Foreword | Kupu Whakataki

This conference would place the intersection of gender and law front and centre. It would seek to prioritise diverse voices and provide an sent research to those who might not otherwise have had that opportunity. In addition, the conference would provide a forum for dialogue across the law profession— undergraduate and postgraduate students, practitioners and academics—as well as create space for personal and professional reflection and the sharing of our stories, thereby placing a feminist lens on the academic conference. That was the kaupapa of the Symposium on Law and Gender: Beyond Patriarchy. Postponed by the challenges of the pandemic and finally taking place online on 1 February 2022, the Symposium was a collaboration between the AUT Law School Te Wānanga Aronui o Tāmaki Makau Rau and New Zealand Women’s Law Journal – Te Aho Kawe Kaupapa Ture a ngā Wāhine

Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems

Concern over global problems induced by rising CO2 has prompted attention on the role of forests and pastures as carbon ‘storage’ because forests and pastures store a large amount of carbon in vegetation biomass and soil. Soil organic matter (SOM) plays a critical role in soil quality and has the potential to cost-effectively mitigate the detrimental effects of rising atmospheric CO2 and other greenhouse gas emissions that cause global warming and climate change(Causarano-Medina, 2006). SOM, an important source of plant nutrients is itself influenced by land use, soil type, parent material, time, climate and vegetation (Loveland &Webb, 2003). Important climatic factors influencing SOM include rainfall and temperature. Within the same isotherm, the SOM content increases with increase in rainfall regime. For the same isohyet, the SOM content...............

Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran

In this study artificial neural network (ANN) models were designed to predict the biomass and grain yield of barley from soil properties; and the performance of ANN models was compared with earlier tested statistical models based on multivariate regression. Barley yield data and surface soil samples (0–30 cm depth) were collected from 1 m2 plots at 112 selected points in the arid region of northern Iran. ANN yield models gave higher coefficient of determination and lower root mean square error compared to the multivariate regression, indicating that ANN is a more powerful tool than multivariate regression. Sensitivity analysis showed that soil electrical conductivity, sodium absorption ratio, pH, total nitrogen, available phosphorus, and organic matter consistently influenced barley biomass and grain yield. A comparison of the two methods to identify the most important factors indicated that while in the ANN analysis, soil organic matter (SOM) was included among the most important factors; SOM was excluded from the most important factors in the multivariate analysis. This significant discrepancy between the two methods was apparently a consequence of the non-linear relationships of SOM with other soil properties. Overall, our results indicated that the ANN models could explain 93 and 89% of the total variability in barley biomass and grain yield, respectively. The performance of the ANN models as compared to multivariate regression has better chance for predicting yield, especially when complex non-linear relationships exist among the factors. We suggest that for further potential improvement in predicting the barley yield, factors other than the soil properties considered such as soil micronutrient status and soil and crop management practices followed during the growing season, need to be included in the models

The hRPC62 subunit of human RNA polymerase III displays helicase activity.

In Eukaryotes, tRNAs, 5S RNA and U6 RNA are transcribed by RNA polymerase (Pol) III. Human Pol III is composed of 17 subunits. Three specific Pol III subunits form a stable ternary subcomplex (RPC62-RPC39-RPC32α/β) being involved in pre-initiation complex formation. No paralogues for subunits of this subcomplex subunits have been found in Pols I or II, but hRPC62 was shown to be structurally related to the general Pol II transcription factor hTFIIEα. Here we show that these structural homologies extend to functional similarities. hRPC62 as well as hTFIIEα possess intrinsic ATP-dependent 3'-5' DNA unwinding activity. The ATPase activities of both proteins are stimulated by single-stranded DNA. Moreover, the eWH domain of hTFIIEα can replace the first eWH (eWH1) domain of hRPC62 in ATPase and DNA unwinding assays. Our results identify intrinsic enzymatic activities in hRPC62 and hTFIIEα

Relationships of barley biomass and grain yields to soil properties within a field in the arid region: Use of factor analysis

Understanding the variability of soil properties and their effects on crop yield is a critical component of site-specific management systems. The objective of this study was to employ factor and multiple regression analyses to determine major soil physical and chemical properties that influence barely biomass and grain yield within a field in the arid region of northern Iran. For this purpose, soil samples and crop-yield data were collected from 108 sites, at regular intervals (20 30 m) in a 5.6 ha field. Soil samples were analysed for total nitrogen (TN), available phosphorus (Pava), available potassium (Kava), cation-exchange capacity(CEC), electrical conductivity (EC), pH, mean weight diameter of aggregates (MWD), water-stable aggregates (WSA), field capacity volumetric (FC), available water-holding capacity (AWHC), bulk density (BD), and calcium carbonate equivalent (CCE). Results of the factor analysis, followed by regression of biomass and grain yield of barley with soil properties, showed that the regression equations developed accounted for 78 and 73% of the total variance in biomass and grain yield, respectively. Study of covariance analysis among soil variables using factor analysis indicated that some of the variation measured could be grouped to indicate a number of underlying common factors influencing barley biomass and grain yields. These common factors were salinity and sodicity, soil fertility, and water availability. The most effective soil variables to barley production in the study area identified as EC, SAR, pH, TN, Pava, AWHC, and FC. In this study, factor analysis was effective to identify the groups of correlated soil variables that were significantly correlated with the within field variability in the yield of the barley crop. Our results also suggest that the approach can be applied to other crops under similar soil and agroclimatic conditions

