Bruno Andreas Walther

Participation in authentic research projects benefits both teachers and students by linking hands‐on field work with experimental biology and quantitative analysis. Plant ecological genetics is especially well suited for classroom integration, but accessible, classroom‐ready outlines are scarce. Arabidopsis thaliana —a wild species in urban environments with a well‐characterized genome and extensive educational resources—provides an ideal model. Here we present a comprehensive, easy‐to‐implement pipeline for secondary‐school–level research‐oriented projects in plant ecological genetics using A. thaliana . The report includes (i) a step‐by‐step experimental workflow for field sampling, phenotypic scoring, and PCR‐sequencing‐based genotyping assays for environment‐sensitive allele monitoring; (ii) classroom‐adapted protocols and quality‐control checks; (iii) background material and a list of concrete, testable research questions; and (iv) downloadable real datasets and analysis templates for guided interpretation. We demonstrate feasibility with pilot field‐survey data from urban school neighborhoods and report outcomes that validate the approach as a proof of concept. Students can learn species identification, formulate hypotheses about ecological adaptation, perform standardized data collection, and apply elementary genetic and statistical analyses—thereby gaining practical laboratory skills and improved scientific literacy. The methods are adaptable to different time frames and resource levels and can be scaled into broader citizen‐science initiatives, allowing classroom findings to contribute meaningfully to ecological genetics research. This project therefore provides a practical bridge between secondary education and place‐based research, fostering engagement with contemporary questions in ecology and evolution.

Plastic production and plastic waste have increased to such an extent that it has become globally ubiquitous. Several studies already have investigated the meso- and microplastic pollution along the German Baltic and North Sea coasts, but were all limited to a few locations. To obtain representative bulk samples from sandy beaches along the entire German coast, we initiated a citizen-science project entitled “Microplastic Detectives.” Here, we describe in detail 1) how we recruited, instructed, and engaged citizen scientists, 2) why we chose bulk sampling over reduced-volume sampling, and 3) the laboratory methods we used. The citizen scientists collected 1,139 samples from 71 locations along the German coast, totalling 2.2 tons of sand. After drying, sieving with a 1 mm sieve, and visual inspection of the retained fraction under a binocular microscope, all putative plastic particles ≥1 mm were analysed by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. 177 out of 1,139 samples (15.5%) contained a total of 260 plastic particles, with a large right-skewed variation among locations. Most of the particles were fragments, foils, foams, fibres, and pellets (96.2% in total), and 89.6% of the particles were made of polyethylene, polypropylene, polyester, and polystyrene. The unweighted mean pollution densities were 4.12 particles m⁻², 0.17 particles kg⁻¹ and 0.27 particles L⁻¹, and the weighted mean pollution densities were 3.77 particles m⁻², 0.11 particles kg⁻¹ and 0.18 particles L⁻¹. These densities are lower than in other similar studies, but previous studies had important methodological differences. We discuss how these differences could have influenced the results and make recommendations for improving future studies. Two important recommendations are 1) to use random or stratified random sampling and 2) to run transects perpendicular (rather than parallel) to the waterline. Our study highlights that large-scale, scientifically rigorous monitoring of meso- and microplastic pollution is possible at the national level, and possibly even at much larger spatial and temporal scales. With the help of local authorities, such a monitoring program could be established.

Plastic production and plastic waste have increased to such an extent that it has become globally ubiquitous. Recent research has highlighted that it has also invaded remote Polar Regions including the Arctic, where it is expected to accumulate over time due to transport from distant sources, rising local anthropogenic activities and increasing fragmentation of existing ocean plastics to microplastics (plastic items −1 and 147.4 particles L⁻¹). These pollution levels could be due to our focus on plastic particles ≥1 mm as well as the relatively small sample sizes used during this initial phase of the project. In addition, the coarse substrate on most beaches might retain fewer plastic particles. The two samples with plastic particles ≥1 mm contained six polyester-epoxide particles and 4920 polypropylene fibres. The latter likely originated from a fishing net and points to possibly accelerated plastic fragmentation processes on Arctic beaches. Since fisheries-related debris is an important source of plastic on Svalbard, a build-up of microplastic quantities can be expected to burden Arctic ecosystems in addition to climate change unless efficient upstream action is taken to combat plastic pollution.

Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.

