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With the ever-increasing need to manage and conserve the ecosystem, as well as the growing business potential for tourism, it is becoming increasingly important to address and align these two domains. Ecotourism enters the scene to provide a solution for anthropogenic interruptions at ecological tourist sites, with its potential to provide sustainable maintenance and development of both the environment and the local communities closely related with natural ecosystems. Tourism is one of the most valuable industries in India and the world, accounting for a considerable portion of most countries' economies. Chamoli is one of the tourist hotspots districts in India. Along with being an ecologically rich zone residing in the Himalayas, it possesses some of the highest peaks and national parks like Nanda Devi and valley of flowers. The study area is also prone to various natural disasters like floods, earthquakes, landslides, and the recent one being the rishi-ganga landslide of February 2021. The use of GIS tools in conjunction with AHP allows for a more streamlined and holistic approach to making scientifically calculated conclusions. We attempted to establish the prospective zones of ecotourism sites in our study region by considering a variety of factors that influence those sites of sustainable zones, such as slope, topographic roughness, elevation, road closeness, river proximity, and proximity to a protected area. The study area's data and information were geospatially analysed to build an ecotourism potential map that can be used as a guide for planning sustainable resource management and development operations in the Chamoli district. Keywords: - Ecotourism (ET), Analytical Hierarchy Process (AHP), Site-Suitability, Chamoli.

Background: The PI3K inhibition has been an appealing approach for anticancer therapy due to its crucial role in cell growth, proliferation, and survival in various cancers. Emerging data suggest the PI3K pathway is also involved in DNA replication and genome stability, making DNA damage response (DDR) inhibitors as an attractive combination treatment for PI3K pathway blockades. Here, to enhance the efficacy of PI3K inhibitors, we investigated the novel approach combining PI3K inhibitors (alpelisib, AZD8835, AZD8186, NVP-BKM120) with DDR blockade using ATR inhibitor (AZD6738) in breast cancer preclinical models. Methods: Hormone receptor-positive breast cancer cells, MCF7 (PIK3CA E545K mutation) and T47D (PIK3CA H1047R mutation) were used. First, cell viability and the synergistic effect of combination drug was carried out by MTT assy. Next, western blot, cell cycle analysis, apoptosis assay, and immunocytochemistry were conducted to validate the hypothesis. Finally, tumor xenograft experiments were conducted using BALB/c nude mice to validate the in vitro data. Results: A synergistic antiproliferative effect was observed over a wide range of combination (PI3K inhibitor + ATR inhibitor) concentrations (even in a lower concentration as 1/10 x IC50) in vitro, suggesting that when alpelisib is combined with an ATR inhibitor, there may be possibility of lowering the effective dosage of alpelisib. After confirmation of the strong synergistic effect in sub-G1/G0-G1 arrest-mediated apoptosis by the combination treatment, we demonstrated that expressions of phospho ATR, ATM, and CHK1 were suppressed but -H2AX markedly increased by combination treatment as compared to PI3K inhibitors and ATR inhibitor alone. This suggests that combination treatment activates apoptotic pathway through enhanced DNA double strand breaks. Finally, the in vivo study showed that the combination treatment group tended to show a greater tumor growth inhibition compared with PI3K inhibitor or ATR inhibitor alone. Conclusion: The combined inhibition of PI3K and ATR showed a synergistic anticancer effect in vitro and in vivo. ATR inhibitor in combination with PI3K inhibitor merits further clinical investigation to enhance the activity of PI3K inhibitor for the treatment of PIK3CA mutated breast cancer patients. Citation Format: Yong Wha Moon, Mithun Gosh, Nahee Park, Kamal Pandey, Nar Bahadur Katwal, Sa Deok Hong. Synergistic activity of PI3K inhibitor in combination with AZD6738, ATR inhibitor in breast cancer preclinical model via DNA damage response pathway [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-26-09.

To evaluate the feasibility of syngeneic mouse models of breast cancer by analyzing the efficacy of immune checkpoint inhibitors (ICIs) and potential predictive biomarkers.To establish the murine triple-negative breast cancer (TNBC) models, JC, 4T1, EMT6, and E0771 cells were subcutaneously implanted into female syngeneic mice. When the tumor reached 50-100 mmThe EMT6 model showed the highest tumor response rate (54%, 6/11) of the syngeneic models: 4T1 (45%, 5/11), JC (40%, 4/10), or E0771 (23%, 3/13). Early changes in tumor size at 7 days post-PD-1 inhibitor treatment predicted the final efficacy of the PD-1 inhibitor. Peripheral blood CD8+ and CD4+ T cells with or without Ki67 expression at 7 days post-PD-1 inhibitor treatment were higher in the finally designated responder group than in the non-responder group. At the time of sacrifice, analyses of tumor infiltrating lymphocytes consistently supported these results. We also demonstrated that retro-orbital blood sampling procedures (baseline, 7 days post-treatment, time of sacrifice) were safe for serum cytokine analyses, suggesting that our preclinical platform may be used for biomarker research using serum cytokines.Our syngeneic mouse model of TNBC is a feasible preclinical platform to evaluate ICI efficacy combined with other drugs and predictive biomarkers in the screening process of immune-oncology drug development.

