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Aim: This observational study investigated the association between remdesivir treatment during hospitalization for COVID-19 and 30-day COVID-19-related and all-cause readmission across different variants time periods. Patients & methods: Hospitalization records for adult patients discharged from a COVID-19 hospitalization between 1 May 2020 to 30 April 2022 were extracted from the US PINC AI Healthcare Database. Likelihood of 30-day readmission was compared among remdesivir-treated and nonremdesivir-treated patients using multivariable logistic regression models adjusted for age, corticosteroid treatment, Charlson comorbidity index and intensive care unit stay during the COVID-19 hospitalization. Analyses were stratified by maximum supplemental oxygen requirement and variant time period (pre-Delta, Delta and Omicron). Results: Of the 440,601 patients discharged alive after a COVID-19 hospitalization, 248,785 (56.5%) patients received remdesivir. Overall, remdesivir patients had a 30-day COVID-19-related readmission rate of 3.0% and all-cause readmission rate of 6.3% compared with 5.4% and 9.1%, respectively, for patients who did not receive remdesivir during their COVID-19 hospitalization. After adjusting for demographics and clinical characteristics, remdesivir treatment was associated with significantly lower odds of 30-day COVID-19-related readmission (odds ratio 0.60 [95% confidence interval: 0.58-0.62]), and all-cause readmission (0.73 [0.72-0.75]). Significantly lower odds of 30-day readmission in remdesivir-treated patients was observed across all variant time periods. Conclusion: Treating patients hospitalized for COVID-19 with remdesivir is associated with a statistically significant reduction in 30-day COVID-19-related and all-cause readmission across variant time periods. These findings indicate that the clinical benefit of remdesivir may extend beyond the COVID-19 hospitalization.

Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air–pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements and the importance of such high-resolution data for new satellite retrievals, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea ice surface emerging from the increasingly available visual image data recorded from uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models.

Objective: To evaluate the efficacy and safety of 2 doses of vilaprisan vs. placebo in participants with symptomatic endometriosis. Design: Multicenter, randomized, double-blind, placebo-controlled, parallel-group phase 2b trial (NCT03573336). The initially planned sample size was 315 patients. Recruitment was paused to assess long-term toxicity findings in rodents; although the findings were assessed as likely to be of limited clinical relevance in humans, the study was closed by the sponsor. During the pause, enrolled patients completed 3 or 6 months of treatment per their assigned regimen. Setting: University hospitals, a regional hospital, and a private clinic. Patients: Premenopausal adults with confirmed endometriosis and moderate-to-severe pelvic pain (≥4/10 on a numerical rating scale) were enrolled. Inclusion required protocol adherence, including ≥24 diary entries, and an average pain score of ≥3.5. Intervention: Participants were randomly assigned 1:1:1 to receive vilaprisan (2 mg), vilaprisan (4 mg), or placebo. Main Outcome Measures: The primary outcome was a change in the 7-day mean “worst pain” (per the endometriosis symptom diary item 1) from baseline to month 3. All analyses were descriptive only. Results: Eight participants were randomly assigned to treatment before the study pause: 6 received vilaprisan (4 mg, n = 4 and 2 mg, n = 2), and 2 received placebo. The 6 vilaprisan recipients experienced an improvement in endometriosis-associated pelvic pain, whereas the 2 placebo recipients experienced no change or increased pain; all 8 participants had decreased use of pain medication. Bleeding intensity decreased from baseline in the vilaprisan group. Conclusion: The study findings suggest that vilaprisan may improve outcomes in patients with endometriosis. Further studies in larger populations would be needed to accurately assess treatment effects. Clinical Trial Registration Number: NCT03573336

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Abstract Despite the wide availability of effective vaccines, COVID-19 continues to be an infectious disease of global importance. Remdesivir is a broad-spectrum antiviral and was the first US Food and Drug Administration-approved treatment for COVID-19. In clinical guidelines, remdesivir is currently the only recommended antiviral for use in hospitalized patients with COVID-19, with or without a supplemental oxygen requirement. It is also recommended for nonhospitalized patients with COVID-19 and hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who are at high risk of progression to severe disease. This narrative review explores the evidence for remdesivir across various clinical outcomes and evolution of clinical guidelines through a survey over time of randomized controlled trials, observational studies, and meta-analyses. Remdesivir, compared to standard of care, appears to improve survival and disease progression in a variety of patient populations with COVID-19 across a spectrum of disease severity and SARS-CoV-2 variant periods. Remdesivir also appears to improve time to clinical recovery, increase rate of recovery, and reduce time on supplemental oxygen and readmission rates. More recent large, real-world studies further support the early use of remdesivir in a range of patient populations, including those with immunocompromising conditions.

Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of melt ponds along the MOSAiC drift at a resolution of 10 m. A novel melt pond parameterization scheme simulates the movement of meltwater under the influence of gravity over a realistic sea ice topography. We evaluated different melt pond parameterization schemes based on remote sensing observations. The absolute deviation of the maximum pond coverage simulated by the new scheme is within 3%, while differences among parameterization schemes exceed 50%. Errors were found to be primarily due to the calculation of macroscopic meltwater loss, which is related to sea ice surface topography. Previous studies have indicated that sea ice with a lower surface roughness has a larger catchment area, resulting in larger pond coverage during the melt season. This study has identified an opposing mechanism: sea ice with lower surface roughness has a larger catchment area connected to the macroscopic flaws of the sea ice surface, which leads to more macroscopic drainage into the ocean and thereby a decrease in melt pond coverage. Experimental simulations showed that sea ice with 46% higher surface roughness, resulting in 12% less macroscopic drainage, exhibited a 38% higher maximum pond fraction. The presence of macroscopic flaws is related to the fragmentation of sea ice cover. As Arctic sea ice cover becomes increasingly fragmented and mobile, this mechanism will become more significant.

Numerous studies have focused on constructing multimodal machine learning models for estimating a person's cognitive load. However, a prevalent limitation is that these models are typically evaluated on data from the same scenario they were trained on. Little attention has been given to their robustness against data distribution shifts, which may occur during deployment. The aim of this paper is to investigate the performance of these models when confronted with a scenario different from the one on which they were trained. For this evaluation, we utilized a dataset encompassing two distinct scenarios: an n-Back test and a driving simulation. We selected a variety of classic machine learning and deep learning architectures, which were further complemented by various fusion techniques. The models were trained on the data from the n-Back task and tested on both scenarios to evaluate their predictive performance. However, the predictive performance alone may not lead to a trustworthy model. Therefore, we looked at the uncertainty estimates of these models. By leveraging these estimates, we can reduce misclassification by resorting to alternative measures in situations of high uncertainty. The findings indicate that late fusion produces stable classification results across the examined models for both scenarios, enhancing robustness compared to feature-based fusion methods. Although a simple logistic regression tends to provide the best predictive performance for n-Back, this is not always the case if the data distribution is shifted. Finally, the predictive performance of individual modalities differs significantly between the two scenarios. This research provides insights into the capabilities and limitations of multimodal machine learning models in handling distribution shifts and identifies which approaches may potentially be suitable for achieving robust results.

Abstract Background Breastfeeding provides the optimal nutrition for infants and offers numerous benefits for both mother and child. The World Health Organisation recommends exclusive breastfeeding during the first 6 months of life and the introduction of complementary feeding between the fifth and seventh months of life. There is a discrepancy between breastfeeding recommendations and the actual duration of breastfeeding. The aim of this study was to analyse breastfeeding behaviour in primiparous women in order to be able to provide support for mothers. Methods In this prospective, questionnaire-based study conducted between 2020 and 2022, primiparous women were asked to complete three questionnaires at three defined survey time points (routine prepartum presentation, postpartum hospitalization, completed sixth month of life). Results A total of 140 women were included and returned all three questionnaires. Fifty-eight percent performed breastfeeding exclusively at least until their baby had reached the age of 6 months, whereas 20% already stopped within the first 6 months. The main reasons given for early cessation were insufficient milk supply and inadequate infant weight gain. A comprehensive level of prepartum knowledge had a significant positive effect on participants’ sense of confidence with breastfeeding. Sociodemographic factors such as age and educational level were also associated with breastfeeding behaviour, but significant corresponding differences in the duration of breastfeeding were not observed. Women with postpartum midwifery care breastfed significantly longer (p

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.

