Evaluation of bacterial antagonists for the management of rhizome rot of cardamom (Elettaria cardamomum Maton)

Among the 90 rhizobacterial isolates screened against rhizome rot pathogens (Pythium vexans, Fusarium oxysporum and Rhizoctonia solani) of cardamom (Elettaria cardamomum) two bacterial strains viz., Pseudomonas fluorescens Pf 51 and Bacillus subtilis B45 were highly inhibitory. P. fluorescens Pf 51 exhibited highest inhibition (42.5%, 44.2% and 41.4% respectively) against P. vexans, F. oxysporum and R. solani. B. subtilis B45 also exhibited highest inhibition (43.2%, 41.2% and 42.4% respectively) against these three pathogens. P. fluorescens Pf51 was compatible with B. subtilis Bs 45. Peat formulation supported the survival of both the strains up to 270 days with a viable population of 4.3 × 107 cfu g-1 and 6.2 × 107 cfu g-1 respectively. Application of antagonists in combination with rhizome bacterization and soil application resulted in 54.0% reduction in rhizome rot over control as compared to single method such as rhizome bacterization (43.0%) or soil application (39.0%). Application of copper oxychloride and carbendazim resulted in 68.0% reduction of rhizome rot. Maximum height (167.21 cm) and number of tillers (30.14) were recorded due to the application of mixture of both the strains through rhizome bacterization and soil application. &nbsp

Evaluation of bacterial antagonists for the management of rhizome rot of cardamom (Elettaria cardamomum Maton)

Among the 90 rhizobacterial isolates screened against rhizome rot pathogens (Pythium vexans, Fusarium oxysporum and Rhizoctonia solani) of cardamom (Elettaria cardamomum) two bacterial strains viz., Pseudomonas fluorescens Pf 51 and Bacillus subtilis B45 were highly inhibitory. P. fluorescens Pf 51 exhibited highest inhibition (42.5%, 44.2% and 41.4% respectively) against P. vexans, F. oxysporum and R. solani. B. subtilis B45 also exhibited highest inhibition (43.2%, 41.2% and 42.4% respectively) against these three pathogens. P. fluorescens Pf51 was compatible with B. subtilis Bs 45. Peat formulation supported the survival of both the strains up to 270 days with a viable population of 4.3 × 107 cfu g-1 and 6.2 × 107 cfu g-1 respectively. Application of antagonists in combination with rhizome bacterization and soil application resulted in 54.0% reduction in rhizome rot over control as compared to single method such as rhizome bacterization (43.0%) or soil application (39.0%). Application of copper oxychloride and carbendazim resulted in 68.0% reduction of rhizome rot. Maximum height (167.21 cm) and number of tillers (30.14) were recorded due to the application of mixture of both the strains through rhizome bacterization and soil application. &nbsp

Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India

Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data collected from India Meteorological Department in northeast region over a period of 118 years. We conducted a comparative analysis of these methods to determine their relative effectiveness in predicting rainfall patterns. Using historical rainfall data from multiple weather stations, we trained and validated our models to forecast future rainfall patterns. Our results indicate that both DMD and LSTM are effective in forecasting rainfall, with LSTM outperforming DMD in terms of accuracy, revealing that LSTM has the ability to capture complex nonlinear relationships in the data, making it a powerful tool for rainfall forecasting. Our findings suggest that data-driven methods such as DMD and deep learning approaches like LSTM can significantly improve rainfall forecasting accuracy in the North-East region of India, helping to mitigate the impact of extreme weather events and enhance the region's resilience to climate change.Comment: Paper is under review at ICMC 202

Protons in the near-lunar wake observed by the Sub-keV Atom Reflection Analyzer on board Chandrayaan-1

Significant proton fluxes were detected in the near wake region of the Moon by an ion mass spectrometer on board Chandrayaan-1. The energy of these nightside protons is slightly higher than the energy of the solar wind protons. The protons are detected close to the lunar equatorial plane at a $140^{\circ}$ solar zenith angle, i.e., ~50$^{\circ}$ behind the terminator at a height of 100 km. The protons come from just above the local horizon, and move along the magnetic field in the solar wind reference frame. We compared the observed proton flux with the predictions from analytical models of an electrostatic plasma expansion into a vacuum. The observed velocity was higher than the velocity predicted by analytical models by a factor of 2 to 3. The simple analytical models cannot explain the observed ion dynamics along the magnetic field in the vicinity of the Moon.Comment: 28 pages, 7 figure

