endpoint. Results The administration of 7.5 or 15 mg/day tolvaptan significantly decreased body weight from baseline on day 7 of treatment compared to that with placebo treatment (P = 0.026; P = 0.001). For the secondary endpoints, changes in abdominal circumference from baseline and improvements in ascites were markedly different in the treatment groups and the placebo group on day 7 (P7.5 = 0.05, P15.0 = 0.002 and P7.5 = 0.037, P15.0 = 0.003), but there was no difference between the 7.5 mg/day and 15 mg/day dosage groups. The 24-h cumulative urine volume was higher in the 7.5 mg/day and 15 mg/day tolvaptan groups than the placebo group (P = 0.002, P P = 0.004). Sodium serum concentrations were higher in patients with hyponatraemia after tolvaptan treatment, with no significant difference between the two dosage groups. The incidence of serious adverse drug reactions was not different between the groups (P = 0.543). Conclusions Tolvaptan treatment at 7.5 mg per day might be a good therapeutic choice for Chinese cirrhotic patients with ascites who did not achieve satisfactory clinical responses to previous treatment regimens with combination therapy with an aldosterone antagonist and an orally administered loop diuretic. Trial registration NCT01349348. Retrospectively registered May 2011.

BACKGROUND Recent research suggests that although prokinetic agents, acid suppressors, and radical treatment for Helicobacter pylori infection may be effective in patients with functional dyspepsia (FD), a large proportion of patients still fail to respond to these treatments or may suffer from severe adverse reactions. Many traditional Chinese medicinal herbs can regulate the status of the entire body and have special advantages in the treatment of functional diseases. The present study was designed to verify the efficacy of Biling Weitong Granules (BLWTG), a traditional Chinese medicinal herbal compound formula, in alleviating epigastric pain syndrome (EPS) in FD patients, in an attempt to provide an effective prescription for the clinical treatment of this disease. AIM To evaluate the clinical efficacy and safety of BLWTG in treating EPS in patients with FD. METHODS In this multicenter, stratified, randomized, double-blind, placebo-controlled, parallel group clinical trial, eligible patients were randomized into the BLWTG and placebo groups who were treated for 6 wk. Efficacy indicators including the severity and frequency of EPS and the time to pain resolution and safety indicators including adverse events were observed and compared. RESULTS The baseline demographic data and clinical characteristics, such as epigastric pain symptoms, pain intensity, and frequency of attacks, were matched between the two groups before randomization. After 6 wk of treatment and after the center effect was eliminated, the epigastric pain was significantly improved in 28.33% and 85.59% of the patients in the placebo and BLWTG groups, respectively (P < 0.05). At 6 wk, the resolution rate of epigastric pain was 15% and 69.49% in the placebo and BLWTG groups, respectively (P < 0.05). The differences of total FD clinical score between these two groups were significant (P < 0.05) at 2, 4, and 6 wk (P < 0.05). The scores of each item and the total score in the Functional Digestive Disorders Quality of Life Questionnaire showed significant differences between the two groups at 6 wk after both the center and interaction effects were eliminated (P < 0.05). There was no significant difference in the incidence of adverse events between the two groups, and no serious adverse event was noted during the observation. CONCLUSION Compared with placebo, BLWTG markedly improved EPS in FD patients without causing serious adverse reactions.

The lithium ion capacitors (LICs) become a potential energy storage device because it has both outstanding energy density of lithium ion batteries (LIBs) and excellent power density of supercapacit...

37. International Cosmic Ray Conference, ICRC 2021, Berlin, Germany, 15 Jul 2021 - 22 Jul 2021; Proceedings of Science 395, 1194 (2022). doi:10.22323/1.395.1194 special issue: "37th International Cosmic Ray Conference : ICRC2021 : 12-23 July 2021 : Berlin, Germany - Online / Editorial Board: Alexander Kappes, Institut für Kernphysik, Westfälische Wilhelms-Universität Münster, D-48149 Münster, Germany ; Bianca Keilhauer, Karlsruhe Institute of Technology, Institute for Astroparticle Physics (IAP)", Published by SISSA, Trieste

Background Increasing evidence indicates that Six2 contributes to tumorigenesis in various tumor including hepatocellular carcinoma (HCC). This study aimed to determine the role of Six2 in HCC and to elucidate the association of Six2 with clinical pathological characteristics. Methods The expressions of Six2 in HCC tumor, para-tumor tissue and portal vein tumor thrombus (PVTT) were detected by tissue microarray technique, immunohistochemistry, real-time RT-PCR and Western blotting. Chi-square and Kaplan-Meier analysis were used to analyze the correlation between Six2 expression and prognosis of HCC patients. Lentivirus mediated Six2 knockdown, spheroid formation assay, proliferation assay and subcutaneous tumor implantation were performed to determine the function of Six2. Results In 274 HCC samples, Six2 was strongly expressed. Kaplan-Meier analysis revealed that high expression of Six2 was correlated with a shorter overall survival (OS) and disease-free survival (DFS). Moreover, Six2 expression was associated with sex, alpha-fetoprotein, tumor size and portal vein invasion. Six2 was highly expressed in PVTT. Six2 knockdown inhibited HCC cell lines proliferation, migration, and self-renewal in vitro and in vivo. In addition, low-expression of Six2 weakened TGF-β induced Smad4 activation and epithelial-mesenchymal transition in HCC cell lines. Conclusions Elevated Six2 expression in HCC tumor patients was associated with negative prognosis. Upregulated Six2 promoted tumor growth and facilitated HCC metastasis via TGF-β/Smad signal pathway.