Purpose Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a complex and challenging task, especially when energy demands are exponentially rising. The purpose of this paper is to review the relevant literature on indoor temperature forecasting in the past two decades and draw inferences on important methodologies with influencing variables and offer future directions. Design/methodology/approach The motivation for this work is based on the research work done in the field of intelligent buildings and energy related sector. The focus of this study is based on past literature on forecasting models and methodologies related to IRT forecasting for building energy management, with an emphasis on data-driven models (statistical and machine learning models). The methodology adopted here includes review of several journals, conference papers, reference books and PhD theses. Selected forecasting methodologies have been reviewed for indoor temperature forecasting contributing to building energy consumption. The models reviewed here have been earmarked for their benefits, limitations, location of study, accuracy along with the identification of influencing variables. Findings The findings are based on 62 studies where certain accuracy metrics and influencing explanatory variables have been reviewed. Linear models have been found to show explanatory relationships between the variables. Nonlinear models are found to have better accuracy than linear models. Moreover, IRT profiles can be modeled with enhanced accuracy and generalizability through hybrid models. Although deep learning models are found to have better performance for this study. Research limitations/implications This is accuracy-based study of data-driven models. Their run-time performance and cost implications review and review of physical, thermal and simulation models is future scope. Originality/value Despite the earlier work conducted in this field, there is a lack of organized and comprehensive evaluation of peer reviewed forecasting methodologies. Indoor temperature depends on various influencing explanatory variables which poses a research challenge for researchers to develop suitable predictive model. This paper presents a critical review of selected forecasting methodologies and provides a list of important methodologies along with influencing variables, which can help future researchers in the field of building energy management sector. The forecasting methods presented here can help to determine appropriate heating, ventilation and air-conditioning systems for buildings.

Carbon-based nanomaterials (CBNs) are often used for potential agricultural applications. Since CBNs applied to plants can easily enter plant organs and reach the human diet, the consequences of the introduction of CBNs into the food chain need to be investigated. We created a platform for a comprehensive investigation of the possible health risks of multiwalled carbon nanotubes (CNTs) accumulated in the organs of exposed tomato plants. Quantification and visualization of CNTs absorbed by plant organs were determined by microwave-induced heating (MIH) and radio frequency (RF) heating methods. Feeding mice with CNT-contaminated tomatoes showed an absence of toxicity for all assessed animal organs. The amount of CNTs accumulated inside the organs of mice fed with CNT-containing fruits was assessed by an RF heating technique and was found to be negligible. Our work provides the experimental evidence that the amount of CNTs accumulated in plant organs as a result of nanofertilization is not sufficient to induce toxicity in mice.

