Composition of breast milk from mothers of premature and full-term infants and its influence in Z-Scores for infant physical growth.

BACKGROUND: Breast milk contains various crucial nutrients and biologically active substances and is ideal for newborns. This study aimed to analyze the composition of breast milk from mothers of premature and full-term infants and its influences on the growth of infants. METHODS: Infant-mother dyads examined at our Hospital (March 2016 to May 2017) were included. Milk was collected at 0-1 month, 2-3 months, and 5-6 months and analyzed using a MIRIS human milk analyzer. Z-scores of weight-for-length (WLZ), weight-for-age (WAZ), and length-for-age (LAZ) were calculated. RESULTS: This study included full-term (> 37 weeks of gestation, n = 177) and premature (< 37 weeks, n = 94) infant-mother dyads. The premature infants showed higher ΔWAZ, ΔLAZ, and ΔWLZ from infancy to toddlerhood for the physical growth speed, compared with term infants (P < 0.001). All proteins and true protein components of breast milk decreased with infants' age (P < 0.001). For premature and full-term infants, differences in ΔWAZ and ΔLAZ from birth to infancy and the difference in ΔLAZ, WAZ, and LAZ in toddlerhood were positively associated with non-protein nitrogen (NPN) (all P < 0.05), while the Z-score differences in ΔWLZ from birth to infancy were negatively associated with NPN (all P < 0.05). For premature babies, from birth to infancy stage, ΔWAZ was positively correlated with NPN and carbohydrates while negatively correlated with dry matter (all P < 0.05), and ΔLAZ correlated with NPN (ß = 0.428, P = 0.005). CONCLUSION: Breastfeeding helped premature infants compensatory growth when compared to term infants. Whileduring early infancy stage ΔWLZ gain was negatively associated with increased amounts of NPN in breast milk. This might mean although NPN increase the Z-scores of weight-for-age and length-for-age, with no rise in adipose tissue mass.

Lightweight Cross-Modal Information Mutual Reinforcement Network for RGB-T Salient Object Detection.

RGB-T salient object detection (SOD) has made significant progress in recent years. However, most existing works are based on heavy models, which are not applicable to mobile devices. Additionally, there is still room for improvement in the design of cross-modal feature fusion and cross-level feature fusion. To address these issues, we propose a lightweight cross-modal information mutual reinforcement network for RGB-T SOD. Our network consists of a lightweight encoder, the cross-modal information mutual reinforcement (CMIMR) module, and the semantic-information-guided fusion (SIGF) module. To reduce the computational cost and the number of parameters, we employ the lightweight module in both the encoder and decoder. Furthermore, to fuse the complementary information between two-modal features, we design the CMIMR module to enhance the two-modal features. This module effectively refines the two-modal features by absorbing previous-level semantic information and inter-modal complementary information. In addition, to fuse the cross-level feature and detect multiscale salient objects, we design the SIGF module, which effectively suppresses the background noisy information in low-level features and extracts multiscale information. We conduct extensive experiments on three RGB-T datasets, and our method achieves competitive performance compared to the other 15 state-of-the-art methods.

Tailoring PTV expansion to improve the dosimetry of post modified radical mastectomy intensity-modulated radiotherapy for left-sided breast cancer patients by using 4D CT combined with cone beam CT.

