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Background: Schwannomas of C1 are rare tumors. Because of the wide variation in their anatomy, and presentation and their rarity, there is no proper understanding of their pathological anatomy nor their optimal management. Methods: A retrospective study of the C1 schwannomas operated by the author between 2000 and 2020 and a PubMed-based search of English literature were done to analyze data on the location and extent of the lesions, surgical approaches used, and operative outcomes. Using this data, a classification system was created indicating the location and extent of these lesions, depending on whether they were extradural (E) or intradural (I); with further subdivisions based on their relation to the vertebral artery, and cervicomedullary junction (CMJ). Results: Six patients with C1 schwannoma were operated by the author in the said period. Literature search yielded 44 cases in 16 publications. The details of location and extent of tumor were available in 22 patients including six of this article. Most of the tumors were intradural (13/22), with six in intradural and extradural compartments. Specific growth patterns were identified and a theory for this is proposed. Different surgical approaches were used; the far lateral and the posterolateral in 9 patients each, and the posterior midline in 7 of 25 patients. Using the available data, preferred surgical approaches are also presented. Conclusions: C1 schwannomas are a rare and complex group of tumors. The classification presented will help in understanding the growth pattern and pathological anatomy and choose the preferred surgical approach. Keywords: C1 schwannomas, classification, growth patterns, vertebral artery, Author(s): Krish Sridhar (corresponding author) [1] Key Message: C1 schwannomas are rare lesions probably because of the variable anatomy of both the cisternal and extradural portions of the nerve. The [...]

Oncocytic follicular cell-derived thyroid neoplasms, formerly known as Hurthle cell tumors, are characterized by expansile nodular growths of thyroid follicular epithelium displaying abundant granular eosinophilic cytoplasm. The topic of oncocytic [...], * Context.--Oncocytic thyroid tumors displaying a papillary growth pattern are rare and may cause diagnostic problems. Objective.--To examine the clinicopathologic features of a series of 32 follicular cell-derived tumors composed of cells with oncocytic cytoplasm and displaying papillary architecture. Design.--Thirty-two cases were collected and studied to assess clinicopathologic features, including immunohistochemical and molecular testing for BRAF V600E. Results.--The patients were 26 women and 6 men, aged 17 to 77 years. The nodules ranged from 0.3 to 6.0 cm. Eighteen cases showed features of oncocytic hyperplastic nodules and were identified against a background of thyroid follicular nodular disease; 4 cases showed features of oncocytic follicular adenoma; and 10 cases corresponded to carcinomas with oncocytic and papillary features. Nuclear features of papillary thyroid carcinoma were absent or exceedingly rare. All cases were negative for HBME-1 and cytokeratin 19 (CK19) and wild type for BRAF V600E. Follow-up in 25 patients showed that all patients with hyperplastic nodules and oncocytic follicular adenomas were alive and well and free of disease from 7 to 20 years. One patient with oncocytic follicular carcinoma showed metastases and died of tumor at 16 months; 2 patients with carcinoma had metastases and recurrence at 6 and 7 years; and 5 patients with invasive tumors were free of disease from 5 to 10 years. Conclusions.--Oncocytic thyroid tumors with papillary features can span a spectrum from benign hyperplastic, to encapsulated neoplastic, to invasive malignant lesions. Owing to their papillary features, it is important not to confuse them for other types of thyroid tumors, such as oncocytic papillary thyroid carcinoma. (Arch Pathol Lab Med. 2024;148:997-1006; doi: 10.5858/arpa.2023-0309-OA)

