Biological Classification Research Articles

IDT: An incremental deep tree framework for biological image classification

Nowadays, breast and cervical cancers are respectively the first and fourth most common causes of cancer death in females. It is believed that, automated systems based on artificial intelligence would allow the early diagnostic which increases significantly the chances of proper treatment and survival. Although Convolutional Neural Networks (CNNs) have achieved human-level performance in object classification tasks, the regular growing of the amount of medical data and the continuous increase of the number of classes make them difficult to learn new tasks without being re-trained from scratch. Nevertheless, fine tuning and transfer learning in deep models are techniques that lead to the well-known catastrophic forgetting problem. In this paper, an Incremental Deep Tree (IDT) framework for biological image classification is proposed to address the catastrophic forgetting of CNNs allowing them to learn new classes while maintaining acceptable accuracies on the previously learnt ones. To evaluate the performance of our approach, the IDT framework is compared against with three popular incremental methods, namely iCaRL, LwF and SupportNet. The experimental results on MNIST dataset achieved 87 % of accuracy and the obtained values on the BreakHis, the LBC and the SIPaKMeD datasets are promising with 92 %, 98 % and 93 % respectively.

Landscapes of Enteric Virome Signatures in Early-Weaned Piglets.

ABSTRACTDiarrhea caused by early-weaning-induced stress can increase mortality rates and reduce growth performance of piglets, seriously harming the livestock industry. To date, studies on the gut microbiome of early-weaned piglets have focused almost exclusively on bacteria, while studies on their gut virome are extremely lacking. Here, we used metagenomic and metatranscriptomic sequencing combined with bioinformatic analysis techniques to preliminarily characterize the intestinal virome of early-weaned piglets at different biological classification levels. The alpha diversity of enteroviruses was generally elevated in early-weaned piglets with diarrhea, compared to healthy piglets, whereas the two groups of piglets showed no significant difference in beta diversity. In addition, the species compositions of the gut virome were similar between healthy piglets and piglets with diarrhea, while their respective dominant species were somewhat different. We also identified 58 differential DNA viruses and 16 differential RNA viruses between the two groups of piglets at all biological taxonomic levels. Of these, 1 (family Dhakavirus) and 6 (phylum Artverviricota, class Revtraviricetes, order Ortervirales, family Retroviridae, genus Gammaretrovirus, and species Kirsten murine sarcoma virus) specific viruses disappeared from the intestines of healthy piglets and piglets with diarrhea, respectively. Moreover, we found that some DNA and RNA viruses formed strong correlations among themselves or between them.IMPORTANCE This study systematically reveals the biological diversity, structure, and composition of intestinal DNA and RNA virus profiles in early-weaned piglets. Furthermore, characteristics of differences in gut viromes between early-weaned healthy piglets and piglets with diarrhea were also elucidated. Importantly, some potential biomarkers for early-weaned piglets with diarrhea were identified. These findings fill a gap for the early-weaned piglet gut virome and lay the foundation for the development of strategies to target enteroviruses for the prevention and treatment of early-weaning-induced piglet diarrhea.

Metabolomic analyses redefine the biological classification of pancreatic cancer and correlate with clinical outcomes.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high heterogeneity, and the postoperative prognosis of different patients often varies greatly. Therefore, the classification of pancreatic cancer patients and precise treatment becomes particularly important. In our study, 1 H NMR spectroscopy was used to analyze the 76 PDAC serum samples and identify the potential metabolic subtypes. The metabolic characteristics of each metabolic subtype were screened out and the relationship between metabolic subtype and the long-term prognosis was further identified. The clinical stages of PDAC did not show the metabolic differences at the serum metabolomic level. And three metabolic subtypes, basic, choline-like and amino acid-enriched types, were defined by the hierarchical cluster analysis of the serum metabolites and the disturbed metabolic pathways. The characteristic metabolites of each PDAC subtype were identified, and the metabolite model was established to distinguish the PDAC patients in the different subtypes. Among the three metabolic subtypes, choline-like type displayed better long-term prognosis compared to the other two types of patients. Metabolic subtypes are of clinical importance and are closer to expressing the heterogeneity in the actual life activities of pancreatic cancer than molecular typing. The excavation of metabolic subtypes based on this will be more in line with clinical reality and more promising to guide clinical precision individualization treatment.

Role of Antimicrobial Peptides in Treatment and Prevention of Mycobacterium Tuberculosis: A Review.

