Nearest-neighbor Distance Research Articles

Structural Stability of Vacancy and Substitutional Defects in g-GaN: A First-Principles Study

Herein, the first-principles calculations of defected monolayer graphene-like gallium nitride (g-GaN) were elucidated. The optimized geometric revealed that the nearest-neighbor distances of N-N (between N atoms) and Ga-Ga (between Ga atoms) were narrowing in most configurations. Interestingly, the VGa has a similar atomic symmetry as the pure g-GaN, which is D3h. Most defected configurations are degraded into C2V symmetries, while the Stone-Wales configuration has the lowest symmetry of CS. The formation energies of V­­Na systems are lower, which implies better energetic stability. For the divacancies, the VGa system is more stable than the V­­Na system. Moreover, the Stone-Wales configuration is energetically more stable than the interchange configuration. Furthermore, the reaction coordinates represent the geometric evolution of each defected system. The result revealed that the N-atoms are consistent with moving outward while the Ga-atoms move inward only for monovacancies and divacancies configurations. In contrast, the N-atoms move inward while the Ga-atoms move outward for substitutions, interchanges, and Stone-Wales configurations. We believe that this finding will be beneficial as the groundwork for future 2D g-GaN-based semiconductor devices. HIGHLIGHTS The V­­Na defective system is more stable than the VGa system in monovacancies and substitutions. The VGaGa defective system is more stable than the V­­Na system in divacancies. The Stone-Wales system is a stable configuration with a Cs symmetry. GRAPHICAL ABSTRACT

Nanoscale organization of cardiac calcium channels is dependent on thyroid hormone status.

Thyroid hormone dysfunction is frequently observed in patients with chronic illnesses including heart failure which increases risk of adverse events. This study examined effects of thyroid hormones (TH) on cardiac T-tubule (TT) integrity, Ca2+ sparks, and nanoscale organization of ion channels in excitation-contraction (EC)-coupling, including L-type calcium channel (Cav1.2), ryanodine receptor-type 2 (RyR2), and junctophilin-2 (Jph2). TH deficiency was established in adult female rats by propyl-thiouracil (PTU) ingestion for 8 weeks; followed by randomization to continued PTU without or with oral triiodo-L-thyronine (T3; 10 ug/kg/d) for two additional weeks (PTU+T3). Confocal microscopy of isolated cardiomyocytes (CM) showed significant misalignment of TTs, and increased Ca2+ sparks in thyroid-deficient CMs. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis of STochastic Optical Reconstruction Microscopy (STORM) images showed decreased (p<0.0001) RyR2 cluster number per cell area in PTU CMs compared to euthyroid (EU) control myocytes, and this was normalized by T3-treatment. Cav1.2 channels and Jph2 localized within 210 nm radius of the RyR2 clusters were significantly reduced in PTU myocytes, and these values were increased with T3 treatment. A significant percentage of the RyR2 clusters in the PTU myocytes had neither Cav1.2 or Jph2, suggesting fewer functional clusters in EC-coupling. Nearest neighbor distances between RyR2 clusters were greater (p<0.001) in PTU cells compared to EU and T3-treated CMs that corresponds to disarray of TTs at the sarcomere z-discs. These results support a regulatory role of T3 in the nanoscale organization of RyR2 clusters and co-localization of Cav1.2 and Jph2 in optimizing EC-coupling.

Active Learning for Deep Visual Tracking.

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this article, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNN model. Under the guidance of active learning, the tracker based on the trained deep CNN model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multiframe collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest-neighbor discrimination method based on the average nearest-neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples' subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active-learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks. Project website: https://sites.google.com/view/altrack/.

