Deep Studies Research Articles

ОСОБЕННОСТИ ГЛУБИННОГО СЕЙСМИЧЕСКОГО СТРОЕНИЯ ТИХООКЕАНСКОГО ТЕКТОНИЧЕСКОГО ПОЯСА В СТВОРЕ ОПОРНОГО ПРОФИЛЯ 8-ДВ

The article presents the results of deep seismic studies over the Sikhote-Alin fragment of the 8-DV reference geophysical profile running across the large fold structures of the Pacific tectonic belt, that is, the Sikhote-Alin orogenic belt with the superimposed Middle Amur sedimentary basin and the East Sikhote-Alin volcanoplutonic belt. A deep seismic section of the Earth's crust and upper mantle with the distribution of longitudinal wave velocities is constructed. In the upper part of the section, P-wave velocities vary from 4.0–5.0 km/s within the Middle Amur sedimentary basin and the eastern part of the Eastern Sikhote-Alin volcanoplutonic belt to 5.5–5.6 km/s in the central part of the Sikhote-Alin orogenic belt. It is found that the thick Paleozoic assemblage of sediments within the Middle Amur sedimentary basin is complexly faulted and folded. Crystalline rocks with compressional wave velocities of 6.0–6.2 km/s occur at depths of 8 to13 km in different parts of the Middle Amur sedimentary basin and at depths of 4 to 6 km within the Sikhote-Alin orogenic complex. Compressional wave velocities of the crust in the Sikhote-Alin fragment of the 8-DV profile are between 6.3 and 6.35 km/s. The crust varies in thickness from 32–34 km in the northern part of the fragment to 36–37 km in its central and eastern parts. Along the Moho, P-wave velocities are 8.1 to 8.3 km/s; a number of areas of reduced Vp from 7.8 to 8.0 km/s were identified in the junction zones of large crustal blocks. P-wave data were used to identify anomalies which were correlated with structural-tectonic zones, deep fault zones, and seismicity. Thus, along the deep Central Sikhote-Alin fault, the Middle Amur basin is identified, which is infilled by low-velocity sediments of considerable thickness, with Paleozoic sediments shifted to between 4 and 5 km at its junction with the Sikhote-Alin orogenic. Zones of increased heterogeneity in the upper crust projected onto the area within the contours of the Middle Amur sedimentary basin are characterized by increased seismicity.

A Census of Star Formation Histories of Massive Galaxies at 0.6 < z < 1 from Spectrophotometric Modeling Using Bagpipes and Prospector

We present individual star formation histories (SFHs) of ∼3000 massive galaxies (log(M */M ⊙) > 10.5) from the Large Early Galaxy Astrophysics Census spectroscopic survey at a lookback time of ∼7 billion yr and quantify the population trends leveraging 20 hr deep-integrated spectra of these ∼1800 star-forming and ∼1200 quiescent galaxies at 0.6 < z < 1.0. Essentially all galaxies at this epoch contain stars of age <3 Gyr, in contrast with older massive galaxies today, facilitating better recovery of previous generations of star formation at cosmic noon and earlier. We conduct spectrophotometric analysis using parametric and nonparametric Bayesian stellar population synthesis modeling tools—Bagpipes and Prospector—to constrain the median SFHs of this mass complete sample and characterize population trends. A consistent picture arises for the late-time stellar mass growth when quantified as t 50 and t 90, corresponding to the age of the Universe when galaxies formed 50% and 90% of their total stellar mass, although the two methods disagree at the earliest formation times (e.g., t 10). Our results reveal trends in both stellar mass and stellar velocity dispersion as in the local Universe—low-mass galaxies with shallower potential wells grow their stellar masses later in cosmic history compared to high-mass galaxies. Unlike local quiescent galaxies, the median duration of late-time star formation (τ SF,late = t 90–t 50) does not consistently depend on the stellar mass. This census sets a benchmark for future deep spectrophotometric studies of the more distant Universe.

Cross-Spatiotemporal Graph Convolution Networks for Skeleton-Based Parkinsonian Gait MDS-UPDRS Score Estimation.