This study compares the utility of multifrequency SAR and Optical multispectral data for land-cover classification of Mumbai city and its nearby regions with a special focus on water body mapping. The L-band ALOS-2 PALSAR-2, X-band TerraSAR-X, C-band RISAT-1, and Sentinel-2 datasets have been used in this work. This work is done as a retrospective study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission. The ALOS-2 PALSAR-2 data has been pre-processed before the implementation of machine learning algorithms for image segmentation. Multi-looking is performed on ALOS-2 PALSAR-2 data to generate square pixels of size 5.78 m and then target decomposition is applied to generate a false-color composite RGB image. While in the case of TerraSAR-X and RISAT-1 datasets, no multi-looking was performed and direct target decomposition was applied to generate false-color composite RGB images. Similarly, for the optical dataset that has a resolution of 10 m, a true color composite, and a false color composite RGB image are generated. For the comparative study between ALOS-2 PALSAR-2 and Sentinel-2 dataset, the RGB images are divided into smaller chunks of size 500*500 pixels each to create a training and testing dataset. Ten image patches were taken from the large dataset, out of which eight patches were used to train the machine learning models Random Forest (RF), K Nearest Neighbor (KNN), and Support Vector Machine (SVM), and two patches were kept for testing and validation purpose. For training the machine learning models, feature vectors are generated using the Gabor filter, Scharr filter, Gaussian filter, and Median filter. For patch 1, the mIOU for true-color composite based Optical image varies from 0.2323 to 0.2866 with the RF classifier performing the best and the mIOU for false-color composite based Optical image varies from 0.4130 to 0.4941 with the RF classifier performing the best while for ALOS-2 PALSAR-2 data, the mIOU varies from 0.4033 to 0.4663 with the RF classifier outperforming the KNN and the SVM classifiers. For patch 2, the mIOU for true-color composite based Optical data varies from 0.3451 to 0.4517 with KNN performing the best and the mIOU for false-color composite based Optical image varies from 0.5156 to 0.5832 with the RF classifier performing the best while for ALOS-2 PALSAR-2 data, the mIOU varies from 0.4600 to 0.5178 with the RF classifier outperforming the KNN and the SVM classifiers. The gap between the performance of ALOS-2 PALSAR-2 data and Sentinel-2 optical data is observed when the IOU of water class is compared, with IOUw for the true-color composite based optical image at a maximum of 0.2525 and for false-color composite based optical image at a maximum of 0.7366 while for ALOS-2 PALSAR-2 data a maximum IOUw of 0.7948 is achieved. The better performance of SAR data as compared to true-color composite based optical image data is due to the misclassification of ground and water classes into urban and forest in the case of the true-color composite based optical dataset which can be attributed to the high similarity between water and forest classes in the case of true-color composite based optical data whereas both these classes are easily separable in case of SAR data. This issue is however resolved by using the false color composite based optical image dataset for the classification task which performs slightly better than ALOS-2 PALSAR-2 data in the overall classification task. However, the SAR data works best in water body detection as notable from the high IOU for water class in the case of SAR data. In addition to the comparative analysis between Sentinel-2 optical and ALOS-2 PALSAR-2 data, land-cover classification has been performed on X-band TerraSAR-X and C-band RISAT-1 data on a single patch and it has been found that the RF classifier performs the best, recording the mIOU 0.5815 for X-band TerraSAR-X data, mIOU of 0.4031 for the C-band RISAT-1 data, and mIOU of 0.6153 for the L-band ALOS-2 data.

Multispectral remote sensing data is available with broad spectral bands in the wavelength range of Visible-NIR-SWIR, and finds its applications in assessment of Land Use Land Cover classes (LULC). There are plenty of onboard sensors which are providing these datasets regularly. However, for detailed LULC classification there is a requirement of distinguishing spectrally similar land cover features, which is possible through hyperspectral data. These datasets are spectrally rich and are available with narrow spectral bandwidth but the availability of such datasets is limited. This is due to requirement of sophisticated detectors and high storage, which makes the acquisition of these datasets challenging. Therefore, there is a opportunity to utilize the available multispectral data for generating hyperspectral data. Existing spectral reconstruction technique is used for simulating hyperspectral bands from multispectral bands in combination with ground spectra. In this study, an attempt has been made to develop a software solution for automating the simulation of hyperspectral data using multispectral data in an open source platform. The developed software solution (HyDAS) consists of modules for satellite data visualization, spectral library creation, spectral unmixing and hyperspectral data simulation. HyDAS toolbox is successfully tested for simulating hyperspectral data from EO-1 ALI multispectral data. The simulated results were validated using the available EO-1 Hyperion data and results were satisfactory with similarity being 80–90%. This open source solution was also tested for simulating hyperspectral bands from EO-1 ALI and Indian Satellite (LISS III & LISS IV) multispectral datasets, the results obtained were satisfactory for species level classification

In this paper, we investigate a commercial nematic liquid crystal (LC) mixture namely E7 dispersed with small concentrations of multi-walled carbon nanotubes (MWCNTs). The dielectric and electro-optical characterizations have been carried out in the homogeneously and vertically aligned LC cells. The electro-optical response of LC molecules has been enhanced by 60% after the addition of MWCNTs, which is attributed to the reduced rotational viscosity in the composites. MWCNTs act like barricades for ionic impurities by reducing them up to ∼34.3% within the dispersion limit of 0.05 wt%. The nematic–isotropic phase transition temperature (TNI) of the E7 LC has also been shifted towards the higher temperature, resulting in a more ordered nematic phase. The enhanced birefringence and orientational order parameter in the LC-MWCNTs are attributed to π-π electron stacking between the LC molecules and the MWCNTs. The outlined merits of the LC-MWCNTs composites evince their suitability for ultrafast nematic-based electro-optical devices.