Soil Surface Salinity Prediction Using ASTER Data: Comparing Statistical and Geostatistical Models

This study was conducted to evaluate the performance of univariate spatial (ordinary kriging- OK), hybrid/multivariate geostatistical methods (regression-kriging- RK, Co-kriging- CK) with multivariate linear regression (MLR) in incorporation with ASTER data in order to predict the spatial variability of surface soil salinity in an arid area in northern Iran. The primary attributes were obtained from grid soil sampling with nested-systematic pattern of 169 samples and the secondary information extracted from spectral data of ASTER satellite images. The principal component analysis, NDVI and some suitable ratioing bands were applied to generate new arithmetic bands. According to validation based RMSE and ME calculated by a validation data set, the predictions for soil salinity were found to be the best and varied in the following order: RK ASTERmultivariate > REG ASTERmultivariate > Co-kriging ASTER> kriging. Overall, this comparative study demonstrated that RK approach was a better predicator than other selected methods to predict spatial variability of soil salinity. The overall results confirmed that using ancillary variables such as remotely sensed data, the accuracy of spatial prediction can further improved

Relationships between soil depth and terrain attributes in a semi arid hilly region in western Iran

Soil depth generally varies in mountainous regions in rather complex ways. Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time, effort and consequently relatively large budget to perform. This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran. For this, one hundred sampling points were selected using randomly stratified methodology, and considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope; and soil depth was actually measured. Eleven primary and secondary topographic attributes were derived from the digital elevation model (DEM) at the study area. The result of multiple linear regression indicated that slope, wetness index, catchment area and sediment transport index, which were included in the model, could explain about 76 % of total variability in soil depth at the selected site. This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale

3D interrelationship between osteocyte network and forming mineral during human bone remodeling

During bone remodeling, osteoblasts are known to deposit unmineralized collagenous tissue (osteoid), which mineralizes after some time lag. Some of the osteoblasts differentiate into osteocytes, forming a cell network within the lacunocanalicular network (LCN) of bone. To get more insight into the potential role of osteocytes in the mineralization process of osteoid, sites of bone formation are three-dimensionally imaged in nine forming human osteons using focused ion beam-scanning electron microscopy (FIB-SEM). In agreement with previous observations, the mineral concentration is found to gradually increase from the central Haversian canal toward pre-existing mineralized bone. Most interestingly, a similar feature is discovered on a length scale more than 100-times smaller, whereby mineral concentration increases from the LCN, leaving around the canaliculi a zone virtually free of mineral, the size of which decreases with progressing mineralization. This suggests that the LCN controls mineral formation but not just by diffusion of mineralization precursors, which would lead to a continuous decrease of mineral concentration from the LCN. The observation is, however, compatible with the codiffusion and reaction of precursors and inhibitors from the LCN into the bone matrix

Pasture degradation effects on soil quality indicators at different hillslope positions in a semiarid region of western Iran

A study was made to determine the influence of pasture degradation on soil quality indicators that included physical, chemical, biological and micromorphological attributes, along the hillslope positions in Chaharmahal and Bakhtiari province, western Iran. Soil samples from different slope positions were collected from 0 to 30 cm depth for physical and chemical properties and from 0 to 15 cm depth for biological properties at two adjacent sites in the two ecosystems: natural pasture and cultivated land. Soil quality indicators including bulk density, mean weight diameter, soil organic carbon (SOC), particulate organic material (POM) in aggregate fractions, total nitrogen, available potassium, available phosphorus, cation exchange capacity, soil microbial respiration (SMR) and microbial biomass C and N were determined. The results showed that SOC decreased cultivation from 1.09 to 0.77 % following pasture degradation. The POM decreased by about 19.35 % in cultivated soils when compared to natural pasture; also, SMR and microbial biomass C and N decreased significantly following pasture degradation. Furthermore, aggregate stability and pore spaces decreased, and bulk density increased in the cultivated soils. Overall, our results showed that long-term cultivation following pasture degradation led to a decline in soil quality in all selected slope positions at the site studied in the semiarid region

Assessing Impacts of Land Use Change on Soil Quality Indicators in a Loessial Soil in Golestan Province, Iran

A study was conducted to determine suitable soil properties as soil quality indicators, using factor analysis in order to evaluate the effects of land use change on loessial hillslope soils of the Shastkola District in Golestan Province, northern Iran. To this end, forty surface soil (0-30 cm) samples were collected from four adjacent sites with the following land uses systems: (1) natural forest, (2) cultivated land, (3) land reforested with olive, and (4) land reforested with Cupressus. Fourteen soil chemical, physical, and biological properties were measured. Factor analysis (FA) revealed that mean weight diameter (MWD), water stable aggregates (WSA), soil organic matter (SOM), and total nitrogen (TN) were suitable for assessing the soil quality in the given ecosystem for monitoring the land use change effects. The results of analysis of variance (ANOVA) and mean comparison showed that there were significant (P< 0.01) differences among the four treatments with regard to SOM, MWD, and sand content. Clearing of the hardwood forest and tillage practices during 40 years led to a decrease in SOM by 71.5%. Cultivation of the deforested land decreased MWD by 52% and increased sand by 252%. The reforestation of degraded land with olive and Cupressus increased SOM by about 49% and 72%, respectively, compared to the cultivated control soil. Reforestation with olive increased MWD by 81% and reforestation with Cupressus increased MWD by 83.6%. The study showed that forest clearing followed by cultivation of the loessial hilly slopes resulted in the decline of the soil quality attributes, while reforestation improved them in the study area