Spatial pattern and landscape change prediction of blowouts in sandy grassland

Blowouts are a common type of wind-eroded landform found in sandy and desertified areas. They also represent a major degradative surface process affecting grassland ecosystems. Blowouts exacerbate changes in surface morphology through their effects on other surface phenomena including vegetation. In this paper, Xilingol League sandy grassland blowouts are taken as the research object, and the U.S. Keyhole satellite data and China’s Gaofen-1 satellite data are used as the data source, and the blowouts are extracted based on the 3 S technology for a total of six periods of high-resolution remote sensing image data in the study area from 1962 to 2023. The Landscape Pattern Index method and Fuzzy Land Use Simulation (FLUS) modelling applied to changes over the last six decades provided spatial evolution parameters for predicting future blowout distributions. Results showed that blowouts affecting the Xilingol grassland area increased by 16.81% over the past 60 years. The patch density (PD) increased by 0.9 per hectare. The mean proximity index (PROX_MN) and mean Euclidean nearest neighbour distance (ENN_MN) showed a tendency to decrease and then increase indicating initial expansion and then merging of adjacent blowouts to create the present landscape. The FLUS model used ten factors to predict changes in blowout distributions from 2023 to 2033. Factors included digital elevation model (DEM), slope, aspect, normalized difference vegetation index (NDVI), mean annual temperature, mean annual precipitation, population density, real GDP, distance to water, and distance to impervious surfaces. It was found that grassland area decreased by 6217.12 hm2 and blowout area decreased by 102.91 hm2. Results of this study can expand understanding of blowout morphodynamics in ecologically sensitive areas.

Burrowing Owls Require Mutualist Species and Ample Interior Habitat Space

Mitigating habitat loss of western burrowing owls (Athene cunicularia hypugaea) often involves relocation from California ground squirrel (Otospermophilus beecheyi) burrows to offsite nest boxes. Naval Air Station Lemoore (NASL), Kings and Fresno counties, California, initiated this approach to displace a regionally important population from airfield grasslands. We examined monitoring data of burrowing owls and fossorial mammals at NASL to assess mitigation options. Occupied nests increased by 33 (61%), with 47 nest box installations in 1997–2001, peaked at 87 in 1999, then declined by 50 through 2013. Although ≥13 nest boxes were occupied in 2000, none were occupied in 2003–2013. Within a 43.1 ha isolated grassland monitored for 13 years, nest site reuse in ground squirrel burrows averaged only 17% between any 2 consecutive years. Compared to the average density across grassland study areas, ground squirrel burrow systems/ha numbered 43% higher within 60 m of occupied nests and non-breeding-season burrows. Vegetation clearing to restore kangaroo rat (Dipodomys n. nitratoides) habitat preceded a 7.4-fold increase in ground squirrel burrow systems and a 4-fold increase in occupied nests, but drought-induced extirpation of ground squirrels eliminated occupied nests from the 43.1 ha grassland study area. Ground cover near occupied nests averaged 58% of the mean vegetation height and 67% of the mean percentage of bare ground in the field. Both nest sites and non-breeding-season burrows occurred &gt;60 m interior to field edges 1.4 times more than expected. Non-breeding-season burrows averaged 328 m from same-year nest sites, and only 7% of non-breeding-season burrows were also used as nest sites. Mitigating habitat loss should be made more effective by fostering natural burrow construction by fossorial mammals on patches of short-stature vegetation that is sufficiently expansive to support breeding colonies of ≥12 pairs averaging ≥60 m from the field’s edge and a separation between non-breeding-season burrows and nest burrows minimally equal to mean nearest-neighbor distances among nests.

Abstract A040: Multiplex fluorescent immunohistochemistry reveals immunosuppressive tumor microenvironment in COMPASS-like complex gene mutated pancreatic cancer