Pancreatic neuroendocrine neoplasms (PanNENs) represent a heterogeneous group of epithelial tumors of the pancreas showing a neuroendocrine differentiation. (1-4) Among all pancreatic malignancies, the prevalence of PanNENs is around 2% [...], * Context.--Pancreatic neuroendocrine neoplasms (PanNENs) represent a heterogeneous group of epithelial tumors of the pancreas showing neuroendocrine differentiation. These neoplasms are classified into well-differentiated pancreatic neuroendocrine tumors (PanNETs), which include G1, G2, and G3 tumors, and poorly differentiated pancreatic neuroendocrine carcinomas (PanNECs), which are G3 by definition. This classification mirrors clinical, histologic, and behavioral differences and is also supported by robust molecular evidence. Objective.--To summarize and discuss the state of the art regarding neoplastic progression of PanNENs. A better comprehension of the mechanisms underpinning neoplastic evolution and progression of these neoplasms may open new horizons for expanding biologic knowledge and ultimately for addressing new therapeutic strategies for patients with PanNENs. Data Sources.--Literature review of published studies and the authors' own work. Conclusions.--PanNETs can be seen as a unique category, where G1-G2 tumors may progress to G3 tumors mainly driven by DAXX/ATRX mutations and alternative lengthening of telomeres. Conversely, PanNECs display totally different histomolecular features more closely related to pancreatic ductal adenocarcinoma, including TP53 and Rb alterations. They seem to derive from a nonneuroendocrine cell of origin. Even the study of PanNEN precursor lesions corroborates the rationale of considering PanNETs and PanNECs as separate and distinct entities. Improving the knowledge regarding this dichotomous distinction, which guides tumor evolution and progression, will represent a critical basis for PanNEN precision oncology. (Arch Pathol Lab Med. 2024;148:975-979; doi: 10.5858/arpa.2022-0417-RA)

Highly pathogenic avian influenza (HPAI) viruses cause high mortality rates in wild birds and farmed poultry, and their potential for adaptation to humans remains a major pandemic threat (2). In [...]

Orthohantavirus is a genus of globally distributed RNA viruses in the family Hantaviridae. In the Americas, the viruses are hosted by native rodent species within the Cricetidae family (2). Although [...]

Biting midge-borne bluetongue virus (BTV), an Orbivirus of the Sedoreoviridae family, can cause epizootic disease in domestic and wild ruminants (2). Bluetongue (BT) is a World Organisation for Animal Health-listed [...]

Mpox is caused by infection with monkeypox vi rus (MPXV). Phylogenomically and clinically, clades I and II can be distinguished; clade II has 2 subcodes, Ha and IIb (1). In [...]

Rickettsioses are undifferentiated febrile illnesses, often accompanied by myalgia and rash, that are caused by intracellular gram-negative bacteria of the genus Rickettsia. Spotted fever group (SFG) Rickettsia are transmitted through [...]

A new species of leptostracan of the genus Nebalia, N. holothurophila, is described from southern California. The new species appears closely associated with the warty sea cucumber, Apostichopus parvimensis (H. L. Clark, 1913), although the exact nature of the relationship is unclear. The leptostracans were found in "transfer tubs" after the sea cucumbers were removed from artificial tidepools and placed in fresh seawater within these tubs. The new species differs from other leptostracans in southern California in having a combination of features not shared by any other species., Leptostracans are small (usually 3 to 12 mm) crustaceans characterized by a large, bivalved carapace that covers nearly all of the thorax and the eight thoracic appendages, a short movable [...]

This paper introduces a novel approach to identifying and classifying herbal plant species using leaf images. While deep learning models have shown remarkable performance in this domain, their decision-making process often lacks transparency, making it challenging to understand the reasoning behind their classifications. The proposed model is applied specifically to the classification of leaf images, providing significant insights into the characteristics of herbal plant species. Unlike existing models, the proposed model uniquely leverages a variational auto-encoder (VAE) to simultaneously achieve high classification accuracy and offer feature-based explanations. The VAE architecture enhances explainability by disentangling classifiable features from non-classifiable ones, allowing for clear visualization of the most important features driving classification decisions. This aspect has not been fully explored in previous works. Our approach demonstrated superior classification accuracy of 99.20% and enhanced explainability through experiments conducted on the BDMediLeaves dataset. Our model enabled visual understanding of important features influencing classification decisions, empowering researchers with actionable insights into botanical image classification. The explainable nature of our model enhances transparency and trust in automated plant species identification systems, crucial for validation and knowledge extraction. This model shows significant potential for broader applications in other crops and diverse image classification tasks, ensuring both transparency and high performance. [ABSTRACT FROM AUTHOR]

