Characteristic Appearance Research Articles

Classification and Distribution of Traditional Grass-Roofed Dwellings in China Based on Deep Learning

Traditional grass-roofed dwellings are important components of Chinese vernacular architecture. Building a comprehensive nationwide database of traditional grass-roofed dwellings is crucial for the inherence of this cultural heritage and its traditional ecological technologies. This study proposes classifying traditional Chinese grass-roofed dwellings into three types according to recognizable appearance features. Based on the YOLOv8 deep learning framework, a recognition model is constructed to recognize and spatially locate various grass-roofed dwellings from the image dataset on a county-level. Further, by conducting spatial overlap analysis with a variety of natural and socio-environmental factors on ArcGIS, their influences on the distribution pattern of traditional grass-roofed dwellings were examined. The study findings are as follows: (1) Traditional grass-roofed dwellings are concentrated on the southeast side of the Hu Line with different distribution patterns according to their types. (2) The natural environment influences the original construction and distribution of traditional grass-roofed dwellings in terms of the growth of grass resources and the ecological adaptability of grass material. (3) The development of economy, population, and urbanization pose challenges to the retention of grass-roofed dwellings. This research provides useful references for the precise preservation of various grass-roofed dwellings and introduced a novel approach for the classification of traditional buildings.

Motion-guided small MAV detection in complex and non-planar scenes

In recent years, there has been a growing interest in the visual detection of micro aerial vehicles (MAVs) due to its importance in numerous applications. However, the existing methods based on either appearance or motion features encounter difficulties when the background is complex or the MAV is too small. In this paper, we propose a novel motion-guided MAV detector that can accurately identify small MAVs in complex and non-planar scenes. This detector first exploits a motion feature enhancement module to capture the motion features of small MAVs. Then it uses multi-object tracking and trajectory filtering to eliminate false positives caused by motion parallax. Finally, an appearance-based classifier and an appearance-based detector that operates on the cropped regions are used to achieve precise detection results. Our proposed method can effectively and efficiently detect extremely small MAVs from dynamic and complex backgrounds because it aggregates pixel-level motion features and eliminates false positives based on the motion and appearance features of MAVs. Experiments on the ARD-MAV dataset demonstrate that the proposed method could achieve high performance in small MAV detection under challenging conditions and outperform other state-of-the-art methods across various metrics.

Hereditary gluten enteropathy in irish setters: evaluation of external parameters, clinical symptoms and fatal risks

Aim. Determine the features of appearance, clinical signs and fatal risks in Irish setters with hereditary gluten enteropathy. Methods. The methods used were observation, eye examination, clinical examination, autopsy, histological. Results. It has been established that in hereditary gluten enteropathy in Irish setters there are violations of the parameters of appearance, weak ability to train, fulminant and chronic forms of damage to the intestinal wall by type of enteropathy, anaphylactic reactions and cerebral disorders. In the dead setters, the causes of death were hypovolemic shock due to intestinal bleeding and combined critical injuries of the brain, heart and lungs. Conclusions. Violations of the parameters of appearance in setters with hereditary gluten enteropathy led to their unsuitability for hunting. Dietary therapy with the exclusion of feed containing cereals was effective in stabilizing the condition of sick setters. Setters with hereditary gluten enteropathy are at risk of developing fatal cardiovascular, pulmonary, and cerebral insufficiency. The validity of the spread of hereditary gluten enteropathy in Irish setters depends on the accounting of the results of multi-year studies by a multidisciplinary group of specialists.

Reaction qualifications in the eyes of the people: An experimental‐philosophical study based on US survey data

AbstractIs it fair for employers to select candidates partly based on how the employers think customers react to the candidates' appearances, that is, based on candidates' reaction qualifications? Both philosophically (in the literature on wrongful discrimination) and empirically, this question has recently been getting attention. Here, we focus on a theory of unfair disadvantages emphasizing (i) whether the possession of the appearance feature in question reflects choices on the part of the candidate and (ii) whether the appearance feature in question reflects values or convictions of importance to the candidate. We scrutinize the theory by evaluating how well these factors map on to folk intuitions about wrongful discrimination using an experimental‐philosophical approach and data from the United States. We do so on the presumption that coherence with folk intuitions is one desideratum for a philosophical theory and that attention should be given to potential discrepancies between philosophical theories and folk intuitions. Our central finding is that there is only general support for factor (i), not (ii), in the American population. Our study also adds to the extant empirical literature on reaction qualifications that tend to focus on the employer's side of the matter, not the candidate's as we do.