Macrophytes, which are foundational to freshwater ecosystems, face significant threats due to habitat degradation globally. Habitat suitability models are vital tools used to investigate the relationship between macrophytes and their environment. This study addresses a critical gap by developing a Geographic information system-based HSM tailored for small lakes, which are often overlooked in ecological studies. We included various abiotic predictors to model the potential macrophyte habitat for several small lakes in southern Bavaria (Germany). Key factors such as the distance to groundwater inflow, depth, availability of photosynthetically active radiation (PAR), and littoral slope were identified as significant predictors of macrophyte occurrence. Notably, the HSM integrates remote sensing-based data to derive PAR availability at the growing depths of the macrophytes using Sentinel-2 MSI data. Integration of an MSI-based time series of PAR availability enabled the introduction of a temporal component allowing monitoring and predicting changes in macrophyte habitats over time. The modeled habitat suitability score correlates highly (R = 0.908) with macrophyte occurrence. We see great promise in using habitat modeling for macrophytes as a tool for water management; in particular, the use of Sentinel-2 MSI data for habitat suitability modeling holds promise for advancing water management. By demonstrating the efficacy of GIS- and remote sensing-based HSM, we pave the way for future applications of this innovative approach in ecological conservation and resource management.

Abstract Background Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. Methods An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. Results The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. Conclusions We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.

Aim: This observational study investigated the association between remdesivir treatment during hospitalization for COVID-19 and 30-day COVID-19-related and all-cause readmission across different variants time periods. Patients & methods: Hospitalization records for adult patients discharged from a COVID-19 hospitalization between 1 May 2020 to 30 April 2022 were extracted from the US PINC AI Healthcare Database. Likelihood of 30-day readmission was compared among remdesivir-treated and nonremdesivir-treated patients using multivariable logistic regression models adjusted for age, corticosteroid treatment, Charlson comorbidity index and intensive care unit stay during the COVID-19 hospitalization. Analyses were stratified by maximum supplemental oxygen requirement and variant time period (pre-Delta, Delta and Omicron). Results: Of the 440,601 patients discharged alive after a COVID-19 hospitalization, 248,785 (56.5%) patients received remdesivir. Overall, remdesivir patients had a 30-day COVID-19-related readmission rate of 3.0% and all-cause readmission rate of 6.3% compared with 5.4% and 9.1%, respectively, for patients who did not receive remdesivir during their COVID-19 hospitalization. After adjusting for demographics and clinical characteristics, remdesivir treatment was associated with significantly lower odds of 30-day COVID-19-related readmission (odds ratio 0.60 [95% confidence interval: 0.58–0.62]), and all-cause readmission (0.73 [0.72–0.75]). Significantly lower odds of 30-day readmission in remdesivir-treated patients was observed across all variant time periods. Conclusion: Treating patients hospitalized for COVID-19 with remdesivir is associated with a statistically significant reduction in 30- day COVID-19-related and all-cause readmission across variant time periods. These findings indicate that the clinical benefit of remdesivir may extend beyond the COVID-19 hospitalization.

Yaws is an endemic disease caused by Treponema pallidum subsp. pertenue (TPE) that primarily affects children in rural regions of the tropics. The endemic character of yaws infections and the expected exclusive reservoir of TPE in humans opened a new opportunity to start a yaws eradication campaign. We have developed a multi-locus sequence typing (MLST) scheme for TPE isolates combining the previously published (TP0548, TP0488) and new (TP0858) chromosomal loci, and we compared this typing scheme to the two previously published MLST schemes. We applied this scheme to TPE-containing clinical isolates obtained during a mass drug administration study performed in the Namatanai District of Papua New Guinea between June 2018 and December 2019. Of 1081 samples collected, 302 (28.5%) tested positive for TPE DNA, from which 255 (84.4%) were fully typed. The TPE PCR-positivity in swab samples was higher in younger patients, patients with single ulcers, first ulcer episodes, and with ulcer duration less than six months. Non-treponemal serological test positivity correlated better with PCR positivity compared to treponema-specific serological tests. The MLST revealed a low level of genetic diversity among infecting TPE isolates, represented by just three distinct genotypes (JE11, SE22, and TE13). Two previously used typing schemes revealed similar typing resolutions. Two new alleles (one in TP0858 and one in TP0136) were shown to arise by intragenomic recombination/deletion events. Compared to samples genotyped as JE11, the minor genotypes (TE13 and SE22) were more frequently detected in samples from patients with two or more ulcers and patients with higher values of specific TP serological tests. Moreover, the A2058G mutation in the 23S rRNA genes of three JE11 isolates was found, resulting in azithromycin resistance.

