Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

Multispectral camouflage and radiative cooling using dynamically tunable metasurface.

With the increasing demand for privacy, multispectral camouflage devices that utilize metasurface designs in combination with mature detection technologies have become effective. However, these early designs face challenges in realizing multispectral camouflage with a single metasurface and restricted modes. Therefore, this paper proposes a dynamically tunable metasurface. The metasurface consists of gold (Au), antimony selenide (Sb2Se3), and aluminum (Al), which enables radiative cooling, light detection and ranging (LiDAR) and infrared camouflage. In the amorphous phase of Sb2Se3, the thermal radiation reduction rate in the mid wave infrared range (MWIR) is up to 98.2%. The echo signal reduction rate for the 1064 nm LiDAR can reach 96.3%. In the crystalline phase of Sb2Se3, the highest cooling power is 65.5 Wm-2. Hence the metasurface can reduce the surface temperature and achieve efficient infrared camouflage. This metasurface design provides a new strategy for making devices compatible with multispectral camouflage and radiative cooling.

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and "NPP-VIIRS-like" night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

Bathymetry of a macro-pulsed chaotic laser based on a 520†nm laser diode subject to free space optical feedback.

We generate a macro-pulsed chaotic laser based on pulse-modulated laser diode subject to free space optical feedback, and demonstrate the performance of suppressing backscattering interference and jamming in turbid water. The macro-pulsed chaotic laser with a wavelength of 520 nm as a transmitter is used with a correlation-based lidar receiver to perform an underwater ranging. At the same power consumption, macro-pulsed lasers have higher peak power than in the continuous-wave form, enabling the former to detect longer ranging. The experimental results show that a chaotic macro-pulsed laser has excellent performance of suppressing the backscattering of water column and anti-noise interference compared with traditional pulse laser, especially by multiple accumulations about 10∼30 times, and target position can still be determined when SNR is -20 dB.

The recombinant subunit vaccine encapsulated by alginate-chitosan microsphere enhances the immune effect against Micropterus salmoides rhabdovirus.

The disease caused by Micropterus salmoides rhabdovirus (MSRV) has brought substantial economic losses to the largemouth bass aquaculture industry in China. Vaccination was considered as a potential way to prevent and control this disease. As a kind of sustained and controlled release system, alginate and chitosan microspheres (SA-CS) are widely used in the development of oral vaccination for fish. Here, we prepared a king of alginate-chitosan composite microsphere to encapsulate the second segment of MSRV glycoprotein (G2 protein) and then evaluated the immune effect of the microsphere vaccine on largemouth bass. Largemouth bass were vaccinated via intragastric immunization by different treatments (PBS, SA-CS, G2 and SA-CS-G2). The results showed that a stronger immune response including serum antibody levels, immune-related physiological indexes (acid phosphatase, alkaline phosphatase, superoxide dismutase and total antioxidant capacity) and the expression of immune-related gene (IgM、IL-8、IL-1ß、CD4、TGF-ß、TNF-α) can be induced obviously with SA-CS-G2 groups compared with G2 groups when fish were vaccinated. Furthermore, fish were injected with a lethal dose of MSRV after immunization for 28 days, and the highest relative percentage survival (54.8%) was observed in SA-CS-G2 group (40 µg per fish), which is significantly higher than that of G2 group (25.8%). This study showed that alginate-chitosan microspheres as the vaccine carrier can effectively improve the immune effect of oral vaccination and induce better immune protection effect against MSRV infection.

LiDAR Echo Gaussian Decomposition Algorithm for FPGA Implementation.

