Partial Classification Research Articles

A partial classification of local rings of order p6

A partial classification of local rings of order p6

Abstract 641: VirMAP for cancer: Characterization of the intratumoral virome in virally-associated cancers and a resource for investigators

Abstract Background: A major barrier to understanding the role of viruses in cancer progression has been the lack of sensitivity of current tools to identify and assemble viral genomes in an untargeted way. Here, we utilize our novel VirMAP algorithm to identify eukaryotic and prokaryotic viruses associated with cancers, using RNA sequencing data from The Cancer Genome Atlas (TCGA). Methods: VirMAP was used to analyze TCGA RNA-Seq data derived from primary tumor tissues, with a focus on cancers with known viral associations (cervical, ovarian, head and neck, liver, stomach and esophagus). Previously published survival data were matched with viral signatures for exploratory analysis. Ancom-BC, a differential abundance pipeline, was utilized to screen for viruses which drive outcome. Overall survival (OS) was assessed using the Kaplan-Meier method, and p-values were calculated using log-rank test. Multivariate analysis (MVA) was done using the Cox proportional hazards model to control for age, sex, stage, and histology. Results: Viral sequence recovery (among 723 billion sequencing reads from 2090 patients) was at least equal to previously published algorithms such as Kraken, but with higher taxonomic resolution. Cancers with high viral loads such as cervical cancer demonstrated high recovery rates (0.012% of all sequencing reads mapped to viral taxa, followed by 0.004% for liver, 0.0018% for head and neck squamous cell, 0.0016% for stomach, 0.0009% for esophageal, and 0.0002% for ovarian). There was a wide range of viral signature richness, with cervical cancer showing the highest viral read count predominated by HPV serotypes; in contrast, stomach cancer had lower abundance but contained over 300 unique viral taxa. Exploratory clinical analysis revealed novel correlations between viral signatures and OS. In cervical cancer, presence of HPV45 predicted worse survival (median OS 837 days vs 4086, p=0.0043); MVA demonstrated hazard ratio (HR) of 5.7 [2.6-11.4, p<0.0001]. In stomach cancer, patients often had more than one viral taxon present (mean 3.7, range 0-18); presence of 7 or more unique taxa predicted worse OS (median OS 403 days vs 1153, p=0.0001). MVA showed HR 1.1 for every additional taxon (p=0.0004). Conclusions: VirMAP performs deep characterization of the tumor-associated virome in both high and low abundance settings, with generation of partial genome reconstructions and strain level taxonomic classification. Utilizing publicly available platforms such as TCGA, clinical and correlative published data can be leveraged to better understand the nuanced pleotropic effects of the tumor virome on cancer progression. Citation Format: Yongwoo D. Seo, Neal Bhutiani, Matthew C. Wong, Ashish V. Damania, Golnaz Morad, Matthew Lastrapes, Alexander J. Lazar, Jennifer A. Wargo, Nadim J. Ajami. VirMAP for cancer: Characterization of the intratumoral virome in virally-associated cancers and a resource for investigators [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 641.

Remote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing

Hyperspectral remote sensing is an emerging technique to develop new cost- and time-effective geophysical mapping methods. To overcome challenges introduced by plant cover in geothermal systems globally, we hypothesise that foliage can be used as a proxy to map underlying surface geothermal activity and heat-flux due to their capability on elemental uptake from geothermal fluids and host rock/soil. This study shows for the first time that foliar elemental mapping can be used to image geothermal systems using both high-resolution airborne and satellite hyperspectral images.This study has specifically targeted kanuka shrub (kunzea ericoides var. microflora) as a proxy media to develop air- and spaceborne hyperspectral solutions to monitor inaccessible, biologically and culturally sensitive geothermal areas. Using high resolution airborne AisaFENIX and PRISMA hyperspectral data, foliar element maps for Ag, As, Ba and Sb have been developed using Kernel Partial Least Squares Regression and Random Forest classification models to track their foliar distribution and develop a conceptual model for metal and thermal induced changes in plants. Our study shows evidence that the created foliar element maps are in concordance with independent LiDAR-retrieved canopy structure and height as well as temperature effects of the underlying geothermal field. This study has proven air- and spaceborne hyperspectral sensors can indeed capture critical information within the VNIR and SWIR regions (e.g. ∼452, ∼500, ∼670, ∼820, ∼970, ∼1180, ∼1400 and ∼2000 nm) that can be used to identify metal and thermal-induced spectral changes in plants reliably (overall accuracy of 0.41–0.66) with remotely sensed imagery. Our non-invasive method using hyperspectral remote sensing can complement existing practices for exploration and management of renewable geothermal resources through timely monitoring from air- and spaceborne platforms.

