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This work introduces a novel approach for predicting the complex element weights of a rectangular planar phased array for a desired beam pattern using physics-aware convolutional neural networks (PA-CNN). An image representation of the desired radiation pattern is provided as input to the proposed network architecture, where PA-CNN learns to recognize spatial features and map input patterns to the amplitude and phase of the antenna element weights. In addition to the classical mean squared error (MSE) loss for regression to true weights, a novel physics-aware loss function is designed to jointly force the PA-CNN to estimate true weight while generating physically consistent beam patterns. The performance of the proposed model is evaluated using metrics such as MSE of estimated weights and traditional radiation pattern metrics such as accuracy of peak direction. The obtained results demonstrate that the models successfully learn the nonlinear relation between antenna parameters and desired beam patterns, where the physics-aware approach outperforms the base CNN cases with an improvement of 6.2% in the MSE between radiation patterns. This approach provides a promising solution for learning complex weights of arrays for synthesizing desired patterns while ensuring physical consistency.

As next-generation communication services and satellite systems expand across diverse frequency bands, there is a pressing need to use the spectrum as efficiently as possible. A possible strategy to achieve this is to facilitate the coexistence of different radio services, for example between telecommunication and microwave remote sensing systems. However, this approach could increase the risk of radio frequency interference (RFI) affecting passive microwave sensors, which are crucial for environmental and geoscience applications. Therefore, the availability of a testbed along with a dataset encompassing a diverse array of scenarios under a controlled environment is very important in order to study coexistence scenarios and reduce the risk of interference associated with them. This study presents a physical environmentally controlled testbed including a passive fully calibrated L-band radiometer with a digital back-end capable of collecting raw in-phase/quadrature (IQ) samples and an active fifth-generation (5G) wireless communication system with the capability of transmitting waveforms with advanced modulations. Various RFI situations such as in-band, transition band, and out-of-band transmission effects are quantified in terms of their effect on the measured brightness temperature. A detailed procedure and publicly accessible dataset are provided to help test the impact of wireless communication on passive sensing, enabling the scientific community to facilitate coexistence research and quantify interference effects on radiometers. This dataset includes publicly available raw radiometer and 5G communication samples along with preprocessed time-frequency representations and true brightness temperature data.

Passive microwave remote sensing is a vital tool for acquiring valuable information regarding the Earth's surface, with significant applications in agriculture, water management, forestry, and various environmental disciplines. Precision agricultural (PA) practices necessitate the availability of field-scale, high-resolution remote sensing data products. This study focuses on the design and development of a cost-effective, portable L-band microwave radiometer capable of operating from an unmanned aircraft system platform to measure high-resolution surface brightness temperature ($T_{B}$). This radiometer consists of a dual-polarized (Horizontal polarized, H-pol and Vertical polarized, V-pol) antenna and a software-defined radio-based receiver system with a 30 MHz sampling rate. The post-processing methodology encompasses the conversion of raw in-phase and quadratic (I&Q) surface emissions to radiation $T_{B}$ through internal and external calibrations. Radiometric measurements were conducted over an experimental site covering both bare soil within an agricultural field and a large water body. The results yielded a high-resolution $T_{B}$ map that effectively delineated the boundaries between land and water, and identified land surface features. The radiometric temperature measurements of the sky and blackbody demonstrated a standard deviation of 0.95 K for H-pol and 0.57 K for V-pol in the case of the sky and 0.39 K for both H-pol and V-pol in the case of the blackbody observations. The utilization of I&Q samples acquired via the radiometer digital back-end facilitates the generation of different time–frequency (TF) analyses through short-time Fourier transform and power spectral density (PSD). The transformation of radiometer samples into TF representations aids in the identification and mitigation of radio frequency interference originating from the instrument itself and external sources.

