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The destructive impact of fungi in agriculture and animal and human health, coincident with increases in antifungal resistance, underscores the need for new and alternative drug targets to counteract these trends. Cellular metabolism relies on many intermediates with intrinsic toxicity and promiscuous enzymatic activity generates others. Fuller knowledge of these toxic entities and their generation may offer opportunities of antifungal development. From this perspective our observation of media-conditional lethal metabolism in respiratory mutants of the opportunistic fungal pathogen Candida albicans was of interest. C. albicans mutants defective in NADH:ubiquinone oxidoreductase (Complex I of the electron transport chain) exhibit normal growth in synthetic complete medium. In YPD medium, however, the mutants grow normally until early stationary phase whereupon a dramatic loss of viability occurs. Upwards of 90% of cells die over the subsequent four to six hours with a loss of membrane integrity. The extent of cell death was proportional to the amount of BactoPeptone, and to a lesser extent, the amount of yeast extract. YPD medium conditioned by growth of the mutant was toxic to wild-type cells indicating mutant metabolism established a toxic milieu in the media. Conditioned media contained a volatile component that contributed to toxicity, but only in the presence of a component of BactoPeptone. Fractionation experiments revealed purine nucleosides or bases as the synergistic component. GC-mass spectrometry analysis revealed acetal (1,1-diethoxyethane) as the active volatile. This previously unreported and lethal synergistic interaction of acetal and purines suggests a hitherto unrecognized toxic metabolism potentially exploitable in the search for antifungal targets.

Abstract Background As cases of visceral leishmaniasis (VL) in India dwindle, there is motivation to monitor elimination progress on a finer geographic scale than sub-district (block). Low-incidence projections across geographically- and demographically- heterogeneous communities are difficult to act upon, and equitable elimination cannot be achieved if local pockets of incidence are overlooked. However, maintaining consistent surveillance at this scale is resource-intensive and not sustainable in the long-term. Methods We analysed VL incidence across 45,000 villages in Bihar state, exploring spatial autocorrelation and associations with local environmental conditions in order to assess the feasibility of inference at this scale. We evaluated a statistical disaggregation approach to infer finer spatial variation from routinely-collected, block-level data, validating against observed village-level incidence. Results This disaggregation approach does not estimate village-level incidence more accurately than a baseline assumption of block-homogeneity. Spatial auto-correlation is evident on a block-level but weak between neighbouring villages within the same block, possibly suggesting that longer-range transmission (e.g., due to population movement) may be an important contributor to village-level heterogeneity. Conclusions Increasing the range of reactive interventions to neighbouring villages may not improve their efficacy in suppressing transmission, but maintaining surveillance and diagnostic capacity in areas distant from recently observed cases - particularly along routes of population movement from endemic regions - could reduce reintroduction risk in currently unaffected villages. The reactive, spatially-targeted approach to VL surveillance limits interpretability of data observed at the village level, and hence the feasibility of routinely drawing and validating inference at this scale.

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

Trial data have shown that post-discharge malaria chemoprevention (PDMC) reduces the risk of readmission and death in children previously hospitalised with severe malarial anaemia. Here, the authors use mathematical modelling to estimate the potential epidemiological impacts of PDMC in malaria-endemic countries in Africa.

Introduction: Post-surgical pain following dental implant placement surgery is typically managed with non-opioid analgesics, including non-steroidal anti-inflammatory drugs (NSAIDs) and acetaminophen. However, the comparative analgesic efficacy of over-the-counter doses of non-steroidal anti-inflammatory drugs and acetaminophen in implant patients is unknown. Therefore, we compared the analgesic and anti-inflammatory effects of naproxen sodium and acetaminophen after surgical placement of one or two dental implants.Methods: Adult patients were treated with naproxen sodium (440 mg loading dose +220 mg q8h, n = 15) or acetaminophen (1,000 mg q6h—max daily dose 3,000 mg, n = 15) for 3 days after implant placement in a randomized, double-blind design. Pain was assessed on a 0–10 scale every 20 min for 6 h after study medication treatment. Tramadol (50 mg) was available as a rescue medication. Plasma and gingival crevicular fluid (GCF) were collected prior to the surgery and 0, 1, 2, 4, 6, 24, and 72 h after surgery for quantification of interleukin (IL)-6, IL-8, and IL-1β levels.Results: Pain scores were significantly lower in patients treated with naproxen sodium compared to those treated with acetaminophen. Inflammatory mediator levels in plasma and gingival crevicular fluid increased after surgery and returned to near baseline levels by 72 h. Plasma IL-6 levels were significantly lower 6 h after surgery in patients treated with naproxen sodium compared to acetaminophen. No differences in inflammatory mediator concentrations in gingival crevicular fluid were observed between the treatment groups. The number of implants placed and body mass index (BMI) influenced inflammatory mediator concentrations in plasma and gingival crevicular fluid, respectively.Discussion: Naproxen sodium was more effective than acetaminophen in reducing post-operative pain and systemic inflammation following surgical placement of one or two dental implants. Further studies are needed to determine whether these findings are applicable to more complex implant cases and how they affect clinical outcomes following implant placement.Clinical Trial Registration:ClinicalTrials.gov, identifier NCT04694300

