Philippa Kaur

Welch and colleagues analysed 3.7 billion AIS messages recorded between 2017 and 2019 in the global Fishing Watch AIS dataset, identifying more than 55,000 suspected intentional disabling events in waters more than 50 nautical miles from shore, amounting to 6% ( >4.9 million hours) of obscured vessel activity. Hotspots of disabling activity were located near several regions of IUU concern and transshipment hotspots, including in the exclusive economic zones of Argentina and West African nations and in the Northwest Pacific. Using individual boosted regression tree models for the four dominant gear types (squid jiggers, trawlers, tuna purse seines and drifting longlines) and a full model that included all suspected disabling events (that is, the four gear types listed above and additional gears such as gillnet and troll), Welch and colleagues found that loitering by transshipment vessels (a proxy for potential transshipment events) was the most important driver in the full model and squid jigger model and more than half of the disabling events by squid jiggers were close enough to undertake transshipment to refrigerated cargo vessels. More

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Russia invades UkraineThe global science community was quick to condemn Russian’s invasion of Ukraine in February. Research organizations moved fast to cut ties with Russia, stopping funding and collaborations, and journals came under pressure to boycott Russian authors.The situation escalated when Russian forces attacked Europe’s largest nuclear power plant, Zaporizhzhia, in March, prompting fears of a nuclear accident. Russian troops continue to occupy the power plant. Since the invasion began, thousands of civilians have been killed and millions displaced; many others, including scientists, have fled the country.The war has affected research in space and climate science, disrupted fieldwork and played a significant part in the global energy crisis. The invasion could also precipitate a new era for European defence research.JWST delights astronomers

Stephans Quintet, a grouping of five galaxies, taken by NASA’s James Webb Space Telescope.Credit: NASA, ESA, CSA, and STScI via Getty

NASA’s James Webb Space Telescope (JWST) — the most complex telescope ever built — reached its destination in space in January after decades of planning. In July, astronomers were awed by the telescope’s first image — of thousands of distant galaxies in the constellation Volans. Since then, the US$10-billion observatory has captured a steady stream of spectacular images, and astronomers have been working feverishly on early data. Insights include detailed observations of an exoplanet, and leading contenders for the most distant galaxy ever seen.NASA also decided not to rename the telescope, despite calls from some astronomers to do so because the telescope’s namesake, a former NASA administrator, held high-ranking government positions in the 1950s and 1960s, when the United States systematically fired gay and lesbian government employees. A NASA investigation “found no evidence that Webb was either a leader or proponent of firing government employees for their sexual orientation”, the agency said in a statement in November.AI predicts protein structuresResearchers announced in July that they had used the revolutionary artificial-intelligence (AI) network AlphaFold to predict the structures of more than 200 million proteins from roughly one million species, covering almost every known protein from all organisms whose genomes are held in databases. The development of AlphaFold netted its creators at the London-based AI company DeepMind, owned by Alphabet, one of this year’s US$3-million Breakthrough prizes — the most lucrative awards in science.AlphaFold isn’t the only player on the scene. Meta (formerly Facebook), in California, has developed its own AI network, called ESMFold, and used it to predict the shapes of roughly 600 million possible proteins from bacteria, viruses and other microorganisms that have not been isolated or cultured. Scientists are using these tools to dream up proteins that could form the basis of new drugs and vaccines.Monkeypox goes global

The monkeypox virus (shown here as a coloured transmission electron micrograph) is related to the smallpox virus.Credit: CDC/Science Photo Library

The rapid global spread of monkeypox (recently renamed ‘mpox’ by the World Health Organization) this year caught many scientists off guard. Previously, the virus had mainly been confined to Central and West Africa, but from May this year, infections started appearing in Europe, the United States, Canada and many other countries, mostly in young and middle-aged men who have sex with men. The virus is related to smallpox, and the circulating strain only rarely causes severe disease or death. But its fast spread led the World Health Organization to declare the global outbreak a ‘public-health emergency of international concern’, the agency’s highest alert level, in July.As cases soared, researchers got to work trying to understand the dynamics of the disease. Studies confirmed that it is transmitted primarily through repeated skin-to-skin contact, and trials of possible treatments got under way. Existing smallpox vaccines were also used to suppress the virus in some countries. Six months after mpox infections first started increasing, vaccination efforts and behavioural changes seemed to have curbed its spread in Europe and the United States. Researchers predict a range of scenarios from here — the most hopeful being that the virus fizzles out in non-endemic countries over the next few months or years.The Moon has a revivalThe Moon has become a popular destination for space missions this year. First off the launch pad, in August, was South Korea’s Danuri probe, which is expected to arrive at its destination in January and orbit the Moon for a year. The mission is the country’s first foray beyond Earth’s orbit and is carrying a host of experiments.Last month, NASA’s hotly anticipated Artemis programme — which aims to send astronauts to the Moon in the next few years — finally kicked off with the launch of an uncrewed capsule called Orion, a joint venture with the European Space Agency. As part of a test flight to see whether the system can transport people safely to the Moon, the capsule flew out past the Moon and made its way back to Earth safely this month.A lunar spacecraft made by a Japanese company launched this month. ispace’s M1 lander is aiming to be the first of several private ventures to land on the surface of the Moon next year. The lander will carry two rovers, one for the United Arab Emirates and another for the Japan Aerospace Exploration Agency, JAXA. The rovers will be a first for both countries.Climate-change funding

