Subclass Level Research Articles

Transcriptomic cell-type specificity of local cortical circuits.

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

Specific long-term changes in anti-SARS-CoV-2 IgG modifications and antibody functions in mRNA, adenovector, and protein subunit vaccines.

Various vaccine platforms were developed and deployed against the COVID-19 disease. The Fc-mediated functions of IgG antibodies are essential in the adaptive immune response elicited by vaccines. However, the long-term changes of protein subunit vaccines and their combinations with messenger RNA (mRNA) vaccines are unknown. A total of 272 serum and plasma samples were collected from individuals who received first to third doses of the protein subunit Medigen, the mRNA (BNT, Moderna), or the adenovector AstraZeneca vaccines. The IgG subclass level was measured using enzyme-linked immunosorbent assay, and Fc-N glycosylation was measured using liquid chromatography coupled to tandem mass spectrometry. Antibody-dependent-cellular-phagocytosis (ADCP) and complement deposition (ADCD) of anti-spike (S) IgG antibodies were measured by flow cytometry. IgG1 and 3 reached the highest anti-S IgG subclass level. IgG1, 2, and 4 subclass levels significantly increased in mRNA- and Medigen-vaccinated individuals. Fc-glycosylation was stable, except in female BNT vaccinees, who showed increased bisection and decreased galactosylation. Female BNT vaccinees had a higher anti-S IgG titer than that of males. ADCP declined in all groups. ADCD was significantly lower in AstraZeneca-vaccinated individuals. Each vaccine produced specific long-term changes in Fc structure and function. This finding is critical when selecting a vaccine platform or combination to achieve the desired immune response.

Weighted Median Inflation Around the World: A Measure of Core Inflation

The standard measure of core or underlying inflation is the inflation rate excluding food and energy prices. This paper constructs an alternative measure, the weighted median inflation rate, for 38 advanced and emerging economies using subclass level disaggregation of the CPI over 1990–2021, and compares the properties of this measure to those of standard core. For quarterly data, we find that the weighted median is less volatile than standard core, more closely related to economic slack, and more closely related to headline inflation over the next year. The weighted median also has a drawback: in most countries, it has a lower average level than headline inflation. We therefore also consider a measure of core inflation that eliminates this bias, which is based on the percentile of sectoral inflation rates that matches the sample average of headline CPI inflation.

Partitioning and in vitro bioaccessibility of apple polyphenols during mechanical and physiological extraction: A hierarchical clustering analysis with LC-ESI-QTOF-MS/MS

Polyphenol partitioning during mechanical (cold-pressing) and physiological (digestion) extraction at the individual polyphenol and subclass level was investigated. UHPLC-ESI-QTOF-MS/MS analysis yielded a comprehensive identification of 45 polyphenols whose semi-quantification revealed a hierarchical clustering strongly determined by polyphenol structure and their location within the apple tissue. For instance, pomace retained most flavonols and flavanols (degree of polymerization DP 5–7), which were highly hydrophobic, hydroxylated, or large (>434 Da), and more abundant in peel. In vitro digestion UHPLC-ESI-QTOF-MS/MS analysis of whole apple (and its corresponding matrix-free extract) clustered polyphenols into five main groups according to their interaction with plant cell walls (PCWs) during each digestion phase. This grouping was not reproduced in pomace, which exhibited a greater matrix effect than whole apple during oral and gastric digestion. Nevertheless, the interaction between most polyphenol groups, including dihydrochalcones, flavanols (DP 1–4) and hydroxycinnamic acid derivatives, and pomace PCWs was lost during intestinal digestion.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

Reduced IgG2 with thrombocytopenia predicts mortality in patients with influenza pneumonia

