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A digital Image Compression Encryption Method (ICEM) is proposed in this work by using the Compressed Sensing (CS), two-dimensional Logistic-adjusted-Sine map, the piecewise asymptotic deterministic system, dynamic diffusion and Hilbert-Logistic permutation. The plain image is pre-processed by a two-dimensional discrete wavelet transform to obtain four kinds of frequency components HH , HL , LH and LL . Dynamic diffusion and the CS are used to process low and high-frequency components, respectively, and then the results are combined to obtain an intermediate cryptographic image. In the process of CS, a measurement matrix is generated by a piecewise asymptotic deterministic random system and a chaotic sequence. To obtain the encrypted image, an improved Hilbert-Logistic permutation method is used to disturb the pixel position of the intermediate cryptographic image. The SHA-256 is utilized to generate the required keys for the encryption process. The proposed ICEM has the ability to resist chosen-plaintext and known-plaintext attacks as the key is selected dynamically. By using the CS the time and space complexity of the algorithm are reduced. It is shown in experimental results that the proposed scheme has good key sensitivity, decryption speed, fast encryption and large key space, and high robustness and security. [ABSTRACT FROM AUTHOR]

Cellulose nanofiber (CNF) is an environmentally friendly material with a high specific surface area, which is an ideal candidate for cathode catalysts in zinc–air batteries (ZABs). However, untreated CNF-based carbon materials suffer from low intrinsic activity and fewer micro-mesopores. Herein, we developed an N-doped porous carbon aerogel from CNF and graphite phase carbon nitride (g-C3N4) via hydrothermal pretreatment and the carbonization process. The CNF can serve as the skeleton of the aerogel that leads to an interconnected porous structure, while g-C3N4 guarantees the in situ N doping and the formation of more nanopores. Therefore, the as-prepared carbon aerogel exhibits excellent oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) performances owing to the successful N doping with sufficient graphite-N and pyridine-N active sites (69.1%) and high specific surface areas (785.4 m2 g−1). The assembled ZABs with a carbon aerogel deliver a specific capacity of 790 mA h g−1 and an energy density of 945.6 W h kg−1 with a long-term stability over 100 h. This work opens a new avenue for the effective conversion of abundant biomass into bifunctional air cathodes for energy-related applications. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks (SNNs) can closely mimic the biological neural network systems. Recently, the SNNs have been developed in hardware circuits to emulate the time encoding and information-processing aspects of the human brain in real-time. However, the hardware SNN systems are suffering from large hardware resource consumption due to the high complexity of computational units. In this paper, a novel hardware SNN system based on stochastic computing is proposed to address this problem. Pair-based spiking-timing-dependent plasticity, coupled with integrate-and-fire neurons are employed to design the SNN. Stochastic computing can simplify the computational components of multipliers, adders, and subtractors in conventional hardware SNNs, hence reduce the hardware resource cost. Experimental results show that compared with the state-of-the-art approaches the proposed SNN system reduces the resource consumption by 58.0% (especially registers by ≥ 65.6%). In the meantime, the maximum normalized root mean square error between the proposed hardware and others is only 0.0097, which can maintain the behaviours of SNN. This work provides a beneficial alternative to the large-scale hardware SNN implementations. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Schizophrenia is a multidimensional disease. This study proposes a new research framework that combines multimodal meta-analysis and genetic/molecular architecture to solve the consistency in neuroimaging biomarkers of schizophrenia and whether these link to molecular genetics. Study Design We systematically searched Web of Science, PubMed, and BrainMap for the amplitude of low-frequency fluctuations (ALFF) or fractional ALFF, regional homogeneity, regional cerebral blood flow, and voxel-based morphometry analysis studies investigating schizophrenia. The pooled-modality, single-modality, and illness duration-dependent meta-analyses were performed using the activation likelihood estimation algorithm. Subsequently, Spearman correlation and partial least squares regression analyses were conducted to assess the relationship between identified reliable convergent patterns of multimodality and neurotransmitter/transcriptome, using prior molecular imaging and brain-wide gene expression. Study Results In total, 203 experiments comprising 10 613 patients and 10 461 healthy controls were included. Multimodal meta-analysis showed that brain regions of significant convergence in schizophrenia were mainly distributed in the frontotemporal cortex, anterior cingulate cortex, insula, thalamus, striatum, and hippocampus. Interestingly, the analyses of illness-duration subgroups identified aberrant functional and structural evolutionary patterns: Lines from the striatum to the cortical core networks to extensive cortical and subcortical regions. Subsequently, we found that these robust multimodal neuroimaging abnormalities were associated with multiple neurobiological abnormalities, such as dopaminergic, glutamatergic, serotonergic, and GABAergic systems. Conclusions This work links transcriptome/neurotransmitters with reliable structural and functional signatures of brain abnormalities underlying disease effects in schizophrenia, which provides novel insight into the understanding of schizophrenia pathophysiology and targeted treatments. [ABSTRACT FROM AUTHOR]

