SALT OVERLY SENSITIVE2 stabilizes phytochrome-interacting factors PIF4 and PIF5 to promote Arabidopsis shade avoidance.

Sun-loving plants trigger the shade avoidance syndrome (SAS) to compete against their neighbors for sunlight. Phytochromes are plant red (R) and far-red (FR) light photoreceptors that play a major role in perceiving the shading signals and triggering SAS. Shade induces a reduction in the level of active phytochrome B (phyB), thus increasing the abundance of PHYTOCHROME-INTERACTING FACTORS (PIFs), a group of growth-promoting transcription factors. However, whether other factors are involved in modulating PIF activity in the shade remains largely obscure. Here, we show that SALT OVERLY SENSITIVE2 (SOS2), a protein kinase essential for salt tolerance, positively regulates SAS in Arabidopsis thaliana. SOS2 directly phosphorylates PIF4 and PIF5 at a serine residue close to their conserved motif for binding to active phyB. This phosphorylation thus decreases their interaction with phyB and posttranslationally promotes PIF4 and PIF5 protein accumulation. Notably, the role of SOS2 in regulating PIF4 and PIF5 protein abundance and SAS is more prominent under salt stress. Moreover, phyA and phyB physically interact with SOS2 and promote SOS2 kinase activity in the light. Collectively, our study uncovers an unexpected role of salt-activated SOS2 in promoting SAS by modulating the phyB-PIF module, providing insight into the coordinated response of plants to salt stress and shade.

Predictive value of the controlling nutritional status (CONUT) score to assess long-term mortality (10 Years) in patients with hypertension.

BACKGROUND AND AIMS: Malnutrition is associated with poor outcomes in patients with chronic diseases. The aim of this study is to investigate the prevalence of malnutrition in patients with hypertension and relationship between malnutrition severity and long-term mortality in these patients. METHODS AND RESULTS: The study included 11,278 patients with hypertension from the National Health and Nutrition Examination Survey database. The degree of malnutrition was assessed using the Controlled Nutritional Status score, with patients divided into normal, mild, and moderate-to-severe groups. After 10 years of follow-up, the results showed that patients who died had higher CONUT scores, poorer nutritional status, and lower albumin, total cholesterol, and lymphocytes than those who survived (P < 0.05). The Kaplan-Meier analysis revealed that patients with poor nutritional status had a significantly higher risk of all-cause death. In the Non-Lipid Lowering Drugs group, the CONUT score (hazard ratio (HR): 1.225; 95% confidence interval (CI): 1.162-1.292; P < 0.0001), as well as mild (HR: 1.532; 95% CI 1.340-1.751; P < 0.0001) and moderate-to-severe malnutrition (HR: 2.797; 95% CI: 1.441-5.428; P = 0.0024), were independent predictors of long-term mortality. The competing risk regression models showed that cardiovascular and cerebrovascular mortality increased with increasing CONUT scores. The results were robust in both subgroup and sensitivity analyses. CONCLUSIONS: Malnutrition significantly impacts long-term mortality in hypertensive patients. The CONUT score may be a useful tool for assessing the nutritional status of patients with hypertension in the non-lipid-lowering population and for predicting their long-term mortality.

Regulation of cryptochrome-mediated blue light signaling by the ABI4-PIF4 module.

ABSCISIC ACID-INSENSITIVE 4 (ABI4) is a pivotal transcription factor which coordinates multiple aspects of plant growth and development as well as plant responses to environmental stresses. ABI4 has been shown to be involved in regulating seedling photomorphogenesis; however, the underlying mechanism remains elusive. Here, we show that the role of ABI4 in regulating photomorphogenesis is generally regulated by sucrose, but ABI4 promotes hypocotyl elongation of Arabidopsis seedlings under blue (B) light under all tested sucrose concentrations. We further show that ABI4 physically interacts with PHYTOCHROME INTERACTING FACTOR 4 (PIF4), a well-characterized growth-promoting transcription factor, and post-translationally promotes PIF4 protein accumulation under B light. Further analyses indicate that ABI4 directly interacts with the B light photoreceptors cryptochromes (CRYs) and inhibits the interactions between CRYs and PIF4, thus relieving CRY-mediated repression of PIF4 protein accumulation. In addition, while ABI4 could directly activate its own expression, CRYs enhance, whereas PIF4 inhibits, ABI4-mediated activation of the ABI4 promoter. Together, our study demonstrates that the ABI4-PIF4 module plays an important role in mediating CRY-induced B light signaling in Arabidopsis.