BackgroundThrombocytopenia is a common disorder during influenza that is related to high mortality. ObjectivesA prospective study was performed to investigate the association of immunoglobulin subclass changes accompanying incident thrombocytopenia with clinical outcomes in patients with severe influenza. Methods96 influenza patients were recruited and divided into two groups, patients with thrombocytopenia (n = 30) and patients without thrombocytopenia (n = 66). Plasma microarrays were used for quantitative analysis of immunoglobulins. The endpoint was 28-day mortality. Continuous platelet count, d-dimer, level of each Ig subclass and other variables were compared between the two groups. Kaplan-Meier curve was taken to analyze the 28-day survival rate of the two groups and Cox regression analysis was performed to identify variables independently associated with 28-day mortality. ResultsPatients with thrombocytopenia had significantly high values of d-dimer at admission and when platelet lowest with high SOFA score. Their IgA2, IgG2, and IgG4 values were also lower than those without thrombocytopenia. Patients without thrombocytopenia had a higher 28-day survival rate than those in the thrombocytopenia group. In the multivariate Cox regression model, age (HR = 1.036, 95%CI = 1.011–1.062), IgG2 (HR = 0.990, 95%CI = 0.982–0.998), platelet minimum within 28 days (HR = 0.991, 95%CI = 0.982–0.999) and d-dimer when platelet lowest (HR = 1.091, 95%CI = 1.047–1.137) were independently related to 28-day mortality. ConclusionDecreased IgG2 may be associated with thrombocytopenia. A coexistence of thrombocytopenia, IgG2 reduction and d-dimer elevation may improve the accuracy of mortality prediction in patients with influenza pneumonia.

Label-free, rapid and highly accurate identification and categorization of leukemia cells via Raman spectroscopy

Leukemia, a highly malignant form of cancer, relies on early diagnosis for effective treatment and patient survival. Currently, leukemia is diagnosed by manually identifying blood cells through recognizing morphological features using staining techniques. In this work, we achieve rapid, accurate, label-free identification and categorization of four important leukemia cell lines to a subclass level using Raman spectroscopy and an identification model. The flat gold film-covered glass substrates ensure cell intact, good morphology and high-quality Raman spectra. Although the shapes of unstained erythrocytes, K562, U937 and Raji B cells are similar, their spectra are different. The sensitivity, the specificity and the accuracy of the unsupervised identification model (PCA-KMCA) of these four cell lines were 93.75%, 96.67%, and 97.50%, respectively. The accuracy of this model is higher than that of the supervised identification model (LDA-KNN) with an accuracy of 90.62%. Further, the unsupervised identification (PCA-KMCA) of Raji B and Jurkat cells could be greatly achieved, with sensitivity of 96.25%, specificity of 97.50% and accuracy of 96.88%. This method has great potential in the early diagnosis of leukemia, which can help doctors determine treatment plans faster and improve the prognosis of patients.

Lipid Profile after Pharmacologic Discontinuation and Restoration of Menstruation in Women with Endometriosis: A 12-Month Observational Prospective Study.

The lipid profile is affected following menstrual cessation (MC). We aimed to evaluate the effects of goserelin-induced MC and subsequent menstrual restoration (MR) on lipid metabolism. Premenopausal women with histologically verified endometriosis (n = 15) received goserelin monthly for 6 months (6mο), resulting in MC, and were followed-up for another 6 months after MR (12mο). Serum total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein a ([Lp(a)] and lipidomics were measured at baseline, 6mo and 12mo. Shotgun quantitative deep lipidomics were determined at the level of lipid class category, subclass, species, and fatty acyl chain lengths and degree of saturation. TC (p = 0.006), LDL-C (p = 0.028), HDL-C (p = 0.002), and apoA1 (p = 0.013) increased during goserelin-induced MC and remained practically unchanged during MR. TG, apoB, and Lp(a) did not change. From the deep lipidomics analysis, multivariate statistical analysis demonstrated profound alterations in lipid species with MC, whereas no statistically valid models could be fitted for the restoration period. In conclusion, GnRH-analog-induced MC alters lipid profiles at various levels, from standard blood lipid and lipoprotein profiles to several lipid species as detected by lipidomics analysis. Changes largely persist for at least 6 m after MR.

Representativeness of China’s Protected Areas in Conserving Its Diverse Terrestrial Ecosystems

Ecological representativeness of protected area networks is essential for leveraging protected areas to achieve conservation goals. On the basis of China’s vegetation map and the spatial data of protected areas, we analyze the representativeness of China’s network of protected areas in protecting distinct types of terrestrial natural ecosystems. Across the major ecosystem classes, wetland, alpine tundra, and grassland have more than 20% of their areas being protected, while shrub, forest, and desert are less covered by protected areas. At the level of ecosystem subclass, 90.2% of the 691 types of natural ecosystems have at least some areas covered by protected areas, but of the rarest natural ecosystem types (i.e., their land areas less than 100 km 2 ), about half were poorly protected (i.e., less than 5% of their extents are protected). A small percentage (14.7%) of natural ecosystem with high human pressure is included in protected areas, whereas 27% of natural ecosystem with the lowest intensity of human pressure occurs in protected areas. To enhance the ecological representativeness of China’s protected areas, we suggest strengthening the protection of underrepresented ecosystems, especially those exposed to high human pressure, by expanding protected area networks and delineating ecological redlines.

