Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.</p

Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy and Aerosol Loaded Conditions: Part 1–Application to RGB Image Restoration Over Land With GOME-2

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection

Sustained axon regeneration induced by co-deletion of PTEN and SOCS3

A formidable challenge in neural repair in the adult central nervous system (CNS) is the long distances that regenerating axons often need to travel in order to reconnect with their targets. Thus, a sustained capacity for axon regeneration is critical for achieving functional restoration. Although deletion of either phosphatase and tensin homologue (PTEN), a negative regulator of mammalian target of rapamycin (mTOR), or suppressor of cytokine signalling 3 (SOCS3), a negative regulator of Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathway, in adult retinal ganglion cells (RGCs) individually promoted significant optic nerve regeneration, such regrowth tapered off around 2 weeks after the crush injury. Here we show that, remarkably, simultaneous deletion of both PTEN and SOCS3 enables robust and sustained axon regeneration. We further show that PTEN and SOCS3 regulate two independent pathways that act synergistically to promote enhanced axon regeneration. Gene expression analyses suggest that double deletion not only results in the induction of many growth-related genes, but also allows RGCs to maintain the expression of a repertoire of genes at the physiological level after injury. Our results reveal concurrent activation of mTOR and STAT3 pathways as key for sustaining long-distance axon regeneration in adult CNS, a crucial step towards functional recovery

Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction

Background: We present a novel method of protein fold decoy discrimination using machine learning, more specifically using neural networks. Here, decoy discrimination is represented as a machine learning problem, where neural networks are used to learn the native-like features of protein structures using a set of positive and negative training examples. A set of native protein structures provides the positive training examples, while negative training examples are simulated decoy structures obtained by reversing the sequences of native structures. Various features are extracted from the training dataset of positive and negative examples and used as inputs to the neural networks.Results: Results have shown that the best performing neural network is the one that uses input information comprising of PSI-BLAST [1] profiles of residue pairs, pairwise distance and the relative solvent accessibilities of the residues. This neural network is the best among all methods tested in discriminating the native structure from a set of decoys for all decoy datasets tested. Conclusion: This method is demonstrated to be viable, and furthermore evolutionary information is successfully used in the neural networks to improve decoy discrimination

A designer hyper interleukin 11 (H11) is a biologically active cytokine

<p>Abstract</p> <p>Background</p> <p>Interleukin 11 (IL-11) is a pleiotropic cytokine with anti-apoptotic, anti-inflammatory and hematopoietic potential. The IL-11 activity is determined by the expression of the IL-11R receptor alpha (IL-11Rα) and the signal transducing subunit β (gp130) on the cell membrane. A recombinant soluble form of the IL-11Rα (sIL-11Rα) in combination with IL-11 acts as an agonist on cells expressing the gp130 molecule. We constructed a designer cytokine Hyper IL-11 (H11), which is exclusively composed of naturally existing components. It contains the full length sIL-11Rα connected with the mature IL-11 protein using their natural sequences only. Such a construct has two major advantages: (i) its components are as close as possible to the natural forms of both proteins and (ii) it lacks an artificial linker what should avoid induction of antibody production.</p> <p>Results</p> <p>The H11 construct was generated, the protein was produced in a baculovirus expression system and was then purified by using ion exchange chromatography. The H11 protein displayed activity in three independent bioassays, (i) it induced acute phase proteins production in HepG2 cells expressing IL-11, IL-11Rα and gp130, (ii) it stimulated the proliferation of B9 cells (cells expressing IL-11Rα and gp130) and (iii) proliferation of Baf/3-gp130 cells (cells not expressing IL-11 and IL-11Rα but gp130). Moreover, the preliminary data indicated that H11 was functionally distinct from Hyper-IL-6, a molecule which utilizes the same homodimer of signal transducing receptor (gp130).</p> <p>Conclusions</p> <p>The biologically active H11 may be potentially useful for treatment of thrombocytopenia, infertility, multiple sclerosis, cardiovascular diseases or inflammatory disorders.</p

Replicating viral vector platform exploits alarmin signals for potent CD8⁺ T cell-mediated tumour immunotherapy.

Viral infections lead to alarmin release and elicit potent cytotoxic effector T lymphocyte (CTL &lt;sup&gt;eff&lt;/sup&gt; ) responses. Conversely, the induction of protective tumour-specific CTL &lt;sup&gt;eff&lt;/sup&gt; and their recruitment into the tumour remain challenging tasks. Here we show that lymphocytic choriomeningitis virus (LCMV) can be engineered to serve as a replication competent, stably-attenuated immunotherapy vector (artLCMV). artLCMV delivers tumour-associated antigens to dendritic cells for efficient CTL priming. Unlike replication-deficient vectors, artLCMV targets also lymphoid tissue stroma cells expressing the alarmin interleukin-33. By triggering interleukin-33 signals, artLCMV elicits CTL &lt;sup&gt;eff&lt;/sup&gt; responses of higher magnitude and functionality than those induced by replication-deficient vectors. Superior anti-tumour efficacy of artLCMV immunotherapy depends on interleukin-33 signalling, and a massive CTL &lt;sup&gt;eff&lt;/sup&gt; influx triggers an inflammatory conversion of the tumour microenvironment. Our observations suggest that replicating viral delivery systems can release alarmins for improved anti-tumour efficacy. These mechanistic insights may outweigh safety concerns around replicating viral vectors in cancer immunotherapy

The Innofusion of Electronic Banking in Indonesia

Even though financial innovation plays an important role in the modern economy, surprisingly there have been few empirical literatures. In developing countries, electronic banking (e-banking), for example, received relatively little attention although has been deployed for years. Indonesia presents unique case of Asian tiger economies, especially after experiencing crisis, financial recession, and economic reform. This research address how e-banking being developed in Indonesia, how it diffused from time to time, what factors that drives and influences the e-banking implementation, how banks manage their e-banking, and what lessons can we learn from it. This study concludes by summarising the case of innofusion of electronic banking in Indonesia. Customers and competitors play an important role in influencing e-banking services adoption. There is also an indication that bigger and older banks are not as innovative and dynamic as their smaller and newer peers. Interestingly, the unavailability of legal protection does not discourage them to partner and cooperate with each other and probably true that it is only 'those' people who drive innovation among Indonesian banks

Notch signaling regulates follicular helper T cell differentiation.

Follicular helper T (TFH) cells are specialized in providing help for B cell differentiation and Ab secretion. Several positive and negative regulators of TFH cell differentiation have been described but their control is not fully understood. In this study, we show that Notch signaling in T cells is a major player in the development and function of TFH cells. T cell-specific gene ablation of Notch1 and Notch2 impaired differentiation of TFH cells in draining lymph nodes of mice immunized with T-dependent Ags or infected with parasites. Impaired TFH cell differentiation correlated with deficient germinal center development and the absence of high-affinity Abs. The impact of loss of Notch on TFH cell differentiation was largely independent of its effect on IL-4. These results show a previously unknown role for Notch in the regulation of TFH cell differentiation and function with implications for the control of this T cell population