As Deep Neural Networks (DNNs) have achieved state-of- the-art performance in various scientific fields and applica- tions, the memory and computational complexity of DNNs have increased concurrently. The increased complexity re- quired by DNNs prohibits them from running on platforms with limited computational resources. This has sparked a re- newed interest in parameter pruning. We propose to replace the standard cross-entropy objective – typically used in clas- sification problems – with the Nonlinear Information Bottle- neck (NIB) objective to improve the accuracy of a pruned net- work. We demonstrate, that our proposal outperforms cross- entropy combined with global magnitude pruning for high compression rates on VGG-nets trained on CIFAR10. With approximately 97% of the parameters pruned, we obtain an accuracy of 87.63% and 88.22% for VGG-16 and VGG-19, respectively, where the baseline accuracy is 91.5% for the un- pruned networks. We observe that the majority of biases are pruned completely, and pruning parameters globally outper- forms layer-wise pruning.

Deep neural networks have recently shown excellent performance on numerous image classification tasks. These networks often need to estimate a large number of parameters and require much training data. When the amount of training data is small, however, a network with high flexibility quickly overfits the training data, resulting in a large model variance and poor generalization. To address this problem, we propose a new, simple yet effective ensemble method called InterBoost for small-sample image classification. In the training phase, InterBoost first randomly generates two sets of complementary weights for training data, which are used for separately training two base networks of the same structure, and then the two sets of complementary weights are updated for refining the training of the networks through interaction between the two base networks previously trained. This interactive training process continues iteratively until a stop criterion is met. In the testing phase, the outputs of the two networks are combined to obtain one final score for classification. Experimental results on four small-sample datasets, UIUC-Sports, LabelMe, 15Scenes and Caltech101, demonstrate that the proposed ensemble method outperforms existing ones. Moreover, results from the Wilcoxon signed-rank tests show that our method is statistically significantly better than the methods compared. Detailed analysis is also provided for an in-depth understanding of the proposed method.

A central use case for the Internet of Things (IoT) is the adoption of sensors to monitor physical processes, such as the environment and industrial manufacturing processes, where they provide data for predictive maintenance, anomaly detection, or similar. The sensor devices are typically resource-constrained in terms of computation and power, and need to rely on cloud or edge computing for data processing. However, the capacity of the wireless link and their power constraints limit the amount of data that can be transmitted to the cloud. While this is not problematic for the monitoring of slowly varying processes such as temperature, it is more problematic for complex signals such as those captured by vibration and acoustic sensors. In this paper, we consider the specific problem of remote anomaly detection based on signals that fall into the latter category over wireless channels with resource-constrained sensors. We study the impact of source coding on the detection accuracy with both an anomaly detector based on Principal Component Analysis (PCA) and one based on an autoencoder. We show that the coded transmission is beneficial when the signal-to-noise ratio (SNR) of the channel is low, while uncoded transmission performs best in the high SNR regime., Presented at SPAWC 2021

Background: Multimodal postoperative analgesia with intravenous ibuprofen can reduce the consumption of opioids and reduce the postoperative pain intensity. Recommended dose in the USA and Europe is 800 mg every 6 hours for the management of moderate to severe postoperative pain. The purpose of this study was to investigate whether 800 mg is needed in the Chinese population.Methods: This is a multicenter, randomized, controlled multi-center trial. Adult patients (18-65 years of age) scheduled for abdominal or orthopedic surgery under general anesthesia with endotracheal intubation were eligible. Subjects were randomized at a ratio of 1:1:1 to receive ibuprofen at 400 or 800 mg or saline every 6 hours for 48 hours after the operation. All subjects received morphine access through patient-controlled intravenous analgesia (PCIA) device, which was set as background infusion rate 0.5 mg/h, bolus 1 mg, 5-minute lock interval and 20-mg 4-hour limit. The primary endpoint was morphine consumption within the first 24 hours after surgery. Results: A total of 396 patients were enrolled (180 men, 216 women; 46±12.4 years of age; 106 patients receiving abdominal surgery, and 290 patients receiving orthopedic surgery). The number of patients was 131 in the placebo group, 132 in the 400-mg group, and 133 in the 800-mg group. The 3 groups did not differ in age, sex, BMI and the type of surgery. Median 24-hour morphine consumption was 39.1 mg (IQR: 29.7, 61.7) mg in the placebo control, 29.8 mg (IQR: 24.3, 43.7) mg in the 400-mg group, and 28.0 mg (IQR: 24.1, 35.9) in the 800-mg group (PConclusions: Intravenous ibuprofen at either 400 or 800 mg every 6 hours could reduce postoperative morphine consumption, with apparently equal efficacy in Chinese population.

