Showing papers by "Wuhan University published in 2017"

Global, regional, and national burden of neurological disorders, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016

Abstract: Summary Background Comparable data on the global and country-specific burden of neurological disorders and their trends are crucial for health-care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study provides such information but does not routinely aggregate results that are of interest to clinicians specialising in neurological conditions. In this systematic analysis, we quantified the global disease burden due to neurological disorders in 2015 and its relationship with country development level. Methods We estimated global and country-specific prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) for various neurological disorders that in the GBD classification have been previously spread across multiple disease groupings. The more inclusive grouping of neurological disorders included stroke, meningitis, encephalitis, tetanus, Alzheimer's disease and other dementias, Parkinson's disease, epilepsy, multiple sclerosis, motor neuron disease, migraine, tension-type headache, medication overuse headache, brain and nervous system cancers, and a residual category of other neurological disorders. We also analysed results based on the Socio-demographic Index (SDI), a compound measure of income per capita, education, and fertility, to identify patterns associated with development and how countries fare against expected outcomes relative to their level of development. Findings Neurological disorders ranked as the leading cause group of DALYs in 2015 (250·7 [95% uncertainty interval (UI) 229·1 to 274·7] million, comprising 10·2% of global DALYs) and the second-leading cause group of deaths (9·4 [9·1 to 9·7] million], comprising 16·8% of global deaths). The most prevalent neurological disorders were tension-type headache (1505·9 [UI 1337·3 to 1681·6 million cases]), migraine (958·8 [872·1 to 1055·6] million), medication overuse headache (58·5 [50·8 to 67·4 million]), and Alzheimer's disease and other dementias (46·0 [40·2 to 52·7 million]). Between 1990 and 2015, the number of deaths from neurological disorders increased by 36·7%, and the number of DALYs by 7·4%. These increases occurred despite decreases in age-standardised rates of death and DALYs of 26·1% and 29·7%, respectively; stroke and communicable neurological disorders were responsible for most of these decreases. Communicable neurological disorders were the largest cause of DALYs in countries with low SDI. Stroke rates were highest at middle levels of SDI and lowest at the highest SDI. Most of the changes in DALY rates of neurological disorders with development were driven by changes in YLLs. Interpretation Neurological disorders are an important cause of disability and death worldwide. Globally, the burden of neurological disorders has increased substantially over the past 25 years because of expanding population numbers and ageing, despite substantial decreases in mortality rates from stroke and communicable neurological disorders. The number of patients who will need care by clinicians with expertise in neurological conditions will continue to grow in coming decades. Policy makers and health-care providers should be aware of these trends to provide adequate services. Funding Bill & Melinda Gates Foundation.

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Abstract: Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level : results from the Global Burden of Disease Study 2015

Abstract: Importance Liver cancer is among the leading causes of cancer deaths globally. The most common causes for liver cancer include hepatitis B virus (HBV) and hepatitis C virus (HCV) infection and alcohol use. Objective To report results of the Global Burden of Disease (GBD) 2015 study on primary liver cancer incidence, mortality, and disability-adjusted life-years (DALYs) for 195 countries or territories from 1990 to 2015, and present global, regional, and national estimates on the burden of liver cancer attributable to HBV, HCV, alcohol, and an “other” group that encompasses residual causes. Design, Settings, and Participants Mortality was estimated using vital registration and cancer registry data in an ensemble modeling approach. Single-cause mortality estimates were adjusted for all-cause mortality. Incidence was derived from mortality estimates and the mortality-to-incidence ratio. Through a systematic literature review, data on the proportions of liver cancer due to HBV, HCV, alcohol, and other causes were identified. Years of life lost were calculated by multiplying each death by a standard life expectancy. Prevalence was estimated using mortality-to-incidence ratio as surrogate for survival. Total prevalence was divided into 4 sequelae that were multiplied by disability weights to derive years lived with disability (YLDs). DALYs were the sum of years of life lost and YLDs. Main Outcomes and Measures Liver cancer mortality, incidence, YLDs, years of life lost, DALYs by etiology, age, sex, country, and year. Results There were 854 000 incident cases of liver cancer and 810 000 deaths globally in 2015, contributing to 20 578 000 DALYs. Cases of incident liver cancer increased by 75% between 1990 and 2015, of which 47% can be explained by changing population age structures, 35% by population growth, and −8% to changing age-specific incidence rates. The male-to-female ratio for age-standardized liver cancer mortality was 2.8. Globally, HBV accounted for 265 000 liver cancer deaths (33%), alcohol for 245 000 (30%), HCV for 167 000 (21%), and other causes for 133 000 (16%) deaths, with substantial variation between countries in the underlying etiologies. Conclusions and Relevance Liver cancer is among the leading causes of cancer deaths in many countries. Causes of liver cancer differ widely among populations. Our results show that most cases of liver cancer can be prevented through vaccination, antiviral treatment, safe blood transfusion and injection practices, as well as interventions to reduce excessive alcohol use. In line with the Sustainable Development Goals, the identification and elimination of risk factors for liver cancer will be required to achieve a sustained reduction in liver cancer burden. The GBD study can be used to guide these prevention efforts.

AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Abstract: Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

Recent Advances in Radical C-H Activation/Radical Cross-Coupling.

Abstract: Research and industrial interest in radical C–H activation/radical cross-coupling chemistry has continuously grown over the past few decades. These reactions offer fascinating and unconventional approaches toward connecting molecular fragments with high atom- and step-economy that are often complementary to traditional methods. Success in this area of research was made possible through the development of photocatalysis and first-row transition metal catalysis along with the use of peroxides as radical initiators. This Review provides a brief and concise overview of the current status and latest methodologies using radicals or radical cations as key intermediates produced via radical C–H activation. This Review includes radical addition, radical cascade cyclization, radical/radical cross-coupling, coupling of radicals with M–R groups, and coupling of radical cations with nucleophiles (Nu).

Observation of topological valley transport of sound in sonic crystals

Abstract: Valleytronics — exploiting a system’s pseudospin degree of freedom — is being increasingly explored in sonic crystals. Now, valley transport of sound is reported for a macroscopic triangular-lattice array of rod-like scatterers in a 2D air waveguide.

Cryo-EM structures of MERS-CoV and SARS-CoV spike glycoproteins reveal the dynamic receptor binding domains

Abstract: The envelope spike (S) proteins of MERS-CoV and SARS-CoV determine the virus host tropism and entry into host cells, and constitute a promising target for the development of prophylactics and therapeutics. Here, we present high-resolution structures of the trimeric MERS-CoV and SARS-CoV S proteins in its pre-fusion conformation by single particle cryo-electron microscopy. The overall structures resemble that from other coronaviruses including HKU1, MHV and NL63 reported recently, with the exception of the receptor binding domain (RBD). We captured two states of the RBD with receptor binding region either buried (lying state) or exposed (standing state), demonstrating an inherently flexible RBD readily recognized by the receptor. Further sequence conservation analysis of six human-infecting coronaviruses revealed that the fusion peptide, HR1 region and the central helix are potential targets for eliciting broadly neutralizing antibodies.

Direct Z-scheme g-C_3N_4/WO_3 photocatalyst with atomically defined junction for H_2 production

Abstract: Mimicking the natural photosynthesis, artificial Z-scheme photocatalysis enables more efficient utilization of solar energy for sustainable chemical fuel production. Herein, a direct Z-scheme g-C_3N_4/WO_3 photocatalyst with host-guest architecture is rationally designed, demonstrating significantly enhanced activities of photocatalytic H_2 production. Unprecedented atomic-scale imaging of both the in-plane and interlayer structures in g-C_3N_4 revealed the well-defined interfaces in such architecture, where the 2D g-C_3N_4 layers stand vertically on the flat facets of WO_3 nanocuboids. Through both experimental and theoretical investigations, mechanistic insights regarding the direct Z-scheme electron transfer from WO_3 to g-C_3N_4 were obtained. The Z-scheme electron transfer was driven by the internal electric field at the interfacial junction, defined by the covalent W-O-N-(C)_2 interaction. Under simultaneous light excitation, this atomically defined junction induces a rapid electron injection from WO_3 to inhibit the fast recombination kinetics within g-C_3N_4 and prolong the charge carrier lifetime of g-C_3N_4, thereby liberating more excited electrons with high reducing power for H_2 production.

