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General General

Non-coding transcriptomic profiles in the sheep mammary gland during different lactation periods.

In Frontiers in veterinary science ; h5-index 25.0

Sheep milk production is a dynamic and multifactorial trait regulated by diverse biological mechanisms. To improve the quality and production of sheep milk, it is necessary to understand the underlying non-coding transcriptomic mechanisms. In this study, ribonucleic acid-sequencing (RNA-seq) was used to profile the expression of microRNAs (miRNAs) and circular RNAs (circRNAs) in the sheep mammary gland at three key lactation time points (perinatal period, PP; early lactation, EL; and peak lactation, PL). A total of 2,369 novel circRNAs and 272 miRNAs were profiled, of which 348, 373, and 36 differentially expressed (DE) circRNAs and 30, 34, and 7 DE miRNAs were detected in the comparison of EL vs. PP, PL vs. PP, and PL vs. EL, respectively. A series of bioinformatics analyses including functional enrichment, machine learning prediction, and competing endogenous RNA (ceRNA) network analyses were conducted to identify subsets of the potential candidate miRNAs (e.g., oar_miR_148a, oar_miR_362, and oar_miR_432) and circRNAs (e.g., novel_circ_0011066, novel_circ_0010460, and novel_circ_0006589) involved in sheep mammary gland development. Taken together, this study offers a window into the dynamics of non-coding transcriptomes that occur during sheep lactation and may provide further insights into miRNA and circRNA that influence sheep mammary gland development.

Chen Weihao, Gu Xinyu, Lv Xiaoyang, Cao Xiukai, Yuan Zehu, Wang Shanhe, Sun Wei

2022

ceRNA, circRNA, lactation, machine learning, mammary gland, miRNA, sheep

General General

Rete ridges: morphogenesis, function, regulation, and reconstruction.

In Acta biomaterialia ; h5-index 89.0

Rete ridges (RRs) are distinct undulating microstructures at the junction of the dermis and epidermis in the skin of humans and certain animals. This structure is essential for enhancing the mechanical characteristics of skin and preserving homeostasis. With the development of tissue engineering and regenerative medicine, artificial skin grafts have made great progress in the field of skin healing. However, the restoration of RRs has been often disregarded or absent in artificial skin grafts, which potentially compromise the efficacy of tissue repair and regeneration. Therefore, this review collates recent research advances in understanding the structural features, function, morphogenesis, influencing factors, and reconstruction strategies pertaining to RRs. In addition, the preparation methods and limitations of tissue-engineered skin with RRs are discussed. STATEMENT OF SIGNIFICANCE: The technology for the development of tissue-engineered skin (TES) is widely studied and reported; however, the preparation of TES containing rete ridges (RRs) is often ignored, with no literature reviews on the structural reconstruction of RRs. This review focuses on the progress pertaining to RRs and focuses on the reconstruction methods for RRs. In addition, it discusses the limitations of existing reconstruction methods. Therefore, this review could be a valuable reference for transferring TES with RR structure from the laboratory to clinical applications in skin repair.

Shen Zhizhong, Sun Lei, Liu Zixian, Li Meng, Cao Yanyan, Han Lu, Wang Jianming, Wu Xunwei, Sang Shengbo

2022-Nov-22

Artificial skin grafts, Reconstruction, Rete ridges, Tissue-engineered skin

General General

A GAN model encoded by CapsEEGNet for visual EEG encoding and image reproduction.

In Journal of neuroscience methods

In last few decades, reading the human mind is an innovative topic in scientific research. Recent studies in neuroscience indicate that it is possible to decode the signals of the human brain based on the neuroimaging data. The work in this paper explores the possibility of building an end-to-end BCI system to learn and visualize the brain thoughts evoked by the stimulating images. To achieve this goal, it designs an experiment to collect the EEG signals evoked by randomly presented images. Based on these data, this work analyzes and compares the classification abilities by several improved methods, including the Transformer, CapsNet and the ensemble strategies. After obtaining the optimal method to be the encoder, this paper proposes a distribution-to-distribution mapping network to transform an encoded latent feature vector into a prior image feature vector. To visualize the brain thoughts, a pretrained IC-GAN model is used to receive these image feature vectors and generate images. Extensive experiments are carried out and the results show that the proposed method can effectively deal with the small sample data original from the less electrode channels. By examining the generated images coming from the EEG signals, it verifies that the proposed model is capable of reproducing the images seen by human eyes to some extent.

