Influence of host plant nitrogen fertilization on haemolymph protein profiles of herbivore Spodoptera exigua and development of its endoparasitoid Cotesia marginiventris

Citation: Chen, Y., Ruberson, J. R., & Ni, X. (2014). Influence of host plant nitrogen fertilization on haemolymph protein profiles of herbivore Spodoptera exigua and development of its endoparasitoid Cotesia marginiventris. Retrieved from http://krex.ksu.eduNitrogen has complex effects on plant-herbivore-parasitoid tri-trophic interactions. The negative effects of host plant low nitrogen fertilization on insect herbivores in many cases can be amplified to the higher trophic levels. In the present study, we examined the impact of varying nitrogen fertilization (42, 112, 196, and 280 ppm) on cotton plants (Gossypium hirsutum L.) on the interactions between the beet armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae), and the hymenopteran endoparasitoid Cotesia marginiventris (Cresson) (Hymenoptera: Braconidae). We predicted that the development and fitness of C. marginiventris would be adversely affected by low host plant nitrogen fertilization through the herbivore S. exigua. The percentage of C. marginiventris offspring developing to emerge and spin a cocoon, and total mortality of parasitized S. exigua larvae were unaffected by nitrogen level. The developmental time of C. marginiventris larvae in S. exigua larvae feeding on low (42 ppm) nitrogen cotton plants was approximately 30% longer than that of those feeding on high (112, 196, and 280 ppm) nitrogen plants. Parasitoid size (length of right metathoracic tibia), a proxy for fitness, of C. marginiventris males was positively affected by nitrogen level. Total amounts of S. exigua haemolymph proteins were not affected by nitrogen level, but were reduced by parasitism by C. marginiventris. Two proteins with molecular weights of ca. 84 and 170 kDa dominated the S. exigua larval haemolymph proteins. Concentrations of the 170 kDa haemolymph protein were unaffected by nitrogen treatment, but parasitism reduced concentrations of the the 170 kDa protein. Concentrations of the 84 kDa protein, on the other hand, were interactively affected by parasitism and nitrogen treatment: higher nitrogen fertilization (112, 196, and 280 ppm) increased protein concentrations relative to the 42 ppm treatment for unparasitized S. exigua larvae, whereas nitrogen treatment had no effects on parasitized larvae. For S. exigua larvae feeding on 42 ppm nitrogen plants, parasitism increased concentration of the 84 kDa protein, while for those feeding on 112, 196, and 280 ppm nitrogen plants, parasitism decreased concentrations of the protein. Possible mechanisms and ecological consequences for the extended development of C. marginiventris on S. exigua hosts grown on low-nitrogen plants are discussed

2-Methyl­sulfanyl-5,6-dihydro-2H-1,3-dithiolo[4,5-b][1,4]dioxin-2-ium tetra­fluoro­borate

The title compound, C6H7O2S3 +·BF4 −, consists of a planar 2-thioxo-1,3-dithiol-4,5-yl unit [maximum deviation from the ring plane = 0.020 (3) Å], with an ethyl­enedi­oxy group fused at the 4,5-positions; the ethyl­enedi­oxy C atoms are disordered over two positions with site-occupancy factors of 0.5. The 1,4-dioxine ring has a twist-chair conformation. Weak cation–anion S⋯F inter­actions [3.022 (4)–3.095 (4) Å] and an S⋯O [3.247 (4) Å] inter­action are present

Understanding Heterogeneity of Automated Vehicles and Its Traffic-level Impact: A Stochastic Behavioral Perspective

This paper develops a stochastic and unifying framework to examine variability in car-following (CF) dynamics of commercial automated vehicles (AVs) and its direct relation to traffic-level dynamics. The asymmetric behavior (AB) model by Chen at al. (2012a) is extended to accommodate a range of CF behaviors by AVs and compare with the baseline of human-driven vehicles (HDVs). The parameters of the extended AB (EAB) model are calibrated using an adaptive sequential Monte Carlo method for Approximate Bayesian Computation (ABC-ASMC) to stochastically capture various uncertainties including model mismatch resulting from unknown AV CF logic. The estimated posterior distributions of the parameters reveal significant differences in CF behavior (1) between AVs and HDVs, and (2) across AV developers, engine modes, and speed ranges, albeit to a lesser degree. The estimated behavioral patterns and simulation experiments further reveal mixed platoon dynamics in terms of traffic throughout reduction and hysteresis