People cross a flooded highway in Sindh province, Pakistan in August.Credit: Waqar Hussein/EPA-EFE/Shutterstock

There were many reasons to feel despondent about the United Nations Climate Change Conference of the Parties (COP27) in Egypt last month, but an agreement on a new ‘loss and damage’ fund was one bright spot. The fund will help low- and middle-income countries to cover the cost of climate-change impacts, such as the catastrophic floods in Pakistan this year, which caused more than US$30 billion worth of damage and economic losses.But calls at COP27 to phase out fossil fuels were blocked by oil-producing states, and many blamed the lack of progress on the energy crisis sparked by Russia’s invasion of Ukraine. High natural-gas prices have led some European nations to rely temporarily on coal. Global carbon emissions from fossil fuels are expected to hit 37.5 billion tonnes this year, a new record. The window to limit warming to 1.5–2 ºC above pre-industrial temperatures is disappearing fast — and might even have passed.Omicron’s offspring drive the pandemicOmicron and its descendants dominated all other coronavirus variants this year. The fast-spreading strain was first detected in southern Africa in November 2021, and quickly spread around the globe. From early on, it was clear that Omicron could evade immune-system defences more successfully than previous variants, which has meant that vaccines are less effective. Throughout the year, a diverse group of immune-dodging offshoots of Omicron has emerged, making it challenging for scientists to predict coming waves of infection.Vaccines based on Omicron variants have been rolled out in some countries in the hope they will offer greater protection than previous jabs, but early data suggest the extra benefit is modest. Nasal sprays against COVID-19 have also become a tool in the vaccine arsenal. The idea is that these stop the virus at the site where it first takes hold. In September, China and India approved needle-free COVID-19 vaccines that are delivered through the nose or mouth, and many similar vaccines are in various stages of development.Pig organs transplanted into people

Surgeons in Baltimore, Maryland transplanted the first pig heart into a person in January.Credit: EyePress News/Shutterstock

In January, US handyman David Bennett became the first person to receive a transplanted heart from a genetically modified pig — a crucial first step in determining whether animals could provide a source of organs for people who need them. Bennett survived for another eight weeks after the transplant, but researchers were impressed that he lived for that long, given that the human immune system attacks non-genetically modified pig organs in minutes. A few months later, two US research groups independently reported transplanting pig kidneys into three people who had been declared legally dead because they did not have brain function. The organs weren’t rejected and started producing urine. Researchers say the next step is clinical trials to test such procedures thoroughly in living people.Elections and science

Luís Inácio Lula da Silva was elected president of Brazil in October.Credit: Fabio Vieira/FotoRua/NurPhoto via Getty

National elections in Brazil, Australia and France brought relief for many researchers. After three years of science-damaging policies under right-wing president Jair Bolsonaro, Brazil narrowly elected leftist labour leader and former president Luiz Inácio Lula da Silva to lead the country in October. Scientists are hopeful that Lula’s return will result in a desperately needed boost to research funding and greater protection for the Amazon rainforest.French researchers were buoyed by President Emmanuel Macron’s victory over far-right candidate Marine Le Pen in April, and the election of Anthony Albanese as prime minister in Australia in May was seen as a good thing for science and climate-change action, too. In China, Xi Jinping cemented his legacy with an historic third term as head of the Chinese Communist Party. Xi has placed science and innovation at the heart of his country’s growth strategy.In other nations, it was unclear how research would fare under new leaders, such as Giorgia Meloni, the far-right candidate elected as Italy’s first female prime minister in October. Science was not a priority for the United Kingdom’s three prime ministers this year, although they have retained previous commitments to raise research funding. After Boris Johnson reisgned, Liz Truss was in the position for just seven weeks before she too resigned and the current Prime Minister Rishi Sunak took over.Environmental push beginsThis week, conservation and political leaders are attempting to finalize a global deal to protect the environment. The UN’s Convention on Biological Diversity Conference of the Parties (COP15) is under way in Montreal, Canada. A new biodiversity treaty, known as the post-2020 Global Diversity Framework, has been delayed by more than two years because of the COVID-19 pandemic. Progress towards an agreement has been slow, and the deal looked under threat when negotiations stalled over financing during international talks in Nairobi in June. Financial pledges from some nations to support biodiversity helped discussions to move forward, but estimates suggest that US$700 billion more is needed annually to protect the natural world. At the meeting, delegates will hopefully agree on targets to stabilize species’ declines by 2030 and reverse them by mid-century. More

