Synthesis and characterization of bis-amide SSE1917 as a microtubule-stabilizing anticancer agent.

Microtubule dynamics are critical for spindle assembly and chromosome segregation during cell division. Pharmacological inhibition of microtubule dynamics in cells causes prolonged mitotic arrest, resulting in apoptosis, an approach extensively employed in treating different types of cancers. The present study reports the synthesis of thirty-two novel bis-amides (SSE1901-SSE1932) and the evaluation of their antiproliferative activities. N-(1-oxo-3-phenyl-1-(phenylamino)propan-2-yl)benzamide (SSE1917) exhibited the most potent activity with GI50 values of 0.331 ± 0.01 µM in HCT116 colorectal and 0.48 ± 0.27 µM in BT-549 breast cancer cells. SSE1917 stabilized microtubules in biochemical and cellular assays, bound to taxol site in docking studies, and caused aberrant mitosis and G2/M arrest in cells. Prolonged treatment of cells with the compound increased p53 expression and triggered apoptotic cell death. Furthermore, SSE1917 suppressed the growth of both mouse and patient-derived human colon cancer organoids, highlighting its potential therapeutic value as an anticancer agent.

Identification of primary metabolites in fungal species of Trichophyton mentagrophyte and Trichophyton rubrum by NMR spectroscopy.

BACKGROUND: Superficial mycoses are fungal infections limited to the outermost layers of the skin and its appendages. The chief causative agents of these mycoses are dermatophytes and yeasts. The diagnosis of dermatophytosis can be made by direct mycological examination with potassium hydroxide (10%-30%) of biological material obtained from patients with suspected mycosis, providing results more rapid than fungal cultures, which may take days or weeks. This information, together with clinical history and laboratory diagnosis, ensures that the appropriate treatment is initiated promptly. However, false negative results are obtained in 5%-15%, by conventional methods of diagnosis of dermatophytosis. OBJECTIVES: To study the metabolic profiles of the commonly occurring dermatophytes by NMR spectroscopy. PATIENTS/MATERIALS: We have used 1D and 2D Nuclear Magnetic Resonance (NMR) experiments along with Human Metabolome Database (HMDB) and Chenomx database search for identification of primary metabolites in the methanol extract of two fungal species: Trichophyton mentagrophyte (T. mentagrophyte) and Trichophyton rubrum (T. rubrum). Both standard strains and representative number of clinical isolates of these two species were investigated. Further, metabolic profiles obtained were analysed using multivariate analysis. RESULTS: We have identified 23 metabolites in the T. mentagrophyte and another 23 metabolites in T. rubrum. Many important metabolites like trehalose, proline, mannitol, acetate, GABA and several other amino acids were detected, which provide the necessary components for fungal growth and metabolism. Altered metabolites were defined between Trichophyton mentagrophyte and T. rubrum strains. CONCLUSION: We have detected many metabolites in the two fungal species T. mentagrophyte and T. rubrum by using NMR spectroscopy. NMR spectroscopy provides a holistic snapshot of the metabolome of an organism. Key metabolic differences were identified between the two fungal strains. We need to perform more studies on metabolite profiling of the samples from these species for their rapid diagnosis and prompt treatment.

Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study.

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies.

INTRODUCTION: Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra. OBJECTIVES: The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals. METHODS: We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS). RESULTS: The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites. CONCLUSION: This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Resolving the phylogenetic relationship of Himalayan snow trout Schizothorax plagiostomus with other species of Schizothoracine using mitochondrial CO-I and Cyt b genes.

BACKGROUND: The classification of the sub-family Schizothoracinae has been debatable due to the overlap in morphological characters. There are discrepancies between classical taxonomy and molecular taxonomy, as well. In the present study, mitochondrial genes CO-I and Cyt b were sequenced to elucidate the phylogenetic status of three species of the genus Schizothorax. METHODS AND RESULTS: In total, 29 samples of three species viz., S. plagiostomus, S. progastus, and S. richardsonii, were collected from rivers of Uttarakhand, India. For phylogenetic analyses, 40 sequences of CO-I and 41 sequences of Cyt b of Schizothoracinae species were downloaded from NCBI. The highest genetic divergence based on CO-I (16.08%) is between S. plagiostomus and Ptychobarbus dipogon, while the lowest divergence (0.00%) is between 10 pairs of species. The highest divergence based on Cyt b (19.43%), is between S. niger and Gymnocypris eckloni, while the lowest divergence (0.00%) is between four pairs of species. The divergence (0.00% for CO-I and 2.38% for Cyt b) between S. chongi and S. kozlovi, seems a case of convergent molecular evolution of the CO-I gene and in this case, CO-I alone cannot be used to differentiate these two species. CONCLUSION: The simultaneous use of two molecular markers along with morphomeristic data is a better strategy for the classification of the sub-family Schizothoracinae. These results will be a resource dataset for determining the taxonomical status of Schizothoracine species and will help in the conservation and commercial production of these commercially important fish species.