Theoretical and Empirical Justifications for The Development of the Questionnaire “Subjective Evaluation of Lukism in Humor”

Introduction. The paper deals with the problem of lookism (discrimination based on appearance) on the example of evaluating humorous posts and memes, which in various social situations can act as triggers of lookism. In order to diagnose sensitivity to Lukism in jokes, we attempted to prepare a basic description of the future structure of the methodology by testing a number of statements obtained by other authors in relation to different types of jokes. The novelty of the study lies in the description of the stages of preparation of the stimulus material, which allows us to study the peculiarities of perception of humor that ridicules the features of appearance and sensitivity to such jokes depending on the respondent’s experience (as a victim or a witness). Objective. To test a number of assumptions that are essential for the development of a questionnaire to examine the subjective evaluation of Lukism in humor. Materials and Methods. The study was conducted in five stages, a total of 410 participants (54 men, 356 women), aged 19 to 46 years, each stage included consideration of different components of perception and evaluation of humor in terms of the perception of it as offensive/containing lukism. Results. At each stage the peculiarities of perception of humorous posts that are distributed in social networks were revealed. It was revealed that the way of design (textual, textual-visual, as well as peculiarities of using different visual components), as well as the personal experience of respondents who faced situations of Lukism and self-assessment of respondents’ appearance influence the perception of a joke as containing or not containing Lukism, as well as the prediction of its assessment by other people as “offensive”. Discussion. Considering memes as complex, multilevel texts of identity, we see in them a significant potential for studying social norms, stereotypes and attitudes about appearance and sensitivity to the assessment of it as “non-standard” and a target for jokes. At the empirical level, we have identified a number of requirements for the selection of jokes to be included in the questionnaire: uniformity of design, use of “swear words”, visual content (images of “realistic people”), etc. An approximate structure of the future questionnaire is also presented.

M-YOLOv8s: An improved small target detection algorithm for UAV aerial photography

The object of UAV target detection usually means small target with complicated backgrounds. In this paper, an object detection model M-YOLOv8s based on UAV aerial photography scene is proposed. Firstly, to solve the problem that the YOLOv8s model cannot adapt to small target detection, a small target detection head (STDH) module is introduced to fuse the location and appearance feature information of the shallow layers of the backbone network. Secondly, Inner-Wise intersection over union (Inner-WIoU) is designed as the boundary box regression loss, and auxiliary boundary calculation is used to accelerate the regression speed of the model. Thirdly, the structure of multi-scale feature pyramid network (MS-FPN) can effectively combine the shallow network information with the deep network information and improve the performance of the detection model. Furthermore, a multi-scale cross-spatial attention (MCSA) module is proposed to expand the feature space through multi-scale branch, and then achieves the aggregation of target features through cross-spatial interaction, which improves the ability of the model to extract target features. Finally, the experimental results show that our model does not only possess fewer parameters, but also the values of mAP0.5 are 6.6% and 5.4% higher than the baseline model on the Visdrone2019 validation dataset and test dataset, respectively. Then, as a conclusion, the M-YOLOv8s model achieves better detection performance than some existing ones, indicating that our proposed method can be more suitable for detecting the small targets.

SimpleTrackV2: Rethinking the Timing Characteristics for Multi-Object Tracking.

Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios. This paper rethinks state prediction and fusion based on target temporal features to address these issues and proposes the SimpleTrackV2 algorithm, building on the previously designed SimpleTrack. Firstly, to address the poor prediction performance of linear motion models in complex scenes, we designed a target state prediction algorithm called LSTM-MP, based on long short-term memory (LSTM). This algorithm encodes the target's historical motion information using LSTM and decodes it with a multilayer perceptron (MLP) to achieve target state prediction. Secondly, to mitigate the effect of occlusion on target state saliency, we designed a spatiotemporal attention-based target appearance feature fusion (TSA-FF) target state fusion algorithm based on the attention mechanism. TSA-FF calculates adaptive fusion coefficients to enhance target state fusion, thereby improving the accuracy of subsequent data association. To demonstrate the effectiveness of the proposed method, we compared SimpleTrackV2 with the baseline model SimpleTrack on the MOT17 dataset. We also conducted ablation experiments on TSA-FF and LSTM-MP for SimpleTrackV2, exploring the optimal number of fusion frames and the impact of different loss functions on model performance. The experimental results show that SimpleTrackV2 handles camera jitter and target occlusion better, achieving improvements of 1.6%, 3.2%, and 6.1% in MOTA, IDF1, and HOTA, respectively, compared to the SimpleTrack algorithm.