Gait impairment in Parkinson's Disease (PD) is quantitatively assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a well-established clinical tool. Objective and efficient PD gait assessment is crucial for developing interventions to slow or halt its advancement. Skeleton-based PD gait MDS-UPDRS score estimation has attracted increasing interest in improving diagnostic efficiency and objectivity. However, previous works ignore the important cross-spacetime dependencies between joints in PD gait. Moreover, existing PD gait skeleton datasets are very small, which is a big issue in deep learning-based gait studies. In this work, we collect a sizable PD gait skeleton dataset by multi-view Azure Kinect sensors. The collected dataset contains 102 PD patients and 30 healthy older adults. In addition, gait data from 16 young adults (aged 24-50 years) are collected to further examine the effect of age on PD gait assessment. For skeleton-based automatic PD gait analysis, we propose a novel cross-spatiotemporal graph convolution network (CST-GCN) to learn complex features of gait patterns. Specifically, a gait graph labeling strategy is designed to assemble and group cross-spacetime neighbors of the root node according to the spatiotemporal semantics of the gait skeleton. Based on this strategy, the CST-GCN module explicitly models the cross-spacetime dependencies among joints. Finally, a dual-path model is presented to realize the modeling and fusion of spatial, temporal, and cross-spacetime gait features. Extensive experiments validate the effectiveness of our method on the collected dataset.

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma.

Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

New insights from paediatric electrophysiology

Visual electrophysiology is uniquely valuable in paediatric practice because it provides objective measures of visual pathway function. The combination of electroretinograms and visual evoked potentials to flashes and patterns enables a functional assessment of the entire pathway, from photoreceptors to striate cortex. This information can be impossible to obtain from children by other means. Electrophysiology complements structural information, often highlighting discrepant structure function relationships.Objective functional electrophysiology facilitates early diagnosis and can delineate potential treatment windows from the youngest ages. Thus visual electrophysiology findings in children contribute to natural history and deep phenotyping studies that inform clinical trials of new treatments not only for ocular disease, but also systemic diseases that involve the eye.This talk will describe ways in which ISCEV tests can be adapted and optimized for young children in clinical practice without loss of diagnostic accuracy, and how electrophysiology can distinguish phenotypes and inform genetic analysis. Examples of the value and limitations of electrophysiology in potential gene trials and other ameliorating treatments will be presented and discussed; including inherited retinal disease such as RPE65, neurodegenerations that affecting the eye such as CLN2 and iron brain disorders such as PKAN and ciliopathies such as BBS.Key words: visual electrophysiology, children, ERG, VEP, ISCEV.

Deblending overlapping galaxies in DECaLS using transformer-based algorithm: A method combining multiple bands and data types

Abstract In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending was inevitable. Such blending posed a potential primary systematic uncertainty for upcoming surveys. Current deblenders predominantly depended on analytical modelling of galaxy profiles, facing limitations due to inflexible and imprecise models. We presented a novel approach, using a U-net structured transformer-based network for deblending astronomical images, which we term the CAT-deblender. It was trained using both RGB and the grz-band images, spanning two distinct data formats present in the Dark Energy Camera Legacy Survey (DECaLS) database, including galaxies with diverse morphologies in the training dataset. Our method necessitated only the approximate central coordinates of each target galaxy, sourced from galaxy detection, bypassing assumptions on neighbouring source counts. Post-deblending, our RGB images retained a high signal-to-noise peak, consistently showing superior structural similarity against ground truth. For multi-band images, the ellipticity of central galaxies and median reconstruction error for r-band consistently lie within $\pm$ 0.025 to $\pm$ 0.25, revealing minimal pixel residuals. In our comparison of deblending capabilities focused on flux recovery, our model showed a mere 1% error in magnitude recovery for quadruply blended galaxies, significantly outperforming SExtractor’s higher error rate of 4.8%. Furthermore, by cross-matching with the publicly accessible overlapping galaxy catalogs from the DECaLS database, we successfully deblended 433 overlapping galaxies. Moreover, we have demonstrated effective deblending of 63 733 blended galaxy images, randomly chosen from the DECaLS database.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer’s performance of 80% according to AUROC was 20–40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

