GDF15 ameliorates sepsis-induced lung injury via AMPK-mediated inhibition of glycolysis in alveolar macrophage.

Growth differentiation factor 15 (GDF15) as a stress response cytokine is involved in the development and progression of several diseases associated with metabolic disorders. However, the regulatory role and the underlying mechanisms of GDF15 in sepsis remain poorly defined. Our study analyzed the levels of GDF15 and its correlations with the clinical prognosis of patients with sepsis. In vivo and in vitro models of sepsis were applied to elucidate the role and mechanisms of GDF15 in sepsis-associated lung injury. We observed strong correlations of plasma GDF15 levels with the levels of C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and lactate as well as Sequential Organ Failure Assessment (SOFA) scores in patients with sepsis. In the mouse model of lipopolysaccharide-induced sepsis, recombinant GDF15 inhibited the proinflammatory responses and alleviated lung tissue injury. In addition, GDF15 decreased the levels of cytokines produced by alveolar macrophages (AMs). The anti-inflammatory effect of glycolysis inhibitor 2-DG on AMs during sepsis was mediated by GDF15 via inducing the phosphorylation of the α-subunit of eukaryotic initiation factor 2 (eIF2α) and the expression of activating transcription factor 4 (ATF4). Furthermore, we explored the mechanism underlying the beneficial effects of GDF15 and found that GDF15 inhibited glycolysis and mitogen-activated protein kinases (MAPK)/nuclear factor-κB (NF-κB) signaling via promoting AMPK phosphorylation. This study demonstrated that GDF15 inhibited glycolysis and NF-κB/MAPKs signaling via activating AMP-activated protein kinase (AMPK), thereby alleviating the inflammatory responses of AMs and sepsis-associated lung injury. Our findings provided new insights into novel therapeutic strategies for treating sepsis.

In Vivo Imaging MicroRNA with Bright Fluorescent RNA Aptamer Through Target-Mediated Entropy-Driven Toehold Exchange.

MicroRNAs (miRNAs) play vital roles in biological activities, but their in vivo imaging is still challenging due to the low abundance and the lack of efficient fluorescent tools. RNA aptamers with high affinity and low background emerge for bioimaging yet suffering from low brightness. We introduce a rational design based on target-mediated entropy-driven toehold exchange (EDTE) to induce the release of RNA aptamer and subsequently light up corresponding fluorophore, which achieves selective imaging of miRNAs with good stability in both living cells and tumor-bearing mouse. Through tailoring recognition unit of the EDTE probes, highly sensitive imaging of different miRNAs including miRNA-125b and miRNA-21 is achieved, confirming its universal bioimaging applications. In comparison with the reported "one-to-one" model, the EDTE strategy shows a remarkable 4.6-time improvement in signal/noise ratio for intracellular imaging of the same miRNA. Particularly, it realizes sensitive imaging of miRNA in vivo, providing a promising tool in investigating functions and interactions of disease-associated miRNAs.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors.

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Sufficiently activated mature natural killer cells derived from peripheral blood mononuclear cells substantially enhance antitumor activity.

BACKGROUND: Peripheral blood-derived natural killer (NK) cells spontaneously lyse tumor cells without prior sensitization. However, NK cells in peripheral blood (PBNK cells) are in a resting state and exhibit inhibitory phenotypes and impaired cytotoxicity. Thus, strengthening the cytotoxic effector function of PBNK cells and improving NK cell expansion in vitro for a convenient allogeneic therapy are essential. MATERIALS AND METHODS: Pure cytokine activation and expansion of NK cells (super NK [SNK]) from peripheral blood mononuclear cells were studied. Markers of activated and inhibited NK cells and cytokine secretion by NK cells were examined using flow cytometry. NK cell antitumor activity in vitro was assessed using lactate dehydrogenase (LDH) cytotoxicity assay and an Incucyte real-time imaging system. Additionally, the function of SNK cells against ascites caused by ovarian cancer in NOD-Prkdc(em26Cd52)il2rg(em26Cd22)/Nju (NCG) mice was determined. In a further investigation of the differences between PBNK and SNK, the mRNA of both cells was sequenced and analyzed. RESULTS: Human peripheral blood mononuclear cells showed selective NK cell expansion upon cytokine activation and culture. Both SNK and PBNK cells expressed activation markers, but at different levels, and SNK cells secreted more cytokines related to cytotoxicity than PBNK cells did. Accordingly, SNK cells exhibited strong antitumor activity ex vivo and improved NCG mice survival after intraperitoneal ovarian cancer transplantation. Mechanistically, SNK cells expressed more genes associated with nucleotide metabolism, fatty acid, and ATP metabolism than PBNK cells. CONCLUSION: SNK cells derived from peripheral blood mononuclear cells have sufficiently activated mature characteristics and high antitumor activity, rendering them a highly promising and essential therapeutic approach for cancer treatment.

