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Abstract Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffective against realistic, non-stationary noise such as multi-talker interference. Recent developments in deep neural network (DNN) algorithms have achieved noteworthy performance in speech enhancement and separation, especially in removing speech noise. However, more work is needed to investigate the potential of DNN algorithms in removing speech noise when tested with listeners fitted with CIs. Here, we implemented two DNN algorithms that are well suited for applications in speech audio processing: (1) recurrent neural network (RNN) and (2) SepFormer. The algorithms were trained with a customized dataset ( $$\sim$$ ∼ 30 h), and then tested with thirteen CI listeners. Both RNN and SepFormer algorithms significantly improved CI listener’s speech intelligibility in noise without compromising the perceived quality of speech overall. These algorithms not only increased the intelligibility in stationary non-speech noise, but also introduced a substantial improvement in non-stationary noise, where conventional signal processing strategies fall short with little benefits. These results show the promise of using DNN algorithms as a solution for listening challenges in multi-talker noise interference.

The Pan-Eurasian Experiment Modelling Platform (PEEX-MP) is one of the key blocks of the PEEX Research Programme. The PEEX MP has more than 30 models and is directed towards seamless environmental prediction. The main focus area is the Arctic-boreal regions and China. The models used in PEEX-MP cover several main components of the Earth’s system, such as the atmosphere, hydrosphere, pedosphere and biosphere, and resolve the physical-chemical-biological processes at different spatial and temporal scales and resolutions. This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system, online integrated, forward/inverse, and socioeconomical modelling, and other approaches with a particular focus on applications in the PEEX geographical domain. The employed high-performance computing facilities, capabilities, and PEEX dataflow for modelling results are described. Several virtual research platforms (PEEX-View, Virtual Research Environment, Web-based Atlas) for handling PEEX modelling and observational results are introduced. The overall approach allows us to understand better physical-chemical-biological processes, Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks, impact, consequences, etc. for population, environment and climate in the PEEX domain. This work was also one of the last projects of Prof. Sergej Zilitinkevich, who passed away on 15 February 2021. Since the finalization took time, the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

Abstract Immune checkpoints (CTLA4 & PD-1) are inhibitory pathways that block aberrant immune activity and maintain self-tolerance. Tumors co-opt these checkpoints to avoid immune destruction. Immune checkpoint inhibitors (ICIs) activate immune cells and restore their tumoricidal potential, making them highly efficacious cancer therapies. However, immunotolerant organs such as the liver depend on these tolerogenic mechanisms, and their disruption with ICI use can trigger the unintended side effect of hepatotoxicity termed immune-mediated liver injury from ICIs (ILICI). Learning how to uncouple ILICI from ICI anti-tumor activity is of paramount clinical importance. We developed a murine model to recapitulate human ILICI using CTLA4+/- mice treated with either combined anti-CTLA4 + anti-PDL1 or IgG1 + IgG2. We tested two forms of antisense oligonucleotides to knockdown caspase-3 in a total liver (parenchymal and non-parenchymal cells) or in a hepatocyte-specific manner. We also employed imaging mass cytometry (IMC), a powerful multiplex modality for immunophenotyping and cell interaction analysis in our model. ICI-treated mice had significant evidence of liver injury. We detected cleaved caspase-3 (cC3), indicating apoptosis was occurring, as well as Nod-like receptor protein 3 (NLRP3) inflammasome activation, but no necroptosis. Total liver knockdown of caspase-3 worsened liver injury, and induced further inflammasome activation, and Gasdermin-D-mediated pyroptosis. Hepatocyte-specific knockdown of caspase-3 reduced liver injury and NLRP3 inflammasome activation. IMC-generated single-cell data for 77,692 cells was used to identify 22 unique phenotypic clusters. Spatial analysis revealed that cC3+ hepatocytes had significantly closer interactions with macrophages, Kupffer cells, and NLRP3hi myeloid cells than other cell types. We also observed zones of three-way interaction between cC3+ hepatocytes, CD8 + T-cells, and macrophages. Our work is the first to identify hepatocyte apoptosis and NLRP3 inflammasome activation as drivers of ILICI. Furthermore, we report that the interplay between adaptive and innate immune cells is critical to hepatocyte apoptosis and ILICI.

