Sampling Pattern Research Articles

Genomic patterns of somatic mutations provide new prognostic, therapeutic, and biological insights in cancer

The mutational landscape of an individual's cancer can inform on its molecular state and be used as prognostic and therapeutic markers. The study by Barbour etal.1 analyzes mutational patterns in bladder cancer samples to uncover new biological insights into the ERCC2 gene function and develop new predictive prognostic tools.

Left Upper Lobectomy vs Trisegmentectomy for Lung Cancer: A Propensity Score–Matched Comparison

BackgroundThe left upper division (segments I-III) and the lingula (segments IV and V) are analogous to the right upper and middle lobes, respectively. Whereas bilobectomy for right upper lobe tumors is rare, left upper division tumors are often resected by left upper lobectomy (LUL) rather than by left upper trisegmentectomy (LU3S). To assess safety and oncologic efficacy of LUL vs LU3S, we compared short- and long-term outcomes after both procedures. MethodsPatients undergoing LUL or LU3S for clinical stage IA-IIA non-small cell lung cancer in the left upper division from January 2006 to December 2020 were identified from an institutional database. Propensity score matching was used to control for clinical differences. ResultsWe identified 229 cases meeting inclusion criteria: 131 (57.2%) LUL, 98 LU3S. After matching, 83 cases were included in each group. Median clinical tumor size was similar for LUL vs LU3S (2.2 cm [interquartile range, 1.6-3.0 cm] vs 2.1 cm [interquartile range, 1.7-2.9 cm]; P = .80). Total lymph nodes sampled did not differ between LUL and LU3S (median, 7 vs 6; P = .36), nor did patterns of N2 sampling (P = .11). Odds of postoperative complications did not differ after LUL vs LU3S (odds ratio, 0.75; 95% CI, 0.39-1.46). No 30-day death was observed. Median follow-up was 72 months. There was no statistically significant difference in 5-year overall survival (75.9% vs 82.1%; P = .28) or locoregional recurrence-free survival (73.7% vs 80.0%; P = .23) after LUL vs LU3S. ConclusionsOur findings suggest that LU3S and LUL have equivalent short- and long-term outcomes in patients with clinical stage IA-IIA non-small cell lung cancer in the left upper division.

Blind power spectrum reconstruction for multi-sinusoid signals with generative multicoset sampling

As the target frequency increases, the Nyquist rate is hard to reach due to hardware limitations. Alternatives to high-rate sampling have attracted considerable research interest. Compressed covariance sensing based on multicoset sampling (MCS) is a prevalent sub-Nyquist sampling strategy for blind power spectrum reconstruction. However, it requires the sampling pattern of MCS to be a minimal sparse ruler. In this paper, a generative multicoset sampling (GMCS) is proposed to minimize the number of cosets. In the sampling stage, a minimal MCS, delay coprime scheme is proposed to ensure the recoverability of the power spectrum. In the recovery stage, a new function, i.e., multifold correlation is constructed for blind power spectrum reconstruction. We prove that the power spectrum of a multi-sinusoid signal (MSS) can be estimated by its multifold correlation and the sample density of multifold correlation can be increased by correlation operations. Owing to multifold correlation, GMCS can generate virtual cosets satisfying the sparse ruler criterion through only two physical cosets and then efficiently recover the power spectrum by least-squares estimation. Moreover, we demonstrate the feasibility of GMCS for linear frequency modulation (LFM) signals. Finally, the effectiveness of GMCS are verified by numerical simulations and blade tip timing experiments (a non-contact vibration measurement). By reducing the average sampling rate and hardware complexity, GMCS opens new avenues for blind sampling and power spectrum reconstruction.

