Generic Classification Research Articles

A Phylogenetically Informed Reclassification of the Rock Daisies (Perityleae; Compositae)

Abstract— Recent phylogenomic analyses of sequence data from chloroplast and nuclear genomes as well as morphological and cytological analyses resolved long standing phylogenetic uncertainty in the rock daisy tribe (Perityleae; Asteraceae) and support reclassification at the generic level to reflect evolutionary relationships. The previously recognized genera Eutetras and Pericome were upheld as clades and continue to be recognized in the new classification. The large genus Perityle as treated in previous taxonomies was found not to be monophyletic and is thus reclassified in four genera, using the available names Laphamia (in an expanded sense), Galinsogeopsis (in an expanded sense), Nesothamnus, and Perityle (in a newly restricted sense). The type species of Perityle belongs to an early diverging lineage of the rock daisy tribe, in a clade with varied chromosome numbers of x = 11, 12, 13, 16, 18, or 19. In addition to the type (Perityle californica), Perityle now includes six other minimum-rank taxa of Perityle, all three taxa of Amauria, and the Desventuradas Islands endemic Lycapsus tenuifolius. Nesothamnus is reinstated as a monotypic genus for the Guadalupe Island endemic shrub Nesothamnus incana. Laphamia and Galinsogeopsis together constitute a clade of woody and herbaceous perennials or annuals with a stabilized base chromosome number of x = 17 (n = 17, 34, 51, 68) that have diversified throughout the Basin and Range Province and the Sierra Madre Occidental of the southwest US and northern Mexico. Laphamia and Galinsogeopsis have overlapping geographic distributions but can be distinguished by a combination of fruit and flower traits. This new generic classification of Perityleae resolves long standing conflict about the circumscription of Perityle without expanding the genus to encompass the entire subtribe Peritylinae and recognizes two independent evolutionary radiations onto island-like rocky habitats in western North America as taxonomically distinct components of this fascinating tribe of composites. To allow for this revised taxonomy, 46 new combinations in Galinsogeopsis, Laphamia, and Perityle are provided, and one new name, Laphamia sanchezii, is adopted.

Assessing Cognitive Workload Using Cardiovascular Measures and Voice.

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

Metagenomics analysis of microbial community distribution in large-scale and step-by-step purification system of swine wastewater

Biological treatment is one of the most widely used methods to treat swine wastewater in wastewater treatment plants. The microbial community plays an important role in the swine slurry treatment system. However, limited information is available regarding the correlation between pollutant concentration and dominant microbial community in swine wastewater. This work aimed to study the profiling of microbial communities and their abundance in the 40 M3/day large-scale and step-by-step treatment pools of swine wastewater. Metagenome sequencing was applied to study the changes of microbial community structure in biochemical reaction pools. The results showed that in the heavily polluted pools, it was mainly Proteobacteria, Cyanobacteria, Chlorella and other strains that could tolerate high concentration of ammonia nitrogen to remove nitrogen and absorb chemical oxygen demand (COD). In the moderately polluted pools, Nitrospirae, Actinobacteria and other strains further cooperated to purify swine wastewater. In the later stage, the emergence of Brachionus indicated the reduction of water pollution. The dominant microbes and their abundance changed with the purification of swine wastewater in different stages. Moreover, the dominant microflora of swine wastewater treatment pools at all levels reflected little difference in phylum classification level, while in genus classification level, the dominant microflora manifested great difference. Findings demonstrated that the microorganisms maintained ecological balance and absorbed the nutrients in the swine wastewater treatment pools, so as to play the role of purifying sewage. Therefore, the stepwise purification of swine wastewater can be realized by adding bacteria and microalgae of different genera.

