Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissection of Developmental Programs and Regulatory Modules Directing Endosperm Transfer Cell and Aleurone Identity in the Syncytial Endosperm of Barley

Endosperm development in barley starts with the formation of a multinucleate syncytium, followed by cellularization in the ventral part of the syncytium generating endosperm transfer cells (ETCs) as first differentiating subdomain, whereas aleurone (AL) cells will originate from the periphery of the enclosing syncytium. Positional signaling in the syncytial stage determines cell identity in the cereal endosperm. Here, we performed a morphological analysis and employed laser capture microdissection (LCM)-based RNA-seq of the ETC region and the peripheral syncytium at the onset of cellularization to dissect developmental and regulatory programs directing cell specification in the early endosperm. Transcriptome data revealed domain-specific characteristics and identified two-component signaling (TCS) and hormone activities (auxin, ABA, ethylene) with associated transcription factors (TFs) as the main regulatory links for ETC specification. On the contrary, differential hormone signaling (canonical auxin, gibberellins, cytokinin) and interacting TFs control the duration of the syncytial phase and timing of cellularization of AL initials. Domain-specific expression of candidate genes was validated by in situ hybridization and putative protein–protein interactions were confirmed by split-YFP assays. This is the first transcriptome analysis dissecting syncytial subdomains of cereal seeds and provides an essential framework for initial endosperm differentiation in barley, which is likely also valuable for comparative studies with other cereal crops.
Integrated Microbiota and Metabolite Changes following Rice Bran Intake during Murine Inflammatory Colitis-Associated Colon Cancer and in Colorectal Cancer Survivors

Simple Summary Heat-stabilized rice bran is a nutrient-dense food ingredient that has shown colorectal cancer control and prevention properties in a suite of carcinogen-induced animal models. This study identified colon cancer protective properties associated with rice bran metabolism in feces that relate to the previously reported findings in gastrointestinal tissue. A longitudinal analysis of fecal microbiota and metabolite changes between diet treatments identified novel mechanisms for rice bran-mediated anti-cancer activity. Fecal metabolites in response to dietary rice bran intake in mice showed overlap with results from adult colorectal cancer survivors after eating rice bran. These molecules may serve as translational dietary biomarkers for colorectal cancer prevention. Abstract Dietary rice bran-mediated inhibition of colon carcinogenesis was demonstrated previously for carcinogen-induced rodent models via multiple anti-cancer mechanisms. This study investigated the role of dietary rice bran-mediated changes to fecal microbiota and metabolites over the time course of colon carcinogenesis and compared murine fecal metabolites to human stool metabolic profiles following rice bran consumption by colorectal cancer survivors (NCT01929122). Forty adult male BALB/c mice were subjected to azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced colitis-associated colon carcinogenesis and randomized to control AIN93M (n = 20) or diets containing 10% w/w heat-stabilized rice bran (n = 20). Feces were serially collected for 16S rRNA amplicon sequencing and non-targeted metabolomics. Fecal microbiota richness and diversity was increased in mice and humans with dietary rice bran treatment. Key drivers of differential bacterial abundances from rice bran intake in mice included Akkermansia, Lactococcus, Lachnospiraceae, and Eubacterium xylanophilum. Murine fecal metabolomics revealed 592 biochemical identities with notable changes to fatty acids, phenolics, and vitamins. Monoacylglycerols, dihydroferulate, 2-hydroxyhippurate (salicylurate), ferulic acid 4-sulfate, and vitamin B6 and E isomers significantly differed between rice bran- and control-fed mice. The kinetics of murine metabolic changes by the host and gut microbiome following rice bran consumption complemented changes observed in humans for apigenin, N-acetylhistamine, and ethylmalonate in feces. Increased enterolactone abundance is a novel diet-driven microbial metabolite fecal biomarker following rice bran consumption in mice and humans from this study. Dietary rice bran bioactivity via gut microbiome metabolism in mice and humans contributes to protection against colorectal cancer. The findings from this study provide compelling support for rice bran in clinical and public health guidelines for colorectal cancer prevention and control.
Molecular phylogenetic and morphological support for the recognition of Friesodielsia lalisae (Annonaceae), a new species from S Thailand

