Sample Position Research Articles

The Characteristics of Component of Lime Mortars with Perilla Oil

A lime is a traditional material that has been used for a long time and has been used in ancient tumb, fortress walls, ondol, buildings, and lime-mortar barrier tombs. Based on the literature records used lime with additive materials, in this study, observed changes in the composition of perilla oil according to curing environment, sampling position, and time tor time mixed or applied with perilla oil among traditional lime additive materials by pyrosis gas chromatography mass spectrometery (Py-GC-MS) and Fourier Transform infrared spectroscopy(FT-IR) analysis. For perilla oil, unsaturated fatty acids such as oleic acid, and linoleic acid, saturated fatty acid, palmitic acid were identified as the main component, and as perilla oil was cured, the ratio of saturated fatty acids such as azelaic acid, palmitic acid, and stearic acid tended to increase. This trend has also been confirmed in lime mortars using perilla oil. In limes mixed with perilla oil, oleic acid is predominantly detected. In the case of lime coated with perilla oil, no characteristic component was identified in the internal sample, and a change in characteristic components were confirmed according to the indoor and outdoor environments on the coated surface. Palmitic acid, stearic acid, and oleic acid were detected as main components in a stable indoor environment, but azelaic acid, palmitic acid, and stearic acid were identified as main components as they were exposed to the climate environment(outdoor). In particular, it is characterized by the fact that it was detected only in mortar applied with azelaic acid. However the possibility of grasping perilla oil using FT-IR was expected to have a detection limit depending on the concentration of perilla oil. Such analysis results are expected to contribute to the discovery of lime additive materials used in cultural heritage and the research on construction technology in the future.

Habitat preferences of Phycis blennoides (Pisces: Gadiformes): environmental factors and fishing-related ontogenic deepening

Phycis blennoides is a component of the ecosystem of the Atlantic waters of northern Spain and a species of commercial interest for fisheries in this region. However, little is known about the distribution of this species. The present study analyses the relationships between different environmental and spatial variables and distribution of P. blennoides. Biological (presence-absence, abundance, biomass and total length) and environmental data were collected from a bottom trawl survey carried out off the northern coast of Spain. These data were used in 2-step generalised additive models to examine both the relative significance of physical factors in influencing P. blennoides distribution and to generate density distribution maps. Additionally, the role of fishing effort on P. blennoides body size distribution with depth was analysed. Geospatial analyses showed that changes in annual abundance did not influence physical habitat preference. Most of the variation in spatial distribution was explained by the sampling position and depth. The analyses showed that P. blennoides exhibited habitat preferences in the waters of Galicia and the Cantabrian Sea, with higher values of presence, abundance and biomass on the upper slope and on fine-grained sediment bottoms. P. blennoides biomass increased with depth, probably due to the increased mean size of individuals with depth. Modelling results suggest that the observed ontogenetic deepening appears to be mainly driven by the life history strategy of the species, but is also reinforced by fishing effort.

Carbon accumulation in the soil and biomass of macauba palm commercial plantations

Commercial cultivation of the Macauba palm is growing due to its agro-energy potential. This study quantified carbon stock in two macauba plantations (4.8 and 9.0 years old), considering carbon stored in soil and biomass. We assessed total organic carbon (TOC) stocks in labile (EOC and LF-C) and non-labile (NL-C) soil fractions, comparing these with a forest regenerating for over 30 years. Soil samples were collected within the palm plantation (rows and inter-rows) and the forest area at five depths (0–10, 10–20, 20–40, 40–60, and 60–100 cm). The impact of plantation age and sampling position on TOC stocks and organic matter fractions across different soil layers (0–100 cm) was assessed. The Carbon Management Index (CMI) was calculated to evaluate carbon recovery. Plantation age and sampling position influenced soil TOC stocks across all depths up to 1 m. Higher TOC and NL-C soil stocks were observed in the older plantation (9.0 years) and inter-row. EOC and LF-C fractions varied by soil layer. The inter-row region of the 9.0-year-old plantation exhibited higher carbon recovery based on the CMI. Over 4.2 years of palm cultivation (between 4.8 and 9.0 years), carbon accumulation in biomass and soil reached 75.36 Mg C ha−1. These findings underscore the potential of commercial macauba plantations to enhance soil carbon stocks rapidly, particularly in inter-row areas. Plantations younger than a decade surpassed the soil carbon stock of a forest regenerating for over 30 years, highlighting significant environmental benefits of macauba cultivation, including soil and biomass carbon sequestration.

YOLOv8-Coal: a coal-rock image recognition method based on improved YOLOv8.