On the robustness of self-supervised representations for multi-view object classification

• Analyse self-supervised and supervised embeddings regarding viewpoint robustness in both controlled and real-world settings. • Controlled setting created by approximating view using a homography on multiple common benchmark datasets. • Performance in-the-wild quantified by analysing the representations on multiple real-world, multi-view datasets. • Demonstrate that self-supervised representations are more robust to both controlled and real-world settings. • Show that self-supervised representations require less context than supervised ones in recognising objects in novel views. It is known that representations from self-supervised pre-training can perform on par, and often better, on various downstream tasks than representations from fully-supervised pre-training. This has been shown in a host of settings such as generic object classification and detection, semantic segmentation, and image retrieval . However, some issues have recently come to the fore that demonstrate some of the failure modes of self-supervised representations, such as performance on non-ImageNet-like data, or complex scenes. In this paper, we show that self-supervised representations based on the instance discrimination objective lead to better representations of objects that are more robust to changes in the viewpoint and perspective of the object. We perform experiments of modern self-supervised methods against multiple supervised baselines to demonstrate this, including approximating object viewpoint variation through homographies , and real-world tests based on several multi-view datasets. We find that self-supervised representations are more robust to object viewpoint and appear to encode more pertinent information about objects that facilitate the recognition of objects from novel views.

La hora del silencio by Cristina Feijóo

Reviewed by: La hora del silencio by Cristina Feijóo Janis Be Cristina Feijóo. La hora del silencio. Buenos Aires: Astier Libros, 2022. 208 p. Cristina Feijóo's most recent novel, La hora del silencio, is as intricate, oneiric, and haunting as the classic work to which it openly pays homage, Juan Rulfo's Pedro Páramo. The gendered retelling—set within the claustrophobic confines of Villa Devoto, a women's prison with striking parallels to Comala—takes place during Argentina's last military dictatorship. The protagonist, Leonora, is a political prisoner who embarks on a journey of self-discovery not unlike her literary predecessor, Juan Preciado. In so doing, Leonora reveals cunning survival strategies to combat the harsh conditions of political imprisonment even as she unveils myriad complexities of the leftist movement from an intensely personal perspective. Narrated primarily in first person and in present tense through the protagonist's prison diary, La hora del silencio is simultaneously intimate and immersive. What's more, the protagonist's persistent efforts to "put [her]self together" (50) mirror the active role required of readers who must similarly work to integrate the novel's disparate narrative fragments. Leonora consciously and overtly channels Damiana Cisneros in order to access her own (back)story, understand her identity, find a sense of purpose and belonging, and ultimately accept her fate. As she confides in her journal, "La vieja Damiana era sabia y con ella hicimos [End Page 327] un pacto: yo contaría mi historia usando su voz y ella contaría historias entramadas con la mía" (11). The graceful ease with which Leonora performs this and other literary assimilations and ventriloquisms becomes plausible given Feijóo's lyrical prose and linguistic agility. The storyline, which incorporates multiple voices and perspectives in addition to distinct registers, unfolds in overlapping loops. Proliferating embedded tales and secondary characters simultaneously echo and shed light upon the protagonist's plight. The result is a highly stylized, complex narrative that can be likened to a tapestry or mosaic. As the novel advances, carefully building to a dramatic conclusion, the protagonist achieves greater self-understanding and eventually finds her own voice. Readers gradually draw closer to Leonora herself, increasingly privy to the internal workings of her conniving mind. As the above might suggest, La hora del silencio defies simple generic classification. Like Rulfo, Feijóo situates her novel beyond the realm of traditional or linear time for "el tiempo es un perro que se muerde la cola" (66) while simultaneously exploring liminal spaces and interior states. Boundaries such as those that delineate consciousness and dreams, the corporeal and the ethereal, life and death remain porous. At times characters inhabit "sueños vivos" (64), which can include meeting fictional characters from other literary works, having out-of-body experiences, fusing or melding with each other, and struggling to distinguish between what is inside or outside of the prison walls or even one's physical body. In short, the masterful work questions the very nature of reality and sanity, while exhibiting traits of both the fantastic and magical realism. At the same time, La hora del silencio represents historical fiction at its finest. Not coincidentally, the cover art showcases a photograph by Daniel Berbedés from the Parque de la memoria—a commemorative public space honoring the victims of state terrorism and dedicated to "remembrance, homage, testimony and reflection." To this end, the novel offers detailed portrayals of the day-to-day hardships that political prisoners endured for years: from strict food rations to cruel punishments, from traumatic searches and seizures to the torments of solitary confinement. A particularly harrowing episode recalls the Masacre del Pabellón 7 when on March 14, 1978 over sixty prisoners died due to police repression and the brutality of prison personnel, specifically from fires, smoke inhalation, being beaten, or from gunshot [End Page 328] wounds. Feijóo artfully conveys the chaos, fear, and uncertainty by dramatically yet realistically narrating the unfolding scene through auditory rather than visual witnessing, given the need to take cover as well as direct orders to stay away from the windows or be shot. Another traumatic event features the political...

AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)

Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20th March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.

Molecular insights on the conflicting generic boundaries in the Carduncellus‐Carthamus complex (Compositae)

AbstractThe Carthamus‐Carduncellus complex is formed by approximately 50 taxa that are widely distributed throughout the Mediterranean basin and western Asia. The generic delineation of this complex has always been controversial. Currently, there are three widely diverging taxonomical proposals, suggesting that the complex is formed by (1) a single genus, Carthamus; (2) two genera, Carduncellus and Carthamus; and (3) four genera, Carduncellus, Carthamus, Femeniasia, and Phonus. The generic classification has varied depending on the importance assigned to some morphological characters, i.e., the morphology/structure of pappus, achenes, and involucral bracts. All these generic changes have complicated the nomenclature of the complex, leading to species wandering between four different genera. To objectively assess the taxonomic delimitation of this complex, we carried out phylogenetic analyses using nuclear (external and internal transcribed spacers of rDNA) and plastid (ndhF, trnH, rpl32, trnT) data, as well as multispecies coalescent model analyses on near‐complete sampling. Phylogenetic reconstructions resolved two monophyletic groups, Carthamus s.str. and Carduncellus s.l. Within the latter, some groups could be differentiated, such as the monophyletic genus Phonus and the monospecific Femeniasia. Our multispecies coalescent analyses strongly support a classification based on four genera in the complex Carthamus‐Carduncellus. In contrast, classifications based on only one or two genera lack relevant support. However, the absence of synapomorphic morphological characters that define the Carduncellus lineage (Carduncellus, Phonus, Femeniasia), and the possible hybridization in the ancestral lineages detected by incongruences between plastid and nuclear markers make it difficult to define clear generic boundaries. We propose maintaining the hypothesis of four genera, which was the first classification supported by molecular evidence, pending a broader study (both molecular and morphological) to reach a more definitive delineation and avoid more unfounded generic changes.

Defining rural in Aotearoa New Zealand: a novel geographic classification for health purposes.

Describe the first specifically designed and validated five-level rurality classification for health purposes in New Zealand that is both data-driven and incorporates heuristic understandings of rurality. Our approach involved: (1) defining the purpose and parameters of a proposed five-level Geographic Classification for Health (GCH); (2) developing a quantitative framework; (3) undertaking co-design with the National Rural Health Advisory Group (NRHAG), and extensive consultation with key stakeholders; (4) testing the validity of the five-level GCH and comparing it to previous Statistics New Zealand (Stats NZ) rurality classifications; and (5) describing rural populations and identifying differences in all-cause mortality using the GCH and previous Stats NZ rurality classifications. The GCH is a technically robust and heuristically valid rurality classification for health purposes. It identifies a rural population that is different to the population defined by generic Stats NZ classifications. When applied to New Zealand's Mortality Collection, the GCH estimates a rural mortality rate 21% higher than for residents of urban areas. These rural-urban disparities are masked by the generic Stats NZ classifications. The development of the five-level GCH embraces both the technical and heuristic aspects of rurality. The GCH offers the opportunity to develop a body of New Zealand rural health literature founded on a robust conceptualisation of rurality.