Previous epidemiological studies have shown that proton pump inhibitor (PPI) may modify the risk of pancreatic cancer. We conducted an updated systematic review and meta-analysis of observational studies assessing the effect of PPI on pancreatic cancer. PubMed, Embase, Scopus, and Web of Science were searched for studies published between 1 January 2000, and 1 May 2022. We only included studies that assessed exposure to PPI, reported pancreatic cancer outcomes, and provided effect sizes (hazard ratio or odds ratio) with 95% confidence intervals (CIs). We calculated an adjusted pooled risk ratio (RR) with 95%CIs using the random-effects model. Eleven studies (eight case–control and three cohorts) that reported 51,629 cases of pancreatic cancer were included. PPI was significantly associated with a 63% increased risk of pancreatic cancer (RR_adj. 1.63, 95%CI: 1.19–2.22, p = 0.002). Subgroup analysis showed that the pooled RR for rabeprazole and lansoprazole was 4.08 (95%CI: 0.61–26.92) and 2.25 (95%CI: 0.83–6.07), respectively. Moreover, the risk of pancreatic cancer was established for both the Asian (RR_adj. 1.37, 95%CI: 0.98–1.81) and Western populations (RR_adj.2.76, 95%CI: 0.79–9.56). The findings of this updated meta-analysis demonstrate that the use of PPI was associated with an increased risk of pancreatic cancer. Future studies are needed to improve the quality of evidence through better verification of PPI status (e.g., patient selection, duration, and dosages), adjusting for possible confounders, and ensuring long-term follow-up.

Plastic pollution is now a worldwide phenomenon affecting all marine ecosystems, but some ecosystems and regions remain understudied. Here, we review the presence and impacts of macroplastics and microplastics for four such ecosystems: mangroves, seagrass meadows, the Arctic Ocean and the deep seafloor. Plastic production has grown steadily, and thus the impact on species and ecosystems has increased, too. The accumulated evidence also indicates that plastic pollution is an additional and increasing stressor to these already ecosystems and many of the species living in them. However, laboratory or field studies, which provide strong correlational or experimental evidence of ecological harm due to plastic pollution remain scarce or absent for these ecosystems. Based on these findings, we give some research recommendations for the future.

Besides competition, positive interactions also play an important role in shaping the social structure of mixed‐species bird flocks. This study aimed to illuminate the interspecific interactions of competition and facilitation in mixed‐species bird flocks. We recorded the foraging behavior and microhabitat use of flocking species in montane coniferous forests of Taiwan under different social contexts. Foraging niche breadth and niche‐overlap with other flocking species were compared between individuals inside and outside of mixed flocks. For the three microhabitat variables (foraging locations, vertical strata and horizontal strata), relationships between niche‐overlaps of heterospecific pairs of these flocking species and their corresponding interspecific associations were determined using a simple linear regression. While in mixed flocks, two understory species, Taiwan fulvetta Fulvetta formosana and yellowish‐bellied bush warbler Horornis acanthizoides , shifted their foraging from shrubs upwards into coniferous trees. Meanwhile, flamecrests Regulus goodfellowi moved downwards vertically within the canopy, and black‐throated tits Aegithalos concinnus spread out horizontally along branches. In addition, flamecrests applied many more sally‐hovers inside of mixed flocks than outside of flocks. All four species are insectivores which might find it more difficult to obtain sufficient food during the colder winters when food resources become scarcer. Therefore, they may be using the increased vigilance afforded by the flock to expand their foraging niches and thus to increase their foraging opportunities inside mixed flocks. Furthermore, niche‐overlaps of heterospecific pairs of the 11 common flocking species were positively correlated with their corresponding interspecific associations on all three microhabitat variables. These results indicate that a greater foraging niche‐overlap between two flocking species would result in higher coexistence of the two species in mixed flocks. Consequently, facilitative interactions occurred in these mixed‐species flocks in addition to competitive interactions.

Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Identification of early gastric cancer (EGC) can ensure quick treatment and reduce significant mortality. Therefore, we aimed to conduct a systematic review with a meta-analysis of current literature to evaluate the performance of the CNN model in detecting EGC. We conducted a systematic search in the online databases (e.g., PubMed, Embase, and Web of Science) for all relevant original studies on the subject of CNN in EGC published between 1 January 2010, and 26 March 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Moreover, a summary receiver operating characteristic curve (SROC) was plotted. Of the 171 studies retrieved, 15 studies met inclusion criteria. The application of the CNN model in the diagnosis of EGC achieved a SROC of 0.95, with corresponding sensitivity of 0.89 (0.88–0.89), and specificity of 0.89 (0.89–0.90). Pooled sensitivity and specificity for experts endoscopists were 0.77 (0.76–0.78), and 0.92 (0.91–0.93), respectively. However, the overall SROC for the CNN model and expert endoscopists was 0.95 and 0.90. The findings of this comprehensive study show that CNN model exhibited comparable performance to endoscopists in the diagnosis of EGC using digital endoscopy images. Given its scalability, the CNN model could enhance the performance of endoscopists to correctly stratify EGC patients and reduce work load.