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Forest plays a crucial role in mitigating soil erosion and preserving organic carbon, especially in mountainous regions of Himalayas. However, limited information exists on soil erosion rate, soil organic carbon stock (SOCS), and associated carbon loss in these areas because of the rugged terrain, which poses challenges for reliable estimation using both traditional and modelling approaches. This study used Fallout Radionuclide- 137 Cs to assess soil erosion and carbon loss across various forest types. Results showed that mixed forests had the lowest erosion rates, while degraded forests had the highest, following the order of mixed forest < oak ( Quercus ) <  Rhododendron  < deodar ( Cedrus ) < pine ( Pinus ) < apple ( Malus ) < degraded forests. Forests with dense canopy and understory cover experiences reduced erosion (5.9 ± 3.6 t ha −1 year −1 ) while degraded forests showed high soil erosion rates (15.5 ± 6.4 t ha −1 year −1 ) with corresponding carbon displacement of 0.75 ± 0.48 and 1.42 ± 0.71 t ha −1 year −1 and carbon emission of 0.23 ± 0.14 and 0.43 ± 0.21 t ha −1 year −1 respectively. SOCS (0–15 cm) was inversely correlated with erosion rates, being highest in mixed forests (73.7 ± 32.2 t ha −1 ) and lowest in apple orchard (23.41 ±  4.3 t ha -1 ) and degraded forests (46.3 ± 19.9 t ha −1 ). These findings underscore the need to maintain forest diversity and canopy cover to arrest soil erosion, enhance carbon sequestration, and to improve ecosystem resilience. Conservation and restoration in degraded areas are essential for climate change mitigation and environmental stability in the mountainous landscapes of Himalayas.

The development of the esophagus and trachea following the septation of the anterior foregut is a highly regulated process involving bidirectional communication between the endoderm and mesoderm. Signaling pathways such as the Bone Morphogenetic Protein family, Wnt/β-catenin, Sonic Hedgehog, and Fibroblast Growth Factor family mediate this complex crosstalk to induce the dorsal-ventral patterning of the anterior foregut as well as lineage specification. Even though the mechanisms are not fully understood, dysregulation of signaling pathways may lead to congenital malformations such as tracheomalacia, laryngeal–tracheal clefts and multiple types of esophageal atresia with/without tracheoesophageal fistula (EA/TEF). Human induced pluripotent stem cells (iPSCs) provide a robust in vitro platform to monitor the normal and abnormal development of esophagus and trachea and to understand the roles of the endoderm and mesoderm during anterior foregut development. Recent studies have demonstrated that direct differentiation of iPSCs into epithelial and mesenchymal lineages can recapitulate the key stages of foregut development. In this regard, in the current paper, we review the signaling pathways involved in the development of organs deriving from the anterior foregut as well as the roles of the endoderm and mesoderm revealed by previous studies. Furthermore, we discuss the use of iPSCs as a valuable model for investigating the bidirectional communications between the endoderm and mesoderm, which can broaden our knowledge and understanding of the critical mechanisms leading to normal and abnormal development of the esophagus and trachea.

Soil micronutrients are critical for plant growth and reproductive processes to complete their key functionality of the life cycle. The availability of micronutrients in hilly and mountainous regions is influenced by topography-controlled soil processes at various hillslope positions. However, the ruggedness of terrains makes it challenging to gather comprehensive data on soil micronutrient status, which in turn limits effective micronutrient management. This issue is particularly significant to the acidic soils of the North-western Himalayas, where agriculture is the primary source of livelihood. Therefore, this study was designed to explore the availability and variability of major micronutrients (Zn, Cu, Fe, and Mn) in the soils of hilly and mountainous landscapes across five different hillslope positions and various cropping systems, and to examine their relationship with physico-chemical properties of soils. The study revealed that all micronutrients were present in sufficient quantities across the hillslope positions. However, a slight tendency toward zinc deficiency was observed at some hillslope positions. Principal component analysis (PCA) highlighted the importance of micronutrients Fe, Mn, Zn, and Cu to pH, organic matter, sand and silt contents in the landscape. It revealed that pH and organic matter were critical factors influencing micronutrient availability, with higher levels of organic matter generally enhancing the availability of these micronutrients. The study further concluded that a complex interplay of topography, soil management practices, and cropping systems influence soil micronutrient availability. The variability observed at the hillslope scale highlights the need for site-specific management approaches. Understanding these factors is crucial for optimizing soil micronutrient management and ensuring sustainable agricultural productivity in the soil erosion-prone fragile Himalayan region.