Consistency-constrained unsupervised video anomaly detection framework based on Co-teaching

Intelligent video surveillance continues to be a vibrant research domain within the field of computer vision. However, existing representation learning frameworks primarily focus on static information extraction frame by frame such as appearance features, they often overlook the valuable dynamic information like optical flow feature inherent in the video data, which is most essential characteristics of sequence data. To mining dynamic features and bridge this gap, our paper introduces a novel anomaly detection framework that balance dynamic information with static information and construct a relationship between appearance features and corresponding optical flow features, where we sets strong consistency constraints, which reduce the loss between dynamic information and corresponding static information, and we leverages collaborative teaching network to ensure a consistent representation of both static and dynamic information for predict. The proposed framework consists of two sets of encoder–decoder pairs complemented by a memory storage module. Operating in parallel with the dual encoder network is a Co-teaching network, with the shared memory module serving as the cornerstone for collaborative training. The Consistency constrained condition guarantees the strong consistency of dynamic and static information in the learned representations. In our experimental phase, we present compelling results that showcase the superior performance of our algorithm across three publicly available datasets.

Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection.

Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient's intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and further fused by a cross-order fusion strategy for FoG detection. Comprehensive experiments on a large in-house dataset collected during clinical assessments demonstrate the effectiveness of the proposed method, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 is achieved for detecting FoG.

Episodic fluid charging and mixing triggers calcite-bearing agates in the Permian Emeishan Basalt, SW China

ABSTRACT Agate, a distinctive form of banded chalcedony, derives its characteristic appearance from the deposition of silica and the presence of impurities. A significant variety of agate-bearing calcite was identified in the Permian Emeishan Basalt located in the southwestern Sichuan Basin. This calcite-bearing agate provides valuable insights into the complex interplay and evolution of fluids and minerals. This study aims to provide a detailed description and analysis of the microscopic textural and geochemical characteristics of the agate, while elucidating the sequence of mineral deposition, fluid properties, and the genetic mechanism underlying its formation. The findings reveal that the agate primarily consists of lamellar calcite and banded chalcedony, exhibiting a variety of textures, thicknesses, and colours. Through the analysis of the transformation and precipitation of epidote and allanite, and the identification of four types of calcites and five micro-silica textures, it has been confirmed that the early stage of fluid in geodes involved the mixing of meteoric water with post-magmatic hydrothermal fluid. The middle and late stages involve the mixing of magmatic-hydrothermal fluid with organic-acid fluid. The charging of silica-rich hydrothermal fluids and the generation of hydrocarbons from organic matter cause fluctuations in SiO2 concentration, pH, and crystallization rates, contributing to the diversity of silica minerals, such as chalcedony and quartz. The interlayered growth observed between chalcedony and calcite is attributed to secondary metasomatism, which occurs after the dehydration and transformation of chalcedony due to increasing temperatures. Notably, the internal texture of the agate is somewhat influenced by pressure variations caused by the episodic charging of the fluid.

Noise-Robust 3D Pose Estimation Using Appearance Similarity Based on the Distributed Multiple Views.

In this paper, we present a noise-robust approach for the 3D pose estimation of multiple people using appearance similarity. The common methods identify the cross-view correspondences between the detected keypoints and determine their association with a specific person by measuring the distances between the epipolar lines and the joint locations of the 2D keypoints across all the views. Although existing methods achieve remarkable accuracy, they are still sensitive to camera calibration, making them unsuitable for noisy environments where any of the cameras slightly change angle or position. To address these limitations and fix camera calibration error in real-time, we propose a framework for 3D pose estimation which uses appearance similarity. In the proposed framework, we detect the 2D keypoints and extract the appearance feature and transfer it to the central server. The central server uses geometrical affinity and appearance similarity to match the detected 2D human poses to each person. Then, it compares these two groups to identify calibration errors. If a camera with the wrong calibration is identified, the central server fixes the calibration error, ensuring accuracy in the 3D reconstruction of skeletons. In the experimental environment, we verified that the proposed algorithm is robust against false geometrical errors. It achieves around 11.5% and 8% improvement in the accuracy of 3D pose estimation on the Campus and Shelf datasets, respectively.