Abstract Background The aim of this retrospective study was to describe the spectrum of genital and associated malformations in women with Mayer-Rokitansky-Küster-Hauser syndrome using evaluated diagnostic procedures and the Vagina Cervix Uterus Adnex – associated Malformation classification system (VCUAM). Methods 290 women with MRKH syndrome were clinically evaluated with using clinical examinations, abdominal and perineal/rectal ultrasound, MRI, and laparoscopy. Results Classification of female genital malformation according to the Vagina Cervix Uterus Adnex – associated Malformation classification system was possible in 284 women (97.9%). Complete atresia of Vagina (V5b) and bilateral atresia of Cervix (C2b) were found in 284 patients (100%). Uterus: bilateral rudimentary or a plastic uterine horns were found in 239 women (84.2%). Adnexa: normal Adnexa were found in 248 women (87.3%). Malformations: associated malformations were found in 126 of 282 evaluable women (44.7%), 84 women (29.6%) had malformations of the renal system. Of 284 women with Mayer-Rokitansky-Küster-Hauser syndrome 212 women (74.7%) could be classified as V5bC2bU4bA0. The most frequent classification was V5bC2bU4bA0M0 (46.8%) diagnosed in 133 of 284 women. Conclusions Complete atresia of vagina and cervix were found in all patients, variable malformations were found with uterus and adnexa. A variety of associated malformations were present, predominantly of the renal system. It is therefore recommended that all patients with genital malformations should be evaluated for renal abnormalities.

Chronic HIV infection may exacerbate atherosclerotic vascular disease, which at advanced stages presents as necrotic plaques rich in crystalline cholesterol. Such lesions can catastrophically rupture precipitating myocardial infarct and stroke, now important causes of mortality in those living with HIV. However, in this population little is known about plaque structure relative to crystalline content and its chemical composition. Here, we first interrogated plaque crystal structure and composition in atherosclerotic SIV-infected macaques using non-linear optical microscopy. By stimulated Raman scattering and second harmonic generation approaches both amorphous and crystalline plaque lipid was detected and the crystal spectral profile indicated a cholesterol ester (CE) dominated composition. Versus controls, SIV+ samples had a greater number of cholesterol crystals (CCs), with the difference, in part, accounted for by crystals of a smaller length. Given the ester finding, we profiled HIV+ plaques and also observed a CE crystalline spectral signature. We further profiled plaques from Ldlr-/- mice fed a high fat diet, and likewise, found CE-dominate crystals. Finally, macrophage exposure to CCs or AcLDL induced auto-fluorescent puncta that co-stained with the LC3B autophagy sensor. In aggregate, we show that atheromatous plaques from mice, macaques and humans, display necrotic cores dominated by esterified CCs, and that plaque macrophages may induce autophagic vesicle formation upon encountering CCs. These findings help inform our knowledge of plaque core lipid evolution and how the process may incite systemic inflammation.

PurposePembrolizumab improved survival in patients with recurrent or metastatic head and neck squamous-cell carcinoma (HNSCC). The aims of this study were to determine if pembrolizumab would be safe, result in pathologic tumor response (pTR), and lower the relapse rate in patients with resectable human papillomavirus (HPV)-unrelated HNSCC.Patients and methodsNeoadjuvant pembrolizumab (200 mg) was administered and followed 2 to 3 weeks later by surgical tumor ablation. Postoperative (chemo)radiation was planned. Patients with high-risk pathology (positive margins and/or extranodal extension) received adjuvant pembrolizumab. pTR was quantified as the proportion of the resection bed with tumor necrosis, keratinous debris, and giant cells/histiocytes: pTR-0 (

In state of the art photoinjector electron sources, thermal emittance from photoemission dominates the final injector emittance. Therefore, low thermal emittance cathode developments and diagnostics are very important. Conventional thermal emittance measurements for the high gradient gun are time-consuming and thus thermal emittance is not measured as frequently as quantum efficiency during the lifetime of photocathodes, although both are important properties for the photoinjector optimizations. In this paper, a single shot measurement of photoemission transverse momentum, i.e., thermal emittance per rms laser spot size, is proposed for photocathode rf guns. By tuning the gun solenoid focusing, the electrons' transverse momenta at the cathode are imaged to a downstream screen, which enables a single shot measurement of both the rms value and the detailed spectra of the photoelectrons' transverse momenta. Both simulations and proof of principle experiments are reported.

