A fundamental study on postmortem submersion interval estimation by metabolomics analyzing of gastrocnemius muscle from submersed rat models in freshwater.

In forensic practice, determining the postmortem submersion interval (PMSI) and cause-of-death of cadavers in aquatic ecosystems has always been challenging task. Traditional approaches are not yet able to address these issues effectively and adequately. Our previous study proposed novel models to predict the PMSI and cause-of-death based on metabolites of blood from rats immersed in freshwater. However, with the advance of putrefaction, it is hardly to obtain blood samples beyond 3 days postmortem. To further assess the feasibility of PMSI estimation and drowning diagnosis in the later postmortem phase, gastrocnemius, the more degradation-resistant tissue, was collected from drowned rats and postmortem submersion model in freshwater immediately after death, and at 1 day, 3 days, 5 days, 7 days, and 10 days postmortem respectively. Then the samples were analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the dynamic changes of the metabolites. A total of 924 metabolites were identified. Similar chronological changes of gastrocnemius metabolites were observed in the drowning and postmortem submersion groups. The difference in metabolic profiles between drowning and postmortem submersion groups was only evident in the initial 1 day postmortem, which was faded as the PMSI extension. Nineteen metabolites representing temporally-dynamic patterns were selected as biomarkers for PMSI estimation. A regression model was built based on these biomarkers with random forest algorithm, which yielded a mean absolute error (± SE) of 5.856 (± 1.296) h on validation samples from an independent experiment. These findings added to our knowledge of chronological changes in muscle metabolites from submerged vertebrate remains during decomposition, which provided a new perspective for PMSI estimation.

Inverse lithography physics-informed deep neural level set for mask optimization.

As the feature size of integrated circuits continues to decrease, optical proximity correction (OPC) has emerged as a crucial resolution enhancement technology for ensuring high printability in the lithography process. Recently, level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution, showcasing its powerful pattern fidelity, especially in advanced processing. However, the massive computational time consumption of ILT limits its applicability to mainly correcting partial layers and hotspot regions. Deep learning (DL) methods have shown great potential in accelerating ILT. However, the lack of domain knowledge of inverse lithography limits the ability of DL-based algorithms in process window (PW) enhancement, etc. In this paper, we propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization. This approach utilizes level set-based ILT as a layer within the DL framework and iteratively conducts mask prediction and correction to significantly enhance printability and PW in comparison with results from pure DL and ILT. With this approach, the computational efficiency is significantly improved compared with ILT. By gearing up DL with the knowledge of inverse lithography physics, ILDLS provides a new and efficient mask optimization solution.

Controllable spin splitting in 2D ferroelectric few-layerγ-GeSe.

γ-GeSe is a new type of layered bulk material that was recently successfully synthesized. By means of density functional theory first-principles calculations, we systematically studied the physical properties of two-dimensional (2D) few-layerγ-GeSe. It is found that few-layerγ-GeSe are semiconductors with band gaps decreasing with increasing layer number; and 2Dγ-GeSe with layer numbern⩾ 2 are ferroelectric with rather low transition barriers, consistent with the sliding ferroelectric mechanism. Particularly, spin-orbit coupling induced spin splitting is observed at the top of valence band, which can be switched by the ferroelectric reversal; furthermore, their negative piezoelectricity also enables the regulation of spin splitting by strain. Finally, excellent optical absorption was also revealed. These intriguing properties make 2D few-layerγ-GeSe promising in spintronic and optoelectric applications.

Galangin Targets HSP90ß to Alleviate Ulcerative Colitis by Controlling Fatty Acid Synthesis and Subsequent NLRP3 Inflammasome Activation.

SCOPE: The purpose of this research is to investigate the specific role of HSP90 paralogs in ulcerative colitis (UC), and to explore the mechanisms behind the inhibitory effects of galangin (Gal) on UC by inhibiting HSP90ß in vivo. METHODS AND RESULTS: In order to achieve this, publicly available gene expression data and molecular biology techniques are used. The results show that the expression of HSP90ß is significantly increased in the mucosal biopsies of UC patients and in the colons of colitis mice, and that there is a significant correlation between HSP90ß levels and disease severity. Then, Gal is found to bind directly to HSP90ß and downregulates the level of p-AKT, as well as the stability and oligomerization of HSP90ß, indicating Gal as an HSP90ß inhibitor. Moreover, the findings reveal that HSP90ß plays a critical role in controlling UC, and that Gal can alleviate colitis by inhibiting HSP90ß and perturbing fatty acid synthesis-mediated NLRP3 inflammasome activation. CONCLUSION: These results not only provide insight into the potential therapeutic use of Gal in the treatment of UC, but also offer new perspectives on the role of HSP90ß in this disease.

