Research Progress of Using Canine Olfactory Search for Human Remains.

In the process of murder investigation, it is of great significance to find the discarded and buried human remains accurately. The main methods of searching for human remains include human visual search, aerial detection, geophysical technology, remote imaging technology and canine olfactory search technique. Canine olfactory search for human remains is a recognized time-effective and non-invasive search method, making dogs the most valuable search tool in forensic investigation. By systematically reviewing and summarizing relevant literature, and based on the theory of volatile organic compound produced by the decomposition of human remains, this paper explores the basic principle of the canine olfactory search technique for human remains. This paper also reviews the application of training canine search technique for human remains in forensic investigation by using human blood, tissue, cadaver putrefying fluid and odor substitutes as sniffing sources. The application prospect of canine olfactory search for human remains was prospected from the perspectives of detection of volatile organic compound during cadaver decay, development of odor substitutes and adsorption devices, and technology tactics used in canine training and use, to provide references for the relevant research of canine olfactory search for human remains in China.

Quantitation of anacetrapib in human and animal adipose by liquid chromatography with mass spectrometric detection.

Cholesteryl ester transfer protein (CETP) inhibitor is a target for both lowering low-density lipoproteins and raising high-density lipoproteins. Anacetrapib was the lead compound in our cholesteryl ester transfer protein inhibitor program. Preclinical studies were initiated to support the safety of anacetrapib deposition in adipose tissue, followed by a clinical trial to evaluate the effects of anacetrapib in people with vascular disease. An ultra-high performance liquid chromatography/tandem mass spectrometry method was developed to determine tissue anacetrapib concentrations in the adipose of three animal species and humans. The assays were validated in the concentration ranges of 5-5000 ng/ml and 0.1-100 µg/ml. The anacetrapib concentrations in adipose tissue from preclinical and clinical studies were determined.

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Water limitation regulates positive feedback of increased ecosystem respiration.

Terrestrial ecosystem respiration increases exponentially with temperature, constituting a positive feedback loop accelerating global warming. However, the response of ecosystem respiration to temperature strongly depends on water availability, yet where and when the water effects are important, is presently poorly constrained, introducing uncertainties in climate-carbon cycle feedback projections. Here, we disentangle the effects of temperature and precipitation (a proxy for water availability) on ecosystem respiration by analysing eddy covariance CO2 flux measurements across 212 globally distributed sites. We reveal a threshold precipitation function, determined by the balance between precipitation and ecosystem water demand, which separates temperature-limited and water-limited respiration. Respiration is temperature limited for precipitation above that threshold function, whereas in drier areas water limitation reduces the temperature sensitivity of respiration and its positive feedback to global warming. If the trend of expansion of water-limited areas with warming climate over the last decades continues, the positive feedback of ecosystem respiration is likely to be weakened and counteracted by the increasing water limitation.

Roles of Nonadiabatic Processes, Reaction Mechanism, and Selectivity in Cu-Catalyzed [2 + 2] Photocycloaddition of Norbornene and Acetone to Oxetane.

Oxetane has been extensively studied for its applications in medicinal chemistry and as a reactive intermediate in synthesis. Experiments report a Cu-catalyzed [2 + 2] photocycloaddition of acetone and norbornene to oxetane, which is proposed to deviate from the conventional Paternò-Büchi reaction. However, its mechanism at the atomic level is not clear. In this study, we used a combination of multistate complete active space second-order perturbation theory (MS-CASPT2) and density functional theory to systematically investigate the reaction mechanism and elucidate the factors contributing to the diastereomeric selectivity. Initially, the formation of the TpCu(Norb) complex is achieved by strong interaction between tris(pyrazolyl)borate Cu(I) (TpCu) and norbornene in the ground state (S0). Upon photoexcitation, TpCu(Norb) eventually decays to the T1 state, in which TpCu(Norb) attacks acetone to initiate subsequent reactions and produces final endo- or exo-oxetane products. All these reactions initially involve the C-C bond formation in the T1 state thereto leading to a ring-opening intermediate. This intermediate then undergoes a nonradiative transition to the S0 state, producing a five-membered ring intermediate, from which the C-O bond is formed, leading to the experimentally dominant exo-product. In contrast, the endo-oxetane formation requires a rearrangement process after the C-C bond is formed because of the large steric effects. As a consequence, the different reaction pathways generating exo- and endo-products exhibit large differences in the free-energy barriers, which results in a diastereomeric selectivity observed experimentally. Additionally, the nonradiative transition is found to play an important role in facilitating these reaction steps. The present computational study provides valuable mechanistic insights into Cu-catalyzed photocycloaddition reactions.

