Tracing early life histories from Roman times to the Medieval era: weaning practices and physiological stress

and cultural factors. While infant feeding strategies vary across different regions and historical eras, the associated transition from breastmilk to solid foods is universally thought to be stressful. However, still little is known about infant feeding practices and possibly associated stress in former times. This also applies to the period of transition from classical antiquity to medieval times, which shaped modern Western civilization. To enhance the understanding of childhood nutrition and stress during this period, we first analyzed stable carbon and nitrogen isotopes in serial dentine samples from the first molars of 38 individuals buried in the region once known as the Roman frontier province of Raetia secunda, now encompassing Southern Bavaria. In addition, we investigated the presence of linear enamel hypoplasia (LEH), known to be a marker of unspecific physiological stress, within their dentition. We used this data to create isotope profiles that display dietary changes in comparison with the occurrence of LEH. We found highly variable δ15N and δ13C values and different shapes of isotope profiles which indicate different nutrition of breastfeeding individuals, complementary foods and post-weaning diets, and individual weaning patterns. For most individuals, the weaning process was completed between the ages of two and three. Interestingly, some females of non-local origin show longer weaning periods, likely displaying the influence of different cultural practices in other communities. We also found that LEH most frequently occurred in the post-weaning phase, which supports the assumption that children were at increased risk once breastfeeding had ceased completely. Furthermore, a change in the post-weaning diet in the seventh century coincided with an increased prevalence of LEH, indicating that the foods chosen or available during this time affected the susceptibility of children to stress. In conclusion, our study unveiled diverse infant feeding strategies practiced across various communities, both in different historical eras and geographical locations

HPO × ELA:Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.</p

Hpo X Ela:Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications

Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features

Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks. The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB.</p

Multimodal imaging techniques to evaluate the anticancer effect of cold atmospheric pressure plasma

Background: Skin cancer is the most frequent cancer worldwide and is divided into non-melanoma skin cancer, including basal cell carcinoma, as well as squamous cell carcinoma (SCC) and malignant melanoma (MM). Methods: This study evaluates the effects of cold atmospheric pressure plasma (CAP) on SCC and MM in vivo, employing a comprehensive approach using multi-modal imaging techniques. Longitudinal MR and PET/CT imaging were performed to determine the anatomic and metabolic tumour volume over three‐weeks in vivo. Additionally, the formation of reactive species after CAP treatment was assessed by non‐invasive chemiluminescence imaging of L‐012. Histological analysis and immunohistochemical staining for Ki‐67, ApopTag®, F4/80, CAE, and CD31, as well as protein expression of PCNA, caspase‐3 and cleaved‐caspase‐3, were performed to study proliferation, apoptosis, inflammation, and angiogenesis in CAP‐treated tumours. Results: As the main result, multimodal in vivo imaging revealed a substantial reduction in tumour growth and an increase in reactive species after CAP treatment, in comparison to untreated tu-mours. In contrast, neither the markers for apoptosis, nor the metabolic activity of both tumour entities was affected by CAP. Conclusions: These findings propose CAP as a potential adjuvant therapy option to established standard therapies of skin cancer

Conformal SiO₂ coating of sub-100 nm diameter channels of polycarbonate etched ion-track channels by atomic layer deposition

Polycarbonate etched ion-track membranes with about 30 µm long and 50 nm wide cylindrical channels were conformally coated with SiO₂ by atomic layer deposition (ALD). The process was performed at 50 °C to avoid thermal damage to the polymer membrane. Analysis of the coated membranes by small angle X-ray scattering (SAXS) reveals a homogeneous, conformal layer of SiO₂ in the channels at a deposition rate of 1.7–1.8 Å per ALD cycle. Characterization by infrared and X-ray photoelectron spectroscopy (XPS) confirms the stoichiometric composition of the SiO₂ films. Detailed XPS analysis reveals that the mechanism of SiO₂ formation is based on subsurface crystal growth. By dissolving the polymer, the silica nanotubes are released from the ion-track membrane. The thickness of the tube wall is well controlled by the ALD process. Because the track-etched channels exhibited diameters in the range of nanometres and lengths in the range of micrometres, cylindrical tubes with an aspect ratio as large as 3000 have been produced

Population genomic analysis of elongated skulls reveals extensive female-biased immigration in Early Medieval Bavaria

Modern European genetic structure demonstrates strong correlations with geography, while genetic analysis of prehistoric humans has indicated at least two major waves of immigration from outside the continent during periods of cultural change. However, population-level genome data that could shed light on the demographic processes occurring during the intervening periods have been absent. Therefore, we generated genomic data from 41 individuals dating mostly to the late 5th/early 6th century AD from present-day Bavaria in southern Germany, including 11 whole genomes (mean depth 5.56×). In addition we developed a capture array to sequence neutral regions spanning a total of 5 Mb and 486 functional polymorphic sites to high depth (mean 72×) in all individuals. Our data indicate that while men generally had ancestry that closely resembles modern northern and central Europeans, women exhibit a very high genetic heterogeneity; this includes signals of genetic ancestry ranging from western Europe to East Asia. Particularly striking are women with artificial skull deformations; the analysis of their collective genetic ancestry suggests an origin in southeastern Europe. In addition, functional variants indicate that they also differed in visible characteristics. This example of female-biased migration indicates that complex demographic processes during the Early Medieval period may have contributed in an unexpected way to shape the modern European genetic landscape. Examination of the panel of functional loci also revealed that many alleles associated with recent positive selection were already at modern-like frequencies in European populations ∼1,500 years ago