To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP50, AP50:95, and AR50:95 were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, optimal localization recall precision (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms.

Organic compounds as temperature calibrants for fast scanning calorimetry

Organic compounds can be used as temperature calibrants in fast scanning calorimetry. Their advantages include ease of surface cleaning of the calorimetric chip and good thermal contact with the chip surface. Among several compounds tested, benzoic acid was identified as a convenient and reliable calibrant for temperatures below approximately 130 °C. However, organic calibrants often exhibit unusual heating rate dependencies of the onset temperatures of melting. This phenomenon can be semi-quantitatively explained by considering different heat flows within the sensor. Notably, the thermal resistance between the heater and thermopile, often overlooked, introduces an additional time constant that can sometimes result in a negative apparent thermal lag. In addition, the onset temperatures are influenced by factors such as sample position, thickness, surface wetting, and spreading. These factors limit the accuracy of transition temperature determinations to approximately ±1 K below 130 °C and ±5 K up to 220 °C.

A custom-made integrated system for thermoluminescence and radioluminescence spectroscopy

Thermoluminescence (TL) and Radioluminescence (RL) are widely used in dosimetry applications. We present a custom-built integrated system, designated LUMI22, for measuring TL, TL spectroscopy, RL, and RL as a function of temperature. LUMI22 includes a heating system based on Kanthal® A1 alloy (FeCrAl), a microcontroller to regulate the temperature ramps (e.g. 1–5 °C/s). To irradiate samples an X-ray tube (Moxtek 50 kV, 50 μA) is powered, controlled, and monitored by an FTC-200 standard controller. The dose rate at the sample position is 0.43 Gy/min. Light collection includes a Photomultiplier Tube (PMT, Hamamatsu H10493-012:HA, 185–850 nm). Additionally, a miniature fiber optic spectrometer (Ocean Optics, QE65000, range 200–1100 nm) coupled with a 1000 μm diameter fiber optic (QP1000- 2-UV-VIS) was employed for TL and RL spectroscopy measurements. To assess the functionality of the system, it was used to measure TL and RL from Al2O3:C,Mg, Al2O3:C and TLD-100 phosphors which have been previously well investigated. The measured TL and RL data were well compared to the published ones, confirming the functionality of the system.

Isothermal thermoluminescence dating of speleothem growth – A case study from Bleßberg cave 2, Germany

Speleothems are a key archive of past climatic and environmental changes. 230Th/U dating is the most commonly used method to determine speleothem ages. However, incorporation of non-radiogenic thorium may hamper 230Th/U dating, and samples older than 600 ka also remain out-of-reach. Calcite exhibits a thermoluminescence (TL) signal at 280 °C with a high characteristic saturation dose, and provides significant potential to date carbonate samples over several million years. Hitherto, the application of TL dating for calcite has mainly been hindered by two factors: 1) a spurious TL signal occurring in the high temperature range, and 2) non-uniform dose rate due to U-series disequilibrium. Here we test an isothermal TL (ITL) dating method on a speleothem sample from Bleßberg cave 2, Germany. We show that the ITL signal measured at 240 °C can completely remove the 280 °C TL peak with a negligible TL contribution from the higher temperature range, thus reducing the influence from the spurious signal. The time-dependent dose rate variation can be simulated using the initial radioactivity of 238U, 234U, 230Th and their decay constants. We use the 230Th/U dating method to provide precise and accurate radiometric ages documenting that the speleothem grew between 425.5 ± 5.4 and 320.5 ± 9.7 ka. The ITL ages (421 ± 23 to 311 ± 23 ka) of four subsamples from the speleothem are consistent with the 230Th/U ages at isochronous sampling positions, showing the general reliability of the ITL dating method. ITL dating provides a pathway to construct chronologies for palaeoclimate reconstructions for speleothems beyond the range of the 230Th/U-method and for samples that are unsuitable for U-series dating methods.

An accessible digital imaging workflow for multiplexed quantitative analysis of adult eye phenotypes in Drosophila melanogaster.

The compound eye of Drosophila melanogaster has long been a model for studying genetics, development, neurodegeneration, and heterochromatin. Imaging and morphometry of adult Drosophila and other insects is hampered by the low throughput, narrow focal plane, and small image sensors typical of stereomicroscope cameras. When data collection is distributed among many individuals or extended time periods, these limitations are compounded by inter-operator variability in lighting, sample positioning, focus, and post-acquisition processing. To address these limitations we developed a method for multiplexed quantitative analysis of adult Drosophila melanogaster phenotypes. Efficient data collection and analysis of up to 60 adult flies in a single image with standardized conditions eliminates inter-operator variability and enables precise quantitative comparison of morphology. Semi-automated data analysis using ImageJ and R reduces image manipulations, facilitates reproducibility, and supports emerging automated segmentation methods, as well as a wide range of graphical and statistical tools. These methods also serve as a low-cost hands-on introduction to imaging, data visualization, and statistical analysis for students and trainees.

