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Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

The spatial context is critical when assessing present-day climate anomalies, attributing them to potential forcings and making statements regarding their frequency and severity in a long-term perspective. Recent international initiatives have expanded the number of high-quality proxy-records and developed new statistical reconstruction methods. These advances allow more rigorous regional past temperature reconstructions and, in turn, the possibility of evaluating climate models on policy-relevant, spatio-temporal scales. Here we provide a new proxy-based, annually-resolved, spatial reconstruction of the European summer (June–August) temperature fields back to 755 CE based on Bayesian hierarchical modelling (BHM), together with estimates of the European mean temperature variation since 138 BCE based on BHM and composite-plus-scaling (CPS). Our reconstructions compare well with independent instrumental and proxy-based temperature estimates, but suggest a larger amplitude in summer temperature variability than previously reported. Both CPS and BHM reconstructions indicate that the mean 20th century European summer temperature was not significantly different from some earlier centuries, including the 1st, 2nd, 8th and 10th centuries CE. The 1st century (in BHM also the 10th century) may even have been slightly warmer than the 20th century, but the difference is not statistically significant. Comparing each 50 yr period with the 1951–2000 period reveals a similar pattern. Recent summers, however, have been unusually warm in the context of the last two millennia and there are no 30 yr periods in either reconstruction that exceed the mean average European summer temperature of the last 3 decades (1986–2015 CE). A comparison with an ensemble of climate model simulations suggests that the reconstructed European summer temperature variability over the period 850–2000 CE reflects changes in both internal variability and external forcing on multi-decadal time-scales. For pan-European temperatures we find slightly better agreement between the reconstruction and the model simulations with high-end estimates for total solar irradiance. Temperature differences between the medieval period, the recent period and the Little Ice Age are larger in the reconstructions than the simulations. This may indicate inflated variability of the reconstructions, a lack of sensitivity and processes to changes in external forcing on the simulated European climate and/or an underestimation of internal variability on centennial and longer time scales.

Three different climate field reconstruction (CFR) methods are employed to reconstruct spatially resolved North Atlantic–European (NAE) and Northern Hemisphere (NH) summer temperatures over the past millennium from proxy records. These are tested in the framework of pseudoproxy experiments derived from two climate simulations with comprehensive Earth system models. Two of these methods are traditional multivariate linear methods (principal component regression, PCR, and canonical correlation analysis, CCA), whereas the third method (bidirectional long short-term memory neural network, Bi-LSTM) belongs to the category of machine-learning methods. In contrast to PCR and CCA, Bi-LSTM does not need to assume a linear and temporally stable relationship between the underlying proxy network and the target climate field. In addition, Bi-LSTM naturally incorporates information about the serial correlation of the time series. Our working hypothesis is that the Bi-LSTM method will achieve a better reconstruction of the amplitude of past temperature variability. In all tests, the calibration period was set to the observational period, while the validation period was set to the pre-industrial centuries. All three methods tested herein achieve reasonable reconstruction performance on both spatial and temporal scales, with the exception of an overestimation of the interannual variance by PCR, which may be due to overfitting resulting from the rather short length of the calibration period and the large number of predictors. Generally, the reconstruction skill is higher in regions with denser proxy coverage, but it is also reasonably high in proxy-free areas due to climate teleconnections. All three CFR methodologies generally tend to more strongly underestimate the variability of spatially averaged temperature indices as more noise is introduced into the pseudoproxies. The Bi-LSTM method tested in our experiments using a limited calibration dataset shows relatively worse reconstruction skills compared to PCR and CCA, and therefore our working hypothesis that a more complex machine-learning method would provide better reconstructions for temperature fields was not confirmed. In this particular application with pseudoproxies, the implied link between proxies and climate fields is probably close to linear. However, a certain degree of reconstruction performance achieved by the nonlinear LSTM method shows that skill can be achieved even when using small samples with limited datasets, which indicates that Bi-LSTM can be a tool for exploring the suitability of nonlinear CFRs, especially in small data regimes.