Evaluation of mixture toxicity literature and chemical space: a data centric approach.

Using the Abstract Sifter tool to analyse PubMed, we reveal published mixture related research most commonly relates to water pollutants, pesticides, environmental pollutants, insecticides, soil pollutants, and chemicals described as persistent, bioaccumulative, and toxic. Furthermore, we discern individual chemicals that also identify as priority chemicals in biomonitoring initiatives and using an ontology-based chemical classification, at the level of the chemical subclass, found these priority chemicals overlap with just 9% of the REACH chemical space.

Metabolomic changes in crown of alfalfa (Medicago sativa L.) during de-acclimation

Alfalfa is a high-quality forage legume species that is widely cultivated at high latitudes worldwide. However, a decrease in cold tolerance in early spring seriously affects regrowth and persistence of alfalfa. There has been limited research on the metabolomic changes that occur during de-acclimation. In this study, a liquid chromatography–mass spectrometry system was used to compare the metabolites in two alfalfa cultivars during a simulated overwintering treatment. In four pairwise comparisons, 367 differential metabolites were identified, of which 31 were annotated according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Many of these metabolites were peptides, carbohydrates, and lipids. At the subclass level, 17 major pathways were revealed to be significantly enriched (P < 0.05). The main differential metabolites included amino acids, peptides and analogs, carbohydrates, and glycerol phosphocholines. A metabolomic analysis showed that the up-regulation of unsaturated fatty acids and amino acids as well as the enhancement of the related metabolic pathways might be an effective strategy for increasing alfalfa cold tolerance. Furthermore, glycerophospholipid metabolism affects alfalfa cold tolerance in early spring. Study results provide new insights about the changes in alfalfa metabolites that occur during de-acclimation, with potential implications for the selection and breeding of cold-tolerant cultivars.

Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation

In object re-identification (ReID), the development of deep learning techniques often involves model updates and deployment. It is unbearable to re-embedding and re-index with the system suspended when deploying new models. Therefore, backward-compatible representation is proposed to enable ``new'' features to be compared with ``old'' features directly, which means that the database is active when there are both ``new'' and ``old'' features in it. Thus we can scroll-refresh the database or even do nothing on the database to update. The existing backward-compatible methods either require a strong overlap between old and new training data or simply conduct constraints at the instance level. Thus they are difficult in handling complicated cluster structures and are limited in eliminating the impact of outliers in old embeddings, resulting in a risk of damaging the discriminative capability of new features. In this work, we propose a Neighborhood Consensus Contrastive Learning (NCCL) method. With no assumptions about the new training data, we estimate the sub-cluster structures of old embeddings. A new embedding is constrained with multiple old embeddings in both embedding space and discrimination space at the sub-class level. The effect of outliers diminished, as the multiple samples serve as ``mean teachers''. Besides, we propose a scheme to filter the old embeddings with low credibility, further improving the compatibility robustness. Our method ensures the compatibility without impairing the accuracy of the new model. It can even improve the new model's accuracy in most scenarios.