Introduction Sarcoidosis remains a debilitating, and sometimes fatal, multiorgan inflammatory granulomatous disease that predominately affects the lung and skin. Patients struggle with fatigue, difficulty breathing, and pain in their joints, [...], Sarcoidosis is a multiorgan granulomatous disease that lacks diagnostic biomarkers and targeted treatments. Using blood and skin from patients with sarcoid and non-sarcoid skin granulomas, we discovered that skin granulomas from different diseases exhibit unique immune cell recruitment and molecular signatures. Sarcoid skin granulomas were specifically enriched for type 1 innate lymphoid cells (ILC1s) and B cells and exhibited molecular programs associated with formation of mature tertiary lymphoid structures (TLSs), including increased CXCL12/CXCR4 signaling. Lung sarcoidosis granulomas also displayed similar immune cell recruitment. Thus, granuloma formation was not a generic molecular response. In addition to tissue-specific effects, patients with sarcoidosis exhibited an 8-fold increase in circulating ILC1s, which correlated with treatment status. Multiple immune cell types induced CXCL12/CXCR4 signaling in sarcoidosis, including Th1 T cells, macrophages, and ILCs. Mechanistically, CXCR4 inhibition reduced sarcoidosis-activated immune cell migration, and targeting CXCR4 or total ILCs attenuated granuloma formation in a noninfectious mouse model. Taken together, our results show that ILC1s are a tissue and circulating biomarker that distinguishes sarcoidosis from other skin granulomatous diseases. Repurposing existing CXCR4 inhibitors may offer a new targeted treatment for this devastating disease.

1. Introduction Pasteurella multocida is a non-motile, Gram-negative, facultative anaerobe, and capsulated coccobacillus. It is the causative agent of a wide range of infections (commonly called 'Pasteurellosis') in animals and [...], Pasteurella multocida causes acute/chronic pasteurellosis in porcine, resulting in considerable economic losses globally. The draft genomes of two Indian strains NIVEDIPm17 (serogroup D) and NIVEDIPm36 (serogroup A) were sequenced. A total of 2182-2284 coding sequences (CDSs) were predicted along with 5-6 rRNA and 45-46 tRNA genes in the genomes. Multilocus sequence analysis and LPS genotyping showed the presence of ST50: genotype 07 and ST74: genotype 06 in NIVEDIPm17 and NIVEDIPm36, respectively. Pangenome analysis of 61 strains showed the presence of 1653 core genes, 167 soft core genes, 750 shell genes, and 1820 cloud genes. Analysis of virulence-associated genes in 61 genomes indicated the presence of nanB, exbB, exbD, ptfA, ompA, ompH, fur, plpB, fimA, sodA, sodC, tonB, and omp87 in all strains. The 61 genomes contained genes encoding tetracycline (54%), streptomycin (48%), sulphonamide (28%), tigecycline (25%), chloramphenicol (21%), amikacin (7%), cephalosporin (5%), and trimethoprim (5%) resistance. Multilocus sequence type revealed that ST50 was the most common (34%), followed by ST74 (26%), ST13 (24%), ST287 (5%), ST09 (5%), ST122 (3%), and ST07 (2%). Single-nucleotide polymorphism and core genome-based phylogenetic analysis clustered the strains into three major clusters. In conclusion, we described the various virulence factors, mobile genetic elements, and antimicrobial resistance genes in the pangenome of P. multocida of porcine origin, besides the rare presence of LPS genotype 7 in serogroup D. Key words: capsular serogroups, genome analysis, Pasteurella multocida, porcine, vaccine

Introduction Plants exhibit near-continuous variation in reproductive strategies, from complete outcrossing to exclusive selflng, to partial or obligate asexual reproduction (Barrett 2002; Hartfleld 2016). Understanding the causes and consequences of [...], In species that primarily reproduce asexually, occasional sex can have important evolutionary consequences. Many plant species in the genus Oenothera have a genetic system called permanent translocation heterozygosity (PTH), which renders plants functionally asexual when they self-fertilize. PTH results from reciprocal translocations of chromosomes that suppress recombination and a balanced lethal mortality of homozygous gametes that prevents independent assortment of alleles. When PTH plants self-fertilize, the offspring are genetically identical to the parents, but when they outcross they can create new genotypes. Here, we sought to determine the outcrossing rate of natural populations of PTH Oenothera biennis L. (Onagraceae). We geno typed 46 parents from 14 populations in Tompkins County, NY, USA, and 754 of their offspring at highly polymorphic microsatellite loci. 99.5% of offspring were genetically identical to their seed parents, whereas 4 offspring out of 754 exhibited allelic mismatches with their seed parent. The estimated outcrossing rate ranged from 0.1% to 0.4%. Our study design also permitted us to estimate mutation rate of microsatellite loci, which ranged from 1.33 x 10 (3) to 3.98 x 10 (3). These results show that 0. biennis typically exhibits a functionally asexual genetic system, but rare outcrossing events can generate novel genotypes that may have important evolutionary consequences. Key words: apomixis, evening primrose, genetic variation, inbreeding, mating system, simple sequence repeats