Cross-talks between perivascular adipose tissue and neighbors: multifaceted nature of nereids

Perivascular adipose tissue (PVAT) is a unique fat depot surrounding blood vessels and plays a vital role in the progression of vascular remodeling and dysfunction. PVAT exhibits remarkable differences in structure, phenotype, origin, and secretome across anatomical locations. The proximity of PVAT to neighboring vascular beds favors a niche for bidirectional communication between adipocytes and vascular smooth muscle cells, endothelial cells, and immune cells. In this review, we update our understanding of PVAT’s regional differences and provide a comprehensive exploration of how these differences impact cross-talks between PVAT and the vascular wall. Different PVAT depots show different degrees of vasoprotective function and resilience to pathological changes such as obesity and vasculopathies, shaping multifaceted interactions between PVAT depots and adjacent vasculatures. The depot-specific resilience may lead to innovative strategies to manage cardiometabolic disorders

FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent Font Effect Generation

Recently, the application of modern diffusion-based text-to-image generation models for creating artistic fonts, traditionally the domain of professional designers, has garnered significant interest. Diverging from the majority of existing studies that concentrate on generating artistic typography, our research aims to tackle a novel and more demanding challenge: the generation of text effects for multilingual fonts. This task essentially requires generating coherent and consistent visual content within the confines of a font-shaped canvas, as opposed to a traditional rectangular canvas. To address this task, we introduce a novel shape-adaptive diffusion model capable of interpreting the given shape and strategically planning pixel distributions within the irregular canvas. To achieve this, we curate a high-quality shape-adaptive image-text dataset and incorporate the segmentation mask as a visual condition to steer the image generation process within the irregular-canvas. This approach enables the traditionally rectangle canvas-based diffusion model to produce the desired concepts in accordance with the provided geometric shapes. Second, to maintain consistency across multiple letters, we also present a training-free, shape-adaptive effect transfer method for transferring textures from a generated reference letter to others. The key insights are building a font effect noise prior and propagating the font effect information in a concatenated latent space. The efficacy of our FontStudio system is confirmed through user preference studies, which show a marked preference (78% win-rates on aesthetics) for our system even when compared to the latest unrivaled commercial product, Adobe Firefly.Comment: Project-page: https://font-studio.github.io

4-(Pyridin-2-yl)-1,3-dithiol-2-one

In the title compound, C8H5NOS2, the non-H atoms are approximately coplanar [maxium deviation = 0.060 (3) Å]. The dihedral angle between the least-squares planes of the pyridine and 1,3-dithiol-2-one rings is 5.96 (17)°. The crystal packing is stabilized by weak inter­molecular C—H⋯O hydrogen bonds and by an S⋯S close contact [3.510 (5) Å]

Domain Generalization for Zero-calibration BCIs with Knowledge Distillation-based Phase Invariant Feature Extraction

The distribution shift of electroencephalography (EEG) data causes poor generalization of braincomputer interfaces (BCIs) in unseen domains. Some methods try to tackle this challenge by collecting a portion of user data for calibration. However, it is time-consuming, mentally fatiguing, and user-unfriendly. To achieve zerocalibration BCIs, most studies employ domain generalization (DG) techniques to learn invariant features across different domains in the training set. However, they fail to fully explore invariant features within the same domain, leading to limited performance. In this paper, we present an novel method to learn domain-invariant features from both interdomain and intra-domain perspectives. For intra-domain invariant features, we propose a knowledge distillation framework to extract EEG phase-invariant features within one domain. As for inter-domain invariant features, correlation alignment is used to bridge distribution gaps across multiple domains. Experimental results on three public datasets validate the effectiveness of our method, showcasing stateof-the-art performance. To the best of our knowledge, this is the first domain generalization study that exploit Fourier phase information as an intra-domain invariant feature to facilitate EEG generalization. More importantly, the zerocalibration BCI based on inter- and intra-domain invariant features has significant potential to advance the practical applications of BCIs in real world