Abstract This article describes the setup and performance of the near and far detectors in the Double Chooz experiment. The electron antineutrinos of the Chooz nuclear power plant were measured in two identically designed detectors with different average baselines of about 400 m and 1050 m from the two reactor cores. Over many years of data taking the neutrino signals were extracted from interactions in the detectors with the goal of measuring a fundamental parameter in the context of neutrino oscillation, the mixing angle $$\theta _{13}$$ θ 13 . The central part of the Double Chooz detectors was a main detector comprising four cylindrical volumes filled with organic liquids. From the inside towards the outside there were volumes containing gadolinium-loaded scintillator, gadolinium-free scintillator, a buffer oil and, optically separated, another liquid scintillator acting as veto system. Above this main detector an additional outer veto system using plastic scintillator strips was installed. The technologies developed in Double Chooz were inspiration for several other antineutrino detectors in the field. The detector design allowed implementation of efficient background rejection techniques including use of pulse shape information provided by the data acquisition system. The Double Chooz detectors featured remarkable stability, in particular for the detected photons, as well as high radiopurity of the detector components.

Abstract Although the currently available antidepressants are well established in the treatment of the major depressive disorder (MDD), there is strong variability in the response of individual patients. Reliable predictors to guide treatment decisions before or in an early stage of treatment are needed. DNA-methylation has been proven a useful biomarker in different clinical conditions, but its importance for mechanisms of antidepressant response has not yet been determined. 80 MDD patients were selected out of >500 participants from the Early Medication Change (EMC) cohort with available genetic material based on their antidepressant response after four weeks and stratified into clear responders and age- and sex-matched non-responders (N = 40, each). Early improvement after two weeks was analyzed as a secondary outcome. DNA-methylation was determined using the Illumina EPIC BeadChip. Epigenome-wide association studies were performed and differentially methylated regions (DMRs) identified using the comb-p algorithm. Enrichment was tested for hallmark gene-sets and in genome-wide association studies of depression and antidepressant response. No epigenome-wide significant differentially methylated positions were found for treatment response or early improvement. Twenty DMRs were associated with response; the strongest in an enhancer region in SORBS2, which has been related to cardiovascular diseases and type II diabetes. Another DMR was located in CYP2C18, a gene previously linked to antidepressant response. Results pointed towards differential methylation in genes associated with cardiac function, neuroticism, and depression. Linking differential methylation to antidepressant treatment response is an emerging topic and represents a step towards personalized medicine, potentially facilitating the prediction of patients’ response before treatment.

Abstract Background Tumor size assessment by MRI is central for staging uterine cervical cancer. However, the optimal role of MRI-derived tumor measurements for prognostication is still unclear. Material and methods This retrospective cohort study included 416 women (median age: 43 years) diagnosed with cervical cancer during 2002–2017 who underwent pretreatment pelvic MRI. The MRIs were independently read by three radiologists, measuring maximum tumor diameters in three orthogonal planes and maximum diameter irrespective of plane (MAXimaging). Inter-reader agreement for tumor size measurements was assessed by intraclass correlation coefficients (ICCs). Size was analyzed in relation to age, International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, histopathological markers, and disease-specific survival using Kaplan–Meier-, Cox regression-, and time-dependent receiver operating characteristics (tdROC) analyses. Results All MRI tumor size variables (cm) yielded high areas under the tdROC curves (AUCs) for predicting survival (AUC 0.81–0.84) at 5 years after diagnosis and predicted outcome (hazard ratios [HRs] of 1.42–1.76, p

Abstract Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework’s performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

Hoivik, Hodneland and colleagues employ convoluted neural networks alongside radiogenomic biomarkers to profile endometrial tumors. They identify an 11-gene high-risk signature validated with retrospective patient data.

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

Abstract We present a search for signatures of neutrino mixing of electron anti-neutrinos with additional hypothetical sterile neutrino flavors using the Double Chooz experiment. The search is based on data from 5 years of operation of Double Chooz, including 2 years in the two-detector configuration. The analysis is based on a profile likelihood, i.e. comparing the data to the model prediction of disappearance in a data-to-data comparison of the two respective detectors. The analysis is optimized for a model of three active and one sterile neutrino. It is sensitive in the typical mass range $${5 \times 10^{-3}}\,\mathrm{eV}^2 \lesssim \varDelta m^2_{41} \lesssim {3 \times 10^{-1}}\,\mathrm{eV}^2 $$ 5 × 10 - 3 eV 2 ≲ Δ m 41 2 ≲ 3 × 10 - 1 eV 2 for mixing angles down to $$\sin ^2 2\theta _{14} \gtrsim {0.02} $$ sin 2 2 θ 14 ≳ 0.02 . No significant disappearance additionally to the conventional disappearance related to $$\theta _{13} $$ θ 13 is observed and correspondingly exclusion bounds on the sterile mixing parameter $$\theta _{14} $$ θ 14 as a function of $$ \varDelta m^2_{41} $$ Δ m 41 2 are obtained.

