Relative mRNA expression levels of pituitary thyroid-stimulating hormone <i>β</i> subunit (<i>tshβ</i>) and quantification of plasma TSH content in female goldfish exposed to 0, 0.... by Xiaona Zhang (489603)

Relative mRNA expression levels of thyrotropin-releasing hormone (<i>trh</i>) and corticotrophin-releasing hormone (<i>crh</i>) in the hypothalamus glands and quantification of pla... by Xiaona Zhang (489603)

Data_Sheet_6_MliR, a novel MerR-like regulator of iron homeostasis, impacts metabolism, membrane remodeling, and cell adhesion in the marine Bacteroidetes Bizionia argentinensis.PD... by Leonardo Pellizza (3743770)

“…We here present a novel transcriptional regulator classified in the MerR superfamily that lacks an EBD domain and has neither conserved metal binding sites nor cysteine residues. …”

Depletion of SAMHD1 induces cell cycle delay and spontaneous DNA damage response. by Kiwon Park (6492266)

Effect of MA-5 on sIBM myoblasts. by Yoshitsugu Oikawa (9721356)

The knocking down of the oncoprotein Golgi phosphoprotein 3 in T98G cells of glioblastoma multiforme disrupts cell migration by affecting focal adhesion dynamics in a focal adhesio... by Cecilia Arriagada (2636104)

“…Here, we analyzed the effect on cell migration that resulted from stable, RNAi-mediated knocking down of GOLPH3 in T98G cells of glioblastoma multiforme, a human glioma cell line with unique features. …”

FOXO1 Suppression Maps to −304/−95 of the Murine <i>Fshb</i> Promoter. by Chung Hyun Park (665717)

Table_1_Single Nucleotide Polymorphisms (SNP) and SNP-SNP Interactions of the Surfactant Protein Genes Are Associated With Idiopathic Pulmonary Fibrosis in a Mexican Study Group; C... by Ata Abbasi (4971352)

“…The goal was to identify SP SNPs and SNP-SNP interactions that associate with IPF as well as SNPs and interactions that may be unique to each of these interstitial diseases or common between them. We observed: 1) in terms of IPF, i) three single SFTPA1 SNPs to associate with decreased IPF risk, ii) three SFTPA1 haplotypes to associate with increased IPF risk, and iii) a number of three-SNP interactions to associate with IPF susceptibility. 2) Comparison of IPF and HP, i) three SFTPA1 and one SFTPB SNP associated with decreased risk in IPF but increased risk in HP, and one SFTPA1 SNP associated with decreased risk in both IPF and HP, ii) a number of three-SNP interactions with the same or different effect pattern associated with IPF and/or HP susceptibility, iii) one of the three-SNP interactions that involved SNPs of SFTPA1, SFTPA2, and SFTPD, with the same effect pattern, was associated with a disease-specific outcome, a decreased and increased risk in HP and IPF, respectively. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Data_Sheet_1_Critical Leaf Magnesium Thresholds and the Impact of Magnesium on Plant Growth and Photo-Oxidative Defense: A Systematic Review and Meta-Analysis From 70 Years of Rese... by Melanie Hauer-Jákli (6851843)

“…We combined all published data relating leaf Mg concentrations to growth and found a critical leaf Mg range for dry weight between 0.1 and 0.2% which was valid for numerous crop species such as wheat, potato, rice, maize, sorghum and barley. …”

Systemic inflammation is decreased in <i>Sharpin^cpdm-Dem</i>, <i>Rag1^−/−</i> double mutant mice but significantly increased in <i>Sharpin^cpdm-Dem</... by Christopher S. Potter (356079)

Fig 1 - by Corantin Maurin (9155070)

SEM and TEM exploration of SARS-CoV-2 viral particles at the ocular surface. by Corantin Maurin (9155070)

Number of cases of syphilis in Japan: 2018–2022. by Akira Komori (8284599)

“…<div><p>Some countries have reported a post-pandemic resurgence in syphilis prevalence, but trend data in the World Health Organization Western Pacific Region (WHO-WPRO), including Japan, are severely lacking. …”

Supplementary Material for: The Clinicopathological Significance and Correlative Signaling Pathways of an Autophagy-Related Gene, Ambra1, in Breast Cancer: a Study of 25 Microarray... by He R. (5904200)

“…<i>In vitro</i> experiments suggested that the proliferation of MDA-MB-231 cells transfected with Ambra1 short hairpin RNA (sh-RNA 2450) showed a decreasing trend at 48 h compared with the control (CK) group. …”

DataSheet1_Gasdermin D could be lost in the brain parenchyma infarct core and a pyroptosis-autophagy inhibition effect of Jie-Du-Huo-Xue decoction after stroke.docx by Chang Zhou (88053)

“…JDHXD suppressed pyroptosis and autophagy by inhibiting NLRP3, thereby alleviating CI. In addition, we present a different observation from previous studies that the expression of GSDMD in the infarct core was lower than that in the peri-infarct and contralateral non-ischemic hemispheres on day 3 of CI.…”