In closed-bore MRI units, assistance systems play a crucial role in overcoming patient access limitations during percutaneous interventions. In this work, we present eGantryMate, a piezo-motor-driven assistance system specifically designed for MR-guided needle interventions in high-field MRI systems. eGantryMate consists of an instrument positioning unit and a control unit equipped with piezo motors, radiofrequency filters, and shielding. Paired with a real-time tracking sequence for automatic marker detection and projection of the instrument trajectory onto the MR image, eGantryMate enables precise and efficient needle interventions. Targeting experiments were performed by inserting a biopsy needle into a series of fiducial targets in a phantom, and usability experiments were conducted in vivo without needle insertion. The results show artifact-free MR imaging, minimal temperature rise on the instrument positioning unit, and precise targeting capabilities. These findings demonstrate eGantryMate’s ability to perform real-time needle alignments and insertions within the magnet bore, highlighting its potential to enhance the acceptability and efficacy of MR-guided interventions.

The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint.

Abstract Current radio frequency (RF) classification techniques assume only one target in the field of view. Multi‐target recognition is challenging because conventional radar signal processing results in the superposition of target micro‐Doppler signatures, making it difficult to recognise multi‐target activity. This study proposes an angular subspace projection technique that generates multiple radar data cubes (RDC) conditioned on angle (RDC‐ω). This approach enables signal separation in the raw RDC, making possible the utilisation of deep neural networks taking the raw RF data as input or any other data representation in multi‐target scenarios. When targets are in closer proximity and cannot be separated by classical techniques, the proposed approach boosts the relative signal‐to‐noise ratio between targets, resulting in multi‐view spectrograms that boosts the classification accuracy when input to the proposed multi‐view DNN. Our results qualitatively and quantitatively characterise the similarity of multi‐view signatures to those acquired in a single‐target configuration. For a nine‐class activity recognition problem, 97.8% accuracy in a 3‐person scenario is achieved, while utilising DNN trained on single‐target data. We also present the results for two cases of close proximity (sign language recognition and side‐by‐side activities), where the proposed approach has boosted the performance.

Abstract Manipulation of antiferromagnetic (AFM) materials as active elements provides a crucial combination of electrical, thermal, and magnetic properties for spintronics. This study shows how the spin current generated in heavy metal is induced by spin‐orbit torque into an adjacent AFM insulator. The bulk unpinned spins of the AFM layer drive a spin current that is transmitted to the top ferromagnet. This mechanism allows the electrical control of the exchange bias, coercive field, and blocking temperature of the system. Further support is provided by a model calculation that quantitatively describes the effect of the spin current injection into the AFM.

BackgroundOptimal timing of pulmonary valve replacement (PVR) in Tetralogy of Fallot (TOF) patients remains challenging. Ventricular wall stress is considered to be an early marker of right ventricular (RV) dysfunction.ObjectivesTo investigate the association of RV wall stresses and their change over time with functional parameters in TOF patients.MethodsTen TOF patients after surgical repair with moderate/severe pulmonary regurgitation were included. At two timepoints (median follow-up time 7.2 years), patient-specific computational biventricular models for wall stress assessment were created using CMR short-axis cine images and echocardiography-based RV pressures. RV ejection fraction (RVEF), NT-proBNP and cardiopulmonary exercise tests were used as outcome measures reflecting RV function. Associations between regional RV diastolic wall stress and RV function were investigated using linear mixed models.ResultsIncreased wall stress correlated with lower RV mass (rrm = −0.70, p = 0.017) and lower RV mass-to-volume (rrm = −0.80, p = 0.003) using repeated measures. Wall stress decreased significantly over time, especially in patients with a stable RVEF (p