Purpose To evaluate the feasibility of syngeneic mouse models of breast cancer by analyzing the efficacy of immune checkpoint inhibitors (ICIs) and potential predictive biomarkers. Methods Four syngeneic mouse models of triple-negative breast cancer (TNBC) JC, 4T1, EMT6 and E0771 cells were injected subcutaneously. When the tumor reached 50–100 mm3, each mouse model was divided into treatment (murine PD-1 antibody) and no-treatment control. Treatment group is further divided into the responder and nonresponder groups. Potential predictive biomarkers were evaluated by analyzing serum cytokines, peripheral blood T cells and tumor infiltrating immune cells. Results The JC model showed the highest tumor response rate (40%, 4/10) of syngeneic models: 4T1 (36%, 4/11), EMT6 (36%, 4/11), or E0771 model (23%, 3/13). Early change of tumor size at 7 days post PD-1 inhibitor treatment predicted the final efficacy of PD-1 inhibitor. Peripheral blood CD8 + and CD4 + T cells with or without Ki67 expression at 7 days post-PD-1 inhibitor treatment were higher in the finally designated responder group than in the nonresponder group. At the time of sacrifice, analyses of tumor infiltrating lymphocytes consistently supported these results. Furthermore, serum IFN-γ at 7 days post-PD-1 inhibitor treatment was also higher in responders than in nonresponders, suggesting that early changes of these markers could be predictive biomarkers of the final efficacy of ICIs. Conclusions Our syngeneic mouse model of TNBC is a feasible preclinical platform to evaluate ICI efficacies combined with other drugs and predictive biomarkers in the screening period of immune-oncology drug development.

Distance learning is the process of creating an educational experience of equal qualitative value for the learner to best suit their needs outside the classroom. Distance Learning Program (DLP) offered by Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), has witnessed steady growth in the last thirteen years benefitted more than 2.65 lakh participants from 2650 network institutes distributed across the country. Under this program, IIRS has successfully established a network of academic and professional Institutions in the country. The Covid-19 pandemic has affected higher education severely. India was under complete lockdown during April and May 2020. In this scenario, to ensure uninterrupted and safe access to learning, IIRS has responded to the crisis by tapping into its existing DLP. During the lockdown and post-lockdown period, IIRS conducted 11 Live and Interactive course/workshop/webinars focusing on Basics and Advanced topics related to Geospatial technology. During this period, IIRS DLP witnessed an exponential growth in terms of number of participation, number of participating institutes and also the percentage of working professionals. IIRS DLP witnessed a fivefold growth in participation benefitting around 57,000 participants from across the country. The number of network institutes also grew by 50% during April–June 2020. Statistics indicate that during January–June IIRS outreach program witnessed 22-fold growth in number of professionals joining the courses. Also, the overall feedback of the course by participants was very encouraging. Even though there are times when nothing can replace the personal attention of a teacher, in the current situation the focus is on utilizing technology effectively for uninterrupted learning.

With progressive climate change and the associated increase in mean temperature, heat stress tolerance has emerged as one of the key traits in the product profile of the maize breeding pipeline for lowland tropics. The present study aims to identify the genomic regions associated with heat stress tolerance in tropical maize. An association mapping panel, called the heat tolerant association mapping (HTAM) panel, was constituted by involving a total of 543 tropical maize inbred lines from diverse genetic backgrounds, test-crossed and phenotyped across nine locations in South Asia under natural heat stress. The panel was genotyped using a genotyping-by-sequencing (GBS) platform. Considering the large variations in vapor pressure deficit (VPD) at high temperature (Tmax) across different phenotyping locations, genome-wide association study (GWAS) was conducted separately for each location. The individual location GWAS identified a total of 269 novel significant single nucleotide polymorphisms (SNPs) for grain yield under heat stress at a p value of –5. A total of 175 SNPs were found in 140 unique gene models implicated in various biological pathway responses to different abiotic stresses. Haplotype trend regression (HTR) analysis of the significant SNPs identified 26 haplotype blocks and 96 single SNP variants significant across one to five locations. The genomic regions identified based on GWAS and HTR analysis considering genomic region x environment interactions are useful for breeding efforts aimed at developing heat stress resilient maize cultivars for current and future climatic conditions through marker-assisted introgression into elite genetic backgrounds and/or genome-wide selection.

Urbanization is increasing with a faster pace in almost all the cities of India, which has somehow prompted the anthropogenic activities eventually influencing the environment in this climate change scenario. This has led to the more prominent urban heat island (UHI) effect in urban areas, significantly raising the surface temperature of urban built-up. To focus on this issue, a spatio-temporal analysis of UHI over the Jaipur site of Rajasthan has been performed using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and MODIS Normalized Difference Vegetation Index (NDVI) for 16 years i.e., from 2003 to 2018 wherein NDVI has been used to analyze the status of vegetation in the urban and non-urban area w.r.t. UHI phenomenon. Study of UHI phenomenon has been done by computing the difference of mean LST between urban area and urban periphery (at a buffer distance of 2 km from urban boundary) both for day and night time. The difference of LST day time observed between urban and non-urban (buffer) is 0.06 °C in May, 2003 and 0.51 °C in May, 2018 whereas, difference of LST night time between urban and area surrounding to urban is 0.58 °C in May, 2003 and 1.41 °C in May, 2018. From the quantitative analysis of land use land cover it has been observed that there is a rapid increase of urban area from 63% in 2003 to 69% in 2018 and vegetation cover decreased from 36% in 2003 to 31% in 2018. The percentage decrease of vegetation cover from 2003 to 2018 is 5% and the percentage increase in urban area is 6% which is still continuing at an even faster rate. Data preparation tasks can be greatly minimized by using analysis ready open data available in the public domain for carrying out such studies. The approach followed in the study will assist researchers to carry out a quick spatio-temporal analysis for the identification of areas susceptible to increasing variability of LST. Also, it provides a basis to understand and manage urban stress, one of the major causes of unalterable damages to the environment.