Revealing the Coral Species Diversity in Xiamen Bay: Spatial Distribution of Genus Astrogorgia (Cnidaria, Alcyonacea, Plexauridae) and Newly Recorded Species

Coral reefs provide a habitat for many marine organisms and support the safety, coastal protection, well-being, and food and economic security of hundreds of millions of people. The focus on coral species diversity cannot be overemphasized. One of them, Astrogorgia, contains many marine natural active substances, and has important scientific research value and application prospects. Most of the current research on the active substances of the genus Astrogorgia is based on unidentified species, and in-depth taxonomic studies are urgently needed. A total of 1185 samples were collected from 2014 to 2021 in the waters of Xiamen Bay. Herein, the morphological identification, electronic microscopy, and gene fragment sequencing methods were used for the taxonomic study. There are three species of Astrogorgia identified, including Astrogorgia lafoa, A. arborea, and A. dumbea. Among them, A. lafoa and A. arborea are newly recorded species in the waters of China. A. lafoa is distributed in Qingyu Island, A. arborea is distributed in Wuyu Island, and A. dumbea is widely distributed in Baiha Reef, Qingyu Island, Wuyu Island, and Xiaobai Island. In this paper, the geographical distribution and the habits of 18 species of Astrogorgia are summarized, and the evolution of family and genus classification of Astrogorgia is discussed. The results enrich the geographical distribution information and coral species diversity records of Astrogorgia in China.

Enhanced deep bottleneck transformer model for skin lesion classification

Background and objectives:Skin cancer is the most common cancer worldwide, and therein the malignant melanoma may lead to less than 5-year life expectancy. Via early-stage detection and recognition, even the deadliest melanoma can be cured to greatly increase the patient’s survival rate. Recently dermoscopy imaging is capable of capturing high-resolution magnified images of the infected skin region to automatic lesion classification, and deep learning network has been witnessed great potential of accurately recognizing different types of skin lesions. This study aims to exploit a novel deep model to enhance the skin lesion recognition performance. Methods:In spite of the remarkable progress, the existing deep network based methods naively deploy the proposed network architectures in generic image classification to the skin lesion classification, and there has still large space for performance improvement. This study presents an enhanced deep bottleneck transformer model, which incorporates self-attention to model the global correlation of the extracted features from the conventional deep models, for boosting the skin lesion performance. Specifically, we exploit an enhanced transformer module via incorporating a dual position encoding module to integrate encoded position vector on both key and query vectors for balance learning. By replacing the bottleneck spatial convolutions of the late-stage blocks in the baseline deep networks with the enhanced module, we construct a novel deep skin lesion classification model to lift the skin lesion classification performance. Results:We conduct extensive experiments on two benchmark skin lesion datasets: ISIC2017 and HAM10000 to verify the recognition performance of different deep models. The three quantitative metrics of accuracy, sensitivity and specificity on the ISIC2017 dataset with our method reach to 92.1%, 90.1% and 91.9%, respectively, which manifests very good balance result between the sensitivity and specificity, while the results on the accuracy and precision for the HAM10000 dataset are 95.84% and 96.1%. Conclusions:Results on both datasets have demonstrated that our proposed model can achieve superior performance over the baseline models as well as the state-of-the-art methods. This superior results using the incorporated model of the transformer of convolution module would inspire further research on the wide application of the transformer-based block for the real scenario without large-scale dataset.

Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations

The present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill diverse (semi-) automated indexing/annotation and management necessities. In this context, aggregated radio content is collected, featuring small input datasets, which are utilized for adaptive classification experiments, without searching, at this point, for a generic pattern recognition solution. Hierarchical and hybrid taxonomies are proposed, firstly to discriminate voice data in radio streams and thereafter to detect single speaker voices, and when this is the case, the experiments proceed into a final layer of gender classification. It is worth mentioning that stand-alone and combined supervised and clustering techniques are tested along with multivariate window tuning, towards the extraction of meaningful results based on overall and partial performance rates. Furthermore, the current work via data augmentation mechanisms contributes to the formulation of a dynamic Generic Audio Classification Repository to be subjected, in the future, to adaptive multilabel experimentation with more sophisticated techniques, such as deep architectures.