14-3-3 proteins regulate photomorphogenesis by facilitating light-induced degradation of PIF3.

14-3-3s are highly conserved phosphopeptide-binding proteins that play important roles in various developmental and signaling pathways in plants. However, although protein phosphorylation has been proven to be a key mechanism for regulating many pivotal components of the light signaling pathway, the role of 14-3-3 proteins in photomorphogenesis remains largely obscure. PHYTOCHROME-INTERACTING FACTOR3 (PIF3) is an extensively studied transcription factor repressing photomorphogenesis, and it is well-established that upon red (R) light exposure, photo-activated phytochrome B (phyB) interacts with PIF3 and induces its rapid phosphorylation and degradation. PHOTOREGULATORY PROTEIN KINASES (PPKs), a family of nuclear protein kinases, interact with phyB and PIF3 in R light and mediate multisite phosphorylation of PIF3 in vivo. Here, we report that two members of the 14-3-3 protein family, 14-3-3λ and κ, bind to a serine residue in the bHLH domain of PIF3 that can be phosphorylated by PPKs, and act as key positive regulators of R light-induced photomorphogenesis. Moreover, 14-3-3λ and κ preferentially interact with photo-activated phyB and promote the phyB-PIF3-PPK complex formation, thereby facilitating phyB-induced phosphorylation and degradation of PIF3 upon R light exposure. Together, our data demonstrate that 14-3-3λ and κ work in close concert with the phyB-PIF3 module to regulate light signaling in Arabidopsis.

MYB30 Is a Key Negative Regulator of Arabidopsis Photomorphogenic Development That Promotes PIF4 and PIF5 Protein Accumulation in the Light.

Phytochromes are red (R) and far-red (FR) light photoreceptors in plants, and PHYTOCHROME-INTERACTING FACTORS (PIFs) are a group of basic helix-loop-helix family transcription factors that play central roles in repressing photomorphogenesis. Here, we report that MYB30, an R2R3-MYB family transcription factor, acts as a negative regulator of photomorphogenesis in Arabidopsis (Arabidopsis thaliana). We show that MYB30 preferentially interacts with the Pfr (active) forms of the phytochrome A (phyA) and phytochrome B (phyB) holoproteins and that MYB30 levels are induced by phyA and phyB in the light. It was previously shown that phytochromes induce rapid phosphorylation and degradation of PIFs upon R light exposure. Our current data indicate that MYB30 promotes PIF4 and PIF5 protein reaccumulation under prolonged R light irradiation by directly binding to their promoters to induce their expression and by inhibiting the interaction of PIF4 and PIF5 with the Pfr form of phyB. In addition, our data indicate that MYB30 interacts with PIFs and that they act additively to repress photomorphogenesis. In summary, our study demonstrates that MYB30 negatively regulates Arabidopsis photomorphogenic development by acting to promote PIF4 and PIF5 protein accumulation under prolonged R light irradiation, thus providing new insights into the complicated but delicate control of PIFs in the responses of plants to their dynamic light environment.

The association between academic achievement, psychological distress, and smartphone addiction: A cross-sectional study among medical students.

This study investigates the relationship between academic achievement, psychological distress, and smartphone addiction in medical students. In total, 513 medical students voluntarily completed a survey that included the Personal Information Questionnaire, the Smartphone Addiction Scale-Short Version (SAS-SV), the Depression, Anxiety and Stress Scale (DASS-21), and the Interaction Anxiousness Scale (IAS). Results showed that 321 participants were screened positive for smartphone addiction and the prevalence of smartphone addiction was 62.6%. We found that the prevalence of smartphone addiction was higher among male rather than female students (67.1% vs 58.2%; p = 0.039). There were significant differences between the smartphone addiction group and the smartphone non-addiction group as per the DASS-21 scores and the IAS scores. In addition, multiple regression indicated that psychological distress including anxiety, stress, depression, and social anxiety might be the predictors of smartphone addiction. However, smartphone addiction was found to have no significant correlation with academic performance in 274 undergraduate medical students. In conclusion, the study revealed the high prevalence of smartphone addiction in medical students. Smartphone addiction was associated with states of depression, anxiety, stress, and social anxiety, and there was no significant relationship between academic performance and smartphone addiction in undergraduate medical students. Further longitudinal research is needed to clarify the causal relationship between smartphone addiction and psychological distress.

p-Block Bismuth Nanoclusters Sites Activated by Atomically Dispersed Bismuth for Tandem Boosting Electrocatalytic Hydrogen Peroxide Production.

