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The Gaddis, a renowned nomadic tribe from the Himalayas, reside in Himachal Pradesh's Chamba and Kangra districts, traversing the Dhaula Dhar range. Their migrations, echoing Lord Shiva's pattern, lead them to Lahaul's high pastures in summer and to Himachal Pradesh's borders in winter. They pay grazing taxes to the Forest Department in summer and to the local panchayat for forest use in winter. Interviews with 42 herders revealed their challenges in adapting to modern times. Despite collecting insights from various department representatives, it's clear that globalization affects the Gaddis just as it does the wider world, pushing the community away from ancestral customs towards new professions and a stationary way of life. While they cherish their heritage, they hesitate to pass this legacy to future generations. This study highlights the Gaddi pastoralists' current struggles and their shift from nomadism to a settled existence. [ABSTRACT FROM AUTHOR]

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument's software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology. [ABSTRACT FROM AUTHOR]

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol−1 K and 403.47 kJ mol−1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg−1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater. [Display omitted] • Chitosan conjugated magnetic pectin loaded Filter mud was used as bioadsorbent. • Bismark Brown R dye was effectively removed from the solution with q max 143 mg g−1. • Pseudo-second-order was proved to be the best-described kinetic model. • ANN and ANFIS model were successfully used to model the process. • The cost estimate for bioadsorbent was presented as US$ 5.809/Kg. [ABSTRACT FROM AUTHOR]

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol−1 K and 403.47 kJ mol−1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg−1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater. [Display omitted] • Chitosan conjugated magnetic pectin loaded Filter mud was used as bioadsorbent. • Bismark Brown R dye was effectively removed from the solution with q max 143 mg g−1. • Pseudo-second-order was proved to be the best-described kinetic model. • ANN and ANFIS model were successfully used to model the process. • The cost estimate for bioadsorbent was presented as US$ 5.809/Kg. [ABSTRACT FROM AUTHOR]