New infrageneric taxa in Leucospermum (Proteaceae) and Tetragonia L. (Aizoaceae) in southern Africa

New infrageneric taxa are described in Leucospermum R.Br. (Proteaceae) and Tetragonia L. (Aizoaceae) to accommodate species that are currently unplaced in the respective generic classifications. Leucospermum section Hamata J.C.Manning is described to accommodate the relatively recently described L. hamatum Rourke and L. harpagonatum Rouke, and L. section Secundifolia J.C.Manning for L. secundifolium Rourke, which was unplaced following the transfer of the type of L. section Xericola Rourke to the genus Vexatorella Rourke. In Tetragonia L., T.series Echinatae J.C.Manning & Klak is described for three species that were leftunplaced following their exclusion from subgenus Tetragonoides DC.. In addition, the three series T. series Chenopodinae, series Pedunculares and series Macranthae are incorrectly attributed to Fenzl and are shown to be combinations by Adamson of unranked taxa published by Fenzl. In Aizoon L., we replace the illegitimate name, A. procumbens (L.f.) Klak, with the new name A. flavescens Klak.

Leaf Morphometric Studies in Some Ipomoea Species of Convolvulaceae Family

Eight species of Ipomoea (Convolvulaceae) were morphometrically studied upon their leaf characters, with the help of taxonomical analysis to solve the relationship between these species. On the basis of taxonomical component analysis, among the studied species, it has been disclosed that the numerical characters such as leaf length, petiole length, leaf breadth and lamina length are positively correlated to resolved taxonomical relation of different species of the same genus. Contribute important role in bringing together the species within a genus using principal component analysis results of five quantitative characters based on similarity matrix reveals significantly the correlation between leaf length to leaf breadth, leaf base nerve number, and the ratio of leaf lamina length to petiole length significantly separates the species from each other. Morphometric characters provided justification for the existing classification of the Ipomoea genus. It also indicates the component matrix after extraction of the characters that contributed strongly in similarity between the selected Ipomoea species. Three characters which include Leaf length, leaf breadth, and the ratio of leaf length to leaf breadth contributed significantly to the delimitation of the species of Ipomoea studied. Morphometric analysis of eight species of Ipomoea quamoclit L.; Ipomoea batatas (L.) Lam.; Ipomoea cairica (L.) Sweet; Ipomoea hederacea Jacq.; Ipomoea obscura (L.) Ker Gawl.; Ipomoea cordatotriloba Dennst.; Ipomoea lacunosa L. and Ipomoea hederifolia L. Using five different quantitative characters provided justification for the existing classification of the Ipomoea genus. This characters which, contributed significantly to the delimitation of the species of Ipomoea studied. We recommend an application of this method in an elaborate taxonomic study of the genus Ipomoea in the future study.

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance

Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing. Machine learning methods are increasingly applied for automated interpretation of imaging and other clinical results for medical diagnosis. In this study, we proposed a novel CAD detection method based on CMR images by utilizing the feature extraction ability of deep neural networks and combining the features with the aid of a random forest for the very first time. It is necessary to convert image data to numeric features so that they can be used in the nodes of the decision trees. To this end, the predictions of multiple stand-alone convolutional neural networks (CNNs) were considered as input features for the decision trees. The capability of CNNs in representing image data renders our method a generic classification approach applicable to any image dataset. We named our method RF-CNN-F, which stands for Random Forest with CNN Features. We conducted experiments on a large CMR dataset that we have collected and made publicly accessible. Our method achieved excellent accuracy (99.18%) using Adam optimizer compared to a stand-alone CNN trained using fivefold cross validation (93.92%) tested on the same dataset.

Singularities of 3-parameter line congruences in

In this paper, we give the generic classification of the singularities of 3-parameter line congruences in $\mathbb {R}^{4}$. We also classify the generic singularities of normal and Blaschke (affine) normal congruences.