Constructing electrocatalysts with p-block elements is generally considered rather challenging owing to their closed d shells. Here for the first time, we present a p-block-element bismuth-based (Bi-based) catalyst with the co-existence of single-atomic Bi sites coordinated with oxygen (O) and sulfur (S) atoms and Bi nanoclusters (Biclu ) (collectively denoted as BiOSSA /Biclu ) for the highly selective oxygen reduction reaction (ORR) into hydrogen peroxide (H2 O2 ). As a result, BiOSSA /Biclu gives a high H2 O2 selectivity of 95 % in rotating ring-disk electrode, and a large current density of 36 mA cm-2 at 0.15 V vs. RHE, a considerable H2 O2 yield of 11.5 mg cm-2 h-1 with high H2 O2 Faraday efficiency of ∼90 % at 0.3 V vs. RHE and a long-term durability of ∼22 h in H-cell test. Interestingly, the experimental data on site poisoning and theoretical calculations both revealed that, for BiOSSA /Biclu , the catalytic active sites are on the Bi clusters, which are further activated by the atomically dispersed Bi coordinated with O and S atoms. This work demonstrates a new synergistic tandem strategy for advanced p-block-element Bi catalysts featuring atomic-level catalytic sites, and the great potential of rational material design for constructing highly active electrocatalysts based on p-block metals.

SICKLE represses photomorphogenic development of Arabidopsis seedlings via HY5- and PIF4-mediated signaling.

Arabidopsis CONSTITUTIVELY PHOTOMORPHOGENIC1 (COP1) and PHYTOCHROME INTERACTING FACTORs (PIFs) are negative regulators, and ELONGATED HYPOCOTYL5 (HY5) is a positive regulator of seedling photomorphogenic development. Here, we report that SICKLE (SIC), a proline rich protein, acts as a novel negative regulator of photomorphogenesis. HY5 directly binds the SIC promoter and activates SIC expression in response to light. In turn, SIC physically interacts with HY5 and interferes with its transcriptional regulation of downstream target genes. Moreover, SIC interacts with PIF4 and promotes PIF4-activated transcription of itself. Interestingly, SIC is targeted by COP1 for 26S proteasome-mediated degradation in the dark. Collectively, our data demonstrate that light-induced SIC functions as a brake to prevent exaggerated light response via mediating HY5 and PIF4 signaling, and its degradation by COP1 in the dark avoid too strong inhibition on photomorphogenesis at the beginning of light exposure.

Nanowire-Seeded Growth of Single-Crystalline (010) ß-Ga2 O3 Nanosheets with High Field-Effect Electron Mobility and On/Off Current Ratio.

2D ß-Ga2 O3 nanosheets, as fundamental materials, have great potential in next generations of ultraviolet transparent electrodes, high-temperature gas sensors, solar-blind photodetectors, and power devices, while their synthesis and growth with high crystalline quality and well-controlled orientation have not been reported yet. The present study demonstrates how to grow single-crystalline ultrathin quasi-hexagonal ß-Ga2 O3 nanosheets with nanowire seeds and proposes a hierarchy-oriented growth mechanism. The hierarchy-oriented growth is initiated by epitaxial growth of a single-crystalline ( 2 - 01 ) ß-Ga2 O3 nanowire on a GaN nanocrystal and followed by homoepitaxial growth of quasi-hexagonal (010) ß-Ga2 O3 nanosheets. The undoped 2D (010) ß-Ga2 O3 nanosheet field effect transistor has a field-effect electron mobility of 38 cm2 V-1 s-1 and an on/off current ratio of 107 with an average subthreshold swing of 150 mV dec-1 . The from-nanowires-to-nanosheets technique paves a novel way to fabricate nanosheets, which has great impact on the field of nanomaterial synthesis and growth and the area of nanoelectronics as well.

Fuzzy State-Driven Cross-Time Spatial Dependence Learning for Multivariate Time-Series Anomaly Detection.