Geochemical analysis of bituminous samples from the Pre-Pottery Neolithic B site of Nahal Efe (Northern Negev, Israel): Earliest evidence in the region and an example of alteration of the Dead Sea bitumen

Thirty-six bituminous samples from the Pre-Pottery Neolithic site of Nahal Efe (8000–7700 cal. BCE, Negev, Israel) were analysed to determine their origins. Most samples are mixtures of bitumen with minerals, predominantly quartz, calcite, dolomite and aragonite. The aragonite is from marine shells, which were crushed to be incorporated into the mixtures.Dead Sea bitumen was identified in all samples. The Nahal Efe geochemical data were compared to samples from Tell Yarmuth (2800–1100 BCE), Tall Dhiban (700–800 cal. BCE), Tel Gezer (1250–700 cal. BCE) and to floating asphalt blocks from the Dead Sea. The stable carbon isotope values of resins and asphaltenes of the Nahal Efe bitumens differ from those of the Dead Sea floating blocks, Tell Yarmuth, Tel Gezer and Tall Dhiban, apparently a consequence of diagenesis. Thus, δ13C values of asphaltenes may not always be a reliable representative of the stable carbon isotope composition of the unaltered source bitumen. Surprisingly, the sterane and terpane patterns of the Nahal Efe samples are not severely biodegraded and clearly indicate a Dead Sea bitumen source. No difference was recorded between mixtures and pure bitumen. Some changes have, however, been observed when using quantitative data on biomarkers. The shifts in δ13C values of resins and asphaltenes is not related to biodegradation but rather to oxidation of the Nahal Efe samples.This is the earliest and most complete evidence of Dead Sea bitumen exploitation and use, which included the preparation of different ‘recipes’, in the Negev and Sinai arid regions.

Effects of disturbances on the spatiotemporal patterns and dynamics of coastal wetland vegetation

The coastal wetlands represented by mangroves are vulnerable to disturbances. To clarify the effects of disturbances quantitatively on mangroves, spatiotemporal changes in land use and land cover (LULC) were compared between the inside and outside of mangroves during 1988 and 2020 in Bangladesh by remote sensing. The mangrove region, which had diverse surface greenness due to natural and anthropogenic disturbances, were surveyed. Changes in greenness were estimated by five vegetation indices (VIs). To increase in the accuracy of land use classification, the five VIs were combined under a random forest (RF) classifier. The five VIs were: normalized difference vegetation index (NDVI), green NDVI (GNDVI), soil-adjusted VI (SAVI), green–red VI (GRVI) and enhanced VI (EVI), using data obtained from Landsat TM (30 m resolution) and Sentinel-2A (10 m) via Google Earth Engine. A Moran’s I analysis showed a random pattern of training samples of LULC classes for integrated RF classification. The classification accuracy was evaluated using the reference point data with multiple comparisons of VIs and κ coefficient. The area was classified into eight LULC classes. Although the respective VIs depicted unique characteristics, the accuracies of them were less than 89 %. The integrated classifier improved the accuracy of 96–97 % with κ of 0.96 in 1988 and 0.97 in 2020. The VI-integrated RF was the most effective classifier for LULC, particularly for separating mangroves from homestead vegetation. The changes for 32 years showed that the dense mangrove occupied 42 % of surveyed area in 1988 but declined ca 622 km2 in 2020, whereas sparse mangrove and aquaculture increased by 487 km2 and 464 km2, respectively, out of the total area of 8,360 km2. The changes in LULC classes were mostly from dense mangroves to sparse mangroves, sparse mangroves to dense mangroves and from cropland to bare land and aquaculture. Inaccessible mangrove degradation should result from natural disturbances, i.e., tropical cyclones, while areas near forest boundaries were distorted by anthropogenic disturbances such as aquaculture development. The restoration of habitat quality, mangrove afforestation and sustainable aquaculture practices were effective indicators for improving the mangrove ecosystems. Since natural and anthropogenic disturbances induce substantial loss of mangrove vegetation, the VI-integrated RF classifier should be applied for monitoring LULC and for developing the sustainable management strategies.