As the existing processing algorithms for LiDAR echo decomposition are time-consuming, this paper proposes an FPGA-based improved Gaussian full-waveform decomposition method. The proposed FPGA architecture consists of three modules: (i) a pre-processing module, which is used to pipeline data reading and Gaussian filtering, (ii) the inflection point coordinate solution module, applied to the second-order differential operation and to calculate inflection point coordinates, and (iii) the Gaussian component parameter solution and echo component positioning module, which is utilized to calculate the Gaussian component and echo time parameters. Finally, two LiDAR datasets, covering the Congo and Antarctic regions, are used to verify the accuracy and speed of the proposed method. The experimental results show that (i) the accuracy of the FPGA-based processing is equivalent to that of PC-based processing, and (ii) the processing speed of the FPGA-based processing is 292 times faster than that of PC-based processing.

Modeling Auger Processes with Nonadiabatic Molecular Dynamics.

Auger-type energy exchange plays key roles in the carrier dynamics in nanomaterials due to strong carrier-carrier interactions. However, theoretical descriptions are limited to perturbative calculations of scattering rates on static structures. We develop an accurate and efficient ab initio technique to model Auger scattering with nonadiabatic molecular dynamics. We incorporate the many-body Coulomb matrix into several surface hopping methods and describe simultaneously charge-charge and charge-phonon scattering in the time-domain and in a nonperturbative, configuration-dependent manner. The approach is illustrated with a CdSe quantum dot. Auger scattering between electrons and holes breaks the phonon bottleneck to electron relaxation. The bottleneck is recovered when electrons and holes are decoupled. The simulations correctly reproduce all experimental processes and time scales, including Auger- and phonon-assisted cooling of hot electrons, intraband carrier relaxation, and carrier recombination. Providing detailed insights into the energy flow, the developed method allows studies of carrier dynamics in nanomaterials with strong carrier-carrier interactions.

Common Defects Accelerate Charge Separation and Reduce Recombination in CNT/Molecule Composites: Atomistic Quantum Dynamics.

Carbon nanotubes (CNTs) are appealing candidates for solar and optoelectronic applications. Traditionally used as electron sinks, CNTs can also perform as electron donors, as exemplified by coupling with perylenediimide (PDI). To achieve high efficiencies, electron transfer (ET) should be fast, while subsequent charge recombination should be slow. Typically, defects are considered detrimental to material performance because they accelerate charge and energy losses. We demonstrate that, surprisingly, common CNT defects improve rather than deteriorate the performance. CNTs and other low dimensional materials accommodate moderate defects without creating deep traps. At the same time, charge redistribution caused by CNT defects creates an additional electrostatic potential that increases the CNT work function and lowers CNT energy levels relative to those of the acceptor species. Hence, the energy gap for the ET is decreased, while the gap for the charge recombination is increased. The effect is particularly important because charge acceptors tend to bind near defects due to enhanced chemical interactions. The time-domain simulation of the excited-state dynamics provides an atomistic picture of the observed phenomenon and characterizes in detail the electronic states, vibrational motions, inelastic and elastic electron-phonon interactions, and time scales of the charge separation and recombination processes. The findings should apply generally to low-dimensional materials, because they dissipate defect strain better than bulk semiconductors. Our calculations reveal that CNT performance is robust to common defects and that moderate defects are essential rather than detrimental for CNT application in energy, electronics, and related fields.

Machine learned Hückel theory: Interfacing physics and deep neural networks.

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Overall, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.

A Chinese CADASIL Family with a Novel Mutation on Exon 10 of Notch3 Gene.

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), which is caused by the Notch3 gene mutation, has its unique clinical and imaging characteristics. Here we present a Chinese family with a novel mutation on exon 10 of Notch3 gene. METHODS: Clinical and MRI data of the three patients in the family during the 7-year follow-up were collected. The CADASIL Scale Score was calculated to evaluate the disease risk of the three patients at their first admission or clinic visit. Five family members underwent genetic test. RESULTS: Genetic test confirmed the diagnosis of CADASIL in this family. A novel mutation of p.C533S on exon 10 of Notch3 gene was detected. The CADASIL score of the proband and her sister was both 17 and that of her brother was 14. CONCLUSIONS: Our report not only expands the mutation spectrum of Notch3 gene in CADASIL, but also shows the distinct heterogeneity of CADASIL patients in the same family with the same mutation.

