Identification and management of tomato brown rugose fruit virus in greenhouses in Mexico.

Tomato brown rugose fruit virus (ToBRFV) has become a cause of great concern in the global tomato agro-industry since its first report in Israel in 2014. Global tomato production is affected by ToBRFV, and management practices are being evaluated. We tested seed lots from international producers as well as greenhouse substrates and water wells as possible sources of virus contamination. We identified a second introduction of ToBRFV in Mexico by a strain closely resembling isolates from the Netherlands and the Middle East. ToBRFV was detected by RT-PCR in seed coats and epicotyls (from commercial seeds and seedlings obtained from infected tomato plants), indicating a transmission rate of 9%. Virus-like particles (VLPs) were found in wells used for irrigation in greenhouses, but these exhibited low infectivity. These findings suggest that water sources could serve as ToBRFV reservoirs. We evaluated four chemical and six thermal methods for sanitizing substrates, plasticware, and other greenhouse utensils, using detached leaf bioassays in Nicotiana rugosa. The most effective chemical sanitization method was treatment with glutaraldehyde plus quaternary ammonium salts and pentapotassium salts. The most effective heat treatment was at 92°C for 30 minutes, which inactivated the virus. Tomato producers could implement these sanitization methods to control ToBRFV.

Fuzzy Synchronization of Chaotic Systems with Hidden Attractors.

Chaotic systems are hard to synchronize, and no general solution exists. The presence of hidden attractors makes finding a solution particularly elusive. Successful synchronization critically depends on the control strategy, which must be carefully chosen considering system features such as the presence of hidden attractors. We studied the feasibility of fuzzy control for synchronizing chaotic systems with hidden attractors and employed a special numerical integration method that takes advantage of the oscillatory characteristic of chaotic systems. We hypothesized that fuzzy synchronization and the chosen numerical integration method can successfully deal with this case of synchronization. We tested two synchronization schemes: complete synchronization, which leverages linearization, and projective synchronization, capitalizing on parallel distributed compensation (PDC). We applied the proposal to a set of known chaotic systems of integer order with hidden attractors. Our results indicated that fuzzy control strategies combined with the special numerical integration method are effective tools to synchronize chaotic systems with hidden attractors. In addition, for projective synchronization, we propose a new strategy to optimize error convergence. Furthermore, we tested and compared different Takagi-Sugeno (T-S) fuzzy models obtained by tensor product (TP) model transformation. We found an effect of the fuzzy model of the chaotic system on the synchronization performance.

Differential Expression of Genes between a Tolerant and a Susceptible Maize Line in Response to a Sugarcane Mosaic Virus Infection

To uncover novel genes associated with the Sugarcane mosaic virus (SCMV) response, we used RNA-Seq data to analyze differentially expressed genes (DEGs) and transcript expression pattern clusters between a tolerant/resistant (CI-RL1) and a susceptible (B73) line, in addition to the F1 progeny (CI-RL1xB73). A Gene Ontology (GO) enrichment of DEGs led us to propose three genes possibly associated with the CI-RL1 response: a heat shock 90-2 protein and two ABC transporters. Through a clustering analysis of the transcript expression patterns (CTEPs), we identified two genes putatively involved in viral systemic spread: the maize homologs to the PIEZO channel (ZmPiezo) and to the Potyvirus VPg Interacting Protein 1 (ZmPVIP1). We also observed the complex behavior of the maize eukaryotic factors ZmeIF4E and Zm-elfa (involved in translation), homologs to eIF4E and eEF1α in A. thaliana. Together, the DEG and CTEPs results lead us to suggest that the tolerant/resistant CI-RL1 response to the SCMV encompasses the action of diverse genes and, for the first time, that maize translation factors are associated with viral interaction.

Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level.

Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifold-based solutions excel at data exploration, but there are infinite surfaces crossing the observations cloud of points. Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error. Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated Δ c HbO 2 and reduced Δ c HbR hemoglobin species by means of radial basis functions (RBF). We evaluate the root mean square error (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability in an experimental dataset of surgical neuroergonomics. Results: Lowest interpolation RMSE was 1.26 e - 4 ± 1.32 e - 8 for Δ c HbO 2 [A.U.] and 4.30 e - 7 ± 2.50 e - 13 [A.U.] for Δ c HbR . Agreement with classical group analysis was JI = 0.89 ± 0.01 for Δ c HbO 2 . Agreement with PPI analysis was JI = 0.83 ± 0.07 for Δ c HbO 2 and JI = 0.77 ± 0.06 for Δ c HbR . IFM successfully decoded group differences [ANOVA: Δ cHbO 2 : F ( 2,117 ) = 3.07 ; p < 0.05 ; Δ c HbR : F ( 2,117 ) = 3.35 ; p < 0.05 ]. Conclusions: IFM provides a pragmatic solution to the problem of choosing the manifold associated to a cloud of points, facilitating the use of manifold-based solutions for the group analysis of fNIRS datasets.

A Monocular SLAM-based Controller for Multirotors with Sensor Faults under Ground Effect.

Multirotor micro air vehicles can operate in complex and confined environments that are otherwise inaccessible to larger drones. Operation in such environments results in airflow interactions between the propellers and proximate surfaces. The most common of these interactions is the ground effect. In addition to the increment in thrust efficiency, this effect disturbs the onboard sensors of the drone. In this paper, we present a fault-tolerant scheme for a multirotor with altitude sensor faults caused by the ground effect. We assume a hierarchical control structure for trajectory tracking. The structure consists of an external Proportional-Derivative controller and an internal Proportional-Integral controller. We consider that the sensor faults occur on the inner loop and counteract them in the outer loop. In a novel approach, we use a metric monocular Simultaneous Localization and Mapping algorithm for detecting internal faults. We design the fault diagnosis scheme as a logical process which depends on the weighted residual. Furthermore, we propose two control strategies for fault mitigation. The first combines the external PD controller and a function of the residual. The second treats the sensor fault as an actuator fault and compensates with a sliding mode action. In either case, we utilize onboard sensors only. Finally, we evaluate the effectiveness of the strategies in simulations and experiments.