The Stabilization of a Nonlinear Permanent-Magnet- Synchronous-Generator-Based Wind Energy Conversion System via Coupling-Memory-Sampled Data Control with a Membership-Function-Dependent H∞ Approach

This study presents the coupling-memory-sampled data control (CMSDC) design for the Takagi–Sugeno (T-S) fuzzy system that solves the stabilization issue of a surface-mounted permanent-magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). A fuzzy CMSDC scheme that includes the sampled data control (SDC) and memory-sampled data control (MSDC) is designed by employing a Bernoulli distribution order. Meanwhile, the membership-function-dependent (MFD) H∞ performance index is presented, mitigating the continuous-time fuzzy system’s disturbances. Then, by using the Lyapunov–Krasovskii functional with the MFD H∞ performance index, the data of the sampling pattern, and a constant signal transmission delay, sufficient conditions are derived. These sufficient conditions are linear matrix inequalities (LMIs), ensuring the global asymptotic stability of a PMSG-based WECS under the designed control technique. The proposed method is demonstrated by a numerical simulation implemented on the PMSG-based WECS. Finally, Rossler’s system demonstrates the effectiveness and superiority of the proposed method.

LUCMT: Learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction

Background and ObjectiveAs a widely used technique for Magnetic Resonance Image (MRI) acceleration, compressed sensing MRI involves two main issues: designing an effective sampling strategy and reconstructing the image from significantly under-sampled K-space data. In this paper, an innovative approach is proposed to address these two challenges simultaneously. MethodsA novel MRI reconstruction method, termed as LUCMT, is implemented by integrating a learnable under-sampling strategy with a reconstruction network based on the Cross Multi-head Attention Transformer. In contrast to conventional static sampling methods, the proposed adaptive sampling scheme is processed optimally by learning the optimal sampling technique, which involves binarizing the sampling pattern by a sigmoid function and computing gradients by backpropagation. And the reconstruction network is designed by using CS-MRI depth unfolding network that incorporates a Cross Multi-head Attention (CMA) module with inertial and gradient descent terms. ResultsT1 brain MR images from the FastMRI dataset are used to validate the performance of the proposed method. A series of experiments are conducted to validate the superior performance of our proposed network in terms of quantitative metrics and visual quality. Compared with other state-of-the-art reconstruction methods, LUCMT achieves better reconstruction performances with more accurate details. Specifically, LUCMT achieves PSNR and SSIM results of 41.87/0.9749, 46.64/0.9868, 50.41/0.9924, and 53.51/0.9955 at sampling rates of 10 %, 20 %, 30 %, and 40 %, respectively. ConclusionsThe proposed LUCMT method can provide a promising way for generating optimal under-sampling mask and accelerating MRI reconstruction accurately.

Analysis of influence mechanism of CO2-water coupling fracturing sandstone

The existence of hard rock layers has a serious impact on coal seam mining, in order to explore the acidification and crushing mechanism of hard sandstone rock layers, this paper adopts the self-developed CO2-water-rock coupling test device to carry out the testing of mechanical properties and internal structural characteristics of rock samples before and after the coupling action of the three sandstones, and analyzes the influencing factors of sandstone CO2 coupling crushing. The study shows that: the lower the temperature of CO2-water-rock coupling, the higher the pore pressure, the higher the volume fraction of CO2 in the coupling fracturing fluid, and the longer the coupling time, the greater the decrease in the mechanical strength of the rock samples, and the more complicated the splitting damage pattern is, and the CO2-water-rock coupling makes the pore and fracture volume fraction and fractal dimension of three kinds of sandstone samples increase to varying degrees, whereas the volume fraction of minerals and the fractal dimension decrease, and the CO2-water-rock coupling results in a decrease in the volume fraction of minerals and fractal dimension, and a decrease in the volume fraction of minerals and fractal dimension. The pore volume fraction and fractal dimension of the three sandstone samples increased to different degrees, while the mineral volume fraction and fractal dimension decreased, and the pore volume fraction and fractal dimension of the three sandstone samples decreased. The damage pattern of sandstone samples after coupling is affected by both chemical and mechanical damages. When using coupling fracturing fluid with 8 % CO2, the degree of mineral dissolution and dissolution is the largest, and the dissolution effect is larger than the precipitation effect, which has the most significant effect on the morphology type and connectivity of microscopic pore cracks, and the study in this paper has certain theoretical and practical value for the chemical softening of sandstone.