Fishery, growth, mortality, and stock assessment of endangered Tor putitora from Tehri dam reservoir, Uttarakhand, Himalayan foothills of India in relation to environmental variables.

The current study determined Tor putitora (Hamilton, 1822) fishery, growth, mortality, and population characteristics using length-frequency data assembled monthly from the Tehri dam reservoir in Uttarakhand from January to December 2022. The estimation data was separated into 40-mm class intervals, and population parameters were investigated and computed using the FiSAT-II software tool. W = 0.0101 L2.996, where a = 0.0101 and b = 2.99, were determined as the length-weight relationships, and the growth performance index (ϕ) was computed to be 5.40. Tor putitora commercial catches in the Tehri dam reservoir were dominated by length groups of 360-399 and 320-359 mm. Different growth parameters were estimated using length-frequency data as L∞ = 987.00 mm, K = 0.26 yr-1, and t0 = -0.0003 years. Z, M, and F mortality coefficients were estimated to be 1.01, 0.27, and 0.73, respectively. At the end of the first, second, third, fourth, fifth, sixth, and seventh years, the fish measured 226, 400, 535, 638, 718, and 780, and 827, respectively. The estimated value of the exploitation rate (E) was 0.73 using the length-converted catch curve approach, which was determined to be somewhat higher than the optimum value (0.50). Tor putitora recruitment patterns from the Tehri dam reservoir reveal that the species only has one recruitment pattern every year, and that solely occurs from June to September. The current exploitation level (0.73) has already exceeded the maximum fishing pressure (Emax = 0.508), indicating that there is a decline in the catch at the current fishing pressure, and a further increase in fishing efforts may lead to a decline in the stock, which may be detrimental to the sustainable fishery of Tor putitora in the Tehri dam reservoir, Uttarakhand, India.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Mitigating greenhouse gas emissions from irrigated rice cultivation through improved fertilizer and water management.

Greenhouse gas (GHG) emissions from agriculture sector play an important role for global warming and climate change. Thus, it is necessary to find out GHG emissions mitigation strategies from rice cultivation. The efficient management of nitrogen fertilizer using urea deep placement (UDP) and the use of the water-saving alternate wetting and drying (AWD) irrigation could mitigate greenhouse gas (GHG) emissions and reduce environmental pollution. However, there is a dearth of studies on the impacts of UDP and the integrated plant nutrient system (IPNS) which combines poultry manure and prilled urea (PU) with different irrigation regimes on GHG emissions, nitrogen use efficiency (NUE) and rice yields. We conducted field experiments during the dry seasons of 2018, 2019, and 2020 to compare the effects of four fertilizer treatments including control (no N), PU, UDP, and IPNS in combination with two irrigation systems- (AWD and continuous flooding, CF) on GHG emissions, NUE and rice yield. Fertilizer treatments had significant (p < 0.05) interaction effects with irrigation regimes on methane (CH4) and nitrous oxide (N2O) emissions. PU reduced CH4 and N2O emissions by 6% and 20% compared to IPNS treatment, respectively under AWD irrigation, but produced similar emissions under CF irrigation. Similarly, UDP reduced cumulative CH4 emissions by 9% and 15% under AWD irrigation, and 9% and 11% under CF condition compared to PU and IPNS treatments, respectively. Across the year and fertilizer treatments, AWD irrigation significantly (p < 0.05) reduced cumulative CH4 emissions and GHG intensity by 28%, and 26%, respectively without significant yield loss compared to CF condition. Although AWD irrigation increased cumulative N2O emissions by 73%, it reduced the total global warming potential by 27% compared to CF irrigation. The CH4 emission factor for AWD was lower (1.67 kg ha-1 day-1) compared to CF (2.33 kg ha-1 day-1). Across the irrigation regimes, UDP increased rice yield by 21% and N recovery efficiency by 58% compared to PU. These results suggest that both UDP and AWD irrigation might be considered as a carbon-friendly technology.