A sensitive "off-on" electrochemiluminescence DNA sensor based on signal cascade amplification circuit and distance-dependent energy transfer.

A sensitive "off-on" electrochemiluminescence (ECL) DNA sensor was constructed based on Exo III-assisted cascade amplification system. In the cascade amplification circuit, target DNA and Exo III cutting substrate were designed into an inverted T-shaped binding mode to form a stable DNA junction, thus effectively triggering Exo III digestion cycle. During the biosensor assembly process, ferrocene (Fc) and distance-dependent ECL resonance energy transfer (ECL-RET) and surface plasmon resonance (SPR) effects were introduced to regulate the ECL of semiconductor quantum dots (QDs). Carboxylated ZnCdSe/ZnS QDs were used as ECL signal probes and K2S2O8 was coreactant, and the initial cathodic ECL signal of QDs was efficiently quenched through electron and energy transfer with Fc and ECL-RET with Au NPs, leaving the system in "off" state. After the products of cascade amplification were introduced into the electrode surface, the single-stranded DNA modified with Fc was displaced, and the distance between Au NPs and QDs became farther, resulting in a transition from ECL-RET to SPR, and then a significant ECL signal boost was achieved, turning the system into "on" state. The combination of efficient cascade amplification system and sensitive "off-on" ECL signal change mode enabled the biosensing platform to detect target DNA with high selectivity (able to distinguish single-base mutated DNA) and ultra-high sensitivity (limit of detection was 31.67 aM, S/N = 3), providing a new perspective for designing highly sensitive and programmable ECL biosensors.

Spatial Topological Structure Design of Porous Ti-6Al-4V Alloy with Low Modulus and Magnetic Susceptibility.

Ti-6Al-4V alloy is widely used as a biomaterial for hard tissue replacement, but its Young's modulus is still higher than that of human bone tissue, which may cause a "stress shielding" effect and lead to implant loosening. In addition, metal implants with low magnetic susceptibility are beneficial for obtaining minimal artifacts in magnetic resonance imaging. To reduce Young's modulus and magnetic susceptibility of Ti-6Al-4V alloy, a series of irregular prismatic porous structure models were designed based on the Voronoi principle, built by changing the irregularity, prism-diameter-to-initial-seed-spacing ratio, and seed number, and studied using finite-element analysis. Porous samples were prepared by selective laser melting and subjected to a compression test and magnetic susceptibility test. The simulation results show that the prism-diameter-to-initial-seed-spacing ratio has the greatest impact on porosity compared with the irregularity and seed number. The simulation-predicted porosity and compression modulus are highly consistent with the measured ones. The irregular prismatic porous Ti-6Al-4V samples exhibit mechanical properties similar to those of human bones and show a magnetic susceptibility of no more than 50% that of compact Ti-6Al-4V. A regulatable irregular prismatic porous structure is feasible for designing porous implants with desirable properties for biomedical applications.

Sensitivity analysis of counterweight double-row pile deformation to weak stratum parameters.

In order to investigate the sensitivity of weak soil parameters on the deformation of balanced double-row piles, a case study was conducted in a deep foundation pit project in Shenzhen City. A variety of analysis methods, including numerical simulation, field measurements, orthogonal experiments, and theoretical analysis, were employed to analyze the impact of three weak soil parameters on the deformation of balanced double-row piles. The research results showed that the deformation of the front and back rows of piles exhibited overturning deformation, gradually decreasing with depth and reaching the maximum at the pile top due to the constraint effect of the balance platform. The numerical simulation results of horizontal displacements for the front and rear piles were in good agreement with the field measurements, confirming the accuracy and reasonableness of the numerical analysis model and parameter selection. Through a series of orthogonal numerical simulation experiments, it was determined that the cohesive strength (C) of soft layers, such as rockfill and silt, is a key factor, the internal friction angle (φ) is an important influencing factor, and the elastic modulus (E) is a general influencing factor. Theoretical analysis was employed to establish the relationship curve between each parameter and the maximum pile deformation, as well as the sensitivity factors, further verifying the impact of these weak soil parameters. The research findings presented in this paper can provide valuable guidance for geotechnical engineers when selecting geological parameters for similar deep excavation projects.