Disaggregation modeling, or downscaling, has become an important discipline in epidemiology. Surveillance data, aggregated over large regions, is becoming more common, leading to an increasing demand for modeling frameworks that can deal with this data to understand spatial patterns. Disaggregation regression models use response data aggregated over large heterogeneous regions to make predictions at fine-scale over the region by using fine-scale covariates to inform the heterogeneity. This paper presents the R package disaggregation, which provides functionality to streamline the process of running a disaggregation model for fine-scale predictions.

Aims Psychoeducative prehabilitation to optimize surgical outcomes is relatively novel in spinal fusion surgery and, like most rehabilitation treatments, they are rarely well specified. Spinal fusion patients experience anxieties perioperatively about pain and immobility, which might prolong hospital length of stay (LOS). The aim of this prospective cohort study was to determine if a Preoperative Spinal Education (POSE) programme, specified using the Rehabilitation Treatment Specification System (RTSS) and designed to normalize expectations and reduce anxieties, was safe and reduced LOS. Methods POSE was offered to 150 prospective patients over ten months (December 2018 to November 2019) Some chose to attend (Attend-POSE) and some did not attend (DNA-POSE). A third independent retrospective group of 150 patients (mean age 57.9 years (SD 14.8), 50.6% female) received surgery prior to POSE (pre-POSE). POSE consisted of an in-person 60-minute education with accompanying literature, specified using the RTSS as psychoeducative treatment components designed to optimize cognitive/affective representations of thoughts/feelings, and normalize anxieties about surgery and its aftermath. Across-group age, sex, median LOS, perioperative complications, and readmission rates were assessed using appropriate statistical tests. Results In all, 65 (43%) patients (mean age 57.4 years (SD 18.2), 58.8% female) comprised the Attend-POSE, and 85 (57%) DNA-POSE (mean age 54.9 years (SD 15.8), 65.8% female). There were no significant between-group differences in age, sex, surgery type, complications, or readmission rates. Median LOS was statistically different across Pre-POSE (5 days ((interquartile range (IQR) 3 to 7)), Attend-POSE (3 (2 to 5)), and DNA-POSE (4 (3 to 7)), (p = 0.014). Pairwise comparisons showed statistically significant differences between Pre-POSE and Attend-POSE LOS (p = 0.011), but not between any other group comparison. In the Attend-POSE group, there was significant change toward greater surgical preparation, procedural familiarity, and less anxiety. Conclusion POSE was associated with a significant reduction in LOS for patients undergoing spinal fusion surgery. Patients reported being better prepared for, more familiar, and less anxious about their surgery. POSE did not affect complication or readmission rates, meaning its inclusion was safe. However, uptake (43%) was disappointing and future work should explore potential barriers and challenges to attending POSE. Cite this article: Bone Jt Open 2022;3(2):135–144.

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms), and phase picking. These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only outperform the baseline under certain conditions, such as when the target problems require features that are similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.

Evaluations of the UK’s contact tracing programme have shown that it has had limited impact on COVID-19 control. Here, the authors show that with high levels of reporting and adherence, contact tracing could reduce transmission, but it should not be used as the sole control measure.