Post-trauma elbow stiffness (PTES) is a common complication after elbow trauma that causes severe upper limb disability. Open elbow arthrolysis (OEA) with radial head arthroplasty (RHA) is an effective method to treat PTES with rotation limitation, or persistent pain/instability after radial head resection. However, no long-term results have been reported for this technique. This study aimed to show the clinical and radiographic outcomes of OEA with RHA over 8 years and compare its efficacy at 3 years (short-term).Patients with PTES treated by OEA with RHA between September 2010 and December 2012 were retrospectively reviewed. Seventeen patients were followed up over 8 years (range, 100-106 months). A bipolar prosthesis of RHA was performed during OEA. Preoperative, 3-year, and 8-year elbow and forearm motion, upper limb function, radiographic outcomes, and complications were recorded.Clinically important improvements in elbow motion and forearm rotation were obtained, from 34° and 58° preoperatively, to 109° and 135° at 3 years, which were maintained over 8 years, to 113° (P = .262) and 134° (P = .489). The Mayo Elbow Performance Index had clinically important increases from the preoperative level of 58 to 94 points at 3 years, and was maintained over 8 years (95 points, P = .422), with 100% reporting excellent to good outcomes. Pain and nerve symptoms were also improved. Complications consisted of new-onset ulnar nerve symptoms in 1 patient, nonclinically significant heterotopic ossification recurrence in 3, humeroulnar arthritis exacerbation in 4, and periprosthetic lucency in 8.OEA with RHA yielded satisfactory short-term outcomes for PTES at 3 years, with substantial improvements in elbow mobility and function, and the results were durable over the long term (8 years).

After cardiopulmonary bypass (CPB), the occurrence of systemic inflammatory response is often accompanied by a persistent compensatory anti-inflammatory response syndrome that can lead to a compromised immune competence termed immunoparalysis, rendering the patients susceptible to infections which is a leading complication following cardiac surgery. However, the underlying mechanisms of CPB-elicited immunoparalysis remain obscure. In this study we showed that peroxiredoxin 1 (Prdx1), a putative cytosolic antioxidant, was released immediately after CPB in a cohort of pediatric patients receiving congenital cardiac surgery. This increased Prdx1 was correlated to a reduced human leukocyte antigen-DR expression and an elevated interleukin-10 (IL-10) production, as well as a hypo-responsiveness of macrophages to endotoxin and a higher incidence of nosocomial infection. We demonstrated that substitution of Ser83 for Cys83 prevented Prdx1 from oligomerization and subsequent binding and internalization to macrophages. These effects mitigated Prdx1-induced IL-10 induction and endotoxin tolerance. Furthermore, after engagement with toll-like receptor (TLR) 4, clathrin-dependent endocytosis is crucial for Prdx1 to elicit IL-10 production in phagocytes. Congruently, inhibition of Prdx1/TLR4 endocytosis in phagocytes reversed the Prdx1/IL-10-mediated hypo-responsiveness to endotoxin. Our findings unveiled the possible mechanisms by which Prdx1 undertakes to cause immunoparalysis, and targeting endocytosis of Prdx1 could be a novel therapeutic approach for postoperative infections associated with CPB., Graphical abstract Image 1, Highlights • Cardiopulmonary bypass-elicited ischemia/reperfusion injury causes release of Prdx1. • High molecular weight forms of Prdx1 interact with TLR4 to initiate sterile inflammation. • Endocytosis of Prdx1/TLR4 induces IL-10 production that leads to immunoparalysis and subsequent septic inflammation. • Inhibition of Prdx1/TLR4 endocytosis reverses Prdx1/IL-10-mediated hypo-responsiveness to endotoxin in phagocytes.

This study to analyze the clinical characteristics of patients with invasive pulmonary aspergillosis (IPA) following influenza A (H1N1) infection. We retrospectively analyzed 10 cases with IPA following H1N1 infection. The clinical manifestations, laboratory examination results, chest computed tomography, and treatments were analyzed. Clinical manifestations: all 10 cases had typical flu-like symptoms at the onset of the disease, among which 7 patients developed dyspnea in the late stage, and 8 patients had hemoptysis. Laboratory examination: the absolute and percentage of peripheral blood lymphocytes in all 10 patients were declined, among which 5 cases were with decreased CD3+ CD4+ T cells/lymphocytes; 9 cases with increased bronchoalveolar lavage fluid galactomannan; 6 cases with increased serum galactomannan; 1 case with bronchoalveolar lavage fluid cultured aspergillus fumigatus; and 2 cases with aspergillus by second-generation sequencing. Chest computed tomography: all patients showed multiple diffused ground-glass opacities at the beginning, along with linear or reticular interstitial changes. Two cases had multiple subarachnoid nodules with halo signs, 3 cases had consolidation in multiple segments of both lungs, 2 cases had cavities, and 4 cases were with pleural effusion. Treatment: 10 patients were treated with antiviral and anti-Aspergillus drugs after admission. Four patients received respiratory support. All 10 cases were cured and discharged. Early diagnosis of IPA in influenza A (H1N1) patients is the key to successful treatment.