In order to successfully induce disease, the fungal pathogen Candida albicans regulates exposure of antigens like the cell wall polysaccharide ß(1,3)-glucan to the host immune system. C. albicans covers (masks) ß(1,3)-glucan with a layer of mannosylated glycoproteins, which aids in immune system evasion by acting as a barrier to recognition by host pattern recognition receptors. Consequently, enhanced ß(1,3)-glucan exposure (unmasking) makes fungal cells more visible to host immune cells and facilitates more robust fungal clearance. However, an understanding of how C. albicans regulates its exposure levels of ß(1,3)-glucan is needed to leverage this phenotype. Signal transduction pathways and their corresponding effector genes mediating these changes are only beginning to be defined. Here, we report that the phosphatase calcineurin mediates unmasking of ß(1,3)-glucan in response to inputs from the Cek1 MAPK pathway and in response to caspofungin exposure. In contrast, calcineurin reduces ß-glucan exposure in response to high levels of extracellular calcium. Thus, depending on the input, calcineurin acts as a switchboard to regulate ß(1,3)-glucan exposure levels. By leveraging these differential ß(1,3)-glucan exposure phenotypes, we identified two novel effector genes in the calcineurin regulon, FGR41 and C1_11990W_A, that encode putative cell wall proteins and mediate masking/unmasking. Loss of either effector caused unmasking and attenuated virulence during systemic infection in mice. Furthermore, immunosuppression restored the colonization decrease seen in mice infected with the fgr41Δ/Δ mutant to wild-type levels, demonstrating a reliance on the host immune system for virulence attenuation. Thus, calcineurin and its downstream regulon are general regulators of unmasking.

Abstract We present a novel, highly versatile, and self-referenced arrival time monitor for measuring the femtosecond time delay between a hard X-ray pulse from a free-electron laser and an optical laser pulse, measured directly on the same sample used for pump-probe experiments. Two chirped and picosecond long optical supercontinuum pulses traverse the sample with a mutually fixed time delay of 970 fs, while a femtosecond X-ray pulse arrives at an instant in between both pulses. Behind the sample the supercontinuum pulses are temporally overlapped to yield near-perfect destructive interference in the absence of the X-ray pulse. Stimulation of the sample with an X-ray pulse delivers non-zero contributions at certain optical wavelengths, which serve as a measure of the relative arrival time of the X-ray pulse with an accuracy of better than 25 fs. We find an excellent agreement of our monitor with the existing timing diagnostics at the SACLA XFEL with a Pearson correlation value of 0.98. We demonstrate a high sensitivity to measure X-ray pulses with pulse energies as low as 30 $$\upmu $$ μ J. Using a free-flowing liquid jet as interaction sample ensures the full replacement of the sample volume for each X-ray/optical event, thus enabling its utility even at MHz repetition rate XFEL sources.

Abstract A θ 13 oscillation analysis based on the observed antineutrino rates at the Double Chooz far and near detectors for different reactor power conditions is presented. This approach provides a so far unique simultaneous determination of θ 13 and the total background rates without relying on any assumptions on the specific background contributions. The analysis comprises 865 days of data collected in both detectors with at least one reactor in operation. The oscillation results are enhanced by the use of 24.06 days (12.74 days) of reactor-off data in the far (near) detector. The analysis considers the ν ¯ e $$ {\overline{\nu}}_e $$ interactions up to a visible energy of 8.5 MeV, using the events at higher energies to build a cosmogenic background model considering fast-neutrons interactions and 9Li decays. The background-model-independent determination of the mixing angle yields sin2(2θ 13) = 0.094 ± 0.017, being the best-fit total background rates fully consistent with the cosmogenic background model. A second oscillation analysis is also performed constraining the total background rates to the cosmogenic background estimates. While the central value is not significantly modified due to the consistency between the reactor-off data and the background estimates, the addition of the background model reduces the uncertainty on θ 13 to 0.015. Along with the oscillation results, the normalization of the anti-neutrino rate is measured with a precision of 0.86%, reducing the 1.43% uncertainty associated to the expectation.