Data sourcesThis study builds on two datasets; 666 livestock anthrax outbreaks collected over 60 years (1957–2017) by the Kenya Directorate of Veterinary Services (KDVS), and 13 reported anthrax outbreaks we investigated between 2017 and 201811,13. These datasets were combined with data from targeted active anthrax surveillance we conducted in 2019–2020 (see below) to define anthrax suitable areas in Kenya, including hotspots, and subsequently assessed effectiveness of livestock vaccination as a control strategy.Targeted active surveillance-collected anthrax data, 2019–2020Active anthrax surveillance was conducted for 12 months between 2019 and 2020 in randomly selected areas to ensure representation of all AEZs of the country. AEZs are land units defined based on the patterns of soil, landforms and climatic characteristics. Kenya has seven AEZs that include agro-alpine, high potential, medium potential, semi-arid, arid, very-arid and desert. In 2013, Kenya devolved governance into 47 semi-autonomous counties that are subdivided into 290 subcounties which are in turn divided into 1450 administrative wards, the smallest administrative units in the country. Using a geographic map that condensed Kenya into five AEZs; agro-alpine, high potential, medium potential, semi-arid, and arid/very arid zones, we randomly selected 4 administrative sub-counties from each AEZ (N = 20). To increase geographic spread of the study and enhance detection of anthrax outbreaks, we surveilled the larger administrative county (consisting of 20 to 45 administrative wards) where the randomly selected sub-counties were located. As shown in Fig. S1, we ultimately carried out the active anthrax surveillance in 18 counties, containing 523 administrative wards, the latter being used for measuring spatial association (see below).We conducted the surveillance between April 2019 and June 2020, through 523 animal health practitioners (AHPs), one in each ward, after intensive training to identify anthrax using a standard case definition, and to collect and electronically transmit the data weekly using telephone-based short messaging system (SMS) to a central server hosted by KDVS. Regarding case definition, any livestock death classified as anthrax through clinical or laboratory diagnosis was considered an anthrax event. Using standard guidelines issued by the KDVS, a clinical diagnosis was made by the AHPs across the country as an acute cattle, sheep or goat disease characterized by sudden death with or without bleeding from natural orifices, accompanied by absence of rigor mortis. Further, if the carcass was accidentally opened, failure of blood to clot and/or the presence of splenomegaly were included. In pigs, symptoms included swelling of the face and neck with oedema. A laboratory confirmed anthrax was diagnosed using Gram and methylene blue stains followed by identification of the capsule and typical rod-shaped B. anthracis in clinical specimens that the AHPs submitted to the central or regional veterinary investigation laboratories in Kenya. One case of anthrax in either species was considered an outbreak.During the surveillance, the programmed server sent prompting texts directly to the AHPs’ cell phones every Friday of each week for the 52 weeks. The AHPs interacted with the platform by responding to prompting questions sent via SMS to their telephones. Data were securely stored in an online encrypted platform which was subsequently downloaded into Ms Excel for analysis. This surveillance detected 119 anthrax outbreaks, whose partial data were used to model effects of climate change on future anthrax distribution in Kenya14. Here, we integrated these active surveillance data with other datasets to conduct detailed ENM and kernel-smoothed density mapping with a goal of refining suitable anthrax areas including crystalizing hotspots in the country.Anthrax outbreak incidence per livestock population by countyWe knew the total number of livestock per county and wards by species for the active surveillance period. The counties represented the level of disease management including vaccine distribution while the wards within counties represented the modeling unit for targeting control. Therefore, we estimated the outbreak incidence as the total number of outbreaks per livestock species per 100,000 head of that species.Ecological niche modeling and validationWe used boosted regression tree (BRT) algorithm as previously published13. In those studies, we estimated the geographic distribution of anthrax in southern Kenya using 69 spatially unique outbreak points (thinned from the 86 outbreaks in the records) and 18 environmental variables resampled to 250 m resolution. In this study, the final experiments were run with a learning rate (lr) = 0.001, bagging fraction (br) = 5, and maximum tree = 2500. We then mapped anthrax suitability as the mean output of the 100 experiments and the lower 2.5% and upper 97.5% mapped as confidence intervals. We determined variable contribution and derived partial dependence