Down-regulation of UBA6 exacerbates brain injury by inhibiting the activation of Notch signaling pathway to promote cerebral cell apoptosis in rat acute cerebral infarction model.

This study aimed to examine the UBA6 role in brain injury mediated by acute cerebral infarction (ACI). In order to screen potential therapeutic targets for ACI, two expression profiles, including GSE97537 and GSE97533 datasets, were downloaded from the GEO database. The Venn method to identify the common DEGs. 68 up-regulated overlapping DEGs and 51 down-regulated overlapping DEGs were used to construct the PPI network by STRING online database. UBA6 was identified as a hub gene by the CytoHubba plugin from Cytoscape. GO and KEGG pathway enrichment analyses were conducted using DAVID online website. UBA6 knockout exacerbated MCAO-mediated brain injury and cell apoptosis in rat brain tissues by H&E and TTC staining and TUNEL assay. The results of flow cytometry and western blot assays further demonstrated that UBA6 inhibition induced the apoptosis of hippocampal neurons and increased cleaved-caspase-3/9 protein levels. Notch1, NICD and Hes1 protein levels were suppressed by down-regulated UBA6. UBA6 was lowly expression in poor prognosis group of 100 patients with ACI. Logistic regression analysis indicated that hypertension, blood glucose, urokinase dose, UBA6 expression and AF were the main risk factors of poor prognosis after thrombolytic therapy for patients with ACI. The ROC curve analysis showed that the sensitivity and specificity of UBA6 was good (sensitivity 100%, specificity 89%, and AUC = 0.772) to be used to evaluate the poor prognosis of ACI. In conclusion, down-regulated UBA6 intensified MCAO-induced brain injury by inhibiting the activation of Notch signaling pathway to promote the apoptosis of hippocampal neurons and was used to predict the poor prognosis of ACI.

Trifluoperazine as an alternative strategy for the inhibition of tumor growth of colorectal cancer.

The development of cancer in patients with schizophrenia is affected by genetic and environmental factors and antipsychotic medication. Several studies found that schizophrenia was associated with decreased risk of some cancers, and the neuroleptic medication might help to reduce the risk of colorectal cancer (CRC). Phenothiazine drugs including trifluoperazine (TFP) are widely used antipsychotic drugs and showed some antitumor effects, we here investigated the potential application of TFP in the treatment of colon cancer. A series doses of TFP were treated to the colon cancer cell line HCT116 and the inhibitory concentration (IC50 ) of TFP for HCT116 was determined by cell counting kit-8. The results indicated that the treatment of TFP impaired the cell vitality of HCT116 in a dose- and time-dependent manner. Meanwhile, the Edu assay demonstrated that the proliferation was also inhibited by TFP, which was accompanied with the induction of apoptosis and autophagy. The expression of CCNE1, CDK4, and antiapoptosis factor BCL-2 was downregulated but the proapoptosis factor BAX was upregulated. The autophagy inhibitor chloroquine could significantly reverse the TFP-induced apoptosis. Moreover, the ability of migration and invasion of HCT116 was found to be suppressed by TFP, which was associated with the inhibition of epithelial-mesenchymal transition (EMT). The function of TFP in vivo was further confirmed. The results showed that the administration of TFP remarkably abrogated the tumor growth with decreased tumor volume and proliferation index Ki-67 level in tumor tissues. The EMT phenotype was also confirmed to be inhibited by TFP in vivo, suggesting the promising antitumor effects of TFP in CRC.

RPC-Based Orthorectification for Satellite Images Using FPGA.

Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area; and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice.

A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching.

Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC's and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.

FPGA-Based On-Board Geometric Calibration for Linear CCD Array Sensors.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules-Input Data, Coefficient Calculation, Adjustment Computation and Comparison-in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (Köln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate; thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

pH-sensitive microfiltration membrane prepared from polyethersulfone grafted with poly(itaconic acid) synthesized by simultaneous irradiation in homogeneous phase.

