Protomelission is an early dasyclad alga and not a Cambrian bryozoan.

The animal phyla and their associated body plans originate from a singular burst of evolution occurring during the Cambrian period, over 500 million years ago1. The phylum Bryozoa, the colonial 'moss animals', have been the exception: convincing skeletons of this biomineralizing clade have been absent from Cambrian strata, in part because potential bryozoan fossils are difficult to distinguish from the modular skeletons of other animal and algal groups2,3. At present, the strongest candidate4 is the phosphatic microfossil Protomelission5. Here we describe exceptionally preserved non-mineralized anatomy in Protomelission-like macrofossils from the Xiaoshiba Lagerstätte6. Taken alongside the detailed skeletal construction and the potential taphonomic origin of 'zooid apertures', we consider that Protomelission is better interpreted as the earliest dasycladalean green alga-emphasizing the ecological role of benthic photosynthesizers in early Cambrian communities. Under this interpretation, Protomelission cannot inform the origins of the bryozoan body plan; despite a growing number of promising candidates7-9, there remain no unequivocal bryozoans of Cambrian age.

Weapon operating pose detection and suspicious human activity classification using skeleton graphs.

Spurt upsurge in violent protest and armed conflict in populous, civil areas has upstretched momentous concern worldwide. The unrelenting strategy of the law enforcement agencies focuses on thwarting the conspicuous impact of violent events. Increased surveillance using a widespread visual network supports the state actors in maintaining vigilance. Minute, simultaneous monitoring of numerous surveillance feeds is a workforce-intensive, idiosyncratic, and otiose method. Significant advancements in Machine Learning (ML) show potential in realizing precise models to detect suspicious activities in the mob. Existing pose estimation techniques have privations in detecting weapon operation activity. The paper proposes a comprehensive, customized human activity recognition approach using human body skeleton graphs. The VGG-19 backbone extracted 6600 body coordinates from the customized dataset. The methodology categorizes human activities into eight classes experienced during violent clashes. It facilitates alarm triggers in a specific activity, i.e., stone pelting or weapon handling while walking, standing, and kneeling is considered a regular activity. The end-to-end pipeline presents a robust model for multiple human tracking, mapping a skeleton graph for each person in consecutive surveillance video frames with the improved categorization of suspicious human activities, realizing effective crowd management. LSTM-RNN Network, trained on a customized dataset superimposed with Kalman filter, attained 89.09% accuracy for real-time pose identification.

Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique.

Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.

Unprecedented Elimination Reactions of Cyclic Aldols: A New Biosynthetic Pathway toward the Taiwaniaquinoid Skeleton.

The acid treatment of 6,7-seco-abietane dialdehydes gives, in high yield, the corresponding derivatives with the 4a-methyltetrahydrofluorene skeleton of taiwaniaquinoids. A mechanism involving the elimination of formic acid from the cyclic aldol intermediate is proposed here. This process can be postulated as a new biogenetic pathway from abietane diterpenes to taiwaniaquinoids. Using this novel reaction, the first enantiospecific synthesis of bioactive natural cupresol and taxodal has been obtained.

Synchronous multicentric giant cell tumour of immature skeleton with epiphysiometaphyseal origin.

Giant cell tumour of bone accounts for 5% of all primary bone tumours. Multicentric giant cell tumour is an infrequent variety be it either synchronous or metachronous accounting for less than 1% of all giant cell tumours. Synchronous multicentric giant cell tumour of foot and ankle with epiphysiometaphyseal origin is unheard of. We delineate a case of soap-bubble appearance lytic lesions at left distal tibia and talus in an early adolescent woman with biopsy proven giant cell tumour for its rarity and its successful management by extended curettage and allogenic impaction bone grafting.

Echinoid skeleton: an insight on the species-specific pattern of the Paracentrotus lividus plate and its microstructural variability.