Revealing and reconstructing the 3D Li-ion transportation network for superionic poly(ethylene) oxide conductor.

Understanding the Li-ions conduction network and transport dynamics in polymer electrolyte is crucial for developing reliable all-solid-state batteries. In this work, advanced nano- X-ray computed tomography combined with Raman spectroscopy and solid state nuclear magnetic resonance are used to multi-scale qualitatively and quantitatively reveal ion conduction network of poly(ethylene) oxide (PEO)-based electrolyte (from atomic, nano to macroscopic level). With the clear mapping of the microstructural heterogeneities of the polymer segments, aluminium-oxo molecular clusters (AlOC) are used to reconstruct a high-efficient conducting network with high available Li-ions (76.7%) and continuous amorphous domains via the strong supramolecular interactions. Such superionic PEO conductor (PEO-LiTFSI-AlOC) exhibites a molten-like Li-ion conduction behaviour among the whole temperature range and delivers an ionic conductivity of 1.87 × 10-4 S cm-1 at 35 °Ï¹. This further endows Li electrochemical plating/stripping stability under 50 µA cm-2 and 50 µAh cm-2 over 2000 h. The as-built Li|PEO-LiTFSI-AlOC|LiFePO4 full batteries show a high rate performance and a capacity retention more than 90% over 200 cycling at 250 µA cm-2, even enabling a high-loading LiFePO4 cathode of 16.8 mg cm-2 with a specific capacity of 150 mAh g-1 at 50 °Ï¹.

Evaluation of fluoride emissions and pollution from an electrolytic aluminum plant located in Yunnan province.

The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 µg/m3 and 9.0 µg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.

A 3D hierarchical cross-modality interaction network using transformers and convolutions for brain glioma segmentation in MR images.

BACKGROUND: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions. PURPOSE: To overcome these problems, in this paper, we propose a 3D hierarchical cross-modality interaction network (HCMINet) using Transformers and convolutions for accurate multi-modal glioma segmentation, which leverages an effective hierarchical cross-modality interaction strategy to sufficiently learn modality-specific and modality-shared knowledge correlated to glioma sub-region segmentation from multi-parametric MR images. METHODS: In the HCMINet, we first design a hierarchical cross-modality interaction Transformer (HCMITrans) encoder to hierarchically encode and fuse heterogeneous multi-modal features by Transformer-based intra-modal embeddings and inter-modal interactions in multiple encoding stages, which effectively captures complex cross-modality correlations while modeling global contexts. Then, we collaborate an HCMITrans encoder with a modality-shared convolutional encoder to construct the dual-encoder architecture in the encoding stage, which can learn the abundant contextual information from global and local perspectives. Finally, in the decoding stage, we present a progressive hybrid context fusion (PHCF) decoder to progressively fuse local and global features extracted by the dual-encoder architecture, which utilizes the local-global context fusion (LGCF) module to efficiently alleviate the contextual discrepancy among the decoding features. RESULTS: Extensive experiments are conducted on two public and competitive glioma benchmark datasets, including the BraTS2020 dataset with 494 patients and the BraTS2021 dataset with 1251 patients. Results show that our proposed method outperforms existing Transformer-based and CNN-based methods using other multi-modal fusion strategies in our experiments. Specifically, the proposed HCMINet achieves state-of-the-art mean DSC values of 85.33% and 91.09% on the BraTS2020 online validation dataset and the BraTS2021 local testing dataset, respectively. CONCLUSIONS: Our proposed method can accurately and automatically segment glioma regions from multi-parametric MR images, which is beneficial for the quantitative analysis of brain gliomas and helpful for reducing the annotation burden of neuroradiologists.

Auto-Spikformer: Spikformer architecture search.

