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Forward genetics is used to identify the genetic basis for a phenotype. The approach involves identifying a mutant organism exhibiting a phenotype of interest and then mapping the causative locus or gene. Bulked‐segregant analysis (BSA) is a quick and effective approach to map mutants using pools of mutants and wild‐type plants from a segregating population to identify linkage of the mutant phenotype, and this approach has been successfully used in plants. Traditional linkage mapping approaches are outdated and time intensive, and can be very difficult. With the highly evolved development and reduction in cost of high‐throughput sequencing, this new approach combined with BSA has become extremely effective in multiple plant species, includingZea mays(maize). While the approach is incredibly powerful, careful experimental design, bioinformatic mapping techniques, and interpretation of results are important to obtain the desired results in an effective and timely manner. Poor design of a mapping population, limitations in bioinformatic experience, and inadequate understanding of sequence data are limitations of these approaches for the researcher. Here, we describe a straightforward protocol for mapping mutations responsible for a phenotype of interest in maize, using high‐throughput sequencing and BSA. Specifically, we discuss relevant aspects of developing a mutant mapping population. This is followed by a detailed protocol for DNA preparation and analysis of short‐read sequences to map and identify candidate causative mutations responsible for the mutant phenotype of interest. We provide command‐line and perl scripts to complete the bioinformatic analysis of the mutant sequence data. This protocol lays out the design of the BSA, bioinformatic approaches, and interpreting the sequencing data. These methods are very adaptable to any forward genetics experiment and provide a step‐by‐step approach to identifying the genetic basis of a maize mutant phenotype. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Basic Protocol: Bulked‐segregant analysis and high‐throughput sequencing to map maize mutants

 

Particle cycling rates in marine systems are difficult to measure directly, but of great interest in understanding how carbon and other elements are distributed throughout the ocean. Here, rates of particle production, aggregation, disaggregation, sinking, remineralization, and transport mediated by zooplankton diel vertical migration were estimated from size-fractionated measurements of particulate organic carbon (POC) concentration collected during the NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) cruise at Station P in summer 2018. POC data were combined with a particle cycling model using an inverse method. Our estimates of the total POC settling flux throughout the water column are consistent with those derived from thorium-234 disequilibrium and sediment traps. A budget for POC in two size fractions, small (1–51 µm) and large (> 51 µm), was produced for both the euphotic zone (0–100 m) and the upper mesopelagic zone (100–500 m). We estimated that POC export at the base of the euphotic zone was 2.2 ± 0.8 mmol m−2 d−1, and that both small and large particles contributed considerably to the total export flux along the water column. The model results indicated that throughout the upper 500 m, remineralization leads to a larger loss of small POC than does aggregation, whereas disaggregation results in a larger loss of large POC than does remineralization. Of the processes explicitly represented in the model, zooplankton diel vertical migration is a larger source of large POC to the upper mesopelagic zone than the convergence of large POC due to particle sinking. Positive model residuals reveal an even larger unidentified source of large POC in the upper mesopelagic zone. Overall, our posterior estimates of particle cycling rate constants do not deviate much from values reported in the literature, i.e., size-fractionated POC concentration data collected at Station P are largely consistent with prior estimates given their uncertainties. Our budget estimates should provide a useful framework for the interpretation of process-specific observations obtained by various research groups in EXPORTS. Applying our inverse method to other systems could provide insight into how different biogeochemical processes affect the cycling of POC in the upper water column. 

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting. 

Governments around the world rely on environmental impact assessment (EIA) to understand the environmental risks of proposed developments.

To examine the basis for these appraisals, we examine the output of EIA processes in jurisdictions within seven countries, focusing on scope (spatial and temporal), mitigation actions and whether impacts were identified as ‘significant’.

We find that the number of impacts characterized as significant is generally low. While this finding may indicate that EIA is successful at promoting environmentally sustainable development, it may also indicate that the methods used to assess impact are biased against findings of significance. To explore the methods used, we investigate the EIA process leading to significance determination.

We find that EIA reports could be more transparent with regard to the spatial scale they use to assess impacts to wildlife. We also find that few reports on mining projects consider temporal scales that are precautionary with regard to the effects of mines on water resources. Across our sample of reports, we find that few EIAs meaningfully consider the different ways that cumulative impacts can interact.

Across countries, we find that proposed mitigation measures are often characterized as effective without transparent justification, and sometimes are described in ways that render the mitigation measure proposal ambiguous.

Across the reports in our sample, professional judgement is overwhelmingly the determinant of impact significance, with little transparency around the reasoning process involved or input by stakeholders.

We argue that the credibility and accuracy of the EIA process could be improved by adopting more rigorous assessment methodologies and empowering regulators to enforce their use.

A freePlain Language Summarycan be found within the Supporting Information of this article.

 

The ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind “upslope” events. Larger biases of at least 50% were found for high wind “pulsed” events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions.

 

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of theWZ+ jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.