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Differences in the bacterial community structure associated with 7 skin sites in 71 healthy people over five days showed significant correlations with age, gender, physical skin parameters, and whether participants lived in urban or rural locations in the same city. While body site explained the majority of the variance in bacterial community structure, the composition of the skin-associated bacterial communities were predominantly influenced by whether the participants were living in an urban or rural environment, with a significantly greater relative abundance of Trabulsiella in urban populations. Adults maintained greater overall microbial diversity than adolescents or the elderly, while the intragroup variation among the elderly and rural populations was significantly greater. Skin-associated bacterial community structure and composition could predict whether a sample came from an urban or a rural resident ~5x greater than random.

The limited capacity to recognise faces under occlusions is a long‐standing problem that presents a unique challenge for face recognition systems and even humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real‐world applications. In this article, the scope to occluded face recognition is restricted and a systematic categorisation that new as well as classic methods fit into is presented. First, the authors explore the kind of the occlusion problem and the type of inherent difficulties that can arise. As a part of this review, face detection under occlusion, a preliminary step in face recognition. Second the authors analyse how the existing face recognition methods cope with the occlusion problem and classify them into three categories, which are given as: 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, the motivations, innovations, pros and cons, and the performance of representative approaches for comparison are analyzed. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed.

The factors driving the composition of gut microbiota are still only partly understood but appear to include environmental, cultural, and genetic factors. In order to obtain more insight into the relative importance of these factors, we analyzed the microbiome composition in subjects of Tibetan or Han descent living at different altitudes. DNA was isolated from stool samples. Using polymerase chain reaction methodology, the 16S rRNA V1-V3 regions were amplified and the sequence information was analyzed by principal coordinates analysis and Lefse analyses. Contrasting the Tibetan and Han populations both living at the 3600 m altitude, we found that the Tibetan microbiome is characterized by a relative abundance of Prevotella whereas the Han stool was enriched in Bacteroides. Comparing the microbiome of Han stool obtained from populations living at different altitudes revealed a more energy efficient flora in samples from those living at higher altitude relative to their lower-altitude counterparts. Comparison of the stool microbiome of Tibetan herders living at 4800 m to rural Tibetans living at 3600 m altitude shows that the former have a flora enriched in butyrate-producing bacteria, possibly in response to the harsher environment that these herders face. Thus, the study shows that both altitude and genetic/cultural background have a significant influence on microbiome composition, and it represents the first attempt to compare stool microbiota of Tibetan and Han populations in relation to altitude.

A huge accumulation of domestic waste has caused serious environmental contamination in rural areas of developing countries (RADIC). The characteristics and management of domestic waste are carefully discussed, based on field surveys and a literature review. The results indicate that the generation in most of RADIC is less than the median of 0.521 kg day −1 per capita in China, and much smaller than in rural areas of developed countries (RADEC). Organic waste and inert waste with an accumulative mass percentage of 72.31% are dominant components of domestic waste in the rural areas of China. There are trends of increasing amounts of kitchen waste, paper/cardboard, and plastic/rubber and a decreasing trend of ash waste. The RADIC composition of domestic waste had a high content of organic waste and a low content of recyclable waste compared to the RADEC. Domestic waste has good compressibility and a light bulk density ranging from 40 to 650 kg m −3 . The moisture, ash, combustible, and calorific values of domestic waste were 53.31%, 18.03%, 28.67%, and 5368 kJ kg −1 , respectively. The domestic waste has an abundance of nutrients including organic matter (39.05%), nitrogen (1.02%), phosphorus (0.50%), and potassium (1.42%). In RADIC, domestic waste can be used as an agricultural manure only after it has been collected and sorted for the potential risk of heavy metal accumulation. Based on these characteristics of domestic waste and the different situations of rural areas, four waste management modes including centralized treatment, decentralized treatment, group treatment, and mobile treatment are designed and discussed.

This study evaluates the prevalence and implications of Non-thyroidal Illness Syndrome (NTIS) in children with acute lymphoblastic leukemia (ALL), marking a focus shift towards pediatric patients who have been less studied in this context. Through a prospective analysis of 96 newly diagnosed ALL patients against healthy controls, we assessed thyroid function at diagnosis and after induction chemotherapy. Our findings highlight a significant reduction in T3/FT3 and FT4 levels in the ALL group, with NTIS prevalence jumping from 44.8% pre-chemotherapy to 74.2% post-chemotherapy, illustrating the profound impact of treatment-related factors and inflammation on thyroid health. Unlike previous beliefs, NTIS’s occurrence was independent of ALL risk categories and induction therapy outcomes. Factors like elevated C-reactive protein, low serum albumin, and lymphoblast count emerged as NTIS risk indicators. Most thyroid dysfunctions normalized post-chemotherapy without needing hormonal interventions, suggesting a transient NTIS that favors conservative management focused on long-term monitoring. This study not only confirms the high incidence of NTIS in pediatric ALL patients but also challenges existing thyroid health management paradigms in pediatric oncology, calling for nuanced treatment approaches.

