Origami-inspired highly stretchable and breathable 3D wearable sensors for in-situ and online monitoring of plant growth and microclimate.

The emerging wearable plant sensors demonstrate the capability of in-situ measurement of physiological and micro-environmental information of plants. However, the stretchability and breathability of current wearable plant sensors are restricted mainly due to their 2D planar structures, which interfere with plant growth and development. Here, origami-inspired 3D wearable sensors have been developed for plant growth and microclimate monitoring. Unlike 2D counterparts, the 3D sensors demonstrate theoretically infinitely high stretchability and breathability derived from the structure rather than the material. They are adjusted to 100% and 111.55 mg cm-2·h-1 in the optimized design. In addition to stretchability and breathability, the structural parameters are also used to control the strain distribution of the 3D sensors to enhance sensitivity and minimize interference. After integrating with corresponding sensing materials, electrodes, data acquisition and transmission circuits, and a mobile App, a miniaturized sensing system is produced with the capability of in-situ and online monitoring of plant elongation and microclimate. As a demonstration, the 3D sensors are worn on pumpkin leaves, which can accurately monitor the leaf elongation and microclimate with negligible hindrance to plant growth. Finally, the effects of the microclimate on the plant growth is resolved by analyzing the monitored data. This study would significantly promote the development of wearable plant sensors and their applications in the fields of plant phenomics, plant-environment interface, and smart agriculture.

CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.

Summary: Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing. Availability and implementation: This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.

Assembling all-wood-derived carbon/carbon dots-assisted phase change materials for high-efficiency thermal-energy harvesters.

The collection and storage of renewable, sustainable and clean energy including wind, solar, and tidal energy has attracted considerable attention because of its promising potential to replace fossil energy sources. Advanced energy-storage materials are the core component for energy harvesters, affording the high-efficiency conversion of these new-style energy sources. Herein, originated from nature, a series of all-wood-derived carbon-assisted phase change materials (PCMs) were purposed by incorporating carbon dots-modified polyethylene glycol matrix into carbon skeletons via a vacuum-impregnation strategy. The resultant PCMs possessed desired anti-leakage capability and superior thermophysical behaviors. In particular, the optimum sample posed high latent heat (131.5 J/g) and well thermal stability, where the corresponding enthalpy still reserved 90 % over 100 heating/cooling cycles. More importantly, the as-fabricated thermal-energy harvester presented prominent capability to strorage and release multiple forms of thermal energy, as well as high-efficiency solar-energy utilization, corresponding to a photothermal conversion efficiency of 88 % in simulated sunlight irradiation, far exceeding some reported PCMs. Overall, with the introduction of wood-derived carbon dots and carbon skeletons, the assembled all-wood-derived carbon-assisted PCMs afforded trinity advantages on thermal performance, cycling stability, and energy conversion efficiency, which provide a promising potential for the practical application in thermal-energy harvesters.

Planting Anion Channels in a Negatively Charged Polyamide Layer for Highly Selective Nanofiltration Separation.

A nanofiltration (NF) membrane with high salt permeation and high retention of small organics is appealing for the treatment of high-salinity organic wastewater. However, the conventional negatively charged NF membranes commonly show high retention of divalent anions (e.g., SO42-), and the reported positively charged NF membranes normally suffer super low selectivity for small organics/Na2SO4 and high fouling potential. In this work, we propose a novel "etching-swelling-planting" strategy assisted by interfacial polymerization and mussel-inspired catecholamine chemistry to prepare a mix-charged NF membrane. By X-ray photoelectron spectroscopy depth profiling and pore size distribution analysis, it was found that such a strategy could not only deepen the positive charge distribution but also narrow the pore size. Molecular dynamics confirm that the planted polyethyleneimine chains play an important role to relay SO42- ions to facilitate their transport across the membrane, thus reversing the retention of Na2SO4 and glucose (43 vs 71%). Meanwhile, due to the high surface hydrophilicity and smoothness as well as the preservation of abundant negatively charged groups (-OH and -COOH) inside the separation layer, the obtained membrane exhibited excellent antifouling performance, even for the coking wastewater. This study advances the importance of vertical charge distribution of NF membranes in separation selectivity and antifouling performance.

MetaLP: An integrative linear programming method for protein inference in metaproteomics.

Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.

Enhancing the Antifouling Ability of a Polyamide Nanofiltration Membrane by Narrowing the Pore Size Distribution via One-Step Multiple Interfacial Polymerization.