as previously described13. As BRTs are a random walk and each experiment randomly resamples training and test data, it was necessary to repeat those outputs along with the map predictions.Here, our goal was to evaluate the BRT models built with records data from 2011 to 2017 data and use the predict function to calculate model accuracy metrics using the 2017–2020 outbreaks as presence points and the sub-counties reporting zero outbreaks during the 2019–2020 active surveillance period as absence points. The model of southern Kenya was projected onto all of Kenya using climate variables clipped to the whole of Kenya. We tested the BRT models in two ways; first, evaluating 2011–2017 data models with holdout data using a random resampling and multi-modeling approach. Here, we report the area under curve (AUC) for each of the original training/testing split into the 69 historical points and the 2017–2020 data serving as independent data, the latter representing true model validation. Second, to determine the total percentage of surveillance data predicted and map areas of anthrax suitability to compare with kernel density estimates (see below), we produced a dichotomized map using the Youden index cutoff17 following Otieno et al.14.Outbreak concentrations from kernel density estimation (KDE)To describe the spatial concentration of reported outbreaks, we calculated descriptive spatial statistics, including the spatial mean, standard distance, and standard deviational ellipse of outbreak locations from the prospective surveillance dataset following Blackburn et al.18 These spatial statistics help to differentiate the geographic focus (spatial mean) and dispersion of outbreak reports from year to year and across the sampling period. We then conducted kernel density estimation (KDE) to visualize the concentration of anthrax outbreaks per square kilometer per year and across the study period18. We used the spatstat package for all KDE analyses using the quadratic kernel function19:$$fleft( x right) = frac{1}{{nh^{2} }} mathop sum limits_{i = 1}^{n} Kleft( {frac{{x – X_{i} }}{h}} right)$$where h is the bandwidth, x-Xi is the distance to each anthrax outbreak i. Finally, K is the quadratic kernel function, defined as:$$Kleft( x right) = frac{3}{4}left( {1 – x^{2} } right), left| x right| le 1$$$$Kleft( x right) = 0,x > 1$$This function was employed to estimate anthrax outbreak concentration across space using each outbreak weighted as one. We calculated the bandwidth (kernel) using hopt that uses the sample size (number of outbreaks) and the standard distance to estimate bandwidth. Finally, we estimated bandwidth for each year and then averaged them to apply the same fixed bandwidth for each year under study in Q-GIS version 3.1.8. The resulting outputs were map surfaces representing the spatial concentrations of outbreaks across the country per 1 km2 for each study year and all study years combined. For this study, we used the cutoff criteria of Nelson and Boots19 to identify outbreak hotspots as areas with density values in the upper 25%, 10%, and 5% of outbreak concentrations. The analyses identified these areas by year (2017–2020) and for all surveillance years combined.Local spatial clustering at the ward levelAnthrax outbreak incidence per livestock speciesThe ENM and KDE-derived maps provide a first estimate of potential risk and outbreak concentration, respectively. We were also interested in estimating anthrax outbreak intensity relative to livestock populations at a local level. For the active surveillance period, we knew the total number of outbreaks per ward (the smallest administrative spatial unit) by livestock species. For this two-year period, we estimated the ward-level outbreak incidence as the total number of outbreaks per livestock species per 10,000 head of that species. To estimate livestock population per ward, we extracted the values in the raster file of the areal weighted gridded livestock of the world data using the zonal statistic routine in Q-GIS version 3.1.8, into the polygon consisting of all pixels per ward as the total population19,20. We calculated outbreak incidence as the number of outbreaks per ward cattle population per 10,000 cattle for each administrative ward. We limited this analysis to those 18 counties participating in the active surveillance study (Fig. S1), as we could appropriately assume any ward with no reports was a ‘true zero’ for the estimation. Given that most reported outbreaks were in domestic cattle (see results below), we here report those results involving cattle alone. Given the overall high number of wards and the high number of wards without outbreaks, we performed the empirical Bayes smoothing and spatial Bayes smoothing routines in GeoDa version 1.12.1.161 to reduce the variance in anthrax incidence estimates20,21. To evaluate smoothing routine performance, we box plotted rates per ward and selected the method with the greatest reduction in outliers21. Smoothed rates were mapped as choropleth map in Q-GIS version 