Tuberculosis (TB) is one of the leading cause of death worldwide, and the world is fighting with this global health emergency from the past 25 year. The current clinical interventions for the management of TB face a number of inherent challenges which includes low patient compliance due to the long therapy regimen, and emerging antimicrobial resistance. Therefore, there is an unmet need of new anti-TB therapeutic agent with enhanced safety profile, which can reduce the duration of therapy, enhanced bioavailability and efficacy against drug resistant forms of TB. Bacteriocins or anti microbial peptides (AMPs) occurring in microbes, human beings and other life forms have been investigated as host defense peptides. Structurally AMPs are short and ionized and play crucial role in innate immunity of host. Some AMPs can kill microbial infections directly while others function indirectly by altering the host defense mechanisms. Amidst rising issue of antibiotic resistance, AMPs are being tested in clinical research as potential antibiotics and novel therapeutics to fight against infections and non-infectious diseases. Studies have also highlighted the ability of AMPs to act against the bacteria spreading tuberculosis. The present review provides information on antimicrobial peptides, highlights their biological role, classification and mode of action in treatment and prevention of tuberculosis. It further mentions the prospects and challenges of developing peptides for their therapeutic applications against mycobacterium tuberculosis.

Nomination of a hunting object in the Komi language

Introduction. The paper deals with two components of the hunting vocabulary, namely large wild animals, and medium-sized forest dwellers. Each component sub-defines the nominative units related to predators and herbivores are distinguished and separately considered in the paper. In the first part of the study, the composition is determined and a brief description of the relevant representatives of the local commercial fauna in the Komi Territory is given. The second part presents a diachronic hierarchy of the analyzed primordial vocabulary, compiled on the basis of the classification of proto-linguistic reconstructions. Materials and Methods. The analyzed material refers mainly to the Komi literary language. The paper considers the names of both native and foreign-language origin. The main methods used by the author in developing the research topic are descriptive, comparative-historical, synchronous-comparative and statistical. Results and Discussion. For the first time the article presents a linguistic analysis using the biological classification of two lexical subgroups belonging to one of the microsystems of the hunting vocabulary in the Komi language. It was established that the object of the study has ancient origins and the designations of primordial origin make its basis. In the composition of this variety of hunting vocabulary it presents Proto-Finno-Ugric, Proto-Finno-Permian, Proto-Permian and Proto-Komi vocabulary. A group of late formations having no genetic correspondences in other related languages appeared in the period of independent existence of the Komi-Zyryan language, after its separation from the Komi-Permian language. The foreign language component belongs to early and late borrowings. The technology of developing this topic, created by the author of the paper, can be used in the material of other related and unrelated languages. Conclusion. The formation and development of the Komi language vocabulary related to large commercially hunted animals and medium-sized wild animals has been going on for many millennia, since the Proto-Finno-Ugric epoch. Most of the names of the analyzed microsystem belong to the primordial vocabulary fund of the Komi language. A foreign-language component consisting of borrowings from 5 external sources took part in its replenishment.

Extinct species identification from late middle Pleistocene and earlier Upper Pleistocene bone fragments and tools not recognizable from their osteomorphological study by an enhanced proteomics protocol

AbstractAncient preserved molecules offer the opportunity of gaining a deeper knowledge on their biological past. However, the development of a proteomic workflow remains a challenge. The analysis of fossils must involve a low quantity of material to avoid damaging the samples. In this study an enhanced proteomic protocol was applied to 5‐milligram samples of about 130,000‐year‐old mammalian bones ranging from the end of the Middle Pleistocene up to the earlier Upper Pleistocene, excavated from Scladina Cave (Sclayn, Belgium). Using sequence homology with modern sequences, a biological classification was successfully achieved and the associated taxonomic ranks to each bone were identified consistently with the information gained from osteomorphological studies and palaeoenvironmental and palaeodietary data. Amino acid substitutions on collagens were identified, thus providing new information on extinct species sequences and helping in taxonomy‐based clustering. Considering samples with no osteomorphological information, such as two fragments of bone retouchers, proteomics successfully identified the families providing paleontologists new information on these objects. Combining osteomorphology studies and amino acid variations identified by proteomics, one of the retouchers was potentially identified as belonging to the Ursus spelaeus species.

Ganodermaovisporum sp. nov. (Polyporales, Polyporaceae) from Southwest China.