PURPOSE: Our study aimed to improve the dosimetry of post modified radical mastectomy intensity-modulated radiotherapy (PMRM-IMRT) for left-sided breast cancer patients by tailoring and minimizing PTV expansion three-dimensionally utilizing 4D CT combined with on-board cone beam CT (CBCT). METHODS: We enrolled a total of 10 consecutive left-sided breast cancer patients to undergo PMRM-IMRT. We measured the intra-fractional CTV displacement attributed to respiratory movement by defining 9 points on the left chest wall and quantifying their displacement by using the 4D CT, and measured the inter-fractional CTV displacement resulting from the integrated effect of respiratory movement, thoracic deformation and set up errors by using CBCT. We created 3 different PMRM-IMRT plans for each of the patients using PTVt (tailored PTV expansion three-dimensionally), PTV0.5 and PTV0.7 (isotropic 0.5- cm and isotropic 0.7- cm expanding margin of CTV), respectively. We performed paired samples t test to establish a hierarchy in terms of plan quality and dosimetric benefits. P < 0.05 was considered statistically significant. RESULTS: The inter-fractional CTV displacement (2.6 ± 2.2 mm vertically, 2.8 ± 2.3 mm longitudinally, and 1.7 ± 1.2 mm laterally) measured by CBCT was much larger than the intra-fractional one (0.5 ± 0.5 mm vertically, 0.5 ± 1.0 mm longitudinally, and 0.3 ± 0.3 mm laterally, respectively) measured by 4D CT. Intensity-modulated radiotherapy with tailored PTV expansion based on inter-fractional CTV displacement had dosimetrical advantages over those with PTV0.5 or those with PTV0.7 owing to its perfect PTV dose coverage and better OARs sparing(especially of heart and left lung). CONCLUSION: The CTV displacement in PMRM-IMRT predominantly arises from inter-fraction rather than from intra-fraction during natural respiration and differs in 3 coordinate axes either inter-fractionally or intra-fractionally. Tailoring and minimizing PTV expansion three-dimensionally significantly improves the dosimetry of PMRM-IMRT for left-sided breast cancer patients.

A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information.

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

Comparison of 2 dynamic conformal arc plans based on high-dose rate flattening filter free beams for peripheral lung cancer. / åŸºäºŽå‘¨å›´åž‹è‚ºç™Œé«˜å‰‚é‡çŽ‡æ— å‡æ•´æ»¤è¿‡å™¨æ¨¡å¼ä¸‹çš„2种动态适形弧计划比较.

OBJECTIVES: To compare 2 dynamic conformal arc plans based on the high dose rate flattening filter free (FFF) beams, and to evaluate the dosimetric differences. METHODS: A total of 20 patients with early peripheral non-small cell lung cancer were selected, and 2 dynamic conformal arc plans were designed in the Eclipse 10.0 treatment planning system (TPS). One of them was based on tumor-center (T-DCA), and the other was based on iso-center (Iso-DCA). Both plans were created by using the Truebeam linear accelerator, based on 6 MV FFF photons with a dose rate at 1 400 monitor unit (MU)/min. All patients received the prescribed dose of 4 800 cGy in 4 fractions (1 200 cGy/fraction). Target coverage and organ at risk limits were planned and designed according to the Radiation Therapy Oncology Group (RTOG) Criteria, and were compared between the T-DCA and the Iso-DCA plans. RESULTS: There was no significant difference in the target coverage between the T-DCA and Iso-DCA plans (P>0.05). Conformal index and homogeneity index had no significant differences (both P>0.05), but the percentage of the maximum dose in any direction 2 cm away from the planned target area (D2 cm) and the ratio of the volume wrapped by the isodose line of 50% prescription dose to the volume of the planned target area (R50%) showed significant differences (both P<0.05). The MU of the Iso-DCA plan was increased by 21% compared with that of the T-DCA plan. Except the maximum dose of spinal cord and esophagus, there was no significant difference in the other dosimetric parameters of the organs at risk between the T-DCA and the Iso-DCA plans (all P>0.05). CONCLUSIONS: The dose fall-off of Iso-DCA plan is better than T-DCA plan, but the T-DCA plan is consistently superior in sparing dose to spinal cord and esophagus, and the T-DCA plan has fewer MU.

[Risk factors for cow's milk protein allergy in infants: a multicenter survey].

