Search Results (856)

We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard ILP solvers. This is achieved by adopting the concept of contact graphs from classical continuous sphere packing problems to the present discrete context, resulting in a reduction technique for the models which enables their efficient solution as well as their decomposition to smaller subproblems. Based on our calculations, we provide a systematic summary of all lower and upper bounds for packings in the smallest Hamming spaces. The known results are reproduced, with some bounds found to be sharp, and the upper bounds improved in some cases. Full article

Introduction: Here we consider the collision of a genetic factor and an essential instigator in Alzheimer’s neuropathogenesis: (i) the Alzheimer’s gene (APOEε4), which downregulates lysosomal autophagy and induces synthesis of (ii) the instigator, interleukin-1β (IL-1β), which drives synthesis of βAPP for Aβ plaques and of MAPKp38 for phosphorylation of tau for formation of neurofibrillary tangles (NFTs), the two cardinal features of AD. Methods: RT-PCR, immunoblotting and immunohistochemistry techniques were used to assess the levels of IL-1β and its signaling cascade in ADε4,4, ε3,3, and age-matched controls (AMC3,3) in hippocampal regions of the brain. Results: IL-1β and its downstream signaling proteins TLR-2, MyD88, NFκB, COX-1, and COX-2 were greater in tissues from ADε4,4 than ADε3,3 or AMC3,3. Cathepsin B, D, and L levels, which play a pivotal role and are necessary for lysosomal autophagy, were lower in ADε4,4 than in ADε3,3 or AMC ε3,3. IL-1β and its downstream signaling cascade TLR-2, MyD88, NFκB, COX-1, and COX-2 expression levels were high in SH-SY5Y and T98G cells transfected with APOεE4. Conclusions: APOEε4 causes Alzheimer’s by downregulating autophagy, thus inducing IL-1β for Aβ plaque and neurofibrillary tangle formation. Full article

If $(X,\mathcal{U})$ is a Hausdorff uniform space, we define the uniform weight $w(X,\mathcal{U})$ as the smallest cardinal $\kappa$ such that $\mathcal{U}$ has a basis of cardinality $\kappa$. An important topological cardinal of a Tychonoff space X is the number of cozero sets of X, which we denote as $z\left(X\right)$. It is known that $w(X,\mathcal{U})\le z(X\times X)$ for every compatible uniformity $\mathcal{U}$ of X. We do not know if $z(X\times X)$ can be replaced by $z\left(X\right)$. We concentrate ourselves in $w(X,{\mathcal{U}}_n)$, where ${\mathcal{U}}_n$ is the fine uniformity of X, i.e., the one having the family of normal covers as a basis. We establish upper bounds for $w(X,{\mathcal{U}}_n)$ using the character and pseudocharacter in extensions of $X\times X$ or using the cardinal $z\left(X\right)$. We also find some generalizations of the equivalence: $w(X,{\mathcal{U}}_n)={\aleph }_0$ if and only if X is metrizable and the set of non-isolated points of X is compact. Full article

There has been much interest in the mathematical investigation of critical sets and unavoidable sets in Latin Squares, Sudoku, and their applications to practical problems in areas such as agriculture and cryptology. This paper considers the associated structures of Strictly Concentric Magic Squares (SCMSs) and Prime Strictly Concentric Magic Squares (PSCMSs). A framework of formal definitions is given that leads to the definitions of critical sets and unavoidable sets. Minimal critical sets are of interest in Latin Squares, and in this article, the cardinality of minimal critical sets of SCMS is given for all n, n odd. Two families of unavoidable sets are established for SCMS, leading to a complete classification of unavoidable sets of minimum PSCMS of order 5. Full article

For a vertex subset S of a graph $G,$ if each vertex of G is either in S or adjacent to some vertex in $S,$ then S is a dominating set of $G.$ Let S be a dominating set of a graph $G.$ If each vertex v not in S has a neighbor u in S such that $(S\backslash \{u\left\}\right)\cup \left\{v\right\}$ is also a dominating set of $G,$ then S is a secure dominating set of $G.$ If each vertex u in S has a neighbor v not in S such that $(S\backslash \{u\left\}\right)\cup \left\{v\right\}$ is also a dominating set of G, then S is a co-secure dominating set of G. The minimum cardinality of a secure (resp. co-secure) dominating set of G is the secure (resp. co-secure) domination number of $G.$ Arumugam et al. proposed the questions to characterize a graph G such that the co-secure domination number of G equals the independence number and the secure domination number of G, respectively. Inspired by those questions, in this paper, we obtain two classes of claw-free graphs such that the co-secure domination number equal the independence number and the secure domination number. Our results provide some theoretical basis of claw-free graphs for networks. Full article