Manipulating Adsorption–Insertion Mechanisms in Nanostructured Carbon Materials for High‐Efficiency Sodium Ion Storage

Abstract: Hard carbon is one of the most promising anode materials for sodium-ion batteries, but the low Coulombic efficiency is still a key barrier. In this paper, a series of nanostructured hard carbon materials with controlled architectures is synthesized. Using a combination of in situ X-ray diffraction mapping, ex situ nuclear magnetic resonance (NMR), electron paramagnetic resonance, electrochemical techniques, and simulations, an “adsorption–intercalation” mechanism is established for Na ion storage. During the initial stages of Na insertion, Na ions adsorb on the defect sites of hard carbon with a wide adsorption energy distribution, producing a sloping voltage profile. In the second stage, Na ions intercalate into graphitic layers with suitable spacing to form NaC x compounds similar to the Li ion intercalation process in graphite, producing a flat low voltage plateau. The cation intercalation with a flat voltage plateau should be enhanced and the sloping region should be avoided. Guided by this knowledge, nonporous hard carbon material has been developed which has achieved high reversible capacity and Coulombic efficiency to fulfill practical application.

Cancer Cell Membrane Camouflaged Cascade Bioreactor for Cancer Targeted Starvation and Photodynamic Therapy.

Abstract: Selectively cuting off the nutrient supply and the metabolism pathways of cancer cells would be a promising approach to improve the efficiency of cancer treatment. Here, a cancer targeted cascade bioreactor (designated as mCGP) was constructed for synergistic starvation and photodynamic therapy (PDT) by embedding glucose oxidase (GOx) and catalase in the cancer cell membrane-camouflaged porphyrin metal–organic framework (MOF) of PCN-224 (PCN stands for porous coordination network). Due to biomimetic surface functionalization, the immune escape and homotypic targeting behaviors of mCGP would dramatically enhance its cancer targeting and retention abilities. Once internalized by cancer cells, mCGP was found to promote microenvironmental oxygenation by catalyzing the endogenous hydrogen peroxide (H2O2) to produce oxygen (O2), which would subsequently accelerate the decomposition of intracellular glucose and enhance the production of cytotoxic singlet oxygen (1O2) under light irradiation. Consequently, mCGP d...

Low-bandgap mixed tin-lead iodide perovskite absorbers with long carrier lifetimes for all-perovskite tandem solar cells

Abstract: Tandem solar cells using only metal-halide perovskite sub-cells are an attractive choice for next-generation solar cells. However, the progress in developing efficient all-perovskite tandem solar cells has been hindered by the lack of high-performance low-bandgap perovskite solar cells. Here, we report efficient mixed tin–lead iodide low-bandgap (∼1.25 eV) perovskite solar cells with open-circuit voltages up to 0.85 V and over 70% external quantum efficiencies in the infrared wavelength range of 700–900 nm, delivering a short-circuit current density of over 29 mA cm−2 and demonstrating suitability for bottom-cell applications in all-perovskite tandem solar cells. Our low-bandgap perovskite solar cells achieve a maximum power conversion efficiency of 17.6% and a certified efficiency of 17.01% with a negligible current–voltage hysteresis. When mechanically stacked with a ∼1.58 eV bandgap perovskite top cell, our best all-perovskite 4-terminal tandem solar cell shows a steady-state efficiency of 21.0%. All-perovskite tandem solar cells hold the promise of high efficiencies whilst safeguarding the ease of fabrication intrinsic to perovskites. Here, Zhao et al. present a certified 17% efficient tin and lead perovskite solar cell, which is integrated as the low-bandgap component of a tandem device with 21% efficiency.