OBJECTIVE: To investigate the risk factors for cow's milk protein allergy (CMPA) among infants through a multicenter clinical study. METHODS: A total of 1 829 infants, aged 1-12 months, who attended the outpatient service of the pediatric department in six hospitals in Shenzhen, China from June 2016 to May 2017 were enrolled as subjects. A questionnaire survey was performed to screen out suspected cases of CMPA. Food avoidance and oral food challenge tests were used to make a confirmed diagnosis of CMPA CMPA. A multivariate logistic regression analysis was used to investigate the risk factors for CMPA. RESULTS: Among the 1 829 infants, 82 (4.48%) were diagnosed with CMPA. The multivariate logistic regression analysis showed that maternal food allergy (OR=4.91, 95%CI: 2.24-10.76, P<0.05), antibiotic exposure during pregnancy (OR=3.18, 95%CI: 1.32-7.65, P<0.05), and the introduction of complementary food at an age of <4 months (OR=3.55, 95%CI: 1.52-8.27, P<0.05) were risk factors for CMPA, while exclusive breastfeeding (OR=0.21, 95%CI: 0.08-0.58, P<0.05) and the introduction of complementary food at an age of >6 months (OR=0.38, 95%CI: 0.17-0.86, P<0.05) were protective factors. CONCLUSIONS: The introduction of complementary food at an age of <4 months, maternal food allergy, and antibiotic exposure during pregnancy are risk factors for CMPA in infants.

Assessment of Cow's milk-related symptom scores in early identification of cow's milk protein allergy in Chinese infants.

BACKGROUND: The diagnosis of cow's milk protein allergy(CMPA) may be easily misdiagnosed due to its lack of specific symptoms. Thus, experts have proposed the use of Cow's milk-related symptom scores (CoMiSS) to predict CMPA. There has been no relevant report on the clinical application value of CoMiSS in Chinese children. This study aimed to evaluate the effect of CoMiSS in early identification of CMPA in Chinese infants. METHODS: We calculated CoMiSS for 38 infants with suspected CMPA diagnosed in the pediatric gastroenterologic clinic in our hospital. After 4 weeks of dietary elimination and symptomatic improvement, these infants returned to our hospital to undergo oral food challenge (OFC). The ROC curve was used to determine the sensitivity and specificity of CoMiSS and evaluate the effect of CoMiSS in early identification of CMPA in Chinese infants. We didn't determine the CoMiSS of presumed healthy infants as control group. RESULTS: Of 38 infants who underwent OFC testing, the average CoMiSS of infants with positive OFC testing was 7.4 ± 2.3, while the average CoMiSS of infants with negative OFC testing was 4.1 ± 1.6, and there was a significant difference between two groups(F = 2.13, P<0.05). The area under the ROC curve (AUC) of CoMiSS was 0.89, and the best diagnostic cut-off point was 5.5. The sensitivity of CoMiSS was 87.5%, while the specificity of CoMiSS was 78.6%. CONCLUSION: CoMiSS is a simple and operable method to screen for CMPA, though there may be a risk of under-diagnosis when CoMiSS≥12 is used as the criterion for early identification of CMPA in Chinese infants. More multi-center studies are needed to evaluate whether the factors such as bloody stool should be included in CoMiSS or CoMiSS≥6 can be used as the criterion for early identification of CMPA in Chinese infants.

New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms.

This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected sensor network cloud platforms. Document vectors are first generated by combining the term frequency-inverse document frequency (TF-IDF) weight factor and the vector space model (VSM). The improved quality hierarchical clustering (IQHC) algorithm then generates document vectors, document indices, and cluster indices, which are encrypted via the k-nearest neighbor algorithm (KNN). MCS-IQHC then returns the top-k search result. A series of experiments proved that the proposed method had better searching efficiency and accuracy in high-privacy sensor cloud network environments, compared to other state-of-the-art methods.

Anti-IL-6 neutralizing antibody modulates blood-brain barrier function in the ovine fetus.