Fungal diversity notes 1387-1511: taxonomic and phylogenetic contributions on genera and species of fungal taxa.

This article is the 13th contribution in the Fungal Diversity Notes series, wherein 125 taxa from four phyla, ten classes, 31 orders, 69 families, 92 genera and three genera incertae sedis are treated, demonstrating worldwide and geographic distribution. Fungal taxa described and illustrated in the present study include three new genera, 69 new species, one new combination, one reference specimen and 51 new records on new hosts and new geographical distributions. Three new genera, Cylindrotorula (Torulaceae), Scolecoleotia (Leotiales genus incertae sedis) and Xenovaginatispora (Lindomycetaceae) are introduced based on distinct phylogenetic lineages and unique morphologies. Newly described species are Aspergillus lannaensis, Cercophora dulciaquae, Cladophialophora aquatica, Coprinellus punjabensis, Cortinarius alutarius, C. mammillatus, C. quercoflocculosus, Coryneum fagi, Cruentomycena uttarakhandina, Cryptocoryneum rosae, Cyathus uniperidiolus, Cylindrotorula indica, Diaporthe chamaeropicola, Didymella azollae, Diplodia alanphillipsii, Dothiora coronicola, Efibula rodriguezarmasiae, Erysiphe salicicola, Fusarium queenslandicum, Geastrum gorgonicum, G. hansagiense, Helicosporium sexualis, Helminthosporium chiangraiensis, Hongkongmyces kokensis, Hydrophilomyces hydraenae, Hygrocybe boertmannii, Hyphoderma australosetigerum, Hyphodontia yunnanensis, Khaleijomyces umikazeana, Laboulbenia divisa, Laboulbenia triarthronis, Laccaria populina, Lactarius pallidozonarius, Lepidosphaeria strobelii, Longipedicellata megafusiformis, Lophiotrema lincangensis, Marasmius benghalensis, M. jinfoshanensis, M. subtropicus, Mariannaea camelliae, Melanographium smilaxii, Microbotryum polycnemoides, Mimeomyces digitatus, Minutisphaera thailandensis, Mortierella solitaria, Mucor harpali, Nigrograna jinghongensis, Odontia huanrenensis, O. parvispina, Paraconiothyrium ajrekarii, Parafuscosporella niloticus, Phaeocytostroma yomensis, Phaeoisaria synnematicus, Phanerochaete hainanensis, Pleopunctum thailandicum, Pleurotheciella dimorphospora, Pseudochaetosphaeronema chiangraiense, Pseudodactylaria albicolonia, Rhexoacrodictys nigrospora, Russula paravioleipes, Scolecoleotia eriocamporesi, Seriascoma honghense, Synandromyces makranczyi, Thyridaria aureobrunnea, Torula lancangjiangensis, Tubeufia longihelicospora, Wicklowia fusiformispora, Xenovaginatispora phichaiensis and Xylaria apiospora. One new combination, Pseudobactrodesmium stilboideus is proposed. A reference specimen of Comoclathris permunda is designated. New host or distribution records are provided for Acrocalymma fici, Aliquandostipite khaoyaiensis, Camarosporidiella laburni, Canalisporium caribense, Chaetoscutula juniperi, Chlorophyllum demangei, C. globosum, C. hortense, Cladophialophora abundans, Dendryphion hydei, Diaporthe foeniculina, D. pseudophoenicicola, D. pyracanthae, Dictyosporium pandanicola, Dyfrolomyces distoseptatus, Ernakulamia tanakae, Eutypa flavovirens, E. lata, Favolus septatus, Fusarium atrovinosum, F. clavum, Helicosporium luteosporum, Hermatomyces nabanheensis, Hermatomyces sphaericoides, Longipedicellata aquatica, Lophiostoma caudata, L. clematidis-vitalbae, Lophiotrema hydei, L. neoarundinaria, Marasmiellus palmivorus, Megacapitula villosa, Micropsalliota globocystis, M. gracilis, Montagnula thailandica, Neohelicosporium irregulare, N. parisporum, Paradictyoarthrinium diffractum, Phaeoisaria aquatica, Poaceascoma taiwanense, Saproamanita manicata, Spegazzinia camelliae, Submersispora variabilis, Thyronectria caudata, T. mackenziei, Tubeufia chiangmaiensis, T. roseohelicospora, Vaginatispora nypae, Wicklowia submersa, Xanthagaricus necopinatus and Xylaria haemorrhoidalis. The data presented herein are based on morphological examination of fresh specimens, coupled with analysis of phylogenetic sequence data to better integrate taxa into appropriate taxonomic ranks and infer their evolutionary relationships.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

