Correct Assignment Research Articles

End-to-end programmable computing systems

Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision.

Rail transit express and local train schedule optimization considering hybrid rolling stock composition

ABSTRACT This study adopts a hybrid rolling stock composition strategy and proposes a mixed-integer linear programming (MILP) model to optimize the rolling stock compositions together with the train timetable according to real-life passenger demand. The optimization objectives are to minimize the number of stranded passengers and rolling stocks. Two types of constraints are included: constraints related to timetabling and constraints related to passenger assignment. In addition, timetable optimization is used for mixed traffic, including local and express train services. Express trains overtake local trains at specific stations; therefore mapping constraints are built to match each train service to the corresponding train order at different stations to ensure a correct passenger assignment. Numerical examples based on the Shanghai Metro Line 16 are implemented to demonstrate the efficiency of the proposed model. The optimization results show that the proposed method can efficiently reduce the number of stranded passengers and the number of rolling stocks required.

Bone Marrow Microenvironment and Hematopoietic Cell Types Are Differentially Affected By Transcriptomic Quality Control Filtering Metrics in Single Cell Data

A crucial step prior to analysis of single cell transcriptomic data includes filtering out low-quality (e.g., dying) cells and artifacts (e.g., empty droplets). Choosing appropriate quality control parameters leads to correct cell assignment, so that the data for downstream analysis is high-quality and is representative of all processed cell types. These metrics include high and low cut-offs for mitochondrial content, total number of genes, number of different genes, and ribosomal content per cell. They have been found to vary based on cell and tissue type, as well biological variables such as age and disease, and technical variables such as sequencing technology. The aim of this study was to investigate the variation of these quality control metrics in a data set that included stem, progenitor, and differentiated cell types of the hematopoietic, mesenchymal, and endothelial lineages. Furthermore, we set out to determine whether applying standard cut-offs may differentially affect different cell types. We calculated individual filtering parameters for each cell type using the R package ddqc (Subramanian et al. Genome Biol. 2022). Thus, any biological variability based on mitochondrial, ribosomal, and gene counts was accounted for. Notably, distribution of measurements for each of the above quality control metrics varied by cluster, and certain cell-type means were adjacent to commonly used cut-off values. For instance, we observed higher content of mitochondrial genes in osteoblasts, sinusoidal endothelial cells, a subset of adipocyte-primed mesenchymal cells (MSPC), as well as hematopoietic progenitor cells, pre-B, and B cells. On the other hand, subsets of erythroblasts, neutrophils, as well as arteriolar endothelial cells had the lowest mitochondrial content. In addition, lower total gene counts and lower gene complexity were found in adipocyte-primed MSPCs, neutrophils, and megakaryocyte-erythrocyte progenitors, while higher counts were found in granulocyte-monocyte progenitor cells, megakaryocyte/erythroid-primed multipotent progenitor cells and subsets of erythroblasts. Applying universal cutoff values to this data set would greatly reduce, and some cases, eliminate cell types that have high mitochondrial content or lower gene counts and complexity, while leaving those cell types on the opposite spectrum unaffected. This would also skew proportions of cells in downstream analyses, thus affecting biological interpretation of the results. Overall, our results demonstrate biological variability of the quality control metric distributions in a single cell transcriptomic data set that includes diverse bone marrow niche and hematopoietic cell types. This reveals novel biological information regarding distinct cell types as well as cautions that applying standard filtering paraments on such a data set would eliminate some cell types and bias downstream analysis for others. Optimization of filtering criteria will be important when broadening single cell analysis to multiplexed approaches incorporating transcriptome, genome, epigenome, and proteome-level data.

Tutton K2Zn(SO4)2(H2O)6 salt: Structural-vibrational properties as a function of temperature and ab initio calculations

In this work, a thorough study of Tutton K2Zn(SO4)2(H2O)6 crystal was performed. Structural, electronic, vibrational, and thermal properties were analyzed and discussed. Calculations based on the density functional theory (DFT) were performed to provide a correct assignment of vibration modes (90 active Raman and 93 active IR), and analyses of band structure and density of states. Powder X-ray diffraction (PXRD) at 300 K showed that the Tutton crystallizes in monoclinic symmetry with space group P21/a. An electronic bandgap of 4.66 eV, typical of insulating material, was estimated by band structure calculation. The thermal analysis in the 300–700 K range by coupled TG-DTA thermogram revealed two endothermal events and one exothermal. Such events were investigated by PXRD and Raman spectroscopy as a function of temperature, where three phase changes associated with dehydration and crystallization were observed. The new phase structures were determined by the Le Bail and Rietveld methods. The thermal-structural findings suggest that the K2Zn(SO4)2(H2O)6 Tutton can be considered a promising thermochemical compound for residential heat storage devices due to a low onset for the dehydration temperature (≈327 K) and a high enthalpy of dehydration (77.4 kJ/ mol H2O).