Green synthesis of novel spiropyrazoline-indolinones in neutral deep eutectic solvents and DFT studies

Novel spiropyrazoline-indolinones (4a–t) have been synthesized successfully in neutral deep eutectic solvents by reacting 5-Cl/Br-isatin (1a–b) with aromatic ketones (2a–b) and a variety of substituted hydrazines (3a–e) in good to excellent yields. This eco-friendly straightforward synthetic protocol discloses good functional group compatibility. The conventional synthetic approach was compared with the greener route of microwave-assisted synthesis of spiropyrazolines using ethanol. This approach utilized mild reaction conditions which furnished high yields in short reaction time employing one pot two-step multicomponent. All new compounds were structurally confirmed by detailed spectroscopic analysis and density functional theory calculations. This method provides efficient access to spiropyrazole derivatives using biodegradable and green solvent.

Machine Learning-Based Survival Analysis Reveals Prognostic Clinical and Genetic Insights for Patients with Cutaneous T-Cell Lymphoma

Introduction: Cutaneous T-cell lymphomas (CTCL) are heterogeneous lymphoproliferative disorders on a spectrum of disease presentation and severity. Around two-thirds of cutaneous T-cell lymphomas can be classified as mycosis fungoides (MF) or Sézary syndrome (SS). While advanced stages of MF and SS are associated with decreased survival and worse outcomes, even early-stage patients can possess a variable course. Numerous deep sequencing studies have fallen short in identifying genetic abnormalities that drive disease pathogenesis and predict prognosis. Large-cell transformation and elevated lactate dehydrogenase levels are associated with worse prognosis in SS; however, such features cannot accurately prognosticate patient survival. There is a need for investigation that may assist in the prognostication of survival in patients with CTCL. Machine learning methods may help to elucidate correlations between clinical and genetic factors to predict disease progression and outcomes. Objective: Using integrated clinical and genomic data from six international sequencing studies, this investigation aimed to perform survival analysis to identify both clinical and genetic features of survival outcomes using artificial intelligence/machine learning methods. Methods: A total of 126 eligible patients were identified, of which 99 had sufficient clinical data and 88 had sufficient clinical and genetic data. Poisson distribution was used to assess genomic data and significant genetic abnormalities for each individual patient were linked with their corresponding clinical outcomes. Genetic inputs included mutational data at a frequency of greater than or equal to four dataset occurrences. Multiple imputation using a random forest model was applied to all included variables (rate of missingness &amp;lt;10%). Overall survival was assessed using three separate cox models fit with patient clinical, laboratory, or treatment covariates. Ten-fold cross validated Least Absolute Shrinkage and Selection Operator (LASSO) was applied using adaptive regularization, and an iterated approach using 100 ten-fold cross validation repeats (resampling validation) to select the lambda value with the highest average C-index. The one-standard-error rule was used for the adaptive LASSO, while the iterated LASSO penalty was relaxed by 0.1. Prior to adjusting the lambdas, the best performing C-index was 0.73 and 0.64 for the adaptive LASSO and iterated LASSO, respectively. Following adjustment, the C-indexes were 0.71 and 0.60. To elucidate genetic candidates, we performed genome-wide association studies (GWAS) which used the false discovery rate (FDR) multiple comparisons correction and adjusted for the first three principal components in a principal component analysis (PCA) that included the imputed non-mutational covariates. Results: We have used standard statistical and machine learning methodologies to elucidate prognostic factors in MF and SS. Using standard cox regression analysis and in agreement with prior investigations, our investigation showed significant associations for age at diagnosis (hazard ratio = 1.06, P&amp;lt;0.001), stage at sampling (hazard ratio =1.99, P=0.007), and lymph node involvement at diagnosis (hazard ratio = 4.59, P&amp;lt;0.001). For the first time, using PCA-adjusted GWAS and iterated and adaptive LASSO, we demonstrate the association of mutated genes with survival in patients with MF and SS (e.g. most significant GWAS hazard ratio of 0.17, P=0.007, FDR=0.35). Moreover, several mutated genes were associated with particularly poor outcomes and high mortality (e.g. most significant GWAS hazard ratio of 5.26, P=0.003, FDR=0.63). Mutated genes with the highest or lowest magnitude using LASSO effect estimates in GWAS (lowest P values) were highly associated with survival outcomes. When present, the mutated genes carried significant prognostic implications for these patients. Conclusions: Taken together, we demonstrate the potential of machine learning and artificial intelligence methodologies for investigation of novel genetic associations with survival prognostication. Future investigations are needed to validate our findings in prospective studies.