The development of keyword spotting (KWS) systems that are accurate in noisy conditions remains a challenge. Towards this goal, in this paper we propose a novel training strategy relying on multi-condition training for noise-robust KWS. By this strategy, we think of the state-of-the-art KWS models as the composition of a keyword embedding extractor and a linear classifier that are successively trained. To train the keyword embedding extractor, we also propose a new ( $C_{N,2}+1$ )-pair loss function extending the concept behind related loss functions like triplet and $N$ -pair losses to reach larger inter-class and smaller intra-class variation. Experimental results on a noisy version of the Google Speech Commands Dataset show that our proposal achieves around 12% KWS accuracy relative improvement with respect to standard end-to-end multi-condition training when speech is distorted by unseen noises. This performance improvement is achieved without increasing the computational complexity of the KWS model.

Over the past decades, remarkable progress on phosphoramidite chemistry-based large-scale de novo oligonucleotide synthesis has been achieved, enabling numerous novel and exciting applications. Among them, de novo genome synthesis and DNA data storage are striking. However, to make these two applications more practical, the synthesis length, speed, cost, and throughput require vast improvements, which is a challenge to be met by the phosphoramidite chemistry. Harnessing the power of enzymes, the recently emerged enzymatic methods provide a competitive route to overcome this challenge. In this review, we first summarize the status of large-scale oligonucleotide synthesis technologies including the basic methodology and large-scale synthesis approaches, with special focus on the emerging enzymatic methods. Afterward, we discuss the opportunities and challenges of large-scale oligonucleotide synthesis on de novo genome synthesis and DNA data storage respectively.

The efficient synthetic route was disclosed to prepare optically active triarylborane-based [5]helicenes, 7B-PhHC and 7B5N-PhHC. Their emission wavelengths are tunable by both the chemical structure modification and the tuning of excited state charge transfer dynamics via selection of appropriate solvents or addition of external F-, enabling the full-color circularly polarized luminescence with moderate to good quantum yields (0.07-0.51) and high luminescence dissymmetry factors (glum > 5 × 10-3).

This study aims to investigate the inflential factors for visit time for tracheobronchial foreign bodies in pediatrics, and to shorten the time of diagnosis and reduce complications. A questionnaire survey was designed and conducted among the caretakers of children with tracheobronchial foreign bodies, and the related inflential factors for visit time were analyzed. The visit time for tracheobronchial foreign body was correlated with the age of the child, the type of foreign body, the educational level of the caretaker, a history of foreign body aspiration were provided, an examination was performed during the visit, the anti-inflammatory and anti-allergic treatment, and transfer to a higher level hospital. Age, history of foreign body aspiration were provided, and anti-inflammatory and anti-allergic treatment were the independent inflential factors for the time of diagnosis (P

Background Drug-induced liver injury (DILI) is the most common reason for a drug to be withdrawn from the market. Apart from stopping the offending drug, no regimens are available for treating idiosyncratic DILI in clinical practice. Methods We carried out a randomized, double-blind, multidoses, active drug controlled, multicentre phase II trial to assess the safety and efficacy of the study drug, magnesium isoglycyrrhizinate (MgIG), as compared to tiopronin, a standard therapy for DILI in China. The primary outcome was the proportion of alanine aminotransferase (ALT) normalization at week 4 after study drug administration. Logistic regression was used to examine the odds of ALT normalization between low dose (Group A) and high dose (Group B) vs active control (Group C). Results One hundred and seventy-four eligible subjects were randomized and enrolled into three groups: 59 in group A, 56 in group B and 59 in group C. It was shown that group A and group B lowered ALT level even at early stage of study drug administration; when compared with Group C (61.02%), the proportions of ALT normalization at week 4 were significantly greater in Group A (84.75%, P = .0029) and Group B (85.71%, P = .0037) respectively. The results from the univariate logistic model showed that the odds of ALT normalized among subjects in Group A were about 3.6 times greater (OR = 3.55, 95% CI: 1.47-8.57, P = .0049) than subjects in Group C. Similar effect was observed among subjects in Group B (OR = 3.83, 95% CI: 1.54-9.55, P = .0039). Conclusions This trial provided preliminary evidence that MgIG is an effective and safe treatment for patients with acute DILI.