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics.

This study aims to test chemometrically partial least squares-discriminant analysis (PLS-DA) classification models to detect the adulteration of patchouli oil (PO) with gurjun balsam oil (GBO) by utilization of Fourier transform infrared spectroscopy. Unsupervised analysis was tested using the pattern recognition method using the principal component analysis model against the original spectrum at wavenumbers 4000-500 cm-1 and at the fingerprint area (1800-600 cm-1). Model testing was also carried out on the spectrum that had been pre-processed using the standard normal variate, second derivative Savitzky-Golay, and normalization approaches. Variable Y samples used were certified reference material (CRM), PO, GBO, and PO forged with GBO (PGBO) with a counterfeiting ratio of 0.5 (v/v) to 10% (v/v) with an interval of 0.5%. The same treatment was carried out on testing of the PLS-DA model. In pattern recognition tests, the best separation of the original spectrum was obtained at wavenumbers 1800-600 cm-1. The model was further tested on PLS-DA by making assumptions or codes for CRM, PO, GBO, and PGBO as +2, +1, 0, and -1, respectively. The results of the model analysis showed that even at the lowest counterfeiting ratio (0.5%), the presence of counterfeiting material was detected by the PLS-DA model. The RMSEC value is close to zero with a value of 0.22, and the R square is close to 1, which is 0.954. This very significant separation is clearly illustrated in the loading plot and bi-plot due to the contribution of chemical compounds in the GBO that undergo vibration at wavenumbers 603, 786, and 1386 cm-1. Validation of the PLS-DA model was carried out strongly using the PLS model, and it showed that the difference between the calibration concentration and the prediction was very low (average 0.45) with an accuracy percent above 99%. The efficacy of the model is further substantiated by the consistent and precise values of sensitivity and selectivity, obtained from both the training set and test set.

Nilpotent Lie algebras of breadth type (0, 3)

For a natural number m, a Lie algebra L over a field k is said to be of breadth type if the co-dimension of the centralizer of every non-central element is m. In this article, we classify finite dimensional nilpotent Lie algebras of breadth type over of odd characteristic up to isomorphism. We also give a partial classification of the same over finite fields of even characteristic, and . We also discuss 2-step nilpotent Camina Lie algebras.

Time-frequency Fusion Method via Convolutional Neural Network for Partial Discharge Classification

To improve the accuracy of partial discharge (PD) pattern recognition by jointing time-domain (TD) and frequency-domain (FD) information, a time-frequency (TF) fusion method via convolution neural network (CNN) is proposed in this paper. Firstly, PD signals are represented by PD waveform images and transformed into the envelope of variational mode decomposition-based Hilbert marginal spectrum (VHMS). Secondly, a fusion network, FuNet involving a 2-dimensional CNN (2D-CNN), a 1D-CNN, and a multilayer perceptron (MLP), is established to join TF information. In FuNet, the 2D-CNN inputted by PD waveform images and 1D-CNN adopted the envelope of VHMS of PD signal as its input are all improved by drawing on the complementary strengths of different convolution layers’ features. Then the MLP will fuse the extracted TD and FD features and classify the PD defects.

