bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal navigation of a smart active particle: directional and distance sensing
Authors: Putzke Mischa , Stark Holger 
Published: June 2023

Abstract
We employ Q learning, a variant of reinforcement learning, so that an active particle learns by itself to navigate on the fastest path toward a target while experiencing external forces and flow fields. As state variables, we use the distance and direction toward the target, and as action variables the active particle can choose a new orientation along which it moves with constant velocity. We explicitly investigate optimal navigation in a potential barrier/well and a uniform/ Poiseuille/swirling flow field. We show that Q learning is able to identify the fastest path and discuss the results. We also demonstrate that Q learning and applying the learned policy works when the particle orientation experiences thermal noise. However, the successful outcome strongly depends on the specific problem and the strength of noise.

Graphical abstract
IS SOLAR AND BIOGAS A BETTER CHOICE THAN ELECTRICITY AND DIESEL?
Authors: AKMAL NADEEM , QASIM MUHAMMAD , SHAH HASSNAIN , ZAMAN SUMIA BINT , FAROOQ WAQAS , AHMAD NAJID 
Published: June 2023

This study measures the cost-effectiveness of alternate energy sources for irrigation in Pakistan. Primary data has been collected from eight districts of three provinces of Pakistan. Overall, 223 farmers were personally interviewed out of which, 58 farmers were using electric tube-well, 95 diesel tube-well, and 35 each for solar and biogas-operated tube-wells. The cost of extracting one cubic meter of water through electricity, diesel, subsidized electricity, biogas, and solar was $0.180, $0.013, $0.011, $0.010, and $0.005, respectively. The paper concludes that water pumping using alternative energy sources is cost-effective. Major constraints to adopting solar-powered water pumping systems were the high upfront cost, the lack of awareness, and the lack of available spare parts. The constraints to adopting biogas were difficulty operating and managing through winter.
Zircon U–Pb geochronology, Nd isotopes and geochemistry of mafic granulites from the Central Indian Tectonic Zone: isotopic constraints on Proterozoic crustal evolution
Authors: Alam Meraj , Kaulina Tatiana V. , Varma Rakhi R. , Ahmad Talat 
Published: June 2023

Abstract

The Central Indian Tectonic Zone (CITZ) comprises northern and southern Indian cratonic blocks and is a tectonic window that is suitable for investigating the Proterozoic crustal evolution because of the presence of a wide variety of lithologies. Geochemical and geochronological data on mafic granulites by previous workers do not ascertain the possibility of mafic protoliths and their coeval link to other CITZ units. Thus, determining the precise timing of the formation of mafic granulites may indicate a connection between metamorphism and fragmentation of the Columbian supercontinent. This study presents zircon U–Pb ages, Nd isotopes and the geochemistry of mafic granulites to evaluate their genesis and timing of metamorphism. The results show the tholeiitic affinity and primary magmatic differentiation of the parental melt. Depletion of Nb, P, Zr and Ti and positive enrichment of Ba, U and Pb indicate the derivation of mafic granulites from a variably enriched subcontinental lithospheric mantle (SCLM) source. The zircon U–Pb ages (1564 ± 8 to 1598 ± 9 Ma) are interpreted as a period of granulite-facies metamorphism. The
T
DM
(depleted-mantle) model ages (2.9–3.4 Ga) of mafic granulites indicate the timing of mafic protolith extraction. The mineral isochron age
c.
1.0 Ga indicates that these rocks underwent some events during an early Neoproterozoic period. Protolith of mafic granulites could be related to the evolution of melts derived from metasomatized SCLM through fractional crystallization processes.
The Cambrian Atlas – Ossa–Morena – North Armorican Rift, West Gondwana: along- and off-axis stratigraphic and volcano-tectonic patterns
Authors: Álvaro J. Javier , Sánchez-García Teresa , Casas Josep Maria 
Published: June 2023