Test of lepton universality with B 0 → K *0 ℓ + ℓ − decays

Abstract: A test of lepton universality, performed by measuring the ratio of the branching fractions of the B$^{0}$ → K$^{*0}$ μ$^{+}$ μ$^{−}$ and B$^{0}$ → K$^{*0}$ e$^{+}$ e$^{−}$ decays, $ {R}_{K^{*0}} $ , is presented. The K$^{*0}$ meson is reconstructed in the final state K$^{+}$ π$^{−}$, which is required to have an invariant mass within 100 MeV/c$^{2}$ of the known K$^{*}$(892)$^{0}$ mass. The analysis is performed using proton-proton collision data, corresponding to an integrated luminosity of about 3 fb$^{−1}$, collected by the LHCb experiment at centre-of-mass energies of 7 and 8 TeV. The ratio is measured in two regions of the dilepton invariant mass squared, q$^{2}$, to be $ {R}_{K^{*0}}=\left\{\begin{array}{l}{0.66_{-}^{+}}_{0.07}^{0.11}\left(\mathrm{stat}\right)\pm 0.03\left(\mathrm{syst}\right)\kern1em \mathrm{f}\mathrm{o}\mathrm{r}\kern1em 0.045<{q}^2<1.1\kern0.5em {\mathrm{GeV}}^2/{c}^4,\hfill \\ {}{0.69_{-}^{+}}_{0.07}^{0.11}\left(\mathrm{stat}\right)\pm 0.05\left(\mathrm{syst}\right)\kern1em \mathrm{f}\mathrm{o}\mathrm{r}\kern1em 1.1<{q}^2<6.0\kern0.5em {\mathrm{GeV}}^2/{c}^4.\hfill \end{array}\right. $

High performance platinum single atom electrocatalyst for oxygen reduction reaction

Abstract: For the large-scale sustainable implementation of polymer electrolyte membrane fuel cells in vehicles, high-performance electrocatalysts with low platinum consumption are desirable for use as cathode material during the oxygen reduction reaction in fuel cells. Here we report a carbon black-supported cost-effective, efficient and durable platinum single-atom electrocatalyst with carbon monoxide/methanol tolerance for the cathodic oxygen reduction reaction. The acidic single-cell with such a catalyst as cathode delivers high performance, with power density up to 680 mW cm-2 at 80 °C with a low platinum loading of 0.09 mgPt cm-2, corresponding to a platinum utilization of 0.13 gPt kW-1 in the fuel cell. Good fuel cell durability is also observed. Theoretical calculations reveal that the main effective sites on such platinum single-atom electrocatalysts are single-pyridinic-nitrogen-atom-anchored single-platinum-atom centres, which are tolerant to carbon monoxide/methanol, but highly active for the oxygen reduction reaction.

Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks

Abstract: In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds.

A highly flexible and sensitive piezoresistive sensor based on MXene with greatly changed interlayer distances

Abstract: Since the successful synthesis of the first MXenes, application developments of this new family of two-dimensional materials on energy storage, electromagnetic interference shielding, transparent conductive electrodes and field-effect transistors, and other applications have been widely reported. However, no one has found or used the basic characteristics of greatly changed interlayer distances of MXene under an external pressure for a real application. Here we report a highly flexible and sensitive piezoresistive sensor based on this essential characteristics. An in situ transmission electron microscopy study directly illustrates the characteristics of greatly changed interlayer distances under an external pressure, supplying the basic working mechanism for the piezoresistive sensor. The resultant device also shows high sensitivity (Gauge Factor ~ 180.1), fast response (<30 ms) and extraordinarily reversible compressibility. The MXene-based piezoresistive sensor can detect human being’s subtle bending-release activities and other weak pressure. MXenes are a family of layered materials which show promise for a variety of (opto-)electronic applications. Here, the authors leverage the variations of the interlayer distance in Ti3C2 under external pressure to devise a piezoresistive sensor.

Superparamagnetic enhancement of thermoelectric performance

Abstract: The ability to control chemical and physical structuring at the nanometre scale is important for developing high-performance thermoelectric materials. Progress in this area has been achieved mainly by enhancing phonon scattering and consequently decreasing the thermal conductivity of the lattice through the design of either interface structures at nanometre or mesoscopic length scales or multiscale hierarchical architectures. A nanostructuring approach that enables electron transport as well as phonon transport to be manipulated could potentially lead to further enhancements in thermoelectric performance. Here we show that by embedding nanoparticles of a soft magnetic material in a thermoelectric matrix we achieve dual control of phonon- and electron-transport properties. The properties of the nanoparticles-in particular, their superparamagnetic behaviour (in which the nanoparticles can be magnetized similarly to a paramagnet under an external magnetic field)-lead to three kinds of thermoelectromagnetic effect: charge transfer from the magnetic inclusions to the matrix; multiple scattering of electrons by superparamagnetic fluctuations; and enhanced phonon scattering as a result of both the magnetic fluctuations and the nanostructures themselves. We show that together these effects can effectively manipulate electron and phonon transport at nanometre and mesoscopic length scales and thereby improve the thermoelectric performance of the resulting nanocomposites.