The skeletal plates of echinoids consist of a peculiar lightweight structure, called stereom, which is organized in a porous three-dimensional lattice-like meshwork. The stereom is characterized by an extremely complex and diverse microarchitecture, largely varying not only from species to species but also among different test plates. It consists of different basic types combined in extremely different ways according to specific functional needs, creating species-specific structural patterns. These patterns can lead to specific mechanical behaviours, which can inspire biomimetic technology and design development. In this framework, the present study aimed to characterize the species-specific pattern of the Paracentrotus lividus interambulacral plate and the main microstructural features regarding its geometrical variability and mechanical responses. The results achieved quantitatively highlighted the differences between the analysed stereom types providing new insights regarding their topological configuration and isotropic and anisotropic behaviour. Interestingly, data also revealed that the galleried stereom present at the tubercle is significantly different from the one located at the suture. These analyses and findings are encouraging and provide a starting point for future research to unravel the wide range of mechanical strategies evolved in the echinoid skeletal structure.

Fabrication of mechanical skeleton of small-diameter vascular grafts via rolling on water surface.

Mimicking the multilayered structure of blood vessels and constructing a porous inner surface are two effective approaches to achieve mechanical matching and rapid endothelialization to reduce occlusion in small-diameter vascular grafts. However, the fabrication processes are complex and time consuming, thus complicating the fabrication of personalized vascular grafts. A simple and versatile strategy is proposed to prepare the skeleton of vascular grafts by rolling self-adhesive polymer films. These polymer films are directly fabricated by dropping a polymer solution on a water surface. For the tubes, the length and wall thickness are controlled by the rolling number and position of each film, whereas the structure and properties are tailored by regulating the solution composition. Double-layer vascular grafts (DLVGs) with microporous inner layers and impermeable outer layers are constructed; a microporous layer is formed by introducing a hydrophilic polymer into a polyurethane (PU) solution. DLVGs exhibit a J-shaped stress-strain deformation profile and compliance comparable to that of coronary arteries, sufficient suture retention strength and burst pressure, suitable hemocompatibility, significant adhesion, and proliferation of human umbilical vein endothelial cells. Freshly prepared PU tubes exhibit good cytocompatibility. Thus, this strategy demonstrates potential for rapid construction of small-diameter vascular grafts for individual customization.

The diachronic trend of female and male stature in Milan over 2000 years.

Stature is a biological trait directly determined by the interaction of genetic and environmental components. As such, it is often evaluated as an indicator for the reconstruction of skeletal biological profiles, past health, and social dynamics of human populations. Based on the analysis of 549 skeletons from the CAL (Collezione Antropologica LABANOF), a study of the diachronic trend of male and female adult stature in Milan (Italy) is being proposed here, covering a time span of about 2000 years, ranging from the Roman era to present-days. The skeletons, from necropolises dedicated to the less wealthy classes of Milanese society, were assigned to one of following five historical periods: Roman Era (first-fifth centuries AD), Early Middle Ages (sixth-tenth centuries AD), Late Middle Ages (eleventh-fifteenth centuries AD), Modern Era (sixteenth-eighteenth centuries AD) and Contemporary Era (nineteenth-twentieth centuries AD), and their stature was estimated according to the regression formulae of Trotter (1970). The collected data were then subjected to statistical analyses with ANOVA using R software. Although stature values showed an ample standard deviation in all periods, statistical analyses showed that stature did not significantly vary across historical periods in Milan for both sexes. This is one of the rare studies showing no diachronic changes in the trend of stature in Europe.

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks.

The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.

Enzymatic α-Ketothioester Decarbonylation Occurs in the Assembly Line of Barbamide for Skeleton Editing.

The decarbonylation reaction has been developed significantly in organic chemistry as an effective approach to various synthetic applications, but enzymatic precedents for this reaction are rare. Based on investigations into the hybrid nonribosomal peptide synthetase (NRPS)-polyketide synthase (PKS) assembly line of barbamide, we report an on-line α-ketothioester decarbonylation reaction that leads to one-carbon truncation of the elongating skeleton. This enzymatic editing reaction occurs in the first round of lipopeptide extension and modification involving the multienzymes BarE and BarF, which successively house an NRPS module to initiate the biosynthesis and a PKS module to catalyze the first round of chain extension. Starting with processing a leucine-derived α-ketoacyl starter, the ketosynthase domain in BarE displays an unusual dual activity that results in net one-carbon chain elongation. It extrudes carbon monoxide from α-keto-isocaproyl thioester and then mediates decarboxylative condenses of the resultant isovaleryl thioester with malonyl thioester to form a diketide intermediate, followed by BarF-based O-methylation to stabilize the enol form of the ß-carbonyl and afford an unusual E-double bond. Biochemical characterization, chemical synthesis, computational analysis, and the experimental outcome of site-directed mutagenesis illustrate the extraordinary catalytic capability of this ketosynthase domain. This work furthers the appreciation of assembly line chemistry and opens the door to new approaches for skeleton editing/engineering of related molecules using synthetic biology approaches.