Hillslope elements and land cover types are primarily determining the spatial variability of soils in the hilly and mountainous landscape. Among the soil forming factors, topography strongly influences pedogenic process and governs the variability of soils in hilly and mountainous landscape. This study mainly focusses on characterizing soil quality parameters distribution based on the hillslope elements and soil–landscape units in the watershed. Digital terrain model (DTM)-derived topographic position index was used to delineate various hillslope elements. Land use/land cover map was generated using random forest machine learning technique. Hillslope elements, land use/land cover types and aspects were integrated in GIS environment to generate soil–landscape unit map of the watershed. Soil samples were collected based on various soil–landscape units to characterize soil quality parameters such as total carbon (TC) soil organic matter (SOM), total nitrogen (TN), aggregate stability (SAS) in the watershed. SOM ranged from 1.6% to 10.05% and higher estimated in shoulder (forest) followed by valley (agriculture) and toe slope (forest). TC and TN contents ranged from 0.93% to 5.84% and 0.11% to 0.38%, respectively. The C:N ratio ranged from 7.96% to 18.31% and high value was found in shoulder (forest) followed by valley (agriculture) and toe slope (forest). SAS under different hillslope elements in the area ranged from 0.0.552 to 0.615 indicating large spatial variation of soil quality parameters. The study indicates that in hilly and mountainous landscape, topography and land cover types have major role in determining soil quality. DTM-based soil–landscape units’ delineation can be helpful to study soil quality variability and can be used to generate soil map for the hilly and mountainous watershed. The significance of this study lies in its potential to make substantial contributions to land use planning, sustainable land management and environmental conservation planning in the challenging and ecologically fragile and sensitive Himalayan region.

Premature ovarian insufficiency (POI) is a significant complication of cytotoxic treatments due to extreme ovarian sensitivity to chemotherapy and radiation. POI is particularly devastating for young girls reaching puberty, because it irreversibly affects their physical and cognitive development. Changes occurring during puberty determine their height, bone health, insulin responsiveness, lipid metabolism, cardiovascular health and cognition. The only available treatment for POI during puberty is hormone replacement therapy (HRT), which delivers non-physiological levels of estrogen, lacks other ovarian hormones and pulsatility, and is not responsive to feedback regulation. Here we report that ovarian allografts encapsulated in a hydrogel-based capsule and implanted in ovariectomized mice restore ovarian endocrine function in immune competent mice. Ovarian tissue from BALB/c mice was encapsulated in poly(ethylene-glycol) (PEG) hydrogels, with a proteolytically degradable core and a non-degradable shell. The dual capsules were implanted subcutaneously in immune competent ovariectomized C57BL/6 mice for a period of 60 days. As expected, non-encapsulated ovarian allografts implanted in a control group sensitized the recipients as confirmed with donor-specific IgG in the serum, which increased 26-fold in the 3 weeks following transplantation (p = 0.02) and infiltration of the graft with CD8 T cells consistent with allo-immunity. In contrast, encapsulation in the Dual PEG capsules prevented sensitization to the allograft in all the recipients with no evidence of lymphocytic infiltration. In summary, the approach of hydrogel-based immunoisolation presents a minimally invasive and robust cell-therapy to restore hormonal balance in ovarian insufficiency. This report is the first to demonstrate the application of a tunable PEG-based hydrogel as an immunoisolator of allogeneic ovarian tissue to restore endocrine function in ovariectomized mice and prevent cell-mediated immune rejection in immune competent mice.

