Global expansion of marine protected areas and the redistribution of fishing effort.

The expansion of marine protected areas (MPAs) is a core focus of global conservation efforts, with the "30x30" initiative to protect 30% of the ocean by 2030 serving as a prominent example of this trend. We consider a series of proposed MPA network expansions of various sizes, and we forecast the impact this increase in protection would have on global patterns of fishing effort. We do so by building a predictive machine learning model trained on a global dataset of satellite-based fishing vessel monitoring data, current MPA locations, and spatiotemporal environmental, geographic, political, and economic features. We then use this model to predict future fishing effort under various MPA expansion scenarios compared to a business-as-usual counterfactual scenario that includes no new MPAs. The difference between these scenarios represents the predicted change in fishing effort associated with MPA expansion. We find that regardless of the MPA network objectives or size, fishing effort would decrease inside the MPAs, though by much less than 100%. Moreover, we find that the reduction in fishing effort inside MPAs does not simply redistribute outside-rather, fishing effort outside MPAs would also decline. The overall magnitude of the predicted decrease in global fishing effort principally depends on where networks are placed in relation to existing fishing effort. MPA expansion will lead to a global redistribution of fishing effort that should be accounted for in network design, implementation, and impact evaluation.

Satellites can reveal global extent of forced labor in the world's fishing fleet.

While forced labor in the world's fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

Synthesis of Group IV Nanowires on Graphene: The Case of Ge Nanocrawlers.

In recent years, there has been a growing interest in using graphene as a synthesis platform for polymers, zero-dimensional (0D) materials, one-dimensional materials (1D), and two-dimensional (2D) materials. Here, we report the investigation of the growth of germanium nanowires (GeNWs) and germanium nanocrawlers (GeNCs) on single-layer graphene surfaces. GeNWs and GeNCs are synthesized on graphene films by gold nanoparticles catalyzed vapor-liquid-solid growth mechanism. The addition of hydrogen chloride gas (HCl) at the nucleation step increased the propensity toward GeNCs growth on the surface. As the time lag before HCl introduction during the nucleation step increased, a significant change in the number of out-of-plane GeNWs versus in-plane GeNCs was observed. The nucleation temperature and time played a key role in the formation of GeNCs as well. The fraction of GeNCs (χNCs) decreased from 0.95 ± 0.01 to 0.66 ± 0.07 when the temperature was kept at 305 °C for 15 s versus maintained at 305 °C throughout the process, respectively. GeNCs exhibit ⟨112⟩ as the preferred growth direction whereas GeNWs exhibit both ⟨112⟩ and ⟨111⟩ as the preferred growth directions. Finally, our growth model suggests a possible mechanism for the preference of an in-plane GeNC growth on graphene versus GeNW on SiO2. These findings open up unique opportunities for fundamental studies of crystal growth on graphene, as well as enable exploration of new electronic interfaces between group IV materials and graphene, potentially toward designing new geometries for hybrid materials sensors.

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers.

A hierarchical machine learning (HML) framework is presented that uses a small dataset to learn and predict the dominant build parameters necessary to print high-fidelity 3D features of alginate hydrogels. We examine the 3D printing of soft hydrogel forms printed with the freeform reversible embedding of suspended hydrogel method based on a CAD file that isolated the single-strand diameter and shape fidelity of printed alginate. Combinations of system variables ranging from print speed, flow rate, ink concentration to nozzle diameter were systematically varied to generate a small dataset of 48 prints. Prints were imaged and scored according to their dimensional similarity to the CAD file, and high print fidelity was defined as prints with less than 10% error from the CAD file. As a part of the HML framework, statistical inference was performed, using the least absolute shrinkage and selection operator to find the dominant variables that drive the error in the final prints. Model fit between the system parameters and print score was elucidated and improved by a parameterized middle layer of variable relationships which showed good performance between the predicted and observed data (R2 = 0.643). Optimization allowed for the prediction of build parameters that gave rise to high-fidelity prints of the measured features. A trade-off was identified when optimizing for the fidelity of different features printed within the same construct, showing the need for complex predictive design tools. A combination of known and discovered relationships was used to generate process maps for the 3D bioprinting designer that show error minimums based on the chosen input variables. Our approach offers a promising pathway toward scaling 3D bioprinting by optimizing print fidelity via learned build parameters that reduce the need for iterative testing.

Symptom changes and crack progression in untreated cracked teeth: One-year findings from the National Dental Practice-Based Research Network.