Molecularly targeted cancer therapy has improved outcomes for patients with cancer with targetable oncoproteins, such as mutant EGFR in lung cancer. Yet, the long-term survival of these patients remains limited, because treatment responses are typically incomplete. One potential explanation for the lack of complete and durable responses is that oncogene-driven cancers with activating mutations of EGFR often harbor additional co-occurring genetic alterations. This hypothesis remains untested for most genetic alterations that co-occur with mutant EGFR. Here, we report the functional impact of inactivating genetic alterations of the mRNA splicing factor RNA-binding motif 10 (RBM10) that co-occur with mutant EGFR. RBM10 deficiency decreased EGFR inhibitor efficacy in patient-derived EGFR-mutant tumor models. RBM10 modulated mRNA alternative splicing of the mitochondrial apoptotic regulator Bcl-x to regulate tumor cell apoptosis during treatment. Genetic inactivation of RBM10 diminished EGFR inhibitor–mediated apoptosis by decreasing the ratio of (proapoptotic) Bcl-xS to (antiapoptotic) Bcl-xL isoforms of Bcl-x. RBM10 deficiency was a biomarker of poor response to EGFR inhibitor treatment in clinical samples. Coinhibition of Bcl-xL and mutant EGFR overcame the resistance induced by RBM10 deficiency. This study sheds light on the role of co-occurring genetic alterations and on the effect of splicing factor deficiency on the modulation of sensitivity to targeted kinase inhibitor cancer therapy.

Cyclin-dependent kinase (CDK) inhibitor drugs (CDKi), such as R-roscovitine and AT7519, induce neutrophil apoptosis in vitro and enhance the resolution of inflammation in a number of in vivo models. This class of compounds are potential novel therapeutic agents that could promote the resolution of acute and chronic inflammatory conditions where neutrophil activation contributes to tissue damage and aberrant tissue repair. In this study we investigated CDKi effects on macrophage pro-inflammatory mediator production and viability. Treatment of human monocyte-derived macrophages (MDMs) with the CDKi AT7519 and R-roscovitine at concentrations that induce neutrophil apoptosis had no significant effect on control or LPS-activated MDM apoptosis and viability, and did not detrimentally affect MDM efferocytosis of apoptotic cells. In addition, enhanced efferocytosis, induced by the glucocorticoid dexamethasone, was also unaffected after a short time treatment with R-roscovitine. Macrophage cytokine responses to inflammatory stimuli are also of importance during inflammation and resolution. As a key target of CDKi, CDK9, is involved in protein transcription via the RNA polymerase II complex, we investigated the effect of CDKi drugs on cytokine production. Our data show that treatment with AT7519 significantly downregulated expression and release of key MDM cytokines IL-6, TNF, IL-10 and IL-1β, as well as markers of pro-inflammatory macrophage polarisation. R-Roscovitine was also able to downregulate inflammatory cytokine protein secretion from MDMs. Using siRNA transfection, we demonstrate that genetic knock-down of CDK9 replicates these findings, reducing expression and release of pro-inflammatory cytokines. Furthermore, overexpression of CDK9 in THP-1 cells can promote a pro-inflammatory phenotype in these cells, suggesting that CDK9 plays an important role in the inflammatory phenotype of macrophages. Overall, this study demonstrates that pharmacological and genetic targeting of CDK9 inhibits an inflammatory phenotype in human MDMs. As such these data indicate that CDK9 may be key to therapeutically targeting pro-inflammatory macrophage functions during chronic inflammation.

Abstract Background Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time. Methods This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries. Results The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3–75.8), 70.1% (43.6–76.0) and 71.8% (46.9–76.4) for the 1991–2000, 2006–2010, and 2016–2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9–74.9) for 1991–2000 and 55.5% (27.1–73.4) for 2011–2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials. Conclusions This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries’ treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.

Abstract Background Neutrophils rapidly respond to and clear infection from tissues, but can also induce tissue damage through excessive degranulation, when acute inflammation proceeds unchecked. A number of key neutrophil functions, including adhesion-dependent degranulation, are controlled by src family kinases. Dasatinib is a potent src inhibitor used in treating patients with chronic myeloid leukaemia and treatment-resistant acute lymphoblastic leukaemia. We hypothesized that dasatinib would attenuate acute inflammation by inhibiting neutrophil recruitment, degranulation and endothelial cell injury, without impairing bacterial clearance, in a murine model of bacteria-induced acute lung injury. C57BL/6 mice received intratracheal Escherichia coli, and were treated with intraperitoneal dasatinib or control. Bacterial clearance, lung injury, and markers of neutrophil recruitment and degranulation were measured. Separately, human blood neutrophils were exposed to dasatinib or control, and the effects on a range of neutrophil functions assessed. Results Dasatinib was associated with a dose-dependent significant increase in E. coli in the mouse lung, accompanied by impairment of organ function, reflected in significantly increased protein leak across the alveolar-capillary membrane. However, the number of neutrophils entering the lung was unaffected, suggesting that dasatinib impairs neutrophil function independent of migration. Dasatinib did not cause direct toxicity to human neutrophils, but led to significant reductions in phagocytosis of E. coli, adhesion, chemotaxis, generation of superoxide anion and degranulation of primary and secondary granules. However, no biologically important effect of dasatinib on neutrophil degranulation was observed in mice. Conclusions Contrary to our starting hypothesis, src kinase inhibition with dasatinib had a detrimental effect on bacterial clearance in the mouse lung and therefore does not represent an attractive therapeutic strategy to treat primary infective lung inflammation. Data from human neutrophils suggest that dasatanib has inhibitory effects on a range of neutrophil functions.