Satellite based water quality monitoring and assessment is a thrust area of research. Present study focuses on use of space observations and ground data for assessment of spatial pattern in water quality parameters of an urban water body in Gorakhpur city of Uttar Pradesh, India. Water quality parameters namely, pH, Total Dissolved Solid, Turbidity, Total Hardness, Dissolved Oxygen and Biological Oxygen Demand were measured from the spatially distributed samples collected from the lake. Multiple linear regression models were developed using Landsat-8 OLI data and water quality sampling data to estimate the spatial patterns. It was observed that all the water quality parameters are significantly correlated with the radiance values of the Landsat-8 OLI sensor. Results of the regression model indicate a good agreement between the measured and estimated value of all the water quality parameters i.e., 82%, 70%, 90%, 66%, 84% and 79% respectively. Also, water quality maps when validated with lab tested value showed 71%, 62%, 71%, 55%, 75% and 86% accuracy. This study provides an effective and quick approach for mapping and planning of surface water (Lake) in urban areas with acceptable level of accuracy.

“Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability. The current scenario in India reveals that the corporate and residential building structures are incorporating various self-sustainable techniques. Out of the multiple factors governing the comfort of smart buildings, indoor room temperature is an important one, since it drives the need of cooling or heating through controlling systems. Around one-third of total energy consumption of commercial buildings in India is attributed to Heating, Ventilation and Air Conditioning (HVAC) systems. Accurate prediction of indoor room temperature helps in creating an efficient equilibrium between energy consumption and comfort level of the building, thus providing opportunities for efficient decision making for energy optimization. Considering Indian climatic and geographical conditions, this paper proposes an efficient decision making approach using Bayesian Dynamic Models (BDM) for short-term indoor room temperature forecasting of a corporate building structure. The results obtained from Bayesian Dynamic linear model, using Expectation Maximization (EM) algorithm, have been compared to standard Auto Regressive Integrated Moving Average (ARIMA) model, and have been found to be more accurate. Forecasting of indoor room temperature is a highly nonlinear phenomenon, so to further improve the accuracy of the linear models, a hybrid modeling approach has been proposed. The inclusion of state-of-the-art nonlinear models such as Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) improves the forecasting accuracy of the linear models significantly. Results show that the hybrid model obtained using BDM and ANN is the best fit model.

Doping of ferric oxide (Fe2O3) nanoparticles in 4-octyloxy-4ʹ cyanobiphenyl (8OCB) liquid crystal compound embedded in sample cells formed by photolithographic technique. The dielectric spectroscop...

Most parts of the Asian tropics are hotspots of climate change effects and associated weather variabilities. One of the major challenges with climate change is the uncertainty and inter-annual variability in weather conditions as crops are frequently exposed to different weather extremes within the same season. Therefore, agricultural research must strive to develop new crop varieties with inbuilt resilience towards variable weather conditions rather than merely tolerance to individual stresses in a specific situation and/or at a specific crop stage. C4 crops are known for their wider adaptation to range of climatic conditions. However, recent climatic trends and associated variabilities seem to be challenging the threshold limit of wider adaptability of even C4 crops like maize. In collaboration with national programs and private sector partners in the region, CIMMYT-Asia maize program initiated research for development (R4D) projects largely focusing on saving achievable yields across range of variable environments by incorporating reasonable levels of tolerance/resistance to major abiotic and biotic stresses without compromising on grain yields under optimal growing conditions. By integrating novel breeding tools like - genomics, double haploid (DH) technology, precision phenotyping and reducing genotype × environment interaction effects, a new generation of maize germplasm with multiple stress tolerance that can grow well across variable weather conditions were developed. The new maize germplasm were targeted for stress-prone environments where maize is invariability exposed to a range of sub-optimal growing conditions, such as drought, heat, waterlogging and various virulent diseases. The overarching goal of the stress-resilient maize program has been to achieve yield potential with a downside risk reduction.