Impaired blood-brain barrier function represents an important component of hypoxic-ischemic brain injury in the perinatal period. Proinflammatory cytokines could contribute to ischemia-related blood-brain barrier dysfunction. IL-6 increases vascular endothelial cell monolayer permeability in vitro. However, contributions of IL-6 to blood-brain barrier abnormalities have not been examined in the immature brain in vivo. We generated pharmacologic quantities of ovine-specific neutralizing anti-IL-6 mAbs and systemically infused mAbs into fetal sheep at 126 days of gestation after exposure to brain ischemia. Anti-IL-6 mAbs were measured by ELISA in fetal plasma, cerebral cortex, and cerebrospinal fluid, blood-brain barrier permeability was quantified using the blood-to-brain transfer constant in brain regions, and IL-6, tight junction proteins, and plasmalemma vesicle protein (PLVAP) were detected by Western immunoblot. Anti-IL-6 mAb infusions resulted in increases in mAb (P < 0.05) in plasma, brain parenchyma, and cerebrospinal fluid and decreases in brain IL-6 protein. Twenty-four hours after ischemia, anti-IL-6 mAb infusions attenuated ischemia-related increases in blood-brain barrier permeability and modulated tight junction and PLVAP protein expression in fetal brain. We conclude that inhibiting the effects of IL-6 protein with systemic infusions of neutralizing antibodies attenuates ischemia-related increases in blood-brain barrier permeability by inhibiting IL-6 and modulates tight junction proteins after ischemia.

Neutralizing anti-interleukin-1ß antibodies modulate fetal blood-brain barrier function after ischemia.

We have previously shown that increases in blood-brain barrier permeability represent an important component of ischemia-reperfusion related brain injury in the fetus. Pro-inflammatory cytokines could contribute to these abnormalities in blood-brain barrier function. We have generated pharmacological quantities of mouse anti-ovine interleukin-1ß monoclonal antibody and shown that this antibody has very high sensitivity and specificity for interleukin-1ß protein. This antibody also neutralizes the effects of interleukin-1ß protein in vitro. In the current study, we hypothesized that the neutralizing anti-interleukin-1ß monoclonal antibody attenuates ischemia-reperfusion related fetal blood-brain barrier dysfunction. Instrumented ovine fetuses at 127 days of gestation were studied after 30 min of carotid occlusion and 24h of reperfusion. Groups were sham operated placebo-control- (n=5), ischemia-placebo- (n=6), ischemia-anti-IL-1ß antibody- (n=7), and sham-control antibody- (n=2) treated animals. Systemic infusions of placebo (0.154M NaCl) or anti-interleukin-1ß monoclonal antibody (5.1±0.6 mg/kg) were given intravenously to the same sham or ischemic group of fetuses at 15 min and 4h after ischemia. Concentrations of interleukin-1ß protein and anti-interleukin-1ß monoclonal antibody were measured by ELISA in fetal plasma, cerebrospinal fluid, and parietal cerebral cortex. Blood-brain barrier permeability was quantified using the blood-to-brain transfer constant (Ki) with α-aminoisobutyric acid in multiple brain regions. Interleukin-1ß protein was also measured in parietal cerebral cortices and tight junction proteins in multiple brain regions by Western immunoblot. Cerebral cortical interleukin-1ß protein increased (P<0.001) after ischemia-reperfusion. After anti-interleukin-1ß monoclonal antibody infusions, plasma anti-interleukin-1ß monoclonal antibody was elevated (P<0.001), brain anti-interleukin-1ß monoclonal antibody levels were higher (P<0.03), and interleukin-1ß protein concentrations (P<0.03) and protein expressions (P<0.001) were lower in the monoclonal antibody-treated group than in placebo-treated-ischemia-reperfusion group. Monoclonal antibody infusions attenuated ischemia-reperfusion-related increases in Ki across the brain regions (P<0.04), and Ki showed an inverse linear correlation (r= -0.65, P<0.02) with anti-interleukin-1ß monoclonal antibody concentrations in the parietal cortex, but had little effect on tight junction protein expression. We conclude that systemic anti-interleukin-1ß monoclonal antibody infusions after ischemia result in brain anti-interleukin-1ß antibody uptake, and attenuate ischemia-reperfusion-related interleukin-1ß protein up-regulation and increases in blood-brain barrier permeability across brain regions in the fetus. The pro-inflammatory cytokine, interleukin-1ß, contributes to impaired blood-brain barrier function after ischemia in the fetus.