Author(s): Bolanle Idowu Oso [sup.1] , Saheed Babajide A. Oseni [sup.1] , Theophilus Adesola Aladekomo [sup.1] , Tewogbade Adeoye Adedeji [sup.2] , Wasiu Adekunle Olowu [sup.3] Author Affiliations: (1) https://ror.org/04snhqa82, [...], Background Acute kidney injury (AKI) is a common cause of mortality and morbidity in asphyxiated newborns. Recent research suggests serum neutrophil gelatinase-associated lipocalin (sNGAL) as an early biomarker of AKI in newborns with perinatal asphyxia. The prospect of sNGAL is yet to be studied in Nigeria, with a huge burden of asphyxia-related neonatal deaths. Methods A cross-sectional analytic study was conducted on 53 asphyxiated term newborns and 53 healthy babies at the newborn unit of the Wesley Guild Hospital Ilesa, Nigeria. sNGAL was assessed in all neonates with perinatal asphyxia at baseline (within 30 min of delivery), 2 h, and 48 h of life. Results Mean sNGAL was significantly higher in asphyxiated newborns than in the control group, 81.4 (45.9) vs. 53.7 (29.2), p < 0.001. However, the mean 2-h sNGAL levels were similar in asphyxiated babies with and without AKI 100.5 (36.7) ng/ml vs. 85.3 (31.4) ng/ml, p = 0.115. The 2-h sNGAL with an AUC of 0.61 at an 83.0 ng/ml cut-off had an acceptable discriminating capability of predicting AKI. The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 51.5%, 50%, and 81%, respectively. Conclusion This study shows that sNGAL levels were significantly elevated in newborns with perinatal asphyxia compared to healthy neonates, but the 2-h sNGAL is less predictive of AKI in newborns with perinatal asphyxia. The negative predictive value is high, and this may find some relevance in the attempts at early exclusion of asphyxiated babies prone to AKI. Graphical abstract

Author(s): Jia He [sup.1] [sup.2] , Zhanjun Jia [sup.1] [sup.2] [sup.3] , Aihua Zhang [sup.1] [sup.2] [sup.3] , Mi Bai [sup.1] [sup.2] [sup.3] [sup.4] Author Affiliations: (1) https://ror.org/04pge2a40, grid.452511.6, Department [...], Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) have been approved in several countries as a supplement or even an alternative to the clinical treatment of anemia in patients with chronic kidney disease (CKD). Activation of HIF by HIF-PHIs effectively increases hemoglobin (Hb) level in CKD patients by inducing multiple HIF downstream signaling pathways. This indicates that HIF-PHIs have effects beyond erythropoietin, while their potential benefits and risks should be necessarily assessed. Multiple clinical trials have largely demonstrated the efficacy and safety of HIF-PHIs in the short-term treatment of anemia. However, in terms of long-term administration, especially over 1 year, the benefits and risks of HIF-PHIs still need to be assessed. Particular attention should be paid to the risk of kidney disease progression, cardiovascular events, retinal diseases, and tumor risk. This review aims to summarize the current potential risks and benefits of HIF-PHIs in CKD patients with anemia and further discuss the mechanism of action and pharmacological properties of HIF-PHIs, in order to provide direction and theoretical support for future studies.

The Pancreatobiliary Pathology Society (PBPS) wants to thank Alain C. Borczuk, MD, and the Archives of Pathology & Laboratory Medicine for highlighting our society by publishing 3 review articles based [...]

The paper focused on occurrence, characterization, and analytical methods of microplastic (MP) pollution in the lentic environment mainly for the Indian scenario. To understand the flow of MP from plastic waste, a material flow diagram was developed using STAN, assigning the transfer coefficients based on existing scientific literature and primary survey from local recycling facilities and industries. The quantity, morphology, and polymers of MP in the water and sediments of the lentic environment were compared for various states from 2011 to 2022. The reasons for the geographical heterogeneity in microplastics may be the migratory routes of MPs in the ecosystems like commercial uses and wastewater characteristics which possibly discharged in lentic system. Factors like particle density, water surface area, water surface depth, wind speed and direction, and water flow size mainly affect MP concentrations in the lentic water body, and mainly PHI and PLI are keys to MP risk analysis. The surface characteristics of MPs reveal that it absorbs many toxic contaminants including heavy metals. The impacts of MP on ecosystem and human health were also discussed. The impacts of socioeconomic conditions on MP concentrations for different states in India were also added. Proposed methods for plastic waste generation control also included which will help for developing policy in future to prevent MP pollution in lentic environments and also motivate future researchers to establish new standardized methods of MP analysis. [ABSTRACT FROM AUTHOR]

Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem.