[Clinical features and risk factors for secondary hemophagocytic lymphohistiocytosis in elderly patients with severe SARS-CoV-2 infection: a multicenter retrospective cohort study].

OBJECTIVE: To explore the incidence of secondary hemophagocytic lymphohistiocytosis (sHLH) in elderly patients with severe SARS-CoV-2 infection, and to analyze and summarize its clinical features and risk factors for early identification of high-risk groups. METHODS: A retrospective cohort study was conducted. From January to May 2020, No. 960 Hospital of People's Liberation Army, the Second Hospital Affiliated to Cheeloo College of Medicine of Shandong Province, the First Rehabilitation Hospital of Shandong Province, the Public Health Clinical Center Affiliated to Shandong University, and Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine received 248 patients over 60 years old who were diagnosed with severe SARS-CoV-2 infection during their assistance to Hubei or support for diagnosis and treatment of SARS-CoV-2 infection in Shandong Province. The clinical data of patients were collected. According to the hemophagocytic lymphohistiocytosis diagnosis scoring (HScore) criteria, the patients were divided into sHLH group (HScore > 169) and non-sHLH group (HScore < 98). The demographic data, clinical features, laboratory results, the proportion of organ failure and 60-day mortality of patients were collected and compared between the two groups. The risk factors of sHLH and 60-day death were evaluated through binary multivariate Logistic regression analysis in elderly patients with severe SARS-CoV-2 infection. The receiver operator characteristic curve (ROC curve) was plotted to analyze the diagnostic value of indicators only or combined for sHLH. RESULTS: Among 248 elderly patients with severe SARS-CoV-2 infection, 82 patients with incomplete data and untraceable clinical outcomes, and 35 patients with HScore of 98-169 were excluded. Finally, 131 patients were enrolled in the final follow-up and statistics, including 25 patients in the sHLH group and 106 patients in the non-sHLH group. Compared with the non-sHLH group, plasma albumin (ALB), hemoglobin (Hb), lymphocyte count (LYM), platelet count (PLT), fibrinogen (Fib) and prealbumin (PAB) in the sHLH group were significantly reduced, while alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), MB isoenzyme of creatine kinase (CK-MB), serum creatinine (SCr), C-reactive protein (CRP), D-dimer, ferritin (Fer), lactate dehydrogenase (LDH), procalcitonin (PCT), cardiac troponin I (cTnI), triglycerides (TG), interleukin-6 (IL-6), total bilirubin (TBil) were significantly higher. The fever and fatigue in the sHLH group were more severe than those in the non-sHLH group, and the patients in the sHLH group had higher rates of shock, acute kidney injury, liver dysfunction, and cardiac injury than the non-sHLH group. The 60-day mortality of patient in the sHLH group was significantly higher than that in the non-sHLH group [84.0% (21/25) vs. 40.6% (43/106), P < 0.01]. Binary multivariate Logistic regression analysis showed that high Fer [odds ratio (OR) = 0.997, 95% confidence interval (95%CI) was 0.996-0.998], D-dimer (OR = 0.960, 95%CI was 0.944-0.977), LDH (OR = 0.998, 95%CI was 0.997-0.999) and TG (OR = 0.706, 95%CI was 0.579-0.860) were independent risk factors for sHLH in elderly patients with severe SARS-CoV-2 infection (all P < 0.01), while elevated Fer (OR = 1.001, 95%CI was 1.001-1.002), LDH (OR = 1.004, 95%CI was 1.002-1.005) and D-dimer (OR = 1.036, 95%CI was 1.018-1.055) were independent risk factors for 60-day death of patients (all P < 0.01). The death risk of the sHLH patients was 7.692 times higher than that of the non-sHLH patients (OR = 7.692, 95%CI was 2.466-23.987, P = 0.000). ROC curve analysis showed that a three-composite-index composed of LDH, D-dimer and TG had good diagnostic value for sHLH in elderly patients with severe SARS-CoV-2 infection [area under the ROC curve (AUC) = 0.920, 95%CI was 0.866-0.973, P = 0.000]. CONCLUSIONS: Elderly patients with severe SARS-CoV-2 infection complicated by sHLH tend to be critically ill and have refractory status and worse prognosis. High Fer, LDH, D-dimer and TG are independent risk factors for sHLH, and are highly suggestive of poor outcome. The comprehensive index composed of LDH, D-dimer and TG has good diagnostic value, and can be used as an early screening tool for sHLH in elderly patients with severe SARS-CoV-2 infection.