Background: Sepsis is an important public health issue, and it is urgent to develop valuable indicators to predict the prognosis of sepsis. Our study aims to assess the predictive value of ICU admission (Neutrophil + Monocyte)/lymphocyte ratio (NMLR) on the 30-day mortality of sepsis patients. Methods: A retrospective analysis was conducted in septic patients, and the data were collected from Medical Information Mart for Intensive Care IV (MIMIC-IV). Univariate and multivariate Cox regression analyses were conducted to investigate the relation between ICU admission NMLR and 30-day mortality. Restricted cubic spline (RCS) was performed to determine the optimum cut-off value of ICU admission NMLR. Survival outcomes of the two groups with different ICU admission NMLR levels were estimated using the Kaplan-Meier method and compared by the log-rank test. Results: Finally, 7292 patients were recruited in the study, of which 1601 died within 30 days of discharge. The non-survival group had higher ICU admission NMLR values than patients in the survival group (12.24 [6.44–23.67] vs. 8.71 [4.81–16.26], P < 0.001). Univariate and multivariate Cox regression analysis demonstrated that ICU admission NMLR was an independent prognostic predictor on 30-day mortality (Univariate: P < 0.001; multivariate: P = 0.011). The RCS model demonstrated the upturn and non-linear relationship between ICU admission NMLR and 30-day mortality (Nonlinearity: P = 0.0124). According to the KM curve analysis,30-day survival was worse in the higher ICU admission NMLR group than that in the lower ICU admission NMLR group (Log rank test, P < 0.0001). Conclusion: The elevated ICU admission NMLR level is an independent risk factor for high 30-day mortality in patients with sepsis. [ABSTRACT FROM AUTHOR]

It is an important research of automatically identifying the type and severity level of crop diseases, which affects food production, disease control, and economic loss prediction. Deep learning-based methods have achieved successful application for disease identification. However, the method of only using a single network model to identify crop disease is simple and limited since networks vary in complexity and architecture. Furthermore, to measure the effectiveness of the measures taken, precisely quantifying the level of severity is necessary, instead of simply dividing the level into serious or not. In response to these problems, a novel system of disease identification and severity estimation is proposed in this paper. Firstly, an ensemble learning model is proposed to recognize crop diseases, which consider the comprehensive outputs of ResNet50, Inceptionv3, and DenseNet121. The relative majority voting is used as the ensemble classifier to obtain the final identification result. Second, a pixel-level segmentation model is proposed to precisely estimate disease severity after the disease is identified, which can track small changes in infected areas and obtain fine-grained resolutions. DeepLab architecture, encoder-decoder, and dilated convolution are integrated into the proposed segmentation model. Furthermore, an attention mechanism is applied to DeepLabv3+ to improve the performance of severity estimation, which can give more weight to disease areas to achieve more precise segmentation. A public grape disease database is used to verify the performance of the proposed system. Experimental results show that compared with an individual model and other existing studies, the proposed disease identification model has stronger robustness and higher identification accuracy. Furthermore, the proposed severity estimation model also achieves a lower severity error and a higher classification accuracy than baseline models of DeepLabv3+, PspNet, and SegNet. [ABSTRACT FROM AUTHOR]

Image encryption is a common way of information protection. However, most of the existing image encryption schemes have a low transmission efficiency and large memory consumption due to ciphertext redundancy. A highly-efficient and secure image algorithm with block compressed sensing (BCS) is designed in this paper to protect image information and reduce redundant data. Firstly, the original image is sparsely processed by discrete wavelet transform (DWT), and then pixel positions in the global scope are scrambled by chaotic sequence. Secondly, several image blocks are obtained by dividing scrambled images. Then, the image blocks are measured by a key-controlled partial Hadamard matrix to reduce the dimension. Finally, image blocks are merged and a proposed randomly selected pixel diffusion (RSPD) is used to further enhance security performance and the anti-statistic analysis ability. Specifically, the SHA-512 values of the original image are used for the key generation, and then the plaintext sensitivity is improved. The pseudo-random sequences that are required for scrambling and diffusion are produced by the Chen's system and logistic map. Results show that the proposed mechanism can effectively reduce the redundant information and has a lower computational complexity and higher security performance than existing methods. [ABSTRACT FROM AUTHOR]

Background: Injury of alveolar epithelial cells and capillary endothelial cells is crucial in the pathogenesis of acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Mesenchymal stem cells (MSCs) are a promising cell source for ALI/ARDS treatment. Overexpression of Fork head box protein M1 (FoxM1) facilitates MSC differentiation into alveolar type II (AT II) cells in vitro. Moreover, FoxM1 has been shown to repair the endothelial barrier. Therefore, this study explored whether overexpression of FoxM1 promotes the therapeutic effect of bone marrow-derived MSCs (BMSCs) on ARDS by differentiation of BMSCs into AT II cells or a paracrine mechanism. Methods: A septic ALI model was established in mice by intraperitoneal administration of lipopolysaccharide. The protective effect of BMSCs-FoxM1 on ALI was explored by detecting pathological variations in the lung, total protein concentration in bronchoalveolar lavage fluid (BALF), wet/dry (W/D) lung weight ratio, oxidative stress levels, cytokine levels, and retention of BMSCs in the lung. In addition, we assessed whether FoxM1 overexpression promoted the therapeutic effect of BMSCs on ALI/ARDS by differentiating into AT II cells using SPC−/− mice. Furthermore, the protective effect of BMSCs-FoxM1 on lipopolysaccharide-induced endothelial cell (EC) injury was explored by detecting EC proliferation, apoptosis, scratch wounds, tube formation, permeability, and oxidative stress, and analyzing whether the Wnt/β-catenin pathway contributes to the regulatory mechanism in vitro using a pathway inhibitor. Results: Compared with BMSCs-Vector, treatment with BMSCs-FoxM1 significantly decreased the W/D lung weight ratio, total BALF protein level, lung injury score, oxidative stress, and cytokine levels. With the detected track of BMSCs-FoxM1, we observed a low residency rate and short duration of residency in the lung. Notably, SPC was not expressed in SPC−/− mice injected with BMSCs-FoxM1. Furthermore, BMSCs-FoxM1 enhanced EC proliferation, migration, and tube formation; inhibited EC apoptosis and inflammation; and maintained vascular integrity through activation of the Wnt/β-catenin pathway, which was partially reversed by XAV-939. Conclusion: Overexpression of FoxM1 enhanced the therapeutic effect of BMSCs on ARDS, possibly through a paracrine mechanism rather than by promoting BMSC differentiation into AT II cells in vivo, and prevented LPS-induced EC barrier disruption partially through activating the Wnt/β-catenin signaling pathway in vitro. [ABSTRACT FROM AUTHOR]