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is highly resistant to chemoradiation and immune therapies. There is limited biomarker to identify subsets of PDAC that responds to immunotherapy or targeted therapy. We have identified a subset of PDACs (approximately 25%) carrying COMPASS-like complex (CLC) gene mutations. Analysis of the clinicopathologic and molecular features of this PDAC subset revealed that these tumors are an aggressive group of PDACs with poor or squamous histologic differentiation, increased rates of TP53 mutations, and poor patient survival compared to control PDACs. Our preliminary data showed that PDACs carrying mutations in two members of the CLC genes (KDM6A and KMT2D) have immunosuppressive tumor microenvironment (TME), suggesting that CLC genes are candidate biomarkers for immunotherapy. However, a comprehensive analysis of the immune composition, immune checkpoint expression, and the relationship between tumor cells and immune cells in CLC gene mutated PDACs compared to controls has not been performed. In this study, we characterized the immune composition in CLC gene mutated PDACs compared to paired control PDACs using tyramide signal amplification multiplex fluorescent immunohistochemistry (mfIHC). Methods: Human PDAC tissue slides were stained with 2 mfIHC panels and cell types were defined by different markers: panel 1 (PanCK, CD163, PD-L1, CD3, CD8, FoxP3) and panel 2 (PanCK, CD163, CD3, CD8, TIGIT, TIM3). Random image sections were selected in each sample (5∼8 sections per sample) and divided into wild-type (WT) group (n=57) and mutant group (n=41) and analyzed using InForm Cell Analysis software. Tissue segmentation training was performed to separate stromal and epithelial elements. Cell percentage, cell density, and nearest neighbor distance of each cell type were calculated using R program. Results: The CLC gene mutation is associated with elevated epithelial cells (ECs) and PDL1+ ECs and increased infiltration of exhausted cytotoxic T cells (TIGIT+/TIM3+ CD8 T cells). CLC gene mutated PDACs are characterized by spatial proximity between exhausted T cells (TIGIT+) and epithelial cells/antigen presenting cells (APCs). Higher number of epithelial cells is associated with reduced infiltration of helper T cells in the mutant PDAC tissue. We also observed that high numbers of APC in proximity to CD4 T cells were positively correlated with the proximity of EC and CD8 cytotoxic T cells as well as the proximity of CD4 T cells and cytotoxic T cells in the WT group but not in the CLC gene mutant group. Conclusion: Our results provide insight into the effects of CLC gene mutations on shaping the immune microenvironment of PDAC and suggest that CLC gene mutated PDACs have an immunosuppressive immune microenvironment than other PDACs and may respond differently to immunotherapy. Citation Format: Shungang Zhang, Elaina Daniels, Jake McGue, Hong Sun Kim, Dafydd Thomas, Timothy Frankel, Jiaqi Shi. Multiplex fluorescent immunohistochemistry reveals immunosuppressive tumor microenvironment in COMPASS-like complex gene mutated pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A040.

OS-PSO: A Modified Ratio of Exponentially Weighted Averages-Based Optical and SAR Image Registration.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Effects of the social environment on movement-integrated habitat selection

BackgroundMovement links the distribution of habitats with the social environment of animals using those habitats. Despite the links between movement, habitat selection, and socioecology, their integration remains a challenge due to lack of shared vocabulary across fields, methodological gaps, and the implicit (rather than explicit) historical development of theory in the fields of social and spatial ecology. Given these challenges can be addressed, opportunity for further study will provide insight about the links between social, spatial, and movement ecology. Here, our objective was to disentangle the roles of habitat selection and social association as drivers of movement in caribou (Rangifer tarandus).MethodsTo accomplish our objective, we modelled the relationship between collective movement and selection of foraging habitats using socially informed integrated step selection function (iSSF). Using iSSF, we modelled the effect of social processes, i.e., nearest neighbour distance and social preference, and movement behaviour on patterns of habitat selection.ResultsBy unifying social network analysis with iSSF, we identified movement-dependent social association, where individuals took shorter steps in lichen habitat and foraged in close proximity to more familiar individuals.ConclusionsOur study demonstrates that social preference is context-dependent based on habitat selection and foraging behaviour. We therefore surmise that habitat selection and social association are drivers of collective movement, such that movement is the glue between habitat selection and social association. Here, we put these concepts into practice to demonstrate that movement is the glue connecting individual habitat selection to the social environment.

The effect of chemical doping on the lithiation processes of the crystalline Si anode ‒ A first-principles study.

We employed first-principles calculations to investigate the effect of chemical doping on the lithiation kinetics and dynamic properties of the c-Si anode. Our abinitio molecular dynamics simulations reveal that phosphorous/arsenic doping can greatly enhance the lithiation kinetics of c-Si, whereas boron doping is unable to produce such an improvement. Our calculations also show that boron doping could enhance Li insertion into c-Si, but phosphorous/arsenic doping tends to increase the insertion energy of Li ions. Although the migration energy barriers of Li ions may slightly increase (decrease) in the boron-(phosphorus-/arsenic-)doped c-Si, these changes were only effective within the range of the nearest-neighbor distance from dopants. Furthermore, it was found that the phosphorus-/arsenic-doped Si can be more ductile and can more easily undergo plastic deformation upon lithiation, while the c-Si matrix becomes more brittle and stiffer when doped with boron. Our simulation results also demonstrate that phosphorous- and arsenic-doping can effectively speed up the Li-induced structural amorphization of c-Si while boron doping appears to severely slow it down. These findings unambiguously indicate that the induced mechanical softening of the c-Si bond network can be the primary factor that leads to the enhanced lithiation kinetics in the n-type doped c-Si anodes.

