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The recession of a coast can destabilize coastal cliffs. The stability of a cliff is controlled by a rock bridge. Identifying the volume-expansion point of rock bridges is crucial to assess cliff stability, but currently there are few identifying methods. Using a numerical analytical tool, we investigate the acoustic emission characteristics during shear tests on rock bridges. Acoustic emission events with a high energy level, i.e., characteristic events which occur at the volume-expansion point of rock bridges, can indicate this point. The characteristic events, the mainshock (the maximum event corresponding to rock-bridge rupture), and the smaller events between them constitute a special activity pattern, as the micro-seismicity during the evolutionary process of a coastal cliff collapse in Mesnil-Val, NW France showed. This pattern arises in rock bridges with different mechanical properties and geometry, or under different loading conditions. Although the energy level of characteristic events and mainshocks changes with the variation of the conditions, the difference of their energy level is approximately constant. The spatial distribution of characteristic events and mainshocks can indicate the location of rock bridges. These findings help to better understand the evolutionary mechanism of collapses and provide guidelines for monitoring the stability of coastal cliffs.

The focus of the present study is to explain the stair-step brittle deformation evolutionary pattern of the landslide and reveal the corresponding internal physical mechanism. Based on the concept of high strength geological body (HSGB), similar to rock bridges in the intermittence jointed rock masses, a conceptual geological model and the corresponding mechanical model are successively outlined. Through reanalysing typical stair-step displacement records of a wedge rockslide, it was found that the HSGB model can reasonably explain the stair-step deformation feature and reveal its internal essence. Finally, a displacement criterion is derived through combining the two-dimensional renormalization group model with the shear creep constitutive model. By means of this displacement criterion, one can quantitatively describe the displacement ratio between the onset of the accelerating creep and brittle rupture of HSGB. In order to make the reader understand this displacement criterion better, a detailed application process is demonstrated through a case study. To some extent, the present study has shed light on the failure prediction of the HSGB-type landslide, whose stability is mainly controlled by HSGB.

Abstract Cell death is a fundamental progress that regulates cell number, tissue homeostasis and organ size in development. The c-Jun N-terminal kinase (JNK) pathway has been evolutionarily conserved from fly to human, and plays essential roles in regulating cell death. To characterize additional genes that regulate JNK signaling, we performed a genetic screen in Drosophila and identified dGLYAT, a novel gene whose function was previously unknown, as a modulator of JNK-mediated cell death. We found that loss of dGLYAT suppressed JNK activation and cell death triggered by over-expression of Egr or Hep, or depletion of puc or lgl in development, suggesting dGLYAT regulates both ectopic and physiological functions of JNK pathway. Furthermore, we showed that loss of dGLYAT inhibits JNK-mediated ROS production, suggesting dGLYAT regulates multiple functions of JNK signaling in vivo.

Early-stage ovarian serous carcinoma is usually difficult to detect in clinical practice. The profiling of protein expression in high-grade serous carcinoma (HGSC) and low-grade serous carcinoma (LGSC) would provide important information for diagnoses and chemotherapy. Here, we performed proteomic profiling of specimens from 13 HGSC and 7 LGSC patients by iTRAQ. A total of 323 proteins that were differentially expressed were identified. After immunohistochemical confirmation of expressed proteins in 166 clinical tissues, asparagine synthetase (ASNS) and filamin A (FLNA) were selected for further functional study. Cisplatin-sensitive (CS; ASNShigh and FLNAlow) and cisplatin-resistant (CR; ASNSlow and FLNAhigh) SKOV3 and OVCAR3 ovarian cancer cell lines were used for subsequent in vitro and in vivo experiments. Notably, ASNS overexpression (ASNS+) or FLNA knockdown (shFLNA) enabled cisplatin-induced apoptosis and autophagy in CR cells. However, ASNS+ and shFLNA promoted and attenuated tumor growth, respectively. In CS cells, ASNS knockdown (shASNS) attenuated clonogenicity, cell proliferation, and the epithelial–mesenchymal transition, whereas FLNA overexpression (FLNA+) protected cells from cisplatin. In vivo, cisplatin resistance was attenuated in mice xenografted with ASNS+, shFLNA, or ASNS+-shFLNA CR cells, whereas xenografts of shASNS or FLNA+ CS cells exhibited resistance to cisplatin. Clinically, all HGSC patients (83/83) responded to cisplatin, while 6 in 41 LGSC patients exhibited cisplatin resistance. These findings identify ASNS and FLNA as distinct biomarkers for HGSC and LGSC, which may have potential value in the prognosis and clinical treatment of serous carcinoma.