Abstract
The Cambrian Atlas – Ossa–Morena – North Armorican Rift extended along West Gondwana from the end of the Pan-African and Cadomian orogenies until the diachronous beginning of drift conditions related to the opening of the Rheic Ocean. The along-axis rift cross-cut the western parts of the Anti-Atlas, High Atlas and Coastal Meseta, which were linked to the Ossa–Morena Zone and the North Armorican Domain, whereas several joint tectonic branches connected with off-axis rift transects of the Central Iberian, West Asturian–Leonese and Cantabrian zones (Iberian Massif), the Central and South Armorican domains, the Occitan Domain, the Pyrenees, and southern Sardinia. The pre-rift unconformity, post-dating the orogenic collapse, is characterized by initial (half-)graben development and subsequent infill, with slope-related breccias and conglomerates controlled by the denudation of surrounding uplands. Synrift pulses show regional extension and are distinctly identifiable on the top of rift shoulders, recording episodes of carbonate production due to their association with karst and hydrothermal processes. The break-up unconformity ranges from volcanic-free angular discordances and paraconformities to generalized uplift and denudation of subaerially exposed areas, associated with the onset of granite-dominant large igneous provinces (LIPs). The Furongian–Tremadocian (Toledanian) and Ordovician (Sardic) phases have been interpreted as due to: (i) Andean-type subduction magmatism reaching the crust in an arc–back-arc setting; (ii) post-collisional decompression melting without significant mantle involvement; and (iii) partial melting of the lower continental crust affected by the underplating of hot mafic magmas linked to superplumes.
Targeting CaMKK2 Inhibits Actin Cytoskeletal Assembly to Suppress Cancer Metastasis
Authors: Mukherjee Debarati , Previs Rebecca A. , Haines Corinne , Al Abo Muthana , Juras Patrick K. , Strickland Kyle C. , Chakraborty Binita , Artham Sandeep , Whitaker Regina S. , Hebert Katherine , Fontenot Jake , Patierno Steven R. , Freedman Jennifer A. , Lau Frank H. , Burow Matthew E. , Chang Ching-Yi , McDonnell Donald P. 
Published: June 2023

Abstract

Triple-negative breast cancers (TNBC) tend to become invasive and metastatic at early stages in their development. Despite some treatment successes in early-stage localized TNBC, the rate of distant recurrence remains high, and long-term survival outcomes remain poor. In a search for new therapeutic targets for this disease, we observed that elevated expression of the serine/threonine kinase calcium/calmodulin (CaM)-dependent protein kinase kinase 2 (CaMKK2) is highly correlated with tumor invasiveness. In validation studies, genetic disruption of CaMKK2 expression or inhibition of its activity with small molecule inhibitors disrupted spontaneous metastatic outgrowth from primary tumors in murine xenograft models of TNBC. High-grade serous ovarian cancer (HGSOC), a high-risk, poor prognosis ovarian cancer subtype, shares many features with TNBC, and CaMKK2 inhibition effectively blocked metastatic progression in a validated xenograft model of this disease. Mechanistically, CaMKK2 increased the expression of the phosphodiesterase PDE1A, which hydrolyzed cyclic guanosine monophosphate (cGMP) to decrease the cGMP-dependent activity of protein kinase G1 (PKG1). Inhibition of PKG1 resulted in decreased phosphorylation of vasodilator-stimulated phosphoprotein (VASP), which in its hypophosphorylated state binds to and regulates F-actin assembly to facilitate cell movement. Together, these findings establish a targetable CaMKK2–PDE1A–PKG1–VASP signaling pathway that controls cancer cell motility and metastasis by impacting the actin cytoskeleton. Furthermore, it identifies CaMKK2 as a potential therapeutic target that can be exploited to restrict tumor invasiveness in patients diagnosed with early-stage TNBC or localized HGSOC.

Significance:
CaMKK2 regulates actin cytoskeletal dynamics to promote tumor invasiveness and can be inhibited to suppress metastasis of breast and ovarian cancer, indicating CaMKK2 inhibition as a therapeutic strategy to arrest disease progression.
Activation of the cGAS/STING Axis in Genome-Damaged Hematopoietic Cells Does Not Impact Blood Cell Formation or Leukemogenesis
Authors: Dressel Nicole , Natusch Loreen , Munz Clara M. , Costas Ramon Santiago , Morcos Mina N.F. , Loff Anja , Hiller Björn , Haase Christa , Schulze Livia , Müller Patrick , Lesche Mathias , Dahl Andreas , Luksch Hella , Rösen-Wolff Angela , Roers Axel , Behrendt Rayk , Gerbaulet Alexander 
Published: June 2023

Abstract

Genome damage is a main driver of malignant transformation, but it also induces aberrant inflammation via the cGAS/STING DNA-sensing pathway. Activation of cGAS/STING can trigger cell death and senescence, thereby potentially eliminating genome-damaged cells and preventing against malignant transformation. Here, we report that defective ribonucleotide excision repair (RER) in the hematopoietic system caused genome instability with concomitant activation of the cGAS/STING axis and compromised hematopoietic stem cell function, ultimately resulting in leukemogenesis. Additional inactivation of cGAS, STING, or type I IFN signaling, however, had no detectable effect on blood cell generation and leukemia development in RER-deficient hematopoietic cells. In wild-type mice, hematopoiesis under steady-state conditions and in response to genome damage was not affected by loss of cGAS. Together, these data challenge a role of the cGAS/STING pathway in protecting the hematopoietic system against DNA damage and leukemic transformation.

