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•Adding an online channel may lead to cannibalization of offline sales.•Adding an offline channel would have a complimentary effect of the existing channels.•Adding mobile channel has a positive effect on the retailer's overall performance.•Better cross-channel integration increases retailer's performance.•Marketing mix strategies have cross-channel effects and need to be optimized taking into consideration these cross-effects. The authors review 50 empirical retailing research papers that have appeared over the last 20 years to take stock of what we know, need to know better, and do not know yet about within-retailer cross-channel effects of omnichannel retail marketing strategies on (a) consumer responses over their purchase journeys, i.e., online and/or offline search, purchase intention, frequency, amount, returns, loyalty, and (b) the retail firm's aggregate outcomes (e.g., sales, costs, profits, product returns) by channel and overall. Specifically, the authors focus on five strategies: (1) the addition of online channel by an offline retailer; (2) the addition (or subtraction) of offline channels by an online retailer; (3) addition of mobile shopping channel (website and/or app) by offline and/or online retailer; (4) cross-channel integration strategies; and (5) retail marketing mix strategies. The author/s integrate findings from empirical research on these strategies into a number of ‘insights’ about ‘what we know’. Prominent among these are the following: Adding a transactional online channel to an offline channel improves the retailer's overall sales even though offline channel sales can be cannibalized to some degree. Adding an offline channel by an online retailer, however, boosts online channel sales as well as overall sales of the retailer. Similarly, adding a mobile shopping channel usually increases customer purchase frequency and amount and overall sales of the retailer in the long-term. Strategies for greater cross-channel integration generally have a positive effect on a retailer's overall performance while online advertising has positive effects on offline channel consideration and sales as well as overall sales of a multichannel retailer. Other insights or findings that need further study or open questions are also identified. The paper closes with managerial implications of the derived empirical insights, and suggestions for future research.

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

X-linked hypophosphatemia (XLH) is the most frequent form of inherited rickets in humans caused by mutations in the phosphate-regulating gene with homologies to endopeptidases on the X-chromosome (PHEX). Hyp mice, a murine homologue of XLH, are characterized by hypophosphatemia, inappropriately low serum vitamin D levels, increased serum fibroblast growth factor-23 (Fgf23), and osteomalacia. Although Fgf23 is known to be responsible for hypophosphatemia and reduced vitamin D hormone levels in Hyp mice, its putative role as an auto-/paracrine osteomalacia-causing factor has not been explored. We recently reported that Fgf23 is a suppressor of tissue nonspecific alkaline phosphatase (Tnap) transcription via FGF receptor-3 (FGFR3) signaling, leading to inhibition of mineralization through accumulation of the TNAP substrate pyrophosphate. Here, we report that the pyrophosphate concentration is increased in Hyp bones, and that Tnap expression is decreased in Hyp-derived osteocyte-like cells but not in Hyp-derived osteoblasts ex vivo and in vitro. In situ mRNA expression profiling in bone cryosections revealed a ~70-fold up-regulation of Fgfr3 mRNA in osteocytes versus osteoblasts of Hyp mice. In addition, we show that blocking of increased Fgf23-FGFR3 signaling with anti-Fgf23 antibodies or an FGFR3 inhibitor partially restored the suppression of Tnap expression, phosphate production, and mineralization, and decreased pyrophosphate concentration in Hyp-derived osteocyte-like cells in vitro. In vivo, bone-specific deletion of Fgf23 in Hyp mice rescued the suppressed TNAP activity in osteocytes of Hyp mice. Moreover, treatment of wild-type osteoblasts or mice with recombinant FGF23 suppressed Tnap mRNA expression and increased pyrophosphate concentrations in the culture medium and in bone, respectively. In conclusion, we found that the cell autonomous increase in Fgf23 secretion in Hyp osteocytes drives the accumulation of pyrophosphate through auto-/paracrine suppression of TNAP. Hence, we have identified a novel mechanism contributing to the mineralization defect in Hyp mice.

