Maximum Likelihood Criterion Research Articles

New insights into freshwater ascomycetes: discovery of novel species in diverse aquatic habitats

During investigations of freshwater fungi in Hunan and Yunnan provinces, China, Chaetopsina yunnanensis sp. nov. (Nectriaceae), Parafuscosporella hunanensis sp. nov. (Fuscosporellaceae), and Pleurotheciella yunnanensis sp. nov. (Pleurotheciaceae) were discovered on submerged decaying wood and branches. Based on phylogenetic analyses, C. yunnanensis formed a separate branch with Chaetopsina pinicola and nested among other Chaetopsina species in Nectriaceae (Hypocreales). Furthermore, hitherto known Chaetopsina beijingensis shared the same branch with Chaetopsina fulva, a type species of the genus, demonstrating their conspecific status. Therefore, C. beijingensis is formally synonymized under C. fulva, with an amended species circumscription. Pa. hunanensis formed a well-separated subclade with the ex-type strain of Parafuscosporella mucosa and clustered with other Parafuscosporella within Fuscosporellaceae (Fuscosporellales). In addition, the genus Parafuscosporella is treated as distinct from Vanakripa due to a lack of phylogenetic evidence in clarifying their congeneric status with the latter. Pl. yunnanensis is found to be sister to Pleurotheciella saprophytica, forming a subclade with Pleurotheciella dimorphospora within the Pleurotheciaceae (Pleurotheciales). Morphologically, C. yunnanensis fits well with the generic concept of Chaetopsina in forming a holomorphic state with hyphomycetous asexual morph producing pigmented, setiform conidiophores, phialidic conidiogenous cells, hyaline conidia, and nectria-like sexual morph. Pa. hunanensis fits well with Parafuscosporella in having acrogenous, apiosporous, versicolored, obovoid to obpyriform conidia. In contrast, Pl. yunnanensis resembles Pl. dimorphospora in forming asexual dimorphism with two types of conidia (Type I, brown, muriform/phragmosporous conidia; Type II, hyaline, amerosporous/didymorsporous conidia). The novelty of taxa is explained with detailed descriptions, photo-micrographic illustrations, polymorphism, and multigene phylogenetic analyses of Bayesian inference and maximum likelihood criteria.

The complete mitochondrial genome of an important medicinal plant, Rehmannia glutinosa (Gaertn.) DC., 1845 (Lamiales, Orobanchaceae)

Rehmannia glutinosa, an extensively utilized Chinese herbal medicine, is highly valued for its medicinal properties. In this study, the complete mitochondrial genome (mitogenome) of R. glutinosa was sequenced and assembled for the first time. The mitogenome is 547,032 bp in length, with an overall GC content of 44.97%. The mitogenome contains 67 unique genes, comprising 43 protein-coding, three rRNA, and 21 tRNA genes, with six protein-coding and nine tRNA genes being chloroplast-derived. The phylogenetic analysis, based on the maximum-likelihood criterion, demonstrated that R. glutinosa is closely related to Aeginetia indica and Castilleja paramensis within the family Orobanchaceae.

Forecasting cognitive functioning: an artificial intelligence approach with Dynamic Bayesian Networks

Abstract Background Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention of cognitive decline are needed. We examined multifactorial trajectories of risk factors, and their associations with SCD using an artificial intelligence approach to build a score that forecasts later SCD. Methods Five repeated surveys (2000-2022) of the Helsinki Health Study (n = 8960, 79% women, aged 40-60 at Phase 1) were used to build dynamic Bayesian networks (DBN) for estimating the odds of SCD. A score-based approach was implemented for learning DBN using the quotient normalized maximum likelihood criterion. The model was used to predict SCD based on the history of consumption of fruit and vegetables, smoking, alcohol consumption, leisure-time physical activity, body mass index, and insomnia symptoms, adjusting for sociodemographic covariates. Results Of the participants, 31-48% reported decline in memory, learning, and concentration in 2022. Physical activity was the strongest predictor of SCD in a 5-year interval, with an odds ratio of 0.76 (95% Bayesian credible interval 0.59-0.99) for physically active compared to inactive participants. Alcohol consumption showed a U-shaped relationship with SCD. Other risk factors had minor effects. Conclusions A new online risk score tool was developed that enables individuals to inspect their own risk profiles, as well as explore potential targets for interventions and their estimated contributions to later SCD. Dynamic decision heatmap was presented as a communication tool to be used at healthcare consultations. Key messages • Potentially modifiable health-related behaviours such as physical activity can contribute to subjective cognitive decline. • Artificial intelligence can be applied to discover the complex interactions between risk factors and subjective cognitive decline, to support preventive healthcare at individual and population levels.