Abstract The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning‐based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top‐down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real‐world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52-4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance-a key component of JE hazard-over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts.

T cells and B cells have been identified in human and murine islets, but the phenotype and role of islet lymphocytes is unknown. Resident immune populations set the stage for responses to inflammation in the islets during homeostasis and diabetes. Thus, we sought to identify the phenotype and effector function of islet lymphocytes to better understand their role in normal islets and in islets under metabolic stress. Lymphocytes were located in the islet parenchyma, and were comprised of a mix of naïve, activated, and memory T cell and B cell subsets, with an enrichment for regulatory B cell subsets. Use of a Nur77 reporter indicated that CD8 T cells and B cells both received local antigen stimulus, indicating that they responded to antigens present in the islets. Analysis of effector function showed that islet T cells and B cells produced the regulatory cytokine IL-10. The regulatory phenotype of islet T cells and B cells and their response to local antigenic stimuli remained stable under conditions of metabolic stress in the diet induced obesity (DIO) model. T cells present in human islets retained a similar activated and memory phenotype in non-diabetic and T2D donors. Under steady-state conditions, islet T cells and B cells have a regulatory phenotype, and thus may play a protective role in maintaining tissue homeostasis.

Abstract The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post‐process model outputs to match observations, existing approaches usually focus on limited, low‐order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre‐assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle‐consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately 1° resolution, ensures spatiotemporal consistency and inter‐field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big‐data enhanced climate predictions.

The detection of foreign antigens in vivo has relied on fluorescent conjugation or indirect read-outs such as antigen presentation. In our studies, we found that these widely used techniques had several technical limitations that have precluded a complete picture of antigen trafficking or retention across lymph node cell types. To address these limitations, we developed a ‘molecular tracking device’ to follow the distribution, acquisition, and retention of antigen in the lymph node. Utilizing an antigen conjugated to a nuclease-resistant DNA tag, acting as a combined antigen-adjuvant conjugate, and single-cell mRNA sequencing, we quantified antigen abundance in the lymph node. Variable antigen levels enabled the identification of caveolar endocytosis as a mechanism of antigen acquisition or retention in lymphatic endothelial cells. Thus, these molecular tracking devices enable new approaches to study dynamic tissue dissemination of antigen-adjuvant conjugates and identify new mechanisms of antigen acquisition and retention at cellular resolution in vivo.

In this paper, the planetary load-sharing behavior and fatigue life of different wind turbine gearboxes when subjected to rotor moments are examined. Two planetary bearing designs are compared – one design using cylindrical roller bearings with clearance and the other design using preloaded tapered roller bearings to support both the carrier and planet gears. Each design was developed and integrated into a 750 kW dynamometer tests, the loads on each planet bearing row were measured and compared to finite-element models. Bearing loads were not equally shared between the set of cylindrical roller bearings supporting the planets even in pure torque conditions, with one bearing supporting up to 46 % more load than expected. A significant improvement in planetary bearing load sharing was demonstrated in the gearbox with preloaded tapered roller bearings with maximum loads 20 % lower than the gearbox with cylindrical roller bearings. Bearing life was calculated with a representative duty cycle measured from field tests. The predicted fatigue life of the eight combined planet and carrier bearings for the gearbox with preloaded tapered roller bearings is 3.5 times greater than for the gearbox with cylindrical roller bearings. The influence of other factors, such as carrier and planet bearing clearance, gravity, and tangential pin position error, is also investigated. The combined effect of gravity and carrier bearing clearance was primarily responsible for unequal load sharing. Reducing carrier bearing clearance significantly improved load sharing, while reducing planet clearance did not. Normal tangential pin position error did not impact load sharing due to the floating sun design of this three-planet gearbox.