Process analytical technology (PAT) for the manufacture of monoclonal antibodies (mAbs) is defined by an integrated set of advanced and automated methods that analyze the compositions and biophysical properties of cell culture fluids, cell-free product streams, and biotherapeutic molecules that are ultimately formulated into concentrated products. In-line or near-line probes and systems are remarkably well developed, although challenges remain in the determination of the absence of viral loads, detecting microbial or mycoplasma contamination, and applying data-driven deep learning to process monitoring and soft sensors. In this review, we address the current status of PAT for both batch and continuous processing steps and discuss its potential impact on facilitating the continuous manufacture of biotherapeutics. Process analytical technology (PAT) has evolved from hardware-based analyses for defined biological, biomolecular, and biochemical analytes to a toolbox that encompasses data analytics and soft sensors to monitor and control monoclonal antibody (mAb) manufacture.Engineered cell lines used in batch processes and continuous manufacturing have helped improve qualities and production rates for mAbs.Data analytics has become increasingly important as sensors become smaller, more robust, and increasingly ubiquitous, with soft sensors enabling determination of a rolling baseline of process conditions and consequences during the production of biologics.In-line sensors utilized for downstream processes provide a template for how such sensors might be used as part of PAT in the real-time monitoring of the manufacture of biotherapeutic proteins in both upstream and downstream unit operations.

The present paper investigates the impact of future scenarios for the past two storms (namely, Vardah and Madi). These two storms had a different intensity over Bay of Bengal, India. The recent study in this region shows that the influence of these Tropical Cyclones (TC) may be severe in future climatic conditions. Different future Representative concentration pathways (RCP) scenarios are investigated in this study. The combined sea level rise (SLR) and high wind intensity in future scenarios has been investigated in the present study for their influence on maximum water level (MWL), wave climate and coastal inundations. The coupled surge and wave models are used to analyze the influence of SLR and wind intensity for different RCPs. The variations of MWL and inundation extent with increase and decrease in wind increment for different RCPs of the TC’s were reported. Overall, for the Far Future RCP 8.5 scenario, the relative percentage of difference (compared to the current scenario) in MWL has increased by 135% for TC Vardah and 180% for TC Madi. Further, the flood area for TC’s Vardah and Madi will increase by 70% and 95% compared to the current scenario. Finally, the study reveals that the extreme wind intensity of the TC’s in future scenarios plays a significant contribution of up to 50% in coastal inundations.

In this work, zinc oxide (ZnO) nanoparticles were synthesized by the sol gel method using zinc acetate as precursor. The synthesized powder was characterized by X-ray diffraction, Fourier transform infrared Spectroscopy (FTIR), Raman, UV–Visible spectroscopy, field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray analysis (EDAX), impedance analysis and photocatalysis activity studies. X-ray diffraction studies indicate that ZnO nanoparticles have single phase with wurtzite hexagonal structure. The lattice parameters were estimated using Scherrer formula. Micro strain, stress, energy density and crystallite size were analysed using Williamson–Hall model. The FTIR spectrum showed the characteristics absorption peak of ZnO at 458.82 cm −1 and authenticates the presence of ZnO nanoparticles. The FE-SEM characterization shows flake like morphology and the presence of chemical element composition is identified in the EDAX analysis. The optical band gap was found to be 3.1 eV. The presence of Zn–O stretching mode was confirmed from Raman spectrum. The electrical properties such as dielectric constant, dielectric loss, and ac conductivity were analyzed from impedance data. The prepared ZnO nanoparticles show good photocatalytic behaviour for methylene blue dye and the rate constant was calculated as 0.0296 min −1 .

Neolitsea pallens (D. Don) Momiyama & H. Hara (Family: Lauraceae), commonly known as Pale Litsea, is an evergreen small tree, distributed in India at altitudes of 1500–3000 m. Traditionally utilized for various purposes, its leaves and bark are used as spices, and the plant is valued in preparing a hair tonic from freshly pressed juice. Secondary metabolites of the leaves have not comprehensively been analysed so far. The objective of the study was to determine the chemical composition of the leaves by analysing their 25% aqueous methanol extract with the aid of ultra-performance liquid chromatography quadrupole time of flight tandem mass spectrometry. Overall, 56 compounds were identified in the study. Phenolics represented by phenolic acids, phenolic glycosides, proanthocyanidins, and flavonoids were the main components of the extract.

