Building Global Leaders through Field Research and Extension Experiences in Belize

One of the most complex agricultural and natural resources challenges of our time is reconciling sustainable global food security and biodiversity conservation. Providing undergraduate students effective, learning experiences to develop technical and cultural competency prepares them to address this challenge and become global leaders in their disciplines. A three-year experiential research and extension project brought together 14 students and 10 faculty mentors to investigate smallholder farmers practicing conservation-compatible adjacent to the Vaca Forest Reserve in Belize. We used an agroecological approach to foster systems-level thinking and develop transdisciplinary skills of undergraduate students. Students completed applied individual research projects that explored the challenge of food security and biodiversity conservation in the tropics, and worked collaboratively with local stakeholders, design and implement extension projects based on research results. Student and faculty teams assessed cropping and soil management practices; social and economic systems; and wildlife, forestry, and ecosystem services. We assessed student learning outcomes with a tool commonly used for evaluating undergraduate research. Students reported learning gains in attitudes and behaviors toward research, mindset towards research, ability to think and work like a scientist, and research skills. Students also reported positive working relationships with mentors and peers, and a high level of publication and presentation outputs. Students reported that their Belize experience helped develop their agroecological and cross-cultural knowledge and skills, and prepared them for their next career steps. We conclude with recommendations for higher education institutions wishing to develop meaningful global undergraduate research experiences that can build the next generation of leaders

White Light Flare Continuum Observations with ULTRACAM

We present sub-second, continuous-coverage photometry of three flares on the dM3.5e star, EQ Peg A, using custom continuum filters with WHT/ULTRACAM. These data provide a new view of flare continuum emission, with each flare exhibiting a very distinct light curve morphology. The spectral shape of flare emission for the two large-amplitude flares is compared with synthetic ULTRACAM measurements taken from the spectra during the large 'megaflare' event on a similar type flare star. The white light shape during the impulsive phase of the EQ Peg flares is consistent with the range of colors derived from the megaflare continuum, which is known to contain a Hydrogen recombination component and compact, blackbody-like components. Tentative evidence in the ULTRACAM photometry is found for an anti-correlation between the emission of these components.Comment: 8 pages, 3 figures. Proceedings of the 16th Workshop on Cool Stars, Stellar Systems, and the Sun (PASP conference series, in press

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed

Temporal tensions: EU citizen migrants, asylum seekers and refugees navigating dominant temporalities of work in England

This article considers the role of temporality in the differential inclusion of migrants. In order to do this we draw on research which examined the working lives of a diverse group of new migrants in North East England: Eastern European migrants arriving from 2004 and asylum seekers and refugees arriving from 1999. In so doing we emphasise both distinct and shared experiences, related to immigration status but also a range of other dimensions of identity. We specifically consider how dominant temporalities regulate the lives of new migrants through degrees, periods and moments of acceleration/deceleration. The paper illustrates the ways in which dominant temporalities control access and non-access to particular, often precarious forms of work – but also how migrants attempt to navigate such restrictions through their own use and constructions of time. We explore this in relation to three 'phases' of time. Firstly, through experiences of the UK asylum system and work prohibition. Secondly for a broader group of participants we explore the speeding up and slowing down of transitions to and progression within work. Lastly, we consider how participants experience everyday temporal tensions between paid employment and unpaid care. Across these phases we suggest that dominant orderings of time and the narratives which make sense of these, represent non-simultaneous temporalities that do not neatly map onto each other

The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, "bogus" candidates from processing artifacts and imperfect image subtractions outnumber real transients by ~ 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS

Selective leaching of copper and zinc from primary ores and secondary mineral residues using biogenic ammonia