Exploring cross-tissue DNA methylation patterns: blood–brain CpGs as potential neurodegenerative disease biomarkers

The difficulty of obtaining samples from certain human tissues has led to efforts to find accessible sources to analyze molecular markers derived from DNA. In this study, we look for DNA methylation patterns in blood samples and its association with the brain methylation pattern in neurodegenerative disorders. Using data from methylation databases, we selected 18,293 CpGs presenting correlated methylation levels between blood and brain (bb-CpGs) and compare their methylation level between blood samples from patients with neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Multiple Sclerosis, and X Fragile Syndrome) and healthy controls. Sixty-four bb-CpGs presented significant distinct methylation levels in patients, being: nine for Alzheimer’s disease, nine for Parkinson’s disease, 28 for Multiple Sclerosis, and 18 for Fragile X Syndrome. Similar differences in methylation pattern for the nine Alzheimer’s bb-CpGs was also observed when comparing brain tissue from patients vs. controls. The genomic regions of some of these 64 bb-CpGs are placed close to or inside genes previously associated with the respective condition. Our findings support the rationale of using blood DNA as a surrogate of brain tissue to analyze changes in CpG methylation level in patients with neurodegenerative diseases, opening the possibility for characterizing new biomarkers.

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model

The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconstruct images at higher acceleration factors and incur long reconstruction time. Furthermore, end-to-end deep learning-based reconstruction algorithms suffer from large model parameters and lack robustness in the reconstruction results. Recently, unrolled deep learning models, have shown immense potential in algorithm stability and applicability flexibility. In this paper, we propose an unrolled deep learning network based on a second-order Half-Quadratic Splitting(HQS) algorithm, where the forward propagation process of this framework strictly follows the computational flow of the HQS algorithm. In particular, we propose a degradation-sense module by associating random sampling patterns with intermediate variables to guide the iterative process. We introduce the Information Fusion Transformer(IFT) to extract both local and non-local prior information from image sequences, thereby removing aliasing artifacts resulting from random undersampling. Finally, we impose low-rank constraints within the HQS algorithm to further enhance the reconstruction results. The experiments demonstrate that each component module of our proposed model contributes to the improvement of the reconstruction task. Our proposed method achieves comparably satisfying performance to the state-of-the-art methods and it exhibits excellent generalization capabilities across different sampling masks. At the low acceleration factor, there is a 0.7% enhancement in the PSNR. Furthermore, when the acceleration factor reached 8 and 12, the PSNR achieves an improvement of 3.4% and 5.8% respectively.

Physicochemical, Phytochemical and Antioxidant Properties of Organic Sweet Potato Flour and Its Application in Breadstick

Sweet potato flour (SPF) is used to produce food products worldwide due to good biological activity in the human diet and low cost. In this experiment, sweet potato tubers were processed into flour including purple-SPF, yellow-SPF and orange-SPF and applied in breadsticks. The proximate, physical, phytochemical and antioxidant properties of flour and breadsticks were investigated. The carbohydrate, moisture, ash, fat, protein and fiber contents of SPF ranged from 82.19 - 85.21, 4.12 - 5.78, 1.30 - 2.46, 0.32 - 0.86, 4.07 - 4.74 and 3.58 - 5.65 %, respectively. The water activity (aw) of SPF ranged from 0.2277 - 0.2695. The phytochemical analysis showed that purple-SPF and purple-SPF based breadstick products exhibited the highest total phenolic contents (TPC), total flavonoid contents (TFC) and total anthocyanin contents (TAC), while total carotenoid contents (TCC) was observed in both orange-SPF and breadstick made from orange-SPF. In addition, purple-SPF and breadstick produced from purple-SPF had the highest antioxidant activity against ABTS, FRAP, DPPH and metal chelating activity. The X-ray diffraction patterns of SPF samples showed a C-type crystal structure, with a crystallinity ranging from 19.43 - 31.05 %. The scanning electron micrographs of SPF granules revealed that they were mostly round and spherical, with a size range between 5 - 28 µm. The viscosity characteristics of yellow-SPF had the highest peak viscosity (307.97 ± 9.87 RVU), trough viscosity (200.81 ± 10.07 RVU), breakdown (108.42 ± 3.13 RVU), final viscosity (277.75 ± 8.60 RVU) and setback (76.95 ± 4.03 RVU). The physical quality showed breadstick from yellow-SPF had the highest hardness and fracturability with values of 11,537.67 ± 190.58 g and 11,143.33 ± 161.97 g, respectively. These results indicate that sweet potato flour composite breadstick has high phytochemical and antioxidant potential and may be applied in the food industry.