Taxonomy of Non-sporulating Bacteria Tested in Biological Control Against Spodoptera frugiperda (J.E. Smith, 1797) (Lepidoptera: Noctuidae)

The complete analysis of the 16S rRNA gene of some non-sporulating bacterial isolates from the soil led us to a reliable taxonomic classification, allowing us to explore its entomopathogenic potential against insect-pest Spodoptera frugiperda. Many studies have shown the rapid development of resistance of S. frugiperda in relation to the methods that have been used in recent decades. Therefore, new research exploring the potential of new soil isolates is important. Thus, a comparative study between the complete and partial taxonomic classification based on 16S rRNA gene was realized as the basis for biological studies. Non-sporulating bacterial isolates were used in lethality bioassays against S. frugiperda larvae to compare bioassays efficacy using Bacillus thuringiensis (Berliner) (Bt) strains and the commercial product Dipel®. The results confirmed the strong resistance of this pest-larvae once it developed until the adulthood phase in all bioassays, applying sporulating or non-sporulating bacteria. Furthermore, an accurate phylogenetic position of the non-sporulating bacterial isolates become possible showing basically three species: Brevibacillus nitrificans, Curtobacterium sp. and Arthrobacter echigonensis. Besides the new biotechnological options for those bacterial isolates, according to the results further research should be done with new bacterial isolates in order to discover its potential to control S. frugiperda, thus assisting Bt in pest control.

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.

A bivariate distribution with convolution of binomials as marginals

The convolution of two binomial random variables has appeared in various settings such as a model in machine maintenance and as a Markov chain model. This article presents a bivariate distribution with this convolution of binomials as marginal distributions. This bivariate distribution arises from sampling with partial classification, and this has potentially useful applications in diverse areas. The distribution generalizes the bivariate binomial distribution and its variants. Some properties of the bivariate distribution have been obtained. A mixture formulation of the distribution has been derived which facilitates computation of probabilities and computer sampling. An application to a real data set is presented to illustrate its usefulness in empirical modeling.

GL-FusionNet: Fusing global and local features to classify deep and superficial partial thickness burn.

Burns constitute one of the most common injuries in the world, and they can be very painful for the patient. Especially in the judgment of superficial partial thickness burns and deep partial thickness burns, many inexperienced clinicians are easily confused. Therefore, in order to make burn depth classification automated as well as accurate, we have introduced the deep learning method. This methodology uses a U-Net to segment burn wounds. On this basis, a new thickness burn classification model that fuses global and local features (GL-FusionNet) is proposed. For the thickness burn classification model, we use a ResNet50 to extract local features, use a ResNet101 to extract global features, and finally implement the add method to perform feature fusion and obtain the deep partial or superficial partial thickness burn classification results. Burns images are collected clinically, and they are segmented and labeled by professional physicians. Among the segmentation methods, the U-Net used achieved a Dice score of 85.352 and IoU score of 83.916, which are the best results among all of the comparative experiments. In the classification model, different existing classification networks are mainly used, as well as a fusion strategy and feature extraction method that are adjusted to conduct experiments; the proposed fusion network model also achieved the best results. Our method yielded the following: accuracy of 93.523, recall of 93.67, precision of 93.51, and F1-score of 93.513. In addition, the proposed method can quickly complete the auxiliary diagnosis of the wound in the clinic, which can greatly improve the efficiency of the initial diagnosis of burns and the nursing care of clinical medical staff.

Deformable Protein Shape Classification Based on Deep Learning, and the Fractional Fokker-Planck and Kähler-Dirac Equations.

The classification of deformable protein shapes, based solely on their macromolecular surfaces, is a challenging problem in protein-protein interaction prediction and protein design. Shape classification is made difficult by the fact that proteins are dynamic, flexible entities with high geometrical complexity. In this paper, we introduce a novel description for such deformable shapes. This description is based on the bifractional Fokker-Planck and Dirac-Kähler equations. These equations analyse and probe protein shapes in terms of a scalar, vectorial and non-commuting quaternionic field, allowing for a more comprehensive description of the protein shapes. An underlying non-Markovian Lévy random walk establishes geometrical relationships between distant regions while recalling previous analyses. Classification is performed with a multiobjective deep hierarchical pyramidal neural network, thus performing a multilevel analysis of the description. Our approach is applied to the SHREC'19 dataset for deformable protein shapes classification and to the SHREC'16 dataset for deformable partial shapes classification, demonstrating the effectiveness and generality of our approach.