Author(s): Hernan Vera-Villalobos [sup.1] , Yashira Miranda-Peña [sup.1] , Francisco Fuentes-Santander [sup.1] [sup.3] , María Teresa Mata [sup.1] [sup.2] , Carlos Riquelme [sup.1] [sup.2] Author Affiliations: (1) https://ror.org/04eyc6d95, grid.412882.5, 0000 [...], Dinoflagellates are protists of high ecological interest due to their ability to produce compounds with biotechnological utility, such as polyunsaturated fatty acids. Two dinoflagellates, Symbiodinium microadriaticum and Perkinsus marinus, play radically different ecological roles. While S. microadriaticum contributes to coral survival by providing nutrients, P. marinus has become a pathogen in oyster culture, causing economic losses. Despite the differences in their ecological role, the mechanism associated which these two organisms have been linked to the production of polyunsaturated fatty acids (PUFAs). Desaturase enzymes play a crucial role in the synthesis of unsaturated fatty acids through the addition of double bonds, especially the [DELTA]4 and [DELTA]5 desaturase enzymes, which are important to producing docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). However, despite their great potential and ecological importance, cellular mechanisms of polyunsaturated fatty acids synthesis and their associated enzymes have not been sufficiently studied in dinoflagellates. This limits efforts to optimizing the production of these metabolites. In this study, an in-silico analysis of [DELTA]4 and [DELTA]5 desaturase sequences from S. microadriaticum and P. marinus was carried out. Bioinformatic characterization showed that genes are related to a membrane-bound proteins with at least 3 transmembrane domains. In addition, three histidine boxes were detected in each enzyme, which are essential for their role as desaturases. In addition, despite distance between protozoan clade and their diverse biological role that S. microadriaticum and P. marinus are involved in, [DELTA]4 and [DELTA]5 desaturases retain high identity domains, even with species from other kingdom such as Plantae and Animalia.

Author(s): J. Vigliano Relva [sup.1] , C. Van Colen [sup.1] , W. Barhdadi [sup.2] , A. J. Daly [sup.2] , M. De Troch [sup.1] Author Affiliations: (1) https://ror.org/00cv9y106, grid.5342.0, 0000 [...], Copepods are aquatic invertebrates with a key role at the basis of marine food webs due to their high biomass as well as their elevated fatty acid (FA) content. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are two FA abundant in copepods which have a well demonstrated role in growth and reproduction in marine organisms. While the majority of research has focused on planktonic copepod species, less is known for benthic species despite their high richness, abundance and their role as the main food source for many fish larvae. Temperature is a key driver of organism&apos;s fitness as well as ecosystem functioning and sea surface temperature is expected to rise under all CO.sub.2 emission scenarios. Thus, understanding how copepods will respond to such changes is crucial given their role in marine food webs. In this study we expose laboratory reared Tachidius discipes, an intertidal benthic copepod to a temperature gradient (12, 15, 18, 21 and 24 °C) including current seasonal variability as well as future scenarios. Survival, FA, growth rates and nauplii production were measured for each temperature. We found decreased survival, increased growth rates and detrimental effects for nauplii production with temperature increase. While relative EPA and DHA decreased with temperature this was not found on absolute levels of these FA. Changes in benthic copepods&apos; biomass as well as their FA composition in response to temperature changes could amplify to higher trophic levels with consequences for food web functioning.