Highly oxidized flavones in Artemisia species – structure revisions and improved UHPLC-MSn analysis

In course of our studies of the aerial parts of Artemisia abrotanum the major methoxyflavonol could be isolated. However, by NMR structural analysis it became obvious that the substitution pattern in ring B differs from reports for casticin (2). The position of methoxyl and hydroxyl groups are interchanged, i.e., the major flavone is actually chrysosplenetin (1). Three structures in A. abrotanum and A. frigida had to be revised. Use of pyridine-d5 instead of DMSO‑d6 made the resolution of the B-ring 1H and 13C NMR signals possible and enabled correct structural assignment by 2D NMR experiments.Results from NMR structure elucidation for A. abrotanum were confirmed by LC-PDA-ESI-MSn analysis when a PFP (pentafluorophenyl) stationary phase with an optimized gradient elution was applied for separation of 1 and 2 instead of a corresponding C-18 phase. Electrospray mass spectrometry (positive and negative mode) with subsequent fragmentation (ESI-MSn) revealed distinctive mass spectral features of both compounds, especially at MS4 level. Several Artemisia extracts including A. annua were analysed on the PFP phase for the presence of 1 and 2.

Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition

Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem. To model such problem, we propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM), in which Graph Matching mechanism is introduced owing to its good performance of excavating the instance and label relationship. The framework explicitly establishes category correlations and instance-label correspondences by modeling the relation among content-aware (instance) and semantic-aware (label) category representations, to facilitate multi-label image understanding and reduce the dependency of large amounts of training samples for each category. Specifically, we first construct an instance spatial graph and a label semantic graph respectively and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Empirical results conducted on generic multi-label image recognition demonstrate the superiority of our proposed method. Moreover, the proposed method also shows advantages in multi-label recognition with partial labels and multi-label few-shot learning, as well as outperforms current state-of-the-art methods with a clear margin. Our code is available at https://github.com/yananwu0510/ML-SGM.

Synthesis of Neocaesalpin A, AA, and Nominal Neocaesalpin K.

The first total synthesis of heavily oxidized cassane-type diterpenoid neocaesalpin A (1) is disclosed. At the heart of the synthesis lies an intermolecular Diels-Alder reaction that rapidly assembles the target framework from commercial materials. A carefully orchestrated sequence of oxidations secured the desired oxygenation pattern. Late-stage release of the characteristic butenolide occurred through a novel mercury(II)-mediated furan oxidation. Successful extension of the route allowed preparation of neocaesalpin AA (2) as well as nominal neocaesalpin K (3) and suggested structural revision of neocaesalpin K, leading to the hypothesis that the two are likely the same natural product with correct assignment as 2.

Synthesis of Neocaesalpin A, AA, and Nominal Neocaesalpin K**

AbstractThe first total synthesis of heavily oxidized cassane‐type diterpenoid neocaesalpin A (1) is disclosed. At the heart of the synthesis lies an intermolecular Diels–Alder reaction that rapidly assembles the target framework from commercial materials. A carefully orchestrated sequence of oxidations secured the desired oxygenation pattern. Late‐stage release of the characteristic butenolide occurred through a novel mercury(II)‐mediated furan oxidation. Successful extension of the route allowed preparation of neocaesalpin AA (2) as well as nominal neocaesalpin K (3) and suggested structural revision of neocaesalpin K, leading to the hypothesis that the two are likely the same natural product with correct assignment as 2.