In Vitro Cytotoxicity and Interaction of Noscapine with Human Serum Albumin: Effect on Structure and Esterase Activity of HSA.

Noscapine is effective to inhibit cellular proliferation and induced apoptosis in nonsmall cell, lung, breast, lymphoma, and prostate cancer. It also shows good efficiency to skin cancer cells. In the current work, we studied the mechanism of interaction between the anticancer drug noscapine (NOS) and carrier protein human serum albumin (HSA) by using a variety of spectroscopic techniques (fluorescence spectroscopy, time-resolved fluorescence, UV-visible, fluorescence resonance energy transfer (FRET), Fourier transform infrared (FTIR), and circular dichroism (CD) spectroscopy), electrochemistry (cyclic voltammetry), and computational methods (molecular docking and molecular dynamic simulation). The steady-state fluorescence results showed that fluorescence intensity of HSA decreased in the presence of NOS via a static quenching mechanism, which involves ground state complex formation between NOS and HSA. UV-visible and FRET results also supported the fluorescence result. The corresponding thermodynamic result shows that binding of NOS with HSA is exothermic in nature, involving electrostatic interactions as major binding forces. The binding results were further confirmed through a cyclic voltammetry approach. The FRET result signifies the energy transfer from Trp214 of HSA to the NOS. Molecular site marker, molecular docking, and MD simulation results indicated that the principal binding site of HSA for NOS is site I. Synchronous fluorescence spectra, FTIR, 3D fluorescence, CD spectra, and MD simulation results reveal that NOS induced the structural change in HSA. In addition, the MTT assay study on a human skin cancer cell line (A-431) was also performed for NOS, which shows that NOS induced 80% cell death of the population at a 320 µM concentration. Moreover, the esterase-like activity of HSA with NOS was also done to determine the variation in protein functionality after binding with NOS.

Dynamics of Ionic Liquid-Assisted Refolding of Denatured Cytochrome c: A Study of Preferential Interactions toward Renaturation.

In vitro refolding of denatured protein and the influence of the alkyl chain on the refolding of a protein were tested using long chain imidazolium chloride salts, 1-methyl-3-octylimidazolium chloride [C8mim][Cl], and 1-decyl-3-methylimidazolium chloride [C10mim][Cl]. The horse heart cytochrome c (h-cyt c) was denatured by urea and guanidinium hydrochloride (GdnHCl), as well as by base-induced denaturation at pH 13, to provide a broad overview of the overall refolding behavior. The variation in the alkyl chain of the ionic liquids (ILs) showed a profound effect on the refolding of denatured h-cyt c. The ligand-induced refolding was correlated to understand the mechanism of the conformational stability of proteins in aqueous solutions of ILs. The results showed that the long chain ILs having the [C8mim]+ and [C10mim]+ cations promote the refolding of alkali-denatured h-cyt c. The IL having the [C10mim]+ cation efficiently refolded the alkali-denatured h-cyt c with the formation of the MG state, whereas the IL having the [C8mim]+ cation, which is known to be compatible for protein stability, shows slight refolding and forms a different transition state. The lifetime results show successful refolding of alkaline-denatured h-cyt c by both of the ILs, however, more refolding was observed in the case of [C10mim][Cl], and this was correlated with the fast and medium lifetimes (τ1 and τ2) obtained, which show an increase accompanied by an increase in secondary structure. The hydrophobic interactions plays an important role in the refolding of chemically and alkali-denatured h-cyt c by long chain imidazolium ILs. The formation of the MG state by [C10mim][Cl] was also confirmed, as some regular structure exists far below the CMC of IL. The overall results suggested that the [C10mim]+ cation bound to the unfolded h-cyt c triggers its refolding by electrostatic and hydrophobic interactions that stabilize the MG state.