RegMamba: An Improved Mamba for Medical Image Registration

Deformable medical image registration aims to minimize the differences between fixed and moving images to provide comprehensive physiological or structural information for further medical analysis. Traditional learning-based convolutional network approaches usually suffer from the problem of perceptual limitations, and in recent years, the Transformer architecture has gained popularity for its superior long-range relational modeling capabilities, but still faces severe computational challenges in handling high-resolution medical images. Recently, selective state-space models have shown great potential in the vision domain due to their fast inference and efficient modeling. Inspired by this, in this paper, we propose RegMamba, a novel medical image registration architecture that combines convolutional and state-space models (SSMs), designed to efficiently capture complex correspondence in registration while maintaining efficient computational effort. Firstly our model introduces Mamba to efficiently remotely model and process potential dependencies of the data to capture large deformations. At the same time, we use a scaled convolutional layer in Mamba to alleviate the problem of spatial information loss in 3D data flattening processing in Mamba. Then, a deformable convolutional residual module (DCRM) is proposed to adaptively adjust the sampling position and process deformations to capture more flexible spatial features while learning fine-grained features of different anatomical structures to construct local correspondences and improve model perception. We demonstrate the advanced registration performance of our method on the LPBA40 and IXI public datasets.

Introduction of the prompt γ-ray neutron activation analysis system at CARR and the first pilot experiment on boron-containing high-temperature alloys

A prompt γ-ray neutron activation analysis system has recently been developed at China advanced research reactor (CARR), the 60 MW research reactor in China Institute of Atomic Energy (CIAE). The system is set at the cold neutron beam guide with a thermal equivalent neutron flux at the sample position of 1.0 × 109 n·cm−2·s−1 with the power of 30 MW, and it is mainly composed of a neutron beam collimator, a sample chamber, a beam stopper, neutron and γ-ray shieldings and a detection system. The detection system can realize three modes of measurement: single, Compton suppression, and pair modes. The detection efficiency was calibrated up to 11 MeV using a set of radionuclides and the (n, γ) reactions of N and Cl. Boron, one of the most important elements in high-temperature alloy material studies, was analyzed in this work, as the first pilot experiment of the CARR-PGNAA system. The analytical sensitivity of 2000 cps/mg-B was obtained. The results verified the feasibility of the CARR-PGNAA system to measure boron in high-temperature alloys, and laid a foundation for the accurate quantification of boron in the next step.

Experimental study on in-situ emulsification of heavy oil in porous media

Under the strategic goal of “dual-carbon”, reducing oil viscosity by in-situ emulsification using chemical method has become a new direction for high-efficiency and low-carbon development of heavy oil reservoirs. This technology can transform the originally immovable heavy oil into movable dispersed oil droplets through in-situ emulsification, and thus enhances heavy oil recovery. However, there is still a lack of sufficient understanding about the characteristics and influencing laws of in-situ emulsification of heavy oil in porous media. So, this paper carried out experimental studies on in-situ emulsification and influencing factors for heavy oil in porous media. The results show that, due to the transport and adsorption loss of viscosity reducer in porous media, the emulsification area expands gradually from the inlet to the outlet. At the same sampling position, with the increase of injection volume, the dispersed oil droplet size decreases, the distribution density increases, and the color of produced liquid changes from light yellow to dark brown. Under the same injection volume, the closer to the outlet, the smaller the particle size of the dispersed oil droplets and the higher the distribution density, which is resulted from the shearing effect of the porous media. With the increase of injection rate, the hydrodynamic shear effect is enhanced, so the in-situ emulsification of heavy oil starts earlier and the emulsification oil ratio curves have a higher slope and distribute more densely. With the increase of agent concentration, the adsorbed number of viscosity reducer molecules on the oil–water interface is increased, making it easier to emulsify the remaining oil. With the increase of pre-conversion oil recovery, the remaining oil in the porous media is more dispersed and the oil–water contact surface increases, so the emulsification is easier and the percentage of dispersed oil droplets in the produced oil phase increases. The research results based on sampling analysis deepen our understanding on in-situ emulsification of heavy oil in porous media, which provides valuable reference for designing suitable viscosity reducer agent and effectively enhancing heavy oil recovery in the future.