Sampling in the shift-invariant space generated by the bivariate Gaussian function

We study the space spanned by the integer shifts of a bivariate Gaussian function and the problem of reconstructing any function in that space from samples scattered across the plane. We identify a large class of lattices, or more generally semi-regular sampling patterns spread along parallel lines, that lead to stable reconstruction while having densities close to the critical value given by Landau's limit. At the critical density, we construct examples of sampling patterns for which reconstruction fails.In the same vein, we also investigate continuous sampling along non-uniformly scattered families of parallel lines and identify the threshold density of line configurations at which reconstruction is possible. In a remarkable contrast with Paley-Wiener spaces, the results are completely different for lines with rational or irrational slopes.Finally, we apply the sampling results to Gabor systems with bivariate Gaussian windows. As a main contribution, we provide a large list of new examples of Gabor frames with non-complex lattices having volume close to 1.

Green synthesis, characterization, and photocatalytic activity of superparamagnetic MgFe2O4@ZnAl2O4 nanocomposites

MgFe2O4@ZnAl2O4 magnetic nanocomposites were synthesized with the easy and green sol–gel method, and their photocatalytic efficiency was followed toward degradation of reactive blue 222 (RB222) dye under visible light irradiation. Prepared nanocomposites were fully characterized. The SEM and TEM images revealed the spherical morphology of the produced nanocomposites, with average size of 20–25 nm. The XRD pattern of sample exhibited the successful synthesis of the MgFe2O4@ZnAl2O4 MNCs with crystallite size 13 nm. The saturation magnetization (Ms) of the nanocomposites was examined using VSM, indicating a value of 6.59 emu/g. The absence of Hc and Mr values confirms the superparamagnetic nature of the nanoparticles. In addition, the surface area was calculated to be 78.109 m2/g utilizing BET analysis, and the band gap was determined to be 1.88 eV by DRS analysis. The photocatalytic, photolysis, and adsorption performance were investigated and result shown photodegradation activity was higher than others. These results confirm the synergetic effect between the MgFe2O4@ZnAl2O4 MNCs and visible light irradiation to degradation of organic dye. The results indicate that rapid degradation of 96% of RB222 dye occurred in just 10 min, with a TOC removal rate of approximately 59%. Furthermore, radical scavenger agents also clarified photodegradation of RB222 dye.

Experimental study on impact behaviour of normal strength mortar at cryogenic temperatures and after freeze-thaw cycles

Concrete structures employed for the storage of Liquefied Natural Gases (LNG) currently undergo temperature as low as −170 °C. These critical engineering structures may also experience dynamic loads such as impact or explosion during service life. Therefore, it is crucial to explore the mechanical response of concrete at intermediate to high strain rates together with low temperatures or cryogenic freeze-thaw (FT) cycles. This study presents experimental research into the combined effects of low temperatures and strain rate on normal strength mortar, providing a preliminary analysis of the dynamic performance of concrete at low and cryogenic temperatures. A Split Hopkinson Pressure Bar (SHPB) device was used to investigate the characteristics of dynamic compression (at strain rates of 40, 80, 120 and 160 s−1) and dynamic split tension (at strain rates of 20, 40, 60 and 80 s−1) of Normal Strength Mortar (NSM) at different low temperatures. In addition, the dynamic compression (at strain rates of 80, 130 and 180 s−1) after cryogenic FT cycles was also explored. The findings revealed that the failure pattern of NSM samples exposed to coupled low temperature or cryogenic FT cycles and high strain rate loading notably varied from that observed under room temperature condition. Both the dynamic compressive and split tensile strengths of NSM increased with the strain rates at all temperatures. At −160 °C, the dynamic compressive and splitting tensile strengths of NSM specimens were greater than those at room temperature (by 10.94 % and 28.29 %, respectively) and −70 °C (by 3.13 % and 4.12 %, respectively). Cryogenic freeze-thaw cycles evidently impacted the material static and dynamic performance. An empirical model was developed to predict the dynamic increase factors (DIFs) for both dynamic compression and splitting tensile strengths of NSM at low/cryogenic temperatures and after freeze-thaw cycles. Scanning electron microscopy (SEM) technique was performed to study and comprehend the microscopic processes and microstructural changes after FT cycles.