Abstract Background The Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP’s routinely-updated malariometric databases and research outputs. Methods and results The current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis. Conclusions malariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community.

The destructive impact of fungi in agriculture and animal and human health, coincident with increases in antifungal resistance, underscores the need for new and alternative drug targets to counteract these trends. Cellular metabolism relies on many intermediates with intrinsic toxicity and promiscuous enzymatic activity generates others. Fuller knowledge of these toxic entities and their generation may offer opportunities of antifungal development. From this perspective our observation of media-conditional lethal metabolism in respiratory mutants of the opportunistic fungal pathogen Candida albicans was of interest. C. albicans mutants defective in NADH:ubiquinone oxidoreductase (Complex I of the electron transport chain) exhibit normal growth in synthetic complete medium. In YPD medium, however, the mutants grow normally until early stationary phase whereupon a dramatic loss of viability occurs. Upwards of 90% of cells die over the subsequent four to six hours with a loss of membrane integrity. The extent of cell death was proportional to the amount of BactoPeptone, and to a lesser extent, the amount of yeast extract. YPD medium conditioned by growth of the mutant was toxic to wild-type cells indicating mutant metabolism established a toxic milieu in the media. Conditioned media contained a volatile component that contributed to toxicity, but only in the presence of a component of BactoPeptone. Fractionation experiments revealed purine nucleosides or bases as the synergistic component. GC-mass spectrometry analysis revealed acetal (1,1-diethoxyethane) as the active volatile. This previously unreported and lethal synergistic interaction of acetal and purines suggests a hitherto unrecognized toxic metabolism potentially exploitable in the search for antifungal targets. [ABSTRACT FROM AUTHOR]

Fluorinated organic compounds are frequently used across the chemical and life sciences. Although a large, structurally diverse pool of alkyl fluorides is nowadays available, synthetic applications trail behind the widely accepted utility of other halides. We envisioned that C(sp2 )-C(sp3 ) cross-coupling reactions of alkyl fluorides with fluorophilic organozinc compounds should be possible through a heterolytic mechanism that involves short-lived ion pairs and uses the stability of the Zn-F bond as the thermodynamic driving force. This would be mechanistically different from previously reported radical reactions and overcome long-standing limitations of organometallic crosscoupling methodology, including competing β-hydride elimination, homodimerization and hydrodefluorination. Here, we show a practical Csp3-F bond functionalization method that expands the currently restricted synthetic space of unactivated primary, secondary and tertiary C(sp3 )-F bonds but also uses benzylic, propargylic and acyl fluorides. Many functional groups and sterically demanding substrates are tolerated, which allows practical carboncarbon bond formation and late-stage functionalization. [ABSTRACT FROM AUTHOR]

Persistent atmospheric ridging in the North Pacific steered storms away and led to the California drought of 2012-16. Here the authors use simulations to show that sea-ice changes trigger reorganization of tropical convection resulting in drying over California.

Viral infection and vaccination both induce lasting persistence of antigens for protective responses. Here the authors show that migratory dendritic cells, independent of the transcription factor BatF3 for their development, contribute to “archived antigen” exchange with lymphatic endothelial cells.

It is well-established that aging impairs memory for associations more than it does memory for single items. Aging also impacts processes involved in online language comprehension, including the ability to form integrated, message-level representations. These changes in comprehension processes could impact older adults’ associative memory performance, perhaps by reducing or altering the effectiveness of encoding strategies that encourage semantic integration. The present study examined age differences in the use of a strategy termed conceptual combination, which involves integrating two words (e.g., “winter” and “salad”) into a single concept (“a salad for winter”). We recorded ERPs while participants studied unrelated noun pairs using a strategy that either did or did not encourage conceptual combination. We also varied the concreteness of the first noun in each pair in order to measure compositional concreteness effects, or ERP differences at the second noun due to the concreteness of the first noun. At the first nouns, older adults showed word-level concreteness effects that were similar to those of younger adults. However, compositional concreteness effects were diminished in older adults, consistent with reduced semantic integration. Older adults’ associative memory performance was better for word pairs studied during the conceptual combination task versus the non-combinatory encoding task; however, the magnitude of the age-related associative memory deficit did not differ between tasks. Finally, analyses of both memory accuracy and trial-by-trial ratings of perceived combination success suggested that older adults had disproportionate difficulty applying the conceptual combination strategy to word pairs that began with abstract nouns. Overall, these results indicate that changes to integrative language processing that occur with age are not independent of – and may sometimes exacerbate – age-related memory decline.