Naphthalimide Fluorescent Skeleton for Facile and Accurate Quantification of Glutathione.

Glutathione (GSH), the most prevalent nonprotein thiol in biological systems, acts as both an antioxidant to manipulate intracellular redox homeostasis and a nucleophile to detoxify xenobiotics. The fluctuation of GSH is closely related to the pathogenesis of diverse diseases. This work reports the construction of a nucleophilic aromatic substitution-type probe library based on the naphthalimide skeleton. After an initial evaluation, the compound R13 was identified as a highly efficient GSH fluorescent probe. Further studies demonstrate that R13 could readily quantify GSH in cells and tissues via a straightforward fluorometric assay with a comparable accuracy to the results from the HPLC. We then used R13 to quantify the content of GSH in mouse livers after X-ray irradiation, revealing that irradiation-induced oxidative stress leads to the increase of oxidized GSH (GSSG) and depletion of GSH. In addition, probe R13 was also applied to investigate the alteration of the GSH level in the Parkinson's mouse brains, showing a decrease of GSH and an increase of GSSG in Parkinson's mouse brains. The convenience of the probe in quantifying GSH in biological samples facilitates further understanding of the fluctuation of the GSH/GSSG ratio in diseases.

BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN.

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.

Impallidane skeleton as a novel triterpenoid methyl ether from rhizomes of Imperata cylindrica var. koenigii f. pallida.

ABSTRUCT: n-Hexane extract of rhizomes of Imperata cylindrica var. koenigii f. pallida yielded five novel skeletal triterpenoids, designed as impallidin (1), impallidol (2), impallidin ozonide (3a, 3b), trisnorimpallidin aldehyde (4), tetranorimpallidin aldehyde (5). Structures of novel compounds were elucidated by mainly 2D NMR and other spectroscopic analysis and chemical correlations. Alternatively, compound 3a, 3b was derivatized from 1 under ozone oxidation condition.

Design and synthesis of novel orexin 2 receptor agonists with a 1,3,5­trioxazatriquinane skeleton.

A novel series of 1,3,5­trioxazatriquinane with multiple effective residues (TriMER) derivatives with amino-methylene side chains was designed and synthesized based on the docking-simulation results between orexin receptors (OXRs) and TriMER-type OXR antagonists. In vitro screening against orexin receptors identified six TriMER derivatives with a cis side-chain configuration, and, among these, 20d and 28d showed full agonist activity against OX2R at a concentration of 10 µM. To determine the absolute stereochemistry of these hit compounds, we also conducted the first asymmetric synthesis of a 1,3,5­trioxazatriquinane skeleton using a Katsuki-Sharpless asymmetric epoxidation as the key reaction and obtained a set of the individual stereoisomers. After evaluating their activity, (+)-20d (EC50 = 3.87 µM for OX2R) and (+)-28d (EC50 = 1.62 µM for OX2R) were determined as eutomers for OX2R agonist activity. Our results provide a new class of skeleton consisting of an (R)-1,3,5­trioxazatriquinane core with flexible methylene linkers and hydrophobic substituents at the terminals of the side chains via carbamates/sulfonamides as OX2R agonists.

Dynamic Edge Convolutional Neural Network for Skeleton-Based Human Action Recognition.

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods.

Future Pose Prediction from 3D Human Skeleton Sequence with Surrounding Situation.