Race in the eye of the beholder: Decomposing perceiver- and target-level variation in perceived racial prototypicality

Perceivers' ability to use multiple sources of information when forming impressions—including top-down, perceiver-level features, and bottom-up, target-level features—is a hallmark of social cognition. We investigate this primary foundation by examining the role of perceiver-level and target-level variation in perceived racial prototypicality in the U.S. In Study 1 (200 unique faces; 2608 raters), we quantified contributions of perceiver- and target-level effects to perceived racial prototypicality. Perceiver- and target-level contributions varied across racial category (Asian, Black, Latine, Middle Eastern, and Multiracial), with Multiracial and Middle Eastern prototypicality being more perceiver-driven. Although several appearance features (e.g., perceived ambiguity, skin tone) related to perceived prototypicality, there were distinctions in how perceivers used them (e.g., some people strongly used skin tone to infer Black prototypicality, while others used this less or not at all). A second study (N = 511) experimentally manipulated race essentialist beliefs. While there was no impact on perceived racial prototypicality, regardless of the category (Asian, Black, Latine, Middle Eastern, Multiracial, and Native American), Middle Eastern, Multiracial, and Native American prototypicality were generally more perceiver-driven than other categories, converging with Study 1. Further, perceivers' social dominance orientation, but not several other individual differences, were associated with less use of each of these categories. Taken together, findings suggest perceived racial prototypicality may originate less from stable individual differences like attitudes and instead reflects both i) differences in perceptions of target features and ii) differences in how people use particular target features in making racial prototypicality judgments.

FORENSIC DNA PHENOTYPING FOR CRIMINAL IDENTIFICATION

The purpose of the work – to carry out an analysis of modern achievements andpossibilities of forensic DNA phenotyping to determine the external characteristics ofa person, his biogeographical origin and age in order to facilitate criminal investigations.Conclusions. Forensic DNA Phenotyping (FDP) involves the prediction of a person’sexternal characteristics regarding appearance, biogeographic origin, and age based onDNA samples from a crime scene to support investigations and help to find unknowncriminals who cannot be identified through forensic STR profiling.DNA-based appearance prediction has moved beyond eye, hair and skin color andincluded new features such as eyebrow color, freckles, hair structure, male pattern hairloss and height. DNA-based biogeographic inference of ancestry has progressed fromcontinental ancestry to detecting subcontinental ancestry and resolving models of commonancestry in genetically mixed individuals. DNA-based age estimation is now possible notonly through blood testing, but also through somatic tissues such as saliva and bone, aswell as new markers and tools for analyzing semen. Technological advances have madeit possible to use forensic DNA technology with greatly increased multiplexing capabilityto simultaneously analyze hundreds of DNA predictors using targeted massively parallelsequencing (MPS). Forensically validated MPS-based FDP tools for DNA-based crimescene prediction are used to identify: several features of appearance; multiregional origin;several appearance features together with multiregional origin; age by different tissue types. Despite recent advances that are likely to increase the impact of FDP in criminal casesin the near future, bringing reliable prediction of appearance, ancestry, and age fromcrime scene DNA to the level of detail and accuracy, that forensic scientists may desire,requires further intensive scientific research, along with technical developments andforensic validation.

Muscularization of the chordae tendinea of the mitral anterior papillary muscle.

The chordae tendineae, described as fibro-collagenous structures, support the leaflets of the atrioventricular valves of the heart in various ways. The chordae tendineae are composed of collagen and elastic fibers. They connect to the ventricular side of the valve leaflets' free border and hinder the leaflets from swinging back into the atrial cavity during systole. Mitral valve chordae tendineae have been classified using a variety of classification systems. To our knowledge, we report a variant chordae tendinea that has yet to be described in the literature. The variant, present only on the mitral anterior papillary muscle, did not show the characteristic appearance of the chorda tendineae. Muscular fibers were observed extending from a larger than usual mitral anterior papillary muscle, inserting into the rough zone of the anterior leaflet. Several tendinous primary and secondary true leaflet chordae emerge from the apical portion of the anterior papillary muscle, inserting into the anterior leaflet's free edge and rough zone. Contraction of this muscular chorda during systole could disrupt the mechanics of valvular closure and result in possible regurgitation across the mitral valve. Additionally, this structure may be subject to rupture during myocardial infarction, leading to valvular dysfunction. The developmental connection between the chordae and papillary muscles could explain the anomalous muscularization of the chordae tendineae observed in this case.