Current mainstream classification methods predominantly employ end-to-end multi-class frameworks. These approaches encounter inherent challenges including high-dimensional feature space complexity, decision boundary ambiguity that escalates with increasing class cardinality, sensitivity to label noise, and limited adaptability to dynamic model expansion. However, human beings may avoid these mistakes naturally. Research indicates that humans subconsciously employ a decision-making process favoring binary outcomes, particularly when responding to questions requiring nuanced differentiation. Intuitively, responding to binary inquiries such as “yes/no” often proves easier for humans than addressing queries of “what/which”. Inspired by the human decision-making hypothesis, we proposes a decision paradigm named the evolutionary binary decision framework (EBDF) centered around binary classification, evolving from traditional multi-classifiers in deep learning. To facilitate this evolution, we leverage the top-N outputs from the traditional multi-class classifier to dynamically steer subsequent binary classifiers, thereby constructing a cascaded decision-making framework that emulates the hierarchical reasoning of a binary decision tree. Theoretically, we demonstrate mathematical proof that by surpassing a certain threshold of the performance of binary classifiers, our framework may outperform traditional multi-classification framework. Furthermore, we conduct experiments utilizing several prominent deep learning models across various image classification datasets. The experimental results indicate significant potential for our strategy to surpass the ceiling in multi-classification performance. Full article

Background/Objectives: Plexiform neurofibromas (pNFs) are one of the cardinal presentations of NF1 patients, often arising during early childhood. Since selumetinib was approved by the FDA in 2020, the long-term side effects and various responses of mitogen-activated protein kinase inhibitors (MEKi) in pediatric patients necessitate a new strategy. We propose that combining selumetinib with heat shock protein 90 inhibitors (HSP90i) can enhance the inhibitory effects as well as reduce the dosage of selumetinib in combination. We validated the synergistic effects and the significantly improved treatment effects of the combination of selumetinib and HSP90i in pNFs. Methods: We used drug screen data mining to predict the combination of selumetinib and HSP90i. Using cell lines and in vivo mouse models for pNFs, we tested a series of combinations with different concentrations. We validated the in vivo inhibitory effects using the transplanted tumors from DhhCreNf1^f/f mouse models. Results: We demonstrated that combining selumetinib and SNX-2112 or retaspimycin can achieve better tumor inhibition with synergistic effects. The combination significantly delays the progression of mouse pNFs. Conclusions: The combination of selumetinib and HSP90i has significant synergistic effects, provides therapeutic inhibitor effects, and reduces the selumetinib dosage in combination. Full article

Re-identification (ReID) has been widely applied in person and vehicle recognition tasks. This study extends its application to a novel domain: insect (silkworm) recognition. However, unlike person or vehicle ReID, silkworm ReID presents unique challenges, such as the high similarity between individuals, arbitrary poses, and significant background noise. To address these challenges, we propose a multi-view network for silkworm ReID, called WormNet, which is built upon an innovative strategy termed extraction purification extraction interaction. Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. Next, a feature mask module (FMM) is employed to purify the features in the spatial domain, thereby reducing the impact of background interference. To further enhance the data representation capabilities of the network, we propose a channel interaction module (CIM), which combines an efficient channel attention network with global response normalization (GRN) in parallel to recalibrate features, enabling the network to learn crucial information at both the local and global scales. Additionally, we introduce a new silkworm ReID dataset for network training and evaluation. The experimental results demonstrate that WormNet achieves an mAP value of 54.8% and a rank-1 value of 91.4% on the dataset, surpassing both state-of-the-art and related networks. This study offers a valuable reference for ReID in insects and other organisms. Full article