Abstract The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Abstract Klotho is an anti-aging protein with several therapeutic roles in the pathophysiology of different organs, such as the skeletal muscle and kidneys. Available evidence suggests that exercise increases Klotho levels, regardless of the condition or intervention, shedding some light on this anti-aging protein as an emergent and promising exerkine. Development of a systematic review and meta-analysis in order to verify the role of different exercise training protocols on the levels of circulating soluble Klotho (S-Klotho) protein. A systematic search of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE through PubMed, EMBASE, CINAHL, CT.gov, and PEDro. Randomized and quasi-randomized controlled trials that investigated effects of exercise training on S-Klotho levels. We included 12 reports in the analysis, comprising 621 participants with age ranging from 30 to 65 years old. Klotho concentration increased significantly after chronic exercise training (minimum of 12 weeks) (Hedge’ g [95%CI] 1.3 [0.69–1.90]; P

Summary: Cells in the human retina must rapidly adapt to constantly changing visual stimuli. This fast adaptation to varying levels and wavelengths of light helps to regulate circadian rhythms and allows for adaptation to high levels of illumination, thereby enabling the rest of the visual system to remain responsive. It has been shown that retinal microRNA (miRNA) molecules play a key role in regulating these processes. However, despite extensive research using various model organisms, light-regulated miRNAs in human retinal cells remain unknown. Here, we aim to characterize these miRNAs. We generated light-responsive human retinal organoids that express miRNA families and clusters typically found in the retina. Using an in-house developed photostimulation device, we identified a subset of light-regulated miRNAs. Importantly, we found that these miRNAs are differentially regulated by distinct wavelengths of light and have a rapid turnover, highlighting the dynamic and adaptive nature of the human retina.

The high peak current of the electron beam was found to be the key parameter for the THz SASE FEL at the Photo Injector Test facility at DESY in Zeuthen (PITZ). A multipurpose bunch compressor was implemented at PITZ to expand the parameter space of proof-of-principle studies on the tunable high-power accelerator-based THz source for pump-probe experiments at the European XFEL. The magnetic chicane, consisting of four rectangular dipole magnets, is designed with a bending angle of 19 degrees, due to limited space in the PITZ original beamline, to compress electron bunches with a beam momentum of 15–20 MeV/c and a charge up to 2 nC. The space charge effect and coherent synchrotron radiation are expected to drastically affect the bunch compressor performance for these parameters, thereby challenging the beam transport throughout the bunch compressor. A staged commissioning strategy was developed in order to achieve optimum bunch compressor operation. The first commissioning procedure establishes electron beam transport throughout the reference path and provides minimum beam momentum dispersion after the bunch compressor. This procedure yielded correlations between dipole magnet currents. As a result, the first bunch compression experiments were performed.

Providing simultaneously autotrophic and heterotrophic carbon sources is a promising strategy to overcome the limits of autotrophic syngas fermentations. D-xylose and L-arabinose are particularly interesting as they can be obtained by the hydrolysis of lignocellulosic biomass. The individual conversion of varying initial concentrations of these pentoses and D-fructose as reference was studied with C. autoethanogenum in fully controlled stirred-tank reactors with a continuous syngas supply. All mixotrophic batch processes showed increased biomass and product formation compared to an autotrophic reference process. Simultaneous CO and D-xylose or L-arabinose conversion was observed in contrast to D-fructose. In the mixotrophic batch processes with L-arabinose or D-xylose, the simultaneous CO and sugar conversion resulted in high final alcohol-to-acid ratios of up to 58 g g−1. L-arabinose was superior as a mixotrophic carbon source because biomass and alcohol concentrations (ethanol and 2,3-butanediol) were highest, and significant amounts of meso-2,3-butanediol (>1 g L−1) in addition to D-2,3-butanediol (>2 g L−1) were solely produced with L-arabinose. Furthermore, C. autoethanogenum could not produce meso-2,3 butanediol under purely heterotrophic conditions. The mixotrophic production of meso-2,3-butanediol from L-arabinose and syngas, both available from residual lignocellulosic biomass, is very promising for use as a monomer for bio-based polyurethanes or as an antiseptic agent.