[Serum adiponectin levels in children with Kawasaki disease].

OBJECTIVE: To explore the change in serum adiponectin levels and its significance in children with Kawasaki disease (KD). METHODS: Forty-five KD patients were enrolled in this study, including 18 with coronary artery lesions (CAL group) and 27 without coronary artery lesions (NCAL group). Twenty healthy children were recruited to the control group. Enzyme-linked immunosorbent assay was used to measure serum adiponectin levels, and an automatic biochemical analyzer was used to measure the levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). RESULTS: The serum adiponectin levels in the CAL and NCAL groups were significantly lower than in the control group during the acute phase, subacute phase, and recovery phase (P<0.01), with lower levels observed during the acute phase and subacute phase (P<0.01). Compared with the NCAL group, the CAL group had significantly higher serum levels of adiponectin during the acute phase and recovery phase (P<0.05). The levels of TC, HDL, and LDL in the NCAL and CAL groups were significantly lower than in the control group (P<0.05). The levels of serum adiponectin in KD patients were positively correlated with the levels of TC, TG, and C-reactive protein and the occurrence of CAL (r=0.31, 0.30, 0.34, and 0.35, respectively; P<0.05). CONCLUSIONS: Children with KD have metabolic disorders of blood lipids and reduced serum adiponectin levels. Reduced serum adiponectin levels may be the result of systemic inflammation, while increased adiponectin levels may be closely associated with the occurrence of CAL.

Diagnosis of DC series arc fault based on multiple wavelet transform and optimal fractional wavelet energy entropy.

In the DC distribution system, the propagation of arc noise can interfere with normal lines, and accurate and timely diagnosis of the location of series arc fault (SAF) is a challenging problem. In this article, a SAF diagnosis method is proposed from a system perspective, which can accurately identify the fault line. First, multiple wavelet transform is used to decompose the currents of different lines, and the fractional wavelet energy entropy is extracted to construct the feature vector. Then, random forest is employed to analyze the importance of features and to select the optimal features. Finally, a kernel extreme learning machine can fuse the features and output the diagnosis results. The offline experimental results indicate that the proposed method has a diagnosis accuracy of 99.82%, which is higher than those of nine comparison methods, and the effectiveness and advancement of the proposed method are verified. The online experimental results show that the proposed method can diagnose SAF within 110 ms, and the diagnosis speed is able to satisfy the requirements of UL1699B. Moreover, under transient conditions, the proposed method can effectively avoid false alarms and maintain stability.

Multisystem inflammatory syndrome in children with Kawasaki disease-like manifestations in MMA.

Not required for Clinical Vignette.

Quality-Aware Selective Fusion Network for V-D-T Salient Object Detection.

Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. However, the quality of the depth and thermal images is often unreliable in some challenging scenarios, which will result in the performance degradation of the two-modal based salient object detection (SOD). Meanwhile, some researchers pay attention to the triple-modal SOD task, namely the visible-depth-thermal (VDT) SOD, where they attempt to explore the complementarity of the RGB image, the depth image, and the thermal image. However, existing triple-modal SOD methods fail to perceive the quality of depth maps and thermal images, which leads to performance degradation when dealing with scenes with low-quality depth and thermal images. Therefore, in this paper, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet. Firstly, except for extracting features, the initial feature extraction subnet can generate a preliminary prediction map from each modality via a shrinkage pyramid architecture, which is equipped with the multi-scale fusion (MSF) module. Then, we design the weakly-supervised quality-aware region selection subnet to generate the quality-aware maps. Concretely, we first find the high-quality and low-quality regions by using the preliminary predictions, which further constitute the pseudo label that can be used to train this subnet. Finally, the region-guided selective fusion subnet purifies the initial features under the guidance of the quality-aware maps, and then fuses the triple-modal features and refines the edge details of prediction maps through the intra-modality and inter-modality attention (IIA) module and the edge refinement (ER) module, respectively. Extensive experiments are performed on VDT-2048 dataset, and the results show that our saliency model consistently outperforms 13 state-of-the-art methods with a large margin. Our code and results are available at https://github.com/Lx-Bao/QSFNet.