Diffuse Common Bile Duct Mural Thickening in a Patient With Pancreatitis

Question: A 64-year-old man experienced acute episodes of abdominal and back pain 5 months ago, followed by jaundice, which prompted him to seek evaluation at his local hospital. Laboratory evaluation was notable for elevated bilirubin 3.4 mg/dL (reference range, 0–1.2 mg/dL), alkaline phosphatase of 1352 U/L (reference range, 50–121 U/L), alanine aminotransferase of 219 U/L (reference range, 12–52 U/L), and lipase 361 U/L (reference range, 13–78 U/L). The patient underwent several endoscopic retrograde cholangiopancreatography (ERCP) procedures with brush cytology and common bile duct (CBD) stent placement for an indeterminate stricture, and endoscopic ultrasound (EUS) examination with fine needle aspiration of the stricture without an identifiable etiology. At presentation for a second opinion, the patient noted resolution of jaundice, but complained of pruritus, greasy stools, flatulence, and bloating, along with a 30-lb weight loss and fatigue. He denied excessive alcohol consumption, smoking, or a history of gallstones. On physical examination, the patient was without jaundice or abdominal pain. Routine laboratory evaluation was unremarkable except for a mildly elevated alanine aminotransferase of 56 U/L and alkaline phosphatase of 260 U/L. Carbohydrate antigen 19-9 and alpha fetoprotein levels were within normal limits. Hepatitis B and C serologies, anti–smooth muscle actin and anti–mitochondrial antibodies were unremarkable. IgG4 subclass was elevated at 172 mg/dL (reference range, 2.4–121 mg/dL). A magnetic resonance cholangiopancreatography showed enhancement of the CBD with mural thickening and pancreatic parenchymal atrophy (Figure A). A repeat ERCP did not show significant intrahepatic biliary duct dilation, but a stricture was noted in the distal CBD. EUS examination demonstrated pancreatic body and tail atrophy with pancreatic head changes consistent with moderate to severe chronic pancreatitis. The entire visualized CBD and cystic duct had circumferential wall thickening (Figure B). Cholangioscopy showed nonspecific findings. Based on the clinical scenario and images, what is the most likely diagnosis? Look on page 827 for the answer and see the Gastroenterology website (www.gastrojournal.org) for more information on submitting your favorite image to Clinical Challenges and Images in GI. Cholangioscopic biliary mucosal biopsies showed active chronic inflammation and increased IgG4-positive cells. Pancreatic head biopsies showed fibrosis with lymphoplasmacytic infiltrate and increased IgG4-positive cells with an IgG4 to IgG ratio of >40%. Cytology and fluorescence in situ hybridization of bile duct brushings were again unremarkable. The patient was initiated on pancreatic enzyme replacement therapy and a slow taper of prednisone. On follow-up 3 months later, his gastrointestinal symptoms had resolved, and weight had stabilized. Likewise, his liver chemistries and IgG4 subclass level had normalized. Follow-up ERCP revealed improvement in the CBD stricture. IgG4-related disease (IgG4-RD) is an immune-mediated, fibroinflammatory, multiorgan disease. It can present with regions of inflammatory thickening or mass formation that is often confused for a malignancy. The disease presents insidiously and is progressive. Most frequently involved organs include the pancreas resulting in type 1 autoimmune pancreatitis with diffuse enlargement, diabetes, and exocrine insufficiency; salivary and lacrimal glands resulting in asymptomatic symmetric enlargement; retroperitoneum resulting in soft tissue mass; and bile ducts resulting in intrahepatic and extrahepatic strictures.1Perugino C.A. Stone J.H. IgG4-related disease: an update on pathophysiology and implications for clinical care.Nat Rev Rheumatol. 2020; 16: 702-714Crossref PubMed Scopus (90) Google Scholar Additionally, our patient had a diffusely thickened CBD and cystic duct wall well-characterized by EUS examination (Figure B). The diagnosis is based on a comprehensive workup to distinguish from conditions presenting similarly. In this case, pancreatic cancer, cholangiocarcinoma, and primary sclerosing cholangitis were carefully ruled out. Various criteria have been proposed to diagnose IgG4-RD that predominantly rely on (1) imaging features, (2) an elevated serum IgG4 with various cut-off values, and (3) histological findings of lymphoplasmacytic infiltrate with fibrosis, a ratio of IgG4-positive plasma cells/IgG-positive cells of >40%, and the number of IgG4-positive plasma cells greater than 10 per high-powered fields, and typical fibrosis, storiform, or obliterative phlebitis.2Löhr J.M. Beuers U. Vujasinovic M. et al.European guideline on IgG4-related digestive disease - UEG and SGF evidence-based recommendations.United European Gastroenterol J. 2020; 8: 637-666Crossref PubMed Scopus (0) Google Scholar,3Umehara H. Okazaki K. Kawa S. et al.The 2020 revised comprehensive diagnostic (RCD) criteria for IgG4-RD.Mod Rheumatol. 2021; 31: 529-533Crossref PubMed Scopus (77) Google Scholar Multiple organ involvement and steroid responsiveness are also considered supportive of IgG4-RD. Our patient met the diagnostic criteria for IgG4-RD with excellent response to steroid treatment.