Application of nanofiltration membranes in industries still has to contend with membrane fouling that causes a significant loss of separation performance. Herein, an innovative approach to design antifouling membranes with a narrowed pore size distribution by interfacial polymerization (IP) assisted by silane coupling agents is reported. An aqueous solution of piperazine anhydrous (PIP) and γ-(2,3-epoxypropoxy) propytrimethoxysilane (KH560) is employed to perform IP with an organic solution of trimesoyl chloride and tetraethyl orthosilicate (TEOS) on a porous support. In accordance with the results of molecular dynamics and dissipative particle dynamics simulations, the reactive additive KH560 accelerates the diffusion rate of PIP to enrich at the reaction boundary. Moreover, the hydrolysis/condensation of KH560 and TEOS at the aqueous/organic interface forms an interpenetrating network with the polyamide network, which regulates the separation layer structure. The characterization results indicate that the polyamide-silica membrane has a denser, thicker, and uniform separation layer. The mean pore size of the polyamide-silica membrane and the traditional polyamide membrane is 0.62 and 0.74 nm, respectively, and these correspond to the geometric standard deviation (namely, pore size distribution) of 1.39 and 1.97, respectively. It is proved that the narrower pore size distribution endows the polyamide-silica membrane with stronger antifouling performance (flux decay ratio decreases from 18.4 to 3.8%). Such a membrane also has impressive long-term antifouling stability during cane molasses decolorization at a high temperature (50 °C). The outcomes of this study not only provide a novel one-step multiple IP strategy to prepare antifouling nanofiltration membranes but also emphasize the importance of pore size distribution in fouling control for various industrial liquid separations.

FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.

Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, we report FineFDR, a framework for FDR assessment at fine-grained levels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.

Deep learning for peptide identification from metaproteomics datasets.

Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. In this paper, we proposed a deep-learning-based algorithm, named DeepFilter, for improving peptide identifications from a collection of tandem mass spectra. The key advantage of the DeepFilter is that it does not need ad hoc training or fine-tuning as in existing filtering tools. DeepFilter is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/DeepFilter. SIGNIFICANCE: The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of MS/MS data sets acquired from metaproteome samples. Systematical experiment results demonstrate that the DeepFilter identified up to 12% and 9% more peptide-spectrum-matches and proteins, respectively, compared with existing filtering algorithms, including Percolator, Q-ranker, PeptideProphet, and iProphet, on marine and soil microbial metaproteome samples with false discovery rate at 1%. The taxonomic analysis shows that DeepFilter found up to 7%, 10%, and 14% more species from marine, soil, and human gut samples compared with existing filtering algorithms. Therefore, DeepFilter was believed to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification from metaproteomics data.

New insights into effect of alkaline cleaning on fouling behavior of polyamide nanofiltration membrane for wastewater treatment.

Membrane fouling is an intractable issue in wastewater treatment by nanofiltration (NF) membrane, and alkaline cleaning is the most effective approach to remove organic fouling on NF membrane. However, it was found that pore swelling of NF membrane induced by alkaline cleaning might reduce cleaning efficiency, and it is never quantified and its effect on membrane fouling behavior is still mysterious. In this work, membrane pore swelling effect (~9.7%, increment of effective pore size) induced by alkaline cleaning (pH 11) is confirmed and its effect on fouling behavior of the polyamide NF membrane is investigated based on experimental and modelling results. It is found that the alkali-induced pore swelling phenomenon would disappear after water filtration at neutral pH for 30 min, and if such cleaned membrane is faced by the small foulants during this pore shrinkage period, the concentration polarization and membrane fouling would be severer, and the subsequent alkaline cleaning is less effective because more foulants enter the enlarged pores and are tightly embedded in the membrane. Thus, the irreversible fouling of the NF membrane increases from 20% to 40% while its permeability recovery declines from 100% to 67% after six fouling/cleaning cycles. When an anionic surfactant sodium dodecyl sulfate (SDS, 10 mM) is added in the alkaline cleaning solution, the adsorption of SDS in/on the membrane can not only improve its hydrophilicity and negative charge, but also quickly eliminate the alkali-induced pore swelling effect and avoid the accumulation of foulants in the pores, thereby enhancing the antifouling performance of the NF membrane. Using the alkaline SDS cleaning, the irreversible fouling of the NF membrane maintains below 10% while its permeability recovery keeps above 100% in six continuous fouling/cleaning cycles.

Fabrication of Antiswelling Loose Nanofiltration Membranes via a "Selective-Etching-Induced Reinforcing" Strategy for Bioseparation.