Genetic Aspects of Implantation Failure

Implantation failure refers to the inability of a fertilized egg, or embryo, to successfully implant itself in the endometrial lining of the uterus, leading to pregnancy loss. The repeated failure of good quality embryo implantation is referred to as recurrent implantation failure (RIF). This can occur for a variety of reasons, including chromosomal abnormalities in the embryo, problems with the endometrium, or issues with the immune system. Factors such as advanced maternal age, obesity, smoking, and certain medical conditions can also increase the risk of implantation failure. While treatment such as in vitro fertilization (IVF) can help to improve the chances of successful implantation, there is currently no definite way to prevent or treat implantation failure.  Patients and healthcare professionals have substantial diagnostic and treatment hurdles as a result of many etiological factors and lack of knowledge about RIF. A number of studies have indicated a correlation between irregular hormone levels, disruptions in angiogenic and immunomodulatory factors, specific genetic polymorphisms, and the prevalence of RIF. Nonetheless, the precise and intricate underlying pathophysiology of RIF remains elusive.  However, many studies are ongoing in this field to understand the underlying causes and to find new ways to help couples achieve pregnancy. This review article extensively explores diverse molecular and genetic facets aimed at enhancing the diagnosis and management of implantation failure

Cardamom agro-environmental interrelationships analysis in Indian cardamom hills

The rainfall pattern seen in the Indian Cardamom Hills (ICH) has been extremely variable and complicated, with El Niño-Southern Oscillation (ENSO) playing a crucial role in shaping this pattern. In light of this, more investigation is required through improved statistical analysis. During the study period, there was greater variability in rainfall and the frequency of rainy days. About 2,730 mm of rainfall was reported in 2018, while the lowest amount (1168.3 mm) was registered for 2016. The largest decrease in decadal rainfall (>65 mm) was given by the decade 1960–1969, followed by 1980–1989 (>40 mm) and 2010–2019 (>10 mm). In the last 60 years of study, there has been a reduction of rainy days by 5 days in the last decade (2000–2009), but in the following decade (2010–2019), it registered an increasing trend, which is only slightly <2 days. The highest increase in decadal rainy days was observed for the 1970–1979 period. The smallest decadal increase was reported for the last decade (2010–2019). Total sunshine hours were the highest (1527.47) for the lowest rainfall year of 2016, while the lowest value (1,279) was recorded for the highest rainfall year (2021). The rainfall characteristics of ICH are highly influenced by the global ENSO phenomenon, both positively and negatively, depending on the global El Nino and La Nina conditions. Correspondingly, below and above-average rainfall was recorded consecutively for 1963–1973, 2003–2016, and 1970–2002. Higher bright forenoon sun hours occurred only during SWM months, which also reported maximum disease intensity on cardamom. The year 2016 was regarded as a poorly distributed year, with the lowest rainfall and the highest bright afternoon sun hours during the winter and summer months (January-May). Over the last three decades, the production and productivity of cardamom have shown a steady increase along with the ongoing local climatic change. Many of our statistical tests resulted in important information in support of temporal climatic change and variability. Maintaining shade levels is essential to address the adverse effects of increasing surface air temperature coupled with the downward trend of the number of rainy days and elevated soil temperature levels

Ethyl 1-cyclo­hexyl-5-(4-meth­oxy­phen­yl)-1H-pyrazole-4-carboxyl­ate

In the title compound, C19H24N2O3, the benzene ring forms a dihedral angle of 65.34 (7)° with the pyrazole ring. The cyclo­hexane ring adopts a chair conformation. In the crystal, mol­ecules are linked into a inversion dimers by pairs of C—H⋯O hydrogen bonds, generating R 2 2(22) ring motifs

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.Comment: Accepted to Transactions of the Association for Computational Linguistics (TACL

Oyster farm management advisory: spacing between farms

The edible oyster Crassostrea madrasensis commonly known as the backwater oyster is farmed in the estuarine regions of Kerala by setting up wooden rack farms from which rens are suspended. Proximity to the homesteads is one of the reasons for this technology to become popular among women self help groups. The farming season is from November/December to June, but may extend to July also depending on the onset of monsoon. At present, the farm structures are near to the shore line in a linear manner, providing space for navigation in the inner part of the estuarine channels. Initially when commercial farming started in Sattar Island in the year 2002, there were only few farms, hence, spacing of farms was not a problem