Abstract: Thirteen species of Friesodielsia Steenis (Annonaceae) and 11 representatives of related genera were included in molecular phylogenetic analyses using up to six plastid DNA regions (psbA-trnH, trnL-trnF intergenic spacers; trnL intron; matK, ndhF, rbcL exons). The results support the recognition of a new species, F. lalisae Damth. , Baka & Chaowasku from Narathiwat, S Thailand, as belonging to one of the two major clades of Friesodielsia. The members of this clade show outer petals that separate at anthesis and have a concave basal portion, while members in the other major clade exhibit outer petals that separate early during developmental stages and have a flat base. Friesodielsia lalisae is described and illustrated. It is most morphologically similar to F. argentea (J. Sinclair) Steenis and F. glauca (Hook. f. & Thomson) Steenis but differs from the two by having dissimilar sepal shape, higher proportion of inner petal to outer petal length and longer inner petals. The new species additionally differs from the former by having different leaf base and from the latter by having denser indumentum on young twigs and shorter flowering pedicels. Narathiwat, a province to which the new species is endemic, seems to be one of the most underexplored areas in Thailand as evidenced by a number of species described based on recent material. The conservation status of the new species is provisionally assessed as Critically Endangered. Citation: Damthongdee A. , Khunarak N. , Kaeokula S. , Saengpho C. , Wiya C. , Ue-aree P., Baka A. , Aongyong K. & Chaowasku T. 2023: Molecular phylogenetic and morphological support for the recognition of Friesodielsia lalisae (Annonaceae) a new species from S Thailand. – Willdenowia 53: 45–55. Version of record first published online on 10 April 2023 ahead of inclusion in April 2023 issue.
The extreme Anthropogen of the Arctic: the formation of the Great Glacier, the emergence a man and the Arctic Ocean in the Early Holocene

The relevance of the article stems from the need to consider recent events in the Arctic in recent time, which are largely contradictory. The aim of the study is to show extreme environmental changes in the Arctic of anthropogenic nature, including the emergence of man and the Arctic Ocean in the Early Holocene. The territory of the Arctic was a landmass with high mountains, in the period preceding the Holocene, on which a large glacier formed 30-18 thousand years ago (Late Pleistocene). The glacier slid irrepressibly southward over great distances, where it covered vast areas of Europe and North America. In the years that followed, a dramatic warming and active melting of the glacier began. By the beginning of the Holocene, the glacier had melted, and a narrow strait of the Atlantic appeared at the foot of Greenland. Coniferous-deciduous forests, numerous rivers and the first human settlements appeared on the flat territory of the Arctic, and various animals – mammoths, horses, bulls - spread out. However, a major catastrophic event occurred by the middle of the Holocene (6.0 thousand years ago), after active volcanism in the Arctic: the collapse of the central part of the Arctic to a depth of about 5 km and the formation of the Central Arctic graben, associated with the appearance of a huge amount of endogenous water. There began a rapid movement of water on the flat parts of the Arctic and the formation of the modern huge, shallow (50-100 m) shelf - the Arctic Ocean. Many human settlements were flooded, animals escaped, in part, on high uplands. For example, huge animal cemeteries were preserved on the Novosibirsk Islands. A new cooling of the climate occurred 4 thousand years ago, and an ice sheet formed on the surface of the ocean, which led to the name of the North Glacial Ocean in Russia. Modern man began to explore the coastal territories of the Arctic shelf since the mid - Holocene, but active industrial development of the Arctic began in the 21st century.
Privacy and Accuracy for Cloud-Fog-Edge Collaborative Driver-Vehicle-Road Relation Graphs

There are three key roles in Intelligent Transportation Systems (ITS): driver, vehicle and road. However, existing static interactions among Driver-Vehicle-Road (DVR) are too passive to reflect the change of driver preferences, vehicle conditions, road conditions, etc. Therefore, we provide a data-driven Cloud-Fog-Edge Collaborative Driver-Vehicle-Road (CFEC-DVR) framework. The framework could self-adaptively evolves through continuous iteration to provide better ITS services for humans. The collaboration among DVR creates a lot of relation data that construct our relation graphs. Cloud brings some privacy risks. Relation graphs have great analytic value. As DVR collaboration, privacy quality and analytic accuracy are three key issues in the framework, we propose a Relation Graph Privacy-Preserving scheme with High Accuracy in our framework, which is named as RGPP-HA. Based on machine learning, our method nearly maximizes the difficulty for attackers to know exactly how many other roles are connected to the attacked role, which enhances the privacy quality. Meanwhile, we find as much valuable information as possible from roles’ encrypted relations for more accurately analytic performance. Based on the experiments, we compare the proposed scheme RGPP-HA with existing classic and relevant schemes. The experimental results show that our scheme has the best privacy quality and analytic accuracy. This further verifies the feasibility of CFEC-DVR framework.
The State of Antioxidant Systems and Lipids’ Peroxidation Under the Action of Complex Substances