Microwave field mapping for EPR-on-a-chip experiments.

Electron paramagnetic resonance-on-a-chip (EPRoC) devices use small voltage-controlled oscillators (VCOs) for both the excitation and detection of the EPR signal, allowing access to unique sample environments by lifting the restrictions imposed by resonator-based EPR techniques. EPRoC devices have been successfully used at multiple frequencies (7 to 360 gigahertz) and have demonstrated their utility in producing high-resolution spectra in a variety of spin centers. To enable quantitative measurements using EPRoC devices, the spatial distribution of the B1 field produced by the VCOs must be known. As an example, the field distribution of a 12-coil VCO array EPRoC operating at 14 gigahertz is described in this study. The frequency modulation-recorded EPR spectra of a "point"-like and a thin-film sample were investigated while varying the position of both samples in three directions. The results were compared to COMSOL simulations of the B1-field intensity. The EPRoC array sensitive volume was determined to be ~19 nanoliters. Implications for possible EPR applications are discussed.

Parameter Optimization Method in Multidimensional Umbrella Sampling.

Umbrella sampling (US) is an effective method for calculating free-energy landscapes (FELs). However, the complexity of controlling the sampling positions complicates multidimensional FEL calculations. In this study, we proposed a method for controlling sampling by optimizing the US parameters. This method comprises the introduction of a target point and the optimization of the parameters to sample a window around this point. We approximated each window to normal distributions using an umbrella integration method and calculated the divergences between the window distributions and the state distributed at the target position by a variationally enhanced sampling method. Thus, the minimization of the divergence facilitated sampling around the target point, after which the parameters could be optimized on the fly while performing equilibration simulation. In practice, our method employs bias potentials with off-diagonal terms, ensuring a more efficient calculation of multidimensional FEL. Additionally, we developed an algorithm for determining the target point for automated FEL search; the algorithm samples in a specified direction while controlling the overlap of distributions. We performed three different FEL calculations as examples: (1) the calculation of the permeation of a water molecule through a lipid bilayer (one-dimensional FEL), (2) the calculation of the internal structural changes in alanine dipeptide in water (two-dimensional FEL), and (3) the calculation of the internal structural changes from a β-strand structure to an α-helix structure in alanine decapeptide (Ala10, 16-dimensional FEL). These results confirmed that our method could control the number of US windows and calculate the high-dimensional FELs that could not be evaluated by the conventional US method.

Investigation on the microstructure and mechanical properties of large-tube forging manufactured by additive forging

Large-tube forgings were formed using nine layers of continuous-casting billet made from 15CrNi3MoV alloy steel via additive forging. The interfacial microstructural evolution under different hot-compression bonding temperatures and strains was investigated using optical microscopy, scanning electron microscopy, and electron backscatter diffraction. The tensile properties of the hot-compression-bonded and tube-forged samples were also evaluated. The results showed that as the hot-compression bonding temperature and strain increased, the bonding interface gradually disappeared and the voids at the bonding interface closed. Finally, the interface was replaced with recrystallised grains. The tensile properties of the hot-compression-bonded samples at different temperatures and strains were identical. The tensile properties of the interface and base samples of the tube forging were comparable, and the fracture morphologies were consistent. The fracture position of the large tensile sample with a length of 1000 mm containing three original interfaces is the base, indicating the complete metallurgical bonding of the forging.

RA-YOLOv8: An Improved YOLOv8 Seal Text Detection Method

To detect text from electronic seals that have significant background interference, blurring, text overlapping, and curving, an improved YOLOv8 model named RA-YOLOv8 was developed. The model is primarily based on YOLOv8, with optimized structures in its backbone and neck. The receptive-field attention and efficient multi-scale attention (RFEMA) module is introduced in the backbone. The model’s ability to extract and integrate local and global features is enhanced by combining the attention on the receptive-field spatial feature of the receptive-field attention and coordinate attention (RFCA) module and the cross-spatial learning of the efficient multi-scale attention (EMA) module. The Alterable Kernel Convolution (AKConv) module is incorporated in the neck, enhancing the model’s detection accuracy of curved text by dynamically adjusting the sampling position. Furthermore, to boost the model’s detection performance, the original loss function is replaced with the bounding box regression loss function of Minimum Point Distance Intersection over Union (MPDIoU). Experimental results demonstrate that RA-YOLOv8 surpasses YOLOv8 in terms of precision, recall, and F1 value, with improvements of 0.4%, 1.6%, and 1.03%, respectively, validating its effectiveness and utility in seal text detection.

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

Isothermal Amplification Using Temperature-Controlled Frequency Mixing Magnetic Detection-Based Portable Field-Testing Platform.