Cross-time spatial dependence (i.e., the interaction between different variables at different time points) is indispensable for detecting anomalies in multivariate time series, as certain anomalies may have time delays in their propagation from one variable to another. However, accurately capturing cross-time spatial dependence remains a challenge. Specifically, real-world time series usually exhibits complex and incomprehensible evolutions that may be compounded by multiple temporal states (i.e., temporal patterns, such as rising, fluctuating, and peak). These temporal states mix and overlap with each other and exhibit dynamic and heterogeneous evolution laws in different time series, making the cross-time spatial dependence extremely intricate and mutable. Therefore, a cross-time spatial graph network with fuzzy embedding is proposed to disentangle latent and mixing temporal states and exploit it to meticulously learn cross-time spatial dependence. First, considering that temporal states are diversiform and their mixing modes are unknown, we introduce a fuzzy state set to uniformly characterize potential temporal states and adaptively generate corresponding membership degrees to depict how these states mix. Further, we propose a cross-time spatial graph, quantifying similarities among fuzzy states and sensing their dynamic evolutions, to flexibly learn mutable cross-time spatial dependence. Finally, we design state diversity and temporal proximity constraints to ensure the differences among fuzzy states and the evolution continuity of fuzzy states. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art models.

Towards consensual representation: Model-agnostic knowledge extraction for dual heterogeneous federated fault diagnosis.

Federated fault diagnosis has attracted increasing attention in industrial cloud-edge collaboration scenarios, where a ubiquitous assumption is that client models have the same architecture. Practically, this assumption cannot always be fulfilled due to requirements for personalized models, thereby resulting in the problem of model heterogeneity. Many approaches dealing with heterogeneous models tend to neglect the issue of representation bias, particularly in the context of non-identically and independently distributed data. In this article, to address the representation bias problem, Federated Model-Agnostic Knowledge Extraction (FedMAKE) is proposed. To bridge the information gap among clients, different from methods with public datasets, we initially develop two novel architecture-independent knowledge carriers. These carriers are derived based on the importance of process variables, without the need for additional datasets. Subsequently, we introduce a bi-directional distillation algorithm utilizing the two knowledge carriers. This algorithm facilitates the mutual transfer of knowledge embedded in carriers between a generative network and client models, thereby enabling the generation of fault data that is unbiased and well-balanced across categories. Furthermore, to mitigate the impact of statistical heterogeneity, we formulate a local objective for each client using two global knowledge carriers to guide local knowledge extraction and constrain client drift. Extensive experiments conducted on two prevalent industry datasets (TE and CWRU) illustrate that our proposed FedMAKE outperforms baseline methods. Specifically, FedMAKE enhances fault diagnosis accuracy by up to 11.7% on the TE dataset and up to 3.31% on the CWRU dataset compared to the sub-optimal method.

Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes.

Typically, industrial processes possess both temporal and spatial dependencies due to intravariable dynamics and intervariable couplings. The two dependencies have different manifestations, indicating diverse process characteristics. However, the existing methods fail to separate temporal and spatial information well, leading to inappropriate representation and inaccurate fault detection and isolation results. This study proposes an explicit representation and customized fault isolation framework to tackle temporal and spatial characteristics, so as to identify and locate anomalies affecting different dependencies. First, we design a double-level separation method for temporal and spatial information. In the first level, we construct two independent auto-encoding modules to extract temporal correlation and spatial graph structure in parallel. In the second level, we propose an information aliasing loss function to guild the two modules to distinguish between temporal and spatial characteristics, further facilitating information separation. By monitoring the explicit temporal and spatial statistics obtained by the two modules, spatiotemporal dependencies of anomalies can be determined for subsequent isolation. Furthermore, we propose a customized isolation strategy for anomalies in temporal and spatial characteristics. By quantifying changes in intravariable temporal dynamics and intervariable spatial graph structure individually, temporal impact and spatial propagation of faults can be finely characterized and isolated. Three examples are adopted to verify the performance of the proposed framework, including a numerical example, a real condensing system of the thermal power plant process, and the Tennessee Eastman benchmark process.

Research on mechanics and acoustic properties of Jute fiber composite material.

In this study, the loss of quality from the oxidative thermal decomposition of jute fiber was explored during the production of reinforced composite materials. Amino silicone oil was used to modify jute fiber, which was then subjected to thermogravimetric analysis. The modified fiber's thermal decomposition temperature was found to be 271 °C, enhancing the composite's thermal stability. The study also investigated how different jute fiber content affected the mechanical and sound absorption properties of composite materials. Results showed that jute fiber composites had better mechanical properties than pure polypropylene materials, and the average sound absorption coefficient of jute polypropylene composites increased with fiber content. Adding jute fiber to polypropylene effectively improved the sound absorption and noise reduction performance of the material. The average sound absorption coefficient of the composite material at a mass content of 20 wt% was 120 % higher than that of the polypropylene matrix material.