Recently, many researchers have been dedicated to using convolutional neural networks (CNNs) to extract global-context features (GCFs) for remote-sensing scene classification. Commonly, accurate classification of scenes requires knowledge about both the global context and local objects. However, unlike the natural images in which the objects cover most of the image, objects in remote-sensing images are generally small and decentralized. Thus, it is hard for vanilla CNNs to focus on both global context and small local objects. To address this issue, this paper proposes a novel end-to-end CNN by integrating the GCFs and local-object-level features (LOFs). The proposed network includes two branches, the local object branch (LOB) and global semantic branch (GSB), which are used to generate the LOFs and GCFs, respectively. Then, the concatenation of features extracted from the two branches allows our method to be more discriminative in scene classification. Three challenging benchmark remote-sensing datasets were extensively experimented on; the proposed approach outperformed the existing scene classification methods and achieved state-of-the-art results for all three datasets.

This study aimed to evaluate the effects of diets supplemented with various levels of complex antioxidants (CA) containing tertiary butylhydroquinone (TBHQ) and tea polyphenols (TP) on growth performance, meat quality of breast and leg muscles, serum biochemistry, and antioxidant capacity of serum, liver, breast meat, jejunum, and ileum in broilers. A total of 600 one-day-old Arbor Acres male broilers with similar body weights were randomly divided into three groups (10 replicates/group, 20 broilers/replicate). Birds in the three experimental groups were fed a basal diet with CA at 0, 300, and 500 mg/kg. The results showed that supplementing with 300 mg/kg CA significantly increased (p < 0.05) 42 d BW and 22–42 d ADG, and markedly decreased (p < 0.05) 22–42 d F: G ratio in comparison to the control group. Birds fed a diet with 300 mg/kg CA had a higher (p < 0.05) pH of chicken meat at 24 h and 48 h post mortem and lower (p < 0.05) yellowness values (b*) of chicken meat at 45 min and 24 h post mortem, along with a lower (p < 0.05) cooking loss. Supplementing with 300 mg/kg CA significantly increased (p < 0.05) serum and liver T-SOD activity, serum T-AOC level, as well as jejunual GST activity, and significantly decreased (p < 0.05) liver MDA content when compared with the control group. These results indicate that diet supplementation with 300 mg/kg CA containing TBHQ and TP could improve growth performance and meat quality by increasing the antioxidant capacity of broilers.

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method.

The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments.

BACKGROUNDDespite an increasing appreciation of the roles that myeloid cells play in tumor progression and therapy, challenges remain in interpreting the tumor-associated myeloid response balance and its translational value. We aimed to construct a simple and reliable myeloid signature for hepatocellular carcinoma (HCC).METHODSUsing in situ immunohistochemistry, we assessed the distribution of major myeloid subtypes in both peri- and intratumoral regions of HCC. A 2-feature-based, myeloid-specific prognostic signature, named the myeloid response score (MRS), was constructed using an L1-penalized Cox regression model based on data from a training subset (n = 244), a test subset (n = 244), and an independent internal (n = 341) and 2 external (n = 94; n = 254) cohorts.RESULTSThe MRS and the MRS-based nomograms displayed remarkable discriminatory power, accuracy, and clinical usefulness for predicting recurrence and patient survival, superior to current staging algorithms. Moreover, an increase in MRS was associated with a shift in the myeloid response balance from antitumor to protumor activities, accompanied by enhanced CD8+ T cell exhaustion patterns. Additionally, we provide evidence that the MRS was associated with the efficacy of sorafenib treatment for recurrent HCC.CONCLUSIONWe identified and validated a simple myeloid signature for HCC that showed remarkable prognostic potential and may serve as a basis for the stratification of HCC immune subtypes.FUNDINGThis work was supported by the National Science and Technology Major Project of China, the National Natural Science Foundation of China, the Science and Information Technology of Guangzhou, the Fundamental Research Funds for the Central Universities, the Guangdong Basic and Applied Basic Research Foundation, and the China Postdoctoral Science Foundation.