Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Abstract Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss 1 . Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity 2 . Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.

Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.

Estrogens play an important role in the control and differentiation of species-typical behavior and in endocrine homeostasis of birds, but the distribution and evolution of cells that contain estrogen receptors in the avian brain are poorly understood. This study therefore surveys 26 species in the avian orders Anseriformes (1 species), Galliformes (2), Columbiformes (3), Psittaciformes (1), Apodiformes (2), and Passeriformes (3 suboscines, 14 oscines). Indirect immunocytochemistry with the estrogen receptor (ER) antibody H222Spy revealed a general pattern of ER-antibody-immunoreactive cells (ER-IRC) in all 26 species, with ER-IRC in consistent, well-defined locations in the limbic forebrain, the midbrain striatum, the hippocampus, the hindbrain, and especially in the preoptic area and the tuberal hypothalamus. For some species, the microdistribution of ER-IRC in some of these general areas differed, such as in the hippocampus and the anterior hypothalamus of suboscine species and in the preoptic area of the Japanese quail. Brains of oscine songbirds of both sexes, unlike brains of nonsongbirds, had ER-IRC in three specific structures of the nonlimbic forebrain: in the area surrounding the nucleus robustus archistriatalis; in the rostral forebrain; and, for all individuals, in the caudale neostriatum, including the nucleus hyperstriatalis ventrale, pars caudale (HVc). Among songbird families or subfamilies, adult males of the Estrildinae had much lower numbers of ER-IRC in HVc than did adult males of the Fringillidae, Paridae, Sturnidae, and Ploceinae. Differences occurred, too, among closely related species: the songbird canary (Serinus canaria) had an ER-IRC area in the rostral forebrain that was lacking in all other songbird species, including other cardueline finches. The cells with ER that are found only in the songbird forebrain but not in reptiles, nonpasserine birds, and nonoscine passerine birds very likely coevolved with steroid-dependent differentiation of vocal control areas. The songbird-specific expression of ER in the forebrain could be an example in which taxon-specific behavior is due to taxon specific neurochemical properties of the brain.

Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases.

Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models.

The present investigation used stable area-specific, neuronal properties instead of Nissl stain to delineate the boundaries of the nucleus hyperstriatalis caudal c (HVc) in the telencephalon of the adult male canary. Immunocytochemical procedures combined with retrograde tracing labeled a large population of perennial long-projecting neurons that contain estrogen receptors in the canary HVc. The HVc area defined by the distribution of these neurons was congruent with the HVc area defined in Nissl-stained sections during the breeding period. The HVc area defined in Nissl-stained preparations showed an extensive seasonal change in size, confirming previous results (Nottebohm: Science, 214:1368-1370, '81). In contrast, the HVc area defined by the distribution of the estrogen receptor containing long-projection neurons showed little or no seasonal change in size. Because these neurons are permanent, the HVc seems to be of rather constant size year round. The internal morphology of the HVc, however, undergoes seasonal alterations, which are reflected in changes in size of the HVc area distinguishable in Nissl-stained sections. The combination of cytoarchitectural criteria of Nissl-stained preparations with area-specific cytochemical and hodological markers to delineate the boundaries of a brain nucleus might give new insights in the partitioning and neuronal plasticity of brain areas.

Remote collaboration systems have become increasingly important in today’s society, especially during times when physical distancing is advised. Industry, research, and individuals face the challenging task of collaborating and networking over long distances. While video and teleconferencing are already widespread, collaboration systems in augmented, virtual, and mixed reality are still a niche technology. We provide an overview of recent developments of synchronous remote collaboration systems and create a taxonomy by dividing them into three main components that form such systems: Environment , Avatars , and Interaction . A thorough overview of existing systems is given, categorising their main contributions to help researchers working in different fields by providing concise information about specific topics such as avatars, virtual environment, visualisation styles, and interaction. The focus of this work is clearly on synchronised collaboration from a distance. A total of 87 unique systems for remote collaboration are discussed, including more than 100 publications and 25 commercial systems.

Visible light can be used as a sole driving force for the fixation of sulfur dioxide into sulfonylated coumarins.

Im Januar 2017 veröffentlichte die Bundesregierung mit der Neuauflage der Nationalen Nachhaltigkeitsstrategie die Fortschreibung zu der erstmalig 2002 vorgelegten Strategie für nachhaltige Entwicklung, die eine grundlegende Neuorientierung erfahren hat. Sie basiert auf den durch die Vereinten Nationen entwickelten Sustainable Development Goals (SDGs) der Agenda 2030. Es folgten Weiterentwicklungen bis zur Deutschen Nachhaltigkeitsstrategie 2025. Der Autor behandelt in seinem Buch systematisch die historischen Etappen zur aktuellen Nachhaltigkeitsstrategie 2025, aber auch eine kritische Auseinandersetzung hierzu. Das Buch richtet sich in erster Linie an Studierende der Fachbereiche Wirtschaft, Soziologie und Politik, aber auch an politische Entscheidungsträger und Interessierte zu diesem Thema.