Author(s): Sabine Holst [sup.1] , Lisa-Renana Kaiser [sup.2] , Ilka Sötje [sup.2] Author Affiliations: (1) https://ror.org/03sd3yf61, grid.500026.1, 0000 0004 0487 6958, Senckenberg am Meer, German Center for Marine Biodiversity Research [...], The negative effects of scyphozoan jellyfish blooms on ecosystems and economy are linked to planula survival, settlement success and subsequent polyp metamorphosis. A particular periderm covered stage formed by the newly settled planula, the planulocyst, has been reported for Cyanea lamarckii Péron and Lesueur, 1810 but not for Cyanea capillata (Linnaeus, 1758) from the Northeast Atlantic. Only a few planulocysts develop to polyps directly after settlement and the excystment process has not been understood in detail. By combining live observations with histological sections and scanning electron microscopy, present results clarified that the excysting C. lamarckii planula secretes a thin periderm stalk within the planulocyst and subsequently the polyp develops at the top of the stalk. No planulocysts but tiny periderm stalks appeared during the polyp development in C. capillata. Experiments with combined temperature (10, 15, 20 °C) and salinity (32, 25) treatments revealed significant effects of temperature on C. lamarckii planula settlement success (highest at 15 °C) and planulocyst excystment (highest at 20 °C) but no significant effects of salinity. Food supply did not affect excystment but enhanced the tentacle development of polyps. Our results demonstrate that studies on early life stages can reveal species-specific morphological differences in scyphozoan polyps which lack other distinct characters. The experimental results indicate that early C. lamarckii life stages are well adapted to environmental salinity changes and that increasing temperature due to global warming can be beneficial for their development which may support their northward distribution and increasing jellyfish populations in the Northeast Atlantic area.

Author(s): Olga V. Yurchenko [sup.1] , Oleg G. Borzykh [sup.1] , Alexander V. Kalachev [sup.1] Author Affiliations: (1) grid.417808.2, 0000 0001 1393 1398, A.V. Zhirmunsky National Scientific Center of Marine [...], The ultrastructure of oogenic cells and mature oocytes has been described in a deep-sea clam, Calyptogena pacifica, for the first time. Specimens were collected in June 2018 from the Piip volcano in the Bering Sea (55.3821° N, 167.2613° E). Female germ cells developed in the branched gonadal tubules. The pattern of oogenic development is similar to that in littoral species. Although somatic accessory cells do not form any follicles around developing oocytes, these cells have a well-developed endoplasmic reticulum, which could be a source of yolk precursors and be involved in vitellogenesis. Lipid droplets appear in early oocytes, but they disappear later, suggesting that developing oocytes use these lipid droplets. At the onset of vitellogenesis, developing oocytes accumulate lipids in the ooplasm facing the gonadal tubule wall. Yolk granules, in contrast, appear at later stages of oogenesis. Mature oocytes, which are 200 [micro]m in diameter, are filled with a significant volume of lipid droplets (occupying 24% of ooplasm) and a smaller volume of yolk granules (~ 8%). The mature oocyte&apos;s features, i.e., the large cell size (~ 200 [micro]m in diameter) and the high abundance of ooplasmic inclusions, are typical of species with lecithotrophic development. The large volume of lipid droplets would enhance buoyancy of the oocytes and provide embryos and larvae with nutrients during dispersal. Symbiotic bacteria were found in accessory cells, in vitellogenic oocytes, and within remnants of degenerating oocytes.

Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem. Classification and prediction have emerged as key avenues for advancing microbial community research in computational biology. This research compares the advantages and disadvantages of using different algorithmic approaches in four subfields of microbiology (pathogen and epidemiology; microbial ecology; drug development; microbiome and taxonomy). [ABSTRACT FROM AUTHOR]

Timely detection and treatment of tomato diseases can effectively improve the quality and yield of tomato. In order to realize the real-time and non-destructive detection of tomato diseases, a tomato leaf disease classification and recognition method based on improved MobileNetV3 was proposed in this study. Firstly, the lightweight convolutional neural network MobileNetV3 was used for transfer learning on the image net data set. The network was initialized according to the weight of the pre training model, so as to realize the transfer and fine adjustment of large-scale shared parameters of the model. The training method of transfer learning could effectively alleviate the problem of model over fitting caused by insufficient data, realized the accurate classification of tomato leaf diseases in a small number of samples, and saved the time cost of network training. Under the same experimental conditions, compared with the three standard deep convolution network models of VGG16, ResNet50 and Inception-V3, the results showed that the overall performance of MobileNetV3 was the best. Next, the impact of the change of loss function and the change of data amplification mode on the identification of tomato leaf diseases were observed by using MobileNetV3 convolution network. For the test of loss value, focal loss and cross entropy function were used for comparison, and for the test of data enhancement, conventional data amplification and mixup hybrid enhancement were used for comparison. After testing, using Mixup enhancement method under focal loss function could improve the recognition accuracy of the model, and the average test recognition accuracy of 10 types of tomato diseases under Mixup hybrid enhancement and focal loss function was 94.68%. On the basis of transfer learning, continue to improve the performance of MobileNetV3 model, the dilated convolution convolution with expansion rate of 2 and 4 was introduced into convolution layer, 1×1 full connection layer after deep convolution of 5×5 was connected to form a perceptron structure in convolution layer, and GLU gating mechanism activation function was used to train the best tomato disease recognition model. The average test recognition accuracy was as high as 98.25%, the data scale of the model was 43.57 MB, and the average detection time of a single tomato disease image was only 0.27s, after ten fold cross validation, the recognition accuracy of the model was 98.25%, and the test results were stable and reliable. The experiment showed that this study could significantly improve the detection efficiency of tomato diseases and reduce the time cost of disease image detection.