Characterization of Trabecular Bone Microarchitecture and Mechanical Properties Using Bone Surface Curvature Distributions.

Understanding bone surface curvatures is crucial for the advancement of bone material design, as these curvatures play a significant role in the mechanical behavior and functionality of bone structures. Previous studies have demonstrated that bone surface curvature distributions could be used to characterize bone geometry and have been proposed as key parameters for biomimetic microstructure design and optimization. However, understanding of how bone surface curvature distributions correlate with bone microstructure and mechanical properties remains limited. This study hypothesized that bone surface curvature distributions could be used to predict the microstructure as well as mechanical properties of trabecular bone. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the histomorphometric parameters (e.g., BV/TV, BS, Tb.Th, DA, Conn.D, and SMI), geometric parameters (e.g., plate area PA, plate thickness PT, rod length RL, rod diameter RD, plate-to-plate nearest neighbor distance NNDPP, rod-to-rod nearest neighbor distance NNDRR, plate number PN, and rod number RN), as well as the apparent stiffness tensor of trabecular bone using various bone surface curvature distributions, including maximum principal curvature distribution, minimum principal curvature distribution, Gaussian curvature distribution, and mean curvature distribution. The results showed that the surface curvature distribution-based deep learning model achieved high fidelity in predicting the major histomorphometric parameters and geometric parameters as well as the stiffness tenor of trabecular bone, thus supporting the hypothesis of this study. The findings of this study underscore the importance of incorporating bone surface curvature analysis in the design of synthetic bone materials and implants.

Mechanical testing of colloidal solids with millipascal stress and single-particle strain resolution.

We introduce a technique, traction rheoscopy, to carry out mechanical testing of colloidal solids. A confocal microscope is used to directly measure stress and strain during externally applied deformation. The stress is measured, with single-mPa resolution, by determining the strain in a compliant polymer gel in mechanical contact with the colloidal solid. Simultaneously, the confocal microscope is used to measure structural change in the colloidal solid with single particle resolution during the deformation. To demonstrate the utility and sensitivity of this technique, we deform a hard-sphere colloidal glass in simple shear, and from the macroscopic shear strain and measured stress determine the stress-strain curve. Using the stress-strain curve and measured shear modulus, we decompose the macroscopic shear strain into an elastic and a plastic component. We also determine a local strain tensor for each particle using the changes in its nearest-neighbor distances. These local strains are spatially heterogeneous throughout the sample, but, when averaged, match the macroscopic strain. A microscopic yield criterion is used to split the local strains into subyield and yielded partitions; averages over these partitions complement the macroscopic elastic-plastic decomposition obtained from the stress-strain curve. By combining mechanical testing with single-particle structural measurements, traction rheoscopy is a unique tool for the study of deformation mechanisms in a diverse range of soft materials.

Random forests with spatial proxies for environmental modelling: opportunities and pitfalls