Biogenic volatile organic compounds (VOCs) play key roles in coral reef ecosystems, where, together with dimethylated sulfur compounds, they are indicators of ecosystem health and are used as defense strategies and infochemicals. Assessment and prediction of the exchange rates of VOCs between the oceans and atmosphere, with implications for atmospheric reactivity and climate, are hampered by poor knowledge of the regulating processes and their temporal variability, including diel cycles. Here, we measured the variation over 36h of the concentrations of DMSPCs (dimethylsulfoniopropionate (DMSP)-related compounds, namely DMSP, dimethylsulfoxide, acrylate, dimethylsulfide, and methanethiol as dimethyl disulfide) and VOCs (COS, CS2, isoprene, the iodomethanes CH3I and CH2ClI, and the bromomethanes CHBr3 and CH2Br2), in surface waters inside the shallow, northern coral-reef lagoon of Mo'orea (French Polynesia) and 4 km offshore, in the tropical open ocean. Comparisons with concurrent measurements of sea surface temperature, solar radiation, biogeochemical variables (nutrients, organic matter), and the abundances and taxonomic affiliations of microbial plankton were conducted with the aim to explain interconnections between DMSPCs, VOCs, and their environment across diel cycles. In open ocean waters, deeper surface mixing and low nutrient levels resulted in low phytoplankton biomass and bacterial activity. Consequently, the diel patterns of VOCs were more dependent on photochemical reactions, with daytime increases for several compounds including dissolved dimethylsulfoxide, COS, CS2, CH3I, and CH2ClI. A eukaryotic phytoplankton assemblage dominated by dinoflagellates and haptophytes provided higher cell-associated DMSP concentrations, yet the occurrence of DMSP degradation products (dimethylsulfide, dimethyl disulfide) was limited by photochemical loss. Conversely, in the shallow back reef lagoon the proximity of seafloor sediments, corals and abundant seaweeds resulted in higher nutrient levels, more freshly-produced organic matter, higher bacterial activity, and larger algal populations of Mamiellales, diatoms and Cryptomonadales. Consequently, DMSP and dimethylsulfoxide concentrations were lower but those of most VOCs were higher. A combination of photobiological and photochemical processes yielded sunny-daytime increases and nighttime decreases of dimethylsulfide, dimethyl disulfide, COS, isoprene, iodomethanes and bromomethanes. Our results illustrate the important role of solar radiation in DMSPC and VOC cycling, and are relevant for the design of sampling strategies that seek representative and comparable measurements of these compounds. [ABSTRACT FROM AUTHOR]

Proton exchange membrane water electrolysis is a highly promising hydrogen production technique for sustainable energy supply, however, achieving a highly active and durable catalyst for acidic water oxidation still remains a formidable challenge. Herein, we propose a local microenvironment regulation strategy for precisely tuning In−RuO2/graphene (In−RuO2/G) catalyst with intrinsic electrochemical activity and stability to boost acidic water oxidation. The In−RuO2/G displays robust acid oxygen evolution reaction performance with a mass activity of 671 A gcat−1 at 1.5 V, an overpotential of 187 mV at 10 mA cm−2, and long‐lasting stability of 350 h at 100 mA cm−2, which arises from the asymmetric Ru−O−In local structure interactions. Further, it is unraveled theoretically that the asymmetric Ru−O−In structure breaks the thermodynamic activity limit of the traditional adsorption evolution mechanism which significantly weakens the formation energy barrier of OOH*, thus inducing a new rate‐determining step of OH* absorption. Therefore, this strategy showcases the immense potential for constructing high‐performance acidic catalysts for water electrolyzers. [ABSTRACT FROM AUTHOR]

Stock price prediction is crucial in stock market research, yet existing models often overlook interdependencies among stocks in the same industry, treating them as independent entities. Recognizing and accounting for these interdependencies is essential for precise predictions. Propensity score matching (PSM), a statistical method for balancing individuals between groups and improving causal inferences, has not been extensively applied in stock interdependence investigations. Our study addresses this gap by introducing PSM to examine interdependence among pharmaceutical industry stocks for stock price prediction. Additionally, our research integrates Improved particle swarm optimization (IPSO) with long short-term memory (LSTM) networks to enhance parameter selection, improving overall predictive accuracy. The dataset includes price data for all pharmaceutical industry stocks in 2022, categorized into chemical pharmaceuticals, biopharmaceuticals, and traditional Chinese medicine. Using Stata, we identify significantly correlated stocks within each sub-industry through average treatment effect on the treated (ATT) values. Incorporating PSM, we match five target stocks per sub-industry with all stocks in their respective categories, merging target stock data with weighted data from non-target stocks for validation in the IPSO-LSTM model. Our findings demonstrate that including non-target stock data from the same sub-industry through PSM significantly improves predictive accuracy, highlighting its positive impact on stock price prediction. This study pioneers PSM's use in studying stock interdependence, conducts an in-depth exploration of effects within the pharmaceutical industry, and applies the IPSO optimization algorithm to enhance LSTM network performance, providing a fresh perspective on stock price prediction research. [ABSTRACT FROM AUTHOR]