Source-term estimation (STE) methods attempt to calculate the most-likely source characteristics of an atmospheric release given concentration observations. The quality of the STE depends partially on the time and space scales of the observations, sensor locations, and release parameters. We quantify the sensitivity of a probabilistic STE algorithm that was previously validated using observational data collected during a controlled tracer release experiment to the spatiotemporal resolution of observing networks. We have also made many improvements to the STE algorithm, which extends applicability to coarser-resolution observational datasets. Improvements include the use of a fully-connected deep neural network model emulator with dynamically optimized architecture and better goodness-of-fit (GoF) metrics to measure the discrepancy between model and observational data. The GoF metrics, which are more robust and resilient than previous metrics, are the Spearman rank-based correlation coefficient and a variation of the binary f1 classification score adapted for regression problems. Using synthetically generated observations over East Asia, the skill of the improved algorithm is quantified over a broad range of sensor configurations and release scenarios. The evaluation is broken into three experiments. First, a validation study shows that the proposed GoF metrics perform more reliably than other possible candidates. Next, data-denial techniques are applied to a single release scenario over the Korean peninsula, where the skill of the inversion is shown to be highly sensitive to the number and location of deployed sensors but less sensitive to temporal resolution. Finally, the STE algorithm is tested for many release locations throughout the geographic model domain, where the STE algorithm performs well for all but a few cases. The results indicate that the STE algorithm provides informative source-parameter posterior probability distributions utilizing data collected by sparse sensor networks; however, the skill of the STE algorithm improves significantly for higher resolution networks. Keywords: Source-term estimation, Dispersion modeling, Bayesian inversion, Machine learning, Nuclear nonproliferation

Inflammation is a necessary dynamic tissue response to injury or infection and it's resolution is essential to return tissue homeostasis and function. Defective or dysregulated inflammation resolution contributes significantly to the pathogenesis of many, often common and challenging to treat human conditions. The transition of inflammation to resolution is an active process, involving the clearance of inflammatory cells (granulocytes), a change of mediators and their receptors, and prevention of further inflammatory cell infiltration. This review focuses on the use of cyclin dependent kinase inhibitor drugs to pharmacologically target this inflammatory resolution switch, specifically through inducing granulocyte apoptosis and phagocytic clearance of apoptotic cells (efferocytosis). The key processes and pathways required for granulocyte apoptosis, recruitment of phagocytes and mechanisms of engulfment are discussed along with the cumulating evidence for cyclin dependent kinase inhibitor drugs as pro-resolution therapeutics.

Lymphatic endothelial cells (LECs) form the structure of the lymphatic vessels and the sinuses of the lymph nodes, positioning them to be key players in many different aspects of the immune response. Following an inflammatory stimulus, LECs produce chemokines that recruit immune cells to the lymph nodes. The recruitment of immune cells aids in the coordination of both LEC and lymph node expansion and contraction. More recent data has demonstrated that to coordinate LEC division and death, cell surface molecules, such as PD-L1 and interferon receptors, are required. During homeostasis, LECs use PD-L1 to maintain peripheral tolerance by presenting specific peripheral tissue antigens in order to eliminate tissue specific responses. LECs also have the capacity to acquire, present, and exchange foreign antigens following viral infection or immunization. Here we will review how lymph node LECs require immune cells to expand and contract in response to an immune stimulus, the factors involved and how direct LEC-immune cell interactions are important for programming immunity.