P022 Sleep Biomarker Phenotyping of Neurodegenerative Disorders Using Artificial Intelligence – A Pilot Study

Abstract Introduction In this pilot study, we explored sleep biomarker risk probabilities for different neurodegenerative disorder (NDD) phenotypes across a spectrum of NDD patients, compared with controls. Methods We analyzed a cohort of patients with different NDD phenotypes who underwent in-home recordings with Sleep Profiler, including Lewy body disease (LBD=20), Alzheimer’s disease dementia (AD=29), and isolated REM sleep behavior disorder (iRBD=19). Controls with an MMSE>28 (CG=61) and patients with Parkinson disease (PD=16) and mild-cognitive-impairment (MCI=41) also participated. We developed a machine-learning classifier that assigned NDD probabilities for LBD, AD, iRBD, and CG. The input variables included: time-REM, non-REM hypertonia, autonomic-activation index, spindle-duration, atypical-N3, time-supine, sleep-efficiency, relative-theta, and theta/alpha. Probabilities >50% were assigned “likely”, and for CG>=50% and a NDD group probability 20-50%, the assignment was normal “plus”. Probability assignments were then made for the NDD and CG groups, then further applied to the PD and MCI patient groups. Results The CG group participants were assigned Normal-Likely=74%, Normal+AD=11%, Normal+iRBD=5%, iRBD-Likely=5%, and AD-Likely=5%. LBD patient distributions were LBD-Likely=70%, iRBD-Likely=5%, AD-Likely=5%, Normal+LBD=5%, Normal+iRBD=5%, Normal+AD=5%, and Normal-Likely=5%. AD group distributions were AD-Likely=71%, LBD-Likely=4%, Normal+AD=14%, Normal+iRBD=4%, Normal-Likely=7%. iRBD patients were characterized with iRBD-Likely=37%, LBD-Likely=5%, AD-Likely=5%, Normal+iRBD=27%, Normal-Likely=26%. PD patients were assigned iRBD-Likely=29%, LBD-likely=14%, AD-Likely=14%, Normal+iRBD=21%, Normal+AD=7%, Normal-Likely=14%. MCI distributions were AD-Likely=45%, LBD-Likely=8%, iRBD-Likely=5%, Normal+AD=21%, Normal-Likely=13%. Conclusions For LBD, AD and CG groups, correct risk assignments were >70% while gross misclassifications were <10%. Classification patterns for PD, MCI and iRBD were disbursed in a manner consistent with the range of severities expected in each group.

Prioritizing forest patches to enhance habitat restoration and connectivity for the endangered saproxylic beetle Rosalia alpina (Coleoptera, Cerambycidae): a modelling approach.

The cerambycid beetle Rosalia alpinais associated with temperate, broadleaved (mainly beech) forests containing dead or decaying wood. This species is protected under the Habitats Directive of the European Union. Given its narrow ecological niche and limited dispersal abilities, habitat fragmentation is a conservation concern for populations of R. alpina. In order to maximise the effectiveness of habitat restoration, a scientifically sound procedure for patch selection is needed. In Gipuzkoa (N Spain), we used Light Detection and Ranging (LiDAR) images to search for 20x20-m cells matching the parameterisation of a predictive and local habitat model for R. alpinaat the tree level. The cells selected under quantitative criteria were clustered to identify potential habitat patches. Conefor Sensinode software was used to estimate the importance of each of those patches in terms of the connectivity of the population, based on dispersal distances of R. alpinaand the probabilities of dispersing events among patches. We identified 380 potential habitat patches, mostly in the south-eastern sector of the study area, which were classified into “core areas” and “connecting areas”. The performance of the model was tested in the field (61% of correct assignations), although the actual occurrence of R. alpinawithin the habitat patches should be assessed in the future. This model represents a step forward in guiding the cost-effective implementation of conservation activities, through a strict preservation of the current core habitat patches and an increase in the size of connecting patches.Therefore, we show that connectivity models combining remote sensing data and local habitat selection can be an aid in conservation planning and restoration actions, probably outperforming less efficient strategies, such as random or expert selection.

MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

BackgroundIdentification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data of isolates to approach this problem. However, making AMR assessments from more easily available metagenomic sequencing data remains a big challenge.ResultsWe present the Metagenomic Sequencing to Antimicrobial Resistance (MGS2AMR) pipeline, which detects antibiotic resistance genes (ARG) and their possible organism of origin within a sequenced metagenomics sample. This in silico method allows for the evaluation of bacterial AMR directly from clinical specimens, such as stool samples. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): the GFA Linear Optimal Path through seed segments (GLOPS) algorithm and the Adapted Dijkstra Algorithm for GFA (ADAG). These novel algorithms improve the sensitivity of ARG detection and aid in species annotation. Tests based on 1200 microbiome samples show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can further be used in many downstream applications, such as evaluating AMR to specific antibiotics in samples from emerging intestinal infections. We demonstrate that the MGS2AMR-derived data is as informative for the entailing prediction models as the whole-genome sequencing (WGS) data. The performance of these models is on par with our previously published method (WGS2AMR), which is based on the sequencing data of bacterial isolates.ConclusionsMGS2AMR can provide researchers with valuable insights into the AMR content of microbiome environments and may potentially improve patient care by providing faster quantification of resistance against specific antibiotics, thereby reducing the use of broad-spectrum antibiotics. The presented pipeline also has potential applications in other metagenome analyses focused on the defined sets of genes.6MP9Kjm5NAthB1ioNbPxBHVideo