Rupture of the cap of an atherosclerotic plaque can lead to thrombotic cardiovascular events. It has been suggested, through computational models, that the presence of microcalcifications in the atherosclerotic cap can increase the risk of cap rupture. However, the experimental confirmation of this hypothesis is still lacking. In this study, we have developed a novel tissue-engineered model to mimic the atherosclerotic fibrous cap with microcalcifications and assess the impact of microcalcifications on cap mechanics. First, human carotid plaque caps were analyzed to determine the distribution, size, and density of microcalcifications in real cap tissue. Hydroxyapatite particles with features similar to real cap microcalcifications were used as microcalcification mimics. Injected clusters of hydroxyapatite particles were embedded in a fibrin gel seeded with human myofibroblasts which deposited a native-like collagenous matrix around the particles, during the 21-day culture period. Second harmonic multiphoton microscopy imaging revealed higher local collagen fiber dispersion in regions of hydroxyapatite clusters. Tissue-engineered caps with hydroxyapatite particles demonstrated lower stiffness and ultimate tensile stress than the control group samples under uniaxial tensile loading, suggesting increased rupture risk in atherosclerotic plaques with microcalcifications. This model supports previous computational findings regarding a detrimental role for microcalcifications in cap rupture risk and can further be deployed to elucidate tissue mechanics in pathologies with calcifying soft tissues.

The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research.

The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical requirements. We propose a two-step design process for generating CS-based schemes suitable for seismic applications. During the first step, uniform random sampling is used to generate a random scheme, which is supported theoretically by the restricted isometry property. Following that, designated samples are added to the random scheme to control the maximum distance between adjacent sources (or receivers). The null space property theoretically justifies the additional samples of the second step. Our sampling method generates sampling patterns with a CS theoretical background, controlled distance between adjacent samples, and a flexible number of active and omitted samples. The robustness of two-step sampling schemes for reallocated samples is investigated and CS reconstruction tests are performed. In addition, using this approach, a CS-based 3D seismic survey is designed, and the distributions of traces in fold maps and rose diagrams are analyzed. It is shown that the two-step scheme is suitable for CS-based seismic surveys and field applications.

Passive remote sensing is a crucial technology for climate studies and Earth science. National Aeronautics and Space Administration's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400–1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1 A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

In MR-guided interventional procedures, RF coils can be attached to the instruments to provide a positive MR signal for device tracking. The signal from these coils can vary strongly over the procedure and mask the surrounding anatomy. The purpose of this study is to introduce and demonstrate a low-cost, vendor- and device-independent interface circuit that allows the interventionalist to adjust the active device signal intensity. In this work a variable attenuator circuit was constructed to control the tip signal of an active coronary artery catheter in real-time from within the MR scanner room. Performance of the attenuator circuit and the active catheter was characterized on the test bench, in a phantom model, and in vivo. The system was used in a pig model at 3T during the introduction of the catheter into the left coronary artery. The circuit could attenuate the amplitude of the tracking coil signal by up to 20 dB. Without attenuation, the tracking coil signal intensity was masking anatomical details of the coronary ostium making it impossible to reliably introduce the catheter into the artery. After interactive adjustment, which was performed in a few seconds by the interventionalist, the improved visualization of the vascular anatomy enabled a rapid insertion of the catheter into the coronary ostium. The vendor-independent variable attenuator provides real-time control of the catheter signal without interrupting the image acquisition. Even though most MRI systems can control the individual signal levels from coils by software, the attenuator hardware is advantageous as it can be integrated into any MR-system, and it provides a direct interface for the interventionalist at the magnet.

This study proposes a low-cost and “proof-of-concept” methodology to obtain high spatial resolution soil moisture (SM) via processing reflected global positioning system (GPS) and a multispectral camera data acquired by small unmanned aircraft system (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off-the-shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm uses in situ SM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210 by 110 m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)’s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 min of flight time at a 15-m altitude, and SM can be mapped with 5 × 5 m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eight in situ SM station datasets via tenfold and leave-one-probe-out cross-validation techniques. Overall, root-mean-square errors (RMSE) of 0.013 m $^{3}$ m$^{-3}$ volumetric SM and R-value of 0.95 [-] are obtained for tenfold cross validation. The proposed model reached an RMSE of 0.033 m$^{3}$ m$^{-3}$ and an R-value of 0.5 [-] in leave-one-probe-out cross validation. While having limited data, the results indicate that high-resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture applications.

