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Autonomous machines have the potential to maintain food production and agroecological farming resilience. However, autonomous complex mixed cropping is proving to be an engineering challenge because of differences in plant height and growth pattern. Strip cropping is technically the simplest mixed cropping system, but widespread use is constrained by higher labor requirements in conventional mechanized farms. Researchers have long hypothesized that autonomous machines (i.e., crop robots) might make strip cropping profitable, thereby allowing farmers to gain additional agroecological benefits. To examine this hypothesis, this study modeled ex‐ante scenarios for the Corn Belt of central Indiana, using the experience of the Hands Free Hectare‐Linear Programming (HFH‐LP) optimization model. Results show that per annum return to operator labor, management, and risk‐taking (ROLMRT) was$568/ha and $ 163/ha higher for the autonomous corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] strip crop farm compared to the whole field sole crop and the conventional strip crop farms, respectively, that were operated by human drivers. The conventional strip cropping practice was found challenging as this cropping system required four times more temporary hired labor than autonomous strip cropping and three times more than whole field sole cropping. Even if autonomous machines need 100% human supervision, the ROLMRT was higher compared to whole field sole cropping. Profitable autonomous strip cropping could restore and improve in‐field biodiversity and ecosystem services through a sustainable techno‐economic and environmental approach that will address the demand for healthier food and promote environmental sustainability. Core Ideas Autonomous machines enable use of alternative mixed crop geometries. Autonomous strip cropping has higher economic payoffs than sole and conventional strip cropping. Even with lower grain prices and full‐time supervision, swarm robots have economic benefits. Autonomous machines could reconcile economic and agroecological goals.

Uncoupling protein 1 (UCP1)-dependent thermogenesis in brown adipose tissue is supported by a burst of mitochondrial reactive oxygen species upon cold exposure. Control of brown fat thermogenesis Thermogenic respiration in brown adipose tissue (BAT) and beige fat relies on the uncoupling protein 1 (UCP1), a mitochondrial inner membrane protein that produces heat by dissipating the mitochondrial proton gradient generated by the respiratory chain. These authors show that UCP1 activation is supported by a burst of mitochondrial reactive oxygen species (ROS) upon cold exposure. ROS are necessary to sulfenylate a critical cysteine residue in UCP1, which drives its activation — in effect, the 'on/off switch' for UCP1. Brown and beige adipose tissues can dissipate chemical energy as heat through thermogenic respiration, which requires uncoupling protein 1 (UCP1) 1 , 2 . Thermogenesis from these adipocytes can combat obesity and diabetes 3 , encouraging investigation of factors that control UCP1-dependent respiration in vivo . Here we show that acutely activated thermogenesis in brown adipose tissue is defined by a substantial increase in levels of mitochondrial reactive oxygen species (ROS). Remarkably, this process supports in vivo thermogenesis, as pharmacological depletion of mitochondrial ROS results in hypothermia upon cold exposure, and inhibits UCP1-dependent increases in whole-body energy expenditure. We further establish that thermogenic ROS alter the redox status of cysteine thiols in brown adipose tissue to drive increased respiration, and that Cys253 of UCP1 is a key target. UCP1 Cys253 is sulfenylated during thermogenesis, while mutation of this site desensitizes the purine-nucleotide-inhibited state of the carrier to adrenergic activation and uncoupling. These studies identify mitochondrial ROS induction in brown adipose tissue as a mechanism that supports UCP1-dependent thermogenesis and whole-body energy expenditure, which opens the way to improved therapeutic strategies for combating metabolic disorders.

Focusing on expanding technical capabilities and new collaborative possibilities, we address 4 challenges for scientists who collect and use video records to conduct research in and on complex learning environments: (a) Selection: How can researchers be systematic in deciding which elements of a complex environment or extensive video corpus to select for study? (b) Analysis: What analytical frameworks and practices are appropriate for given research problems? (c) Technology: What technologies are available and what new tools must be developed to support collecting, archiving, analyzing, reporting, and collaboratively sharing video? and (d) Ethics: How can research protocols encourage broad video sharing and reuse while adequately protecting the rights of research participants who are recorded?

National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% [4·0–7·9] of variation), GDP per capita (4·2% [1·8–6·6] of variation), and the proportion of infections attributable to seasonality (2·1% [95% uncertainty interval 1·7–2·7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% [18·4–67·6] of variation), GDP per capita (3·1% [0·3–8·6] of variation), and national mean BMI (1·1% [0·2–2·6] of variation). 44·4% (29·2–61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7–17·8) for government trust and 40·3% (24·3–51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%. Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario. Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000). The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic. Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom

One of the main characteristics of the transmissible isoform of the prion protein (PrP(Sc)) is its partial resistance to proteinase K (PK) digestion. Diagnosis of prion disease typically relies upon immunodetection of PK-digested PrP(Sc) following Western blot or ELISA. More recently, researchers determined that there is a sizeable fraction of PrP(Sc) that is sensitive to PK hydrolysis (sPrP(Sc)). Our group has previously reported a method to isolate this fraction by centrifugation and showed that it has protein misfolding cyclic amplification (PMCA) converting activity. We compared the infectivity of the sPrP(Sc) versus the PK-resistant (rPrP(Sc)) fractions of PrP(Sc) and analyzed the biochemical characteristics of these fractions under conditions of limited proteolysis. Our results show that sPrP(Sc) and rPrP(Sc) fractions have comparable degrees of infectivity and that although they contain different sized multimers, these multimers share similar structural properties. Furthermore, the PK-sensitive fractions of two hamster strains, 263K and Drowsy (Dy), showed strain-dependent differences in the ratios of the sPrP(Sc) to the rPrP(Sc) forms of PrP(Sc). Although the sPrP(Sc) and rPrP(Sc) fractions have different resistance to PK-digestion, and have previously been shown to sediment differently, and have a different distribution of multimers, they share a common structure and phenotype.