With diverse selectivity, higher permeance, and good antifouling property, loose polyamide nanofiltration (NF) membranes can be potentially deployed in various bioseparation applications. However, the loose NF membrane with a low crosslinking degree generally suffers from the alkali-induced pore swelling during chemical cleaning, resulting in degradation of separation performance with time. In this work, we conceive a novel strategy to tailor the separating layer through alkaline post-etching following the interfacial polymerization process, where piperazine and tannic acid (TA) were used as water-phase monomers, and trimesoyl chloride (TMC) and ferric acetylacetonate were employed as organic monomers in n-hexane. Thereinto, the polyester network formed by TA and TMC was selectively etched by alkaline treatment, thus obtaining a loose NF membrane, whose structure and performance could be facilely tailored by controlling the TA ratio and the etching pH. As a result, the well-designed loose NF membrane exhibited higher flux, better selectivity, and more stable separation performance in a long-term filtration of diluted cane molasses. Interestingly, the obtained loose NF membrane showed excellent antiswelling ability during alkaline cleaning because of network locking induced by Fe3+ chelation, decrease in the carboxyl proportion (more hydroxyl generation due to the ester bond hydrolysis), and enhanced interface interaction between the separation layer and the sublayer attributed to catechol adhesion effect. Therefore, such a "selective-etching-induced reinforcing" strategy could endow the polyamide NF membrane with both loose and antiswelling separation layer in a reliable and scalable way, which provides a new perspective for preparing highly selective and stable NF membrane for resource recovery.

Mining Potential Effects of HUMIRA in Twitter Posts Through Relational Similarity.

HUMIRA, a biologic therapy, has been approved to treat autoimmune diseases and been marketed in many countries worldwide. Much like other medications, it demonstrates many effects on the human body. It is important to understand its effects from the information generated by its users, and social media is one of the venues its users share their experience with the medication. To understand what HUMIRA effects were reported on Twitter, we utilized a relational similarity-based approach to infer HUMIRA effects based upon known medication-effect relations of other medications. With a corpus of 3.6 million preprocessed, "clean" tweets, a total of 55 effects were identified, and among them, 46 were previously observed, and nine were potentially unreported after verification with six reliable sources. The results not only indicate that many HUMIRA effects shared by the Twitter users are consistent with those previously reported, but also demonstrate the power and utility of our method, making it applicable to studying effects of other medications shared by Twitter users.

Custom-Tailoring Loose Nanofiltration Membrane for Precise Biomolecule Fractionation: New Insight into Post-Treatment Mechanisms.

Loose nanofiltration (NF) membranes with diverse selectivity can meet the great demands in various bioseparation applications. Thus, a facile strategy to tune the properties such as pore size, surface charge, and hydrophilicity of the NF membrane is required to produce tailor-made loose NF membranes without changing the existing production line. Herein, we systematically investigated the post-treatment of the nascent poly(piperazine amide) NF membranes using different reagents (organic acids, weak bases, organic solvents and ionic liquid (IL)). Various characterizations revealed that the skin/separation layer became looser and permeance was promoted with the decrease of salt rejection in varying degrees. It was found that the O/N ratio did not rigorously represent the cross-linking degree of the skin layer, because besides the hydrolysis of the residual acyl chloride impeding the amido bond formation, the breaking of existing amido bonds and the grafting of free trimesoyl chloride molecules on the nascent membranes could also increase the O/N ratio during post-treatments. Then three mechanisms including hydrolysis, swelling rearrangement and capping reaction effects were proposed to better understand the membrane properties variations. All these effects resulted in larger pore size of the NF membrane, and the hydrolysis/capping effect might increase negative charge and hydrophilicity on the membrane, while the swelling rearrangement could produce less defective skin structure. These three effects might be involved together during a single treatment. Finally, the NF membrane post-treated by N-hexane could efficiently separate antibiotics and NaCl with the highest permeate flux, whereas the one post-treated by ionic liquid outperformed others for the decoloration of cane molasses (much more efficient than NF270, DL, and NTR7450 membranes). The long-term operating stability of the post-treated membranes selected was also confirmed by a continuous crossflow filtration for 15 h with regular alkaline cleaning.

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

OBJECTIVE: To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation. METHODS: Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. RESULTS: The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. CONCLUSIONS: Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. KEY POINTS: • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Imaging Characteristics of USPIO Nanoparticles (<5 nm) as MR Contrast Agent In Vitro and in the Liver of Rats.