Die utb-Reihe „Frag doch einfach!“ beantwortet Fragen, die sich nicht nur Studierende stellen. Im Frage-Antwort-Stil geben Expert*innen kundig Auskunft und verraten alles Wissenswerte rund um ein Thema. In diesem Band werden unter anderem Antworten auf diese Fragen zu lesen sein: Warum hat die erste Euphorie von der Rio Konferenz 1992 nachgelassen? Was bringt uns die Agenda 2030 und die deutsche Nachhaltigkeitsstrategie? Gibt es öffentliche Einrichtungen, die Verantwortung für das Nachhaltigkeitsziel übernehmen? Gibt es positive Beispiele zu nachhaltigem Konsum? Die wichtigsten Fachbegriffe werden zudem prägnant vorgestellt und es wird verraten, welche Websites, YouTube-Videos und Bücher das Wissen aus diesem Band vertiefen können.

Fucoidan is a heterogeneous group of polysaccharides isolated from marine organisms, including brown algae and marine invertebrates. The physicochemical characteristics and potential bioactivities of fucoidan have attracted substantial interest in pharmaceutical industries in the past few decades. These polysaccharides are characterized by possessing sulfate ester groups that impart negatively charged surfaces, low/high molecular weight, and water solubility. In addition, various promising bioactivities have been reported, such as antitumor, immunomodulatory, and antiviral effects. Hence, the formulation of fucoidan has been investigated in the past few years in diverse pharmaceutical dosage forms to be able to reach their site of action effectively. Moreover, they can act as carriers for various drugs in value-added drug delivery systems. The current work highlights the attractive biopharmaceutical properties of fucoidan being formulated in oral, inhalable, topical, injectable, and other advanced formulations treating life-quality-affecting diseases. Therefore, the present work points out the current status of fucoidan pharmaceutical formulations for future research transferring their application from in vitro and in vivo studies to clinical application and market availability.

Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges. This study introduces a novel in-memory-computing (IMC) crossbar macro utilizing a multi-level ferroelectric field-effect transistor (FeFET) cell for multi-bit multiply and accumulate (MAC) operations. The proposed 1FeFET-1R cell design stores multi-bit information while minimizing device variability effects on accuracy. Experimental validation was performed using 28 nm HKMG technology-based FeFET devices. Unlike traditional resistive memory-based analog computing, our approach leverages the electrical characteristics of stored data within the memory cell to derive MAC operation results encoded in activation time and accumulated current. Remarkably, our design achieves 96.6% accuracy for handwriting recognition and 91.5% accuracy for image classification without extra training. Furthermore, it demonstrates exceptional performance, achieving 885.4 TOPS/W-nearly double that of existing designs. This study represents the first successful implementation of an in-memory macro using a multi-state FeFET cell for complete MAC operations, preserving crossbar density without additional structural overhead.

The recent advancement of large language models presents numerous opportunities for teaching and learning. Despite widespread public debate regarding the use of large language models, empirical research on their opportunities and risks in education remains limited. In this work, we demonstrate the qualities and shortcomings of using ChatGPT 3.5 for physics task development by prospective teachers. In a randomized controlled trial, 26 prospective physics teacher students were divided into two groups: the first group used ChatGPT 3.5 to develop text-based physics tasks for four different concepts in the field of kinematics for 10th-grade high school students, while the second group used a classical textbook to create tasks for the same concepts and target group. The results indicate no difference in task correctness, but students using the textbook achieved a higher clarity and more frequently embedded their questions in a meaningful context. Both groups adapted the level of task difficulty easily to the target group but struggled strongly with sufficient task specificity, i.e., relevant information to solve the tasks was missing. Students using ChatGPT for problem posing rated high system usability but experienced difficulties with output quality. These results provide insights into the opportunities and pitfalls of using large language models in education.

Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD sets a new state of the art in the unsupervised setting. Our method can incorporate ground-truth anomaly maps during training and using even a few of these (~5) improves performance significantly. Finally, using FCDD's explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks.