Components of metabolic syndrome might be predictors of the therapeutic outcome of psychiatric symptom in schizophrenia, whereas clinical results are inconsistent and an intrinsic therapeutic link between weaker psychiatric symptoms and emergent metabolic syndrome remains unclear. This study aims to reveal the relationship and illustrate potential mechanism by exploring the alteration of cerebellar functional connectivity (FC) in schizophrenia patients with comorbidity metabolic syndrome. Thirty-six schizophrenia patients with comorbidity of metabolic syndrome (SCZ-MetS), 45 schizophrenia patients without metabolic syndrome (SCZ-nMetS) and 39 healthy controls (HC) were recruited in this study. We constructed FC map of cerebello-cortical circuit and used moderation effect analysis to reveal complicated relationship among FC, psychiatric symptom and metabolic disturbance. Components of metabolic syndrome were significantly correlated with positive symptom score and negative symptom score. Importantly, the dysconnectivity between cognitive module of cerebellum and left middle frontal gyrus in SCZ-nMetS was recuperative increased in SCZ-MetS, and was significantly correlated with general symptom score. Finally, we observed significant moderation effect of body mass index on this correlation. The present findings further supported the potential relationship between emergence of metabolic syndrome and weaker psychiatric symptom, and provided neuroimaging evidence. The mechanism of intrinsic therapeutic link involved functional change of cerebello-cortical circuit. [ABSTRACT FROM AUTHOR]

The robustness and imperceptibility of the watermarking algorithm are a pair of contradictory properties. When the robustness increases, the imperceptibility decreases. Most strategies proposed in the literature usually concentrate on improving robustness, but few consider the relationship between robustness and imperceptibility. To deal with this problem, a robust blind image watermarking method is proposed in this work, which is composed of a logistic-sine-COupling map, discrete waVElet transform, and appRoximate maximum eigenvalue of Schur decomposition, namely COVER. In the proposed COVER, an objective function and a two-stage search method are designed to determine the optimal embedding coefficient, thus balancing the imperceptibility and robustness of this work. Specifically, the host image is transformed into four sub-bands by discrete wavelet transform, and the low-low sub-band is further separated into non-overlapping blocks which can be calculated to get the approximate maximum eigenvalues (AMEs) of Schur decomposition. Especially, each AME is not contained in the real Schur decomposition, which effectively reduces the computational complexity. Then, the original AMEs of the host image are updated to complete the embedding process according to the binary feature of encrypted watermark and the proposed embedding method. Finally, the watermarked image is obtained. The experimental results show that all peak signal noise ratio values are above 42 dB, structural similarity index measure values are above 0.98, normalized correlation values are above 0.9, and all bit errors rates are less than 0.14. Compared with six state-of-the-art schemes, the proposed COVER has strong robustness, good imperceptibility, large capacity, and low computational complexity. [ABSTRACT FROM AUTHOR]

Recent encryption schemes are not sensitive enough to plain-images, which leads to low robustness and easy vulnerability to attacks. By employing chaotic maps and Cellular Automata, a novel image encryption algorithm is proposed in this work to increase the sensitivity to plain-images and improve security. Firstly, initial values of the two-dimensional Logistic-Sine-Coupling Map and the Logistic-Sine-Cosine Map are calculated by the SHA-256 of the original image, and the process of diffusion is conducted. Secondly, the key matrices are produced by iterating chaotic maps in the process of permutation. The diffused image is scrambled by the index matrices, which are produced by sorting every row or column of the key matrices. Finally, the scrambled image is transformed into cipher-image by using Cellular Automata. Experimental results and theoretical analysis show that the proposed scheme has good security as it can effectively resist various attacks. [ABSTRACT FROM AUTHOR]

As an adjuvant drug, alprostadil lipid microsphere injection (Lipo-PGE1) is one of the best-selling drugs in China in recent years. However, the off-label use of Lipo-PGE1 is very common. This study aimed to investigate the use of Lipo-PGE1 and evaluate the clinical effects and economic benefits after administrative intervention on inappropriate use of Lipo-PGE1 in neurosurgical patients in a Chinese tertiary hospital. Administrative interventions were implemented from January to December 2018 by reducing the procurement volume of Lipo-PGE1, judging the rationality of medical records, and establishing reward and punishment mechanisms. Administrative interventions significantly decreased prescription rate (49.98% vs 22.49%), utilization (22,311 DDDs vs 8334 DDDs), drug use density (43.52 DDDs/TID vs 15.84 DDDs/TID), total expenditure (3.58 million RMB vs 1.30 million RMB), and average expenditure (2025.04 RMB vs 1466.49 RMB) of Lipo-PGE1. To our delight, these intervention effects were maintained or even better in the 1-year post-intervention period. Moreover, in the intervention and post-intervention phases, the Lipo-PGE1 use for no indications as well as inappropriate drug dose, frequency, menstruum type, combination, and contraindication were markedly reduced. Besides, the mean costs (P < 0.001), and mean duration (P < 0.001) of Lipo-PGE1 were also obviously decreased. The administrative intervention obviously reduced the off-label use of Lipo-PGE1. However, there still remains a number of inappropriate uses of Lipo-PGE1. To further improve the rational use of Lipo-PGE1, combination of administrative intervention and real-time clinical pharmacists intervention should be implemented. [ABSTRACT FROM AUTHOR]