The Role of Bismuth in Suppressing the CO Poisoning in Alkaline Methanol Electrooxidation: Switching the Reaction from the CO to Formate Pathway.

While tuning the electronic structure of Pt can thermodynamically alleviate CO poisoning in direct methanol fuel cells, the impact of interactions between intermediates on the reaction pathway is seldom studied. Herein, we contrive a PtBi model catalyst and realize a complete inhibition of the CO pathway and concurrent enhancement of the formate pathway in the alkaline methanol electrooxidation. The key role of Bi is enriching OH adsorbates (OHad) on the catalyst surface. The competitive adsorption of CO adsorbates (COad) and OHad at Pt sites, complementing the thermodynamic contribution from alloying Bi with Pt, switches the intermediate from COad to formate that circumvents CO poisoning. Hence, 8% Bi brings an approximately 6-fold increase in activity compared to pure Pt nanoparticles. This notion can be generalized to modify commercially available Pt/C catalysts by a microwave-assisted method, offering opportunities for the design and practical production of CO-tolerance electrocatalysts in an industrial setting.

Unexpected spontaneous symmetry breaking and diverse ferroicity in two-dimensional mono-metal phosphorus chalcogenides.

Mono-metal phosphorus trichalcogenides (MPX3) have attracted intensive interest due to their intriguing magnetic properties and potential applications. Generally, single-layer two-dimensional (2D) MPX3 are believed to be centrosymmetric. However, we discovered that unexpected spontaneous symmetry breaking may occur in some 2D MPX3, i.e., vertical P-P dimers move out of the plane and become tilted, leading to the structural stability being enhanced, the inversion symmetry being simultaneously broken, and ferroelectricity or ferroelasticity emerging. By systematically investigating the family (176) of 2D MPX3, we found that 34 members undergo such symmetry breaking during geometric optimization, in which ten are identified to be dynamically stable. We show that the mismatch between the triangular sublattice of P-P dimers and the hexagonal sublattice of M atoms and the variable accommodation of P lone-pair electrons in different valence states of M atoms play dominant roles in the inversion symmetry breaking and the emergence of ferroicity. We obtained a ferroic atlas of the whole 2D MPX3 family, which also includes many stable antiferromagnetic and non-ferroic members that have never been reported. Our work not only presents ferroelectricity in the 2D MPX3 family but also reveals how diverse ferroicity emerges with various spontaneous symmetry breakings, which will be helpful for further exploration of 2D ferroic materials.

Pd/Co Catalyst with High Pd Atom Utilization Efficiency for Nitrobenzene Hydrogenation at Room Temperature: Experimental and DFT Studies.

Enhancing catalytic performance as well as reducing catalyst cost are the eternal pursuit for the catalysis community. Herein, a simple and effective palladium-doped cobalt (Pd/Co) catalyst with high Pd atom utilization efficiency was synthesized via galvanic replacement reaction for the selective hydrogenation of nitrobenzene with H2 at room temperature, delivering >99 % yield of aniline with up to 158 times higher catalytic activity than commercial palladium powder. Detailed characterizations and DFT calculations revealed that Co-Pd interaction leads to a decrease in electron density of Pd and the distance between Pd atoms that results in the enhanced catalytic performance. Further experiments indicated that the Pd/Co catalyst serves as a highly efficient, selective, and recyclable catalyst for a range of nitroarene substrates. This work might provide a green and sustainable methodology to design and synthesize highly active catalysts with high utilization efficiency of the noble metals in fundamental and applied research.

Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation.

Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.

Inferring Postmortem Submersion Interval in Rats Found in Water Based on Vitreous Humor Metabolites.