During the past few decades, there has been a growing trend towards the use of smartphone-based analysis systems. This is mainly due to its ubiquity, its increasing computing capacity, its relatively low cost and the ability to acquire and process data at the same time. Furthermore, there are many sensors integrated into a smartphone, for example a complementary metal-oxide semiconductor (CMOS) sensor. A CMOS sensor enables optical analysis for example by using it as a colorimeter, photometer or spectrometer. This review explores the current state-of-the-art smartphone-based optical analysis systems in various areas of application. It is organized into three sections, each of which investigates one class of smartphone-based devices: (i) smartphone-based colorimeters (ii) smartphone-based photo- and spectrometers and (iii) smartphone-based fluorimeters.

Small regulatory RNAs (sRNAs) that act by base-pairing were first discovered in so-called accessory DNA elements--plasmids, phages, and transposons--where they control replication, maintenance, and transposition. Since 2001, a huge body of work has been performed to predict and identify sRNAs in a multitude of bacterial genomes. The majority of chromosome-encoded sRNAs have been investigated in E. coli and other Gram-negative bacteria. However, during the past five years an increasing number of sRNAs were found in Gram-positive bacteria. Here, we outline our current knowledge on chromosome-encoded sRNAs from low-GC Gram-positive species that act by base-pairing, i.e., an antisense mechanism. We will focus on sRNAs with known targets and defined regulatory mechanisms with special emphasis on Bacillus subtilis.

Nitrosamines occur widespread in food, drinking water, cosmetics, as well as tobacco smoke and can arise endogenously. More recently, nitrosamines have been detected as impurities in various drugs. This is of particular concern as nitrosamines are alkylating agents that are genotoxic and carcinogenic. We first summarize the current knowledge on the different sources and chemical nature of alkylating agents with a focus on relevant nitrosamines. Subsequently, we present the major DNA alkylation adducts induced by nitrosamines upon their metabolic activation by CYP450 monooxygenases. We then describe the DNA repair pathways engaged by the various DNA alkylation adducts, which include base excision repair, direct damage reversal by MGMT and ALKBH, as well as nucleotide excision repair. Their roles in the protection against the genotoxic and carcinogenic effects of nitrosamines are highlighted. Finally, we address DNA translesion synthesis as a DNA damage tolerance mechanism relevant to DNA alkylation adducts.

Extracting useful visual cues for the downstream tasks is especially challenging under low-light vision. Prior works create enhanced representations by either correlating visual quality with machine perception or designing illumination-degrading transformation methods that require pre-training on synthetic datasets. We argue that optimizing enhanced image representation pertaining to the loss of the downstream task can result in more expressive representations. Therefore, in this work, we propose a novel module, FeatEnHancer, that hierarchically combines multiscale features using multi-headed attention guided by task-related loss function to create suitable representations. Furthermore, our intra-scale enhancement improves the quality of features extracted at each scale or level, as well as combines features from different scales in a way that reflects their relative importance for the task at hand. FeatEnHancer is a general-purpose plug-and-play module and can be incorporated into any low-light vision pipeline. We show with extensive experimentation that the enhanced representation produced with FeatEnHancer significantly and consistently improves results in several low-light vision tasks, including dark object detection (+5.7 mAP on ExDark), face detection (+1.5 mAP on DARK FACE), nighttime semantic segmentation (+5.1 mIoU on ACDC), and video object detection (+1.8 mAP on DarkVision), highlighting the effectiveness of enhancing hierarchical features under low-light vision.

IP Routers use sophisticated forwarding table (FIB) lookup algorithms that minimize lookup time, storage, and update time. This paper presents SMALTA, a practical, near-optimal FIB aggregation scheme that shrinks forwarding table size without modifying routing semantics or the external behavior of routers, and without requiring changes to FIB lookup algorithms and associated hardware and software. On typical IP routers using the FIB lookup algorithm Tree Bitmap, SMALTA shrinks FIB storage by at least 50%, representing roughly four years of routing table growth at current rates. SMALTA also reduces average lookup time by 25% for a uniform traffic matrix. Besides the benefits this brings to future routers, SMALTA provides a critical easy-to-deploy one-time benefit to the installed base should IPv4 address depletion result in increased routing table growth rate. The effective cost of this improvement is a sub-second delay in inserting updates into the FIB once every few hours. We describe SMALTA, prove its correctness, measure its performance using data from a Tier-1 provider as well as Route-Views. We also describe an implementation in Quagga that demonstrates its ease of implementation.

Abstract Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple‐stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co‐occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open‐source and interactive version of the dataset ( https://jamesaorr.shinyapps.io/freshwater‐multiple‐stressors/ ). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple‐stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.