Spontaneous Transformation of Murine Oviductal Epithelial Cells: A Model System to Investigate the Onset of Fallopian-Derived Tumors

High-grade serous carcinoma (HGSC) is the most lethal ovarian cancer histotype. The fallopian tube secretory epithelial cells (FTSECs) are a proposed progenitor cell type. Genetically altered FTSECs form tumors in mice; however, a spontaneous HGSC model has not been described. Apart from a subpopulation of genetically predisposed women, most women develop ovarian cancer spontaneously, which is associated with aging and lifetime ovulations. A murine oviductal cell line (MOELOW) was developed and continuously passaged in culture to mimic cellular aging (MOEHIGH). The MOEHIGH cellular model exhibited a loss of acetylated tubulin consistent with an outgrowth of secretory epithelial cells in culture. MOEHIGH cells proliferated significantly faster than MOELOW, and the MOEHIGH cells produced more 2D foci and 3D soft agar colonies as compared to MOELOW. MOEHIGH were xenografted into athymic female nude mice both in the subcutaneous and the intraperiteonal compartments. Only the subcutaneous grafts formed tumors that were negative for cytokeratin, but positive for oviductal markers such as oviductal glycoprotein 1 and Pax8. These tumors were considered to be poorly differentiated carcinoma. The differential molecular profiles between MOEHIGH and MOELOW were determined using RNA-Seq and confirmed by protein expression to uncover pathways important in transformation, like the p53 pathway, the FOXM1 pathway, WNT signaling, and splicing. MOEHIGH had enhanced protein expression of c-myc, Cyclin E, p53 and FOXM1 with reduced expression of p21. MOEHIGH were also less sensitive to cisplatin and DMBA, which induce lesions typically repaired by base-excision repair. A model of spontaneous tumorogenesis was generated starting with normal oviductal cells. Their transition to cancer involved alterations in pathways associated with high-grade serous cancer in humans

Crop geometry and dripper spacing in turmeric (Curcuma longa L.) raised with single bud transplants

Field experiments were conducted to study the effect of dripper spacing and plant spacing on growth, yield and WUE of turmeric (Curcuma longa) raised with rhizome bud seedling at Agricultural Research Station, Tamil Nadu Agricultural University, Bhavanisagar, Erode Dt., Tamil Nadu, India. The treatment structure includes two dripper spacing viz., 60 cm and 40 cm with combination of four plant spacing viz., 30 cm x 15 cm, 30 cm x 20 cm, 30 cm x 25 cm and 45 cm x 15 cm and with conventional (furrow / surface) irrigation method as control. The trials were laid out in randomized block design and replicated thrice. The irrigation is being applied once in three days through drip at 80 per cent PE which is common for all the treatments except the conventional method. The fertigation is being given once in ten days by following the fertigation schedule of turmeric. The results revealed that drip irrigation at 80 per cent Pan Evaporation (PE) with 60 cm dripper spacing and crop spacing of 30 cm x 20 cm recorded higher rhizome yield as well as high income and B:C ratio. Significantly lowest yield was observed in the control treatment (conventional method). Turmeric cultivated under drip irrigation system resulted in saving of 48 per cent of water when compared to conventional method with a WUE ranging from 19.4 to 26.5 kg ha-1mm-

Biomass and Carbon Stock Estimation in Woody Grass (\u3cem\u3eDendrocalamus strictus\u3c/em\u3e L.) in Doon Valley, India