Ganoderma is a white-rot fungus with a cosmopolitan distribution and includes several economically important species. This genus has been extensively researched due to its beneficial medicinal properties and chemical constituents with potential nutritional and therapeutic values. Traditionally, species of Ganoderma were identified solely based on morphology; however, recent molecular studies revealed that many morphology-based species are conspecific. Furthermore, some type species are in poor condition, which hinders us from re-examining their taxonomic characteristics and obtaining their molecular data. Therefore, new species and fresh collections with multigene sequences are needed to fill the loopholes and to understand the biological classification system of Ganoderma. In a survey of Ganoderma in Guizhou Province, southwest China, we found a new species growing on soil and, herein, it was identified by both morphology and phylogenetic evidence. Hence, we propose a new species, Ganodermaovisporum sp. nov. This species is characterised by an annual, stipitate, laccate basidiome, with a red-brown to brownish-black pileus surface and pale white pores, duplex context, clavate pileipellis terminal cells, trimitic hyphal system, ellipsoid basidiospores with dark brown eusporium bearing coarse echinulae and an obtuse turgid appendix. Phylogenetic analyses confirmed that the novel species sisters to G.sandunense with high bootstrap support. Furthermore, the RPB2 sequence of G.sandunense is supplied for the first time. Notably, we re-examined the type specimen of G.sandunense and provide a more precise description of the duplex context, pileipellis terminal cells and basidia. All species collected are described and illustrated with coloured photographs. Moreover, we present an updated phylogeny for Ganoderma, based on nLSU, ITS, RPB2 and TEF1-α DNA sequence data and species relationships and classification are discussed.

BERT contextual embeddings for taxonomic classification of bacterial DNA sequences

Biological taxonomic classification is an important task needed for the identification and discovery of organisms, as well as the inference of their evolutionary relationships. The order and structure of biological sequence components has an essential and primary role in what the sequence’s identity and function is. In order to be able to efficiently differentiate between different bacterial categories, interactions and positions of the biological components in sequences must be known — which is an essential challenge in biological sequence classification. In this light, a considerable amount of recent research has been made to explore efficient representations of biological sequences such as spectral k-mer representation, one-hot encoding, Hilbert space curves and classical word embeddings such as Word2Vec. This paper identifies the taxonomic classification of bacterial 16S rRNA genes at five resolutions mapping hierarchical taxonomic ranks. A Bidirectional Encoder Representations from Transformers (BERT) model is pretrained using biological sequences, which to the best of our knowledge is the first time BERT has been trained with such sequences. A complete prediction model is then proposed – BioSeqBERT-CNN – that initially extracts contextual embeddings representations of DNA sequences using the pretrained BERT model. Extracted representations are further used for taxonomic classification through a Convolutional Neural Network (CNN). For boosting the deep learning classification performance, a data augmentation step is applied. Classification with the original dataset on the most fine-grained rank produced an accuracy of 93.5%, which surpasses that of recent works by 1.5–24.3%. Using data augmentation, an accuracy of 99.9% is achieved, which exceeds values of recent works by a minimum and maximum of 7.9% and 30.7%, respectively on the most fine-grained taxonomic rank. This exhibits promising performance promoting the study of using contextual embeddings to represent biological sequences, with Deep Learning networks.

A general locomotion control framework for multi-legged locomotors

Serially connected robots are promising candidates for performing tasks in confined spaces such as search and rescue in large-scale disasters. Such robots are typically limbless, and we hypothesize that the addition of limbs could improve mobility. However, a challenge in designing and controlling such devices lies in the coordination of high-dimensional redundant modules in a way that improves mobility. Here we develop a general framework to discover templates to control serially connected multi-legged robots. Specifically, we combine two approaches to build a general shape control scheme which can provide baseline patterns of self-deformation (‘gaits’) for effective locomotion in diverse robot morphologies. First, we take inspiration from a dimensionality reduction and a biological gait classification scheme to generate cyclic patterns of body deformation and foot lifting/lowering, which facilitate the generation of arbitrary substrate contact patterns. Second, we extend geometric mechanics, which was originally introduced to study swimming at low Reynolds numbers, to frictional environments, allowing the identification of optimal body–leg coordination in this common terradynamic regime. Our scheme allows the development of effective gaits on flat terrain with diverse numbers of limbs (4, 6, 16, and even 0 limbs) and backbone actuation. By properly coordinating body undulation and leg placement, our framework combines the advantages of both limbless robots (modularity and narrow profile) and legged robots (mobility). Our framework can provide general control schemes for rapid deployment of general multi-legged robots, paving the way toward machines that can traverse complex environments. In addition, we show that our framework can also offer insights into body–leg coordination in living systems, such as salamanders and centipedes, from a biomechanical perspective.