Coverings with congruent and non-congruent hyperballs generated by doubly truncated Coxeter orthoschemes

After the investigation of the congruent and non-congruent hyperball packings related to doubly truncated Coxeter orthoscheme tilings [Acta Univ. Sapientiae, Mathematica, 11, 2 (2019), 437–459], we consider the corresponding covering problems. In Non-fundamental trunc-simplex tilings and their optimal hyperball packings and coverings in hyperbolic space the authors gave a partial classification of supergroups of some hyperbolic space groups whose fundamental domains will be integer parts of truncated tetrahedra, and determined the optimal congruent hyperball packing and covering configurations belonging to some of these classes. In this paper, we complement these results with the investigation of the non-congruent covering cases and the remaining congruent cases. We prove, that between congruent and non-congruent hyperball coverings the thinnest belongs to the $\{7,3,7\}$ Coxeter tiling with density $\approx 1.26829$. This covering density is smaller than the conjectured lower bound density of L. Fejes Tóth for coverings with balls and horoballs. We also study the local packing arrangements related to $\{u,3,7\}$ $(6< u < 7, ~ u\in \mathbb{R})$ doubly truncated orthoschemes and the corresponding hyperball coverings. We prove, that these coverings are achieved their minimum density at parameter $u\approx 6.45953$ with covering density $\approx 1.26454$ which is smaller than the above record-small density, but this hyperball arrangement related to this locally optimal covering can not be extended to the entire $\mathbb{H}^3$. Moreover, we see that in the hyperbolic plane $\mathbb{H}^2$ the universal lower bound of the congruent circle, horocycle, hypercycle covering density $\sqrt{12}/\pi$ can be approximated arbitrarily well also with non-congruent hypercycle coverings generated by doubly truncated Coxeter orthoschemes.

Distance-regular Cayley graphs over dicyclic groups

The characterization of distance-regular Cayley graphs originates from the problem of identifying strongly regular Cayley graphs, or equivalently, regular partial difference sets. In this paper, a partial classification of distance-regular Cayley graphs on dicyclic groups is obtained. More specifically, it is shown that every distance-regular Cayley graph on a dicyclic group is a complete graph, a complete multipartite graph, or a non-antipodal bipartite distance-regular graph with diameter 3 satisfying some additional conditions.

The completion of the classification of the finite dense near 2d-gons, d ∈ {3,4}, with four points on each line

We complete the classification of all finite dense near hexagons and octagons with four points on each line by filling the gaps that exist in the current (partial) classification results. The filling of these gaps will be possible after a prior investigation in which we obtain several classification and nonexistence results regarding finite near hexagons that satisfy the following three properties for certain s,t2∈N∖{0,1}: every line contains precisely s+1 points; every two points at distance 2 have either 2 or t2+1 common neighbours; if Q is a quad of order (s,t2), then Γ2(Q) does not contain lines.

Learning from undercoded clinical records for automated International Classification of Diseases (ICD) coding.

To develop an unbiased objective for learning automatic coding algorithms from clinical records annotated with only partial relevant International Classification of Diseases codes, as annotation noise in undercoded clinical records used as training data can mislead the learning process of deep neural networks. We use Medical Information Mart for Intensive Care III as our dataset. We employ positive-unlabeled learning to achieve unbiased loss estimation, which is free of misleading training signal. We then utilize reweighting mechanism to compensate for the imbalance between positive and negative samples. To further close the performance gap caused by poor quality annotation, we integrate the supervision provided by the automatic annotation tool Medical Concept Annotation Toolkit which can ease the heavy burden of manual validation. Our benchmarking results show that positive-unlabeled learning with reweighting outperforms competitive baseline methods over a range of missing label ratios. Integrating supervision provided by annotation tool further boosted the performance. Considering the annotation noise and severe imbalance, unbiased loss estimation and reweighting mechanism are both important for learning from undercoded clinical records. Unbiased loss requires the estimation of false negative ratios and estimation through trained models is practical and competitive. The combination of positive-unlabeled learning with reweighting and supervision provided by the annotation tool is a promising solution to learn from undercoded clinical records.