Sensitive magnetic nucleic acid (NA) detection via frequency mixing magnetic detection (FMMD) requires amplified NA samples for which a reliable temperature control is necessary. The feasibility of recombinase polymerase amplification (RPA) was studied within a newly integrated temperature-controlled sensor unit of a mobile FMMD based setup. It has been demonstrated that the inherently generated heat of the low frequency (LF) excitation signal of FMMD can be utilized and controlled by means of pulse width modulation (PWM). To test control performance in a point of care (PoC) setting with changing ambient conditions, a steady state and dynamic response model for the thermal behavior at the sample position of the sensor were developed. We confirmed that in the sensor unit of the FMMD device, RPA performs similar as in a temperature-controlled water bath. For narrow steady state temperature regions, a linear extrapolation suffices for estimation of the sample position temperature, based on the temperature feedback sensor for PWM control. For any other ambient conditions, we identified and validated a lumped parameter model (LPM) performing with high estimation accuracy. We expect that the method can be used for NA amplification and magnetic detection using FMMD in resource-limited settings.

Unmutated RRAS2 emerges as a key oncogene in post-partum-associated triple negative breast cancer

BackgroundBreast cancer (BC) is the most common cancer in women, with triple negative BC (TNBC) accounting for 20% of cases. While early detection and targeted therapies have improved overall life expectancy, TNBC remains resistant to current treatments. Although parity reduces the lifetime risk of developing BC, pregnancy increases the risk of developing TNBC for years after childbirth. Although numerous gene mutations have been associated with BC, no single gene alteration has been identified as a universal driver. RRAS2 is a RAS-related GTPase rarely found mutated in cancer.MethodsConditional knock-in mice were generated to overexpress wild type human RRAS2 in mammary epithelial cells. A human sample cohort was analyzed by RT-qPCR to measure RRAS2 transcriptional expression and to determine the frequency of both a single-nucleotide polymorphism (SNP rs8570) in the 3’UTR region of RRAS2 and of genomic DNA amplification in tumoral and non-tumoral human BC samples.ResultsHere we show that overexpression of wild-type RRAS2 in mice is sufficient to develop TNBC in 100% of females in a pregnancy-dependent manner. In human BC, wild-type RRAS2 is overexpressed in 68% of tumors across grade, location, and molecular type, surpassing the prevalence of any previously implicated alteration. Still, RRAS2 overexpression is notably higher and more frequent in TNBC and young parous patients. The increased prevalence of the alternate C allele at the SNP position in tumor samples, along with frequent RRAS2 gene amplification in both tumors and blood of BC patients, suggests a cause-and-effect relationship between RRAS2 overexpression and breast cancer.ConclusionsHigher than normal expression of RRAS2 not bearing activating mutations is a key driver in the majority of breast cancers, especially those of the triple-negative type and those linked to pregnancy.

EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology

Transcriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFβ signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.

The Eurasian Arctic Ocean along the MOSAiC drift in 2019–2020: An interdisciplinary perspective on physical properties and processes

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020), a year-long drift with the Arctic sea ice, has provided the scientific community with an unprecedented, multidisciplinary dataset from the Eurasian Arctic Ocean, covering high atmosphere to deep ocean across all seasons. However, the heterogeneity of data and the superposition of spatial and temporal variability, intrinsic to a drift campaign, complicate the interpretation of observations. In this study, we have compiled a quality-controlled physical hydrographic dataset with best spatio-temporal coverage and derived core parameters, including the mixed layer depth, heat fluxes over key layers, and friction velocity. We provide a comprehensive and accessible overview of the ocean conditions encountered along the MOSAiC drift, discuss their interdisciplinary implications, and compare common ocean climatologies to these new data. Our results indicate that, for the most part, ocean variability was dominated by regional rather than seasonal signals, carrying potentially strong implications for ocean biogeochemistry, ecology, sea ice, and even atmospheric conditions. Near-surface ocean properties were strongly influenced by the relative position of sampling, within or outside the river-water influenced Transpolar Drift, and seasonal warming and meltwater input. Ventilation down to the Atlantic Water layer in the Nansen Basin allowed for a stronger connectivity between subsurface heat and the sea ice and surface ocean via elevated upward heat fluxes. The Yermak Plateau and Fram Strait regions were characterized by heterogeneous water mass distributions, energetic ocean currents, and stronger lateral gradients in surface water properties in frontal regions. Together with the presented results and core parameters, we offer context for interdisciplinary research, fostering an improved understanding of the complex, coupled Arctic System.