Acoustic beamforming using maximum likelihood criteria with Laplacian model of the desired signal

Acoustic beamforming is a technique used in audio engineering and signal processing to filter sound waves in the spatial domain. Usually, an array of microphones is used to capture sound from different directions. These array signals are then combined to reinforce each other in the desired direction while cancelling the noise or interference from other directions. An important application of acoustic beamforming is the cocktail party scenario where multiple people speak simultaneously in a noisy room. To capture the speech of only the desired speaker, acoustic beamforming is used. Usually, in such cases, the target speech is modelled as a zero-mean complex Gaussian distributed random variable. However, the target speech coefficients are sparse in the time-frequency domain. Hence, in our work, we model the speech coefficients using a zero-mean circular Laplacian distribution. After modelling the target speech, we formulate a beamformer based on the maximum likelihood criteria. We add a distortionless constraint to the proposed beamformer to further improve performance. The final solution of the proposed beamformer encourages sparsity, indicating that it models the target speech better than the complex Gaussian distribution. Simulations show the effectiveness of the proposed beamformer in capturing the target speech and rejecting interfering speakers.

Joint state and process inputs estimation for state-space models with Student’s t-distribution

This paper proposes a joint state and unknown inputs (UIs) discrete-time estimation method for industrial processes, represented by a state-space model. To cope with the outliers in process data, the measurement noise is characterized by the Student’s t-distribution. The identification of UIs is accomplished through the recursive expectation–maximization (REM) approach. Specifically, in the E-step, a recursively calculated Q-function is formulated by the maximum likelihood criterion, and the states and the variance scale factor are estimated iteratively. In the M-step, UIs are updated analytically together with the degree of freedom is updated approximately. The effectiveness of the proposed algorithm is validated using a quadruple water tank process and a continuous stirred tank reactor. It shows that the proposed method significantly enhances the robustness and estimation accuracy of state and UIs in industrial processes, effectively handling outliers and reducing computational demands for real-time applications.

Direct position determination algorithm based on wideband array signal model in external illuminator system

Abstract Target location and tracking are among the most critical applications for external illuminator systems. Most traditional array signal direct position determination (DPD) techniques are based on narrowband signals. With the advancement of communication technology, various wideband signals are extensively utilized in communication systems, leading to the failure of some traditional DPD techniques. To address this issue, this article proposes a new DPD algorithm for wideband array signals. First, we suppress the direct wave (DW) interference in the received signal. Then, a cost function for the target coordinate is constructed based on the Maximum Likelihood (ML) criterion, and a phase matching matrix is created for the problem of unknown signal transmission time. Finally, the position estimation is obtained from the peak of the cost function. Simulation results demonstrate that the proposed algorithm outperforms traditional two-step localization methods based on the time difference of arrival of signals, particularly in cases where DW interference is present in the received signal.

Помехоустойчивость когерентного приема двоичных сигналов с огибающей вида приподнятый косинус в гидроакустическом канале связи