Abstract. Spatial proxies, such as coordinates and distance fields, are often added as predictors in random forest (RF) models without any modifications being made to the algorithm to account for residual autocorrelation and improve predictions. However, their suitability under different predictive conditions encountered in environmental applications has not yet been assessed. We investigate (1) the suitability of spatial proxies depending on the modelling objective (interpolation vs. extrapolation), the strength of the residual spatial autocorrelation, and the sampling pattern; (2) which validation methods can be used as a model selection tool to empirically assess the suitability of spatial proxies; and (3) the effect of using spatial proxies in real-world environmental applications. We designed a simulation study to assess the suitability of RF regression models using three different types of spatial proxies: coordinates, Euclidean distance fields (EDFs), and random forest spatial prediction (RFsp). We also tested the ability of probability sampling test points, random k-fold cross-validation (CV), and k-fold nearest neighbour distance matching (kNNDM) CV to reflect the true prediction performance and correctly rank models. As real-world case studies, we modelled annual average air temperature and fine particulate air pollution for continental Spain. In the simulation study, we found that RFs with spatial proxies were poorly suited for spatial extrapolation to new areas due to significant feature extrapolation. For spatial interpolation, proxies were beneficial when both strong residual autocorrelation and regularly or randomly distributed training samples were present. In all other cases, proxies were neutral or counterproductive. Random k-fold cross-validation generally favoured models with spatial proxies even when it was not appropriate, whereas probability test samples and kNNDM CV correctly ranked models. In the case studies, air temperature stations were well spread within the prediction area, and measurements exhibited strong spatial autocorrelation, leading to an effective use of spatial proxies. Air pollution stations were clustered and autocorrelation was weaker and thus spatial proxies were not beneficial. As the benefits of spatial proxies are not universal, we recommend using spatial exploratory and validation analyses to determine their suitability, as well as considering alternative inherently spatial modelling approaches.

Quantitative dispersion characterization of cement particles in hardened cement matrix

Optimizing the cement particle distribution in hardened cement matrix is one of the effective means to utilize cement efficiently, and obtaining information about cement distribution is a prerequisite for this. In view of this, this paper investigates the quantitative dispersion of cement particles in hardened cement paste from an experimental perspective. Cement pastes with different water-binder (W/B) ratios were prepared as the objects. BSE images of these samples were acquired for further analysis, including Delaunay triangulation algorithm, nearest neighbor distance function G(r) and fractal theory. Results showed that when the W/B ratio increases from 0.36 to 0.42, the average cement particle spacing rises from 32.7 μm to 34.9 μm and the proportion of particles with spacing between particle centroids <20 μm decreases from 41.96 % to 25.98 %. Similarly, the increasing W/B ratio causes the nearest neighbor distance function G(r) to shift towards larger distances. When the W/B ratio is 0.34, the median distance between particles (R50) exhibits the most significant change. Additionally, the intricate details of cement particle spacing information can be accurately described by fractal theory. Parameters such as R50 and the proportion of particles with spacing between centroids <20 μm demonstrate strong fits with various fractal parameters. Notably, they exhibit optimal fitting with the spectral width of the generalized fractal spectrum, with an R2 value of 0.99.

KNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation

Abstract. Random and spatial cross-validation (CV) methods are commonly used to evaluate machine-learning-based spatial prediction models, and the performance values obtained are often interpreted as map accuracy estimates. However, the appropriateness of such approaches is currently the subject of controversy. For the common case where no probability sample for validation purposes is available, in Milà et al. (2022) we proposed the nearest-neighbour distance matching (NNDM) leave-one-out (LOO) CV method. This method produces a distribution of geographical nearest-neighbour distances (NNDs) between test and training locations during CV that matches the distribution of NNDs between prediction and training locations. Hence, it creates predictive conditions during CV that are comparable to what is required when predicting a defined area. Although NNDM LOO CV produced largely reliable map accuracy estimates in our analysis, as a LOO-based method, it cannot be applied to the large datasets found in many studies. Here, we propose a novel k-fold CV strategy for map accuracy estimation inspired by the concepts of NNDM LOO CV: the k-fold NNDM (kNNDM) CV. The kNNDM algorithm tries to find a k-fold configuration such that the empirical cumulative distribution function (ECDF) of NNDs between test and training locations during CV is matched to the ECDF of NNDs between prediction and training locations. We tested kNNDM CV in a simulation study with different sampling distributions and compared it to other CV methods including NNDM LOO CV. We found that kNNDM CV performed similarly to NNDM LOO CV and produced reasonably reliable map accuracy estimates across sampling patterns. However, compared to NNDM LOO CV, kNNDM resulted in significantly reduced computation times. In an experiment using 4000 strongly clustered training points, kNNDM CV reduced the time spent on fold assignment and model training from 4.8 d to 1.2 min. Furthermore, we found a positive association between the quality of the match of the two ECDFs in kNNDM and the reliability of the map accuracy estimates. kNNDM provided the advantages of our original NNDM LOO CV strategy while bypassing its sample size limitations.