Urban vacant land can provide important habitat for birds, especially in cities with high concentrations of residential vacancy. Understanding which vegetation features best support urban biodiversity can inform greening strategies that benefit both wildlife and residents. This study addressed two questions: (1) How does bird species composition reflect the potential conservation value of these neighborhoods? (2) Which vegetation structures predict bird abundance across a fine-grained urban landscape? To answer these questions, we conducted avian and vegetation surveys across 100 one-hectare plots in St. Louis, Missouri, USA. These surveys showed that species richness was positively associated with canopy cover (β = 0.32, p = 0.003). Canopy cover was also the strongest predictor of American Robin (Turdus migratorius) and Northern Cardinal (Cardinalis cardinalis) abundance (β = 1.9 for both species). In contrast, impervious surfaces and abandoned buildings were associated with generalist species. European Starling (Sturnus vulgaris) abundance was strongly and positively correlated with NMS Axis 1 (r = 0.878), while Chimney Swift (Chaetura pelagica) abundance was negatively correlated (r = −0.728). These findings underscore the significance of strategic habitat management in promoting urban biodiversity and addressing ecological challenges within urban landscapes. They also emphasize the importance of integrating biodiversity goals into urban planning policies to ensure sustainable and equitable development. Full article

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems. Full article

To explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives affect the fusion of nonlinear moving-target data and the spatial segmentation of such targets. We propose a differential-view nonlinear multi-target tracking approach that integrates the Gaussian mixture, jump Markov nonlinear system, and the cardinalized probability hypothesis density (GM-JMNS-CPHD). The method begins by partitioning the observation space based on the boundaries of distinct viewpoints. Next, it applies a combined technique—the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SOS (stochastic outlier selection)—to identify outliers near these boundaries. To achieve accurate detection, the posterior intensity is split into several sub-intensities, followed by reconstructing the multi-Bernoulli cardinality distribution to model the target population in each subregion. The algorithm’s computational complexity remains on par with the standard GM-JMNS-CPHD filter. Simulation results confirm the proposed method’s robustness and accuracy, demonstrating a lower error rate compared to other benchmark algorithms. Full article

Periodontitis is an inflammatory disease that affects the periodontal supporting tissues. Its cardinal clinical manifestations encompass gingival inflammation, periodontal pocket formation, and alveolar bone resorption. Urolithin A (UA), a gut microbiota-derived metabolite of ellagitannins, is known for its anti-inflammatory and osseous-protective properties. Nonetheless, the impact of UA on periodontitis remains unknown. To investigate the preventive effect of UA, we employed a lipopolysaccharide (LPS)-induced inflammation model in RAW 264.7 mouse macrophages, a receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast differentiation model, and a ligature-induced periodontitis model in mice. The expression of inflammatory factors (tumor necrosis factor-α, TNF-α; interleukin-6, IL-6) was analyzed to assess anti-inflammatory efficacy. Bone loss in mice with periodontitis was assessed through histological and imaging techniques, including haematoxylin and eosin staining to evaluate alveolar bone morphology, Masson’s trichrome staining to visualize collagen fiber distribution, and micro-computed tomography scanning to quantify bone structural parameters. Additionally, we investigated the underlying mechanisms by examining osteoclast activity through tartrate-resistant acid phosphatase staining and the expression levels of proteins RANKL and osteoprotegerin (OPG). We found that UA reduced IL-6 and TNF-α levels in vitro and in vivo, inhibited osteoclast differentiation, and decreased the RANKL/OPG ratio in periodontitis mice. Full article

Large-scale bioprocesses often suffer from spatial heterogeneities, which impact microbial performance and often lead to phenotypic population heterogeneity. To better understand these effects at the single-cell level, this study applied, for the first time, automated real-time flow cytometry (ART-FCM) to monitor L-phenylalanine production with an Escherichia coli triple reporter strain in a fed-batch process with glycerol as the carbon source. The strain was cultivated in both a well-mixed stirred-tank bioreactor (STR) and a scale-down two-compartment bioreactor (TCB), consisting of an STR and a coiled flow inverter (CFI) in bypass, to simulate spatial heterogeneities. ART-FCM enabled autonomous, high-frequency sampling every 20 min, allowing for real-time tracking of fluorescence signals linked to growth (rrnB-mEmerald), oxygen availability (narGHIJ-CyOFP1), and product formation (aroFBL-mCardinal2). The STR exhibited uniform reporter expression and higher biomass accumulation, while the TCB showed delayed product formation and pronounced phenotypic diversification depending on the set mean residence time in the CFI. Single-cell fluorescence distributions revealed that the shorter mean residence time in the CFI resulted in pronounced subpopulation formation, whereas longer exposure attenuated heterogeneity, indicating transcriptional adaptation. This finding highlights a critical aspect of scale-down studies: increased exposure duration to perturbations can enhance population robustness. Overall, this study demonstrates the relevance of ART-FCM, in combination with a multi-reporter strain, as a pioneering tool for capturing dynamic cellular behavior and correlating it to process performance, providing deeper insights into microbial heterogeneity under fluctuating bioprocess conditions. Full article