Abstract Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, $$p = 0.06$$ p = 0.06 ). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, $$p=0.08$$ p = 0.08 , $$p=0.60$$ p = 0.60 , and $$p=0.05$$ p = 0.05 ). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

We evaluated how ranges of four endemic and non-endemic aquatic ostracode species changed in response to long-term (glacial–interglacial cycles) and abrupt climate fluctuations during the last 155 kyr in the northern Neotropical region. We employed two complementary approaches, fossil records and species distribution models (SDMs). Fossil assemblages were obtained from sediment cores PI-1, PI-2, PI-6 and Petén-Itzá 22-VIII-99 from the Petén Itzá Scientific Drilling Project, Lake Petén Itzá, Guatemala. To obtain a spatially resolved pattern of (past) species distribution, a downscaling cascade is employed. SDMs were reconstructed for the last interglacial (∼120 ka), the last glacial maximum (∼22 ka) and the middle Holocene (∼6 ka). During glacial and interglacial cycles and marine isotope stages (MISs), modelled paleo-distributions and paleo-records show the nearly continuous presence of endemic and non-endemic species in the region, suggesting negligible effects of long-term climate variations on aquatic niche stability. During periods of abrupt ecological disruption such as Heinrich Stadial 1 (HS1), endemic species were resilient, remaining within their current areas of distribution. Non-endemic species, however, proved to be more sensitive. Modelled paleo-distributions suggest that the geographic range of non-endemic species changed, moving southward into Central America. Due to the uncertainties involved in the downscaling from the global numerical to the highly resolved regional geospatial statistical modelling, results can be seen as a benchmark for future studies using similar approaches. Given relatively moderate temperature decreases in Lake Petén Itzá waters (∼5 ∘C) and the persistence of some aquatic ecosystems even during periods of severe drying in HS1, our data suggest (1) the existence of micro-refugia and/or (2) continuous interaction between central metapopulations and surrounding populations, enabling aquatic taxa to survive climate fluctuations in the northern Neotropical region.

Candida spp. are an important source of systemic and mucosal infections in immune compromised populations. However, drug resistance or toxicity has put limits on the efficacy of current antifungals. The C. albicans cell wall is considered a good therapeutic target due to its roles in viability and fungal pathogenicity. One potential method for improving antifungal strategies could be to enhance the detection of fungal cell wall antigens by host immune cells. (1,3)-glucan, which is an important component of fungal cell walls, is a highly immunogenic epitope. Consequently, multiple host pattern recognition receptors, such as dectin-1, complement receptor 3 (CR3), and the ephrin type A receptor A (EphA2) are capable of recognizing exposed (unmasked) (1,3)-glucan moieties on the cell surface to initiate an anti-fungal immune response. However, (1,3)-glucan is normally covered (masked) by a layer of glycosylated proteins on the outer surface of the cell wall, hiding it from immune detection. In order to better understand possible mechanisms of unmasking (1,3)-glucan, we must develop a deeper comprehension of the pathways driving this phenotype. In this review, we describe the medical importance of (1,3)-glucan exposure in anti-fungal immunity, and highlight environmental stimuli and stressors encountered within the host that are capable of inducing changes in the levels of surface exposed (1,3)-glucan. Furthermore, particular focus is placed on how signal transduction cascades regulate changes in (1,3)-glucan exposure, as understanding the role that these pathways have in mediating this phenotype will be critical for future therapeutic development.

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters-two topics that were largely absent from coronavirus research in the two years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic.

Little is known about how much water container-grown plants require for maximum growth and value. This study answered some fundamental questions on the subject.