Introduction: The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties. Recent advancements in SNN architecture, such as Spikformer, have demonstrated promising outcomes. However, we observe that Spikformer may exhibit excessive energy consumption, potentially attributable to redundant channels and blocks. Methods: To mitigate this issue, we propose a one-shot Spiking Transformer Architecture Search method, namely Auto-Spikformer. Auto-Spikformer extends the search space to include both transformer architecture and SNN inner parameters. We train and search the supernet based on weight entanglement, evolutionary search, and the proposed Discrete Spiking Parameters Search (DSPS) methods. Benefiting from these methods, the performance of subnets with weights inherited from the supernet without even retraining is comparable to the original Spikformer. Moreover, we propose a new fitness function aiming to find a Pareto optimal combination balancing energy consumption and accuracy. Results and discussion: Our experimental results demonstrate the effectiveness of Auto-Spikformer, which outperforms the original Spikformer and most CNN or ViT models with even fewer parameters and lower energy consumption.

Combination of pemetrexed with bevacizumab for non-small-cell lung cancer: a meta-analysis study.

BACKGROUND: Combining pemetrexed with bevacizumab may have some potential in improving the efficacy in patients with non-small-cell lung cancer (NSCLC), and this meta-analysis aims to explore the impact of pemetrexed addition to bevacizumab on treatment efficacy for NSCLC. METHODS: PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases were systematically searched, and we included randomized controlled trials (RCTs) assessing the effect of pemetrexed addition to bevacizumab on treatment efficacy in patients with NSCLC. Overall survival and progression-free survival were included in this meta-analysis. RESULTS: Four RCTs were finally included in the meta-analysis. Overall, compared with bevacizumab for NSCLC, pemetrexed addition showed significantly improved overall survival (hazard ratio [HR] = 0.87; 95% confidence interval [CI] = 0.76 to 0.99; P = 0.03), survival rate (odd ratio [OR] = 1.41; 95% CI = 1.06 to 1.86; P = 0.02), progression-free survival (HR = 0.63; 95% CI = 0.55 to 0.72; P < 0.00001) and progression-free survival rate (OR = 1.92; 95% CI = 1.38 to 2.67; P < 0.00001), but led to the increase in grade ≥ 3 adverse events (OR = 2.15; 95% CI = 1.62 to 2.84; P < 0.00001). CONCLUSIONS: Pemetrexed addition may be effective to improve treatment efficacy for NSCLC compared to bevacizumab treatment.

Designed Synthesis of an Aluminum Molecular Ring Based Rotaxane and Polyrotaxane.

Mechanically interlocked molecules, such as rotaxanes, have drawn significant attention within supramolecular chemistry. Although a variety of macrocycles have been thoroughly explored in rotaxane synthesis, metal-organic macrocycles remain relatively under-investigated. Aluminum molecular rings, with their inner cavities and numerous binding sites, present a promising option for constructing rotaxanes. Here, we introduce an innovative "ring-donor···axle-acceptor" motif utilizing Al8 molecular rings, enabling the stepwise assembly of molecules, complexes, and polymers through tailored coordination chemistry. This novel approach can not only be applied to macrocycle-based systems like catenanes but also enhance specific functionalities progressively.

Intense Circularly Polarized Luminescence Induced by Chiral Supramolecular Assembly: The Importance of Intermolecular Electronic Coupling.

Herein we report on circularly polarized luminescence (CPL) emission originating from supramolecular chirality of organic microcrystals with a |glum| value up to 0.11. The microcrystals were prepared from highly emissive difluoroboron ß-diketonate (BF2dbk) dyes R-1 or S-1 with chiral binaphthol (BINOL) skeletons. R-1 and S-1 exhibit undetectable CPL signals in solution but manifest intense CPL emission in their chiral microcrystals. The chiral superstructures induced by BINOL skeletons were confirmed by XRD analysis. Spectral analysis and theoretical calculations indicate that intermolecular electronic coupling, mediated by the asymmetric stacking in the chiral superstructures, effectively alters excited-state electronic structures and facilitates electron transitions perpendicular to BF2bdk planes. The coupling increases cosθµ,m from 0.05 (monomer) to 0.86 (tetramer) and triggers intense optical activity of BF2bdk. The results demonstrate that optical activity of chromophores within assemblies can be regulated by both orientation and extent of intermolecular electronic couplings.

[Corrigendum] P2Y2 receptor promotes the migration and invasion of breast cancer cells via EMT­related genes Snail and E­cadherin.