Solid Metal Chemical and Thermal Injury Management.

Solid metals may create a variety of injuries. White phosphorous (WP) is a metal that causes both caustic and thermal injuries. Because of its broad use in munitions and smoke screens during conflicts and wars, all military clinicians should be competent at WP injury identification and acute therapy, as well as long-term consequence recognition. English-language manuscripts addressing WP injuries were curated from PubMed and Medline from inception to January 31, 2024. Data regarding WP injury identification, management, and sequelae were abstracted to construct a Scale for the Assessment of Narrative Review Articles guideline-consistent narrative review. White phosphorous appears to be ubiquitous in military conflicts. White phosphorous creates a characteristic wound appearance accompanied by smoke, a garlic aroma, and spontaneous combustion on contact with air. Decontamination and burning prevention or cessation are key and may rely on aqueous irrigation and submersion or immersion in substances that prevent air contact. Topical cooling is a key aspect of preventing spontaneous ignition as well. Disposal of all contaminated clothing and gear is essential to prevent additional injury, especially to rescuers. Long-term sequelae relate to phosphorous absorption and may lead to death. Chronic or repeated exposure may induce jaw osteonecrosis. Tactical Combat Casualty Care recommendations do not currently address WP injury management. Education and management regarding WP acute injury and late sequelae is essential for acute battlefield and definitive facility care. Resource-replete and resource-limited settings may use related approaches for acute management and ignition prevention. Current burn wound management recommendations should incorporate specific WP management principles and actions for military clinicians at every level of skill and environment.

Automatic Face Detection of Farm Images Based on an Enhanced Lightweight Deep Learning Model

In the realm of precise management, artificial intelligence has garnered significant attention and adoption, particularly within the domain of smart agriculture. In modern animal husbandry, animal face detection is conducive to individual identification, expression detection and behavior analysis of animals, and this technological advancement holds immense importance in fostering the advancement of intelligent farming practices. In order to solve the challenge of face detection caused by similar appearance features (color, texture, etc.) and no obvious feature differences between the solid-color goats and sheep in the natural environment, this research introduces a novel approach for face detection by combining the capabilities of YOLOv5 and a convolutional block attention module (CBAM). First, datasets of goats and sheep with different angles, scales and densities were constructed. Second, the basic framework of YOLOv5 was used for object detection. To address the obstacle posed by the limited presence of distinguishing features on the faces of goats and sheep, this study aims to overcome the challenge of extracting informative facial characteristics. The CBAM block was introduced to construct the YOLOv5-CBAM model to improve the feature extraction ability. Finally, 2412 images were selected and divided into training set and verification set according to 8:1. The experimental results of this dataset show that the proposed YOLOv5-CBAM model yielded remarkable results with a precision rate of 0.970, a recall rate of 0.890, a mAP@0.5 score of 0.935, an frames per second (FPS) of 140.845, and a model size of 14.680[Formula: see text]MB. In comparison to other approaches such as Faster R-CNN, SSD, YOLOv3, and YOLOv5, the proposed model demonstrated superior performance in some aspects. In addition, it excelled in both lightweight design and overall effectiveness, and it is well-suited for real-time detection of animal faces in real-world farming settings, ensuring efficient identification and monitoring of animals within practical agricultural environments.

A framework for detecting fighting behavior based on key points of human skeletal posture

Detecting fights from videos and images in public surveillance places is an important task to limit violent criminal behavior. Real-time detection of violent behavior can effectively ensure the personal safety of pedestrians and further maintain public social stability. Therefore, in this paper, we aim to detect real-time violent behavior in videos. We propose a novel neural network model framework based on human pose key points, called Real-Time Pose Net (RTPNet). Utilize the pose extractor (YOLO-Pose) to extract human skeleton features, and classify video level violent behavior based on the 2DCNN model (ACTION-Net). Utilize appearance features and inter frame correlation to accurately detect fighting behavior. We have also proposed a new image dataset called VIMD (Violence Image Dataset), which includes images of fighting behavior collected online and captured independently. After training on the dataset, the network can effectively identify skeletal features from videos and locate fighting movements. The dataset is available on GitHub (https://github.com/ChinaZhangPeng/Violence-Image-Dataset). We also conducted experiments on four datasets, including Hockey-Fight, RWF-2000, Surveillance Camera Fight, and AVD dataset. These experimental results showed that RTPNet outperformed the most advanced methods in the past, achieving an accuracy of 99.4% on the Hockey-Fight dataset, 93.3% on the RWF-2000 dataset, and 93.4% on the Surveillance Camera Fight dataset, 99.3% on the AVD dataset. And with speeds capable of reaching 33fps, state-of-the-art results are achieved with faster speeds. In addition, RTPNet can also have good detection performance in violent behavior in complex backgrounds.