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

Integral Approach in Discourse Studies (Voice Messaging Study)

Introduction. The article considers the issue of integral approach towards the study of a new form of discourse – voice message (VM), which possesses its social and communicative features. The topic is of particular interest to linguists and humanity studies’ scientists, since an efficient interdisciplinary approach in research is an actual issue. The nature of voice messaging is at the intersection of the interests of sociology, cognitive science, axiology and linguistics, so the use of the integral approach allows us to fully study this discursive phenomenon. The scientific novelty of the work lies in the fact that the integral approach in discourse studies has been poorly studied, and in the absence of deep studies of the voice messaging.Methodology and sources. To study the voice messaging in line with integral approach, a linguistic modeling method is used to create the integral model based on K. Wilber’s idea of AQAL (all quadrants all levels). Discursive analysis and the sociological survey method are used to describe the nature of the voice message. Using the categorical method of “formal- logical definition of the concept”, a definition of a voice message is given.Results and discussion. The use of the integral approach made it possible to create an integral model of voice communication, consisting of 4 sectors: social, cognitive, cultural and linguistic. Each sector presents a series of qualitative findings that help determine the place and role of this communicative phenomenon in the modern paradigm.Conclusion. The social sector presents the VM as a popular form of communication among people of different ages, genders and occupations, compensating for the shortcomings of modern live communication due to the dynamism of life. From the point of view of cultural studies, the communication of the VM suggests that the modern Russian person treats time as an exhaustible valuable resource; Russian-speaking culture becomes individualistic and focuses on the speaker; society values spirituality and the ability to reflect. The daily communication via VM consists of the genre repertoire, which has a high degree of emotionality: explanation, story, heart-to-heart conversation, expression of empathy, request, apology and gratitude. The cognitive sector has given knowledge about the most common emotions in the VM – joy, sadness and surprise.

Auditory stimulus reconstruction from ECoG with DNN and self-attention modules

Auditory stimulus reconstruction is a technique that predicts sound from neural activities. Decoding speech from human brains using neuronal networks allows us to understand auditory information processing and study hearing abilities effectively. However, current approaches produce low-quality reconstruction of speech which is problematic for applications of the reconstruction technology to practical uses such as evaluation of hearing characteristics of individuals or functional preservation during neurosurgical procedures. To tackle this issue, we used a self-attention (SA) module which are showing promising advances in deep neural network studies. We investigated the effect of including neural activity from a longer span on the reconstruction accuracy, and the learning behavior of self-attention modules. Our results showed that self-attention modules that focus on the presence or absence of auditory stimuli used relationships between features over a longer period (over 1000 ms) and produced better reconstructions than a convoluted neural network (CNN) model using features over a shorter period (300 ms) and a multilayer perceptron (MLP) model not using the features and reconstructed with attention to presence and absence of auditory stimuli. These findings imply that long and sparse relationships of time-series information on neural activities derived from SA modules improve the reconstruction performance.

Wildfire in the geological record: Application of Quaternary methods to deep time studies

For hundreds of millions of years wildfire has influenced, and been influenced by, plant evolution, biogeochemical cycling, and climate. Wildfire has even been implicated as one of the causative agents of mass extinctions. The deep time geologic record offers demonstrated, but underdeveloped, potential to advance knowledge on the role of wildfire in the Earth system. Herein, we present and discuss the geologic history of wildfire and methods for its reconstruction. We argue that application of the numeric approaches to wildfire reconstruction often used in Quaternary studies would advance understanding of deep time wildfire. Application of numeric methods increases statistical rigour, with the intent of reducing bias and increasing accuracy. For example, numeric methods offer a means to robustly calibrate the provenance and taphonomy of particles used to reconstruct wildfire, and to quantify uncertainties. Statistical methods should be used to assess the fidelity of new chemical proxies of wildfire, such as the types, amounts, distributions, and isotope signatures of polycyclic aromatic hydrocarbons, to represent source area and fuel type.

