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There is a critical need to generate environmentally relevant microplastics (MPs) and nanoplastics (NPs) to better investigate their behavior in laboratory settings. Environmental MPs are heterogenous in size and shape, unlike monodisperse and uniform microspheres commonly used as surrogates. Cryogenic grinding, or cryomilling, was successfully utilized to transform polystyrene (PS) bulk material into heterogenous micro and nano fragments. Fourier-Transform Infrared (FTIR) spectroscopy confirmed that this approach did not alter polymer surface chemistry. The number of milling cycles (time of milling) and frequency of grinding (intensity of milling) were varied to investigate the role cryomilling parameters had on generated MP characteristics. The resulting particle size distributions of cryomilled samples were measured and compared. Coulter Counter and Nanoparticle Tracking Analysis (NTA) were used to measure the particle size distributions at the micro and nanoparticle size ranges, respectively. Microspheres were used to determine what camera settings yielded more accurate sizing and to reduce bias in the NTA analysis. Increasing milling cycles generally increased the number of smaller particles. The evolution of the measured size distributions indicated that small nanosized fragments broke off from larger MPs during cryomilling, steadily eroding larger MP fragments. The number of milling cycles was observed to more consistently impact the size distributions of fragments compared to the frequency of milling. This study offers both analysis of the cryomilling process and recommendations for generating more realistic PS MP/NPs for examining environmental fate and effects.

 

For short-wavelength infrared (SWIR) avalanche photodiodes, a separate absorption, charge, and multiplication design is widely used. AlInAsSb on an InP substrate is a potential multiplication layer with a lattice match to absorber candidates across the SWIR. Our new measurements demonstrate that AlInAsSb on InP is a promising multiplier candidate with a relatively low dark current density of 10−4 A/cm2 at a gain of 30; a high gain, measured up to 245 in this study; and a large differentiation of electron and hole ionization leading to a low excess noise, measured to be 2.5 at a gain of 30. These characteristics are all improvements over commercially available SWIR detectors incorporating InAlAs or InP as the multiplier. We measured and analyzed gain for multiple wavelengths to extract the ionization coefficients as a function of an electric field over the range 0.33–0.6 MV/cm.

 

This Complete Evidence-based Practice paper will describe how three different public urban research universities designed, executed, and iterated Summer Bridge programming for a subset of incoming first-year engineering students over the course of three consecutive years. There were commonalities between each institution’s Summer Bridge, as well as unique aspects catering to the specific needs and structures of each institution. Both these commonalities and unique aspects will be discussed, in addition to the processes of iteration and improvement, target student populations, and reported student outcomes. Finally, recommendations for other institutions seeking to launch or refine similar programming will be shared. Summer Bridge programming at each of the three institutions shared certain communalities. Mostly notably, each of the three institutions developed its Summer Bridge as an additional way to provide support for students receiving an NSF S-STEM scholarship. The purpose of each Summer Bridge was to build community among these students, prepare them for the academic rigor of first-year engineering curriculum, and edify their STEM identity and sense of belonging. Each Summer Bridge was a 3-5 day experience held in the week immediately prior to the start of the Fall semester. In addition to these communalities, each Summer Bridge also had its own unique features. At the first institution, Summer Bridge is focused on increasing college readiness through the transition from summer break into impending coursework. This institution’s Summer Bridge includes STEM special-interest presentations (such as biomedical or electrical engineering) and other development activities (such as communication and growth mindset workshops). Additionally, this institution’s Summer Bridge continues into the fall semester via a 1-credit hour First Year Seminar class, which builds and reinforces student networking and community beyond the summer experience. At the second institution, all students receiving the NSF S-STEM scholarship (not only those who are first-year students) participate in Summer Bridge. This means that S-STEM scholars at this institution participate in Summer Bridge multiple years in a row. Relatedly, after the first year, Summer Bridge transitioned to a student-led and student-delivered program, affording sophomore and junior students leadership opportunities, which not only serve as marketable experience after graduation, but also further builds their sense of STEM identity and belonging. At the third institution, a special focus was given to building community. This was achieved through several means. First, each day of Summer Bridge included a unique team-oriented design challenge where students got to work together and know each other within an engineering context, also reinforcing their STEM identities. Second, students at this institution’s Summer Bridge met their future instructors in an informal, conversational, lunch setting; many students reported this was one of their favorite aspects of Summer Bridge. Finally, Summer Bridge facilitated a first connect between incoming first-year students and their peer mentors (sophomore and junior students also receiving the NSF S-STEM scholarship), with whom they would meet regularly throughout the following fall and spring semesters. Each of the three institutions employed processes of iteration and improvement for their Summer Bridge programming over the course of two or three consecutive years. Through each version and iteration of Summer Bridge, positive student outcomes are demonstrated, including direct student feedback indicating built community among students and the perception that their time spent during Summer Bridge was valuable. Based on the experiences of these three institutions, as well as research on other institutions’ Summer Bridge programming, recommendations for those seeking to launch or refine similar Summer Bridge programming will also be shared. 