Abstract 5957: Molecular characteristics of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08)

Abstract Microbiome analysis has attracted attention in recent years as a tool to monitor individual health status. In particular, gut microbiota is closely related to autoimmunity, and detailed study of gut microbiota may enable prediction of the health status of individuals.Here, we aimed to conduct a detailed study to identify gastric cancer-specific gut microbiota and compare the data between patients with gastric cancer and healthy individuals. Toward this, we extracted DNA from feces of patients with advanced gastric cancer enrolled in the observational/translational study, the DELIVER trial (JACCRO GC-08: UMIN000030850) which evaluated clinical outcomes of nivolumab and developed host-related biomarkers for nivolumab in 500 patients with advanced gastric cancer, and obtained their sequence data at an average of 5 Gbps using whole-genome shotgun sequencing. We then performed genus classification and functional pathway analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG).We analyzed differences in bacterial composition using the data of 476 Japanese patients with advanced gastric cancer (median age 70y, median BMI 20.0, 71% male, 37.0% of patients with relapse, 55.5% with previous gastrectomy) and public database of 106 healthy cases. Bifidobacterium, Anaerostipes, and Parabacteroides were predominant in the healthy individuals, whereas Streptococcus, Lactobacillus, and Odoribacter were predominant in patients with advanced gastric cancer. Furthermore, the KEGG pathway analysis showed that butanoate and pyruvate metabolism were enriched in the healthy individuals, whereas factors such as ABC transporters and ribosomes were enriched in the patients with advanced gastric cancer. Clustering analysis broadly classified the patients with advanced gastric cancer and healthy individuals into two clusters, and the clustering using pathway data enabled clearer classification of the patients with advanced gastric cancer and healthy individuals than the clustering using flora analysis. Additionally, the healthy individuals had higher bacterial flora diversity compared to the patients with advanced gastric cancer.We thus successfully identified the molecular characteristic of microbial genera and functional pathways in the gut in patients with advanced gastric cancer. We conclude that fecal metagenomic shotgun sequencing analysis might be used to detect gastric cancer. Citation Format: Ryo Matoba, Hiroshi Iijima, Yasuhiro Sakamoto, Ryohei Kawabata, Atsushi Ishiguro, Yusuke Akamaru, Yosuke Kito, Hiroshi Yabusaki, Jin Matsuyama, Masazumi Takahashi, Akitaka Makiyama, Takahisa Suzuki, Masahiro Tsuda, Hisateru Yasui, Jun Hihara, Toru Masuzawa, Hiroyuki Okuda, Junji Kawada, Shuhei Suzuki, Hisato Kawakami, Takako Eguchi Nakajima, Kei Muro, Wataru Ichikawa, Masashi Fujii, Yu Sunakawa. Molecular characteristics of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5957.

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

BackgroundEvery year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance.MethodsA novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification.ResultsThe best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus.ConclusionsTo our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations.Graphical abstract

Ablation Study On Feature Group Importance For Automated Essay Scoring

Grading of written academic essays by humans requires significant effort. It is a time-consuming task and is vulnerable to human biases. Ever since the introduction of modern computing, this has been one of the many automations being explored. Researches in automated essay scoring have been on-going, where the majority of the researches in recent years are based on extracting multiple linguistic features and using them to build a classification model for automated essay scoring. The 3 main types of features used are lexical, grammatical, and semantic. In our work, we conducted an ablation study to discover the engineered features that has the weakest influence. We did this using a generic feature engineering and classification approach that was used by the winners of the Automated Student Assessment Prize (ASAP). This is to mitigate biases that may have addressed specific feature engineering or models. Our results show that a semantic feature called the prompt has been the weakest feature in influencing the models. From further investigations, this was due to it being over-fitted in the classification model.

Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks

Despitethe proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported. As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity.

Genus classification of Triprojectacites Mtchedlishvili, 1961 emend. Stanley 1970

Triprojectacites Mtchedlishvili, 1961 emend. Stanley 1970 is an important pollen group of the Northern Hemisphere palynofloras during the Late Cretaceous. It is featured by three, or occasionally four or five, projections at the equatorial zone of the body with colpate apertures and endexinous thickenings. This pollen group is very useful in the stratigraphic division and correlation for its high diversity, short stratigraphic interval, and rapid evolution. However, the diversity and complex morphology make the pollen difficult to describe and classify, which brought discrepancies in the understanding and application of genus and species. In the present article, we make a re-evaluation of the previously proposed genera by using an improved, clearly defined morphological terminology based on standardised measurements. Finally, eight genera are screened out to set a new classification system from the 39 genera that have been proposed in relation with Triprojectacites. A diagnostic key is compiled for the convenience of differentiating them.