Direct accurate Eu anomaly analysis in very high Ba/Eu silicate samples by triple-quadrupole ICP-MS in MS/MS mass shift mode

The concentration of Eu relative to neighbouring rare earth elements (REE) is an important tool in petrology and geobiology. In most sample matrices, precise and accurate middle REE concentration data can be obtained by direct ICP-MS analysis with careful correction of interferences (chiefly, BaO+ on Eu+ and NdO+ on Gd+). However, geologically interesting samples, including sediments from mid-ocean-ridge and other hydrothermal settings, certain limestones, Precambrian cherts, and some alkali-feldspars have such high Ba/Eu ratios that signals on both Eu isotopes are dominated by BaO. This necessitates high resolution mass spectrometry, highly accurate oxide-correction in these matrices, or requires the separation of the REE from the Ba-rich matrix prior to ICP-MS analysis. Here we report experiments with a triple-quadrupole ICP-MS where Eu was analysed as the mass shifted EuO+ after reaction with N2O or O2 for analysis of Palaeoarchaean cherts. Results were compared to conventional REE analysis with a generic oxide correction as well as individual sample oxide correction. It was found that the triple-quadrupole ICP-MS method with N2O reaction and the conventional ICP-MS analysis with individual sample oxide correction yielded indistinguishable middle REE data for samples with Ba/Eu of up to 125,000. The other two methods overestimated the Eu concentration in samples with Ba/Eu > 10,000, leading to spuriously high positive Eu anomalies, which could be misinterpreted as dominant high-temperature hydrothermal venting into the ancient ocean, for which there is currently no convincing evidence. The triple-quadrupole ICP-MS method with N2O reaction offers a fast, low-blank and high-sensitivity solution to REE analysis of high Ba/Eu geological matrices. Based on the insight from these experiments, we propose criteria with which legacy Eu anomaly data can be assessed for accuracy and inclusion into deep time secular trend studies.

A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++

ObjectiveRadiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations.Methods105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time.ResultsThe average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively.ConclusionUNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.

Multi-Omics Analysis Reveals That TET2 Loss Epigenetically Primes Monocytes for Inflammatory Responses Via AP-1 Signaling