MPGE and RootRank: A sufficient root cause characterization and quantification framework for industrial process faults.

Root cause diagnosis can locate abnormalities of industrial processes, ensuring production safety and manufacturing efficiency. However, existing root cause diagnosis models only consider pairwise direct causality and ignore the multi-level fault propagation, which may lead to incomplete root cause descriptions and ambiguous root cause candidates. To address the above issue, a novel framework, named multi-level predictive graph extraction (MPGE) and RootRank scoring, is proposed and applied to the root cause diagnosis for industrial processes. In this framework, both direct and indirect Granger causalities are characterized by multi-level predictive relationships to provide a sufficient characterization of root cause variables. First, a predictive graph structure with a sparse constrained adjacency matrix is constructed to describe the information transmission between variables. The information of variables is deeply fused according to the adjacency matrix to consider multi-level fault propagation. Then, a hierarchical adjacency pruning (HAP) mechanism is designed to automatically capture vital predictive relationships through adjacency redistribution. In this way, the multi-level causalities between variables are extracted to fully describe both direct and indirect fault propagation and highlight the root cause. Further, a RootRank scoring algorithm is proposed to analyze the predictive graph and quantify the fault propagation contribution of each variable, thereby giving definite root cause identification results. Three examples are adopted to verify the diagnostic performance of the proposed framework, including a numerical example, the Tennessee Eastman benchmark process, and a real cut-made process of cigarette. Both theoretical analysis and experimental verification show the high interpretability and reliability of the proposed framework.

Modulating the Asymmetric Atomic Interface of Copper Single Atoms for Efficient CO2 Electroreduction.

Cu single-atom catalysts (Cu SACs) have been considered as promising catalysts for efficient electrocatalytic CO2 reduction reactions (ECRRs). However, the reports on Cu SACs with an asymmetric atomic interface to obtain CO are few. Herein, we rationally designed two Cu SACs with different asymmetric atomic interfaces to explore their catalytic performance. The catalyst of CuN3O/C delivers high ECRR selectivity with an FECO value of above 90% in a wide potential window from -0.5 to -0.9 V vs RHE (in particular, 96% at -0.8 V), while CuCO3/C delivers poor selectivity for CO production with a maximum FECO value of only 20.0% at -0.5 V vs RHE. Besides, CuN3O/C exhibited a large turnover frequency (TOF) up to 2782.6 h-1 at -0.9 V vs RHE, which is much better than the maximum 4.8 h-1 of CuCO3/C. Density functional theory (DFT) results demonstrate that the CuN3O site needs a lower Gibbs free energy than CuCO3 in the rate-determining step of CO desorption, leading to the outstanding performance of CuN3O/C on the process of ECRR-to-CO. This work provides an efficient strategy to improve the selectivity and activity of the ECRR via regulating asymmetric atomic interfaces of SACs by adjusting the coordination atoms.

Slow Down to Go Better: A Survey on Slow Feature Analysis.

Temporal data contain a wealth of valuable information, playing an essential role in various machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal feature extraction models, has been deeply explored in two decades of development. SFA extracts slowly varying features as high-level representations of temporal data. Its core idea of "slow" has been proven to be consistent with the nature of biological vision and beneficial in capturing significant temporal information for various tasks. So far, SFA has evolved into numerous improved versions and is widely applied in many fields such as computer vision, industrial control, remote sensing, signal processing, and computational biology. However, there currently lacks an insightful review of SFA. In this article, a comprehensive overview of SFA and its extensions is provided for the first time. The formulation and optimization of SFA are introduced. Two mainstream solutions, geometric interpretation, and a gradient-based training method of SFA are presented and discussed. Following that, a taxonomy of the current progress of SFA is proposed. We classify improved versions of SFA into six categories, including dual-input SFA (DISFA), online slow feature analysis (OSFA), probabilistic SFA (PSFA), multimode SFA, nonlinear SFA, and discrete labeled SFA. For each category, we illustrate its main ideas, mathematical principles, and applicable scenarios. In addition, the practical applications of SFA are summarized and presented. Finally, we bring new insights into SFA according to its research status and provide potential research directions, which may serve as a good reference for promoting future work.

Genomic profiling reveals non-small cell lung cancer with common mutations of EGFR exon 20 and exon 21: a case report.