Iron nanoparticles have an increasingly more and more important role in MR molecular imaging due to their novel magnetic and surface chemical properties. They provide new possibilities for noninvasive diagnosis and treatment monitoring, especially for tissues that are rich in macrophages. The smaller size and prolongation of the plasma half-life change the in vivo fate of ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles captured by liver in reticuloendothelial system (RES) or mononuclear phagocytic system (MPS). However, there is still a lack of MR imaging studies on the liver assessing USPIO nanoparticles <5 nm in size to reflect its absorption and clearance properties. In this study, we used MRI to study the in vitro phantom and in vivo rat liver imaging characteristics of USPIO nanoparticles (<5 nm). The results showed that USPIO nanoparticles (<5 nm) could potentially reduce longitudinal and transverse relaxation times and showed similar T 1 relaxation rates compared with commercial gadolinium chelates. In addition, USPIO nanoparticles (<5 nm) in vivo demonstrated both positive (T 1) and negative (T 2) liver contrast enhancement in healthy rats' liver. Furthermore, USPIO nanoparticles showed relatively good in vitro biocompatibility and fast clearance (within 45.17 minutes after intravenous injection) in the normal liver. Taken together, these data might inspire a new personalized and precise diagnostic tool and stimulate new applications for specific targeted molecular probes.

Mussel-Inspired and Magnetic Co-functionalization of Hexagonal Boron Nitride in Poly(vinylidene fluoride) Composites Toward Enhanced Thermal and Mechanical Performance for Heat Exchangers.

Thermal-conductive polymer composites have developed to be a hotspot in academic and industrial fields recently. Both efficient heat transport and superior mechanical properties are of critical significance for high-performance thermal-conductive devices. In this paper, magnetic hexagonal boron nitride (mhBN) with excellent interfacial compatibility is successfully synthesized by dopamine and magnetic co-functionalization, and mhBN-poly(vinylidene fluoride) (mhBN-PVDF) composites with a combination of high thermal conductivity and outstanding mechanical property are achieved due to the integration of excellent interfacial interaction and aligned filler architecture into one material's system. The thermal conductivity increases from 0.35 W/(m K) for the 20 wt % pure BN-PVDF to 0.82 W/(m K) after the dopamine modification and further to 1.43 W/(m K) after alignment. The effective medium approximation model demonstrates that the optimization of mhBN orientation and decrease of thermal resistance are two major factors for the enhancement of thermal conductivity. Moreover, the mhBN-PVDF composites also exhibit excellent tensile strength (168.5 MPa at 15 wt % content) and impact strength (20-30 kJ/m2). The significantly enhanced thermal and mechanical properties result in the excellent heat exchange ability and durability in the heat exchange test, which is important for potential application in the heat exchange industry.

Effects of Loading Frequency and Specimen Geometry on High Cycle and Very High Cycle Fatigue Life of a High Strength Titanium Alloy.

Titanium alloys have been widely used in the structural parts of deep-sea equipment and aviation industries. In this paper, the effects of loading frequency and specimen geometry on the high cycle and very high cycle fatigue life of the high strength titanium alloy Ti-6Al-2Sn-2Zr-3Mo-X is investigated by conventional fatigue test and ultrasonic frequency fatigue test. The results indicate that ultrasonic frequency could enhance the fatigue life of the highstrength titanium alloy compared with that under conventional frequency, and the frequency effect is related to the stress amplitude. This phenomenon is explained by the heat generation in specimens and heat dissipation, in combination with the high strain rate leading to the higher yield strength in the ultrasonic fatigue test. Moreover, it is indicated that the effect of specimen geometry on the fatigue life of the highstrength titanium alloy could be evaluated from the view of control volume.

Identifying tweets of personal health experience through word embedding and LSTM neural network.

BACKGROUND: As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. In this study, we developed a method that combines word embedding and long short-term memory (LSTM) model without the need to engineer any specific features. Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences. RESULTS: Statistical analyses of the results of 10-fold cross-validations of our method and conventional methods indicate that there exist significant differences (p < 0.01) in performance measures of accuracy, precision, recall, F1-score, and ROC/AUC, demonstrating that our approach outperforms the conventional methods in identifying PETs. CONCLUSION: We presented an efficient and effective method of identifying health-related personal experience tweets by combining word embedding and an LSTM neural network. It is conceivable that our method can help accelerate and scale up analyzing textual data of social media for health surveillance purposes, because of no need for the laborious and costly process of engineering features.