In this study, we propose a novel system for automatic optical inspection (AOI) by integrating the Dobot Magician four-axis manipulator and machine vision. In the proposed system, the attitude control for the end joint rotates the claw kit to clamp the workpiece moving on the conveyor belt and performs color classification. The system conducts feature detection by examining the workpiece from different angles through cameras by using the working platform Python. Furthermore, the system identifies the positions of detected features and the center point and inclination angle of the workpiece. Finally, the Dobot Magician manipulator moves to the appropriate clamping position to pick up the object and then moves the object to the specified position. In this study, objects are preliminarily classified on the conveyor belt by using a manipulator through a photoelectric switch and a color recognition sensor, and the Hough transform is used to calculate the center point and rotation angle of the object for image recognition. To integrate image processing and Dobot manipulator motion control to complete the automatic simulation platform, an AOI setup is built for a production line. The system cooperates with the Python extension kit Tkinter to establish a user-friendly human--machine interface environment, which promotes the development and applicability of the automatic production platform. [ABSTRACT FROM AUTHOR]

The optical properties of rocks and minerals provide a reliable way to measure their chemical and mineralogical composition due to the specific reflection behaviors, which is also the key insight behind most automatic identification and classification approaches. However, the inter-category spectral similarity poses a great challenge to the automatic identification and classification tasks because of the diversity of rocks and minerals. Therefore, this paper develops a recognition and classification approach of rocks and minerals using the highly discriminative representation derived from their raw spectral signatures. More specifically, a transformer-based classification approach integrated with category-aware contrastive learning is constructed and trained in an end-to-end manner, which would force instances of the same category to remain close-by while pushing instances of a dissimilar category far apart in the high-dimensional feature space, in order to produce the highly discriminative feature representation of the rocks and minerals. From both qualitative and quantitative views, experiments are conducted on the laboratory sample dataset with 30 types of rocks and minerals shared from the National Mineral Rock and Fossil Specimens Resource Center, and the spectral information of the laboratory rocks and minerals is captured using a multi-spectral sensor, with a duplicated payload of the counterpart onboard the Zhurong rover. Quantitative results demonstrate that the developed approach can effectively distinguish 30 types of rocks and minerals, with a high overall accuracy of 96.92%. Furthermore, the developed approach is remarkably superior to other existing methods, with average differences of 4.75% in the overall accuracy. Furthermore, we also visualized the derived highly discriminative features of different types of rocks and minerals by projecting them onto a two-dimensional map, where the same categories tend to be modeled by nearby locations and the dissimilar categories by distant locations with high probability. It can be observed that, compared with those in the raw spectral feature space, the clusters are formed better in the derived highly discriminative feature space, which further confirms the promising representation capability. [ABSTRACT FROM AUTHOR]

The agricultural yield of any country provides the base for the development of that nation. Sustainable growth needs to maintain crop production up to a certain level that depends on the research of their disease detection and treatment. The general approaches available in the literature follow attributes extraction and training a classifier model for leaf image classification that limits accuracy. The proffered technique eliminates the redundant information from the image dataset. We initially localize the region of interest in terms of the color attributes of leaf image based on the mixture model for region growing. The feature extraction is performed through a proposed deep convolutional neural network model followed by the classification of the leaf images. The deep learning model uses color images to learn the attributes that show different patterns that can be distinguished with the help of a convolutional neural network model. The execution measure of the proposed model is investigated using the PlantVillage dataset. The simulating replica outcomes show that the performance of the proposed model is far better as compared to the existing well-known methods of the domain with mean classifying accuracy and area under the characteristics curve of 95.35% and 94.7%, individually. [ABSTRACT FROM AUTHOR]