OBJECTIVES: The metabolomics technique of LC-MS/MS combined with data analysis was used to detect changes and differences in metabolic profiles in the vitreous humor of early rat carcasses found in water, and to explore the feasibility of its use for early postmortem submersion interval (PMSI) estimation and the cause of death determination. METHODS: The experimental model was established in natural lake water with 100 SD rats were randomly divided into a drowning group (n=50) and a postmortem (CO2 suffocation) immediately submersion group (n=50). Vitreous humor was extracted from 10 rats in each group at 0, 6, 12, 18 and 24 h postmortem for metabolomics analyses, of which 8 were used as the training set to build the model, and 2 were used as test set. PCA and PLS multivariate statistical analysis were performed to explore the differences in metabolic profiles among PMSI and causes of death in the training set samples. Then random forest (RF) algorithm was used to screen several biomarkers to establish a model. RESULTS: PCA and PLS analysis showed that the metabolic profiles had time regularity, but no differences were found among different causes of death. Thirteen small molecule biomarkers with good temporal correlation were selected by RF algorithm. A simple PMSI estimation model was constructed based on this indicator set, and the data of the test samples showed the mean absolute error (MAE) of the model was 0.847 h. CONCLUSIONS: The 13 metabolic markers screened in the vitreous humor of rat corpses in water had good correlations with the early PMSI. The simplified PMSI estimation model constructed by RF can be used to estimate the PMSI. Additionally, the metabolic profiles of vitreous humor cannot be used for early identification of cause of death in water carcasses.

Ferroelectric and Room-Temperature Ferromagnetic Semiconductors in the 2D MIMIIGe2X6 Family: First-Principles and Machine Learning Investigations.

Inspired by experimentally discovering ferromagnetism and ferroelectricity in two-dimensional (2D) CrGeTe3 and CuInP2S6 with similar geometric structures, respectively, we systematically investigated ferroic properties in a large family of 2D MIMIIGe2X6 (MI and MII = metal elements, X = S/Se/Te) by combining high-throughput first-principles calculations and the machine learning method. We identified 12 stable 2D multiferroics containing simultaneously ferromagnetic (FM) and ferroelectric (FE) properties and 35 2D ferromagnets without FE polarization. Particularly, the predicted FM Curie temperatures (TC) of eight 2D FM+FE semiconductors are close to or above room temperature. The ferroelectricity originates from the spontaneous geometric symmetry breaking induced by the unexpected shift of Ge-Ge atomic pairs and the emergence of Ge lone pair electrons, which also strengthens the p-d orbital hybridization between X atoms and metal atoms, leading to enhanced super-super-exchange interactions and raising the FM TC. Our findings not only enrich the family of 2D ferroic materials and present room-temperature FM semiconductors but also disclose the mechanism of the emerging ferroelectricity and enhanced ferromagnetism, which sheds light on the realization of high temperature multiferroics as well as FM semiconductors.

High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials.

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the ab initio Bayesian active learning method that combines active learning and high-throughput ab initio calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search. This approach shows the enormous advantages for the cases with unbalanced distribution of target properties. It can be readily applied to seek a broad range of advanced materials.

Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials.

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of the regression learning model for accurately predicting properties of materials. We apply it to investigate a large family (2135) of two-dimensional hexagonal binary compounds focusing on ferroelectric properties and find that the performance of the model for electric polarization is indeed greatly improved, where 38 stable ferroelectrics with out-of-plane polarization including 31 metals and 7 semiconductors are screened out. By unsupervised learning, actionable information such as how the number and orbital radius of valence electrons, ionic polarizability, and electronegativity of constituent atoms affect polarization was extracted. Our voting data-driven method not only reduces the size of materials data for constructing a reliable learning model but also enables one to make precise predictions for targeted functional materials.

Long-Wavelength AIE-Based Fluorescent Probes for Mitochondria-Targeted Imaging and Photodynamic Therapy of Hepatoma Cells.

With this research, we have developed two long-wavelength theranostic probes (DCMT and DCMC) with aggregation-induced emission (AIE)-based properties for image-guided photodynamic therapy (PDT) of hepatoma cells. Introduction of a triphenylamine or carbazole group to a dicyanomethylene-4H-pyran dye with long-wavelength fluorescence emission produces the AIE-based probes, which were subsequently modified with triphenyl-phosphonium cation for actively targeting the mitochondria of hepatoma cells. Solution-based experiments show that the probes exhibit a mixed photophysical mechanism of twisted-intramolecular charge transfer and AIE at different aggregation states. The molecular aggregation of the probes also leads to an enhanced ability for oxygen photosensitization, suggesting their potential for PDT of cancer cells. Our subsequent cell-based assays show that the probes localize in the mitochondria of hepatoma cells and the use of light leads to cell death through the intracellular production of reactive oxygen species.

Large family of two-dimensional ferroelectric metals discovered via machine learning.