Loss of function mutations in TET2 are common in clonal hematopoiesis (CH) and myelodysplastic syndromes (MDS). Increased inflammation and stem cell capacity have been reported in Tet2 KO mouse models, but molecular mechanisms and cell type-specific contributions to explain this phenotype are lacking. To create models of CH-like TET2 deficiency, we transplanted Mx1-CRE Tet2 fl/fl (Tet2 KO) or Tet2 fl/fl (both CD45.1 +) in CD45.2 + recipientsfollowing radiation-free conditioning with ACK2 (anti-cKit) antibody injection. Six months post-transplant, we flow sorted CD45.1 +Tet2 KO and Tet2 fl/fl Lin -Sca-1 +cKit + (LSK) cells, granulocyte-monocyte progenitors (GMP), and monocytes for RNA and assay for transposase-accessible chromatin (ATAC) sequencing. Differential gene expression analysis identified 902 (468 up, 434 down) differentially expressed genes (DEGs) in LSK, 134 (95 up, 39 down) in GMP, and 474 (125 up, 349 down) in monocytes (p adj&amp;lt;0.05). Gene set enrichment analysis (GSEA) identified that the top Hallmark gene sets in Tet2 KOLSK cells were related to MYC targets, oxidative phosphorylation, and E2F targets. In contrast, GMP and monocytes were most enriched for IFN-alpha and gamma gene sets along with IL6/JAK/STAT3 signaling. ATACseq revealed increased accessibility of myeloid-related transcription factor (TF) binding motifs in Tet2 KOLSK cells (CEBPA, PU.1-IRF(ETS:IRF), and AP-1 TF FOSL2(bZIP)), while prediction of cis-regulating regions via GREAT identified downregulated peaks associated with stem cell maintenance and upregulated peaks associated with myeloid mediated immunity. Peaks upregulated in Tet2 KOGMP cells were enriched for the motifs recognized by CEBPE, ISRE(IRF)) and the AP-1 TF JUND, while upregulated peaks in monocytes were enriched for RUNX1 and CEBPA motifs, as well as AP-1 TF FOS::JUNB. GREAT analysis in GMPs identified cis-regulatory regions related to myeloid mediated immunity, while monocytes possessed those associated with biotic stimuli. To better dissect heterogeneity across primitive and mature cell types driven by TET2 loss, we performed scRNAseq on Tet2 KO and Tet2 WT wholebone marrow (WBM) and sorted LSK cells. We identified 18 transcriptionally distinct cell subsets in a combined analysis of WBM. Tet2 KOprogenitor and myeloid subsets were all enriched for gene sets related to IFN-alpha and gamma and immune signaling. Interestingly, primitive and cycling HSPCs were also enriched for MYC target and oxidative phosphorylation gene sets, while these were negatively enriched for GMP, monocyte progenitor, and monocyte Tet2 KO subsets. Further, the IL-6/JAK/STAT3 signaling gene set was enriched in later myeloid cell types, but not in early or cycling HSPCs. These observations led us to hypothesize that Tet2 KO BM cell populations have differentially enhanced phosphoprotein signaling responses to key cytokines. We used a 36-antibody mass cytometry panel to perform deep immunophenotyping and signaling response studies in Tet2 KO BM. FlowSOM analysis identified clusters corresponding to cKIT +CD34 + myeloid progenitors, a Ly6C +CD34 +cKIT + progenitor, and Ly6Chi monocyte clusters were enriched in Tet2 KO BM, while manual gating revealed expansion of LSK, MPP3, and monocytes. We found that activation of pSTAT3 and pSTAT1, in response to either IL-6 or IFNγ, respectively, were significantly higher in Tet2 KO Ly6Chi monocytes, while the more primitive Ly6C +CD34 +cKIT + progenitors demonstrated only an increased response to IFNγ at pSTAT1. Further, prolonged exposure to IL-6 in immortalized Tet2 KO Hoxb8-ER GMP-like cells led to significantly higher levels of pSTAT3 over 4 hours. Since AP-1 TFs are drivers of inflammation and were enriched in our ATACseq dataset, we tested the effects of an AP-1 inhibitor on the Tet2 KO Hoxb8-ER cells. We found that the AP-1 inhibitor T5224 dose dependently reduced the expansion of Tet2 KO Hoxb8 progenitor cells in base media and inhibited the IL-6 mediated expansion of Tet2 KO Hoxb8 cells. Our data show that TET2 loss rewires BM HSPCs towards an inflammatory, myeloid phenotype. Starting at the GMP stage, we see increases in phosphoprotein responses to IFNγ, and then the addition of hypersensitivity to IL-6 in monocytes. TET2 loss leads to an increase in accessible AP-1 motifs in Tet2 KO cells, while inhibition of AP-1 reduces fitness of cells, suggesting a possible therapeutic strategy for TET2-mutated myeloid malignancy.

Taxonomic and Phylogenomic Assessment Identifies Phytopathogenicity Potential of Stenotrophomonas maltophilia Complex.