Comparative Analysis of Methicillin-Resistant Staphylococcus pseudintermedius Prevalence and Resistance Patterns in Canine and Feline Clinical Samples: Insights from a Three-Year Study in Germany.

The emergence of methicillin-resistant Staphylococcus pseudintermedius (MRSP) presents a significant public health concern globally, particularly within veterinary medicine. MRSP's resistance to multiple antibiotics is limiting treatment options and potentially leading to severe infections in companion animals. This study aimed to understand antimicrobial resistance in dogs and cats, focusing on MRSP resistance patterns and its prevalence in Germany. We analyzed results of bacterial diagnostic samples from canines and felines, sourced from a German veterinary diagnostic microbiology laboratory between 2019 and 2021. This dataset included samples from 3491 veterinary practices, covering 33.1% of veterinary practices and clinics in Germany. MRSP rates were detailed by host species, sample types and co-resistance patterns. Analysis of 175,171 bacterial examination results revealed S. pseudintermedius in 44,880 samples, yielding a 25.6% isolation rate. S. pseudintermedius was more prevalent in dogs (35.0%) than cats (3.6%). Methicillin resistance was found in 7.5% of all S. pseudintermedius isolates. MRSP prevalence was higher in feline samples (16.1%, 95% CI 14.4-17.8) compared to canine samples (7.1%, 95% CI 6.8-7.0). S. pseudintermedius showed high resistance rates to ampicillin (cats: 48.6%, dogs: 67.6%) and clindamycin (cats: 37.2%, dogs: 32.7%), while MRSP exhibited high co-resistance to clindamycin (cats: 82.8%, dogs: 85.4%) and sulfamethoxazole + trimethoprim (cats: 66.4%, dogs: 66.2%). Our study revealed distinct resistance patterns of MRSP in cats compared to dogs, highlighting the need for tailored treatment approaches and the importance of antimicrobial resistance surveillance.

Comprehensive landscape of m6A regulator-related gene patterns and tumor microenvironment infiltration characterization in gastric cancer

The epigenetic regulation of N6-methyladenosine (m6A) has attracted considerable interest in tumor research, but the potential roles of m6A regulator-related genes, remain largely unknown within the context of gastric cancer (GC) and tumor microenvironment (TME). Here, a comprehensive strategy of data mining and computational biology utilizing multiple datasets based on 28 m6A regulators (including novel anti-readers) was employed to identify m6A regulator-related genes and patterns and elucidate their underlying mechanisms in GC. Subsequently, a scoring system was constructed to evaluate individual prognosis and immunotherapy response. Three distinct m6A regulator-related patterns were identified through the unsupervised clustering of 56 m6A regulator-related genes (all significantly associated with GC prognosis). TME characterization revealed that these patterns highly corresponded to immune-inflamed, immune-excluded, and immune-desert phenotypes, and their TME characteristics were highly consistent with different clinical outcomes and biological processes. Additionally, an m6A-related scoring system was developed to quantify the m6A modification pattern of individual samples. Low scores indicated high survival rates and high levels of immune activation, whereas high scores indicated stromal activation and tumor malignancy. Furthermore, the m6A-related scores were correlated with tumor mutation loads and various clinical traits, including molecular or histological subtypes and clinical stage or grade, and the score had predictive values across all digestive system tumors and even in all tumor types. Notably, a low score was linked to improved responses to anti-PD-1/L1 and anti-CTLA4 immunotherapy in three independent cohorts. This study has expanded the important role of m6A regulator-related genes in shaping TME diversity and clinical/biological traits of GC. The developed scoring system could help develop more effective immunotherapy strategies and personalized treatment guidance.