Background: Epidermal growth factor receptor (EGFR) gene is one of the most common driver genes for non-small cell lung cancer (NSCLC). The PIONEER study showed that 51.4% of unselected Asian patients with advanced lung adenocarcinoma have EGFR-sensitive mutations. EGFR mutations mainly occur in the first four [18-21] exons of the intracellular tyrosine kinase (TK) region. At present, there are more than 30 types of mutations in the TK region, including exon 19 deletion mutation (19Del) and exon 21 L858R mutation (L858R) which are the most common types of sensitive mutations, accounting for more than 90% of all EGFR mutations. About 10% of NSCLC patients with EGFR mutations are rare mutation types, including exon 18 point mutation (G719X), exon 20 point mutation (S768I), exon 19 point mutation (L747S), exon 21 point mutation (L833V), etc. About 1% of NSCLC patients have primary double mutations of EGFR. Case Description: In this present study, we identified a 59-year-old female patient with no smoking history had double mutations in EGFR exon 20 R776S mutation and exon 21 L858R mutation by next-generation sequencing (NGS). Conclusions: This observation may explore a new mechanism study for EGFR-TKIs and provide a new direction for clinical treatment of NSCLC.

Microenvironment Modulation in Carbon-Supported Single-Atom Catalysts for Efficient Electrocatalytic CO2 Reduction.

The electrocatalytic CO2 reduction reaction (ECRR) becomes an effective way to reduce excess CO2 in the air and a promising strategy to maintain carbon balance. Carbon-supported single-atom catalysts (C-SACs) is a kind of cost savings and most promising catalysts for ECRR. For C-SACs, the key to achieving efficient ECRR performance is to adjusting the electronic structure of the central metal atoms by modulating their microenvironment of the catalysts. Not only the coordination numbers and hetero-atom coordination, but also the regulation of diatomic sites have a great influence on the performance of C-SACs. This review mainly focuses on recent studies for the microenvironment modulation in C-SACs for efficient ECRR. We hope that this review can contribute readers a comprehensive insight in the current research status of C-SACs for ECRR, as well as provide help for the rational design of C-SACs with better ECRR performance.

Molecular and Biological Characterization of the First Mymonavirus Identified in Fusarium oxysporum.

We characterized a negative sense single-stranded RNA mycovirus, Fusarium oxysporum mymonavirus 1 (FoMyV1), isolated from the phytopathogenic fungus Fusarium oxysporum. The genome of FoMyV1 is 10,114 nt, including five open reading frames (ORFs1-5) that are non-overlapping and linearly arranged. The largest, ORF5, encodes a large polypeptide L containing a conserved regions corresponding to Mononegavirales RNA-dependent RNA polymerase and mRNA-capping enzyme region V; the putative functions of the remaining four ORFs are unknown. The L protein encoded by ORF5 shared a high amino acid identity of 65% with that of Hubei rhabdo-like virus 4, a mymonavirus that associated with arthropods. However, the L protein of FoMyV1 also showed amino acid similarity (27-36%) with proteins of mynonaviruses that infect the phytopathogenic fungi Sclerotinia sclerotiorum and Botrytis cineaea. Phylogenetic analysis based on L protein showed that FoMyV1 is clustered with the members of the genus Hubramonavirus in the family Mymonaviridae. Moreover, we found that FoMyV1 could successfully transfer by hyphal anastomosis to a virus-free strain. FoMyV1 reduced the vegetative growth and conidium production of its fungal host but did not alter its virulence. To the best of our knowledge, this is not only the first mymonavirus described in the species F. oxysporum, but also the first Hubramonavirus species found to infect a fungus. However, the incidence of FoMyV1 infections in the tested F. oxysporum strains was only 1%.

Co-immunoprecipitation Assays to Detect In Vivo Association of Phytochromes with Their Interacting Partners.

The red (R)/far-red (FR) light absorbing phytochromes are one of the major photoreceptor classes in plants. Phytochromes exist in two distinct but interconvertible forms: the R light-absorbing Pr form and the FR light-absorbing Pfr form. Upon photoactivation by light, phytochromes physically interact with their partners to transduce the light signal. Co-immunoprecipitation (Co-IP) is one of the most efficient techniques to study these protein-protein interactions in vivo. However, the co-IP procedure for phytochromes needs to be modified to allow their formation of Pr or Pfr form. Here, we describe a detailed co-IP procedure to examine which form of phytochrome (Pr or Pfr) is preferentially associated with their interacting partners in vivo.