National Aeronautics and Space Administration's Cyclone Global Navigation Satellite System (CYGNSS) mission has gained significant attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDMs), containing important Earth surface information from GNSS reflection measurements. Previous studies considered only designed features from CYGNSS DDM, whereas the whole DDM image is affected by SM, inundation, and vegetation. This paper presents a deep learning (DL) based framework for estimating SM in the Continental United States by leveraging spaceborne GNSS-R DDM observations provided by the CYGNSS constellation along with remotely sensed geophysical data. A data-driven approach utilizing convolutional neural networks (CNNs) is developed to determine complex relationships between the reflected measurements and surface parameters which can provide improved SM estimation. The model is trained jointly with three types of processed DDM images of analog power, effective scattering area, and bistatic radar cross-section with other auxiliary geophysical information such as elevation, soil properties, and vegetation water content (VWC). The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a 9 km resolution with VWC less than 5 kg/m2. The mean unbiased root-mean-square difference between concurrent CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0366 m3/m3 with a correlation coefficient of 0.93 over fivefold cross-validation and 0.0333 m3/m3 with a correlation coefficient of 0.94 over year-based cross-validation at spatial resolution of 9 km and temporal resolution similar to CYGNSS mission.

Many neurological disorders are analyzed and treated with implantable electrodes. Many patients with such electrodes have to undergo MRI examinations – often unrelated to their implant - at the risk of radio-frequency induced heating. The number of electrode contact sites of these implants keeps increasing due to improvements in manufacturing and computational algorithms. Electrode grids with multiple receive channels couple to the RF fields present in MRI, but, due to their proximity, a combination of leads has a coupling response which is not a superposition of the individual leads’ response. To investigate the problem of RF-induced heating of coupled multi-lead implants, temperature mapping was performed on a set of intra-cranial electroencephalogram (icEEG) electrode grid prototypes with increasing number of contact sites (1-16). Additionally, electric field measurements were used to investigate the radio-frequency heating characteristics of the implants in different media combinations, simulating the device being partially immersed inside the patient.MR measurements show RF-induced heating up to 19.6 K for the single electrode, reducing monotonically with larger number of contact sites to a minimum of 0.9 K for the largest grid. The SAR calculated from temperature measurements agrees well with electric field mapping: The same trend is visible for different insertion lengths, however, the energy dissipated by the whole implant varies with the grid size and insertion length. Thus, in the tested circumstances, a larger electrode number either reduced or had a similar risk of RF induced heating, indicating, that the size of electrode grids is a design parameter, which can be used to change an implants RF response and in turn to reduce the risk of RF induced heating and improve the safety of patient with neuro-implants undergoing MRI examinations.

Abstract A deep‐learning‐based approach to estimating the number of coherent sources in radar is presented. A proper estimate of the number of sources in a signal enables improved angle‐of‐arrival (AoA) estimation common in applications such as radar, sonar, and communication systems. Many AoA estimators utilised in these areas require accurate estimates of the number of sources for enhanced performance. Herein, a robust method that performs well under the existence of coherent sources is developed. The proposed method is based on deep learning and it is shown to perform better than state‐of‐the‐art versions of the Akaike Information Criteria (AIC), Minimum Description Length (MDL), and Exponentially Embedded Families (EEF) estimators, which employ spatial smoothing of the covariance matrix to handle coherent signals.