With the number of easily accessible ores depleting, alternate primary and secondary sources are required to meet the increasing demand of economically important metals. Whilst highly abundant, these materials are of lower grade with respect to traditional ores, thus highly selective and sustainable metal extraction technologies are needed to reduce processing costs. Here, we investigated the metal leaching potential of biogenic ammonia produced by a ureolytic strain of Lysinibacillus sphaericus on eight primary and secondary materials, comprised of mining and metallurgical residues, sludges and automotive shredder residues (ASR). For the majority of materials, moderate to high yields (30–70%) and very high selectivity (>97% against iron) of copper and zinc were obtained with 1 mol L−1 total ammonia. Optimal leaching was achieved and further refined for the ASR in a two-step indirect leaching system with biogenic ammonia. Copper leaching was the result of local corrosion and differences in leaching against the synthetic (NH4)2CO3 control could be accounted for by pH shifts from microbial metabolism, subsequently altering free NH3 required for coordination. These results provide important findings for future sustainable metal recovery technologies from secondary materials.This work was conducted under the financial support of the Strategic Initiative Materials in Flanders (SIM) (SBO-SMART: Sustainable Metal Extraction from Tailings, grant no. HBC.2016.0456) and the European Union’s Horizon 2020 research and innovation programme, Metal Re-covery from Low-Grade Ores and Wastes Plus (METGROW+, grant no. 690088) . FV acknowledges support by the Flemish Agency for Inno-vation and Entrepreneurship (Vlaio) via a Baekeland PhD fellowship (HBC.2017.0224) and by the Research & Development Umicore Group. We would like to thank Pieter Ostermeyer and Karel Folens for assis-tance with thermodynamic modelling and CMET and ECOCHEM group members and SMART/METGROW+partners for valuable discussions throughout the projec

Low-intensity microwave irradiation does not substantially alter gene expression in late larval and adult Caenorhabditis elegans.

Reports that low-intensity microwave radiation induces heat-shock reporter gene expression in the nematode, Caenorhabditis elegans, have recently been reinterpreted as a subtle thermal effect caused by slight heating. This study used a microwave exposure system (1.0 GHz, 0.5 W power input; SAR 0.9-3 mW kg-1 for 6-well plates) that minimises temperature differentials between sham and exposed conditions (≤0.1 °C). Parallel measurement and simulation studies of SAR distribution within this exposure system are presented. We compared 5 Affymetrix gene-arrays of pooled triplicate RNA populations from sham-exposed L4/adult worms against 5 gene-arrays of pooled RNA from microwave-exposed worms (taken from the same source population in each run). No genes showed consistent expression changes across all 5 comparisons, and all expression changes appeared modest after normalisation (≤ 40% up- or down-regulated). The number of statistically significant differences in gene expression (846) was less than the false-positive rate expected by chance (1131). We conclude that the pattern of gene expression in L4/adult C. elegans is substantially unaffected by low-intensity microwave radiation; the minor changes observed in this study could well be false positives. As a positive control, we compared RNA samples from N2 worms subjected to a mild heat-shock treatment (30ºC) against controls at 26 ºC (2 gene arrays per condition). As expected, heat-shock genes are strongly up-regulated at 30ºC, particularly an hsp-70 family member (C12C8.1) and hsp-16.2 . Under these heat-shock conditions, we confirmed that an hsp-16.2::GFP transgene was strongly up-regulated, whereas two non-heat-inducible transgenes (daf-16::GFP; cyp-34A9::GFP) showed little change in expression

Cisternal Organization of the Endoplasmic Reticulum during Mitosis

The endoplasmic reticulum (ER) of animal cells is a single, dynamic, and continuous membrane network of interconnected cisternae and tubules spread out throughout the cytosol in direct contact with the nuclear envelope. During mitosis, the nuclear envelope undergoes a major rearrangement, as it rapidly partitions its membrane-bound contents into the ER. It is therefore of great interest to determine whether any major transformation in the architecture of the ER also occurs during cell division. We present structural evidence, from rapid, live-cell, three-dimensional imaging with confirmation from high-resolution electron microscopy tomography of samples preserved by high-pressure freezing and freeze substitution, unambiguously showing that from prometaphase to telophase of mammalian cells, most of the ER is organized as extended cisternae, with a very small fraction remaining organized as tubules. In contrast, during interphase, the ER displays the familiar reticular network of convolved cisternae linked to tubules