Objective: When facing major emergency public accidents, men and women may react differently. Our research aimed to assess the influence of gender difference on social support, information preference, biological rhythm, psychological distress, and the possible interaction among these factors during the COVID-19 pandemic. Methods: In this cross-sectional study, 3,237 respondents aged 12 years and older finished the online survey. Levels of social support, information preference, biological rhythm, and psychological distress were assessed using validated scales. A path analysis was conducted to explore possible associations among these variables. Results: The path analysis indicated that women with high levels of social support had a lower possibility of biological rhythm disorders and lower levels of somatization symptoms of psychological distress during the COVID-19 pandemic. The influence of social support on somatization symptoms was exerted via biological rhythm. Women tended to believe both negative and positive information, while men preferred more extreme information. Conclusion: Our results highlighted gender difference in study variables during the COVID-19 pandemic and the importance of social support in alleviating psychological distress and biological rhythm disorders. Moreover, we confirmed that information preference differed significantly by somatization symptoms of psychological distress, suggesting extra efforts to provide more individualized epidemic information. Longitudinal research is required to further explore casual inferences. [ABSTRACT FROM AUTHOR]

Protecting digital images is crucial, and image encryption schemes based on chaotic systems have been extensively studied where image permutation is a critical process. The updating processes of particle swarm optimization algorithm have the ability to change the rules of particles motion, and it can be employed to permutate image. In this work, an image encryption scheme based on the updating processes of particle swarm optimization algorithm and hyperchaotic system is proposed. Specifically, a key generation mechanism combined with secure hash algorithm 256 hash is first introduced to generate the initial values of hyperchaotic complex Lü system. Then, the plain image is confused by the position and velocity updating processes of particle swarm optimization algorithm. In addition, an adaptive diffusion mechanism is designed and applied to the confused image to obtain the final cipher image. Simulation results and security analysis show that the proposed scheme exhibits good performance of sophisticated dynamic behavior, high sensitivity to key, and effectively resisting the typical cryptanalysis attacks, and provides an alternative to real-time image encryption application. [ABSTRACT FROM AUTHOR]

Intelligent traffic light control is one of the modern approaches to solve traffic congestion, where reinforcement learning is a widely used method. Conventionally, reinforcement learning is used to determine whether to change the current phase (or choose a traffic phase) after each small interval. One major drawback of these approaches is that it makes the current traffic light phase duration uncertain before the current phase terminates. Directly determining the duration of the traffic light phase can effectively avoid this shortcoming. An adaptive traffic light timing system is proposed in this paper which can directly control the phase duration. In the proposed system, the Q-learning algorithm is employed and the action space is defined as all possible phase durations. In addition, the reward function is redefined to guide the agent to balance more traffic metrics, and the state is redefined to reduce the state space. Finally, the proposed system is evaluated by equal, unequal, and complex traffic scenarios. Results show that the proposed system has a better performance compared with other methods in controlling traffic lights, even on complex traffic scenarios. [ABSTRACT FROM AUTHOR]

Currently, effective therapies for advanced gastric cancer with systemic metastasis are lacking. Pharmacological research has been slowly progressing over the past decades. Here, we report the case of a 56-year-old female with human epidermal growth factor receptor 2 (HER2) expression (IHC 2+/FISH-) in gastric cancer with systemic metastasis. The first-line therapeutic regime consisted of systemic administration of camrelizumab, local arterial infusion of oxaliplatin and arterial embolization, oral apatinib, and PS scheme (oral tegafur-gimeracil-oteracil (S-1) and paclitaxel (PTX), which was administered both intraperitoneally and systemically). After the treatment, a 3-month progression-free survival (PFS) was observed. Due to the occurrence of CTCAE grade 4 adverse reactions, the patient could not tolerate chemotherapy. In the second line of treatment, we replaced the PS scheme with disitamab vedotin and continued the use of carrilizumab and apatinib. After four cycles, efficacy evaluation showed that it was stable disease (SD), only CTCAE 1/2 grade adverse reactions occurred, and endoscopy examination showed local tumor control with a reduction in the ulcer lesion. At the time of submission of the current manuscript, a 6-month PFS was achieved and the treatment was continued. Due to the safety and efficacy of disitamab vedotin observed in our case, we propose that disitamab vedotin could be a promising drug for the treatment of advanced gastric cancer patients with HER2 expression. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have the potential to closely mimic the information processing of biological brains, by using massive neurons that are interconnected in a complex network. Recent researches have considered using electronic hardware circuits to SNN implementations to meet real-time processing requirements. Network-on-Chips (NoCs) have been widely used to develop such SNN circuits as their interconnections can offer stable interconnectivity for neuron communications with high throughput and real-time execution. However, its scalability is limited due to expensive and complex NoC routers which leads to high energy consumption and large area utilization. Therefore, a minimally buffered deflection router (MBDR) is proposed in this work to address the scalability challenge of the hardware SNNs. It employs a deflection router technique to remove most of the inter-buffers and other expensive components of the conventional routers. Moreover, a novel flow controller is developed in MBDR to further reduce power consumption. Compared to existing approaches, experimental results show that based on 90-nm CMOS technology the area and power consumption of the proposed router are reduced by ~ 86% and ~ 88%, respectively. In the meantime, system throughput is maintained at a high level. [ABSTRACT FROM AUTHOR]