This study is part of a larger dissertation that examined the assessment culture within Ghana’s education system. This paper specifically investigates the diverse conceptions of assessment held by students and how these perceptions shape their learning-related behaviours and assessment experiences. In total, 405 junior high school (Grades 7–9) and senior high school (Grades 10–12) students responded to the Students’ Conceptions of Assessment Inventory (SCoA-VI). Second-order confirmatory factor analysis (CFA) and latent profile analysis (LPA) were conducted to identify distinct patterns in students’ conceptions of assessment. To explore potential associations between students’ conceptions of assessment and their demographics, chi-square (crosstabulation) analyses were conducted. Three primary patterns emerged: mixed, improvement and negative conceptions of assessment. These three distinct conceptions, held by students within the same educational system, shaped their assessment experiences in different ways. Demographic factors, such as school division, class level and geographic location, significantly influenced these conceptions. Junior high students generally held more positive views than senior high students, who face high-stakes examination pressures. Urban students tended to have more positive conceptions of assessment than rural students. Implications for policy and practice are discussed.

Undergirded by \"economic theory of the principal-agent problem\", the study investigated secondary school teachers' perception of the influence of large-scale testing accountability on teaching and learning. Cross-sectional survey design was used. Simple random sampling was also employed to select 200 teachers for this study. The exploratory factor analysis results showed that although large-scale testing accountability serves as a motivational factor for teachers to be more productive, it puts pressure on teachers and students. Teachers reported teaching to the test (i.e. washback) and spending a lot of instructional time preparing students for the test. Teachers suggested that large-scale testing results should not be seen as the sole basis for teacher effectiveness. Rather, they indicated that large-scale testing results should be used to foster equitable distribution of educational resources and support teachers' professional development to enhance teaching and learning. Implications for policy and practice are discussed.

ABSTRACT Classroom assessment and grading play central roles in education, with important impacts on teachers and students. This study examined the interactions between Canadian teachers’ and students’ conceptions of assessment and approaches to grading. 219 teachers and students completed a survey with two scales: Teachers’ Conceptions of Assessment (TCOA) and Teachers’ Approaches to Grading (TAG). Factor analysis of the TCOA scale showed four factors: assessment to improve teaching and learning, negative or irrelevant assessment, assessment for student and school accountability, and inaccurate assessment. Analysis of the TAG survey also showed four factors: social-emotional pressures for grade increases/changes, situational considerations for grade increases/changes, contextual-based grading, and achievement-based grading. Discriminant analysis showed that four out of these eight factors from the two scales had the strongest effects on teachers’ and students’ membership in their respective groups. The results contribute to a more complete understanding of assessment cultures as conceived by teachers and students.

Land degradation is accelerating in the Himalayan ecosystem, resulting in the loss of soil nutrients due to severe erosion. Soil erosion presents a significant environmental challenge, resulting in both on-site and off-site consequences, such as reduced soil productivity and siltation in reservoirs. Soil erodibility (K factor), an inherent soil property, determines the susceptibility of soils to erosion. Sampling across hilly and mountainous terrain pose challenges due to its complex landscape. Despite these challenges, it is essential to study K factor variations in different land use/land cover types to comprehend the threat of erosion. Digital soil mapping offers an opportunity to overcome this limitation by providing spatial predictions of soil properties. The objective of our study is to map the spatial distribution of soil erodibility using the Random Forest (RF) model, a machine learning method based on sampled in situ soil data and environmental covariates. We collected 556 surface soil samples from the mountainous catchment (Tehri dam catchment) using the stratified random sampling approach. The model performed satisfactorily in both training ( r 2  = 0.91; RMSE = 0.00185) and testing ( r 2  = 0.45; RMSE = 0.00318) phases. Subsequently, we generated a digital map with a resolution of 12.5 m to depict the distribution of the K factor. Our analysis revealed that key environmental variables influencing the prediction of the K factor included geology, mean NDVI, and climatic factors. The average K factor value was estimated at 0.0304 and ranging from 0.0251 to 0.0400 t ha h ha −1  MJ −1  mm −1 . A higher K factor was observed in the barren land (0.0344) primarily located in the higher and trans-Himalayan region of seasonally snow-covered areas. These areas typically feature young soils with weak soil formation and unstable soil aggregates. Subsequently cropland/cultivated soils (0.0307) exhibited higher K factor values due to the breakdown of soil aggregates by ploughing activities and exposing carbon to decomposition. The average K factor value of evergreen (0.0294) and deciduous (0.0295) forests were the lowest compared to other land use/land cover types indicating the role of forests in resisting soil erosion. By assessing and predicting soil erodibility, land planners and farmers can implement erosion control measures to protect soil health, prevent sedimentation in water bodies, and sustain agricultural productivity in the Himalayas.

The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region.