Significance:
Loss of cGAS/STING signaling does not impact DNA damage–driven leukemogenesis or alter steady-state, perturbed or malignant hematopoiesis, indicating that the cGAS/STING axis is not a crucial antioncogenic mechanism in the hematopoietic system.
See related commentary by Zierhut, p. 2807
The African-centric P47S Variant of TP53 Confers Immune Dysregulation and Impaired Response to Immune Checkpoint Inhibition
Authors: Stieg David C. , Parris Joshua L. D. , Yang Tyler Hong Loong , Mirji Gauri , Reiser Sarah Kim , Murali Nivitha , Werts Madison , Barnoud Thibaut , Lu David Y. , Shinde Rahul , Murphy Maureen E. , Claiborne Daniel T. 
Published: June 2023

The tumor suppressor TP53 is the most frequently mutated gene in cancer and is mutationally inactivated in 50% of sporadic tumors. Inactivating mutations in TP53 also occur in Li Fraumeni syndrome (LFS). In addition to germline mutations in TP53 in LFS that completely inactivate this protein, there are many more germline mutant forms of TP53 in human populations that partially inactivate this protein: we call these partially inactivating mutations “hypomorphs.” One of these hypomorphs is a SNP that exists in 6%–10% of Africans and 1%–2% of African Americans, which changes proline at amino acid 47 to serine (Pro47Ser; P47S). We previously showed that the P47S variant of p53 is intrinsically impaired for tumor suppressor function, and that this SNP is associated with increased cancer risk in mice and humans. Here we show that this SNP also influences the tumor microenvironment, and the immune microenvironment profile in P47S mice is more protumorigenic. At basal levels, P47S mice show impaired memory T-cell formation and function, along with increased anti-inflammatory (so-called “M2”) macrophages. We show that in tumor-bearing P47S mice, there is an increase in immunosuppressive myeloid-derived suppressor cells and decreased numbers of activated dendritic cells, macrophages, and B cells, along with evidence for increased T-cell exhaustion in the tumor microenvironment. Finally, we show that P47S mice demonstrate an incomplete response to anti-PD-L1 therapy. Our combined data suggest that the African-centric P47S variant leads to both intrinsic and extrinsic defects in tumor suppression.

Significance:
Findings presented here show that the P47S variant of TP53 influences the immune microenvironment, and the immune response to cancer. This is the first time that a naturally occurring genetic variant of TP53 has been shown to negatively impact the immune microenvironment and the response to immunotherapy.
Classification of gene expression dataset for type 1 diabetes using machine learning methods
Authors: AlRefaai Noor , Zaki AlRashid Sura 
Published: June 2023

Type 1 diabetes (T1D) disease is considered one of the most prevalent chronic diseases in the world, it causes a high level of glucose in the human blood. Despite the seriousness of this disease, T1D may affect people and their condition develops to an advanced stage without feeling it, which makes it difficult to control the disease. Early prediction of the presence of this disease may significantly reduce its risks. There are many attempts to overcome this disease, some of them are heading towards biological solutions and others towards bioinformatic solutions. Several studies have used a single feature selection method with a machine learning (ML) model to predict or classify T1D. In this paper, ML techniques were used for classification, such as Naive Bayes (NB), support vector machine (SVM), and random forest (RF) using a T1D gene expression dataset that has a multiclass to classify the genes associated with this disease. The proposed model can identify the genes related to T1D with high efficiency, which helps a lot in predicting a person carrying the disease before symptoms appear. The highest accuracy of 89.1% was obtained when applying SVM with chi2 as the feature selection method.
Stacking ensemble learning for optical music recognition
Authors: Ferano Francisco Calvin Arnel , Zahra Amalia , Kusuma Gede Putra 
Published: June 2023

The development of music culture has resulted in a problem called optical music recognition (OMR). OMR is a task in computer vision that explores the algorithms and models to recognize musical notation. This study proposed the stacking ensemble learning model to complete the OMR task using the common western musical notation (CWMN) musical notation. The ensemble learning model used four deep convolutional neural networks (DCNNs) models, namely ResNeXt50, Inception-V3, RegNetY-400MF, and EfficientNet-V2-S as the base classifier. This study also analysed the most appropriate technique to be used as the ensemble learning model’s meta-classifier. Therefore, several machine learning techniques are determined to be evaluated, namely support vector machine (SVM), logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), decision tree (DT), and Naïve Bayes (NB). Six publicly available OMR datasets are combined, down sampled, and used to test the proposed model. The dataset consists of the HOMUS_V2, Rebelo1, Rebelo2, Fornes, OpenOMR, and PrintedMusicSymbols datasets. The proposed ensemble learning model managed to outperform the model built in the previous study and succeeded in achieving outstanding accuracy and F1-scores with the best value of 97.51% and 97.52%, respectively; both of which were achieved by the LR meta-classifier.
Traumatic Brain Injury and Long-Term Risk of Stroke Among US Military Veterans
Authors: Schneider Andrea L.C. , Peltz Carrie B. , Li Yixia , Bahorik Amber , Gardner Raquel C. , Yaffe Kristine 
Published: June 2023

BACKGROUND:
Traumatic brain injury (TBI) is associated with significant morbidity, but the association of TBI with long-term stroke risk in diverse populations remains less clear. Our objective was to examine the long-term associations of TBI with stroke and to investigate potential differences by age, sex, race and ethnicity, and time since TBI diagnosis.