Abstract Glutathione‐S‐transferases (GSTs) are a diverse group of phase II detoxification enzymes which primarily evoke tissue protection via glutathione conjugation to xenobiotics and reactive oxygen species. Given their cytoprotective properties, potential changes in GST expression during AKI has pathophysiologic relevance. Hence, we evaluated total GST activity, and the mRNA responses of nine cytosolic GST isotypes (GST alpha1, kappa1, mu1/5, omega1, pi1 sigma1, theta1, zeta1 mRNAs), in five diverse mouse models of AKI (glycerol, ischemia/reperfusion; maleate, cisplatin, endotoxemia). Excepting endotoxemia, each AKI model significantly reduced GST activity (~35%) during both the AKI “initiation” (0‐4 h) and “maintenance” phases (18 or 72 h). During the AKI maintenance phase, increases in multiple GST mRNAs were observed. However, no improvement in GST activity resulted. Increased urinary GST excretion followed AKI induction. However, this could not explain the reduced renal GST activity given that it also fell in response to ex vivo renal ischemia (i.e., absent urinary excretion). GST alpha, a dominant proximal tubule GST isotype, manifested 5–10‐fold protein increases following AKI, arguing against GST proteolysis as the reason for the GST activity declines. Free fatty acids (FFAs) and lysophospholipids, which markedly accumulate during AKI, are known to bind to, and suppress, GST activity. Supporting this concept, arachidonic acid addition to renal cortical protein extracts caused rapid GST activity reductions. Based on these results, we conclude that diverse forms of AKI significantly reduce GST activity. This occurs despite increased GST transcription/translation and independent of urinary GST excretion. Injury‐induced generation of endogenous GST inhibitors, such as FFAs, appears to be a dominant cause.

This paper tries to consider recent developments in popular music studies in Turkey by tracing contemporary debates, particular challenges and emerging themes. In order to achieve this task, firstly, history of popular music studies in Turkey is briefly presented in comparison to those of the Anglophone World. Secondly, recent developments are considered within the political context of last two decades in Turkey where hot discussions have revolved around underlying theme of ‟Republic”. In this mileu, the theoretical premises behind the lack of critical discourses about the neoliberal transformation and Islamization processes, and indifference to the struggles against them are discussed. Thirdly, recent achievements of popular music studies in Turkey are presented. As a result, we mainly tried to discuss why certain critical studies are lacking which diminishes plurality rather than the plurality of the existing ones.

Objective: To evaluate the antibacterial and antioxidant activities and to identify the volatile bioactive compounds present in different crude extracts of the seaweed Caulerpa racemosa var. cylindracea. Methods: Caulerpa racemosa harvested from the intertidal zone of Mostaganem coast (N 35°54’37.94”, E 0°3’17.37”) was subjected to Soxhlet extraction using methanol, chloroform, and hexane solvents. Antioxidant properties were assessed by using 2,2’-diphenyl-1- picrylhydrazyl (DPPH), 2,2’-azino-bis(3-ethylbenzothiazoline- 6-sulfonic acid) (ABTS) and β-carotene bleaching assays. The antibacterial activity was evaluated on six standard bacterial strains using the agar disc diffusion method. The GC-MS analysis was performed using non-polar and polar capillary columns. Results: The chloroform extract of Caulerpa racemosa exhibited higher contents of polyphenols [(123.91±1.46) mg gallic acid equivalent/ g dry extract] and tannins [(59.28±5.43) mg catechin equivalent/ g dry extract] (P

Atherosclerotic plaque rupture in coronary arteries, an important trigger of myocardial infarction, is shown to correlate with high levels of pressure-induced mechanical stresses in plaques. Finite element (FE) analyses are commonly used for plaque stress assessment. However, the required information of heterogenous material properties of atherosclerotic coronaries remains to be scarce. In this work, we characterized the component-wise mechanical properties of atherosclerotic human coronary arteries. To achieve this, we performed ex vivo inflation tests on post-mortem human coronary arteries and developed an inverse FE modeling (iFEM) pipeline, which combined high-frequency ultrasound deformation measurements, a high-field magnetic resonance-based artery composition characterization, and a machine learning-based Bayesian optimization (BO) with uniqueness assessment. By using the developed pipeline, 10 cross-sections from five atherosclerotic human coronary arteries were analyzed, and the Yeoh material model constants of the fibrous intima and arterial wall components were determined. This work outlines the developed pipeline and provides the knowledge of non-linear, multicomponent mechanical properties of atherosclerotic human coronary arteries.