Molecular conformation and packing: their critical roles in the emission performance of mechanochromic fluorescence materials

Abstract: Mechanochromic fluorescence (MCF) materials are a sort of smart material whose photophysical properties are sensitive to mechanical stimulation, such as photoluminescence color, fluorescence quantum yield and emission lifetime. Recently, an increasing number of studies have shown that these photophysical properties can be affected greatly by the molecular packing and conformation, enabling the rapid development of functional materials with mechanochromic fluorescence properties. In this review, we focus on MCF materials with distinct emission properties and various molecular arrangements, especially the inherent correlation between molecular packing modes and emissive behaviors. Many of the selected representative examples possess polymorphism, offering the possibility of exploring different emissions from the exact molecular packing in single crystals. Correspondingly, some remarks are made on the outlook for the next developments in MCF materials and the required thinking about the structure–packing–performance relationship.

Accelerated photocatalytic degradation of organic pollutant over metal-organic framework MIL-53(Fe) under visible LED light mediated by persulfate

Abstract: Photocatalysis based on metal-organic frameworks (MOFs) is being actively investigated as a potential technology in visible light harvesting and utilizing processes. Herein we report that MIL-53(Fe), an earth-abundant Fe-containing MOF material, shows photocatalytic activity for the degradation of Acid Orange 7 (AO7) from aqueous solution under visible LED light irradiation, yet the photocatalytic performance of bare MIL-53(Fe) was not satisfactory due to the fast recombination of photoinduced electron-hole pairs. This can be effectively overcome by adding the external electron acceptor (e.g., persulfate, PS) to the catalytic process. The accelerated photocatalytic degradation of AO7 is demonstrated by the result that the degradation efficiency of AO7 in the MIL-53(Fe)/PS/Vis process reached almost 100% within 90 min as compared to only 24% under the identical experimental conditions for the MIL-53(Fe)/Vis process. To investigate the mechanism of the MIL-53(Fe)/PS/Vis process, photoluminescence (PL) spectra, electrochemical measurements and electron paramagnetic resonance (EPR) analysis were performed. It was concluded that the efficient separation of photogenerated electrons and holes by the introduced PS and the subsequent formation of reactive radicals resulting from the activation of PS by photogenerated electrons accounted for the accelerated photocatalytic degradation of AO7 in the MIL-53(Fe)/PS/Vis process. Furthermore, the applicability of MIL-53(Fe) used in the persulfate-mediated photocatalytic process was systematically investigated in terms of the identification of reactive radicals, the reusability and stability of the photocatalyst, as well as the effect of operating parameters. The findings of this work highlighted the great potential of MOFs as photocatalysts and elucidated a new opportunity for persulfate remediation of contaminated water.

Nest-like NiCoP for Highly Efficient Overall Water Splitting

Abstract: The investigation of high-efficiency nonprecious electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is of great significance for renewable energy technologies. Here, we provide a successive hydrothermal, oxidation, and phosphidation method to fabricate a 3D nest-like ternary NiCoP/carbon cloth (CC) electrocatalyst with superior catalytic activity and stability toward HER/OER. Nest-like NiCoP/CC requires overpotentials of 44 and 62 mV to reach the current density of 10 mA cm–2 in acidic and alkaline media, respectively, toward HER. For OER, the NiCoP/CC exhibits high active and durable performance with an overpotential of 242 mV at current density of 10 mA cm–2 in alkaline solutions. Furthermore, the practical application of NiCoP/CC as a bifunctional catalyst for overall water splitting reaction yields current densities of 10 and 100 mA cm–2 at 1.52 and 1.77 V, respectively.

Metal-Ion-Modified Black Phosphorus with Enhanced Stability and Transistor Performance.