Potentialities of soil taxa common in the landscape of valleys in the arid Mediterranean region

The study was carried out to establish soil map. Land evaluation was made according to MicroLEIS models. The study area is Naghamish valley, North Western coast of Egypt. The study indicated that the study area could be distinguished into seven landforms. Twenty-two soil profiles, spatially distributed upon Sentinel-2A satellite image classification to represent the landforms of wadi Naghamish's basin. They were morphologically described, their chemical and physical properties were determined, and their diagnostic characteristics were pointed. Results point out, in terms of topography, profile depth and soil texture, ten soil units could be identified. The study area could be distinguished into five sub great soil groups. Cervatana model reveals that 64.25% of soils are considered non-productive, 22.64% is moderately capable, whereas only 0.54% and 2.64% of the total area range from excellent to good capable land for agricultural production, respectively. The rest of the area occupying 9.94% is associated with urban settlement and escarpment landform units. At subclass level, there are S3l and S3r, referring to moderate capability with slight constraint severity and S3lr specifying those affected by severe soil constraints and erosion risk. Regarding soil suitability for some selected crops, the results of Almagra model indicated that the soils formed on the coastal plain vary from suitable to marginally suitable, in contradiction of soils formed on the wadi course, piedmont plain and tableland of plateau vary from marginally suitable to unsuitable in general for most tested crop as affected by limiting factors related to very shallow to shallow depth, high lime content, light soil texture and very poor to poor drainage.

Evaluation of nine major satellite soil moisture products in a typical subtropical monsoon region with complex land surface characteristics

Assessing accuracies of satellite soil moisture (SM) products in areas with strong anthropogenic activities, abundant precipitation, and dense vegetation is important but limited. In this study, performances of nine satellite SM products, including the European Space Agency Climate Change Initiative (ESA CCI), and the ascending and descending products of the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), Chinese Fengyun-3C (FY3C), and Soil Moisture Active Passive (SMAP), were evaluated against in-situ SM in the Yangtze River Delta (YRD) from 2015 to 2018. Results showed that the ESA CCI outperformed other products, with averaged correlation coefficient (R) of 0.522, correlation coefficient for SM anomalies (Rano) of 0.419, and unbiased root mean square error (ubRMSE) of 0.040 m3 m−3, followed by the SMAP_A and SMAP_D. Overestimation was observed in the AMSR2 products (Bias >0.081 m3 m−3), while underestimation was found in the FY3B and FY3C products (Bias < −0.104 m3 m−3). Moreover, R values decreased with the increase in water area percentage (WAP), land cover heterogeneity (GSI), fractional vegetation cover (FVC), and precipitation (PRE). Threshold values were observed at 3% for the WAP, 0.05 for the FVC, and 900 mm yr−1 for the PRE, below which sharp declines of accuracy were observed. The WAP, GSI, and FVC were divided into five sub-classes with their values increasing with the sub-class level (I to V). Correlations coefficients between these perturbing factors and R (correlation between satellite and in-situ SM) increased as sub-class levels increased. Under sub-classes I, II, and III, these three factors had limited influences on accuracies of satellite SM products. However, under sub-classes IV and V, significant impacts of these three perturbing factors were observed. Moreover, when sub-classes of the PRE increased from I to V, correlations between Bias and PRE decreased first and then increased. These findings can help to determine suitable satellite SM products in the YRD and similar areas.

Immunoglobulin-G subclass pattern among children with mucopolysaccharidosis attending the genetic clinic of Alexandria University Children’s Hospital