The digital Creole

This article argues that the Creole is, at once, both a technological construct of the plantation economies of the colonized Atlantic world and a moment of interpretation, constantly negotiating time, place, and interpellations. By placing the Creole identity firmly without the borders of either racial or biological classification, this position works toward an understanding of the new social constructions afforded by the new world of online spaces. Centring the Caribbean as the engine of modernity – the New World – and referencing the role it has played in what we have come to know as cultural studies through the work of Stuart Hall, I argue that the new world of online spaces is analogous to the region in this moment – a thirdspace of limitless possibilities – and offer the logic of the digital Creole as a means of unravelling what it means to exist in a digital world.

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein–ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

Automatic classification of snoring sounds from excitation locations based on prototypical network

Snoring site is greatly associated with the treatment approach of Obstructive Sleep Apnea (OSA), a serious chronic sleep disorder. There are more demands to recognize snoring sounds automatically from different excitation locations with high accuracy in clinical practice. Based on the original division of Munich Passau Snore Sound Corpus (MPSSC) with limited four types of snoring sounds. A meta-learning algorithm named prototypical network embedded with a convolutional neural network (CNN) in the prototypical network is used to classify these snoring sounds. The result shows that CNN with six-layer convolution and complement-cross-entropy loss function could well solve the problem of imbalance distribution of the MPSSC dataset to yield the highest unweighted average recall with the value of 78.85% in the development set and 77.13% in the test set under all test conditions. Compared with the challenge baseline, this paper yields a high improvement with a value of 18.63% (p < 0.05) in the test set. It indicates that the simplicity and effectiveness of the prototypical network make it a promising approach for biological signal classification with a relatively small dataset.

PET-based classification of corticobasal syndrome

IntroductionCorticobasal degeneration (CBD) is the most common neuropathological substrate for clinically diagnosed corticobasal syndrome (CBS), while identifying CBD pathology in living individuals has been challenging. This study aimed to examine the capability of positron emission tomography (PET) to detect CBD-type tau depositions and neuropathological classification of CBS. MethodsSixteen CBS cases diagnosed by Cambridge's criteria and 12 cognitively healthy controls (HCs) underwent PET scans with 11C-PiB, 11C-PBB3, and 18F-FDG, along with T1-weighted magnetic resonance imaging. Amyloid positivity was assessed by visual inspection of 11C-PiB retentions. Tau positivity was judged by quantitative comparisons of 11C-PBB3 binding to HCs. ResultsSixteen CBS cases consisted of two cases (13%) with amyloid and tau positivities indicative of Alzheimer's disease (AD) pathologies, 11 cases (69%) with amyloid negativity and tau positivity, and three cases (19%) with amyloid and tau negativities. Amyloid(−), tau(+) CBS cases showed increased retentions of 11C-PBB3 in the frontoparietal areas, basal ganglia, and midbrain, and reduced metabolism in the precentral gyrus and thalamus relative to HCs. The enhanced tau probe retentions in the frontal gray and white matters partially overlapped with metabolic deficits and atrophy and correlated with Clinical Dementia Rating scores. ConclusionsPET-based classification of CBS was in accordance with previous neuropathological reports on the prevalences of AD, non-AD tauopathies, and others in CBS. The current work suggests that 11C-PBB3-PET may assist the biological classification of CBS and understanding of links between CBD-type tau depositions and neuronal deteriorations leading to cognitive declines.

Antioxidant Potential, Acute Toxicity Profile and Bioavailability Studies of Water Soluble Analogue of Curcumin

Curcumin, a bright yellow colored phytochemical is the principal and active ingredient - curcuminoids present in the plant Curcuma longa which belonging to the family Zingiberaceae. Curcumin has said to be metabolized in intestine and liver and metabolizes to form glucuronide and sulfate conjugate thus exhibiting its restricted oral bioavailability. It has been studied till Phase-II clinical study, but its bioavailability is still an issue (Biological Classification System-IV {BCS-Class-IV}). Nonetheless, curcumin can be successfully formulated for oral administration, but care needs to be taken with formulation design to ensure consistent bioavailability. There are numerous studies in support of excellent antioxidant activity, safe toxicity profile and bioavailability of conventional and modulated curcumin. The aim of the present study was to investigate the antioxidant potential, acute toxicity profile and bioavailability of water soluble part of curcumin. Objective of research is to determine the antioxidant potential and acute toxicity of aqueous soluble part of curcumin .