Identifying the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism: Case study of the Yangtze River Economic Belt, China.

Boosting the coordination and symbiosis of urbanization and forest ecological security is notably critical for promoting regional green and sustainable development and achieving emission peak and carbon neutrality goals. However, there was still a lack of in-depth analysis of the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism. On the basis of the data from 844 counties in the Yangtze River Economic Belt, this paper explored the spatial differences and influencing factors of the coupling coordination degree of urbanization and forest ecological security. The results manifested that: i) There were apparent spatial disparities in the urbanization index, forest ecological security index, comprehensive index, coupling degree and coupling coordination degree of the Yangtze River Economic Belt. Among them, the spatial pattern of coupling coordination degree had a strong consistency with urbanization index, that is, areas with higher urbanization index also had higher coupling coordination degree. ii) Based on coupling feature identification, it was found that 249 'problem areas' were mainly located in Yunnan Province, southeastern Guizhou Province, central Anhui Province, and central and eastern Jiangsu Province. The main factor for the formation was due to the lag of urbanization in coordinated development. iii) Among the socioeconomic indicators, population structure (0.136), per capita year-end financial institutions loan balance (0.409) and per capita fixed asset investment (0.202) all had a positive impact on coupling coordination degree, while location conditions (-0.126) had a negative impact. Among the natural indicators, soil organic matter (-0.212) and temperature (-0.094) had a negative impact on coupling coordination degree. iv) During the process of coordinated development, it was necessary to increase financial investment and financial support, actively formulate policies to attract talents, enhance the education and publicity of ecological civilization, and develop a green circular economy. The above measures can promote the harmonious development of urbanization and forest ecological security in the Yangtze River Economic Belt.

DNA-framework-based multidimensional molecular classifiers for cancer diagnosis.

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.

FRET cascade miRNA addition probe from non-crosstalk DNA photonic wire assisted with clustering algorithm for early diagnosis of acute myocardial infarction.

Early and accurate diagnosis of acute myocardial infarction (AMI) can significantly reduce patient mortality. A variety of miRNAs are found to dysregulate in AMI patients, but the up- or down-regulation of a specific miRNA may not be evident in the early stage, making it difficult to achieve accurate diagnosis. Here, proposing the design that DNA photonic wire (PW) with no spectral crosstalk would make an excellent template for miRNA conjoint analysis, we report the construction of a miRNA addition probe for the additive analysis of two up-regulated miRNAs (miR-133a and miR-208a) for early diagnosis of AMI in clinical serum samples. A three-dye non-crosstalk DNA PW is built to form the two-step fluorescence resonance energy transfer (FRET) cascade system, in which three paths can blocking the FRET cascade for separate or additive analysis of the two miRNAs. K-Means clustering algorithm is further utilized to classify the output signals of the miRNA addition probe, achieving a 100% accurate diagnosis of early AMI in both the training (n = 40) and validation (n = 19) cohorts of clinical serum samples.

The enhancement of enzyme cascading via tetrahedral DNA framework modification.

Enzyme clustering is widely used in many organisms to increase the catalytic efficiency of cascade reactions. Inspired by nature, organizing enzymes within a cascade reaction also draws much attention in both basic research and industrial processes. An important step for organizing enzymes precisely in vitro is enzyme modification. However, modifying enzymes without sacrificing their activity remains challenging until now. For example, labeling enzymes with DNA, one of the well-established enzyme modification methods, has been shown to significantly reduce the enzymatic activity. Herein we report an enzyme conjugation method that can rescue the reduction of enzymatic activity caused by DNA labeling. We demonstrate that immobilizing DNA-modified enzymes on the vertex of TDNs (tetrahedral DNA nanostructures) enhances the enzymatic activity compared with their unmodified counterparts. Using this strategy, we have further developed an ultra-sensitive and high-throughput electrochemical biosensor for sarcosine detection, which holds great promise for prostate cancer screening.

Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment.

In recent years, the monitoring range of source location technology has developed from being one-dimensional and two-dimensional to being three-dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.

Chinese university students' conceptions of feedback and the relationships with self-regulated learning, self-efficacy, and English language achievement.