Significance Functional connectivity within the default mode network in patients with major depressive disorder has been frequently reported to be abnormal but with contradicting directions in previous studies with small sample sizes. In creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, we found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration. These findings suggest that default mode network functional connectivity remains a prime target for understanding the pathophysiology of depression, with particular relevance to revealing mechanisms of effective treatments., Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Power transmission with HTS dc cables has the characteristics of high capacity, high efficiency, more compact size, and environmental friendly. It has become an alternative and revolutionary upgrade of the traditional dc power transmission. The self-acting limit function during its quenching for fault current is unique to HTS dc cables. Fault-current-limiting capability of the HTS DC cable in a line-commutated converter (LCC) HVDC system is therefore analyzed with HTS current-limiting features in different short-circuit situations to identify the HTS dc transmission system design and operation features and verification achieved with the HTS dc cable characteristics and parameters, especially the peak value of the fault currents in the LCC HVDC systems.

SET-CAN/NUP214 fusion is a recurrent event most commonly seen in T-cell acute lymphoblastic leukemia (T-ALL). It is related to resistance to glucocorticoids and chemotherapy; however, the reported prognosis of T-ALL with SET-CAN/NUP214 fusion is diverse, and the optimal treatment option remains undetermined. Here, we present the treatment process of an illuminating case of T-ALL with SET-CAN/NUP214 fusion. The patient showed early resistance to routine VICLP chemotherapy (at 15th day, 79.2% blasts), but the leukemia burden was significantly reduced after 28-day induction chemotherapy (18.85% blasts), even though she still didn’t achieve complete remission (CR) after a second course of high-dose methotrexate (3 g/m2) and pegaspargase. Ex vivo drug sensitivity screening using a panel of 165 kinds of cytotoxic drugs, targeted therapy drugs, combination chemotherapy drugs, etc., was conducted on the refractory leukemia cells, which showed extensive resistance to various regimens. Surprisingly, AML-like scheme DAE scheme (daunorubicin + cytarabine + etoposide) and carfilzomib showed the highest ex vivo inhibition rate. The patient received DAE regimen chemotherapy, and finally achieved complete remission and received allogenic hematopoietic stem cell transplantation (allo-HSCT). According to our own findings and a literature survey, we found that T-ALL patients with SET-CAN/NUP214 fusion usually shows early resistance to chemotherapy, but they have a delayed response, and the CR rate is not compromised; thus, a chemotherapy regimen featuring a 28-day long course, such as that used in GRAALL 2003 or 2005, is recommended for induction therapy. For refractory patients, AML-like therapy such as DAE or CLAG in combination with asparaginase may be beneficial. In addition, carfilzomib may be a useful therapeutic drug and is worthy of further study. Allo-HSCT improves prognosis and we recommend HSCT if possible. Additional chromosomal or molecular events may affect the prognosis, and further investigation is needed. We believe that through proper treatment, the prognosis of patients with SET-CAN/NUP214 fusion can be greatly improved, at least not worse than that of other T-ALL patients.

Background The coronavirus disease 2019 (COVID-19) pandemic has led to dramatic disruptions to orthopedic services. The purpose of this study is to quantify the reinstatement of elective orthopedic surgeries of our institution in Shanghai, China, and share our first-hand experiences of how this region is managing the post-outbreak period. Methods The number of patients receiving elective orthopedic surgeries was analyzed in the timeframe of 8 months since the start of the pandemic (from January 20 to September 16) and compared with the patients receiving the same treatment during the same period in 2019. And a detailed workflow for handling patients about to receive elective surgeries in the COVID-19 post-outbreak period was described. Results The number of the selective surgeries in the first 3 months only accounted for 31.72% of the same period in 2019 (p = 0.0031), and the ratio reached 97.47% when it came to the last 5 months (p > 0.9999). The selective surgeries even surpassed the pre-epidemic level in months 7 and 8. And the difference of the surgeries was not significant in the whole eight observed months between 2019 and 2020 (p = 0.1526). No health care providers or hospitalized patients in orthopedic departments in Shanghai have been infected nosocomially. Conclusions Elective orthopedic surgeries have been fully recovered from the COVID-19 pandemic in our institution, and the new normalcy established during the post-outbreak period helped this region co-exist with the impact of the virus well. Trial registration Retrospectively registered, registration number: ChiCTR2000039711, date of registration: November 6, 2020

One of the beyond-5G developments that is often highlighted is the integration of wireless communication and radio sensing. This paper addresses the potential of communication-sensing integration of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the potential for high throughput and efficient multiplexing of wireless links, an LIS can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment, we develop sensing techniques that leverage the usage of computer vision combined with machine learning. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.