Human pose prediction is vital for robot applications such as human-robot interaction and autonomous control of robots. Recent prediction methods often use deep learning and are based on a 3D human skeleton sequence to predict future poses. Even if the starting motions of 3D human skeleton sequences are very similar, their future poses will have variety. It makes it difficult to predict future poses only from a given human skeleton sequence. Meanwhile, when carefully observing human motions, we can find that human motions are often affected by objects or other people around the target person. We consider that the presence of surrounding objects is an important clue for the prediction. This paper proposes a method for predicting the future skeleton sequence by incorporating the surrounding situation into the prediction model. The proposed method uses a feature of an image around the target person as the surrounding information. We confirmed the performance improvement of the proposed method through evaluations on publicly available datasets. As a result, the prediction accuracy was improved for object-related and human-related motions.

The Polysaccharidic Nature of the Skeleton of Marennine as Determined by NMR Spectroscopy.

The water-soluble blue-green pigment marennine, produced and partly excreted by the diatom Haslea ostrearia, and known for a long time for its role in the greening of oysters, was isolated from the culture medium, purified, and analyzed by Nuclear Magnetic Resonance (NMR) in order to gain insight into its chemical structure. The spectra show mainly carbohydrates of a complex composition, apparently highly branched, and with a mass in the order of 10 kDa. There are, in addition, some signals of aliphatic and, much weaker, aromatic groups that present aglycons. The latter might be responsible for the color. These carbohydrates are always associated with the blue-green color and cannot be separated from it by most treatments; they are interpreted as constituting the frame of the pigment. NMR after hydrolysis identifies the most abundant monosaccharides in marennine as galactose, xylose, mannose, rhamnose, and fucose.

A Near-Infrared Fluorescent Probe for Recognition of Hypochlorite Anions Based on Dicyanoisophorone Skeleton.

A novel near-infrared (NIR) fluorescent probe (SWJT-9) was designed and synthesized for the detection of hypochlorite anion (ClO-) using a diaminomaleonitrile group as the recognition site. SWJT-9 had large Stokes shift (237 nm) and showed an excellent NIR fluorescence response to ClO- with the color change under the visible light. It showed a low detection limit (24.7 nM), high selectivity, and rapid detection (within 2 min) for ClO-. The new detection mechanism of SWJT-9 on ClO- was confirmed by 1H NMR, MS spectrum, and the density functional theory (DFT) calculations. In addition, the probe was successfully used to detect ClO- in HeLa cells.

Crystal structure of tubulin-barbigerone complex enables rational design of potent anticancer agents with isoflavone skeleton.

BACKGROUND: Isoflavones possess many biological activities, including anti-inflammatory and anticancer effects. Microtubules (composed of αß-tubulin heterodimers) are described as one possible cellular target of some of these isoflavones. However, the binding of tubulin to isoflavones has not been extensively studied, and until now, no crystal structure of the tubulin-isoflavone complex has been solved, and details of the isoflavone-tubulin interaction remain elusive. PURPOSE: Barbigerone is an isoflavone mainly found in the genus Milletti, such as the edible leguminous plant Millettia ferruginea, with anticancer activity. This study aims to confirm the cellular target of barbigerone and to study its anticancer mechanism. METHOD: Surface plasmon resonance assays and X-ray crystallography were used to study the interaction of barbigerone with tubulin protein. Immunofluorescence, in-cell and in vitro tubulin polymerization assays were employed to investigate the mechanism. MTT assays, cell clonal formation assays, wound healing assays, tube formation assays and H460 xenograft models were conducted to evaluate the in vitro and in vivo anticancer activities of barbigerone and one of its derivatives, 0412. RESULTS: Here, we found that barbigerone binds to tubulin to inhibit tubulin polymerization. Moreover, we solved the X-ray crystal structure of the tubulin-barbigerone complex at 2.33 Å resolution, which unambiguously determined the orientation and position of barbigerone in the colchicine-binding site. Illuminated by the X-ray data, we synthetized and obtained a more active isoflavone, 0412. Both barbigerone and 0412 inhibit cancer cell proliferation, tubulin polymerization, migration of HeLa cells and capillary-like tube formation of HUVECs, induce G2/M phase cell cycle arrest and apoptosis, and exhibit anticancer activity in an H460 xenograft model. CONCLUSION: In all, through biochemical and X-ray crystal structure results, we identified tubulin as the cellular target of one isoflavone, barbigerone, and proved that the tubulin-barbigerone complex plays a guiding role in obtaining a more active compound, 0412. These studies provide a crucial research basis for the development of isoflavones as anticancer candidate compounds.