The article presents the prospects for nanomaterials’ application in medicine, biology, and pharmacology. The possibility of nanoparticles’ usage as contrast agents for tomography, luminophores, and biologically active substances is shown. At the same time, the potential toxic effect on living organisms of the concerned substances is indicated, in particular, the possibility of free-radicals’ formation. These substances are capable of triggering the chain reaction of free-radicals’ transformations, which can result in the development of pathological processes. To clarify this issue, effects of the complex substance composed of lanthanides’ nanoparticles on the biochemical parameters of free-radical processes have been studied in experiment. The results show that infusion of the preparation induces an increase in the content of diene conjugates and TBA-reactive products. Moreover, the content of sulfhydryl groups reduces significantly as compared with the intact group. Catalase activity also decreases, while superoxide dismutase activity increases. Thus, the results show that the substance concerned affects the processes of free-radical oxidation. However, the effect depends on a number of factors, and additional studies are needed to determine the contribution of each of them.
Detection of bolts and nuts of automobile sheet metal parts based on YOLOV7

Bolts and nuts are the most common parts on automobile sheet metal parts. Due to the complex shape of plate parts, traditional detection methods mostly rely on manual work, and there are problems such as slow detection speed and low accuracy. To solve these problems, this paper uses a target detection method based on YOLOV7 to realize the automatic detection of bolts and nuts on automobile sheet metal parts. By training the automobile sheet metal parts data set for 300 epochs, the final map0.5 reaches 94.89%, and the MAP0.5:0.9 reaches 75.26%. Compared with the test of the model in the coco data set, the map0.5 increases from 69.7% to 94.89%. It shows that the detection effect of the model is very good for this task.
Robot Global Relocalization Based on Multi-sensor Data Fusion

In order to solve the problems of low localizable accuracy, slipping and “kidnapping”, which may lead to get “ LOST” in robot localization, a multi-sensor data fusion localization method that integrates the wheel encoder, the Inertial Measurement Unit (IMU), the optical flow s ensor a nd t he laser is proposed. Firstly, the Federal Kalman Filter (FKF) is used to fuse multi-sensor data information to obtain more accurate localization; Secondly, when the robot loses its global localization, the optical flow s ensor, t he r angefinder an d IM U ar e fu sed to obtain the localization as a priori for the prediction step in the resampling of Adaptive MonteCarlo Localization (AMCL). Finally, the Conditional Variational Autoencoder (CVAE) is used for training to further optimize the priori localization. The algorithm without correct initial values is converted into an algorithm with fuzzy localization. Experimental results based on real scenarios showed that the multi-sensor data fusion not only helped to obtain more accurate and stable location, but also significantly reduced the “LOST” problem in case of the robot being kidnapped compared to the pre-optimized AMCL algorithm.
Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.
Leray and LANSmodelling of turbulent mixing

Mathematical regularisation of the nonlinear terms in the Navier-Stokes equations is found to provide a systematic approach to deriving subgrid closures for numerical simulations of turbulent flow. By construction, these subgrid closures imply existence and uniqueness of strong solutions to the corresponding modelled system of equations. We will consider the large eddy interpretation of two such mathematical regularisation principles, i.e., Leray and LANSregularisation. The Leray principle introduces a smoothed transport velocity as part of the regularised convective nonlinearity. The LANSprinciple extends the Leray formulation in a natural way in which a filtered Kelvin circulation theorem, incorporating the smoothed transport velocity, is explicitly satisfied. These regularisation principles give rise to implied subgrid closures which are implemented in large eddy simulations of turbulent mixing. Comparison with filtered direct numerical simulation data and with predictions obtained from popular dynamic eddy-viscosity modelling shows that these mathematical regularisation models provide considerably more accuracy at a lower computational cost than the dynamic approaches. In particular, the regularisation models perform especially well in capturing the flow features characteristic of the smaller resolved scales. Variations in spatial resolution and Reynolds number establish that the Leray model is more robust but also slightly less accurate than the LANS � model. The LANSmodel retains more of the small-scale variability in the resolved solution. However, this requires a corresponding increase in the required spatial resolution. When using second order finite volume discretisation, the potential accuracy of the implied LANSmodel is found to be realized by using a grid spacing that is not larger than the length scalethat appears in the definition of this model. This paper is associated with the focus-issue Cascade Dynamics: Fundamentals and Modelling.
A Mask Self-Supervised Learning-Based Transformer for Bearing Fault Diagnosis With Limited Labeled Samples