Anti-high mobility group box-1 antibody therapy for traumatic brain injury.

OBJECTIVE: High mobility group box-1 (HMGB1) plays an important role in triggering inflammatory responses in many types of diseases. In this study, we examined the involvement of HMGB1 in traumatic brain injury (TBI) and evaluated the ability of intravenously administered neutralizing anti-HMGB1 monoclonal antibody (mAb) to attenuate brain injury. METHODS: Traumatic brain injury was induced in rats or mice by fluid percussion. Anti-HMGB1 mAb or control mAb was administered intravenously after TBI. RESULTS: Anti-HMGB1 mAb remarkably inhibited fluid percussion-induced brain edema in rats, as detected by T2-weighted magnetic resonance imaging; this was associated with inhibition of HMGB1 translocation, protection of blood-brain barrier (BBB) integrity, suppression of inflammatory molecule expression, and improvement of motor function. In contrast, intravenous injection of recombinant HMGB1 dose-dependently produced the opposite effects. Experiments using receptor for advanced glycation end product (RAGE)(-/-) , toll-like receptor-4 (TLR4)(-/-) , and TLR2(-/-) mice suggested the involvement of RAGE as the predominant receptor for HMGB1. INTERPRETATION: Anti-HMGB1 mAb may provide a novel and effective therapy for TBI by protecting against BBB disruption and reducing the inflammatory responses induced by HMGB1.

Edge-Guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images.

Optical remote sensing images (RSIs) have been widely used in many applications, and one of the interesting issues about optical RSIs is the salient object detection (SOD). However, due to diverse object types, various object scales, numerous object orientations, and cluttered backgrounds in optical RSIs, the performance of the existing SOD models often degrade largely. Meanwhile, cutting-edge SOD models targeting optical RSIs typically focus on suppressing cluttered backgrounds, while they neglect the importance of edge information which is crucial for obtaining precise saliency maps. To address this dilemma, this article proposes an edge-guided recurrent positioning network (ERPNet) to pop-out salient objects in optical RSIs, where the key point lies in the edge-aware position attention unit (EPAU). First, the encoder is used to give salient objects a good representation, that is, multilevel deep features, which are then delivered into two parallel decoders, including: 1) an edge extraction part and 2) a feature fusion part. The edge extraction module and the encoder form a U-shape architecture, which not only provides accurate salient edge clues but also ensures the integrality of edge information by extra deploying the intraconnection. That is to say, edge features can be generated and reinforced by incorporating object features from the encoder. Meanwhile, each decoding step of the feature fusion module provides the position attention about salient objects, where position cues are sharpened by the effective edge information and are used to recurrently calibrate the misaligned decoding process. After that, we can obtain the final saliency map by fusing all position attention cues. Extensive experiments are conducted on two public optical RSIs datasets, and the results show that the proposed ERPNet can accurately and completely pop-out salient objects, which consistently outperforms the state-of-the-art SOD models.

Visible-light driven photocatalytic performance of eco-friendly cobalt-doped ZnO nanoarrays: Influence of morphology, cobalt doping, and photocatalytic efficiency.