Background: Performing accurate estimated fetal weights (EFWs) is a critical skill developed in obstetrics residency training. Resident physicians are often the first to perform EFWs on obstetric patients when they enter care. Evaluating residents' accuracy in performing EFWs is crucial for assessing their achievement in residency training milestones and providing patient care. Methods: As part of an educational initiative program between 2014 and 2020, postgraduate year 1 (PGY1) and postgraduate year 2 (PGY2) residents performed EFW measurements on 10 term (>37w0d) patients using ultrasound and Leopold's maneuver and 10 preterm (>24w0d and

Prolactin (PRL) is elevated in B-cell-mediated lymphoproliferative diseases and promotes B-cell survival. Whether PRL or PRL receptors drive the evolution of B-cell malignancies is unknown. We measure changes in B cells after knocking down the pro-proliferative, anti-apoptotic long isoform of the PRL receptor (LFPRLR) in vivo in systemic lupus erythematosus (SLE)- and B-cell lymphoma-prone mouse models, and the long plus intermediate isoforms (LF/IFPRLR) in human B-cell malignancies. To knockdown LF/IFPRLRs without suppressing expression of the counteractive short PRLR isoforms (SFPRLRs), we employ splice-modulating DNA oligomers. In SLE-prone mice, LFPRLR knockdown reduces numbers and proliferation of pathogenic B-cell subsets and lowers the risk of B-cell transformation by downregulating expression of activation-induced cytidine deaminase. LFPRLR knockdown in lymphoma-prone mice reduces B-cell numbers and their expression of BCL2 and TCL1. In overt human B-cell malignancies, LF/IFPRLR knockdown reduces B-cell viability and their MYC and BCL2 expression. Unlike normal B cells, human B-cell malignancies secrete autocrine PRL and often express no SFPRLRs. Neutralization of secreted PRL reduces the viability of B-cell malignancies. Knockdown of LF/IFPRLR reduces the growth of human B-cell malignancies in vitro and in vivo. Thus, LF/IFPRLR knockdown is a highly specific approach to block the evolution of B-cell neoplasms.

Cochlear implant (CI) systems differ in terms of electrode design and signal processing. It is likely that patients fit with different implant systems will experience different percepts when presented speech via their implant. The sound quality of speech can be evaluated by asking single-sided-deaf (SSD) listeners fit with a cochlear implant (CI) to modify clean signals presented to their typically hearing ear to match the sound quality of signals presented to their CI ear. In this paper, we describe very close matches to CI sound quality, i.e., similarity ratings of 9.5 to 10 on a 10-point scale, by ten patients fit with a 28 mm electrode array and MED EL signal processing. The modifications required to make close approximations to CI sound quality fell into two groups: One consisted of a restricted frequency bandwidth and spectral smearing while a second was characterized by a wide bandwidth and no spectral smearing. Both sets of modifications were different from those found for patients with shorter electrode arrays who chose upshifts in voice pitch and formant frequencies to match CI sound quality. The data from matching-based metrics of CI sound quality document that speech sound-quality differs for patients fit with different CIs and among patients fit with the same CI.

Objective A skewed percentage of industry payments goes to “key opinion leaders” (KOLs) whose prominence and influence has increased with time. Given that KOL is neither precisely defined nor quantifiable, we turned to the level of industry payments as a surrogate quantifiable metric and assessed the associations between industry payments to US rheumatologists and their authorships of publications in high‐impact rheumatology journals. Methods Payments to US rheumatologists during the 2015‐2020 interval were obtained from the Centers for Medicare and Medicaid Services Open Payments database, and authorships were tallied for calendar year 2021 publications in the four rheumatology journals (Lancet Rheumatol, Nat Rev Rheumatol, Ann Rheum Dis, Arthritis Rheumatol) with the highest 2021 journal impact factors and journal citation indicators. Differences between groups were determined by chi‐squared test, unpaired Student's t‐test, one‐way analysis of variance (ANOVA), Mann–Whitney rank sum test, and Kruskal–Wallis one‐way ANOVA on ranks. Correlations were calculated using Spearman rank order. A P value ≤0.05 was considered significant. Results There were 278 individual US rheumatologists who received industry payments and served as authors of publications in the four high‐impact rheumatology journals. Non–research‐associated payments to these individuals strongly correlated with research‐associated payments. Payments to male US rheumatologists were greater than those to their female counterparts, and payments strongly correlated with the number of publications among male authors but only weakly, and often not significantly, among female authors. Conclusion A substantial fraction of the authorships in calendar year 2021 publications in four high‐impact rheumatology journals arose from a very small percentage of all US rheumatologists who had received industry payments during the 2015‐2020 interval. Payments to male US rheumatologist‐authors were strikingly different from those to female US rheumatologist‐authors, and further investigation is needed to explain the glaring difference in payments.