Understanding how megaherbivores incorporate habitat features into their foraging behavior is key toward understanding how herbivores shape the surrounding landscape. While the role of habitat structure has been studied within the context of predator–prey dynamics and grazing behavior in terrestrial systems, there is a limited understanding of how structure influences megaherbivore grazing in marine ecosystems. To investigate the response of megaherbivores (green turtles) to habitat features, we experimentally introduced structure at two spatial scales in a shallow seagrass meadow in The Bahamas. Turtle density increased 50‐fold (to 311 turtles ha⁻¹) in response to the structures, and turtles were mainly grazing and resting (low vigilance behavior). This resulted in a grazing patch exceeding the size of the experimental setup (242 m²), with reduced seagrass shoot density and aboveground biomass. After structure removal, turtle density decreased and vigilance increased (more browsing and shorter surfacing times), while seagrass within the patch partly recovered. Even at a small scale (9 m²), artificial structures altered turtle grazing behavior, resulting in grazing patches in 60% of the plots. Our results demonstrate that marine megaherbivores select habitat features as foraging sites, likely to be a predator refuge, resulting in heterogeneity in seagrass bed structure at the landscape scale.

 

Abstract Where does the carbon released by burning fossil fuels go? Currently, ocean and land systems remove about half of the CO 2 emitted by human activities; the remainder stays in the atmosphere. These removal processes are sensitive to feedbacks in the energy, carbon, and water cycles that will change in the future. Observing how much carbon is taken up on land through photosynthesis is complicated because carbon is simultaneously respired by plants, animals, and microbes. Global observations from satellites and air samples suggest that natural ecosystems take up about as much CO 2 as they emit. To match the data, our land models generate imaginary Earths where carbon uptake and respiration are roughly balanced, but the absolute quantities of carbon being exchanged vary widely. Getting the magnitude of the flux is essential to make sure our models are capturing the right pattern for the right reasons. Combining two cutting-edge tools, carbonyl sulfide (OCS) and solar-induced fluorescence (SIF), will help develop an independent answer of how much carbon is being taken up by global ecosystems. Photosynthesis requires CO 2 , light, and water. OCS provides a spatially and temporally integrated picture of the “front door” of photosynthesis, proportional to CO 2 uptake and water loss through plant stomata. SIF provides a high-resolution snapshot of the “side door,” scaling with the light captured by leaves. These two independent pieces of information help us understand plant water and carbon exchange. A coordinated effort to generate SIF and OCS data through satellite, airborne, and ground observations will improve our process-based models to predict how these cycles will change in the future. 