On Some Families of Codes Related to the Even Linear Codes Meeting the Grey–Rankin Bound

Bounds for the parameters of codes are very important in coding theory. The Grey–Rankin bound refers to the cardinality of a self-complementary binary code. Codes meeting this bound are associated with families of two-weight codes and other combinatorial structures. We study the relations among six infinite families of binary linear codes with two and three nonzero weights that are closely connected to the self-complementary linear codes meeting the Grey–Rankin bound. We give a construction method and partial classification results for such codes. The properties of the codes in the studied families and their relations help us in constructing codes of a higher dimension from codes with a given dimension.

On a quantum martingale convergence theorem

It is well known in quantum information theory that a positive operator-valued measure (POVM) is the most general kind of quantum measurement. Mathematically, a quantum probability is a normalized POVM, namely, a function on certain subsets of a (locally compact and Hausdorff) sample space that satisfies the formal requirements for a probability measure and whose values are positive operators acting on a complex Hilbert space. A quantum random variable is an operator-valued function which is measurable with respect to a quantum probability. In this work, we study quantum random variables and generalize several classical limit results to the quantum setting. We prove a quantum analogue of the Lebesgue-dominated convergence theorem and use it to prove a quantum martingale convergence theorem. This quantum martingale convergence theorem is of particular interest since it exhibits nonclassical behavior; even though the limit of the martingale exists and is unique, it is not explicitly identifiable. However, we provide a partial classification of the limit through a study of the space of all quantum random variables having quantum expectation zero.

On the Girth of Three-Dimensional Algebraically Defined Graphs with Multiplicatively Separable Functions

For a field $\mathbb{F}$ and functions $f,g,h,j\colon\mathbb{F}\to \mathbb{F}$, we define $\Gamma_\mathbb{F}(f(X)h(Y),g(X) j(Y))$ to be a bipartite graph where each partite set is a copy of $\mathbb{F}^3$, and a vertex $(a,a_2,a_3)$ in the first partite set is adjacent to a vertex $[x,x_2,x_3]$ in the second partite set if and only if \[a_2+x_2=f(a)h(x) \quad \text{and} \quad a_3+x_3=g(a)j(x).\] In this paper, we completely classify all such graphs by girth in the case $h=j$ (subject to some mild restrictions on $h$). We also present a partial classification when $h\neq j$ and provide some applications.

Top-k Partial Label Machine.

To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement disambiguations, by either identifying the ground-truth label or averaging the candidate labels. However, these methods can be easily misled by the false-positive labels in the candidate label set. We find that these ambiguities often originate from the noise caused by highly correlated or overlapping candidate labels, which leads to the difficulty in identifying the ground-truth label on the first attempt. To give the trained models more tolerance, we first propose the top-k partial loss and convex top-k partial hinge loss. Based on the losses, we present a novel top-k partial label machine (TPLM) for partial label classification. An efficient optimization algorithm is proposed based on accelerated proximal stochastic dual coordinate ascent (Prox-SDCA) and linear programming (LP). Moreover, we present a theoretical analysis of the generalization error for TPLM. Comprehensive experiments on both controlled UCI datasets and real-world partial label datasets demonstrate that the proposed method is superior to the state-of-the-art approaches.

Sixteen-dimensional compact translation planes with automorphism groups of dimension at least 35

Abstract The present paper investigates 16-dimensional compact translation planes with automorphism groups of dimension d between 35 and 37; planes with groups of higher dimensions have been classified by Hähl. We obtain a complete classification for d = 37 (up to isomorphisms). It turns out that these planes have Lenz type V and are already described in a recent paper of Hähl and Meyer [10]. Moreover, we give a partial classification for d = 35 and d = 36. The latter case will be completely finished in a forthcoming paper [16] of the author, while the case where d = 35 is completed except for groups whose maximal compact subgroups are 9-dimensional.