Background: Recently CDK4/6 inhibitors are being widely used to treat advanced HR-positive breast cancer. Despite promising clinical outcomes, almost all patients eventually acquire resistance to CDK4/6 inhibitors. Hence, understanding the mechanisms of acquired resistance to CDK4/6 inhibitors is crucial to develop alternate treatment strategy. Therefore, we screened genes associated with palbociclib resistance through genomics and transcriptomics in breast cancer preclinical models. Methods: We generated palbociclib-resistant cell lines, MCF7-PR and T47D-PR by exposing MCF7 and T47D cells to palbociclib for over 9 months. After confirming the acquired resistance through in vitro assays, we performed whole exome sequencing (WES) and mRNA microarray to compare genomic and transcriptomic landscape between palbociclib-sensitive and resistant cells. Real-time PCR was performed to confirm differentially expressed genes. Results: Microrray analysis revealed 651 differentially expressed genes (DEGs) (fold change ≥2) by comparing MCF7 vs. MCF7-PR cells. WES also revealed 107 mutated genes by comparing T47D vs. T47D-PR cells. Further, GO annotation of both the DEGs and mutated genes found deregulation of immune pathway commonly in MCF7-PR and T47D-PR (FDR Citation Format: Yong Wha Moon, Eunbyeol Lee, Sohyun Hwang, Kamal Pandey, Nahee Park, Jin Hur, Young Bin Cho, Seung Ki Kim, Seung Ah Lee, Hee-Jung An, Joohyuk Sohn. Deregulated immune pathway associated with palbociclib resistance in breast cancer preclinical models: Integrative analysis of genomics and transcriptomics [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-21-06.

Purpose The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment. Design/methodology/approach A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature. Findings ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs. Research limitations/implications The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability. Practical implications Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context. Social implications This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large. Originality/value A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.

Carbon-based nanomaterials (CBNs) such as carbon nanotubes (CNTs) and graphene can be beneficial to crops exposed to abiotic stresses such as drought and high salinity. Our findings suggest that the improvement observed in stressed crops treated with CBNs can be associated with CBN-induced restoration of gene expression. When subjected to salt stress, sorghum seedlings showed modified expression in 51 stress-related genes. The introduction of CNTs or graphene into the salty growth medium resulted in the restoration of the expression of 29 affected genes, resembling that of untreated sorghum seedlings. RNA-Seq approach allowed us to analyze the total gene expression of CBN-treated rice exposed to water-deficit stress and gene expression of CBN-treated tomato plants exposed to salt stress. The application of CNTs or graphene resulted in full or partial restoration of expression of 458 and 1620 genes, respectively, affected by water-deficit stress in rice. Similarly, CBN treatment of NaCl-exposed tomato seedlings led to full or partial restoration of 1639 and 1391 salt-affected transcripts, respectively. Of the genes with restored expression, many of them were identified as major stress-response genes and major transcriptional factors (aquaporins, dehydrins, and heat shock proteins/co-chaperons

Simple Summary Cyclin-dependent kinase (CDK) 4/6 inhibitors, in combination with endocrine therapies, are now the standard of care for patients with metastatic hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer. Despite the effectiveness of CDK4/6 inhibitors, acquired resistance occurs in almost all cases. We developed and used a palbociclib-resistant preclinical model and studied the overcoming strategies, using FDA-approved chemotherapy in combination with a CDK4/6 inhibitor. We demonstrated that sequential abemaciclib treatment following eribulin-enhanced anti-tumor activity in vitro and in vivo on the CDK4/6 inhibitor-resistant cells by more effectively inhibiting the G2/M cell cycle phase. The sequential combination of abemaciclib following eribulin could be an effective treatment strategy in overcoming resistance to CDK4/6 inhibitors in HR-positive breast cancer. Abstract Breast cancer remains a leading cancer burden among women worldwide. Acquired resistance of cyclin-dependent kinase (CDK) 4/6 inhibitors occurs in almost all hormone receptor (HR)-positive subtype cases, comprising 70% of breast cancers, although CDK4/6 inhibitors combined with endocrine therapy are highly effective. CDK4/6 inhibitors are not expected to cooperate with cytotoxic chemotherapy based on the basic cytotoxic chemotherapy mode of action that inhibits rapidly proliferating cells. The palbociclib-resistant preclinical model developed in the current study investigated whether the combination of abemaciclib, CDK4/6 inhibitor with eribulin, an antimitotic chemotherapy could be a strategy to overcome palbociclib-resistant HR-positive breast cancer. The current study demonstrated that sequential abemaciclib treatment following eribulin synergistically suppressed CDK4/6 inhibitor-resistant cells by inhibiting the G2/M cell cycle phase more effectively. The current study showed the significant association of the pole-like kinase 1 (PLK1) level and palbociclib resistance. Moreover, the cumulative PLK1 inhibition in the G2/M phase by each eribulin or abemaciclib proved to be a mechanism of the synergistic effect. The synergistic antitumor effect was also supported by in vivo study. The sequential combination of abemaciclib following eribulin merits further clinical trials to overcome resistance to CDK4/6 inhibitors in HR-positive breast cancer.