AbstractChronic pain is associated with mental and physical health difficulties and is prevalent among veterans. Cannabis has been put forth as a treatment for chronic pain, and changes in laws, attitudes, and use patterns have occurred over the past 2 decades. Differences in prevalence of nonmedical cannabis use and cannabis use disorder (CUD) were examined across 2 groups: veterans or nonveterans and those reporting or not reporting recent pain. Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (2012-2013; n = 36,309) were analyzed using logistic regression. Prevalence differences (PDs) for 3 cannabis outcomes (1) past-year nonmedical cannabis use, (2) frequent (≥3 times a week) nonmedical use, and (3) DSM-5 CUD were estimated for those reporting recent moderate to severe pain (veterans or nonveterans) and veterans reporting or not reporting recent pain. Difference in differences was calculated to investigate PDs on outcomes associated with residence in a state with medical cannabis laws (MCLs). Associations between physical and mental health and cannabis variables were tested. The results indicated that the prevalence of recent pain was greater among veterans (PD = 7.25%, 95% confidence interval (CI) [4.90-9.60]). Among veterans, the prevalence of frequent cannabis use was greater among those with pain (PD = 1.92%, 98% CI [0.21-3.63]), and among veterans residing in a state with MCLs, the prevalence of CUD was greater among those reporting recent pain (PD = 3.88%, 98% CI [0.36-7.39]). Findings failed to support the hypothesis that cannabis use improves mental or physical health for veterans with pain. Providers treating veterans with pain in MCL states should monitor such patients closely for CUD.

ObjectiveThe Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not.MethodsWe analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use.ResultsAmong veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40).ConclusionsHeterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

Abstract Rapid warming and human exploitation threaten boreal forests. Understanding links among vegetation, climate, and people in this vast biome requires highly resolved long‐term records that integrate regional inputs. We developed an 850‐year pollen‐based record of supraregional vegetation change using a southern Greenland ice core and atmospheric modeling that identified the boreal and mixed‐conifer forests of eastern Canada as the dominant pollen source regions. Conifer pollen increased ∼1400 CE at the onset of the cooler and drier Little Ice Age. A subsequent decline began ∼1650 CE and a statistically significant pollen change after 1760 CE suggests ecological consequences of the Little Ice Age cooling and initial human exploitation that persisted until recent decades. These supraregional changes are broadly consistent with local records and demonstrate intensification of human impacts on northern forests, suggesting a shift from a climate‐modulated to an increasingly human‐controlled system during recent centuries.

Abstract The heat wave in late June of 2021 (PNW21) set new temperature records in the Pacific Northwest (PNW). In Lytton the highest temperature ever recorded in Canada was measured. Several studies have already explored this extreme event in detail, however, here we compare the atmospheric air mass transport and heating processes associated with this heat wave with the 34 other most extreme heat events in the same region during the period 1960–2021, using a long backtracking time of 25 days. We found significant differences in the heat waves. During PNW21 most of the air was coming from the Philippine Sea, with more than 40% of the air located south of 15°N, and anomalous advection of sensible and latent heat from the Tropics was the dominant cause of PNW21. The latent heat was efficiently converted into sensible heat by precipitation, which was unique, as most other extremes experienced net diabatic cooling.

Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatiotemporal locations of the particles that move independently from a regular grid. Traditionally, these high-resolution data have been obtained by interpolating the meteorological parameters from the gridded data of a meteorological model or reanalysis, e.g., using linear interpolation in space and time. However, interpolation is a large source of error for these models. Reducing them requires meteorological input fields with high space and time resolution, which may not always be available and can cause severe data storage and transfer problems. Here, we interpret this problem as a single-image super-resolution task. That is, we interpret meteorological fields available at their native resolution as low-resolution images and train deep neural networks to upscale them to a higher resolution, thereby providing more accurate data for Lagrangian models. We train various versions of the state-of-the-art enhanced deep residual networks for super-resolution (EDSR) on low-resolution ERA5 reanalysis data with the goal to upscale these data to an arbitrary spatial resolution. We show that the resulting upscaled wind fields have root-mean-squared errors half the size of the winds obtained with linear spatial interpolation at acceptable computational inference costs. In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we find that absolute horizontal transport deviations of calculated trajectories from “true” trajectories calculated with un-degraded 0.5∘ × 0.5∘ winds are reduced by at least 49.5 % (21.8 %) after 48 h relative to trajectories using linear interpolation of the wind data when training on 2∘ × 2∘ to 1∘ × 1∘ (4∘ × 4∘ to 2∘ × 2∘) resolution data.

Malignant human B cells express autocrine prolactin (PRL), which has the potential to engage the long isoform and intermediate form of the PRL receptor to promote proliferation and/or survival.