3.1.8 using the four equal area bins.Spatial cluster analysisWe used Local Moran’s I16 to test for spatial cluster of livestock anthrax in cattle using the smoothed outbreak incidence estimates. The Local Moran’s I statistic tests whether individual wards are part of spatial cluster, like incidence estimates surrounded by similar estimate (high-high or low-low) or spatial outliers where wards with significantly high or low estimates are surrounded by dissimilar values (high-low or low–high). The local Moran’s I is written as16:$$I_{i} = Z_{i} sum W_{ij} Z_{j}$$where Ii is the statistic for a ward i, Zi is the difference between the incidence at i and the mean anthrax incidence rate for all of wards in the study, Zj is the difference between anthrax risk at ward j and the mean for all wards. Wij is the weights matrix. In this study, the 1st order queen contiguity was employed. Here, Wij equals 1/n if a ward shared a boundary or vertex and 0 if not. For this study, Local Moran’s I was performed on the wards using 999 permutations and p = 0.05 using GeoDa version 1.12.1.161.Assessing effectiveness of cattle vaccination in burden hotspotsAs a first estimate of how we might scale up livestock anthrax vaccination efforts in Kenya, we slightly adjusted a simple published anthrax outbreak simulation model in a cattle population. For this study we applied an early mathematical approach of Funiss and Hahn22 to simulate anthrax at the ward level. While other recent models are available23,24, these are difficult to parameterize or require time series data we could not derive with the surveillance approach in this study. Like the more recent models, Funiss and Hahn22 assumed anthrax transmission was driven by cattle accessing spore-contaminated environments. Here the proportion of infected cattle each day depended on the population of susceptible animals in the population and probability of getting infected. This probability depends on environmental contamination (“a”), and a fraction of anthrax carcasses in the environment on a day (“f,”). Each day, the newly infected cattle are transferred to an incubation period vector, “d,” waiting to die following a probability “p”. In this model, all infected animals, “n,” die following the incubation periods given by the vector, “p”, in which pi is the probability of a cow dying i days after the infection. Following death, the cattle are transferred to a carcass state, providing a direct infection source to the susceptible cattle via environmental contamination. Environmental contamination “a,” is therefore defined as the number of spores ingested by an animal in a day. This environmental contamination depends on spores from carcasses and an assumed spore decay rate γ22.The complete set of difference equations with a daily time step is given by:$${text{S}}_{(t + 1)} = {text{S}}_{(t)} – {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)$$$${text{I}}_{(t + 1)} = {text{I}}_{(t)} + {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{{text{t}}} + gamma {text{f}}_{{{text{t}} + {1}}} } right)}} } right)$$where the expression (left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)) denotes the probability of an animal becoming infected and at + γft+1 is the mean number of spores ingested by a cow in a day. The equation for environmental contamination, a, is given by:$${text{a}}_{t + 1} {-}{text{a}}_{{text{t}}} = alpha {text{a}}_{{text{t}}} + beta {text{c}}_{{{text{t}} + {1}}}$$The newly infected animals die after a certain number of days. The distribution of incubation periods is given by the vector, p. On each day, the new cases are placed in a due-to-die vector, d, and when they die, they are subsequently moved down one step to fresh carcasses, ft. The fresh carcasses provide a direct source of infection to the susceptible cattle via the ‘fresh carcass term’, γ. These carcasses decay or are scavenged or disposed by man. The equation expressing the disseminating carcasses, c, is:$${text{C}}_{t + 1} – {text{c}}_{t} = {text{f}}_{t + 1} – delta {text{c}}_{t}$$The model parameters variables are provided in Table 1 and are similar to those used by Funiss and Hahn22 to generate a standard run. We ran the model for one year and extrapolated to cattle population in the identified hotspot wards.Table 1 Model parameters and variables.Full size table More

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Mild shading facilitates sesquiterpenoid accumulation and growth in Atractylodes lancea rhizomeTo determine a concrete shading value for the production of high-quality and high-yielding AR, we examined the major compounds, including the sesquiterpenoids hinesol (Hin), β-eudesmol (Edu), and atractylone (Atl), and the polyacetylene atractylodin (Atd), as well as the biomass of AR at different growth stages (Fig. 1A–C) under various light intensities. The sum of these four volatile oils as the total volatile oil content was subsequently analyzed. The results revealed that the accumulation of volatile oils was significantly different (p  More