Fish communicate with water flow to enhance a school's social network.

Schooling fish rely on a social network created through signaling between its members to interact with their environment. Previous studies have established that vision is necessary for schooling and that flow sensing by the lateral line system may aid in a school's cohesion. However, it remains unclear to what extent flow provides a channel of communication between schooling fish. Based on kinematic measurements of the speed and heading of schooling tetras (Petitella rhodostoma), we found that compromising the lateral line by chemical treatment reduced the mutual information between individuals by ∼13%. This relatively small reduction in pairwise communication propagated through schools of varying size to reduce the degree and connectivity of the social network by more than half. Treated schools additionally showed more than twice the spatial heterogeneity of fish with unaltered flow sensing. These effects were much more substantial than the changes that we measured in the nearest-neighbor distance, speed and intermittency of individual fish by compromising flow sensing. Therefore, flow serves as a valuable supplement to visual communication in a manner that is revealed through a school's network properties.

DNA mismatch repair defect and intratumor heterogeneous deficiency differently impact immune responses in diffuse large B-cell lymphoma

ABSTRACT Deficient (d) DNA mismatch repair (MMR) is a biomarker predictive of better response to PD-1 blockade immunotherapy in solid tumors. dMMR can be caused by mutations in MMR genes or by protein inactivation, which can be detected by sequencing and immunohistochemistry, respectively. To investigate the role of dMMR in diffuse large B-cell lymphoma (DLBCL), MMR gene mutations and expression of MSH6, MSH2, MLH1, and PMS2 proteins were evaluated by targeted next-generation sequencing and immunohistochemistry in a large cohort of DLBCL patients treated with standard chemoimmunotherapy, and correlated with the tumor immune microenvironment characteristics quantified by fluorescent multiplex immunohistochemistry and gene-expression profiling. The results showed that genetic dMMR was infrequent in DLBCL and was significantly associated with increased cancer gene mutations and favorable immune microenvironment, but not prognostic impact. Phenotypic dMMR was also infrequent, and MMR proteins were commonly expressed in DLBCL. However, intratumor heterogeneity existed, and increased DLBCL cells with phenotypic dMMR correlated with significantly increased T cells and PD-1+ T cells, higher average nearest neighbor distance between T cells and PAX5+ cells, upregulated immune gene signatures, LE4 and LE7 ecotypes and their underlying Ecotyper-defined cell states, suggesting the possibility that increased T cells targeted only tumor cell subsets with dMMR. Only in patients with MYC¯ DLBCL, high MSH6/PMS2 expression showed significant adverse prognostic effects. This study shows the immunologic and prognostic effects of genetic/phenotypic dMMR in DLBCL, and raises a question on whether DLBCL-infiltrating PD-1+ T cells target only tumor subclones, relevant for the efficacy of PD-1 blockade immunotherapy in DLBCL.

Scale effect of landscape characteristics on undergrowth vegetation variance with different ecological traits

Herbaceous understory vegetation plays a significant role in temperate forested ecosystems, with its diversity strongly dependent on both local and landscape conditions. However, the contribution of these factors across different spatial scales (e.g., scale effect) remains inadequately understood. The objective of this study was to determine the most important landscape and local characteristics influencing the species richness of the herb layer. We surveyed and sampled 53 sites in a mountainous forest within the Baihuashan National Nature Reserve of Beijing. Using generalized linear models, we investigated the simultaneous effect of local (e.g. tree density, average tree height, and cover of tree, shrub, and ground vegetation) and landscape (e.g. Area-weighted Mean Patch Size, Area-weighted Mean Euclidean Nearest Neighbor Distance, Area-weighted Mean Fractal Dimension Index, forest area, and shrubbery area) variables on the richness of species in the understory herb layer. Variation partitioning was used to examine the individual contribution of local and landscape variables. Our results identified 366 plant species from 95 families across 53 sampling sites. Significant differences in species richness were found among life forms, seed dispersal types, and habitat preferences (P < 0.001). Parsimonious models indicated that combining local and landscape factors at a 2 km scale explained the most variation in phanerophytes (R2 = 0.658). Locally, average tree height significantly influenced the species richness of biotic dispersal, phanerophytes, and geophytes. At the landscape level, variables such as the Area-weighted Mean Fractal Dimension Index and farmland area significantly impacted plant community traits across different scales. Understanding these effects is crucial for developing effective sustainable forest ecosystem management strategies and for establishing robust ecological conservation policies.