BAFF, a vital B cell survival and differentiation factor, has three receptors: B‐cell maturation antigen (BCMA), transmembrane activator and CAML interactor (TACI) and BR3. Although B cells are greatly reduced in B6.Baff−/− (which harbour no BAFF) and B6.Br3−/− mice (which harbour supra‐normal levels of BAFF), the distributions of B cell subsets and relationships between Foxp3+ and CD4+ cells in these mice differ. Using a large panel of B6 congenic knockout and/or transgenic mice, we demonstrate that (1) supra‐normal levels of BAFF per se do not explain the phenotypic differences between B6.Baff−/− and B6.Br3−/− mice; (2) B cells are expanded in B6.Taci−/− mice, with preferential expansion of follicular (FO) B cells at the expense of CD19+CD21−/loCD23−/lo B cells but without the preferential expansion of Foxp3+ cells observed in B6 mice bearing a Baff transgene; (3) despite no expansion in total B cells, percentages of FO B cells and marginal zone B cells are higher and percentages of CD19+CD21−/loCD23−/lo B cells are lower in young B6.Bcma−/− mice, consistent with the inability of B6.Br3−/−.Taci−/− mice to recapitulate the B cell profile of B6.Baff−/− mice; and (4) percentages of Foxp3+ cells in B6.Br3−/−.Taci−/− mice are intermediate between those in B6.Br3−/− and B6.Taci−/− mice despite the B cell profile of B6.Br3−/−.Taci−/− mice strongly resembling that of B6.Br3−/− mice. Collectively, our findings point to a non‐redundant role for each of the BAFF receptors in determining the ultimate lymphocyte profile of the host. This may have clinically relevant ramifications in that the degree that a candidate therapeutic agent blocks engagement of any given individual BAFF receptor may affect its clinical utility. [ABSTRACT FROM AUTHOR]

Abstract Military conflicts result in local environmental damage, but documenting regional and larger scale impacts such as heavy metal pollution has proven elusive. Anthropogenic emissions of bismuth (Bi) include coal burning and various commodity productions but no emission estimates over the past century exist. Here we used Bi measurements in ice cores from the French Alps to show evidence of regional-scale Bi pollution concurrent with the Spanish Civil War and World War II. Tracers of the main sources of Bi emissions measured in the same ice—coal-burning, steel- and aluminum-industry, alloy and other metal processing—indicate a major, previously undocumented additional emissions source that we attribute to military activities between 1935 and 1945 Common Era (CE) in western Europe. These include the use of bismuth for low-melting point alloys for shells, thin-walled aluminum alloy aircraft oil, and munitions.

Black Carbon is an important climate forcer with poorly constraint historic emission fluxes and therefore large emission uncertainty. Here, ice-core data are combined with modelling to reconstruct historical emissions of Black carbon and finding gaps with the existing inventories, which implies potential climate sensitivity biases

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.

Abstract Objective The Department of Veterans Affairs’ (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. Methods We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen’s d of group differences in absolute risk by past-year VA healthcare use. Results Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40). Conclusions Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

Using the example of sulfur hexafluoride (SF6), we investigate the use of Lagrangian particle dispersion models (LPDMs) for inverse modeling of greenhouse gas (GHG) emissions and explore the limitations of this approach. We put the main focus on the impacts of baseline methods and the LPDM backward simulation period on the a posteriori emissions determined by the inversion. We consider baseline methods that are based on a statistical selection of observations at individual measurement sites and a global-distribution-based (GDB) approach, where global mixing ratio fields are coupled to the LPDM back-trajectories at their termination points. We show that purely statistical baseline methods can cause large systematic errors, which lead to inversion results that are sensitive to the LPDM backward simulation period and can generate unrealistic global total a posteriori emissions. The GDB method produces a posteriori emissions that are far less sensitive to the backward simulation period and that show a better agreement with recognized global total emissions. Our results show that longer backward simulation periods, beyond the often used 5 to 10 d, reduce the mean squared error and increase the correlation between a priori modeled and observed mixing ratios. Also, the inversion becomes less sensitive to biases in the a priori emissions and the global mixing ratio fields for longer backward simulation periods. Further, longer periods might help to better constrain emissions in regions poorly covered by the global SF6 monitoring network. We find that the inclusion of existing flask measurements in the inversion helps to further close these gaps and suggest that a few additional and well-placed flask sampling sites would have great value for improving global a posteriori emission fields.

Warm and moist air-mass intrusions into the Arctic are more frequent than the past decades. Here, the authors show that warm air mass intrusions from northern Eurasia inject record amounts of aerosols into the central Arctic Ocean strongly impacting atmospheric chemistry and cloud properties.