Stenotrophomonas maltophilia is a versatile bacterium found in plants, water, air, and even hospital settings. Deep taxono phylogenomics studies have revealed that S. maltophilia is a complex of several hidden species that are not differentiated using conventional approaches. In the last two decades, there have been increasing reports of S. maltophilia as a pathogen of diverse plants. Hence, proper taxonogenomic assessment of plant-pathogenic strains and species within the S. maltophilia complex (Smc) is required. In the present study, we formally propose a taxonomic amendment of Pseudomonas hibiscicola and P. beteli, reported as pathogens of Hibiscus rosa-sinensis and Betelvine (Piper betle) plants, respectively, as a misclassified member species of the Smc. Recently, a novel species of the genus, S. cyclobalanopsidis, was reported as a leaf spot pathogen of the oak tree genus Cyclobalanopsis. Interestingly, our investigation also revealed S. cyclobalanopsidis as another plant-pathogenic member species of the Smc lineage. In addition, we provide deep phylo-taxonogenomic evidence that S. maltophilia strain JZL8, reported as a plant pathogen, is a misclassified strain of S. geniculata, making it the fourth member species of the Smc harboring plant-pathogenic strains. Therefore, a proper taxonomic assessment of plant-pathogenic strains and species from the Smc is required for further systematic studies and management.

Uprooting Entrenched Gender Biases in Climate-Smart Agriculture

This paper discusses the barriers to and enabling conditions for designing and implementing gender-responsive climate-smart agriculture (CSA) initiatives. Through examples, the paper identifies lessons and promising practices for addressing gender issues across the range of CSA interventions from farm level practices and systems approaches to the enabling environment. Through two deep dive case studies, the paper showcases the potential for CSA to contribute to gender-transformative results and offers recommendations for doing so.

Self‐training approach for crack detection using synthesized crack images based on conditional generative adversarial network

AbstractUrban infrastructure plays a crucial role in determining the quality of life for citizens. However, given the increasing number of aging infrastructures, regular inspections are essential to prevent accidents. Deep learning studies have been conducted to detect structural damage and ensure high accuracy and reliability of these inspections. However, these detection algorithms often face challenges due to scarcity of damage data. To overcome this issue, this paper proposes a method for synthesizing crack images and utilizing them for crack detection. Initially, crack images are synthesized from labeled images by using a conditional generative adversarial network. Subsequently, a new self‐training method is implemented wherein the synthesized crack images from the prediction images were incorporated into the learning process to enhance data diversity. The proposed approach yields promising results with a mean intersection over union of 80.34% and F1‐score of 76.31% on average. The proposed method can aid further research on virtual image generation for crack detection, seeking to reduce the reliance on extensive image collection.

A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas

BackgroundThe anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a laborious and time-intensive process. Previous deep learning-based studies have mainly been focused on automatic tumor segmentation or predicting genetic/histological features. However, few studies have specifically addressed the identification of infiltrated brain areas. To bridge this gap, we aim to develop a model that can simultaneously identify infiltrated brain areas and perform accurate segmentation of gliomas.MethodsWe have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: identifying infiltrated brain areas segmentation of gliomas. The multi-task model leverages shaped location and boundary information to enhance the performance of both tasks. Our retrospective study involved 354 glioma patients (grades II-IV) with single or multiple brain area infiltrations, which were divided into training (N = 270), validation (N = 30), and independent test (N = 54) sets. We evaluated the predictive performance using the area under the receiver operating characteristic curve (AUC) and Dice scores.ResultsOur multi-task model achieved impressive results in the independent test set, with an AUC of 94.95% (95% CI, 91.78–97.58), a sensitivity of 87.67%, a specificity of 87.31%, and accuracy of 87.41%. Specifically, for grade II-IV glioma, the model achieved AUCs of 95.25% (95% CI, 91.09–98.23, 84.38% sensitivity, 89.04% specificity, 87.62% accuracy), 98.26% (95% CI, 95.22–100, 93.75% sensitivity, 98.15% specificity, 97.14% accuracy), and 93.83% (95%CI, 86.57–99.12, 92.00% sensitivity, 85.71% specificity, 87.37% accuracy) respectively for the identification of infiltrated brain areas. Moreover, our model achieved a mean Dice score of 87.60% for the whole tumor segmentation.ConclusionsExperimental results show that our multi-task model achieved superior performance and outperformed the state-of-the-art methods. The impressive performance demonstrates the potential of our work as an innovative solution for identifying tumor-infiltrated brain areas and suggests that it can be a practical tool for supporting clinical decision making.