Masking the immunogenic cell wall epitope ß(1,3)-glucan under an outer layer of mannosylated glycoproteins is an important virulence factor deployed by Candida albicans during infection. Consequently, increased ß(1,3)-glucan exposure (unmasking) reveals C. albicans to the host's immune system and attenuates its virulence. We have previously shown that activation of the Cek1 MAPK pathway via expression of a hyperactive allele of an upstream kinase (STE11ΔN467) induced unmasking. It also increased survival of mice in a murine disseminated candidiasis model and attenuated kidney fungal burden by ≥33 fold. In this communication, we utilized cyclophosphamide-induced immunosuppression to test if the clearance of the unmasked STE11ΔN467 mutant was dependent on the host immune system. Suppression of the immune response by cyclophosphamide reduced the attenuation in fungal burden caused by the STE11ΔN467 allele. Moreover, specific depletion of neutrophils via 1A8 antibody treatment also reduced STE11ΔN467-dependent fungal burden attenuation, but to a lesser extent than cyclophosphamide, demonstrating an important role for neutrophils in mediating fungal clearance of unmasked STE11ΔN467 cells. In an effort to understand the mechanism by which Ste11ΔN467 causes unmasking, transcriptomics were used to reveal that several components in the Cek1 MAPK pathway were upregulated, including the transcription factor CPH1 and the cell wall sensor DFI1. In this report we show that a cph1ΔΔ mutation restored ß(1,3)-glucan exposure to wild-type levels in the STE11ΔN467 strain, confirming that Cph1 is the transcription factor mediating Ste11ΔN467-induced unmasking. Furthermore, Cph1 is shown to induce a positive feedback loop that increases Cek1 activation. In addition, full unmasking by STE11ΔN467 is dependent on the upstream cell wall sensor DFI1. However, while deletion of DFI1 significantly reduced Ste11ΔN467-induced unmasking, it did not impact activation of the downstream kinase Cek1. Thus, it appears that once stimulated by Ste11ΔN467, Dfi1 activates a parallel signaling pathway that is involved in Ste11ΔN467-induced unmasking.

Climate reconstructions are means to extract the signal from uncertain paleo-observations, so-called proxies. It is essential to evaluate these reconstructions to understand and quantify their uncertainties. Similarly, comparing climate simulations and proxies requires approaches to bridge the temporal and spatial differences between both and to address their specific uncertainties. One way to achieve these two goals is so-called pseudoproxies. These are surrogate proxy records within the virtual reality of a climate simulation. They in turn depend on an understanding of the uncertainties of the real proxies including the noise characteristics disturbing the original environmental signal. Common pseudoproxy approaches so far concentrate on data with high temporal resolution over the last approximately 2000 years. Here we provide a simple but flexible noise model for potentially low-resolution sedimentary climate proxies for temperature on millennial timescales, the code for calculating a set of pseudoproxies from a simulation, and one example of pseudoproxies. The noise model considers the influence of other environmental variables, a dependence on the climate state, a bias due to changing seasonality, modifications of the archive (for example bioturbation), potential sampling variability, and a measurement error. Model, code, and data allow us to develop new ways of comparing simulation data with proxies on long timescales. Code and data are available at https://doi.org/10.17605/OSF.IO/ZBEHX (Bothe et al., 2018).

The scarcity of long instrumental records, uncertainty in reconstructions, and insufficient skill in model simulations hamper assessing how regional precipitation changed over past centuries. Here, we use standardized precipitation data to compare a regional climate simulation, reconstructions, and long observational records of seasonal (March to July) mean precipitation in England and Wales over the past 350 years. The Standardized Precipitation Index is a valuable tool for assessing agreement between the different sources of information, as it allows for a comparison of the temporal evolution of percentiles of the precipitation distributions. These evolutions are not consistent among reconstructions, a regional simulation, and instrumental observations for severe and extreme dry and wet conditions. The lack of consistency between the different data sets may be due to the dominance of internal climate variability over the impact of natural exogenous forcing conditions on multi-decadal timescales. The disagreement between sources of information reduces our confidence in inferences about the origins of hydroclimate variability for small regions. However, it is encouraging that there is still some agreement between a regional simulation and observations. Our results emphasize the complexity of hydroclimate changes during the recent centuries and stress the necessity of a thorough understanding of the processes affecting forced and unforced precipitation variability.

We report the first large-acceptance measurement of the beam–spin asymmetry for deuteron photodisintegration (γ→d→pn) in the photon energy range 420

(1) Imaging of pharmaceutical compounds in tissue is an increasingly important subsection of Mass Spectrometry Imaging (MSI). Identifying proper target engagement requires MS platforms with high sensitivity and spatial resolution. Three prominent categories of drugs are small molecule drugs, antibody-drug conjugate payloads, and protein degraders. (2) We tested six common MSI platforms for their limit of detection (LoD) on a representative compound for each category: a Matrix-Assisted Laser Desorption/Ionization (MALDI) Fourier Transform Ion Cyclotron, a MALDI-2 Time-of-Flight (ToF), a MALDI-2 Trapped Ion Mobility Spectrometry ToF, a Desorption Electrospray Ionization Orbitrap, and 2 Atmospheric Pressure-MALDI Triple Quadrupoles. Samples were homogenized tissue mimetic models of rat liver spiked with known concentrations of analytes. (3) We found that the AP-MALDI-QQQ platform outperformed all 4 competing platforms by a minimum of 2- to 52-fold increase in LoD for representative compounds from each category of pharmaceutical. (4) AP-MALDI-QQQ platforms are effective, cost-efficient mass spectrometers for the identification of targeted analytes of interest.