In recent years, transformer has become an effective tool for fault diagnosis, but it has been shown that a sufficient amount of labeled data is usually required to train a transformer model. However, a few labeled data can be obtained in the actual industrial process, and labeling a large quantity of training samples is costly. To reduce the demand for training labeled samples, this article proposes a mask self-supervised learning-based transformer (MSFormer) for bearing fault diagnosis of multistage centrifugal fans in petrochemical units under the condition of limited samples. In mask self-supervised learning (SSL), unlabeled samples can be used to mine robust representations of fault signals and potential relationships between subsequences to obtain a pretrained model with well-generalized parameters. Then, a few labeled samples are utilized to fine-tune by supervised learning to enable MSFormer the discrimination ability to identify different bearing fault types. The effectiveness of the proposed method is fully validated on the multistage centrifugal fan dataset and the Case Western Reserve University (CWRU) motor bearing dataset. The experimental results demonstrate that MSFormer is effective in reducing the number of labeled training samples, and compared to state-of-the-art methods, MSFormer has superior diagnosis performance under the condition of limited labeled samples.
Deep Learning for Operating Performance Assessment of Industrial Processes with Layer Attention-Based Stacked Performance-Relevant Denoising Auto-Encoders

The operating performance of a process may degenerate due to process interferences and operation errors, which cancel the benefits of technology design and economic production. Traditional operating performance assessment methods are either lack of real-time due to the post-analysis or difficult to distinguish performance grades for process data with weak differences and strong noise interferences based on shallow learning structures. In this paper, a new layer attention-based stacked performance-relevant denoising auto-encoder (LA-SPDAE) is proposed for the operating performance assessment of industrial processes. It overcomes the defect that the original SDAE ignores task-relevant information in training and only uses the feature of the last hidden layer to complete special tasks. In this study, the original SDAE is improved by optimizing the cross-entropy loss of the performance grade labels in the layer-wise pretraining, which is named stacked performance-relevant denoising auto-encoder (SPDAE), and the performance-relevant features can be extracted under supervision. Moreover, for making good use of performance-relevant features of each layer, they are fused by adaptive weights based on the layer attention mechanism. In the case study of cyanide leaching, the assessment accuracy of the proposed LA-SPDAE model is up to 99.85% under the corrupted proportion of 20%, and the advantage is still maintained as the proportion increases to 80%, which demonstrates the superiority of LA-SPDAE compared with conventional deep neural networks and shallow learning structures.
New probiotics (Lactobacillus plantarum and Saccharomyces cerevisiae) supplemented to fermented rice straw-based rations on digestibility and rumen characteristics in vitro

Objective: This research was arranged to explore the effect of supplementation of a combination of Lactobacillus plantarum and Saccharomyces cerevisiae as a new probiotic in fermented rice straw-based rations on in vitro digestibility and ruminal characteristics. Materials and Methods: A randomized group design with 3 types of treatment and 4 replications as a group was used in this study. A probiotic inoculum containing L. plantarum and S. cerevisiae with 1 × 1010 colony-forming unit (CFU)/ml. Treatments were followed by: P1 = complete rations without probiotics (control), P2 = P1 supplemented 0.5% probiotics, and P3 = P1 supplemented 1% probiotics. Substrate complete rations were based on the fermented rice straw and concentrate (60%:40%). Parameters of digestibility and rumen fermentation products were determined after 48 h of incubation. Results: Probiotics supplemented with fermented rice straw-based rations significantly increased (p < 0.05) digestibility and rumen characteristics in vitro. Supplementation with 1% probiotics (P3) produces the highest digestibility compared to other treatments: in-vitro dry matter digestibility (IVDMD) (55%), in-vitro organic matter digestibility (IVOMD) (58.28%), in-vitro crude protein digestibility (IVCPD) (84.42%), in-vitro acid detergent fiber digestibility (IVADFD) (53.99%), in-vitro neutral detergent fiber digestibility (IVNDFD) (58.39%), and in-vitro cellulose digestibility (IVCLD) (67.12%). Rumen pH (6.76–6.80) did not change significantly (p > 0.05) due to supplemented probiotics. Probiotic supplementation in rations significantly (p < 0.05) increased the content of NH3 and total volatile fatty acid (VFA). Supplementation with 1% probiotic (P3) showed the highest concentration of NH3 (26.56 mg/100 ml) and was also followed by the total VFA (115.75 mM) compared to the control (22.59 mg/100 ml and 103.00 mM, respectively). Conclusion: Supplementation of 1% probiotics (combination of L. plantarum and S. cerevisiae) containing 1 × 1010 CFU/ml in fermented rice straw-based rations increases nutrient digestibility, that is, IVDMD, IVOMD, IVCPD, IVADFD, IVNDFD, and IVCLD, and also increases rumen fermentation, which is the concentration of NH3 and total VFA.