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Osteosarcoma (OS) is the most common bone cancer in children and young adults. Solid tumors are characterized by intratumoral hypoxia, and hypoxic cells are associated with the transformation to aggressive phenotype and metastasis. The proteome needed to support an aggressive osteosarcoma cell phenotype remains largely undefined. To link metastatic propensity to a hypoxia-induced proteotype, we compared the protein profiles of two isogenic canine OS cell lines, POS (low metastatic) and HMPOS (highly metastatic), under normoxia and hypoxia. Label-free shotgun proteomics was applied to comprehensively characterize the hypoxia-responsive proteome profiles in the OS cell phenotypes. Hypothesis-driven parallel reaction monitoring was used to validate the differential proteins observed in the shotgun data and to monitor proteins of which we expected to exhibit hypoxia responsiveness, but which were absent in the label-free shotgun data. We established a “distance” score (| z HMPOS  −  z POS |), and “sensitivity” score (| z Hypoxia  −  z Normoxia ) to quantitatively evaluate the proteome shifts exhibited by OS cells in response to hypoxia. Evaluation of the sensitivity scores for the proteome shifts observed and principal component analysis of the hypoxia-responsive proteins indicated that both cell types acquire a proteome that supports a Warburg phenotype with enhanced cell migration and proliferation characteristics. Cell migration and glucose uptake assays combined with protein function inhibitor studies provided further support that hypoxia-driven adaption of pathways associated with glycolytic metabolism, collagen biosynthesis and remodeling, redox regulation and immunomodulatory proteins typify a proteotype associated with an aggressive cancer cell phenotype. Our findings further suggest that proteins involved in collagen remodeling and immune editing may warrant further evaluation as potential targets for anti-metastatic treatment strategies in osteosarcoma.

The reliability and stability of the seal at the fuel-supply end of a rocket-engine turbopump are important factors in determining safety. Conventional single-stage floating rings used for the isolation-sealing of cryogenic media are highly susceptible to operational instability during startup and shutdown, which places demands on the seals’ structure, size, and material properties. In this study, a two-stage floating-ring isolation seal with a non-slotted main sealing surface was designed using a tangential air-intake mode. Based on the full-size three-dimensional finite-volume model, the leakage characteristics of the floating ring during operation were calculated, taking into account the effect of the “inlet effect” on the seal’s performance. Combining the temperature and pressure distributions of the sealing system under cryogenic operating conditions, calculated using a numerical simulation, a reliability analysis of various inlet directions and two kinds of floating-ring schemes was carried out on a self-constructed service-performance test bench. The results indicate that the main wear location of the non-slotted floating ring occurs on the auxiliary sealing surface, with stable working performance. When the inlet direction and spindle-rotation direction are the same, this is more conducive to ensuring the stability of seal performance in practical applications. The results of the current research are instructive for designing floating-ring isolation seals for turbine pumps.

Coronary microvascular dysfunction (MVD) is a syndrome of abnormal regulation of vascular tone, particularly during increased metabolic demand. While there are several risk factors for MVD, some of which are similar to those for coronary artery disease (CAD), the cause of MVD is not understood. We hypothesized that MVD in symptomatic non-elderly subjects would be characterized by specific lipidomic profiles. Subjects (n = 20) aged 35–60 years and referred for computed tomography coronary angiography (CTA) for chest pain but who lacked obstructive CAD (>50% stenosis), underwent quantitative regadenoson stress-rest myocardial contrast echocardiography (MCE) perfusion imaging for MVD assessment. The presence of MVD defined by kinetic analysis of MCE data was correlated with lipidomic profiles in plasma measured by liquid chromatography and high-resolution mass spectrometry. Nine of twenty subjects had evidence of MVD, defined by reduced hyperemic perfusion versus other subjects (beta-value 1.62 ± 0.44 vs. 2.63 ± 0.99 s−1, p = 0.009). Neither the presence of high-risk but non-obstructive CAD on CTA, nor CAD risk factors were different for those with versus without MVD. Lipidomic analysis revealed that patients with MVD had lower concentrations of long-carbon chain triacylglycerols and diacylglycerols, and higher concentrations of short-chain triacylglycerols. The diacylglycerol containing stearic and linoleic acid classified all participants correctly. We conclude that specific lipidomic plasma profiles occur in MVD involving saturated long-chain fatty acid-containing acylglycerols that are distinctly different from those in non-obstructive CAD. These patterns could be used to better characterize the pathobiology and potential treatments for this condition.