Deep learning has the potential to enhance the output of in‐line, on‐line, and at‐line instrumentation used for process analytical technology in the pharmaceutical industry. Here, we used Raman spectroscopy‐based deep learning strategies to develop a tool for detecting microbial contamination. We built a Raman dataset for microorganisms that are common contaminants in the pharmaceutical industry for Chinese Hamster Ovary (CHO) cells, which are often used in the production of biologics. Using a convolution neural network (CNN), we classified the different samples comprising individual microbes and microbes mixed with CHO cells with an accuracy of 95%–100%. The set of 12 microbes spans across Gram‐positive and Gram‐negative bacteria as well as fungi. We also created an attention map for different microbes and CHO cells to highlight which segments of the Raman spectra contribute the most to help discriminate between different species. This dataset and algorithm provide a route for implementing Raman spectroscopy for detecting microbial contamination in the pharmaceutical industry. We use Raman spectroscopy to identify microbial contaminants that are common in the pharmaceutical industry. These contaminants span across Gram‐negative bacteria, Gram‐positive bacteria, and fungi. The use of a convolution neural network achieves identification accuracy in the range of 95%–100%.

Certain nonlinear systems can switch between dynamical attractors occupying different regions of phase space, under variation of parameters or initial states. In this work we exploit this feature to obtain reliable logic operations. With logic output 0/1 mapped to dynamical attractors bounded in distinct regions of phase space, and logic inputs encoded by a very small bias parameter, we explicitly demonstrate that the system hops consistently in response to an external input stream, operating effectively as a reliable logic gate. This system offers the advantage that very low-amplitude inputs yield highly amplified outputs. Additionally, different dynamical variables in the system yield complementary logic operations in parallel. Further, we show that in certain parameter regions noise aids the reliability of logic operations, and is actually necessary for obtaining consistent outputs. This leads us to a generalization of the concept of Logical Stochastic Resonance to attractors more complex than fixed point states, such as periodic or chaotic attractors. Lastly, the results are verified in electronic circuit experiments, demonstrating the robustness of the phenomena. So we have combined the research directions of Chaos Computing and Logical Stochastic Resonance here, and this approach has potential to be realized in wide-ranging systems.

Rho family proteins are prenylated by geranylgeranyltransferase type I (GGTase-I), which normally target proteins to membranes for GTP-loading. However, conditional deletion of GGTase-I in mouse macrophages increases GTP-loading of Rho proteins, leading to enhanced inflammatory responses and severe rheumatoid arthritis. Here we show that heterozygous deletion of the Rho family gene Rac1 , but not Rhoa and Cdc42 , reverses inflammation and arthritis in GGTase-I-deficient mice. Non-prenylated Rac1 has a high affinity for the adaptor protein Ras GTPase-activating-like protein 1 (Iqgap1), which facilitates both GTP exchange and ubiquitination-mediated degradation of Rac1. Consistently, inactivating Iqgap1 normalizes Rac1 GTP-loading, and reduces inflammation and arthritis in GGTase-I-deficient mice, as well as prevents statins from increasing Rac1 GTP-loading and cytokine production in macrophages. We conclude that blocking prenylation stimulates Rac1 effector interactions and unleashes proinflammatory signaling. Our results thus suggest that prenylation normally restrains innate immune responses by preventing Rac1 effector interactions. Macrophage specific deletion of GGTase-I, a prenylation enzyme, in mice induces inflammatory response and rheumatoid arthritis. Here the authors show that GGTase-I deficiency and the resulting reduction of RAC1 prenylation increase RAC1 interaction with the adaptor protein IQGAP1, leading to GTP-loading of RAC1 and enhanced proinflammatory cytokine production.