Timely detection and treatment of tomato diseases can effectively improve the quality and yield of tomato. In order to realize the real-time and non-destructive detection of tomato diseases, a tomato leaf disease classification and recognition method based on improved MobileNetV3 was proposed in this study. Firstly, the lightweight convolutional neural network Mobile‐ NetV3 was used for transfer learning on the image net data set. The network was initialized according to the weight of the pre training model, so as to realize the transfer and fine adjustment of large-scale shared parameters of the model. The training method of transfer learning could effectively alleviate the problem of model over fitting caused by insufficient data, realized the accurate classification of tomato leaf diseases in a small number of samples, and saved the time cost of network training. Under the same experimental conditions, compared with the three standard deep convolution network models of VGG16, ResNet50 and Inception- V3, the results showed that the overall performance of MobileNetV3 was the best. Next, the impact of the change of loss function and the change of data amplification mode on the identification of tomato leaf diseases were observed by using MobileNetV3 convolution network. For the test of loss value, focal loss and cross entropy function were used for comparison, and for the test of data enhancement, conventional data amplification and mixup hybrid enhancement were used for comparison. After testing, using Mixup enhancement method under focal loss function could improve the recognition accuracy of the model, and the average test recognition accuracy of 10 types of tomato diseases under Mixup hybrid enhancement and focal loss function was 94.68%. On the basis of transfer learning, continue to improve the performance of MobileNetV3 model, the dilated convolution convolution with expansion rate of 2 and 4 was introduced into convolution layer, 1×1 full connection layer after deep convolution of 5×5 was connected to form a perceptron structure in convolution layer, and GLU gating mechanism activation function was used to train the best tomato disease recognition model. The average test recognition accuracy was as high as 98.25%, the data scale of the model was 43.57 MB, and the average detection time of a single tomato disease image was only 0.27s, after ten fold cross validation, the recognition accuracy of the model was 98.25%, and the test results were stable and reliable. The experiment showed that this study could significantly improve the detection efficiency of tomato diseases and reduce the time cost of disease image detection. [ABSTRACT FROM AUTHOR]

The optical properties of rocks and minerals provide a reliable way to measure their chemical and mineralogical composition due to the specific reflection behaviors, which is also the key insight behind most automatic identification and classification approaches. However, the inter-category spectral similarity poses a great challenge to the automatic identification and classification tasks because of the diversity of rocks and minerals. Therefore, this paper develops a recognition and classification approach of rocks and minerals using the highly discriminative representation derived from their raw spectral signatures. More specifically, a transformer-based classification approach integrated with category-aware contrastive learning is constructed and trained in an end-to-end manner, which would force instances of the same category to remain close-by while pushing instances of a dissimilar category far apart in the high-dimensional feature space, in order to produce the highly discriminative feature representation of the rocks and minerals. From both qualitative and quantitative views, experiments are conducted on the laboratory sample dataset with 30 types of rocks and minerals shared from the National Mineral Rock and Fossil Specimens Resource Center, and the spectral information of the laboratory rocks and minerals is captured using a multi-spectral sensor, with a duplicated payload of the counterpart onboard the Zhurong rover. Quantitative results demonstrate that the developed approach can effectively distinguish 30 types of rocks and minerals, with a high overall accuracy of 96.92%. Furthermore, the developed approach is remarkably superior to other existing methods, with average differences of 4.75% in the overall accuracy. Furthermore, we also visualized the derived highly discriminative features of different types of rocks and minerals by projecting them onto a two-dimensional map, where the same categories tend to be modeled by nearby locations and the dissimilar categories by distant locations with high probability. It can be observed that, compared with those in the raw spectral feature space, the clusters are formed better in the derived highly discriminative feature space, which further confirms the promising representation capability.

Wafer map defect pattern recognition is an indispensable part in the semiconductor manufacturing process. Wafer map defect pattern recognition and classification can locate the cause of failures in the manufacturing process. In this study, we propose a deep convolutional neural network architecture incorporating improved convolutional block attention module (I-CBAM) for pattern recognition and classification of single-type wafer map defects. The proposed method consists of three steps: First, a multi-branch attention module is designed, which focuses more on the main information of defect clusters, extracts richer feature information, and improves feature extraction capabilities. Secondly, I-CBAM is integrated into the ResneXt50 backbone network to extract wafer map defect features. Finally, the extracted features are fed to a support vector machine combined with an error-correcting output code to classify the wafer map defect pattern. The experimental results on the real-world datasets WM-811K show that the accuracy rate of the I-CBAM-ResneXt50 can reach 96.96%. Compared with the existing algorithms, the proposed model can achieve better recognition and classification results. [ABSTRACT FROM AUTHOR]