In China, under the influence of examination-driven culture and teacher-centered ways of learning, students' self-regulated learning (SRL) capabilities, self-efficacy, and actual English proficiency are greatly hindered. Given this situation, the Chinese Ministry of Education has promulgated the use of formative assessment in the College English curriculum at the tertiary level since 2004. Feedback, as an integrated part of formative assessment, takes up the largest proportion of the Chinese College English classroom assessment and intends to facilitate SRL and learning. However, whether feedback could facilitate students' SRL and learning has not been fully investigated in this context in China. Therefore, this study first explored how students self-reported their conceptions of feedback, SRL, and self-efficacy, and second, the relationships among these constructs and their English language achievement in the College English course. A questionnaire was used to collect data on students' conceptions of feedback, SRL, self-efficacy, and self-perceived English language proficiency. Their English test scores as an indicator of English language achievement were also collected. A total of 538 participants from a university in Northern China participated in this study. Data were analyzed using descriptive statistics, exploratory factor analyses, Pearson correlation analyses, and multiple regression analyses. The results found that Chinese students from the College English course reported a high level of conceptions of teacher and peer feedback, SRL, and self-efficacy, yet a low level of Teacher/Peer Feedback Ignored. For the relationships among these variables, students' conceptions of feedback contributed to SRL and self-efficacy. Besides, self-efficacy was found to be the strongest predictor for self-perceived English language proficiency and standardized English test scores, both indicators for English language achievement. From the theoretical perspective, this study addressed the research gap in the literature by examining four constructs together, that is, students' conceptions of feedback, SRL, self-efficacy, and English language achievement within a university context in China. From the pedagogical angle, the results can also support teachers in their feedback practices to facilitate students' SRL, self-efficacy, and learning.

PIGN spatiotemporally regulates the spindle assembly checkpoint proteins in leukemia transformation and progression.

Phosphatidylinositol glycan anchor biosynthesis class N (PIGN) has been linked to the suppression of chromosomal instability. The spindle assembly checkpoint complex is responsible for proper chromosome segregation during mitosis to prevent chromosomal instability. In this study, the novel role of PIGN as a regulator of the spindle assembly checkpoint was unveiled in leukemic patient cells and cell lines. Transient downregulation or ablation of PIGN resulted in impaired mitotic checkpoint activation due to the dysregulated expression of spindle assembly checkpoint-related proteins including MAD1, MAD2, BUBR1, and MPS1. Moreover, ectopic overexpression of PIGN restored the expression of MAD2. PIGN regulated the spindle assembly checkpoint by forming a complex with the spindle assembly checkpoint proteins MAD1, MAD2, and the mitotic kinase MPS1. Thus, PIGN could play a vital role in the spindle assembly checkpoint to suppress chromosomal instability associated with leukemic transformation and progression.

DNA Assembly-Based Stimuli-Responsive Systems.

Stimuli-responsive designs with exogenous stimuli enable remote and reversible control of DNA nanostructures, which break many limitations of static nanostructures and inspired development of dynamic DNA nanotechnology. Moreover, the introduction of various types of organic molecules, polymers, chemical bonds, and chemical reactions with stimuli-responsive properties development has greatly expand the application scope of dynamic DNA nanotechnology. Here, DNA assembly-based stimuli-responsive systems are reviewed, with the focus on response units and mechanisms that depend on different exogenous stimuli (DNA strand, pH, light, temperature, electricity, metal ions, etc.), and their applications in fields of nanofabrication (DNA architectures, hybrid architectures, nanomachines, and constitutional dynamic networks) and biomedical research (biosensing, bioimaging, therapeutics, and theranostics) are discussed. Finally, the opportunities and challenges for DNA assembly-based stimuli-responsive systems are overviewed and discussed.

A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs.

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476-0.9831 and 14.4965-24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs.

Stomatin-like Protein 2 Promotes Tumor Cell Survival by Activating the JAK2-STAT3-PIM1 Pathway, Suggesting a Novel Therapy in CRC.