Bamboos commonly kown as woody grass are one of the most important species particularly in Asia, where it is frequently considered as the ―timber of the poor‖ (Rao et al., 1985). With about 23 genera and 136 species, India is the second largest reservoir of bamboos, next only to China (SFR, 2013 and Nath et al., 2009). Bamboos occur extensively in the managed ecosystems of India—both as plantations (and in agroforestry (scattered clumps, hedgerows on farm boundaries etc. Dendrocalamus strictus L. is most commonly found bamboo in India. It is widely distributed in dry deciduous forests and grows rapidly in all climatic conditions and occupies about 53 % of total bamboo area in India. It grows better in the drier parts and on sandstone, granite and coarse grained soils with low moisture- retaining capacity and soils with pH range 5.5–7.6. It grows more than 8 feet in 6–8 months. The species is used widely for as raw material in paper mills and also for variety of purposes such as construction, agricultural implements, musical instruments, furniture etc. The species is also suitable for reclamations of degraded and ravine lands. The accurate assessment of biomass estimates of a forest is important for many applications (Brown, 2002; Chave et al., 2004; Arora et al., 2014; Verma et al., 2014). In recent years, the carbon cycle has become an important issue in the world and plants play a major role in carbon storage. Biomass estimation enables us to estimate the amount of carbon dioxide that can be sequestered from the atmosphere. However, most of the carbon and biomass studies focus on assessing the capability of trees viz., poplar, eucalyptus, shisham, chir teak, subabul etc. The studies related to biomass and carbon stock estimation in bamboos is limited. The present study examine specifically the above ground stand biomass, biomass structure and C storage in D. strictus

Conservation agriculture: A pathway to achieving sustainable development goals

Conservation agriculture (CA) is an approach to optimize farm and watershed performance. It integrates local and national economic systems while considering societal, environmental and institutional frameworks. This approach engages value chains supported by public, private and civil sectors. CA seeks to harmonize the use of natural resources with population needs, employing sustainable intensification to meet human demands effectively and preventing the loss of arable land. Conservation agriculture directly influences all sustainable development goals (SDGs) by leveraging the core principles of minimum soil disturbance, permanent soil cover and crop rotation. Conservation agriculture can prove to be a viable option for meeting the targets of the sustainable agenda. This practice supports environmental, social and economic justice, which creates a holistic developmental route that supports the burgeoning population. Conservation agriculture relies on a knowledge-based strategy to reduce production costs, enabling farmers to adopt new technologies more readily. While CA demonstrates significant benefits across scales, its adoption remains constrained by socioeconomic factors and limited mechanization in the smallholder context. Advancing CA requires a multidisciplinary, participatory research paradigm coupled with policy support, institutional support and capacity building for farmers. CA offers a sustainable framework that ensures sustainable intensification and environmental stewardship in the long term

Beyond the Grind: Leveraging Data Analysis and Machine Learning for the Quantification and Enhancement of Work-Life Balance

This research aims to comprehensively investigate the dynamics of work-life balance and to develop predictive models using machine learning techniques to assess and predict the factors influencing work-life equilibrium. The study leverages a dataset containing 15,973 responses obtained from the global work-life survey conducted by Authentic-Happiness.com. The survey comprises 23 questions, providing a multifaceted view of how individuals manage their personal and professional lives. Initial Exploratory Data Analysis (EDA) uncovers five key dimensions: "Healthy Body," "Healthy Mind," "Expertise," "Connection," and "Meaning." These dimensions are explored to gain insights into their significance in relation to work-life balance. Subsequently, an extensive set of machine learning regression models, including Linear Regression, Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor, XGBoost, LightGBM, CatBoost, Support Vector Machine, K Nearest Neighbors, K-Means Regression, Ridge and Lasso Regression, Principal Component Analysis, RANSAC, Quartile Regression, GAM, Huber Regression, RBF Kernel Regression, and SGD Regression, are employed to predict work-life balance scores. Performance evaluation is based on metrics such as Mean Squared Error (MSE) and R-squared (R²). The research uncovers a holistic understanding of work-life balance and identifies significant predictors. The comparative analysis of machine learning models reveals their effectiveness in predicting work-life balance, highlighting the models that perform optimally. This research contributes valuable insights into the intricate factors that underlie work-life balance, offering a data-driven perspective that can inform personal choices, organizational strategies, and policy decisions. The application of machine learning techniques underscores the potential for addressing contemporary challenges associated with achieving a harmonious work-life equilibrium