Subsequently to the publication of the above paper, an interested reader drew to the authors' attention that there appeared to be two instances of overlapping data panels comparing between the cell migration and invasion assay data shown in Figs. 4 and 6 on p. 143 and 145, respectively, such that data which were intended to represent the results from differently performed experiments had apparently been derived from the same original sources. In addition, the authors themselves realized that incorrect western blotting data for Snail protein in Fig. 10A on p. 147 had been included in the figure.  The authors were able to re­examine their original data files, and realized that the affected data panels in these figures had inadvertently been incorporated into them incorrectly. The revised versions of Figs. 4, 6, and 10, featuring the correct data for the 'NC / Control' panels in Fig. 4B and C and the 'siRNA2 / ATP 12 h' panels in Fig. 4A and B, a replacement data panel for the 'siRNA1 / Control' experiment in Fig. 6, and the correct western blotting data for Snail protein in Fig. 10A (together with a revised histogram for the MCF7 cell line relating to Fig. 10A) are shown on the next three pages. The authors wish to emphasize that the errors made in compiling these figures did not affect the overall conclusions reported in the paper, and they are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this corrigendum. All the authors agree to the publication of this corrigendum, and also apologize to the readership for any inconvenience caused. [Oncology Reports 39: 138­150, 2018; DOI: 10.3892/or.2017.6081].

Synthesis and photoluminescence properties of polymer chains based on pre-designed heterometallic Al4Ln4 molecular rings.

Presented herein is a series of chain compounds based on pre-designed heterometallic aluminum-lanthanide (Al-Ln) Al4Ln4 molecular rings. Their photoluminescence quantum yield (PLQY) with Eu3+ (30.41%) and Tb3+ (41.44%) is at a high level among the clusters containing four Ln ions. This study significantly extends the family of Al-Ln heterometallic clusters and demonstrates the synergistic effect of heterometallic ions in enhancing their properties.

Construction of Highly Accurate Machine Learning Potential Energy Surfaces for Excited-State Dynamics Simulations Based on Low-Level Data Sets.

Machine learning is capable of effectively predicting the potential energies of molecules in the presence of high-quality data sets. Its application in the construction of ground- and excited-state potential energy surfaces is attractive to accelerate nonadiabatic molecular dynamics simulations of photochemical reactions. Because of the huge computational cost of excited-state electronic structure calculations, the construction of a high-quality data set becomes a bottleneck. In the present work, we first built two data sets. One was obtained from surface hopping dynamics simulations at the semiempirical OM2/MRCI level. Another was extracted from the dynamics trajectories at the CASSCF level, which was reported previously. The ground- and excited-state potential energy surfaces of ethylene-bridged azobenzene at the CASSCF computational level were constructed based on the former low-level data set. Although non-neural network machine learning methods can achieve good or modest performance during the training process, only neural network models provide reliable predictions on the latter external test data set. The BPNN and SchNet combined with the Δ-ML scheme and the force term in the loss functions are recommended for dynamics simulations. Then, we performed excited-state dynamics simulations of the photoisomerization of ethylene-bridged azobenzene on machine learning potential energy surfaces. Compared with the lifetimes of the first excited state (S1) estimated at different computational levels, our results on the E isomer are in good agreement with the high-level estimation. However, the overestimation of the Z isomer is unimproved. It suggests that smaller errors during the training process do not necessarily translate to more accurate predictions on high-level potential energies or better performance on nonadiabatic dynamics simulations, at least in the present case.

Isothiocyanate intermediates facilitate divergent synthesis of N-heterocycles for DNA-encoded libraries.

The versatile reactivity of isothiocyanate intermediates enabled the diversity-oriented synthesis (DOS) of N-heterocycles in a DNA-compatible manner. We first reported a mild in situ conversion of DNA-conjugated amines to isothiocyanates. Subsequently, a set of diverse transformations was successfully developed to construct 2-thioxo-quinazolinones, 1,2,4-thiadiazoles, and 2-imino thiazolines. Finally, the feasibility of these approaches in constructing DELs was further demonstrated through enzymatic ligation and mock pool preparation. This study demonstrated the advantages of combining in situ conversion strategies with DOS, which effectively broadened the chemical and structural diversity of DELs.

Quantum Computation of Conical Intersections on a Programmable Superconducting Quantum Processor.