Synthesis, Computational, Antimicrobial, Antioxidant, and ADME Study of 2-(3,4-Dimethoxyphenyl)-4H-Chromen-4-One

The DMSO/I2 mediated cyclization reaction of (E)-3-(3,4-dimethoxyphenyl)-1-(2-hydroxyphenyl)prop-2-en-1-one afforded the 2-(3,4-dimethoxyphenyl)-4H-chromen-4-one (DMPC). DMPC was characterized by FT-IR, 1H NMR, 13C NMR and HRMS techniques. DMPC was screened for their in vitro antibacterial activity against four bacterial strains namely E. coli, B. subtilis, S. aureus, and Streptococcus spp. and antifungal activity against R. oryzae, P. chrysogenum, A. niger, and C. albicans fungal strains. The synthesized compounds exhibited significant antibacterial and antifungal properties. DMPC possesed potent antibacterial activity against Streptococcus spp. while the powerful antifungal action was found against P. chrysogenum. Besides % free radical scavenging activity was accessed using DPPH and OH assays. DMPC was found to be more active DPPH radical scavenger than OH radical. Theoretical investigation of DMPC was carried out by density functional theory (DFT) method at the B3LYP/6-311++G(d,p) level of theory. Computational investigation of molecular structure, MESP, and HOMO–LUMO was performed to study DMPC. The calculated energy gap of FMOs was found to be 4.25 eV for DMPC. In the MESP plot, the most electronegative potential region found around the oxygen atom. The theoretical and experimental NMR was correlated and correct NMR assignments have been made. Based on the physicochemical features, lipophilicity, water solubility, pharmacokinetic characteristics, and drug similarity matching investigations, the DMPC was revealed to have favorable biological qualities.

Strategies of socio-economic development of regions: indicators of innovative development

The article highlights the issue of the correct and legitimate assignment of indicators of innovative development in the strategies of socio-economic development of the subjects of the Russian Federation. For the purposes of the analysis, the sections devoted to the innovative development of 84 existing regional strategies are studied. It is noted that the quantitative and qualitative composition of indicators for assessing the state and development of innovative processes currently used in regional strategies cannot be called complete, rational, meeting the requirements of legislation on strategic planning and methodological recommendations of the relevant department. On the one hand, the average number of profile indicators for all strategies is 3 units, which is not enough to fully assess the state of the innovation process within the framework of strategic planning. On the other hand, the total number of indicators in all strategies exceeds 39 units, which complicates the comparative analysis of strategies, federal methodological recommendations are not always taken into account. Proposals have been developed for the process of developing regional strategies to assign indicators of regional development, bringing their structure closer to the composition of indicators used to assess innovative development in the documents of strategic planning at the federal level — the Strategy of Innovative Development of the Russian Federation and the Concept of Technological Development of the Russian Federation. It is established that the directions of innovative development provided for by these documents correspond to the theoretical scheme of the main stages of the innovation process. In this regard, the establishment of a set of indicators, based on the structure adopted in these documents, will, on the one hand, make the assessment of regional innovative development more systematic, on the other — to a greater extent link regional strategic planning documents with documents of the federal level. It is also recognized that it is necessary to improve the procedure for monitoring and controlling the development and implementation of regional strategies with a mandatory element in the form of checking the compliance of indicators adopted in the strategy with Federal State Statistics Service data.