In order to remove the organic pollution from water environment, Co2+-doped ZnO nanoarray photocatalyst was prepared through a hydrothermal process. The influences of Co2+ doping amount and hydrothermal temperature on the nanostructure and photocatalytic performance of Co2+-doped ZnO nanoarray were discussed in detail. The standard ZnO structure and nanoarray morphology of Co2+-doped ZnO samples were achieved and the absorption of visible light was also realized through Co2+ doping. The 2% Co2+-doped ZnO nanoarray prepared at 95 °C exhibited excellent photocatalytic activity and could degrade 96% of methylene blue solution within 120 min under visible light. Furthermore, the as-prepared 2% Co2+-doped ZnO nanoarray still maintained 91% for removal rate after 3 cycles of photocatalytic degradation, showed good photocatalytic activity and recyclability. All results indicate that ZnO nanoarray with Co2+ doping has a potential application in visible light photocatalysis for environmental protection and pollution control.

Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis.

Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.

Structure and luminescence investigation of Gd3+-sensitized perovskite CaLa4Ti4O15:Eu3+: A novel red-emitting phosphor for high-performance white light-emitting diodes and plants lighting.

A novel perovskite CaLa4Ti4O15:Eu3+ red-emitting phosphor was synthesized via a sol-combustion method, and Gd3+ was further co-doped into structure to improve the luminescence performance. The effects of Eu3+ doping and Gd3+ co-doping concentrations on the microstructure and luminescence properties were investigated. The red emission peaks of as-prepared phosphors originate from the 5D0→7Fj electron transitions of Eu3+ ions. Under 273 nm excitation, the luminescence intensity of Eu3+ was significantly enhanced through the energy transfer between Gd3+ and Eu3+ in CaLa4Ti4O15, and the luminescence intensity was also improved even under the excitation of 394 nm. By combining red-emitting CaLa4Ti4O15:Eu3+, Gd3+ phosphor with commercial blue and green phosphors on n-UV chip (λ = 395 nm), an eye-friendly w-LEDs with appropriate correlated color temperature (4761 K) and high color rendering index (Ra = 93.1) has been realized. The electroluminescence spectrum of the packaged red LED have an excellent match with the PR absorption of plants. In addition, when introducing CaLa4Ti4O15:Eu3+, Gd3+ phosphor into a commercial w-LED with YAG:Ce3+, the adjustable chromaticity parameters like CCT and CRI values can be obtained. These results demonstrated that the as-prepared CaLa4Ti4O15:Eu3+, Gd3+ phosphor is an outstanding candidate as the red component for the application of w-LEDs and plants lighting.

Deconstruction of the Optimal Design of Urban Road Interchange Based on the Integration of Smart Transportation and Big Data.

Urban interchange is the core hub connecting various regions, and it is of great significance for alleviating the problem of traffic congestion. In the process of urban interchange design, it is impossible to strictly control the traffic volume, interchange types, and standards by relying on traditional technologies. Smart transportation and big data are emerging technologies based on data, which can provide technical support for design and decision making. Based on this, this paper first uses smart transportation and big data technology to predict the traffic volume of Nancheng New District, so as to calculate the future development trend of the target area. Then, on the basis of traffic volume, the article uses smart transportation and big data technology to optimize the original urban interchange design scheme from the aspects of traffic capacity, safety, economic benefits, and environmental benefits. Finally, the article evaluates the optimized urban interchange scheme by means of comprehensive quantitative indicators and evaluation methods. Experiments show that the traffic capacity of the interchange on the outer ring road optimized by smart transportation and big data has increased to 72.6%, and the environmental coordination has increased from 45.2% to 55.2%. Moreover, the design aesthetics of the urban interchange after optimized design based on smart transportation and big data has increased to 65.9%. In addition, the comprehensive evaluation value of the urban interchange after optimization of smart transportation and big data reached 82.6. This fully shows that the optimal design of urban interchange based on the integration of smart transportation and big data can greatly improve the traffic capacity of urban roads.