Abstract BACKGROUND Stereotactic radiosurgery (SRS) is a safe and effective treatment for acromegaly. OBJECTIVE To improve understanding of clinical and dosimetric factors predicting biochemical remission. METHODS A single-institution cohort study of nonsyndromic, radiation-naïve patients with growth hormone-producing pituitary adenomas (GHA) having single-fraction SRS between 1990 and 2017. Exclusions were treatment with pituitary suppressive medications at the time of SRS, or <24 mo of follow-up. The primary outcome was biochemical remission—defined as normalization of insulin-like growth factor-1 index (IGF-1i) off suppression. Biochemical remission was assessed using Cox proportional hazards. Prior studies reporting IGF-1i were assessed via systematic literature review and meta-analysis using random-effect modeling. RESULTS A total of 102 patients met study criteria. Of these, 46 patients (45%) were female. The median age was 49 yr (interquartile range [IQR] = 37-59), and the median follow-up was 63 mo (IQR = 29-100). The median pre-SRS IGF-1i was 1.66 (IQR = 1.37-3.22). The median margin dose was 25 Gy (IQR = 21-25); the median estimated biologically effective dose (BED) was 169.49 Gy (IQR = 124.95-196.00). Biochemical remission was achieved in 58 patients (57%), whereas 22 patients (22%) had medication-controlled disease. Pre-SRS IGF-1i ≥ 2.25 was the strongest predictor of treatment failure, with an unadjusted hazard ratio (HR) of 0.51 (95% CI = 0.26-0.91, P = .02). Number of isocenters, margin dose, and BED predicted remission on univariate analysis, but after adjusting for sex and baseline IGF-1i, only BED remained significant—and was independently associated with outcome in continuous (HR = 1.01, 95% CI = 1.00-1.01, P = .02) and binary models (HR = 2.27, 95% CI = 1.39-5.22, P = .002). A total of 24 patients (29%) developed new post-SRS hypopituitarism. Pooled HR for biochemical remission given subthreshold IGF-1i was 2.25 (95% CI = 1.33-3.16, P < .0001). CONCLUSION IGF-1i is a reliable predictor of biochemical remission after SRS. BED appears to predict biochemical outcome more reliably than radiation dose, but confirmatory study is needed. Graphical Abstract Graphical Abstract

One of the main characteristics of the transmissible isoform of the prion protein (PrPSc) is its partial resistance to proteinase K (PK) digestion. Diagnosis of prion disease typically relies upon immunodetection of PK-digested PrPSc following Western blot or ELISA. More recently, researchers determined that there is a sizeable fraction of PrPSc that is sensitive to PK hydrolysis (sPrPSc). Our group has previously reported a method to isolate this fraction by centrifugation and showed that it has protein misfolding cyclic amplification (PMCA) converting activity. We compared the infectivity of the sPrPSc versus the PK-resistant (rPrPSc) fractions of PrPSc and analyzed the biochemical characteristics of these fractions under conditions of limited proteolysis. Our results show that sPrPSc and rPrPSc fractions have comparable degrees of infectivity and that although they contain different sized multimers, these multimers share similar structural properties. Furthermore, the PK-sensitive fractions of two hamster strains, 263K and Drowsy (Dy), showed strain-dependent differences in the ratios of the sPrPSc to the rPrPSc forms of PrPSc. Although the sPrPSc and rPrPSc fractions have different resistance to PK-digestion, and have previously been shown to sediment differently, and have a different distribution of multimers, they share a common structure and phenotype.

Data on the detailed clinical progression of COVID-19 in conjunction with epidemiological and virological characteristics are limited. In this case series, we describe the first 12 US patients confirmed to have COVID-19 from 20 January to 5 February 2020, including 4 patients described previously 1 – 3 . Respiratory, stool, serum and urine specimens were submitted for SARS-CoV-2 real-time reverse-transcription polymerase chain reaction (rRT-PCR) testing, viral culture and whole genome sequencing. Median age was 53 years (range: 21–68); 8 patients were male. Common symptoms at illness onset were cough ( n  = 8) and fever ( n  = 7). Patients had mild to moderately severe illness; seven were hospitalized and demonstrated clinical or laboratory signs of worsening during the second week of illness. No patients required mechanical ventilation and all recovered. All had SARS-CoV-2 RNA detected in respiratory specimens, typically for 2–3 weeks after illness onset. Lowest real-time PCR with reverse transcription cycle threshold values in the upper respiratory tract were often detected in the first week and SARS-CoV-2 was cultured from early respiratory specimens. These data provide insight into the natural history of SARS-CoV-2. Although infectiousness is unclear, highest viral RNA levels were identified in the first week of illness. Clinicians should anticipate that some patients may worsen in the second week of illness. Detailed clinical and virologic characteristics of the first 12 individuals with COVID-19 in the United States from the US Centers for Disease Control and Prevention.

We investigated the duration of Ebola virus (EBOV) RNA and infectious EBOV in semen specimens of 5 Ebola virus disease (EVD) survivors. EBOV RNA and infectious EBOV was detected by real-time RT-PCR and virus culture out to 290 days and 70 days, respectively, after EVD onset.