Background Pulmonary cryptococcosis (PC) is an infection typically diagnosed in immunocompromised or immunocompetent patients, which can lead to severe disease if not treated appropriately. We aimed to determine the association between clinical manifestations, computed tomography (CT) findings, and host immune status with the serum cryptococcal antigen (CRAG) test results of PC patients. Methods The clinical data of 378 PC patients over a 12-year period were retrospectively reviewed at Shanghai Pulmonary Hospital (Shanghai, China). Serum CRAG was detected by a latex agglutination (LA) test using CryptoTrol (Immuno-Mycologics Inc., Norman, OK, USA). Patients were categorized according to their serum LA results, and their clinical characteristics were analyzed: 244 of 378 patients showed positive serum LA results and 134 had negative results. Results Immunocompromised hosts (ICH) were more likely to present positive LA results. The ICH group had higher titers of LA test than the non-immunocompromised host (NICH) group. Patients with negative LA results often had no symptoms and their CT findings presented a solitary nodule or mass, while LA-positive patients had variable symptoms such as cough, expectoration, fever, etc. A large diversity of CT manifestations were observed in the LA-positive patients, such as multiple nodules, patchy shadows, interstitial infiltrates, and diffuse granular shadows. Patients with a solitary nodule or mass had lower titers than did the patients with other manifestations. The clinical characteristics of LA-positive patients were different from those of LA-negative patients. Conclusions Serum CRAG test results were found to be associated with the clinical manifestations, CT findings, and host immune status of PC patients.

Lithium-ion hybrid capacitors (LIHCs) are considered as promising next-generation energy storage devices with high energy density, large power density, and ultra-long cycle life. Anode material is an important factor affecting LIHCs. Here, we prepared hexagonal MnF2 with good morphology by solvothermal method. MnF2 was used as the anode to deliver a high specific capacity of 330 mAh g−1 after 1050 cycles at 0.1 A g−1. The specific capacity of MnF2 electrode, after 5000 cycles at 2 A g−1, can also reach 65 mAh g−1. LIHCs of MnF2 as anode and AC as cathode offer an excellent energy density of 81 Wh kg−1 at 95 W kg−1 and power density of 6840 W kg−1 at 26 Wh kg−1. Also, the capacity retention rate is 72% after 3000 cycles at 1 A g−1. The implementation of MnF2//AC, an energy storage device, extends the practical application of MnF2 in energy storage.

Background To assess the prevalence and associated risk of potentially inappropriate medications (PIMs) prescribing in community-dwelling elderly patients in China and to examine the most frequently used PIMs. This will provide a reference for the formulation of medication manuals for the community-dwelling elderly and further standardize the use of medications in elderly patients. Methods We conducted a cross-sectional retrospective study from April 1, 2020 to April 30, 2020. Data from elderly patients aged ≥65 years were collected from the Hengjie (N=2,294), Loujiang (N=3,972), and Tongxing communities (N=1,969) in Suzhou. The frequency of PIMs was detected using the 2019 Beers criteria and the 2017 Chinese criteria. Chi-square (for categorical variables), ANOVA (for continuous variables as applicable), and logistic regression were used to describe and identify potential predictors of PIMs. Results A total of 8,235 elderly patients were examined. Using the Chinese criteria, the prevalence of PIMs was 37.07%, which was slightly higher than that found using the 2019 Beers criteria (32.16%). The most prescribed PIMs were estazolam (21.53%) and insulin (4.60%) based on the Chinese criteria. Logistic regression analysis showed that advanced age, polypharmacy, and comorbid disease of patients were associated with a high risk of PIMs. Furthermore, the educational background and professional title of physicians were also associated with PIMs. Conclusions Given the high prevalence of PIMs in the Chinese community-dwelling elderly population, the implementation of evidence-based interventions to promote rational clinical drug use could improve their quality of life.

Background Psoas hematoma rarely occurs in patients with spondylolisthesis who undergo posterior lumbar interbody fusion (PLIF) surgery. Case presentation Here we reported a case of a 57-year-old male patient diagnosed with spondylolisthesis who underwent PLIF at the local hospital. Seven days post-surgery, abdominal pain occurred, and the pain in the right lower limb gradually increased. The computerized tomography (CT) indicated a formation of hematoma around the psoas muscle. Digital-subtraction angiography (DSA) suggested a vascular injury, a rupture of the right segmental artery of the lumbar vertebral level 4. The patient then received DSA vascular embolization, after which the lower lumbar segmental artery active bleeding was stopped. One month after discharge, the abdominal hematoma was gradually absorbed, and the pain in the waist, leg, and abdomen disappeared. Conclusion Symptoms such as abdominal pain, abdominal distension, and exacerbation of lower limb pain, may suggest the occurrence of psoas hematoma after PLIF. DSA vascular embolization is suggested as the first treatment approach for this type of complication.