Goals: The main purpose of this work is to develop a methodology for determining the parameters of binary signals, at which the signals become relatively invariant to frequency distortions in the marine environment. The frequency distortion of the signals is caused by the uneven frequency response of the attenuation of the marine environment. The main part of this technique is to assess the effect of frequency distortion of signals on the noise immunity of reception. In accordance with this, the error probabilities of signal receivers with various types of manipulation are determined, which are optimal in the absence of distortion. Methods: The provisions of applied hydroacoustics, the theory of random processes and the theory of transmission of discrete messages are used. The main content: The paper considered a model of a single-beam hydroacoustic communication channel, characteristic of the deep sea, when the receiver or transmitter is located in the depths of the sea. The transmission coefficient of the channel is used as a transmission coefficient with a Gaussian amplitude -frequency response and a linear phase-frequency response. The error probabilities of coherent receivers of binary signals with amplitude, frequency and phase manipulation with an envelope of the raised cosine type are determined. Coherent receivers optimal by the criterion of maximum likelihood under the action of white Gaussian noise and the absence of distortion in the marine environment are considered as receivers. A logarithmic measure of increasing the probability of error is introduced, which characterizes the deterioration of noise immunity due to frequency distortions in the channel. For some typical cases, the values of signal parameters that are relatively invariant to frequency distortions in the marine environment are determined. Results: Expressions of the error probability of coherent receivers of binary signals with amplitude, frequency and phase manipulation with an envelope of the raised cosine type are found. A logarithmic measure of the relative increase in the probability of error compared to the case of no distortion is introduced. The functional dependence of this measure on the duration of sending the signal, the carrier frequency and the initial phase of the signal, as well as on the communication range and the signal-to-noise ratio is determined. On the plane of the carrier frequency, signal duration, for each type of signal, the boundary of the region above which the signals are relatively invariant to frequency distortions in the marine environment is constructed, a comparison is made with the case of signals with a sinusoidal envelope. For communication ranges R = 1.5 km and 3 km and typical carrier frequencies, the minimum values of the duration of invariant signals are given. Expressions of the error probability of coherent receivers of binary signals with amplitude, frequency and phase manipulation with an envelope of the raised cosine type are found. A logarithmic measure of the relative increase in the probability of error compared to the case of no distortion is introduced. The functional dependence of this measure on the duration of sending the signal, the carrier frequency and the initial phase of the signal, as well as on the communication range and the signal-to-noise ratio is determined. On the plane of the carrier frequency, signal duration, for each type of signal, the boundary of the region above which the signals are relatively invariant to frequency distortions in the marine environment is constructed, a comparison is made with the case of signals with a sinusoidal envelope. For communication ranges R = 1.5 km and 3 km and typical carrier frequencies, the minimum values of the duration of invariant signals are given.

Statistical learning based blind image watermarking approach

Image watermarking technology poses a significant challenge, requiring a delicate balance between robustness, imperceptibility, and capacity. To solve the balancing problem, a statistical learning based blind image watermarking technique is proposed in this paper. The idea of the proposed method to solve the balancing problem is to enhance imperceptibility and robustness while accommodating sufficient watermarks. Firstly, the local low-order pseudo-Zernike moments (PZM) magnitude in the discrete non-separable Shearlet transform (DNST) domain is constructed as the embedding domain. To ensure stability in embedding positions, we propose an edge embedding strategy. Secondly, by analyzing the statistical characteristics and multi-correlations of DNST-PZM magnitudes, a multi-correlation vector model based on the skew student's-t mixture (SSM) and hidden Markov tree (HMT) is designed. Finally, leveraging the vector SSM-HMT model and the maximum likelihood (ML) criterion, we derive the closed-form decoder expression. Comparative analysis with state-of-the-art methods demonstrates the superior imperceptibility and robustness of our proposed approach in accommodating the same capacity watermark.

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models.

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

Maximum likelihood based direction estimation for noncircular signals

In the direction estimation for the signals incident on a sensor array, maximum likelihood (ML) based methods can provide superior performance than subspace based ones such as the multiple signal classification (MUSIC). When the incoming signals are noncircular the exploitation of the property can allow us to improve estimation performance. Based on the deterministic ML criterion, a direction estimation method for strictly noncircular signals is proposed that can outperform the conventional alternating maximization (AM) with no use of noncircularity. Using a generalized Gaussian probability density function, we approach the ML estimation, which is formulated as a nonlinear multidimensional problem. The application of the alternating projection to the multidimensional problem leads to the maximization of an objective function of two variables associated with the direction and the initial phase of a noncircular signal. Theoretically optimizing the phase variable, we obtain the maximum through one-dimensional search with respect to the direction variable only. The complexity of the proposed noncircular AM (NC-AM) is far less than that of the existing ML based method, noncircular decoupled maximization (NC-DM). Moreover, simulation results demonstrate that NC-AM outperforms NC-DM as well as the noncircular MUSIC.

The half-logistic (Type-I)-skew-t model : Comparison of different estimation approaches using monte carlo simulations

In the paper, we looked at the problem of employing several non-Bayesian estimation approaches to estimate the parameters of the Type-I half-logistic skew-t (TIHLST) model. The maximum likelihood, least squares, weighted least squares, Anderson-Darling, maximum product of spacing, and Cramer-von Mises are the six methods for TIHLST parameter estimation that we examined. The Monte Carlo simulations were used to assess the TIHLST parameter estimations’ finite sample performance. The partial and overall ranks of the estimating techniques for every combination of parameters were used to determine the ordering performance of the six criteria. Based on the overall ranks, the results indicate that the criteria performance is ranked from best to worst using the following estimators: maximum likelihood, weighted least squares, maximum product of spacing, least squares, and Cramer-von Mises for all parameter combinations. Based on an overall rank of 34, we infer that the maximum likelihood criterion fared better than all other criteria. For the TIHLST model, we thereby affirm the supremacy of the maximum likelihood criterion with an overall rank of 34 and the Anderson-Darling criterion with an overall rank of 57.5.

Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

Passive localization of multiple sources while the receiver is in motion is a fundamental problem in wireless communication with practical applications in various fields such as acoustic event localization and object tracking. In this study, we propose a novel method for three dimensional (3D) angle of arrival (AOA) localization using a moving receiver with a limited number of sensors. In the proposed method, we record signals from sources in a series of time windows. Then we estimate the elevation and azimuth AOAs of sources along with their pairing by using the proposed three low-complexity deep learning (DL)-based AOA estimators. Following the initial AOA estimation, a low-complexity off-grid based refinement technique is employed to further enhance the accuracy of the estimated elevation and azimuth AOAs based on the maximum likelihood (ML) criterion. Next, a multi-source 3D-localization algorithm is proposed to estimate the positions of sources across the recorded time windows. The simulation results validate the effectiveness and efficiency of the proposed method. The results highlight that the DL-based AOA and location estimators outperform recent studies, while also demonstrating robustness in the face of practical imperfections, such as situations where the receiver’s position or direction is uncertain during movement. Our complexity analysis verifies that the proposed method outperforms existing methods in terms of speed and the number of computations. This enhanced computational efficiency makes the proposed method highly attractive and practically significant for application in various fields.

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

Molecular phylogeny and character-mapping support the synonymy of Cordobia and Gallardoa in Mionandra (Malpighiaceae)

Background and aims – Cordobia, Gallardoa, Mionandra, and Peixotoa (Stigmaphylloid clade, Malpighiaceae) are four small, closely related genera comprising shrubs or lianas endemic to South American savannas, dry forests, and temperate steppes. Their generic limits have significantly changed in the last century, and past molecular phylogenetic studies of Malpighiaceae have not tested the morphological characters of this group to identify synapomorphies supporting these clades/genera. Material and methods – We sampled the monospecific Cordobia and Gallardoa, one species of Mionandra (out of 2 spp.), nine species of Peixotoa (out of 29 spp.), and a single species of Camarea and Janusia as outgroups. Bayesian and Maximum Likelihood analyses were carried out for this clade based on five molecular markers (i.e. ETS, ITS, PHYC, matK, and ndhF). A set of 16 macromorphological characters was scored and coded for identifying synapomorphies under the Maximum Likelihood criteria. Key results – Our molecular phylogeny recovered Peixotoa as monophyletic and sister to the clade comprising Cordobia + Gallardoa + Mionandra, strongly corroborating previous phylogenetic studies of Malpighiaceae. The character-mapping analyses recovered two synapomorphies supporting the Cordobia + Gallardoa + Mionandra + Peixotoa clade, six supporting Mionandra s.l. (i.e. Cordobia + Gallardoa + Mionandra), and five supporting Peixotoa. Cordobia and Gallardoa are proposed as synonyms of Mionandra, alongside the necessary combinations, typifications, and identification keys. Conclusions – Morphological characters related to the degree of connation of the stipules, leaf indumentum type, petiole length, inflorescence architecture, number of flowers per inflorescence, presence of a peduncle in the 1-flowered cincinni, sepal connation, posture and texture, petal width and margin integrity, staminode presence, shape and size, and the shape of the apex of styles were key in circumscribing these lineages. Mionandra s.l. is proposed and characterised, including a new combination, an identification key to distinguish its species, a distribution map, and taxonomy notes.

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty.

Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

In Situ Calibration of Antenna Arrays for Positioning With 5G Networks

Owing to the ubiquity of cellular communication signals, positioning with the fifth-generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas. Unfortunately, although the widely employed antenna arrays in 5G remote radio units (RRUs) facilitate the measurement of the direction of arrival (DOA), DOA-based positioning performance is severely degraded by array errors. This article proposes an in situ calibration framework with a user terminal (UT) transmitting 5G reference signals at several known positions in the actual operating environment and the accessible RRUs estimating their array errors from these reference signals. Further, since sub-6 GHz small-cell RRUs deployed for indoor coverage generally have small-aperture antenna arrays, while 5G signals have plentiful bandwidth resources, this work segregates the multipath components via super-resolution delay estimation based on the maximum likelihood criteria. This differs significantly from existing in situ calibration works which resolve multipaths in the spatial domain. The superiority of the proposed method is first verified by numerical simulations. We then demonstrate via field test with commercial 5G equipment that, a reduction of 46.7% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1{\text -}\sigma$</tex-math> </inline-formula> DOA estimation error can be achieved by in situ calibration using the proposed method.