This article presents a reliability analysis method and a series of case studies of different architectures for interlock systems of large and complex research infrastructures. Interlock systems play a crucial role in the protection of different types of machines, including present and future particle accelerators and fusion experiments. These infrastructures require multibillion Euros investments and accidents could cause irreparable damage. Protection systems are needed to prevent damage from an unintended release of large amounts of stored energy or power. Interlock systems define the signal exchange between the sensors that detect non-nominal conditions and the actuators that bring the machine into a safe state through a protection shutdown. The design of machine protection systems in general and interlock systems, in particular, is caught between the desired machine safety and machine availability, and the requirements vary between different infrastructures. For some infrastructures, interlock systems must be designed to strictly avoid unintentional shutdowns, as these can have a significant impact on the lifetime of vital equipment or their primary operational purpose. For others, unintentional shutdowns due to a failure in the interlock system are acceptable as long as their number is small compared to protection shutdowns caused by failures of other equipment, in order to maximize their scientific output. The case studies presented in the article compare different interlock architectures based on the probability of specific failure scenarios occurring.

Abstract The nature and cause of deep earthquakes remain enduring unknowns in the field of seismology. We present new models of thermal structures of subducted slabs traced to mantle transition zone depths that permit a detailed comparison between slab pressure/temperature (P/T) paths and hydrated/carbonated mineral phase relations. We find a remarkable correlation between slabs capable of transporting water to transition zone depths in dense hydrous magnesium silicates with slabs that produce seismicity below ∼300‐km depth, primarily between 500 and 700 km. This depth range also coincides with the P/T conditions at which oceanic crustal lithologies in cold slabs are predicted to intersect the carbonate‐bearing basalt solidus to produce carbonatitic melts. Both forms of fluid evolution are well represented by sublithospheric diamonds whose inclusions record the existence of melts, fluids, or supercritical liquids derived from hydrated or carbonate‐bearing slabs at depths (∼300–700 km) generally coincident with deep‐focus earthquakes. We propose that the hydrous and carbonated fluids released from subducted slabs at these depths lead to fluid‐triggered seismicity, fluid migration, diamond precipitation, and inclusion crystallization. Deep focus earthquake hypocenters could track the general region of deep fluid release, migration, and diamond formation in the mantle. The thermal modeling of slabs in the mantle and the correlation between sublithospheric diamonds, deep focus earthquakes, and slabs at depth demonstrate a deep subduction pathway to the mantle transition zone for carbon and volatiles that bypasses shallower decarbonation and dehydration processes.

Interleukin-12 (IL-12) is a potent, pro-inflammatory type 1 cytokine that has long been studied as a potential immunotherapy for cancer. Unfortunately, IL-12's remarkable antitumor efficacy in preclinical models has yet to be replicated in humans. Early clinical trials in the mid-1990's showed that systemic delivery of IL-12 incurred dose-limiting toxicities. Nevertheless, IL-12's pleiotropic activity, i.e., its ability to engage multiple effector mechanisms and reverse tumor-induced immunosuppression, continues to entice cancer researchers. The development of strategies which maximize IL-12 delivery to the tumor microenvironment while minimizing systemic exposure are of increasing interest. Diverse IL-12 delivery systems, from immunocytokine fusions to polymeric nanoparticles, have demonstrated robust antitumor immunity with reduced adverse events in preclinical studies. Several localized IL-12 delivery approaches have recently reached the clinical stage with several more at the precipice of translation. Taken together, localized delivery systems are supporting an IL-12 renaissance which may finally allow this potent cytokine to fulfill its considerable clinical potential. This review begins with a brief historical account of cytokine monotherapies and describes how IL-12 went from promising new cure to ostracized black sheep following multiple on-study deaths. The bulk of this comprehensive review focuses on developments in diverse localized delivery strategies for IL-12-based cancer immunotherapies. Advantages and limitations of different delivery technologies are highlighted. Finally, perspectives on how IL-12-based immunotherapies may be utilized for widespread clinical application in the very near future are offered.