Highlights • Two subtypes with distinct default mode network profiles exist in major depression. • Subtypes of major depression are robust in validation datasets across brain atlases. • Hyper- & hypo-connectivity DMN subgroups have comparable clinical symptom variables. • Future studies should examine whether two subtypes have differing treatment response., Background Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. Methods The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. Results Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. Conclusions Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective application of information theory for classification problems. Based on this loss, many other loss functions have been proposed,~\emph{e.g.}, by adding intra-class and inter-class constraints to enhance the discriminative ability of the learned features. However, these loss functions fail to consider the connections between the feature distribution and the model structure. Aiming at addressing this problem, we propose a channel correlation loss (CC-Loss) that is able to constrain the specific relations between classes and channels as well as maintain the intra-class and the inter-class separability. CC-Loss uses a channel attention module to generate channel attention of features for each sample in the training stage. Next, an Euclidean distance matrix is calculated to make the channel attention vectors associated with the same class become identical and to increase the difference between different classes. Finally, we obtain a feature embedding with good intra-class compactness and inter-class separability.Experimental results show that two different backbone models trained with the proposed CC-Loss outperform the state-of-the-art loss functions on three image classification datasets., accepted by ICPR2020

Food safety issue remains a challenge worldwide. Common substances in food can pose a great threat to human health including but not limited to food borne-pathogens, heavy metals, mycotoxins, pesticides, herbicides, veterinary drugs, allergens and illegal additives. To develop rapid, low-cost, portable and on-site detection methods of those contaminants and allergens to ensure food safety, gold nanoparticles (AuNPs) of versatile shapes and morphologies such as nanorods, nanoclusters, nanoflowers, nanostars, nanocages, nanobipyramids and nanowires have been employed as probes because they possess extraordinary properties that can be used to design biosensors enabling detecting various contaminants and allergens. By means of surface modification, AuNPs can directly or indirectly sense specific targets based on different mechanisms, such as hydrogen bonds, nucleic acid hybridization, aptamer-target binding, antigen-antibody recognition, enzyme inhibition, and enzyme-mimicking activity. AuNPs can induce a distinct color change from red to blue when they transform from a monodispersed state to an aggregated state in liquid solution, which can be observed by naked eyes. If Raman molecules are functionalized on AuNPs, their aggregation will alter the interparticle distance and induce the surface-enhanced Raman scattering that can be employed for highly sensitive detection. Ultra-small AuNPs such as Au nanoclusters also feature in fluorescence that enable a fluorescent readout. The formats of AuNPs for food safety detection in real world range broadly including but not limited to films, fibers, liquid solutions, tapes, chips and lateral flow strips. In this review, recent applications of AuNPs-based biosensors for food safety detection will be discussed, mainly in the aspect of different contaminants and allergens encountered in food samples.

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning , achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features ; visual features ; deep learning methods ; fusion techniques ; training targets and objective functions . In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals , since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets , given their central role in the development of data-driven approaches, and evaluation methods , because they are generally used to compare different systems and determine their performance.

Background The present study aimed to assess the value of bronchial and cavity contraction percentages in differentiating benign and malignant pulmonary cavities. Methods Forty-two patients with pulmonary cavities were scanned by dual-phase computed tomography (CT). Then, the cavity and bronchial contraction percentages were respectively measured, the differences between the benign and malignant cavities were compared, and the best diagnostic critical point for differentiating benign and malignant cavities was obtained through the receiver operator characteristic (ROC) curve of the diagnostic test. Results The contraction percentage of the bronchial end with benign cavities was significantly higher than that of the bronchial end with malignant cavities (P P Conclusion The dual-phase CT scanning of the bronchial and cavity contraction percentage can distinguish between benign and malignant cavities.

The stability and safety of the operation of an aero engine is very important for the flight of the aircraft, so its health status must be monitored at all times to ensure its safe operation. The traditional prediction algorithm for the remaining useful life (RUL) is often only shallow, and it is not possible to dig deep into the historical information of the sensor. Moreover, the traditional single sensor predicts the life, which often results in low or inefficient data utilization. Implementation issues. This paper presents a method for predicting remaining engine life based on multi-sense fusion. First, pre-process the data and extract, then, reconstruct the remaining sensor data through singular value decomposition (SVD); input the feature data into the deep GRU neural network for training to obtain the remaining engine life prediction Results; Finally, the commercial modular aero-propulsion system simulation (C-MAPSS) was selected for simulation experiments to verify the feasibility of the method.

For aero-engines, bearing fault monitoring and diagnosis can greatly improve its reliability. Based on the successful application of deep learning algorithms in the mapping of complex nonlinear systems, this paper proposes an aero-engine bearing fault diagnosis method that combines convolutional neural networks and support vector machine. Firstly, the Short-time Fourier transform is used to perform time-frequency analysis on time series signals (including nine faults and one normal) to obtain corresponding time-frequency images. Then, the convolutional neural network is used to extract the feature of the obtained time-frequency images. Furthermore, the support vector machine is used to classify and diagnose the faults from the extracted feature. Finally, based on the data set of Case Western Reserve University, the feasibility is validated. The results show that the proposed method has achieved a bearing fault recognition rate of more than 99%, which can effectively complete the fault diagnosis task.

