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Conspicuously absent, in particular, is a description of the central role of women in forming the medical systems established independently by the Jews in the interwar period, during the Holocaust and in its aftermath.3 From the end of the nineteenth century until the outbreak of World War II, following the opening of universities in Europe to women, the number of female university students grew steadily. The first Jewish female university students largely came from assimilated families that did not observe the religious traditions, but others came from modern-Orthodox homes and completed their studies while maintaining a religious lifestyle.6 This trend among Jewish women to study medicine produced a rise in the number and proportion of Jewish women doctors throughout Europe. [...]no nationwide Jewish medical organization was created, as in Poland, but the percentage of Jews in medicine was still far greater than their percentage in the general population, and the same was true of Jewish women.9 For example, on the eve of World War I, Jewish women constituted 11% of all students at Prussian universities and almost 30% of all the medical students. In 1932, on the eve of Hitler's rise to power, female Jewish physicians comprised 40% of the 722 female doctors in Berlin, and their number was even higher in Vienna.10 At that time, about 17% (9,000) of the approximately 53,000 doctors in Germany were Jewish, though Jews constituted only 0.8% of the general population.11 Although a few women succeeded in entering academia as lecturers and researchers, many found their niche in private practice, usually specializing in pediatrics, gynecology, ophthalmology, or, particularly after World War I, psychiatry.

Several polymorphic DNA restriction endonuclease fragments hybridizing with xenotropic and ecotropic envelope virus probes map adjacent to minor histocompatibility and lymphocyte (H/Ly) antigen-encoding loci. Viral DNA restriction fragments are associated with Ly-17 on chromosome 1, H-30, H-3, and H-13 on chromosome 2, Ly-21 on chromosome 7, H-28 on chromosome 3, and H-38 (chromosomal location as yet undetermined). In each case no recombinant can be found between the H/Ly locus in question and the virus-related restriction fragment, suggesting that linkage is very tight. Although some viral loci map to locations where no H/Ly has yet been mapped, the frequency and tightness of linkage in the seven instances described, coupled with the large number of as yet unmapped H/Ly loci, suggests that the associations found are significant.

The region of chromosome 2 between H-13 and H-3 has been shown to contain loci coding for a variety of other alloantigens, including Ly-4 and the locus coding for β2-microglobulin. Herein we show that Ly-6 and Ly-11 are coded for by genes in a segment of chromosome 2 adjacent to the H-3--H-13 region and that this segment of chromosome also contains the tightly linked loci coding for antigens Ala-1, DAG, H9/25, H-30, Ly-8, and ThB. In addition, at least one locus (and probably more) affecting susceptibility to leukemia induction is found within this gene cluster.

A common link between the induction of leukemia by (i) fractionated doses of x-irradiation and (ii) radiation leukemia virus in mice may be established by the observation that the segment of chromosome 2 between the loci for the minor histocompatibility antigen H-30 and color coat agouti (H-30-A) includes distinct loci involved in susceptibility to leukemogenesis induced by factors i and ii.

IntroductionDetecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.Materials and methodsThis study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.ResultsSuccessful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.ConclusionMulti-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.