Background Quantification of drug-target binding is critical for confirming that drugs reach their intended protein targets, understanding the mechanism of action, and interpreting dose-response relationships. For covalent inhibitors, target engagement can be inferred by free target levels before and after treatment. Targeted mass spectrometry assays offer precise protein quantification in complex biological samples and have been routinely applied in pre-clinical studies to quantify target engagement in frozen tumor tissues for oncology drug development. However, frozen tissues are often not available from clinical trials so it is critical that assays are applicable to formalin-fixed, paraffin-embedded (FFPE) tissues in order to extend mass spectrometry-based target engagement studies into clinical settings. Methods Wild-type RAS and RASG12C was quantified in FFPE tissues by a highly optimized targeted mass spectrometry assay that couples high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) with internal standards. In a subset of samples, technical reproducibility was evaluated by analyzing consecutive tissue sections from the same tumor block and biological variation was accessed among adjacent tumor regions in the same tissue section. Results Wild-type RAS protein was measured in 32 clinical non-small cell lung cancer tumors (622–2525 amol/µg) as measured by FAIMS-PRM mass spectrometry. Tumors with a known KRASG12C mutation (n = 17) expressed a wide range of RASG12C mutant protein (127–2012 amol/µg). The variation in wild-type RAS and RASG12C measurements ranged 0–18% CV across consecutive tissue sections and 5–20% CV among adjacent tissue regions. Quantitative target engagement was then demonstrated in FFPE tissues from 2 xenograft models (MIA PaCa-2 and NCI-H2122) treated with a RASG12C inhibitor (AZD4625). Conclusions This work illustrates the potential to expand mass spectrometry-based proteomics in preclinical and clinical oncology drug development through analysis of FFPE tumor biopsies.

The recent development of the accurate and efficient semilocal density functionals on the third rung of Jacob's ladder of density functional theory such as the revised regularized strongly constrained and appropriately normed (r2SCAN) density functional could enable the rapid and highly reliable prediction of the elasticity and temperature dependence of thermophysical parameters of refractory elements and their intermetallic compounds using quasi-harmonic approximation (QHA). Here, we present a comparative evaluation of the equilibrium cell volumes, cohesive energy, mechanical moduli, and thermophysical properties (Debye temperature and thermal expansion coefficient) for 22 transition metals using semilocal density functionals, including local density approximation (LDA), the Perdew-Burke-Ernzerhof (PBE) and PBEsol generalized gradient approximations (GGA), and the r2SCAN meta-GGA. PBEsol and r2SCAN deliver the same level of accuracies for structural, mechanical and thermophysical properties. Otherwise, PBE and r2SCAN perform better than LDA and PBEsol for calculating cohesive energies of transition metals. Among the tested density functionals, r2SCAN provides an overall well-balanced performance for reliably computing the cell volumes, cohesive energies, mechanical properties, and thermophysical properties of various 3d, 4d, and 5d transition metals using QHA. Therefore, we recommend that r2SCAN could be employed as a workhorse method to evaluate the thermophysical properties of transition metal compounds and alloys in the high throughput workflows.