Abstract. Land surface modellers need measurable proxies toconstrain the quantity of carbon dioxide (CO2) assimilated bycontinental plants through photosynthesis, known as gross primary production(GPP). Carbonyl sulfide (COS), which is taken up by leaves through theirstomates and then hydrolysed by photosynthetic enzymes, is a candidate GPPproxy. A former study with the ORCHIDEE land surface model used a fixedratio of COS uptake to CO2 uptake normalised to respective ambientconcentrations for each vegetation type (leaf relative uptake, LRU) tocompute vegetation COS fluxes from GPP. The LRU approach is known to havelimited accuracy since the LRU ratio changes with variables such asphotosynthetically active radiation (PAR): while CO2 uptake slows underlow light, COS uptake is not light limited. However, the LRU approach hasbeen popular for COS–GPP proxy studies because of its ease of applicationand apparent low contribution to uncertainty for regional-scaleapplications. In this study we refined the COS–GPP relationship andimplemented in ORCHIDEE a mechanistic model that describes COS uptake bycontinental vegetation. We compared the simulated COS fluxes againstmeasured hourly COS fluxes at two sites and studied the model behaviour andlinks with environmental drivers. We performed simulations at a global scale,and we estimated the global COS uptake by vegetation to be −756 Gg S yr−1,in the middle range of former studies (−490 to −1335 Gg S yr−1). Basedon monthly mean fluxes simulated by the mechanistic approach in ORCHIDEE, wederived new LRU values for the different vegetation types, ranging between0.92 and 1.72, close to recently published averages for observed values of1.21 for C4 and 1.68 for C3 plants. We transported the COS using the monthlyvegetation COS fluxes derived from both the mechanistic and the LRUapproaches, and we evaluated the simulated COS concentrations at NOAA sites.Although the mechanistic approach was more appropriate when comparing tohigh-temporal-resolution COS flux measurements, both approaches gave similarresults when transporting with monthly COS fluxes and evaluating COSconcentrations at stations. In our study, uncertainties between these twoapproaches are of secondary importance compared to the uncertainties in theCOS global budget, which are currently a limiting factor to the potential ofCOS concentrations to constrain GPP simulated by land surface models on theglobal scale. 

Using medical images to evaluate disease severity and change over time is a routine and important task in clinical decision making. Grading systems are often used, but are unreliable as domain experts disagree on disease severity category thresholds. These discrete categories also do not reflect the underlying continuous spectrum of disease severity. To address these issues, we developed a convolutional Siamese neural network approach to evaluate disease severity at single time points and change between longitudinal patient visits on a continuous spectrum. We demonstrate this in two medical imaging domains: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. Our patient cohorts consist of 4861 images from 870 patients in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) cohort study and 10,012 images from 3021 patients in the Multicenter Osteoarthritis Study (MOST), both of which feature longitudinal imaging data. Multiple expert clinician raters ranked 100 retinal images and 100 knee radiographs from excluded test sets for severity of ROP and osteoarthritis, respectively. The Siamese neural network output for each image in comparison to a pool of normal reference images correlates with disease severity rank (ρ = 0.87 for ROP andρ = 0.89 for osteoarthritis), both within and between the clinical grading categories. Thus, this output can represent the continuous spectrum of disease severity at any single time point. The difference in these outputs can be used to show change over time. Alternatively, paired images from the same patient at two time points can be directly compared using the Siamese neural network, resulting in an additional continuous measure of change between images. Importantly, our approach does not require manual localization of the pathology of interest and requires only a binary label for training (same versus different). The location of disease and site of change detected by the algorithm can be visualized using an occlusion sensitivity map-based approach. For a longitudinal binary change detection task, our Siamese neural networks achieve test set receiving operator characteristic area under the curves (AUCs) of up to 0.90 in evaluating ROP or knee osteoarthritis change, depending on the change detection strategy. The overall performance on this binary task is similar compared to a conventional convolutional deep-neural network trained for multi-class classification. Our results demonstrate that convolutional Siamese neural networks can be a powerful tool for evaluating the continuous spectrum of disease severity and change in medical imaging.

 

Most of the world's crops depend on pollinators, so declines in both managed and wild bees raise concerns about food security. However, the degree to which insect pollination is actually limiting current crop production is poorly understood, as is the role of wild species (as opposed to managed honeybees) in pollinating crops, particularly in intensive production areas. We established a nationwide study to assess the extent of pollinator limitation in seven crops at 131 locations situated across major crop-producing areas of the USA. We found that five out of seven crops showed evidence of pollinator limitation. Wild bees and honeybees provided comparable amounts of pollination for most crops, even in agriculturally intensive regions. We estimated the nationwide annual production value of wild pollinators to the seven crops we studied at over $1.5 billion; the value of wild bee pollination of all pollinator-dependent crops would be much greater. Our findings show that pollinator declines could translate directly into decreased yields or production for most of the crops studied, and that wild species contribute substantially to pollination of most study crops in major crop-producing regions.