Background:. There have been many technical and scientific advances over the last decade in peripheral nerve surgery. Human acellular nerve graft (HANA) has become increasingly popular but current practice patterns among hand surgeons have yet to be defined. Coding practices may not have kept up with this innovation. A 26 question survey of hand surgeons was performed to evaluate the adoption of HANA, and current coding and billing practices. The survey was sent to hand surgeons trained in orthopedic, plastic, general, and neuro surgery. The survey was designed and implemented by the Mayo Clinic Survey Center. Results:. Four hundred sixty-one responses to the survey were received. Most respondents currently use HANA (70%). Of those surgeons who do use HANA, nearly all use it less than 10 times per month (98%). There was no significant difference in the use of HANA across different specialties. There was a significant difference in HANA use depending on practice type with higher use by those in group private practice (57%) compared with academic practice (28%), solo practice (12%), and other practice environment (3%). There was a significant difference in HANA use depending on the number of years in practice. Those in practice less than 5 years used HANA the most (32%), followed by > 20 years in practice (27%), 6–10 years in practice (16%), 16–20 years in practice (14%), and 11–15 years in practice (11%). When asked the Current Procedural Terminology code they would use to bill for the procedure of choice, the most common response was 64910 (nerve repair with synthetic conduit or vein allograft). Conclusions:. HANA has surpassed nerve conduit as the traditional gold standard in our study with nearly 70% of hand surgeons using HANA in their practice and a greater percentage of respondents choosing HANA as their first choice to repair as compared with nerve conduit, nerve autograft, or vein graft. There remains confusion regarding appropriate billing practices for the use of HANA. Due to its common use, a Current Procedural Terminology code should specifically designated for the use of HANA in the hand.

Cytotoxic bile acids, such as deoxycholic acid (DCA), are responsible for hepatocyte cell death during intrahepatic cholestasis. The mechanisms responsible for this effect are unclear, and recent studies conflict, pointing to either a modulation of plasma membrane structure or mitochondrial-mediated toxicity through perturbation of mitochondrial outer membrane (MOM) properties. We conducted a comprehensive comparative study of the impact of cytotoxic and cytoprotective bile acids on the membrane structure of different cellular compartments. We show that DCA increases the plasma membrane fluidity of hepatocytes to a minor extent, and that this effect is not correlated with the incidence of apoptosis. Additionally, plasma membrane fluidity recovers to normal values over time suggesting the presence of cellular compensatory mechanisms for this perturbation. Colocalization experiments in living cells confirmed the presence of bile acids within mitochondrial membranes. Experiments with active isolated mitochondria revealed that physiologically active concentrations of DCA change MOM order in a concentration- and time-dependent manner, and that these changes preceded the mitochondrial permeability transition. Importantly, these effects are not observed on liposomes mimicking MOM lipid composition, suggesting that DCA apoptotic activity depends on features of mitochondrial membranes that are absent in protein-free mimetic liposomes, such as the double-membrane structure, lipid asymmetry, or mitochondrial protein environment. In contrast, the mechanism of action of cytoprotective bile acids is likely not associated with changes in cellular membrane structure.

Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

In California, coccidioidomycosis is a disease acquired by inhaling spores of Coccidioides immitis, a fungus found in certain arid regions, including the San Joaquin Valley, California, USA, where 8 state prisons are located. During 2011, we reviewed coccidioidomycosis rates at 2 of the prisons that consistently report >80% of California’s inmate cases and determined inmate risk factors for primary, severe (defined as pulmonary coccidioidomycosis requiring >10 hospital days), and disseminated coccidioidomycosis (defined by hospital discharge International Classification of Disease, Ninth Revision code). Inmates of African American ethnicity who were >40 years of age were at significantly higher risk for primary coccidioidomycosis than their white counterparts (odds ratio = 2.0, 95% CI 1.5–2.8). Diabetes was a risk factor for severe pulmonary coccidioidomycosis, and black race a risk factor for disseminated disease. These findings contributed to a court decision mandating exclusion of black inmates and inmates with diabetes from the 2 California prisons with the highest rates of coccidioidomycosis.

This study aimed to evaluate the agronomical and environmental aspects in corn, soil characteristics and leachate resulting from the material combination of swine wastewater and mineral fertilizer in the long run. The experimental area has a history of swine wastewater application for experiments conducted since 2006, this being the seventh crop cycle in the area. Four doses of swine wastewater (0; 100; 200 and 300 m³ ha-1) associated with presence and absence of mineral fertilizer were applied, consisting of eight treatments. The results obtained were subjected to variance analysis and means compared by Tukey test at 5% significance. We conclude that the swine wastewater can be partially used as an alternative fertilizer, since the P and Mn are complemented with specific fertilization. There must be carefulness about Fe+2, Cu+2 and Zn+2 accumulation in soil, and also to the concentrations of Fe+2, Cu+2, Zn+2, NO3- + NO2- and Na+ in leachate which exceeded the limits at the highest SWW applied levels.