Sensing in the swarm: Spectro-temporal variation may facilitate self-recognition of echoes for bats flying in dense groups

Most biosonar models predict that bats rely on the correct assignment of an echo from a broadcast signal to successfully perceive their environment, which should be difficult for bats in dense groups. Brazilian free-tailed bats (Tadarida brasiliensis) form some of the largest aggregations on the planet and have flexible characteristics of their echolocation signals. We hypothesize they use subtle variations in spectro-temporal characteristics to facilitate echolocation in dense groups. To record from inside the swarm, we used both a stationary microphone that opportunistically captured the passing bat swarm, and a custom video and acoustic recorder carried by a trained hawk that flew through the swarm. We computed spectrograms from the acoustic recordings and extracted “time-frequency ridges.” These ridges were fit to low-order polynomials to generate model frequency modulation functions, which in turn uniquely determine continuous time-series representations of modeled emission waveforms. Standard signal detection methods (cross correlation and background normalization) enabled quantitative estimation of detection performance, including rejection of interfering emissions and echoes. Our results demonstrate that subtle but specific variation in spectro-temporal shape can constitute the basis of call differentiation, which may be an adaptive strategy used to reject acoustic signals from conspecifics when echolocating in dense swarms.

A molecular networking-assisted automatic database screening strategy for comprehensive annotation of small molecules in complex matrices

Liquid chromatography-tandem with high-resolution mass spectrometry (LCHRMS) has proven challenging for annotating multiple small molecules within complex matrices due to the complexities of chemical structure and raw LCHRMS data, as well as limitations in previous literatures and reference spectra related to those molecules. In this study, we developed a molecular networking assisted automatic database screening (MN/auto-DBS) strategy to examine the combined effect of MS1 exact mass screening and MS2 similarity analysis. We compiled all previously reported compounds from the relevant literatures. With the development of a Python software, the in-house database (DB) was created by automatically calculating the m/z and data from experimental MS1 hits were rapid screened with DB. We then performed a feature-based molecular network analysis on the auto-MS2 data for supplementary identification of unreported compounds, including clustered FBMN and annotated GNPS compounds. Finally, the results from both strategies were merged and manually curated for correct structural assignment. To demonstrate the applicability of MN/auto-DBS, we selected the Huangqi-Danshen herb pair (HD), commonly used in prescriptions or patent medicines to treat diabetic nephropathy and cerebrovascular disease. A total of 223 compounds were annotated, including 65 molecules not previously reported in HD, such as aromatic polyketides, coumarins, and diarylheptanoids. Using MN/auto-DBS, we can profile and mine a wide range of complex matrices for potentially new compounds.

A peptide nucleic acid probe-based multiplex qPCR assay for rapid and accurate detection and quantification of fish-pathogenic Edwardsiella species

Edwardsiellosis causes severe economic losses in the aquaculture industry worldwide. Accurate identification of the fish pathogenic Edwardsiella species is crucial for preventing disease spread and providing effective treatment strategies in the aquaculture industry. These species have high phenotypic and genetic similarities (> 99% in 16S rRNA sequences), which makes their differentiation challenging. In this study, we developed a multiplex real-time PCR diagnostic assay based on the single-copy DNA gyrase subunit B (gyrB) gene using a peptide nucleic acid (PNA) probe to discriminate among four fish pathogenic Edwardsiella species, E. tarda, E. piscicida, E. anguillarum, and E. ictaluri. Through phylogenetic analysis based on gyrB sequences (1462 bp), 57 Korean Edwardsiella isolates were affiliated to E. piscicida, E. anguillarum, and E. tarda, accounting for host and geographical origin specificity. The assay targeted a conserved region in gyrB, and designed four probes, each capable of distinguishing among the four Edwardsiella species, even in cases of 1–3 nucleotide mismatch, based on differences in melting temperature. The assay exhibited high specificity and sensitivity, with reaction efficiency in the range 85–100% and a reliable quantifiable limit of 102 copies per reaction for all fluorescence signals. In addition, it could detect and quantify the four species, even in mixed infections, allowing accurate identification within 2 h in a single reaction. This PNA probe-based multiplex qPCR assay could be a practical and reliable diagnostic tool in the field and provide correct taxonomic assignment for further research on the widened host range and niche, pathogenicity, and susceptibility of each Edwardsiella species, to help establish appropriate treatment and vaccine strategies.

Regulation of tolerances of associated parts of mechanisms of livestock farms during repair

The paper considers an algorithm for optimizing the efficient distribution of resources that determine the cost of assembly work, subject to restrictions that ensure high quality assembly of livestock machines and equipment. To determine the required tolerance values for parts, one can use the known methods of parametric optimization. Therefore, the application to the solution of the problem of the method of Lagrangian multipliers as a result of the calculations obtained the optimal tolerances of the details of the angle. Upon completion of the calculations, the optimal assembly parameters of the node for various values are obtained; the result is presented in graphical form. There are various methods for calculating component tolerances of mating parts. Methods based on the statistical evaluation of tolerances and finding their root-mean-square deviations make it possible to reduce the degree of risk when expanding the tolerance limits for mating parts and reduce assembly losses. The correct assignment of tolerances allows one to achieve significant savings in material resources and reduce production costs. The most effective solutions may be those obtained by implementing optimization procedures that ensure the minimization of the quality criterion under the conditions of restrictions determined by the components of the assembly assembly process.

