First records of two mealybug species in Brazil and new potential pests of papaya and coffee

Five mealybug (Hemiptera: Pseudococcidae) plant pest species: Dysmicoccus grassii (Leonardi), Ferrisia malvastra (McDaniel), Ferrisia virgata (Cockerell), Phenacoccus tucumanus Granara de Willink, and Pseudococcus elisae Borchsenius are recorded for the first time in the state of Espírito Santo, Brazil. These are the first records of D. grassii in Brazil, from papaya (Carica papaya, Caricaceae), and from coffee (Coffea canephora, Rubiaceae). Ferrisia malvastra is also newly recorded in Brazil, where it was found on Bidens pilosa (Asteraceae). Ferrisia virgata was collected from an unidentified weed and Phenacoccus tucumanus from Citrus sp. (Rutaceae). Plotococcus capixaba Kondo was found on pitanga (Eugenia cf. pitanga, Myrtaceae) and Pseudococcus elisae on Coffea canephora, which are new host records for these mealybugs

Erwartungsbildung über den Wahlausgang und ihr Einfluss auf die Wahlentscheidung

Erwartungen über den Wahlausgang haben einen festen Platz sowohl in Rational-Choice-Theorien des Wählerverhaltens als auch in stärker sozialpsychologisch orientierten Ansätzen. Die Bildung von Erwartungen und ihr Einfluss auf die Wahlentscheidung ist dabei jedoch ein noch relativ unerforschtes Gebiet. In diesem Beitrag werden anhand von Wahlstudien für Belgien, Österreich und Deutschland verschiedene Fragen der Erwartungsbildung und ihrer Auswirkungen untersucht. Zunächst wird die Qualität der Gesamterwartungen analysiert und verschiedene Faktoren identifiziert, die einen systematischen Einfluss auf die Erwartungsbildung haben. Im zweiten Schritt wenden wir uns den Einzelerwartungen über verschiedene Parteien und Koalitionen zu und finden eine moderate Verzerrung zugunsten der präferierten Parteien und Koalitionen. Dabei kann gezeigt werden, dass der Effekt des Wunschdenkens mit dem politischen Wissen und dem Bildungsgrad abnimmt. Schließlich werden in einem letzten Schritt zwei unterschiedliche Logiken für die Auswirkungen von Erwartungen getestet, das rationale Kalkül des koalitionsstrategischen Wählens zur Vermeidung der Stimmenvergeudung sowie der sozialpsychologisch begründete Bandwagon-Effekt. Das Ausmaß an politischem Wissen scheint dabei eine zentrale vermittelnde Variable zwischen den beiden Logiken zu sein

Refining Kidney Survival in 383 Genetically Characterized Patients With Nephronophthisis

Introduction: Nephronophthisis (NPH) comprises a group of rare disorders accounting for up to 10% of end-stage kidney disease (ESKD) in children. Prediction of kidney prognosis poses a major challenge. We assessed differences in kidney survival, impact of variant type, and the association of clinical characteristics with declining kidney function. Methods: Data was obtained from 3 independent sources, namely the network for early onset cystic kidney diseases clinical registry (n = 105), an online survey sent out to the European Reference Network for Rare Kidney Diseases (n = 60), and a literature search (n = 218). Results: A total of 383 individuals were available for analysis: 116 NPHP1, 101 NPHP3, 81 NPHP4 and 85 NPHP11/TMEM67 patients. Kidney survival differed between the 4 cohorts with a highly variable median age at onset of ESKD as follows: NPHP3, 4.0 years (interquartile range 0.3–12.0); NPHP1, 13.5 years (interquartile range 10.5–16.5); NPHP4, 16.0 years (interquartile range 11.0–25.0); and NPHP11/TMEM67, 19.0 years (interquartile range 8.7–28.0). Kidney survival was significantly associated with the underlying variant type for NPHP1, NPHP3, and NPHP4. Multivariate analysis for the NPHP1 cohort revealed growth retardation (hazard ratio 3.5) and angiotensin-converting enzyme inhibitor (ACEI) treatment (hazard ratio 2.8) as 2 independent factors associated with an earlier onset of ESKD, whereas arterial hypertension was linked to an accelerated glomerular filtration rate (GFR) decline. Conclusion: The presented data will enable clinicians to better estimate kidney prognosis of distinct patients with NPH and thereby allow personalized counseling

Metal Bioavailability in the Sava River Water

Metals present one of the major contamination problems for freshwater systems, such as the Sava River, due to their high toxicity, persistence, and tendency to accumulate in sediment and living organisms. The comprehensive assessment of the metal bioavailability in the Sava River encompassed the analyses of dissolved and DGT-labile metal species of nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the river water, as well as the evaluation of the accumulation of five metals (Cd, Cu, Fe, Mn, and Zn) in three organs (liver, gills, and gastrointestinal tissue) of the bioindicator organism, fish species European chub (Squalius cephalus L.).This survey was conducted mainly during the year 2006, in two sampling campaigns, in April/May and September, as periods representative for chub spawning and post-spawning. Additionally, metal concentrations were determined in the intestinal parasites acanthocephalans, which are known for their high affinity for metal accumulation. Metallothionein concentrations were also determined in three chub organs, as a commonly applied biomarker of metal exposure. Based on the metal concentrations in the river water, the Sava River was defined as weakly contaminated and mainly comparable with unpolluted rivers, which enabled the analyses of physiological variability of metal and metallothionein concentrations in the chub organs, as well as the establishment of their constitutive levels

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14