A total of 16 OsS40 genes of Oryza sativa were identified in our previous work, but their functions remain unclear. In this study, 13 OsS40 members were knocked out using the CRISPR/cas9 gene-editing technology. After screening phenotype characterization of CRISPR/Cas9 mutants compared to WT, five oss40s mutants exhibited a stay-green phenotype at 30 days after heading. Moreover, increased grain size and grain weight occurred in the oss40-1, oss40-12 , and oss40-14 lines, while declined grain weight appeared in the oss40-7 and oss40-13 mutants. The transcript levels of several senescence-associated genes (SAGs), chlorophyll degradation-related genes (CDGs), as well as WRKY members were differentially decreased in the five stay-green oss40s mutants compared to WT. Five oss40 mutants also exhibited a stay-green phenotype when the detached leaves were incubated under darkness for 4 days. OsSWEET4 and OsSWEET1b were significantly upregulated, while OsSWEET1a and OsSWEET13 were significantly downregulated in both oss40-7 and oss40-14 compared to WT. Furthermore, these five OsS40 displayed strong transcriptional activation activity and were located in the nucleus. Most of the OsS40 genes were downregulated in the oss40-1, oss40-7 , and oss40-12 mutants, but upregulated in the oss40-13 and oss40-14 mutants, indicating coordinated regulation among OsS40 members. These results suggest that OsS40-1, OsS40-7, OsS40-12, OsS40-13 , and OsS40-14 are senescence-associated genes, involved in the senescence and carbon allocation network by modulating other OsS40 members, SWEET member genes, and senescence-related gene expression. [ABSTRACT FROM AUTHOR]

Background: Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). Methods: We first used histopathological image features and machine‐learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA‐HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. Results: Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5‐year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5‐year AUC = 0.860) and test set (5‐year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high‐risk and low‐risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. Conclusions: Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC. [ABSTRACT FROM AUTHOR]

Purpose: Pulmonary vascular endothelial cell (EC) injury is recognized as one of the pathological factors of acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Bone marrow mesenchymal stem cell (BMSC)-based cytotherapy has attracted substantial attention over recent years as a promising therapeutic approach for ALI/ARDS; however, its use remains limited due to inconsistent efficacy. Currently, gene modification techniques are widely applied to MSCs. In the present study, we aimed to investigate the effect of BMSCs overexpressing Homeobox B4 (HOXB4) on lipopolysaccharide (LPS)-induced EC injury. Methods: We used LPS to induce EC injury and established EC-BMSC coculture system using transwell chambers. The effect of BMSCs on ECs was explored by detecting EC proliferation, apoptosis, migration, tube formation, and permeability, and determining whether the Wnt/β-catenin pathway is involved in the regulatory mechanism using XAV-939, inhibitor of Wnt/ β-catenin. Results: As compared to BMSCWT, BMSCHOXB4 coculture promoted EC proliferation, migration, and tube formation after LPS stimulation and attenuated LPS-induced EC apoptosis and vascular permeability. Mechanistically, BMSCHOXB4 coculture prevented LPS-induced EC injury by activating the Wnt/β-catenin pathway, which is partially reversible by XAV-939. When cocultured with BMSCHOXB4, pro-inflammatory factors were dramatically decreased and anti-inflammatory factors were greatly increased in the EC medium compared to those in the LPS group (P< 0.05). Additionally, when compared to BMSCWT coculture, the BMSCHOXB4 coculture showed an enhanced modulation of IL-6, TNF-α, and IL-10, but there was no statistically significant effect on IL-1β and IL-4. Conclusion: Coculturing of BMSCHOXB4 prevented LPS-induced EC injury by reversing the inactivation of the Wnt/β-catenin signaling pathway. An in vivo study remains warranted to ascertain whether engraftment of BMSCHOXB4 can be an attractive strategy for the treatment of ALI/ARDS. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients. Method: We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients. Results: A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models. Conclusion: In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients. [ABSTRACT FROM AUTHOR]

The purpose of this study was to use a murine model to determine if melatonin can protect the inner ear from radiation-induced damage. A total of 81 4-week-old Balb/c mice were randomly divided into five groups: control group; 50 mg/kg melatonin group; 5 mg/kg melatonin+radiotherapy group; 50 mg/kg melatonin+radiotherapy group; radiotherapy group. The radiotherapy groups received 16 Gy irradiation and melatonin was administered by intraperitoneal injection 30 min before radiotherapy. On days 3 and 7 after irradiation the function of outer hair cells was determined by auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAEs) testing, pathological changes of inner ear cells were observed by light microscopy, and the expression of prestin mRNA was determined. ABR thresholds were increased and wave I latencies were extended after radiotherapy; however, the increases were lower in the groups that received melatonin (P  < 0.05). DPOAEs showed radiotherapy-induced hearing loss at 8–12 kHz, and hearing loss was greater on day 7 than day 3. However, hearing loss was less in the melatonin groups (P  < 0.05). Histopathological examination showed irradiation resulted in breaks and distortion of the cochlear basement membrane, disruption of the stria vascularis, and swelling of outer hair cells. Melatonin reduced these changes. Radiotherapy upregulated prestin mRNA expression. Radiotherapy-induced upregulation of prestin was decreased in the melatonin groups (P  < 0.05), and the decrease was greater in the 50 mg/kg melatonin group (P  < 0.05). Melatonin protects against radiation-induced cochlear damage by reducing damage to outer hair cells. [ABSTRACT FROM AUTHOR]