Global Context Relation-Guided Feature Aggregation Network for Salient Object Detection in Optical Remote Sensing Images

With the rapid development of deep neural networks, salient object detection has achieved great success in natural images. However, detecting salient objects from optical remote sensing images still remains a challenging task due to the diversity of object types, scale, shape and orientation variations, as well as cluttered backgrounds. Therefore, it is impractical to directly leverage methods designed for natural images to detect salient objects in optical remote sensing images. In this work, we present an end-to-end deep neural network for salient object detection in optical remote sensing images via global context relation-guided feature aggregation. Since the objects in remote sensing images often have a scattered distribution, we design a global context relation module to capture the global relationships between different spatial positions. In order to effectively integrate low-level appearance features as well as high-level semantic features for enhancing the final performance, we develop a feature aggregation module with the global context relation information as guidance and embed it into the backbone network to refine the deep features in a progressive manner. Instead of using traditional binary cross entropy as a training loss which treats all pixels equally, we design a weighted binary cross entropy to capture local surrounding information of different pixels. Extensive experiments on three public datasets are conducted to validate the efficiency of the proposed network and the results demonstrate that our proposed method consistently outperforms other competitors.

Phytotoxic responses of acrocarpous moss Campylopus schmidii as bioindicators in copper and cadmium contaminated environments: A comprehensive assessment

Mosses play a vital role in environmental research as reliable biomonitoring tools. This study aims to understand the accumulation and distribution patterns of Cu and Cd in the acrocarpous moss [Campylopus schmidii (Müll. Hal.) A. Jaeger] (C.schmidii). In controlled in vitro experiments, C.schmidii cultures were exposed to varying concentrations of copper (Cu) and cadmium (Cd) stress (0, 10, 25, 50 μmol/L) in aquatic media. The study systematically evaluated the moss's response, including observing appearance features, oxidative traits, and accumulation characteristics. Scanning electron microscopy with energy-dispersive X-ray spectroscopy analyses were employed. They aimed to characterize and determine the distribution of metal particles in different parts of the mosses under high concentration treatments (50 μmol/L Cd, 50 μmol/L Cu, 50 μmol/L Cu and Cd). Results indicated that C.schmidii exhibited greater tolerance to Cu compared to Cd, as evidenced by significantly higher soluble protein content and lipid peroxidation with increasing concentrations. However, Cd stress induced severe damage, including widespread chlorosis, reduced chlorophyll content, and surface fragmentation. Both Cu and Cd were found to stimulate antioxidant levels by increasing the activity of hydrogen peroxide and peroxidase, thus reducing the accumulation of free radicals in C.schmidii. Additionally, the results revealed differential metal distribution. Higher Cu (2.23%) and lower Cd (0.54%) accumulation were observed at the bottom of gametophores, with Cd content 180.46% higher than Cu at the top. This study provides valuable insights into the potential application of acrocarpous mosses for biomonitoring and phytoremediation. It suggests specific strategies for metal deposition and absorption, such as utilizing upper, younger parts for Cd absorption and lower parts for Cu remediation in soil.

General Dietary Recommendations for People with Down Syndrome.

Down syndrome (DS) is caused by trisomy of chromosome 21 and is associated with characteristic features of appearance, intellectual impairment to varying degrees, organ defects, and health problems typical of this syndrome. Studies on the frequency of consumption of food products in this group show many irregularities, in particular too low consumption of vegetables and fruits, wholegrain cereal products and dairy products, and excessive consumption of meat products and sweets. It is necessary to correct eating habits. The diets of people with trisomy 21 should be consistent with the recommendations of rational nutrition for the general population and take into account specific dietary modifications related to the occurrence of diseases and health problems characteristic of this syndrome.