Aplikasi Teknologi QR Code Pada Identifikasi Tumbuhan Di Wisata De-Djawatan

De-Djawatan Tourism Destinations are known by various tourists both local and non-local with their natural beauty in the form of large and lush trees that are still beautiful or natural. De-Djawatan tourism does not yet have detailed information either manually or digitally related to the biological and general classification of plants in that location. As smartphone technology develops, the information needed to be very easy to obtain. QR Code is one method to obtain complete information. QR Code is a type of two-dimensional image that displays data in the form of text. This community partnership program aims to provide appropriate technology in the form of the QR code method in determining the identification of plants found in De-Djawatan tourist destinations. This aims to make it easier for tourists to find information on various plants both biologically and in general.&#x0D; The stages of program implementation start from the observation stage, media creation, and implementation. The results of this program using the QR Code can include the identity of the identification results of plants, such as division, order, family, genus and species of plants in the De-Djawatan location. In this QR Code method there are features such as Home, Collection, and Admin. Before the QR Code is created, the document data is encrypted and then used as a QR Code. The result of scanning the QR Code is in the form of a Uniform Resource Locator (URL) that points to the document data. When the URL is executed on the web browser, the system performs a description of the URL, then it is matched with the database, and then displayed to the web browser. The final result is a Document Validation application in the form of a QR Code which is used to convey plant identification information, such as division, order, family, genus and species of plants.

The Reaction Specificity of Mammalian ALOX15 Orthologs is Changed During Late Primate Evolution and These Alterations Might Offer Evolutionary Advantages for Hominidae.

Arachidonic acid lipoxygenases (ALOXs) have been implicated in the immune response of mammals. The reaction specificity of these enzymes is decisive for their biological functions and ALOX classification is based on this enzyme property. Comparing the amino acid sequences and the functional properties of selected mammalian ALOX15 orthologs we previously hypothesized that the reaction specificity of these enzymes can be predicted based on their amino acid sequences (Triad Concept) and that mammals, which are ranked in evolution below gibbons, express arachidonic acid 12-lipoxygenating ALOX15 orthologs. In contrast, Hominidae involving the great apes and humans possess 15-lipoxygenating enzymes (Evolutionary Hypothesis). These two hypotheses were based on sequence data of some 60 mammalian ALOX15 orthologs and about half of them were functionally characterized. Here, we compared the ALOX15 sequences of 152 mammals representing all major mammalian subclades expressed 44 novel ALOX15 orthologs and performed extensive mutagenesis studies of their triad determinants. We found that ALOX15 genes are absent in extant Prototheria but that corresponding enzymes frequently occur in Metatheria and Eutheria. More than 90% of them catalyze arachidonic acid 12-lipoxygenation and the Triad Concept is applicable to all of them. Mammals ranked in evolution above gibbons express arachidonic acid 15-lipoxygenating ALOX15 orthologs but enzymes with similar specificity are only present in less than 5% of mammals ranked below gibbons. This data suggests that ALOX15 orthologs have been introduced during Prototheria-Metatheria transition and put the Triad Concept and the Evolutionary Hypothesis on a much broader and more reliable experimental basis.

Iselin as Object of Desire in Knut Hamsun's Pan and Victoria

Iselin as Object of Desire in Knut Hamsun's <i>Pan</i> and <i>Victoria</i>

Biological Tomato Leaf Disease Classification using Deep Learning Framework

Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.

Updates in the classification of ependymal neoplasms: The 2021 WHO Classification and beyond.

Ependymal neoplasms occur at all ages and encompass multiple tumor types and subtypes that develop in the supratentorial compartment, the posterior fossa, or the spinal cord. Clinically, ependymomas represent a very heterogeneous group of tumors from rather benign subependymomas to very aggressive and often deadly childhood ependymomas of the posterior fossa. Newly identified biological markers and classification schemes, e. g. based on global DNA methylation profiling, have led to the definition of 10 types of ependymal tumors and an improved prediction of patients’ outcome by applying the new classification system. While the exact genetic basis for several ependymoma types still remains unclear, the knowledge about ependymoma driving events has significantly increased within the last decade and contributed to a classification based on molecular characteristics and localization rather than histological features alone. Convincing evidence is now pointing towards gene fusions involving ZFTA or YAP1 causing the development of supratentorial ependymomas. Also, H3, EZHIP, or TERT mutations have been detected in a fraction of infratentorial ependymal tumors. Finally, MYCN amplifications have recently been identified in spinal ependymomas, in addition to the previously known mutations in NF2. This review summarizes how recent findings regarding biology, molecular tumor typing, and clinical outcome have impacted the classification of ependymomas as suggested by the updated 2021 WHO CNS tumor classification system. We focus on changes compared to the previous classification of 2016 and discuss how a formal grading could evolve in the future and guide clinicians to treat ependymoma patients.

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.