Abstract Purpose In contemporary total knee arthroplasty (TKA), most often, the goal is to align the femoral component to the epicondylar axis (EA). The posterior condylar axis (PCA) is easier to define than the EA, and thus the relationship of PCA to the EA is then used instead to align the femoral component to the EA. However, the relationship of PCA to EA is not constant and has been reported to differ between varus and valgus knees and with increasing deformity. The aim of this large MRI-based study was to evaluate the relationship between PCA and EA with varying coronal deformity especially with increasing valgus deformity. Methods EA, PCA, AP (Whiteside’s line) and the mechanical axis were obtained from 474 magnetic resonance imaging (MRI) scans used to create patient-specific instrumentation (PSI) for the Biomet Signature (Warsaw, NJ) system. Results The relationship of EA relative to the PCA showed considerable heterogeneity in both varus and valgus groups. In the valgus group, there was statistically greater external rotation (P < 0.05) of the EA from the PCA with a mean of 2.52° (range − 1.9° to 6°) compared to the varus group with a mean of 2.03° (range − 3.9° to 6.9°). This relationship did not significantly change with increasing severity of coronal malalignment. Externally rotating the femoral cutting guide by 3° from the PCA, 11% (42 of 382) of varus knees would lie outside of ± 3° from EA. In valgus knees, externally rotating the femoral cutting block by 3° or 5° from the PCA, 6.5% (6 of 92) and 33.7% (31 of 92) of knees, respectively, would lie outside of ± 3° from EA. Conclusion The relationship of PCA to EA is heterogeneous and is not altered significantly with increasing valgus coronal deformity. External rotation beyond 3° from PCA in valgus knees may lead to significant femoral component malrotation in a large proportion cases.

Abstract Background Tin protoporphyrin (SnPP), a heme oxygenase 1 (HO‐1) inhibitor, triggers adaptive tissue responses that confer potent protection against acute renal‐ and extra‐renal tissue injuries. This effect is mediated, in part, via SnPP‐induced activation of the cytoprotective Nrf2 pathway. However, it remains unclear as to whether SnPP can also upregulate humoral cytokine defenses, either in healthy human subjects or in patients with CKD. If so, then systemically derived cytokines could contribute SnPP‐induced tissue protection. Methods SnPP (90 mg IV) was administered over 2 hr to six healthy human volunteers (HVs) and 12 subjects with stage 3–4 CKD. Plasma samples were obtained from baseline upto 72 hr post injection. Two representative anti‐inflammatory cytokines (IL‐10, TGFβ1), and a pro‐inflammatory cytokine (TNF‐α), were assayed. Because IL‐6 has been shown to induce tissue preconditioning, its plasma concentrations were also assessed. In complementary mouse experiments, SnPP effects on renal, splenic, and hepatic IL‐10, IL‐6, TGFβ1, and TNF‐α production (as gauged by their mRNAs) were tested. Tissue HO‐1 mRNA served as an Nrf2 activation marker. Results SnPP induced marked (~5–7x) increases in plasma IL‐10 and IL‐6 concentrations within 24–48 hr, and to equal degrees in HVs and CKD patients. SnPP modestly raised plasma TGFβ1 without impacting plasma TNF‐α levels. In mouse experiments, SnPP did not affect IL‐6, IL‐10, TNF‐α, or TGFβ1 mRNAs in kidney despite marked renal Nrf2 activation. Conversely, SnPP increased splenic IL‐10 and hepatic IL‐6/TGFβ1 mRNA levels, suggesting these organs as sites of extra‐renal cytokine generation. Conclusions SnPP can trigger cytoprotective cytokine production, most likely in extra‐renal tissues. With ready glomerular cytokine filtration, extra‐renal/renal “organ cross talk” can result. Thus, humoral factors seemingly can contribute to SnPP’s cytoprotective effects.