Abstract: Black phosphorus (BP), a burgeoning elemental 2D semiconductor, has aroused increasing scientific and technological interest, especially as a channel material in field-effect transistors (FETs). However, the intrinsic instability of BP causes practical concern and the transistor performance must also be improved. Here, the use of metal-ion modification to enhance both the stability and transistor performance of BP sheets is described. Ag+ spontaneously adsorbed on the BP surface via cation-π interactions passivates the lone-pair electrons of P thereby rendering BP more stable in air. Consequently, the Ag+ -modified BP FET shows greatly enhanced hole mobility from 796 to 1666 cm2 V-1 s-1 and ON/OFF ratio from 5.9 × 104 to 2.6 × 106 . The mechanisms pertaining to the enhanced stability and transistor performance are discussed and the strategy can be extended to other metal ions such as Fe3+ , Mg2+ , and Hg2+ . Such stable and high-performance BP transistors are crucial to electronic and optoelectronic devices. The stability and semiconducting properties of BP sheets can be enhanced tremendously by this novel strategy.

The Strong Light-Emission Materials in the Aggregated State: What Happens from a Single Molecule to the Collective Group.

Abstract: The strong light emission of organic luminogens in the aggregated state is essential to their applications as optoelectronic materials with good performance. In this review, with respect to the aggregation-induced emission and room-temperature phosphorescence luminogens, the important role of molecular packing modes is highlighted. As demonstrated in the selected examples, the molecular packing status in the aggregate state is affected by many factors, including the molecular configurations, the inherent electronic properties, the special functional groups, and so on. With the consideration of all these parameters, the strong fluorescence and phosphorescence in the aggregated state could be achieved in the rationally designed organic luminogens, providing some guidance for the further development.

A high quantum yield molecule-protein complex fluorophore for near-infrared II imaging.

Abstract: Fluorescence imaging in the second near-infrared window (NIR-II) allows visualization of deep anatomical features with an unprecedented degree of clarity. NIR-II fluorophores draw from a broad spectrum of materials spanning semiconducting nanomaterials to organic molecular dyes, yet unfortunately all water-soluble organic molecules with >1,000 nm emission suffer from low quantum yields that have limited temporal resolution and penetration depth. Here, we report tailoring the supramolecular assemblies of protein complexes with a sulfonated NIR-II organic dye (CH-4T) to produce a brilliant 110-fold increase in fluorescence, resulting in the highest quantum yield molecular fluorophore thus far. The bright molecular complex allowed for the fastest video-rate imaging in the second NIR window with ∼50-fold reduced exposure times at a fast 50 frames-per-second (FPS) capable of resolving mouse cardiac cycles. In addition, we demonstrate that the NIR-II molecular complexes are superior to clinically approved ICG for lymph node imaging deep within the mouse body. Near-infrared (NIR) fluorescence imaging >1,000 nm allows deep tissue imaging, but available organic dyes display poor brightness and temporal resolution. Here, the authors synthesize a NIR dye that, upon binding serum proteins, exhibits a 110-fold increase in intensity, giving an 11% quantum yield.

Inorganic nanomaterials for printed electronics: a review

Abstract: Owing to their capability of bypassing conventional high-priced and inflexible silicon based electronics to manufacture a variety of devices on flexible substrates by using large-scale and high-volume printing techniques, printed electronics (PE) have attracted increasing attention in the field of manufacturing industry for electronic devices This simple and cost-effective approach could enhance current methods of constructing a patterned surface for nanomaterials and offer opportunities for developing fully-printed functional devices, especially offering the possibility of ubiquitous low-cost and flexible devices This review presents a summary of work to date on the inorganic nanomaterials involved in PE applications, focused on the utilization of inorganic nanomaterials-based inks in the successful preparation of printed conductive patterns, electrodes, sensors, thin film transistors (TFTs) and other micro-/nanoscale devices The printing techniques, sintering methods and printability of functional inks with their associated challenges are discussed, and we look forward so you can glimpse the future of PE applications

Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network

Abstract: In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the impressive effectiveness of deep neural networks has recently been employed to overcome the drawbacks of the traditional linear models and boost the fusion accuracy However, the existing methods are mainly based on simple and flat networks with relatively shallow architectures, which severely limits their performance In this letter, the concept of residual learning is introduced to form a very deep convolutional neural network to make the full use of the high nonlinearity of the deep learning models Through both quantitative and visual assessments on a large number of high-quality MS images from various sources, it is confirmed that the proposed model is superior to all the mainstream algorithms included in the comparison, and achieves the highest spatial–spectral unified accuracy