We propose here a new idea for quantum teleportation of superposed coherent state which is not only almost perfect, in principle, but also feasible experimentally. We use entangled resource $\sim |\alpha,\frac{\alpha}{\sqrt{2}}\rangle-|-\alpha,-\frac{\alpha}{\sqrt{2}}\rangle$ in contrast with the usual $\sim |\alpha,\alpha\rangle-|-\alpha,-\alpha \rangle$ (both states unnormalized). Bob receives state which is then superposition of the states $|\pm \frac{\alpha}{\sqrt{2}}\rangle$ . Bob mixes these with even or odd coherent states involving superposition of states $|\pm \frac{\alpha}{\sqrt{2}}\rangle$ to obtain a two-mode state which is one of $\sim |I,0\rangle \pm |0,I\rangle$, $|I\rangle$ being the information state. Bob then obtains the teleported information by using interaction of one of these modes in two cavities with resonant two-level atoms. This scheme results in average fidelity of $\simeq 0.95$ for $|\alpha|^2 \simeq 10$, which increases with $|\alpha|^2$ and tends to 1 asymptotically, varying as $1-\frac{\pi^2}{16|\alpha|^2}+\frac{\pi^2(\pi^2+8)}{256|\alpha|^4}$ for large values of $|\alpha|^2$., Comment: 12 pages, 5 figures

In this study, we have investigated the electrical and anchoring energy of ferroelectric liquid crystal (FLC) with the dispersion of SiO2 nanoparticles (NPs). Due to their higher average spontaneou...

Controlled quantum teleportation of superposed coherent states using GHZ entangled 3-mode coherent states is studied. Proposed scheme can be implemented experimentally using linear optical components such as a symmetric lossless beam splitter, two phase-shifters and two photon counters. Fidelity is found close to unity for appreciable mean number of photons in coherent states and is 0.99 for mean photon number equal to two., Comment: A preliminary version was presented at National Laser Symposium-26 held at BARC, Mumbai, in December 2017

Herein, we have discussed change in dielectric and optical properties due to dispersion of Strontium hardystonite (Sr-HT) in the pure nematic liquid crystal (LC). The two different (0.5% wt./wt. and 1.0% wt./wt.) concentration of Sr-HT are dispersed in the pure nematic LC. After the dispersion of Sr-HT we have observed faster response and lower threshold voltage in comparison to pure nematic LC. The higher value of dielectric anisotropy calculated from the dielectric measurement and it has been suggested that lower voltage is required for switching of nematic molecules. The faster response and low threshold voltage will be very beneficial for application point of view used in making flat panel displays and lower power consumption devices. The UV absorbance and optical transmittance is reduced for Sr-HT dispersed nematogen. Photoluminescence (PL) spectra suggested the shifting in emission peak for Sr-HT dispersed nematogen due to different molecular interaction taking place between nematic LC and Sr-HT molecules. In addition to this Fourier Transform Infrared Spectroscopy (FTIR) for pure and Sr-HT dispersed nematogen has also been done.

In present work, Cu:ZnO nanoparticles (NPs) used to disperse in pure nematic liquid crystal N-(4-methoxybenzylidene)-4-butylaniline (MBBA). Due to their larger dipole moment of NPs have stronger in...

We propose a scheme of controlled entanglement diversion of a bi-partite entangled coherent state using GHZ type entangled coherent state as a resource. The scheme involves only linear optical devices such as phase shifters, beam splitters and photon counters. Average fidelity of diversion is calculated and plotted. It is 2/3 for very small coherent amplitudes and approaches unity for appreciable coherent amplitude.

Deregulation of the cyclin D‐CDK4/6‐INK4‐RB pathway leading to uncontrolled cell proliferation, is frequently observed in breast cancer. Currently, three selective CDK4/6 inhibitors have been FDA approved: palbociclib, ribociclib and abemaciclib. Despite promising clinical outcomes, intrinsic or acquired resistance to CDK4/6 inhibitors has limited the success of these treatments; therefore, the development of various strategies to overcome this resistance is of great importance. We highlight the various mechanisms that are directly or indirectly responsible for resistance to CDK4/6 inhibitors, categorizing them into two broad groups; cell cycle‐specific mechanisms and cell cycle‐nonspecific mechanisms. Elucidation of the diverse mechanisms through which resistance to CDK4/6 inhibitors occurs, may aid in the design of novel therapeutic strategies to improve patient outcomes. This review summarizes the currently available knowledge regarding mechanisms of resistance to CDK4/6 inhibitors, and possible therapeutic strategies that may overcome this resistance as well.