Lineage tracing involves the identification of all ancestors and descendants of a given cell, and is an important tool for studying biological processes such as development and disease progression. However, in many settings, controlled time-course experiments are not feasible, for example when working with tissue samples from patients. Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process. Given a single-cell image from one of the stages, ImageAEOT generates an artificial lineage of this cell based on the population characteristics of the other stages. These lineages can be used to connect subpopulations of cells through the different stages and identify image-based features and biomarkers underlying the biological process. To validate our method, we apply ImageAEOT to a benchmark task based on nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used.

An important prerequisite to select a reliable phase change material (PCM) for thermal energy storage applications is to test it under application conditions. In the case of solid–liquid PCM, a large amount of thermal energy can be stored and released in a small temperature range around the solid–liquid phase transition. Therefore, to test the long-term stability of solid–liquid PCM, they are subjected to melting and solidification processes taking into account the conditions of the intended application. In this work, 18 experimental devices to investigate the long-term stability of PCM are presented. The experiments can be divided into thermal cycling stability tests, tests on PCM with stable supercooling, and tests on the stability of phase change slurries (PCS). In addition to these experiments, appropriate methods to investigate a possible degradation of the PCM are introduced. Considering the diversity of the investigated devices and the wide range of experimental parameters, further work toward a standardization of PCM stability testing is recommended.

Aim of study: The aim of this study was to analyze the effects of initial spacing on early growth and carbon sequestration rates in Turkish red pine plantations up to 12 years old, established with improved seeds and deep soil cultivation. Area of study: The study was conducted on experimental sites established in two locations within the Turkish red pine natural distribution areas, namely Duacı and Nebiler close to Antalya city. Material and methods: Data were collected from the experimental sites established as a Nelder design (fan-shaped), with 72 rays and 18 arcs (circles), and trees were planted (almost square) at distances ranging from 1.15 to 4.77 m. Soil type of both sites is loamy, with soil clay content varying between 70-87% in Duacı and 51-70% in Nebiler. Soils are deep being more than one m in both sites, but rockier in Nebiler, providing better soil drainage in this site. Main results: The results showed that mean total height was greater at closer spacing than those of wider spacing until age eight. Growth retardation at wider spacing in early years may be related to water loss due to evaporation in hot summer days and weed suppression. Following the age eight, competition among trees appears to be the major factor reducing the growth and carbon fixation. Diameter at breast height and individual tree volume increased, while stand volume, mean annual volume increment and annual carbon storage per hectare considerably decreased for wider spacing. Our results suggest that in order to obtain higher yield and more carbon fixation, short rotation plantations should initially be established in closer spacing, followed by thinning in subsequent years as required by silvicultural concerns. In this context, spacing 3.0 × 1.0 m or 3.0 × 1.5 m (3.0 and 4.5 m2 growing area per tree, respectively) seems to be more plausible, providing farm machinery for maintenance and harvesting. We also found that mean annual volume increment per unit area can be considerably increased by using improved seed and deep soil cultivation in plantations. Research highlights: Results encourage managers for short rotation management in industrial plantations using closer spacing in terms of carbon sequestration as well as wood production. Keywords: Pinus brutia Ten.; spacing; forest growth and yield; carbon sequestration; Nelder experimental design. Abbreviations used: TFS: Turkish Forest Service; OGM: General Directorate of Forestry; Dbh: Diameter at breast height.