AIM: To describe the clinical results of combined Ahmed valve implantation and 23-gauge vitrectomy for medically uncontrolled neovascular glaucoma (NVG) secondary to proliferative diabetic retinopathy (PDR). METHODS: The medical records of medically uncontrolled NVG patients with PDR who underwent Ahmed valve implantation and 23-gauge vitrectomy between March 2016 and December 2018 were reviewed. Enrolled patients had at least 6-month follow-up. Panretinal photocoagulation (PRP), anti-vascular endothelial growth factor, surgery and medication history were documented. RESULTS: Eleven eyes of 11 patients were included in our study. The visual acuity improved in 8 eyes and remained unchanged in 3 eyes. The preoperative intraocular pressure (IOP) was significantly decreased at the last follow-up (48.8±4.3 to 17.0±1.5 mm Hg, P

BackgroundThe 2019 novel coronavirus (COVID-19) has continuous outbreaks around the world. Lung is the main organ that be involved. There is a lack of clinical data on the respiratory sounds of COVID-19 infected pneumonia, which includes invaluable information concerning physiology and pathology. The medical resources are insufficient, which are now mainly supplied for the severe patients. The development of a convenient and effective screening method for mild or asymptomatic suspicious patients is highly demanded.MethodsThis is a retrospective case series study. 10 patients with positive results of nucleic acid were enrolled in this study. Lung auscultation was performed by the same physician on admission using a hand-held portable electronic stethoscope delivered in real time via Bluetooth. The recorded audio was exported, and was analyzed by six physicians. Each physician individually described the abnormal breathing sounds that he heard. The results were analyzed in combination with clinical data. Signal analysis was used to quantitatively describe the most common abnormal respiratory sounds.ResultsAll patients were found abnormal breath sounds at least by 3 physicians, and one patient by all physicians. Cackles, asymmetrical vocal resonance and indistinguishable murmurs are the most common abnormal breath sounds. One asymptomatic patient was found vocal resonance, and the result was correspondence with radiographic computed tomography. Signal analysis verified the credibility of the above abnormal breath sounds.ConclusionsThis study describes respiratory sounds of patients with COVID-19, which fills up for the lack of clinical data and provides a simple screening method for suspected patients.

For certain applications, keyword spotting (KWS) requires some degree of personalization. This is the case for KWS for hearing assistive devices, e.g., hearing aids, where only the device user should be allowed to trigger the KWS system. In this paper, we first develop a new realistic hearing aid experimental framework. Next, using this framework we show that the performance of a state-of-the-art multi-task deep learning architecture exploiting cepstral features for joint KWS and users' own-voice/external speaker detection drops significantly. To overcome this problem, we use phase difference information through GCC-PHAT (Generalized Cross-Correlation with PHAse Transform)-based coefficients along with log-spectral magnitude features. In addition, we demonstrate that working in the perceptually-motivated constant-Q transform (CQT) domain instead of in the short-time Fourier transform (STFT) domain allows for the generation of compact and coherent features which provide superior KWS performance. Our experimental results show that our CQT-based proposal achieves a relative KWS accuracy improvement of around 18% compared to using cepstral features while dramatically decreasing the number of multiplications in the multi-task architecture, which is key in the context of low-resource devices like hearing assistive devices.

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

In this paper, we present a deep-learning-based framework for audio-visual speech inpainting, i.e., the task of restoring the missing parts of an acoustic speech signal from reliable audio context and uncorrupted visual information. Recent work focuses solely on audio-only methods and generally aims at inpainting music signals, which show highly different structure than speech. Instead, we inpaint speech signals with gaps ranging from 100 ms to 1600 ms to investigate the contribution that vision can provide for gaps of different duration. We also experiment with a multi-task learning approach where a phone recognition task is learned together with speech inpainting. Results show that the performance of audio-only speech inpainting approaches degrades rapidly when gaps get large, while the proposed audio-visual approach is able to plausibly restore missing information. In addition, we show that multi-task learning is effective, although the largest contribution to performance comes from vision., Comment: Accepted at ICASSP 2021

Both acoustic and visual information influence human perception of speech. For this reason, the lack of audio in a video sequence determines an extremely low speech intelligibility for untrained lip readers. In this paper, we present a way to synthesise speech from the silent video of a talker using deep learning. The system learns a mapping function from raw video frames to acoustic features and reconstructs the speech with a vocoder synthesis algorithm. To improve speech reconstruction performance, our model is also trained to predict text information in a multi-task learning fashion and it is able to simultaneously reconstruct and recognise speech in real time. The results in terms of estimated speech quality and intelligibility show the effectiveness of our method, which exhibits an improvement over existing video-to-speech approaches., Comment: Accepted to Interspeech 2020

The location information of the items on storage rack in warehouse is critical for planning and control in the dynamic manufacturing environment. In this study, we explored to use wireless signals from passive radio frequency identification (RFID) system to track the location of items on the storage racks in warehouse. To overcome the problems caused by uncertainties of sensor data stream, a predictive model for localization application is presented in this paper. The autonomous learning multimodel system (ALMMo) is used to construct a localization model. The wireless signals from passive RFID reference tags on the rack are captured constantly. The online localization model is updated by using the passive RFID sensor data stream.