In the field of remote sensing image applications, RGB and infrared image object detection is an important technology. The object detection performance can be improved and the robustness of the algorithm will be enhanced by making full use of their complementary information. Existing RGB-infrared detection methods do not explicitly encourage RGB and infrared images to achieve effective multimodal learning. We find that when fusing RGB and infrared images, cross-modal redundant information weakens the degree of complementary information fusion. Inspired by this observation, we propose a redundant information suppression network (RISNet) which suppresses cross-modal redundant information and facilitates the fusion of RGB-Infrared complementary information. Specifically, we design a novel mutual information minimization module to reduce the redundancy between RGB appearance features and infrared radiation features, which enables the network to take full advantage of the complementary advantages of multimodality and improve the object detection performance. In addition, in view of the drawbacks of the current artificial classification of lighting conditions, such as the subjectivity of artificial classification and the lack of comprehensiveness (divided into day and night only), we propose a method based on histogram statistics to classify lighting conditions in more detail. Experimental results on two public RGB-infrared object detection datasets demonstrate the superiorities of our proposed method over the state-of-the-art approaches, especially under challenging conditions such as poor illumination, complex background, and low contrast.

Hypoxia-inducible factor-1 (HIF-1) is a master driver of glucose metabolism in cancer cells. Here, we demonstrate that a HIF-1α anti-sense lncRNA, HIFAL, is essential for maintaining and enhancing HIF-1α-mediated transactivation and glycolysis. Mechanistically, HIFAL recruits prolyl hydroxylase 3 (PHD3) to pyruvate kinase 2 (PKM2) to induce its prolyl hydroxylation and introduces the PKM2/PHD3 complex into the nucleus via binding with heterogeneous nuclear ribonucleoprotein F (hnRNPF) to enhance HIF-1α transactivation. Reciprocally, HIF-1α induces HIFAL transcription, which forms a positive feed-forward loop to maintain the transactivation activity of HIF-1α. Clinically, high HIFAL expression is associated with aggressive breast cancer phenotype and poor patient outcome. Furthermore, HIFAL overexpression promotes tumor growth in vivo, while targeting both HIFAL and HIF-1α significantly reduces their effect on cancer growth. Overall, our results indicate a critical regulatory role of HIFAL in HIF-1α-driven transactivation and glycolysis, identifying HIFAL as a therapeutic target for cancer treatment. HIF1alpha is reported to drive tumourigenesis through activating glycolysis. Here, the authors show that HIFAL, the HIF1alpha antisense long non-coding RNA, and HIF1alpha form a positive feed-forward loop which is essential for HIF1a-mediated metabolic reprogramming and oncogenic role.

Reliable long-range onboard perception is a prerequisite for future railway safety systems, where potential obstacles must be recognized under long braking distances, sparse far-field returns, and strongly constrained rail-corridor geometry. This paper presents Rail-BEV as an initial reproducible baseline study for LiDAR-centric, sensor-aware bird's-eye-view (BEV) railway obstacle perception. LiDAR is used as the primary geometric sensing modality, while a front-center RGB camera provides lightweight auxiliary visual evidence through calibrated LiDAR-to-image projection. The aligned geometric and visual cues are organized within a unified railway-oriented BEV backend that integrates geometry-aware fusion, rail-geometry prediction, and lightweight inference-time structural refinement. Evaluation was conducted on a scene-isolated railway benchmark with range-stratified center-distance matching, and all model variants were assessed on independent test sequences rather than on validation-selected checkpoints. Compared with CenterPoint and BEVFusion baselines evaluated under the same settings, Rail-BEV achieved the highest overall mAP of 0.6669, with particularly improved long-range pedestrian perception. The controlled ablation further shows that front-view RGB evidence improves the LiDAR-only baseline from 0.5612 to 0.5750 mAP, while ROI-based rail-corridor refinement further increases mAP to 0.5916 and Rail-BEV mIoU to 0.1193. These results indicate that LiDAR-centered sensing, lightweight visual assistance, and coarse rail-aware structural reasoning can be jointly organized to support reproducible long-range railway obstacle perception. This study also clarifies the remaining limitations in rail-geometry quality, calibration robustness, sensor degradation, and strict railway-oriented localization.

The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, −3.4 % for PM10, −1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25°  ×  0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix.

Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.