Genomic data resolve phylogenetic relationships of Australian mat-rushes, Lomandra (Asparagaceae: Lomandroideae)

Abstract Lomandra is the largest genus in Asparagaceae subfamily Lomandroideae and possesses economic, ecological, and ethnobotanical significance in Australia. Lomandra comprises four sections, L. section Capitatae, L. section Macrostachya, L. section Typhopsis and L. section Lomandra, the latter comprising series Lomandra and series Sparsiflorae, all recognized based solely on morphology. In this study, phylogenetic relationships were estimated for 79 Lomandroideae individuals, including 45 Lomandra species and subspecies (c. 63% of species and subspecies diversity). We generated genome-scale plastome sequence data and used maximum likelihood and Bayesian inference criteria for phylogenetic estimation. Lomandra was non-monophyletic, with Xerolirion divaricata nested within it. Two major clades were recovered: Capitatae–Macrostachya (CM) and Lomandra–Typhopsis (LT). The CM clade included a monophyletic Lomandra section Capitatae with a base chromosome number x = 7, and L. section Macrostachya (x = 8); the LT clade included L. sections Typhopsis and Lomandra, both x = 8. Section Lomandra series Lomandra and series Sparsiflorae were both recovered as non-monophyletic. Morphological characters were assessed to identify combinations of characters that characterize clades. A base chromosome number of x = 8 was plesiomorphic for Lomandra. The largest number of Lomandra species occupy the Mediterranean ecoregion and occupancy of sclerophyll vegetation was reconstructed as ancestral for the genus.

First report of Phakopsora euvitis causing leaf rust disease on grapevine (Vitis labrusca L.) in India.

Grapevine (Vitis labrusca L.), a member of the family Vitaceae and native of North America, is grown as a table grape. During the survey for the grapevine diseases in May 2022, we noticed numerous yellow pustules of rust on lower side of leaves of 'Bangalore Bule' in Nandi village (13°22'59.7"N77°42'33.4"E), Chikkaballapur district of Karnataka state of India. The crop was at maturity stage and the rust disease severity was determined using the scale given by Angelotti et al. (2008), which was up to 10%. The disease symptoms were numerous small raised yellow pustules on the abaxial surface corresponding to adaxial surface chlorotic spots. In severe conditions, spots cover the entire leaf and defoliation occurs. Similar disease symptoms were reported by Ono (2000); Weinert et al. (2003); and Primiano et al. (2017). The pathogenicity test was performed on cuttings of 'Bangalore Bule' grapevine in a glasshouse at 25±1°C. The urediniospores were collected from diseased leaves using a brush, 3×104 ml-1 suspension in distilled water was used for inoculation on the abaxial surface of leaves. Control plants were sprayed with distilled water. The leaves developed symptoms in 15-17 days after inoculation, and the pathogen was confirmed by symptomatology and microscopic observation of urediniospores. Urediniospores were short-pedicellate, sessile, obovoid to obovoid-ellipsoid, and uniformly echinulate with 42.98-32.54 x 31.37-25.15 µm in size. The aecial stage of the Phakopsora has been reported on an alternate host, Meliosma simplicifolia (Hosagoudar 1988). As the internal transcribed spacer (ITS) region offers some utility in the molecular detection of the Phakopsora genus (Rush et al. 2019), the pathogen was confirmed by studying the different regions in the ITS such as ITS1, 5.8S rRNA, and ITS2. Total DNA was extracted from urediniospore mass using the Macherey-Nagel kit (Duren, Germany) by following the manufacturer's protocol. The quantity of isolated DNA was checked using an Qubit 3.0 fluorometer (Invitrogen) before being subjected to polymerase chain reaction (PCR) amplification in a thermocycler (Eppendorf-vapo.protect) using ITS1 and ITS4 primers (IDT, Singapore) targeting ITS1, 5.8S rRNA, and ITS2 regions, and the obtained amplicon (~700 bp) was purified using Macherey- Nagel Nucleospin gel and PCR clean-up kit (Duren, Germany) as per the manufacturer's protocol and sequenced by Sanger's dideoxy chain-termination method [ABI 3730 (48 capillaries) electrophoresis]. The sequence was edited in BioEdit (https://bioedit.software.informer.com/7.2/) and aligned in MUSCLE, and the phylogenetic tree was constructed in MEGA 11 using the neighbor-joining method by following the maximum likelihood criterion (Kumar et al. 2018). The sequence data was deposited at NCBI (accession number OP221661). The basic local alignment search tool (BLAST) search sequence of the isolate Nandi-KA in GenBank revealed 97.91% homology with sequence of Phakopsora sp. (accession number KC815548.1) and 96.87% with Phakopsora euvitis (accession number AB354790.1). Based on disease symptoms, fungal morphology, the pathogenicity test, and ITS sequence, the fungus was identified as Phakopsora euvitis, the pathogen causing grapevine leaf rust disease. Though there are similar disease symptoms observed on grapevine in India (EPPO 2016); the pathogen was not confirmed. To our knowledge, this is the first report of Phakopsora euvitis causing leaf rust disease in grapevine (V. labrusca) in India.