OBJECTIVES: The study objective was to: (1) quantify symptom (pain) and crack changes during one year of follow-up, among teeth that had at least one visible crack at baseline but which did not receive treatment for those cracks; (2) identify any patient traits/behaviors and external tooth/crack characteristics correlated with these changes. METHODS: In this observational study, 209 National Dental Practice-Based Research Network dentists enrolled a convenience sample of 2858 subjects, each with a single, vital posterior tooth with at least one observed external crack; 1850 teeth remained untreated after one year of follow-up and were the cohort for analyses. Data were collected at the patient-, tooth-, and crack-level at baseline, one-year follow up (Y1), and interim visits. Associations between changes in symptoms and cracks were identified, as were changes in symptoms associated with baseline treatment recommendations. RESULTS: Changes in pain symptoms were observed in 32% of patients; decreases were twice as common as increases (23% vs. 10%). More changes were observed in cold pain than in biting pain and spontaneous pain combined; 2% had increases in biting pain and 2% in spontaneous pain. Only 6% had an increase in the number of cracks. Changes in pain symptoms were not associated with an increase in the number of cracks, but were associated with baseline treatment recommendations. Specifically, pain symptom changes (especially decreases) were more common when the tooth was recommended for treatment at baseline. CONCLUSIONS: Cracked teeth that have not received treatment one year after baseline do not show meaningful progression as measured by increased symptoms or number of cracks during follow-up. CLINICAL SIGNIFICANCE: Untreated cracked teeth, most of which were recommended for monitoring at baseline and some of which were recommended for treatment but did not receive treatment, remained relatively stable for one year with little progression of cracks or symptoms.

Fisheries governance in the face of climate change: Assessment of policy reform implications for Mexican fisheries.

Climate change is driving shifts in the abundance and distribution of marine fish and invertebrates and is having direct and indirect impacts on seafood catches and fishing communities, exacerbating the already negative effects of unsustainably high fishing pressure that exist for some stocks. Although the majority of fisheries in the world are managed at the national or local scale, most existing approaches to assessing climate impacts on fisheries have been developed on a global scale. It is often difficult to translate from the global to regional and local settings because of limited relevant data. To address the need for fisheries management entities to identify those fisheries with the greatest potential for climate change impacts, we present an approach for estimating expected climate change-driven impacts on the productivity and spatial range of fisheries at the regional scale in a data-poor context. We use a set of representative Mexican fisheries as test cases. To assess the implications of climate impacts, we compare biomass, harvest, and profit outcomes from a bioeconomic model under contrasting management policies and with and without climate change. Overall results show that climate change is estimated to negatively affect nearly every fishery in our study. However, the results indicate that overfishing is a greater threat than climate change for these fisheries, hence fixing current management challenges has a greater upside than the projected future costs of moderate levels of climate change. Additionally, this study provides meaningful first approximations of potential effects of both climate change and management reform in Mexican fisheries. Using the climate impact estimations and model outputs, we identify high priority stocks, fleets, and regions for policy reform in Mexico in the face of climate change. This approach can be applied in other data-poor circumstances to focus future research and policy reform efforts on stocks now subject to additional stress due to climate change. Considering their growing relevance as a critical source of protein and micronutrients to nourish our growing population, it is urgent for regions to develop sound fishery management policies in the short-term as they are the most important intervention to mitigate the adverse effects of climate change on marine fisheries.

Improved fisheries management could offset many negative effects of climate change.

The world's oceans supply food and livelihood to billions of people, yet species' shifting geographic ranges and changes in productivity arising from climate change are expected to profoundly affect these benefits. We ask how improvements in fishery management can offset the negative consequences of climate change; we find that the answer hinges on the current status of stocks. The poor current status of many stocks combined with potentially maladaptive responses to range shifts could reduce future global fisheries yields and profits even more severely than previous estimates have suggested. However, reforming fisheries in ways that jointly fix current inefficiencies, adapt to fisheries productivity changes, and proactively create effective transboundary institutions could lead to a future with higher profits and yields compared to what is produced today.

A handheld point-of-care genomic diagnostic system.

The rapid detection and identification of infectious disease pathogens is a critical need for healthcare in both developed and developing countries. As we gain more insight into the genomic basis of pathogen infectivity and drug resistance, point-of-care nucleic acid testing will likely become an important tool for global health. In this paper, we present an inexpensive, handheld, battery-powered instrument designed to enable pathogen genotyping in the developing world. Our Microfluidic Biomolecular Amplification Reader (µBAR) represents the convergence of molecular biology, microfluidics, optics, and electronics technology. The µBAR is capable of carrying out isothermal nucleic acid amplification assays with real-time fluorescence readout at a fraction of the cost of conventional benchtop thermocyclers. Additionally, the µBAR features cell phone data connectivity and GPS sample geotagging which can enable epidemiological surveying and remote healthcare delivery. The µBAR controls assay temperature through an integrated resistive heater and monitors real-time fluorescence signals from 60 individual reaction chambers using LEDs and phototransistors. Assays are carried out on PDMS disposable microfluidic cartridges which require no external power for sample loading. We characterize the fluorescence detection limits, heater uniformity, and battery life of the instrument. As a proof-of-principle, we demonstrate the detection of the HIV-1 integrase gene with the µBAR using the Loop-Mediated Isothermal Amplification (LAMP) assay. Although we focus on the detection of purified DNA here, LAMP has previously been demonstrated with a range of clinical samples, and our eventual goal is to develop a microfluidic device which includes on-chip sample preparation from raw samples. The µBAR is based entirely around open source hardware and software, and in the accompanying online supplement we present a full set of schematics, bill of materials, PCB layouts, CAD drawings, and source code for the µBAR instrument with the goal of spurring further innovation toward low-cost genetic diagnostics.