Feasibility study of a space-based high pulse energy 2 µm CO2 IPDA lidar.

Sustained high-quality column carbon dioxide (CO2) atmospheric measurements from space are required to improve estimates of regional and continental-scale sources and sinks of CO2. Modeling of a space-based 2 µm, high pulse energy, triple-pulse, direct detection integrated path differential absorption (IPDA) lidar was conducted to demonstrate CO2 measurement capability and to evaluate random and systematic errors. Parameters based on recent technology developments in the 2 µm laser and state-of-the-art HgCdTe (MCT) electron-initiated avalanche photodiode (e-APD) detection system were incorporated in this model. Strong absorption features of CO2 in the 2 µm region, which allows optimum lower tropospheric and near surface measurements, were used to project simultaneous measurements using two independent altitude-dependent weighting functions with the triple-pulse IPDA. Analysis of measurements over a variety of atmospheric and aerosol models using a variety of Earth's surface target and aerosol loading conditions were conducted. Water vapor (H2O) influences on CO2 measurements were assessed, including molecular interference, dry-air estimate, and line broadening. Projected performance shows a <0.35 ppm precision and a <0.3 ppm bias in low-tropospheric weighted measurements related to column CO2 optical depth for the space-based IPDA using 10 s signal averaging over the Railroad Valley (RRV) reference surface under clear and thin cloud conditions.

Double-pulse 2-µm integrated path differential absorption lidar airborne validation for atmospheric carbon dioxide measurement.

Field experiments were conducted to test and evaluate the initial atmospheric carbon dioxide (CO2) measurement capability of airborne, high-energy, double-pulsed, 2-µm integrated path differential absorption (IPDA) lidar. This IPDA was designed, integrated, and operated at the NASA Langley Research Center on-board the NASA B-200 aircraft. The IPDA was tuned to the CO2 strong absorption line at 2050.9670 nm, which is the optimum for lower tropospheric weighted column measurements. Flights were conducted over land and ocean under different conditions. The first validation experiments of the IPDA for atmospheric CO2 remote sensing, focusing on low surface reflectivity oceanic surface returns during full day background conditions, are presented. In these experiments, the IPDA measurements were validated by comparison to airborne flask air-sampling measurements conducted by the NOAA Earth System Research Laboratory. IPDA performance modeling was conducted to evaluate measurement sensitivity and bias errors. The IPDA signals and their variation with altitude compare well with predicted model results. In addition, off-off-line testing was conducted, with fixed instrument settings, to evaluate the IPDA systematic and random errors. Analysis shows an altitude-independent differential optical depth offset of 0.0769. Optical depth measurement uncertainty of 0.0918 compares well with the predicted value of 0.0761. IPDA CO2 column measurement compares well with model-driven, near-simultaneous air-sampling measurements from the NOAA aircraft at different altitudes. With a 10-s shot average, CO2 differential optical depth measurement of 1.0054±0.0103 was retrieved from a 6-km altitude and a 4-GHz on-line operation. As compared to CO2 weighted-average column dry-air volume mixing ratio of 404.08 ppm, derived from air sampling, IPDA measurement resulted in a value of 405.22±4.15 ppm with 1.02% uncertainty and 0.28% additional bias. Sensitivity analysis of environmental systematic errors correlates the additional bias to water vapor. IPDA ranging resulted in a measurement uncertainty of <3 m.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

Molecular investigation of the interaction between ionic liquid type gemini surfactant and lysozyme: A spectroscopic and computational approach.

Herein, we are reporting the interaction of ionic liquid type gemini surfactant, 1,4-bis(3-dodecylimidazolium-1-yl) butane bromide ([C12-4-C12 im]Br2) with lysozyme by using Steady state fluorescence, UV-visible, Time resolved fluorescence, Fourier transform-infrared (FT-IR) spectroscopy techniques in combination with molecular modeling and docking method. The steady state fluorescence spectra suggested that the fluorescence of lysozyme was quenched by [C12-4-C12 im]Br2 through static quenching mechanism as confirmed by time resolved fluorescence spectroscopy. The binding constant for lysozyme-[C12-4-C12 im]Br2 interaction have been measured by UV-visible spectroscopy and found to be 2.541 × 10(5) M(-1). The FT-IR results show conformational changes in the secondary structure of lysozyme by the addition of [C12-4-C12 im]Br2. Moreover, the molecular docking study suggested that hydrogen bonding and hydrophobic interactions play a key role in the protein-surfactant binding. Additionally, the molecular dynamic simulation results revealed that the lysozyme-[C12-4-C12 im]Br2 complex reaches an equilibrium state at around 3 ns.