The security of chaotic cryptographic system can be theoretically evaluated by using conventional statistical tests and numerical simulations, such as the character frequency test, entropy test, avalanche test and SP 800-22 tests. However, when the cryptographic algorithm operates on a cryptosystem, the leakage information such as power dissipation, electromagnetic emission and time-consuming can be used by attackers to analyze the secret keys, namely the Side Channel Analysis (SCA) attack. In this paper, a cryptanalysis method is proposed for evaluating the security of a chaotic block cryptographic system from a hardware perspective by utilizing the Template Attacks (TAs). Firstly, a chaotic block cryptographic system is described briefly and implemented based on an Atmel XMEGA microcontroller. Then the TA using a multivariate Gaussian model is introduced. In order to reduce computational complexity and improve the efficiency of TA, the Hamming weight is used in this work to model power consumption traces. The proposed TA method has the following advantages including (a) using the sum of difference to select points of interest of traces, (b) using a data processing method to minimize the influences on power information modeling from the redundant sampling points, and (c) all the traces are aligned precisely before establishing the templates. Experimental results show that the TA can be used to attack the chaotic cryptographic systems and is more efficient, i.e. ∼ 32% less attack traces than correlation power analysis, when the templates are properly built. [ABSTRACT FROM AUTHOR]

Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN. [ABSTRACT FROM AUTHOR]

In this paper, a color image encryption method using the memristive hyperchaotic system and deoxyribonucleic acid (DNA) encryption is proposed. First, the pseudo-random sequences are generated by a keystream generation mechanism based on a memristive hyperchaotic system and the plain image. Due to this, the memristive hyperchaotic system has a complex dynamical behavior and is highly sensitive to initial conditions, the proposed keystream generation mechanism is highly random which is also dependent on the plain images. Second, a permutation based on the cycle-shift operation is designed to eliminate the correlations between adjacent pixels in the plain images. Then, the scrambled sequences are processed by DNA encryption to increase the system ability to defense the brute force attacks. Finally, the cipher image is obtained after the diffusion and interaction among red, green and blue components. Experimental analysis and performance comparisons show that the proposed method has high security, good efficiency and strong robustness under different attacks. [ABSTRACT FROM AUTHOR]

As a complex psychiatric disorder, schizophrenia is interpreted as a "dysconnection" syndrome, which is linked to abnormal integrations in between distal brain regions. Recently, neuroimaging has been widely adopted to investigate how schizophrenia affects brain networks. Furthermore, some studies reported frequency dependence of the abnormalities of functional network in schizophrenia, however, dynamic functional connectivity with frequency dependence is rarely used to explore changes in the whole brain of patients with schizophrenia (SZ). Therefore, in the current study, dynamic functional connectivity strength (dFCS) was performed on resting-state functional magnetic resonance data from 96 SZ patients and 121 healthy controls (HCs) at slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.25 Hz) frequency bands and further assessed whether the altered dFCS was correlated to clinical symptoms in SZ patients. Results revealed that decreased dFCS of schizophrenia were found in salience, auditory, sensorimotor, visual networks, while increased dFCS in cerebellum, basal ganglia, and prefrontal networks were observed across different frequency bands. Specifically, the thalamus subregion of schizophrenic patients exhibited enhanced dynamic FCS in slow-5 and slow-4, while reduced in slow-3. Moreover, in slow-5 and slow-4, significant interaction effects between frequency and group were observed in the left calcarine cortex, the bilateral inferior orbitofrontal gyrus, and anterior cingulum cortex (ACC). Furthermore, the altered dFCS of insula, thalamus (THA), calcarine cortex, orbitofrontal gyrus, and paracentral lobule were partial correlated with clinical symptoms of SZ patients in slow-5 and slow-4 bands. These results demonstrate the abnormalities of dFCS in schizophrenia patients is rely on different frequency bands and may provide potential implications for exploring the neuropathological mechanism of schizophrenia. [ABSTRACT FROM AUTHOR]

As a basic and crucial security requirement for Wireless Sensor Networks (WSNs), authentication is generally used to prevent various communication attacks such as Denial-of-Service (DoS) attack. A novel broadcast authentication framework is proposed in this paper, where an Identity-Based Signature scheme by using the Extended Chaotic Maps (ECM-IBS) is designed to authenticate all broadcast messages and specifically, a chaos-based hash function is used for message authentication in the WSNs. It is implemented using a WSN hardware device of CC2530 and its performance is analyzed under various methods. Performance analysis and experimental results show that the proposed ECM-IBS scheme has a quick signature generation speed, low energy consumption and short verification delay, and can be applied to WSN applications. [ABSTRACT FROM AUTHOR]

The securities of chaotic cryptographic systems are widely evaluated by conventional tests such as the character frequency test, entropy test and avalanche test. However, when the chaotic cryptosystem is in operation, side channel information such as power consumption, and electromagnetic radiation is leaked. The side channel information can be used to attack the cryptosystem, e.g. the side channel attack (SCA), which is a threat for the security of chaotic cryptographic systems. This paper proposes a chaotic block cryptographic algorithm that can resist the SCA, with the aim of enhancing the security of chaotic cryptosystems. Masking and hiding mechanisms are used in this work. By using the former, the intermediate data correlated with the plaintexts/keys are masked by random numbers, thus no direct correlation exists between the power consumption and the plaintexts/keys and the first order SCA can be counteracted. By using the latter, additional noise is added to the side channel information by randomizing the operation sequence of the algorithm. Combining these two methods, the higher order SCA can be counteracted. To evaluate the security of the proposed system, the correlation power analysis attacks are carried out based on the target device of an Atmel XMEGA microcontroller. For the proposed system, the correlation coefficient calculated from the correct key is not larger than the incorrect keys. However, for the unprotected cipher system, the correlation coefficient calculated from the correct key is 0.8 and the coefficients calculated from the incorrect keys are less than 0.5, i.e. the system can be attacked. Experimental results demonstrate that the proposed cryptosystems can counteract the correlation power analysis attacks and maintain the security performance for the chaotic cryptographic systems. [ABSTRACT FROM AUTHOR]