The First Complete Mitochondrial Genome of the Genus Pachycondyla (Formicidae, Ponerinae) and Insights into the Phylogeny of Ants.

Ants are the standout group among eusocial insects in terms of their exceptional species richness and ecological dominance. The phylogenetic relationships among the group remain elusive. Mitochondrial genome sequences, as a kind of molecular marker, have been widely utilized in the phylogenetic analysis of insects. However, the number of ant mitogenomes published is still very limited. In this study, we utilized next-generation sequencing to determine the complete mitogenome of Pachycondyla annamita (Formicidae, Ponerinae). This is the first mitogenome from the genus Pachycondyla. Two gene rearrangements were identified in the mitogenome, the transposition of trnQ and trnM and the transposition of trnV and rrnS. The secondary structures of tRNAs were predicted. The tRNA genes trnR and trnS1 lacked the dihydrouridine (DHU) arm, and the trnE lacked the TΨC (T) arm. Phylogenetic analyses of the mitochondrial protein-coding genes under maximum likelihood (ML) and Bayesian inference (BI) criteria resulted in conflicting hypotheses. BI analysis using amino acid data with the site-heterogeneous mixture model produced a tree topology congruent with previous studies. The Formicidae was subdivided into two main clades, namely the "poneroid" clade and the "formicoid" clade. A sister group relationship between Myrmicinae and Formicinae was recovered within the "formicoid" clade.

Оптимальная нелинейная фильтрация оценок информационного воздействия в стохастической модели информационного противоборства

A computationally efficient algorithmic solution to the problem of optimal nonlinear filtering of information impact estimates in a generalized stochastic model of information warfare is developed in the article. The formed solution is applicable in the presence of heterogeneous rules for measuring the parameters of the information warfare model, on the basis of which a pair of systems of stochastic differential equations is formed. According to the criterion of maximum likelihood according to the determined evolution of the a posteriori conditional probability density function at a given observation interval, the evaluation of the information impact in the optimal nonlinear filtering model is performed. Taking into account the probability addition theorem, as the probability of the sum of two joint events, the density functions of which are established from the numerical solution of the corresponding robust Duncan-Mortensen-Zakai equations, finding a posteriori conditional probability density function at a given time is performed. For the first event, it is assumed that the first system of stochastic differential equations is the equation of state, and the second – is the equation of observation. For the second event, their definition is set in reverse order. The solution of the robust Duncan-Mortensen-Zakai equation is carried out in the formulation of the Galerkin spectral method when sampling the observation interval into subintervals and reducing the initial solution to a numerical recurrent study of the sequence of subtasks using the so-called Yau-Yau's algorithm, which assumes an estimate of the probability measure from the solution of the direct Kolmogorov equation with its subsequent correction by observation. To highlight the features of the algorithmic implementation of the compiled solution, an algorithm for optimal nonlinear filtering of information impact estimates in a generalized stochastic model of information confrontation when specifying the listing of the function implementing it, which is represented by a pseudocode, has been formed. To identify the preference of the compiled algorithmic solution for optimal nonlinear filtering of information impact assessments, a series of computational experiments on large-volume test samples was carried out. The result of the information impact assessment obtained by the proposed algorithm is compared with the determined solution: 1) by the average sample values from the observation models; 2) by an ensemble extended Kalman filter; 3) by a filtering algorithm involving a numerical study of the Duncan-Mortensen-Zakai equation. According to the conducted a posteriori study, quantitative indicators that establish the gain of the compiled algorithm and the limits of its applicability are highlighted.