This article reviews the state of popular music studies in Turkey in comparison to IASPM's Anglo-American history of popular music studies. The main focus is on the similarities and dissimilarities in studies based on shifts in paradigms: firstly the shift from the sociology of arabesk music in Turkey and sociology of rock music in the Anglo-American popular music studies, to the sociology of popular music; secondly, a parallel shift in theoretical premises, from Marxism to postmodern theories. Mapping the achievements of popular music studies in Turkey in relation to the study of popular musics that have dominated specific eras, it is shown that despite earlier divergences from the 1990s onwards, Turkish popular music studies gradually converged with international popular music studies.

Alpha-1-antitrypsin (AAT) is a hepatic stress protein with protease inhibitor activity. Recent evidence indicates that ischemic or toxic injury can evoke selective changes within kidney that resemble a hepatic phenotype. Hence, we tested the following: i) Does acute kidney injury (AKI) up-regulate the normally renal silent AAT gene? ii) Does rapid urinary AAT excretion result? And iii) Can AAT's anti-protease/anti-neutrophil elastase (NE) activity protect injured proximal tubule cells? CD-1 mice were subjected to ischemic or nephrotoxic (glycerol, maleate, cisplatin) AKI. Renal functional and biochemical assessments were made 4-72 hrs later. Rapidly following injury, 5-10 fold renal cortical and isolated proximal tubule AAT mRNA and protein increases occurred. These were paralleled by rapid (>100 fold) increases in urinary AAT excretion. AKI also induced marked increases in renal cortical/isolated proximal tubule NE mRNA. However, sharp NE protein levels declines resulted, which strikingly correlated (r, -0.94) with rising AAT protein levels (reflecting NE complexing by AAT/destruction). NE addition to HK-2 cells evoked ∼95% cell death. AAT completely blocked this NE toxicity, as well as Fe induced oxidant HK-2 cell attack. Translational relevance of experimental AAT gene induction was indicated by ∼100-1000 fold urinary AAT increases in 22 AKI patients (matching urine NGAL increases). We conclude: i) AKI rapidly up-regulates the renal cortical/proximal tubule AAT gene; ii) NE gene induction also results; iii) AAT can confer cytoprotection, potentially by blocking/reducing cytotoxic NE accumulation; and iv) marked increases in urinary AAT excretion in AKI patients implies clinical relevance of the AKI- AAT induction pathway.

Studies of experimental acute kidney injury (AKI) are critically dependent on having precise methods for assessing the extent of tubular cell death. However, the most widely used techniques either provide indirect assessments (e.g., BUN, creatinine), suffer from the need for semi-quantitative grading (renal histology), or reflect the status of residual viable, not the number of lost, renal tubular cells (e.g., NGAL content). Lactate dehydrogenase (LDH) release is a highly reliable test for assessing degrees of in vitro cell death. However, its utility as an in vivo AKI marker has not been defined. Towards this end, CD-1 mice were subjected to graded renal ischemia (0, 15, 22, 30, 40, or 60 min) or to nephrotoxic (glycerol; maleate) AKI. Sham operated mice, or mice with AKI in the absence of acute tubular necrosis (ureteral obstruction; endotoxemia), served as negative controls. Renal cortical LDH or NGAL levels were assayed 2 or 24 hrs later. Ischemic, glycerol, and maleate-induced AKI were each associated with striking, steep, inverse correlations (r, -0.89) between renal injury severity and renal LDH content. With severe AKI, >65% LDH declines were observed. Corresponding prompt plasma and urinary LDH increases were observed. These observations, coupled with the maintenance of normal cortical LDH mRNA levels, indicated the renal LDH efflux, not decreased LDH synthesis, caused the falling cortical LDH levels. Renal LDH content was well maintained with sham surgery, ureteral obstruction or endotoxemic AKI. In contrast to LDH, renal cortical NGAL levels did not correlate with AKI severity. In sum, the above results indicate that renal cortical LDH assay is a highly accurate quantitative technique for gauging the extent of experimental acute ischemic and toxic renal injury. That it avoids the limitations of more traditional AKI markers implies great potential utility in experimental studies that require precise quantitation of tubule cell death.