Self-calibration and laser energy monitor validations for a double-pulsed 2-µm CO2 integrated path differential absorption lidar application.

Double-pulsed 2-µm integrated path differential absorption (IPDA) lidar is well suited for atmospheric CO2 remote sensing. The IPDA lidar technique relies on wavelength differentiation between strong and weak absorbing features of the gas normalized to the transmitted energy. In the double-pulse case, each shot of the transmitter produces two successive laser pulses separated by a short interval. Calibration of the transmitted pulse energies is required for accurate CO2 measurement. Design and calibration of a 2-µm double-pulse laser energy monitor is presented. The design is based on an InGaAs pin quantum detector. A high-speed photoelectromagnetic quantum detector was used for laser-pulse profile verification. Both quantum detectors were calibrated using a reference pyroelectric thermal detector. Calibration included comparing the three detection technologies in the single-pulsed mode, then comparing the quantum detectors in the double-pulsed mode. In addition, a self-calibration feature of the 2-µm IPDA lidar is presented. This feature allows one to monitor the transmitted laser energy, through residual scattering, with a single detection channel. This reduces the CO2 measurement uncertainty. IPDA lidar ground validation for CO2 measurement is presented for both calibrated energy monitor and self-calibration options. The calibrated energy monitor resulted in a lower CO2 measurement bias, while self-calibration resulted in a better CO2 temporal profiling when compared to the in situ sensor.

Evaluation of an airborne triple-pulsed 2 µm IPDA lidar for simultaneous and independent atmospheric water vapor and carbon dioxide measurements.

Water vapor and carbon dioxide are the most dominant greenhouse gases directly contributing to the Earth's radiation budget and global warming. A performance evaluation of an airborne triple-pulsed integrated path differential absorption (IPDA) lidar system for simultaneous and independent monitoring of atmospheric water vapor and carbon dioxide column amounts is presented. This system leverages a state-of-the-art Ho:Tm:YLF triple-pulse laser transmitter operating at 2.05 µm wavelength. The transmitter provides wavelength tuning and locking capabilities for each pulse. The IPDA lidar system leverages a low risk and technologically mature receiver system based on InGaAs pin detectors. Measurement methodology and wavelength setting are discussed. The IPDA lidar return signals and error budget are analyzed for airborne operation on-board the NASA B-200. Results indicate that the IPDA lidar system is capable of measuring water vapor and carbon dioxide differential optical depth with 0.5% and 0.2% accuracy, respectively, from an altitude of 8 km to the surface and with 10 s averaging. Provided availability of meteorological data, in terms of temperature, pressure, and relative humidity vertical profiles, the differential optical depth conversion into weighted-average column dry-air volume-mixing ratio is also presented.

Spectroscopic and molecular modelling analysis of the interaction between ethane-1,2-diyl bis(N,N-dimethyl-N-hexadecylammoniumacetoxy)dichloride and bovine serum albumin.

Several spectroscopic approaches namely fluorescence, time-resolved fluorescence, UV-visible, and Fourier transform infra-red (FT-IR) spectroscopy were employed to examine the interaction between ethane-1,2-diyl bis(N,N-dimethyl-N-hexadecylammoniumacetoxy)dichloride (16-E2-16) and bovine serum albumin (BSA). Fluorescence studies revealed that 16-E2-16 quenched the BSA fluorescence through a static quenching mechanism, which was further confirmed by UV-visible and time-resolved fluorescence spectroscopy. In addition, the binding constant and the number of binding sites were also calculated. The thermodynamic parameters at different temperatures (298 K, 303 K, 308 K and 313 K) indicated that 16-E2-16 binding to BSA is entropy driven and that the major driving forces are electrostatic interactions. Decrease of the α-helix from 53.90 to 46.20% with an increase in random structure from 22.56 to 30.61% were also observed by FT-IR. Furthermore, the molecular docking results revealed that 16-E2-16 binds predominantly by electrostatic and hydrophobic forces to some residues in the BSA sub-domains IIA and IIIA.