Poly(itaconic acid) (PIA) was grafted onto polyethersulfone (PES) by homogeneously phased γ-ray irradiation. Kinetic polymerization observed was studied by analyzing the effect of irradiation dosages and monomer concentrations. Then, a pH-sensitive microfiltration (MF) membrane was prepared from these PES-g-PIA polymers with different degrees of grafting under phase inversion method. Finally, the contact angles, morphologies, pore sizes, deionized water permeability and filtration performance for aqueous polyethylene glycols solution of the MF membranes were studied. The results show that grafting PIA groups onto PES molecular chains endowed the MF membranes with effective pH-sensitive properties.

Estimation of coniferous forest aboveground biomass with aggregated airborne small-footprint LiDAR full-waveforms.

Forest aboveground biomass (AGB) is critical for assessing forest productivity and evaluating carbon sequestration rates. Discrete-return LiDAR has been widely used to estimate forest AGB, however, fewer studies have estimated the coniferous forest AGB using airborne small-footprint full-waveform LiDAR data. The objective of this study was to extract a suite of newly proposed metrics from airborne small-footprint full-waveform LiDAR data and to evaluate the ability of these metrics in estimating coniferous forest AGB. To achieve this goal, each waveform was first preprocessed, including de-noising, smoothing, and normalization. Next, all the waveforms within each plot were aggregated into a large pseudo waveform and the return energy profile was generated. Then, the foliage profile was retrieved from the return energy profile based on the Geometric Optical and Radiative Transfer (GORT) model. Finally, a series of new return energy profile metrics and foliage profile metrics were extracted to estimate forest AGB. Simple linear regression was conducted to assess the correlation between each LiDAR metric and forest AGB. Stepwise multiple regression analysis was then carried out to select important prediction metrics and establish the optimal forest AGB estimation model. Results indicated that both return energy profile and foliage profile based height-related metrics were strongly correlated to forest AGB. The energy weighted canopy height (HEweight) (R = 0.88) and foliage area weighted height (HFweight) (R = 0.89) all had the highest correlation coefficients with forest AGB in return energy profile metrics and foliage profile metrics respectively. Energy height percentiles and foliage height percentiles also had the ability to explain AGB variation. The energy-related metrics, foliage area-related metrics, and bounding volume-related metrics derived from the return energy profile and foliage profile were not all sensitive to forest AGB. This study also concluded that combining return energy profile metrics and foliage profile metrics could improve the accuracy of forest AGB estimation, and the optimal model contained the metrics of HFweight, HEweight, and VolumemaxHE, which is the product of the maximum canopy return energy profile amplitude (maxCE) and the maximum height of return energy profile (maxHE).

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review.

As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

Expression of metastasis-associated protein 3 in human brain glioma related to tumor prognosis.

Glioma represents a disparate group of tumors characterized by high invasion ability, and therefore it is of clinical significance to identify molecular markers and therapeutic targets for better clinical management. Previously, metastasis-associated protein family (MTA) is considered to promote tumor cell invasion and metastasis of human malignancies. Recently, the newly identified MTA3 has been shown to play conflicting roles in human malignancies, while the expression pattern and potential clinical significance of MTA3 in human glioma have not been addressed yet. In the present study, we investigated the protein expression of MTA3 by immunohistochemistry assay and analyzed its association with glioma prognosis in 186 cases of patients. Results showed that MTA3 expression was decreased in glioma compared with that in normal brain (P < 0.05). In addition, tumors with high MTA3 expression were more likely to be of low WHO grade (P = 0.005) and reserve of body function (P = 0.014). Survival analysis showed that decreased MTA3 expression was independently associated with unfavorable overall survival of patients (P < 0.001). These results provide the first evidence that MTA3 expression was decreased in human glioma and negatively associated with prognosis of patients, suggesting that MTA3 may play a tumor suppressor role in glioma.