Pathophysiological and atrophic changes in the cerebellum have been well‐documented in schizophrenia. Reduction of gray matter (GM) in the cerebellum was confirmed across cognitive and motor cerebellar modules in schizophrenia. Such abnormalities in the cerebellum could potentially have widespread effects on both sensorimotor and cognitive symptoms. In this study, we investigated how reduction change in the cerebellum affects the static and the dynamic functional connectivity (FC) between the cerebellum and cortical/subcortical networks in schizophrenia. Reduction of GM in the cerebellum was confirmed across the cognitive and motor cerebellar modules in schizophrenic subjects. Results from this study demonstrates that the extent of reduction of GM within cerebellum correlated with increased static FCs between the cerebellum and the cortical/subcortical networks, including frontoparietal network (FPN), and thalamus in patients with schizophrenia. Decreased GM in the cerebellum was also associated with a declined dynamic FC between the cerebellum and the FPN in schizophrenic subjects. The severity of patients' positive symptom was related to these structural‐functional coupling score of cerebellum. These findings identified potential cerebellar driven functional changes associated with positive symptom deficits. A post hoc analysis exploring the effect of changed FC within cerebellum, confirmed that a significant positive relationship, between dynamic FCs of cerebellum–thalamus and intracerebellum existed in patients, but not in controls. The reduction of GM within the cerebellum might be associated with modulation of cerebellum–thalamus, and contributes to the dysfunctional cerebellar‐cortical communication in schizophrenia. Our results provide a new insight into the role of cerebellum in understanding the pathophysiological of schizophrenia. [ABSTRACT FROM AUTHOR]

The basic processing units in brain are neurons and synapses that are interconnected in a complex pattern and show many surprised information processing capabilities. The researchers attempt to mimic this efficiency and build artificial neural systems in hardware device to emulate the key information processing principles of the brain. However, the neural network hardware system has a challenge of interconnecting neurons and synapses efficiently. An efficient, low-cost routing architecture (ELRA) is proposed in this paper to provide a communication infrastructure for the hardware spiking neuron networks (SNN). A dynamic traffic arbitration strategy is employed in ELRA, where the traffic status weights of input ports are calculated in real-time according to the channel traffic statuses and the port with the largest traffic status weight is given a high priority to forward packets. This strategy enables the router to serve congested ports preferentially, which can balance the overall network traffic loads. Experimental results show the feasibility of ELRA under various traffic scenarios, and the hardware synthesis result using SAED 90 nm technology demonstrates it has a low hardware area overhead which maintains scalability for large-scale SNN hardware implementations. [ABSTRACT FROM AUTHOR]

A novel low cost interconnected architecture (LCIA) is proposed in this paper, which is an efficient solution for the neuron interconnections for the hardware spiking neural networks (SNNs). It is based on an all-to-all connection that takes each paired input and output nodes of multi-layer SNNs as the source and destination of connections. The aim is to maintain an efficient routing performance under low hardware overhead. A Networks-on-Chip (NoC) router is proposed as the fundamental component of the LCIA, where an effective scheduler is designed to address the traffic challenge due to irregular spikes. The router can find requests rapidly, make the arbitration decision promptly, and provide equal services to different network traffic requests. Experimental results show that the LCIA can manage the intercommunication of the multi-layer neural networks efficiently and have a low hardware overhead which can maintain the scalability of hardware SNNs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient and self-adapting colour-image encryption algorithm based on chaos and the interactions among multiple red, green and blue (RGB) layers. Our study uses two chaotic systems and the interactions among the multiple layers to strengthen the cryptosystem for the colour-image encryption, which can achieve better confusion and diffusion performances. In the confusion process, we use the novel Rubik’s Cube Scheme (RCS) to scramble the image. The significant advantage of this approach is that it sufficiently destroys the correlation among the different layers of colour image, which is the most important feature of the randomness for the encryption. The theoretical analysis and experimental results show that the proposed algorithm can improve the encoding efficiency, enhances the security of the cipher-text, has a large key space and high key sensitivity, and is also able to resist statistical and exhaustive attacks. [ABSTRACT FROM AUTHOR]

Background: Chimeric antigen receptor T (CAR T) cells immunotherapy is rapidly developed in treating cancers, especially relapsed or refractory B-cell malignancies.Methods: To assess the efficacy and safety of CAR T therapy, we analyzed clinical trials from PUBMED and EMBASE.Results: Results showed that the pooled response rate, 6-months and 1-year progression-free survival (PFS) rate were 67%, 65.62% and 44.18%, respectively. We observed that received lymphodepletion (72% vs 44%, P = 0.0405) and high peak serum IL-2 level (85% vs 31%, P = 0.04) were positively associated with patients' response to CAR T cells. Similarly, costimulatory domains (CD28 vs CD137) in second generation CAR T was positively associated with PFS (52.69% vs 33.39%, P = 0.0489). The pooled risks of all grade adverse effects (AEs) and grade ≥ 3 AEs were 71% and 43%. Most common grade ≥ 3 AEs were fatigue (18%), night sweats (14%), hypotension (12%), injection site reaction (12%), leukopenia (10%), anemia (9%).Conclusions: In conclusion, CAR T therapy has promising outcomes with tolerable AEs in relapsed or refractory B-cell malignancies. Further modifications of CAR structure and optimal therapy strategy in continued clinical trials are needed to obtain significant improvements. [ABSTRACT FROM AUTHOR]