With the development of alternative energy technologies, energy production from renewable sources is gaining wide application. One of the types of renewable energy sources is solar power. In the past 5 years, solar cells have become very popular for both private electricity microgeneration and large power plants. There are two main options for installing solar photovoltaic panels: on the roof of a house or the ground; on specially made frames. When installing solar cells on the roof, it is not always possible to choose a tilt angle that is appropriate for all seasons, since the angle is mainly adjusted to the plane of the roof. When installing solar cells on the ground, it is usually possible to choose both the orientation relative to the cardinal points and the tilt angle relative to the ground. There are various theories about the best tilt angle of solar cells for producing the most amount of energy during the year. Therefore, the aim of the present research study is to develop an original research methodology for determining an optimal tilt angle for solar cells. The research study examined six different tilt angles of solar cells, 0°, 30°, 35° 40° 45° and 50°, orienting the cells towards the south. The research study used 18 identical monocrystalline solar panels with a power of 20 W. Three solar panels were set at each angle. This way, the experiment had three replications at each angle of the solar cells. The measurements were recorded by a GWL840 data logger with an interval of 10 s. The experiment was conducted by placing all solar cell modules on the roof of the building at Lat. 56.66181° and Long. 23.75238°. During the experimental period, the highest efficiency was found for the solar panels set at 50° and 40°, reaching the total solar irradiation of 266.61 Wm⁻² and 266.27 Wm⁻², respectively. Full article

Background/Objectives: The colour changes related to inflammation have yet to be quantified despite the fact that the intensification of gingival colour is one of the cardinal signs of gingival inflammation. We aimed to (1) assess the effect of periodontal treatment on gingival colour according to biotype and initial degree of inflammatory severity and (2) identify the relationship between the plaque index and gingival colour and compare the colour of moderately or severely inflamed gums to the colour of gums with mild inflammation. Methods: Forty-three participants with visual signs of inflammation were included in the study. CIELAB colour coordinates were measured using a spectrophotometer at the distal papilla, free gingival margin, middle zone of the attached gingiva, and mucogingival line proximate to the maxillary central incisor pre-treatment and four weeks post treatment with periodontal debridement. Each participant’s gingival biotype, plaque and gingival indices, age, and gender were also recorded. Results: The biotype and plaque index were both found to influence the colour of inflamed gums, albeit only the a* coordinate in two of the zones examined (the distal papilla and free gingival margin). Statistically significant differences were also found between the a* coordinates of participants with and without plaque at the distal papilla and the free gingival margin. The severity of inflammation also influenced the L* coordinates of gingival colour (p < 0.001) in all participants in all four zones examined. Periodontal treatment only affected the L* coordinate of gingival colour at the distal papilla and the free gingival margin. Conclusions: When gingival inflammation occurs, participants with thin biotype and plaque have redder gums, and moderately or severely inflamed gingiva have a visually perceptible darker colour. Periodontal treatment significantly increases the lightness of the distal papilla and the free gingival margin. Further, periodontal treatment creates a perceptible colour change in all zones under study, which is not dependent on either the patient’s biotype or initial severity of inflammation. Clinical Significance. Digital spectrophotometric analysis provides a valuable technological enhancement to quantify gingival colour and record changes in gingival colour during the course of periodontal treatment. Regardless of the initial severity of gingival inflammation, a perceptible increase in the L* coordinate (gums become visibly lighter) can be expected after periodontal treatment. Regardless of the initial severity of inflammation, a perceptible increase in the L* coordinate can be expected (gums become visibly lighter) after treatment. Full article