METHODS:
Retrospective cohort study of US military veterans aged 18+ years receiving healthcare in the Veterans Health Administration system between October 1, 2002 and September 30, 2019. Veterans with TBI were matched 1:1 to veterans without TBI on age, sex, race and ethnicity, and index date, yielding 306 796 veterans with TBI and 306 796 veterans without TBI included in the study. In primary analyses, Fine-Gray proportional hazards models adjusted for sociodemographics and medical/psychiatric comorbidities were used to estimate the association between TBI and stroke risk, accounting for the competing risk of mortality.

RESULTS:

Participants were a mean age of 50 years, 9% were female, and 25% were of non-White race and ethnicity. Overall, 4.7% of veterans developed a stroke over a median follow-up of 5.2 years. Veterans with TBI had 1.69 times (95% CI, 1.64–1.73) increased risk of any stroke (ischemic or hemorrhagic) compared to veterans without TBI. This increased risk was highest in the first-year post-TBI diagnosis (hazard ratio [HR], 2.16 [95% CI, 2.03–2.29]) but remained elevated for 10+ years. Similar patterns were observed for secondary outcomes, with associations of TBI with hemorrhagic stroke (HR, 3.92 [95% CI, 3.59–4.29]) being stronger than with ischemic stroke (HR, 1.56 [95% CI, 1.52–1.61]). Veterans with both mild (HR, 1.47 [95% CI, 1.43–1.52]) and moderate/severe/penetrating injury (HR, 2.02 [95% CI, 1.96–2.09]) had increased risk of stroke compared to veterans without TBI. Associations of TBI with stroke were stronger among older compared to younger individuals (
P
interaction-by-age<0.001) and were weaker among Black veterans compared to other race and ethnicities (
P
interaction-by-race<0.001).

CONCLUSIONS:
Veterans with prior TBI are at increased long-term risk for stroke, suggesting they may be an important population to target for primary stroke prevention measures.
The CESM2 Single-Forcing Large Ensemble and Comparison to CESM1: Implications for Experimental Design
Authors: Simpson Isla R. , Rosenbloom Nan , Danabasoglu Gokhan , Deser Clara , Yeager Stephen G. , McCluskey Christina S. , Yamaguchi Ryohei , Lamarque Jean-Francois , Tilmes Simone , Mills Michael J. , Rodgers Keith B. 
Published: June 2023

Abstract
Single-forcing large ensembles are a relatively new tool for quantifying the contributions of different anthropogenic and natural forcings to the historical and future projected evolution of the climate system. This study introduces a new single-forcing large ensemble with the Community Earth System Model, version 2 (CESM2), which can be used to separate the influences of greenhouse gases, anthropogenic aerosols, biomass burning aerosols, and all remaining forcings on the evolution of the Earth system from 1850 to 2050. Here, the forced responses of global near-surface temperature and associated drivers are examined in CESM2 and compared with those in a single-forcing large ensemble with CESM2’s predecessor, CESM1. The experimental design, the imposed forcing, and the model physics all differ between the CESM1 and CESM2 ensembles. In CESM1, an “all-but-one” approach was used whereby everything except the forcing of interest is time evolving, while in CESM2 an “only” approach is used, whereby only the forcing of interest is time evolving. This experimental design choice is shown to matter considerably for anthropogenic aerosol-forced change in CESM2, due to state dependence of cryospheric albedo feedbacks and nonlinearity in the Atlantic meridional overturning circulation (AMOC) response to forcing. This impact of experimental design is, however, strongly dependent on the model physics and/or the imposed forcing, as the same sensitivity to experimental design is not found in CESM1, which appears to be an inherently less nonlinear model in both its AMOC behavior and cryospheric feedbacks.
Seasonal and Geographical Variations in Fundamental Weather Patterns during Extreme Precipitation as Identified from Omega Equation Forcing
Authors: Swenson Leif M. , Grotjahn Richard 
Published: June 2023

Abstract
We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.
Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas
Authors: Pan Ting , Su Chun-Qiu , Tang Wen-Tian , Lin Jie , Lu Shan-Shan , Hong Xun-Ning 
Published: June 2023

Background Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. Purpose To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. Material and Methods This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. Results Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp−90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis ( P < 0.05). Conclusion Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.