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of "ferroelectric metal", however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics. The ferroelectricity origins from spontaneous symmetry breaking induced by the opposite displacements of bimetal atoms, and the full-d-orbital coinage metal elements cause larger displacements and polarization than other elements. For 2D ferroelectric metals, the odd electrons per unit cell without spin polarization may lead to a half-filled energy band around Fermi level and is responsible for the metallicity. It is revealed that the conducting electrons mainly move on a single-side surface of the 2D layer, while both the ionic and electric contributions to polarization come from the other side and are vertical to the above layer, thereby causing the coexistence of metallicity and ferroelectricity. Van der Waals heterostructures based on ferroelectric metals may enable the change of Schottky barrier height or the Schottky-Ohmic contact type and induce a dramatic change of their vertical transport properties. Our work greatly expands the family of 2D ferroelectric metals and will spur further exploration of 2D ferroelectric metals.

P22077 inhibits LPS-induced inflammatory response by promoting K48-linked ubiquitination and degradation of TRAF6.

Inflammation is a biological process associated with multiple human disorders such as autoimmune diseases and metabolic diseases. Therefore, alleviation of inflammation is important for disease prevention or treatment. Recently, deubiquitinating enzymes (DUBs), especially ubiquitin specific protease-7 (USP7) attracts increasing attention as a potential drug target for inflammation. As an inhibitor of USP7, P22077 has been used to study the roles of USP7 in inflammatory response and neuroblastoma growth. However, the role and precise mechanism of P22077 in anti-inflammatory is still indistinct. In this study, we demonstrated that P22077 could attenuate the release of pro-inflammatory factors including TNF-α, IL-1ß, IL-6 and NO, suppress mRNA expression of COX-2 and iNOS, and inhibit activation of NF-κB and MAPKs signaling pathways in Raw264.7 cells and mouse peritoneal macrophages after LPS stimulation. In vivo study showed that P22077 could relieve inflammatory response and reduce the lung injury in C57BL/6 mice with LPS-induced endotoxemia. Mechanically, P22077 might play an anti-inflammatory role by promoting tumor necrosis factor receptor-associated factor 6 (TRAF6) degradation via K48-linked polyubiquitination. These findings provide a rationale for the role of the P22077 in anti-inflammatory pathway and the promising clinical application of P22077 to treat inflammatory diseases.

Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning.

Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict two-dimensional octahedral oxyhalides with improved optoelectronic properties. We develop an effective machine-learning model based on an expansive data set generated from density functional calculations including the geometric and electronic properties of 300 two-dimensional octahedral oxyhalides. Our model accelerates the screening of potential optoelectronic materials of 5000 two-dimensional octahedral oxyhalides. The distorted stacked octahedral factors proposed in our model play essential roles in the machine-learning prediction. Several potential two-dimensional optoelectronic octahedral oxyhalides with moderate band gaps, high electron mobilities, and ultrahigh absorbance coefficients are successfully hypothesized.

Water source identification for plants growing on karst rock outcrops based on rainfall-exclusion experiment.

Although lack of soil coverage, rock outcrops with developed fractures in karst region can maintain water consumption of plants with different life forms. Water sources for plants on these habi-tats are unclear. Isolated rocky outcrop with relatively simple sources of water was selected for this study. We focused on typical plant species that still thrived after excluding rainfall (removing the water supply to the shallow water) over one year, compared with the same plant species living without rain shelter (always receiving rainfall supplies). Water sources of three representative tree species (Radermachera sinica, Celtis biondii, and Pittosporum tonkinense) were analyzed by using stable isotope techniques and combining with the measurement of plant water potential. The results showed that all the three species depended on deep water sources with similar isotopic values to spring water under rain-sheltered condition, during the rainy season, which explained why plants could still grow normally after rainfall-exclusion over one year. The predawn water potential of R. sinica and P. tonkinense under rain-sheltered condition was not significantly different from those living in natural conditions, which indicated both species were not under water stress. The predawn water potential of C. biondii under rain-sheltered condition was significantly lower than individuals living in natural conditions, which indicated it was under water stress. Under natural condition, water isotope values of stems of all the three species were significantly lower than that under rain-sheltered condition and were within the range of fluctuation of recent rainwater isotope values, indicating that these plants relied on shallow water sources that dominated by recent rainfall. Under both rain-sheltered and natural conditions, there was no obvious difference between the predawn water potential and the midday water potential of P. tonkinense, showing a conservative water use strategy. The midday water potential of other two species was significantly lower than the predawn water potential, showing a profligate/opportunistic water use strategy. Our results indicated that the ability to utilize shallow and deep water sources is key for the plants growing on the habitat of Karst rock outcrops where they could adapt different water environments and maintain diversified water use strategies under the condition with no soil coverage.