Lymphocyte Infiltration Score and Spatial Characteristics Refined the Prognosis and Denosumab Treatment Responsiveness Indicators for Giant Cell Tumor of Bone.

The prognostic value of lymphocyte infiltration score (LIS) and its nearest neighbor distance to tumor cells (NNDTC) in giant cell tumor of bone (GCTB) is currently not well established. This study aims to characterize LIS and NNDTC and examine their correlation with denosumab treatment responsiveness, clinicopathologic features, and patient prognosis. Using multiplexed quantitative immunofluorescence, LIS was evaluated in 253 tumor specimens, whereas NNDTC was computed using HALO software. Subsequently, we analyzed the association of these parameters with patient outcomes (progression-free survival [PFS] and overall survival [OS]), clinicopathologic features, and denosumab treatment responsiveness. Low LIS was indicative of both poor PFS and OS (both P < .001). In addition, LIS was significantly associated with sex (P = .046), Enneking staging (P < .001), Ki-67 expression (P = .007), and denosumab treatment responsiveness (P = .005). Lower CD8+ (tumor interior [TI]) NNDTC, and CD3+ (TI) NNDTC were associated with worse PFS (P = .003 and .038, respectively), whereas lower CD8+ (TI) NNDTC was associated with worse OS (P = .001), but CD8+ (tumor infiltrating margin) NNDTC had the opposite effect (P = .002). Moreover, NNDTC showed a correlation with several clinicopathologic features. Importantly, LIS outperformed Enneking and Campanacci staging systems in predicting the clinical outcomes of GCTB. These findings suggest that LIS is a reliable predictive tool for clinically relevant outcomes and response to denosumab therapy in patients with GCTB. These parameters may prove to be useful in guiding prognostic risk stratification and therapeutic optimization for patients.

Photocatalytic and electrochemical investigation of Mn and Sn co-doped ZnO nanoparticles: Effect of doping concentration

The chemical precipitation method is employed to prepare the Mn and Sn co-doped ZnO nanoparticles with various doping concentrations. Debye Scherrer’s formula, Williamson-Hall equation and Halder-Wagner methods are used to calculate the crystallite size of the prepared nanoparticles. The addition of dopants enhances the periodicity of the atoms. The bond lengths, second nearest neighbor distances and bond angles are examined. The purity of the samples is confirmed using energy dispersive X-ray analysis technique. The process of doping reduces the maximum optical absorption. The dopants reduce the band gap of the prepared nanoparticles and the band gap ranges from 3.07 eV to 3.03 eV. The charge carrier concentrations are calculated from the optical band gap. The Hall coefficient of the prepared nanoparticles increases due to the addition of the dopants. The refractive index of the material is also examined. The quenching is observed in the photoluminescence spectrum due to the dopants. The dielectric properties and electric modulus depend on doping concentrations of Mn and Sn. The degradation of the Congo red and methylene blue dyes with prepared nanoparticles as catalysis is reported. The specific charge of the Mn and Sn co-doped ZnO-based electrode is analyzed using cyclic voltammetric study and galvanostatic charge–discharge technique.

Melting curves of MgSiO3 and CaSiO3 perovskites with cubic structure at extreme conditions

In this work, we derive the analytical expression of Helmholtz free energy, mean nearest neighbor distance between two atoms, lattice constant, and molar volume using statistical moment method (SMM). Then, we present how to determine the melting temperature of cubic perovskite at high pressures by combining SMM and work-heat equivalence principle. The results of the melting theory of cubic perovskite are applied to two perovskites MgSiO3 and CaSiO3 under extreme conditions of the Earth’s lower mantle. Our obtained melting curves of MgSiO3 up to 1400 GPa and CaSiO3 up to 136 GPa were compared with available experiment and other calculations and were in good agreement. The present study provides an effective theoretical tool for finding the melting curves of strongly anharmonic materials under extreme conditions. Our theoretical calculations would be helpful to clarify geological formation process at the Earth’s lower mantle.