Despite intensive efforts, a considerable proportion of colorectal cancer (CRC) patients develop local recurrence and distant metastasis. Stomatin-like protein 2 (SLP-2), a member of the highly conserved stomatin superfamily, is upregulated across cancer types. However, the biological and functional roles of SLP-2 remain elusive in CRC. Here, we report that high SLP-2 expression was found in CRC tissues and was linked to tumor progression and tumor cell differentiation. Additionally, high SLP-2 expression correlated with poor overall survival (OS) in CRC patients (p < 0.001). SLP-2 knockout (SLP-2KO), generated by CRISPR/Cas9, reduced cell growth, migration, and invasion; induced apoptosis in CRC cells; and reduced tumor xenograft growth in vivo. A 181-compound library screening showed that SLP-2KO produced resistance to JAK2 inhibitors (NVP-BSK805 and TG-101348) and a PIM1 inhibitor (SGI-1776), revealing that the JAK2-STAT3-PIM1 oncogenic pathway was potentially controlled by SLP-2 in CRC. In vitro and in vivo, TG-101348 combined with SGI-1776 was synergistic in CRC (combination index [CI] < 1). Overall, our findings suggest that SLP-2 controls the JAK2-STAT3-PIM1 oncogenic pathway, offering a rationale for a novel therapeutic strategy with combined SGI-1776 and TG-101348 in CRC. Additionally, SLP-2 may be a prognostic marker and biomarker for sensitivity to JAK2 and PIM1 inhibitors.

Comparative Study on the Fungicidal Activity of Metallic MgO Nanoparticles and Macroscale MgO Against Soilborne Fungal Phytopathogens.

Engineered nanoparticles have provided a basis for innovative agricultural applications, specifically in plant disease management. In this interdisciplinary study, by conducting comparison studies using macroscale magnesium oxide (mMgO), we evaluated the fungicidal activity of MgO nanoparticles (nMgO) against soilborne Phytophthora nicotianae and Thielaviopsis basicola for the first time under laboratory and greenhouse conditions. In vitro studies revealed that nMgO could inhibit fungal growth and spore germination and impede sporangium development more efficiently than could macroscale equivalents. Indispensably, direct contact interactions between nanoparticles and fungal cells or nanoparticle adsorption thereof were found, subsequently provoking cell morphological changes by scanning electron microscopy/energy-dispersive spectrometry (SEM/EDS) and transmission electron microscopy (TEM). In addition, the disturbance of the zeta potential and accumulation of various modes of oxidative stress in nMgO-exposed fungal cells accounted for the underlying antifungal mechanism. In the greenhouse, approximately 36.58 and 42.35% decreases in tobacco black shank and black root rot disease, respectively, could testify to the efficiency by which 500 µg/ml of nMgO suppressed fungal invasion through root irrigation (the final control efficiency reached 50.20 and 62.10%, respectively) when compared with that of untreated controls or mMgO. This study will extend our understanding of nanoparticles potentially being adopted as an effective strategy for preventing diversified fungal infections in agricultural fields.

Examining the spatiotemporal change of forest resource carrying capacity of the Yangtze River Economic Belt in China.

Rapid urbanization and the excessive human harvesting of forests have led to a continuous decline in the carrying capacity of forests in China. As a result, quantitative means of measuring forest resource carrying capacity are greatly needed, with a view to identifying problem areas and their causes and formulating effective response strategies. This paper puts forward a framework and methodology for constructing a forest resource carrying capacity index (FRCCI). To do this, we first calculate a forest ecological security index (FESI), using an evaluation index system. Ideal FESI values are then simulated by introducing a forest ecological location coefficient (FELC), and the FRCCI is obtained as the difference between the ideal FESI and the FESI. The study considers the 1086 counties that compose the Yangtze River Economic Belt in China, using forest and socioeconomic data for 2015. The resulting FRCCI values indicate that the forests of Yunnan province are generally in a state of "no overload," while Sichuan, Guizhou, Chongqing, Hunan, Hubei, Jiangxi, and Zhejiang provinces occupied a state of "critical overload" and Anhui and Jiangsu provinces experienced "general overload." The spatial pattern of the FRCCI in the study region presented significant centralization, with high FRCCI values mainly clustered in areas in the upper reaches of the Yangtze River and low FRCCI values mainly clustered in areas in the midstream and downstream reaches of the River. The study identifies 416 counties identified as forest carrying capacity problem areas (38.31% of the study area); these areas were mainly concentrated in Shanghai and Anhui province. We argue that a number of measures would be helpful in improving FRCCI values, including promoting the forest state index by strengthening reforestation as well as afforestation, reducing the external pressure on forests by means of energy saving and emission reduction strategies, and formulating comprehensive policy measures to promote the carrying capacity of forests in the whole study area and in the problem areas.