Conical intersections (CIs) are pivotal in many photochemical processes. Traditional quantum chemistry methods, such as the state-average multiconfigurational methods, face computational hurdles in solving the electronic Schrödinger equation within the active space on classical computers. While quantum computing offers a potential solution, its feasibility in studying CIs, particularly on real quantum hardware, remains largely unexplored. Here, we present the first successful realization of a hybrid quantum-classical state-average complete active space self-consistent field method based on the variational quantum eigensolver (VQE-SA-CASSCF) on a superconducting quantum processor. This approach is applied to investigate CIs in two prototypical systems─ethylene (C2H4) and triatomic hydrogen (H3). We illustrate that VQE-SA-CASSCF, coupled with ongoing hardware and algorithmic enhancements, can lead to a correct description of CIs on existing quantum devices. These results lay the groundwork for exploring the potential of quantum computing to study CIs in more complex systems in the future.

Nonadiabatic Molecular Dynamics in Momentum Space Beyond Harmonic Approximation: Hot Electron Relaxation in Photoexcited Black Phosphorus.

We simulated hot-electron relaxation in black phosphorus using the nonadiabatic molecular dynamics (NA-MD) approach with a non-Condon effect in momentum space beyond the harmonic approximation. By comparing simulations at the Γ point in a large supercell with those using a few k-points in a smaller supercell─while maintaining the same number of electronic states within the same energy range, we demonstrate that both setups yield remarkably consistent energy relaxation times, regardless of the initial state energy. This consistency arises from the complementary effects of supercell size in real space and the number of k-points in the reciprocal space. This finding confirms that simulations at a single k-point in large size supercells are an effective approximation for NA-MD with a non-Condon effect. This approach offers significant advantages for complex photophysics, such as intervalley scattering and indirect bandgap charge recombination, and is particularly suitable for large systems without the need for a harmonic approximation.

Visualization of transcatheter aortic valve implantation from the perspective of bibliometric analysis.

Transcatheter aortic valve implantation (TAVI) was originally devised as a treatment for patients with aortic stenosis (AS). It has since emerged as a beneficial alternative to surgical aortic valve replacement (SAVR), extending its reach to a broader array of patients. Our objective was to illustrate the developmental trends and focus areas in TAVI research. We sourced a total of 11,480 research papers on TAVI, published between 1994 and 2022, from the Web of Science Core Collection (WoSCC) database. We conducted a bibliometric analysis of these publications, generating cooperation maps, performing co-citation analysis of journals and references, and carrying out a cluster analysis of keywords. Our findings indicate that TAVI research grapples with numerous clinical challenges. We created knowledge maps that highlight contributing countries/institutions, authors, journals with high publication and citation rates, and notable references in this domain. North America and Europe have been at the forefront of research within the TAVI field. The institutions and authors from these regions exert significant influence in this area of study. Beginning in 2009, China has progressively expanded its research on TAVI over the past two decades. We anticipate that future research will increasingly focus on three key areas: implementation scope, lifelong management, outcomes and predicting the risk of TAVI. Research on TAVI is flourishing. Cooperation among different countries and institutions in this field must be strengthened in the future, especially for developing counties.

C-C chemokine ligand 2 signaling promotes skeletal muscle wasting in non-tumor and breast tumor mouse models.

Despite advancements in treatment, approximately 25% of breast cancer patients experience long-term skeletal muscle wasting (SMW), which limits mobility, reduces drug tolerance and adversely impacts survival. By understanding the underlying molecular mechanisms of SMW, we may develop new strategies to alleviate this condition and improve the lives of breast cancer patients. Chemokines are small soluble factors that regulate homing of immune cells to tissues during inflammation. In breast cancers, overexpression of the C-C chemokine ligand 2 (CCL2) correlates with unfavorable prognosis. Elevated levels of CCL2 in peripheral blood indicate possible systemic effects of this chemokine in breast cancer patients. Here, we investigated the role of CCL2 signaling on SMW in a tumor and non-tumor context. In vitro, increasing concentrations of CCL2 inhibits myoblast and myotube function through C-C chemokine receptor 2 (CCR2) dependent mechanisms involving JNK, SMAD3 and AMPK signaling. In healthy mice, delivery of recombinant CCL2 protein promotes SMW in a dose dependent manner. In vivo knockdown of breast tumor derived CCL2 partially protects against SMW. Overall, chronic, upregulated CCL2/CCR2 signaling positively regulates SMW, with implications on therapeutic targeting.