Epithelial ovarian cancer remains the leading cause of mortality among all gynecologic malignancies owing to recurrence and ultimate development of chemotherapy resistance in the majority of patients. In the chemotherapy-resistant ovarian cancer preclinical model, we investigated whether AZD6738 (an ataxia telangiectasia and Rad3-related (ATR) inhibitor) could synergize with belotecan (a camptothecin analog and topoisomerase I inhibitor). In vitro, both chemotherapy-resistant and chemotherapy-sensitive ovarian cancer cell lines showed synergistic anti-proliferative activity with a combination treatment of belotecan and AZD6738. The combination also demonstrated synergistic tumor inhibition in mice with a chemotherapy-resistant cell line xenograft. Mechanistically, belotecan, a DNA-damaging agent, increased phospho-ATR (pATR) and phospho-Chk1 (pChk1) in consecutive order, indicating the activation of the DNA repair system. This consequently induced G2/M arrest in the cell cycle analysis. However, when AZD6738 was added to belotecan, pATR and pChk1 induced by belotecan alone were suppressed again. A cell cycle analysis in betotecan showed a sub-G1 increase as well as a G2/M decrease, representing the release of G2/M arrest and the induction of apoptosis. In ascites-derived primary cancer cells from both chemotherapy-sensitive and -resistant ovarian cancer patients, this combination was also synergistic, providing further support for our hypothesis. The combined administration of ATR inhibitor and belotecan proved to be synergistic in our preclinical model. This combination warrants further investigation in a clinical trial, with a particular aim of overcoming chemotherapy resistance in ovarian cancer.

Salinity stress is one of the major abiotic stress, which has drastically resulted in a global reduction of agricultural productivity. Around 20% of the global irrigated land falls under the category of saline soil. The elevated amount of soluble salts present in the cultivable land poses severe problems for crop survival and affects various physiological and metabolic processes of the plants. Enhancing the quality of saline soils by chemical methods have shown limited achievements as they also disturb the balance of natural soil ecosystem. Thus, an alternative strategy to compensate for the excessive amounts of salts present in the soil with no harm to the environment is required. Salt-tolerant microorganisms or halophiles can survive in the harsh saline environment and are also important in plant growth and survival. Lately, the plant growth-promoting rhizobacteria, mycorrhiza, and fungal endophytes have been extensively studied for the reclamation of extremely saline habitats. Thus, understanding of the molecular mechanisms and identification of salt-tolerant microorganisms which can benefit crop survivability under extreme saline conditions without harming the ecosystem are necessary. Here, we provide an overview of our current understanding of salt stress and various microorganisms including bacteria, fungi, and endophytes useful to enhance the production of crops under saline environment.

In the twenty-first century, a key obstacle is meeting the hunger needs of rising population every day. Thus, the efficiency of cultivated land and food fortification needs to be improved. The sole objective of the latest agronomic practices, particularly with regard to food crops, is to maximize grain size and weight, even at the price of nutritional value, aiding in few cases to scarcity in micronutrients (termed as hidden hunger). Life-threatening health situations and ailments such as birth defects, heart disease, cancer, nerve disorders, and several others can be caused by hidden hunger. Micronutrient deficiencies are caused by the lessen amount of micronutrients in food crops and are one of the major threats affecting more than two million people worldwide. Plant growth-promoting microbes (PGPM) are categorized in two major groups: plant growth-promoting rhizobacteria (PGPR) and plant growth-promoting fungi (PGPF), which are feasible ways to solve this issue. In staple foods, the use of biofortified nutrients and mineral content can be enhanced. These microbes help to increase micronutrient uptake (e.g., phosphorous, iron, selenium, and zinc). Plant-microbe relationships and our expanding understanding of these interactions can help to biofortify crops with micronutrients in a sustainable and environmentally friendly manner. Biofortification of food crops is indeed espoused as a novel method not only to raise micronutrient concentration in edible food crops but also to boost crop productivity on scarce fertile soils. PGPR are soil bacteria that are responsible of colonizing the rhizosphere and strengthening the development of plants through a wide range of different mechanisms, such as organic matter mineralization, biological control of soil-borne pathogens, N2 biological fixation, and root growth development. The nutrients are mobilized by microorganisms through multiple methods, such as chelation, acidification, exchange reactions, and organic acid release. In addition, several pieces of information reveal that plant inoculation at both physiological and molecular levels with PGPR will have significant effects on plants.