Background Mucopolysaccharidosis is an inherited lysosomal-storage disease, due to deficiency in lysosomal enzymes degrading glycosaminoglycans, causing accumulation of undegraded substrate within the lysosomes. These substrates are related to widespread inflammation, as well as the release of various proinflammatory immune mediators and autophagy dysfunction. Oxidative stress, abnormal mitochondrial function, and disruption in homeostasis also play a role in mucopolysaccharide (MPS) pathogenesis. Defects in immunological parameters in MPS have been described and postulated as explanations for the increased severity of infections seen in MPS children. Most of these infections are of the respiratory tract. Aim The aim was to estimate the total level of immunoglobulin G (IgG) and its subclasses in children with MPS and find out the relation if any between the estimated levels and the recurrence of infections. Patients and methods The study was conducted on 35 children with MPS, either newly diagnosed or previously diagnosed cases attending the genetic clinic of Alexandria University Children’s Hospital for follow-up in the period from July 2019 to July 2020. ELISA was used for estimation of the levels of total IgG and its subtypes (IgG1, IgG2, IgG3, and IgG4). Results The levels of total IgG and its subtypes (IgG1, IgG2, IgG3, and IgG4) were measured in all studied cases. A positive significant correlation between IgG3 and repeated upper respiratory-tract infections (URTIs), that 68.2% of cases with high levels of IgG3 had repeated URTIs, while only 31.8% of cases with normal levels of IgG3 had repeated URTIs with P value of 0.001. Conclusion Children with MPS are especially prone to repeated URTIs more than six times/year, this increased susceptibility to infections has been linked to abnormal parameters of the immune system. IgG subclasses are a significant predictor of recurrent URTIs in patients with MPS disease. Therefore, measurement of IgG subclass level, especially IgG3 level, provides a new strategy to more aggressive treatments for high-risk groups.

Patent text modeling strategy and its classification based on structural features

To find the optimal combination of text modeling for rapid and accurate classification of patent texts and solve the severe problem of manual classification of patent texts in the face of massive patent scientific and technological information. In order to improve the efficiency of patent text automatic classification, the patent texts are split and spliced into 15 different modeling combinations based on the content structure of patent text. Through the 360 comparative experiments, the optimal modeling combination of patent text classification under different classification levels is obtained. The experimental results show that the use of full-text content is not always the best modeling choice in patent classification task. The modeling combination of title, abstract and specification (TAD) is more suitable for patent text classification task. In the subclass level, the highest classification accuracy can be obtained by selecting the specification (d) of patent text. The patent text modeling strategy proposed in this paper provides a good support for improving the effect of patent text classification.

A proposed order-level classification in Oligochaeta (Annelida, Clitellata).

The purpose of our contribution is to propose a robust and practical order-level classification of the families of Oligochaeta, that is, non-leech Clitellata. The order level is mandatory in Linnaean rank-based classification and is also required in many internet-based biodiversity databases. However, it has received little attention in oligochaete systematics, and the few available order-level classifications of Oligochaeta no longer represent phylogenetic relationships adequately. Our proposal is based on corroborated molecular phylogenetic evidence and takes as benchmarks class level for Clitellata, subclass level for Oligochaeta and Hirudinea, and order level for Crassiclitellata, the monophylum that includes most of the earthworm taxa. As a result, eleven orders are proposed: Alluroidida Timm Martin, 2015; Capilloventrida Timm, n. ordo; Crassiclitellata Jamieson, 1988; Enchytraeida Kasprzak, 1984; Haplotaxida Brinkhurst Jamieson, 1971; Lumbriculida Brinkhurst Jamieson, 1971; Moniligastrida Brinkhurst Jamieson, 1971; Narapida Timm, n. ordo; Parvidrilida Timm, n. ordo; Randiellida Jamieson, 1988; Tubificida Jamieson, 1978. This order-level classification is robust and easily adaptable to future insights into phylogenetic relationships.

Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework

This research aims at studying the ability of using diffuse reflectance spectroscopy (DRS) for discriminating or classifying coal samples into different ranks. Spectral characteristics such as the shape of the spectral profile, slope, absorption intensity of coal samples of different ranks ranging from lignite A to semi-anthracite were studied in the Vis-NIR-SWIR (350–2500 nm) range. A number of classification algorithms (Logistic Regression, Random Forest, and SVM) were trained using the DRS dataset of coal samples. Class imbalances present in the dataset were handled using different approaches (SMOTE and Oversampling of minority classes), which improved the classification accuracy. Coal samples were initially classified into broad classes viz., lignite, sub-bituminous, bituminous, and anthracite with an accuracy of 0.98 and F1 score of 0.75. Later, the same samples were further classified into sub-class levels. The sub-class level classification also obtained good results with an accuracy of 0.77 and F1 score of 0.64. The results demonstrate the effectiveness of rapid coal classification systems based on DRS dataset in combination with different machine learning-based classification algorithms.

Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.

Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.