Semi‐supervised classification to determine the provenance of archaeological obsidian samples

AbstractIn archaeometry, the correct assignment of archaeological artefacts with their original geological sources continues to be an indispensable tool for determining exploitation patterns, trade routes, tool production and the movement of goods in ancient times. Statistically, Mahalanobis distance has been an integral part of the artefact provenance methodology. However, this method presents disadvantages when some of the theoretical assumptions are not fulfilled. In this way and with a focus on the veracity of the correct assignment of archaeological samples to their geological sources, we present a technique that employs a semi‐supervised classification based on finite mixture models. This procedure uses labelled and unlabelled data to assign samples to groups with the advantage that it does not depend on the sample size of the groups as required by Mahalanobis distance. This procedure was first applied in a controlled experiment where the exact origin of the samples is known in order to verify the accuracy of the method; afterwards, it was applied to artefacts recovered from the archaeological site of Xalasco, Tlaxcala (Mexico), from both surface and excavation collections and analysed with a portable X‐ray fluorescence spectrometer. The resulting groups defined that the ancient dwellers of Xalasco exploited six supply sources located in Central Mexico.

Machine Learning in Antibody Diagnostics for Inflammatory Bowel Disease Subtype Classification.

Antibody testing in inflammatory bowel disease (IBD) can add to diagnostic accuracy of the main subtypes Crohn's disease (CD) and ulcerative colitis (UC). Whether modern modeling techniques such as supervised and unsupervised machine learning are of value for finer distinction of subtypes such as IBD-unclassified (IBD-U) is not known. We determined the antibody profile of 100 adult IBD patients from the Swiss IBD cohort study with known subtype (50 CD, 50 UC) as well as of 76 IBD-U patients. We included ASCA IgG and IgA, p-ANCA, MPO- and PR3-ANCA, and xANCA measurements for computing different antibody panels as well as machine learning models. The AUC of an optimized antibody panel was 85% (95%CI, 78-92%) to distinguish CD from UC patients. The antibody profile of IBD-U patients was closely related to UC. No specific antibody profile was predictive for IBD-U nor for re-classification. The panel diagnostic was in favor of UC reclassification prediction with a correct assignment rate of 69.2-73.1% depending on the cut-off applied. Supervised machine learning could not distinguish between CD, UC, and IBD-U. More so, unsupervised machine learning suggested only two distinct clusters as a likely number of IBD subtypes. Antibodies in IBD are supportive in confirming clinical determined subtypes CD and UC but have limited capacity to predict IBD-U and reclassification during follow-up. In terms of antibody profiles, IBD-U is not a distinct subtype of IBD.

Assessing Onchocerca volvulus Intensity of Infection and Genetic Diversity Using Mitochondrial Genome Sequencing of Single Microfilariae Obtained before and after Ivermectin Treatment.

Onchocerciasis is a neglected tropical disease targeted for elimination using ivermectin mass administration. Ivermectin kills the microfilariae and temporarily arrests microfilariae production by the macrofilariae. We genotyped 436 microfilariae from 10 people each in Ituri, Democratic Republic of the Congo (DRC), and Maridi County, South Sudan, collected before and 4-5 months after ivermectin treatment. Population genetic analyses identified 52 and 103 mitochondrial DNA haplotypes among the microfilariae from DRC and South Sudan, respectively, with few haplotypes shared between people. The percentage of genotype-based correct assignment to person within DRC was ~88% and within South Sudan ~64%. Rarefaction and extrapolation analysis showed that the genetic diversity in DRC, and even more so in South Sudan, was captured incompletely. The results indicate that the per-person adult worm burden is likely higher in South Sudan than DRC. Analyses of haplotype data from a subsample (n = 4) did not discriminate genetically between pre- and post-treatment microfilariae, confirming that post-treatment microfilariae are not the result of new infections. With appropriate sampling, mitochondrial haplotype analysis could help monitor changes in the number of macrofilariae in a population as a result of treatment, identify cases of potential treatment failure, and detect new infections as an indicator of continuing transmission.