Chaos has been used in cryptography for years and many chaotic cryptographic systems have been proposed. Their securities are often evaluated by conducting conventional statistical tests, however few studies have referred to the security issue of the chaotic hardware cryptographic systems. This paper evaluates the security of the chaotic cryptographic system from a hardware perspective by using the side channel analysis attack. First, a chaotic block cryptosystem is designed and implemented based on an Atmel microcontroller. Then the conventional statistical security tests, including SP 800-22 test, characters frequency test, avalanche test, are used to verify its security performance. In the meantime, the correlation power analysis attack is carried out for the security evaluation. Experimental results demonstrate that even though the chaotic cryptographic system can pass the conventional statistical tests, it still has the probability to be attacked from a hardware perspective using the leaked side channel information such as execution time and power consumption. This paper proposes another way to analyze the security of the chaotic cryptosystem, which can aid designing mechanisms to enhance the security of the hardware cryptosystems in the future. [ABSTRACT FROM AUTHOR]

This paper proposes a parallel digital image encryption algorithm based on a piecewise linear chaotic map (PWLCM) and a four-dimensional hyper-chaotic map (FDHCM). Firstly, two decimals are obtained based on the plain-image and external keys, using a novel parallel quantification method. They are used as the initial value and control parameter for the PWLCM. Then, an encryption matrix and four chaotic sequences are constructed using the PWLCM and FDHCM, which control the permutation and diffusion processes. The proposed algorithm is implemented and tested in parallel based on a graphics processing unit device. Numerical analysis and experimental results show that the proposed algorithm achieves a high encryption speed and a good security performance, which provides a potential solution for real-time image encryption applications. [ABSTRACT FROM AUTHOR]

Objectives: Inconsistent data on detection of albumin expression by ribonucleic acid (RNA) in situ hybridization have been reported. We investigated the utility of RNAscope (Advanced Cell Diagnostics, Hayward, CA) in detection of albumin in hepatocellular carcinomas (HCCs), intrahepatic cholangiocarcinomas (ICCs), and carcinomas from various organs using manual and automated staining.Methods: RNAscope for albumin detection was performed on 482 cases on tissue microarray sections and on 22 cases of ICC, including 14 surgical resection and eight core biopsy specimens.Results: Thirty-six of 37 (97%) HCCs had detectable mRNA, whereas all non-HCC and non-ICC cases, except one lung adenocarcinoma, were negative for albumin. Fourteen of 22 ICCs (64%) were positive for albumin.Conclusions: RNAscope for albumin is highly sensitive and specific for identifying HCCs and is highly specific and moderately sensitive for detection of ICCs; however, rare carcinomas (non-HCC, non-ICC, and those with no hepatoid histomorphology) can also have aberrant expression of albumin. [ABSTRACT FROM AUTHOR]

The chaotic map has complex dynamics under ideal conditions however it suffers from the problem of performance degradation in the case of finite computing precision. In order to prevent the dynamics degradation, in this paper the continuous Chen chaotic system is used to perturb both the inputs and parameters of Chebyshev map to minimize the chaotic degradation phenomenon under finite precision. Experimental evaluations and corresponding performance analysis demonstrate that the Chebyshev chaotic map has a good randomness and complex dynamic performance by using the proposed perturbation method, and some attributes of the proposed system are stronger than the original system (e.g. chaos attractor and approximate entropy). Finally, the corresponding pseudorandom number generator (PRNG) is constructed by this method and then its randomness is evaluated via NIST SP800-22 and TestU01 test suites, respectively. Statistical test results show that the proposed PRNG has high reliability of randomness, thus it can be used for cryptography and other potential applications. [ABSTRACT FROM AUTHOR]

A novel image encryption algorithm based on the total plain image and tent map is proposed in this work. The pixels' value of a plain image and an initial key are firstly quantified to be a decimal to be used as the initial value of tent map. The quantification algorithm is mainly based on multiplication operation. Therefore, the tiny changes of the plain image can lead to very different quantification results. Then, a digital sequence generated from tent map is transformed to two ergodic sequences which are used for the following permutation, and the diffusion is controlled at the same time. In the diffusion process, the pixel values are modified based on the division and modulus operations according to eight special chaotic values. Finally, the performance analysis and test experiments are achieved, and the results illustrate that there is a high security against several common attacks. [ABSTRACT FROM AUTHOR]

BACKGROUND Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (SCC) is a unique form of carcinoma that is important to identify for prognosis and treatment. Immunohistochemistry (IHC) for p16 (also known as cyclin-dependent kinase inhibitor 2A, multiple tumor suppressor 1) is used as a surrogate marker for transcriptionally active, high-risk HPV. The primary objective of this study was to correlate p16 IHC of cell blocks from fine-needle aspirations (FNAs) with surgical pathology specimens of HPV-related oropharyngeal SCC. METHODS In total, 48 patients who had a diagnosis of oropharyngeal or nonoropharyngeal SCC and also had an FNA that demonstrated metastatic SCC with available cell block material were identified. IHC for p16 was evaluated on both FNA cell blocks and surgical pathology specimens. In situ hybridization for high-risk HPV messenger RNA was performed on 31 of the FNA cell blocks. RESULTS Although partial p16 staining was observed in the majority of cell blocks, there was concordance in 47 of 48 FNAs (98%) with surgical pathology specimens when strong positive p16 staining of at least 15% of tumor cells in FNA cell block material was present. In addition, high-risk HPV RNA in situ hybridization demonstrated a high correlation with p16 staining in surgical pathology specimens (96%) and FNAs (93%). CONCLUSIONS There was excellent correlation between p16 IHC of FNA cell blocks and surgical pathology specimens using a cutoff of at least 15% positive staining in cell blocks. The recommended threshold (70% positive staining) for surgical pathology specimens may yield a high rate of false-negative results if applied to FNA cell blocks. Cancer (Cancer Cytopathol) 2015;123:723-731. © 2015 American Cancer Society. [ABSTRACT FROM AUTHOR]