Single-pixel compressive imaging via single photon counting

Single-pixel compressive imaging reconstructs a target scene with many pixels by using a single-pixel detector to measure the power variations as small sequences of sampling patterns are applied. While it boasts remarkable capabilities, its practical applications remain a challenge in the photon-starved regime where signal-to-noise is low. To address this challenge, we propose to combine quantum parametric mode sorting (QPMS) and deep neural networks (DNN) to overcome low signal-to-noise for faithful image construction. We benchmark our approach in a telecom-LiDAR system against that using direct photon counting detection. Our results show that with only 25 sampling patterns (corresponding compression ratio ∼0.043%), QPMS plus DNN give structural similarity index measure and peak signal-to-noise ratio on average above 22 dB and 0.9, respectively, much higher than those with direct detection (DD). The details of our targets from QPMS are more clearly compared with from DD. Notably, such high performance is sustained even in the presence of 500 times stronger in-band background noise, while DD fails. The high efficiency and robust noise rejection promise potential applications in various fields, especially in photon-starving scenarios.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

A binary-medium-based constitutive model for rocks under cyclic loading

The present study extends the existing three-scale constitutive model to investigate the mechanical response of mudstone under cyclic loading conditions. The research framework encompasses macro, meso, and micro scales, with the mudstone sample treated as a macro Representative Volume Element (RVE) at the macroscale, bonded and frictional elements with distinct mesostructures at the mesoscale, and three-phase composites with varying micro-component contents at the microscale. The transitions at micro, meso, and macro scales are realized through various homogenization methods and the refined binary medium framework.To enhance the model’s predictive capabilities under cyclic loading, isotropic and kinematic hardening effects are introduced to capture the elastoplastic behavior of the frictional elements. To further enhance the model’s numerical stability and convergence, the fixed-point iteration method is introduced to leverage the relationships among macroscopic strain, and the strains of the bonded and frictional elements within the binary-medium framework. Model verification against experimental data enables the prediction of stress and deformation patterns in mudstone samples subjected to cyclic loading, with a notable reduction in model parameters. The model’s enhanced capabilities in capturing material behavior and numerical stability make it a valuable tool for analyzing the response of mudstone structures to cyclic loading scenarios.

Influence of BMI, Cigarette Smoking and Cryopreservation on Tyrosine Phosphorylation during Sperm Capacitation.

Capacitation involves tyrosine phosphorylation (TP) as a key marker. Lifestyle-related factors, such as obesity and smoking, are recognized for their adverse effects on semen quality and male fertility, yet the underlying mechanisms, including their potential impact on TP, remain unclear. Moreover, the effect of sperm cryopreservation on TP at the human sperm population level is unexplored. Flow cytometry analysis of global TP was performed on pre-capacitated, post-capacitated and 1- and 3-hours' incubated fresh and frozen-thawed samples from sperm donors (n = 40). Neither being overweight nor smoking (or both) significantly affected the percentage of sperm showing TP. However, elevated BMI and smoking intensity correlated with heightened basal TP levels (r = 0.226, p = 0.003) and heightened increase in TP after 3 h of incubation (r = 0.185, p = 0.017), respectively. Cryopreservation resulted in increased global TP levels after capacitation but not immediately after thawing. Nonetheless, most donors' thawed samples showed increased TP levels before and after capacitation as well as after incubation. Additionally, phosphorylation patterns in fresh and frozen-thawed samples were similar, indicating consistent sample response to capacitation stimuli despite differences in TP levels. Overall, this study sheds light on the potential impacts of lifestyle factors and cryopreservation on the dynamics of global TP levels during capacitation.