Scout-Based Automated Tube Potential Selection Technique (kV Assist) in Enhanced Chest Computed Tomography: Effects on Radiation Exposure and Image Quality.

OBJECTIVE: The aim of our study was to assess radiation dose reduction and image quality for enhanced chest CT examinations with a scout-based automated tube potential selection technique (kV Assist) compared with a standard 120-kV protocol. METHODS: Prospective study of enhanced chest CT examinations was performed in 100 consecutive patients with kV Assist and in 100 consecutive patients with conventional 120-kV protocol on a multislice CT (Discovery CT750 HD). The body mass index, CT dose index volume, and dose length product were recorded from the examination protocol. Image noise and CT value was measured on region of interest, signal-to-noise ratio, and contrast-to-noise ratio was calculated. The subjective image quality was assessed by two radiologists blinded to the respective protocol with the use of a 3-grade scale (3, superior quality; 2, moderate quality; 1, inferior quality). RESULTS: With kV Assist, the percentages of patients being scanned using 80, 100, and 120 kV were 12.0%, 80.0%, and 8.0%, respectively. The kilovolt setting was related with body mass index (r = 0.565, P = 0.000). Compared with the conventional 120 kV protocol, kV Assist allowed for an overall average decrease of 30.6% in CT dose index volume (kV Assist, 11.05 ± 4.78 mGy; 120 kV, 15.92 ± 6.89 mGy) (P < 0.001) and 32.3% in dose length product (kV Assist, 386.41 ± 184.02 mGy cm; 120 kV, 571.14 ± 286.68 mGy cm) (P < 0.001). In the kV Assist, mean attenuation of regions of interest inside the aorta was significantly higher than that in 120-kV protocols (kV Assist, 310.27 ± 73.70 HU; 120 kV, 239.44 ± 47.65 HU) (P < 0.001), resulting in increased contrast-to-noise ratio (kV Assist, 26.69 ± 7.78; 120 kV, 21.38 ± 6.05) (P < 0.001). There was no significant difference in subjective image quality scores between the 2 groups. CONCLUSIONS: The use of attenuation-based kV Assist technique enables significant dose reduction in enhanced chest CT scan while improving arterial enhancement and preserving image quality at adequate levels.

Hollow spheres consisting of Ti0.91O2/CdS nanohybrids for CO2 photofixation.

Here we report multilayer hollow spheres consisting of alternating ultrathin Ti0.91O2 nanosheets and CdS nanoparticles via exquisite layer-by-layer self-assembly to achieve an all solid-state Z-scheme system with 7-times enhancement of the CH4-production rate relative to pure Ti0.91O2 hollow spheres, due to a greatly prolonged lifetime of charge carriers.

Long-Term Effects of Novel Combination Coating Anti-CD34 Antibody Applied on Sirolimus-Eluting Stents.

OBJECTIVES: We investigated whether the combination coating of a novel "prohealing coating" hyaluronan-chitosan (HC) and anti-CD34 antibody applied on an SES (HCASES) can reduce neointimal formation while promoting endothelialization compared to either agent alone. BACKGROUND: Drug-eluting stents have considerably reduced the incidence of in-stent restenosis compared with bare metal stents. However, the beneficial effect of drug elution is overshadowed by delayed re-endothelialization as well as later "catch-up" proliferation related to the drug. METHODS: Three different stents: Sirolimus-eluting stents (SES), Genous anti-CD34 antibody stents (GS), and the combination of HC-anti-CD34 antibody with sirolimus-eluting stents (HCASES) were deployed in 54 normal porcine coronary arteries and harvested for scanning electron microscopy (SEM) and histological analysis at 60, 90, and 120 days. RESULTS: At 60 and 90 days, SEM analysis showed stent surface endothelial coverage was nearly completed in the HCASES (87 ± 3%, 95 ± 3%) compared with that in the SES (68 ± 6%, 77 ± 8%, P = 0.03). Histological examination at 90 days showed that the HCASES group had less percentage of stenosis than the GS group (P < 0.05). At 120 days, SEM showed a significantly higher extent of endothelial coverage above struts in the HCASES (96 ± 2%) and the GS (95 ± 3%) as compared with the SES group (66 ± 3%; P = 0.02). The HCASES group showed less stenosis than that in the GS group (P < 0.05), but it was not significantly different from the SES group (P = 0.063). CONCLUSIONS: Histological and SEM analyses demonstrate that the HCASES can reduce neointimal formation and inflammation while promoting endothelialization in the long term.