Deng Xin, Wang Zhongyin, Liu Ke, Xiang Xiaohong

2022-Nov-22

BCI, Deep learning, EEG, EEGNet, Vision stimulus generation

General General

Synthesis of heated aluminum oxide particles impregnated with Prussian blue for cesium and natural organic matter adsorption: Experimental and machine learning modeling.

In Chemosphere

Heated aluminum oxide particles impregnated with Prussian blue (HAOPs-PB) are synthesized for the first time using different molar ratios of aluminum sulfate and PB to improve the adsorption of cesium (133Cs+) and natural organic matter (NOM) from an aqueous solution. The Cs+ adsorption from various aqueous solutions, including surface, tap and deionized water by synthesized HAOPs-PB, is investigated. The influencing factors such as HAOPs-PB mixing ratio, pH and dosage are studied. In addition, pseudo 1st and 2nd order is tested for adsorption kinetics study. A machine learning model is developed using gene expression programming (GEP) to evaluate and optimize the adsorption process for Cs+ and NOM removal. Synthesized adsorbent showed maximum adsorption at a 1:1 M ratio of aluminum sulfate and PB in DI, tap, and surface water. The pseudo 2nd order kinetics model described the Cs + adsorption by HAOPs-PB more accurately that indicating physiochemical adsorption. Adsorption of Cs+ showed an increasing trend with higher HAOPs-PB concentration, while high pH also favored the adsorption. Maximum NOM adsorption is found at a higher HAOPs-PB dosage and a neutral pH value. Furthermore, the proposed GEP model shows outstanding performance for Cs+ adsorption modeling, whereas a modified-GEP model presents promising results for NOM adsorption prediction for testing dataset by learning the relationship between inputs and output with R2 values of 0.9348 and 0.889, respectively.

Yaqub Muhammad, Nguyen Mai Ngoc, Lee Wontae

2022-Nov-22

Adsorption, Cesium, Gene expression programming, Heated aluminum oxide particles, Natural organic matter, Prussian blue

Surgery Surgery

Practice-Based Learning and Improvement: Improving Morbidity and Mortality Review Using Natural Language Processing.

In The Journal of surgical research

INTRODUCTION : Practice-Based Learning and Improvement, a core competency identified by the Accreditation Council for Graduate Medical Education, carries importance throughout a physician's career. Practice-Based Learning and Improvement is cultivated by a critical review of complications, yet methods to accurately identify complications are inadequate. Machine-learning algorithms show promise in improving identification of complications. We compare a manual-supplemented natural language processing (ms-NLP) methodology against a validated electronic morbidity and mortality (MM) database, the Morbidity and Mortality Adverse Event Reporting System (MARS) to understand the utility of NLP in MM review.

METHODS : The number and severity of complications were compared between MARS and ms-NLP of surgical hospitalization discharge summaries among three academic medical centers. Clavien-Dindo (CD) scores were assigned to cases with identified complications and classified into minor (CD I-II) or major (CD III-IV) harm.

RESULTS : Of 7774 admissions, 987 cases were identified to have 1659 complications by MARS and 1296 by ms-NLP. MARS identified 611 (62%) cases, whereas ms-NLP identified 670 (68%) cases. Less than one-third of cases (299, 30.3%) were detected by both methods. MARS identified a greater number of complications with major harm (457, 46.30%) than did ms-NLP (P < 0.0001).

CONCLUSIONS : Both a prospectively maintained MM database and ms-NLP review of discharge summaries fail to identify a significant proportion of postoperative complications and overlap 1/3 of the time. ms-NLP more frequently identifies cases with minor complications, whereas prospective voluntary reporting more frequently identifies major complications. The educational benefit of reporting and analysis of complication data may be supplemented by ms-NLP but not replaced by it at this time.

Kobritz Molly, Patel Vihas, Rindskopf David, Demyan Lyudmyla, Jarrett Mark, Coppa Gene, Antonacci Anthony C

2022-Nov-22

Morbidity review, Natural language processing, PBLI, Quality review, Surgical education

General General

A novel framework-based meta-analysis for in-depth characterization of microplastic pollution and associated ecological risks in Chinese Bays.

In Journal of hazardous materials

Among aquatic ecosystems, bays are ubiquitously contaminated with microplastics (MPs, size <5 mm), but a comprehensive understanding of their pollution characterization in Chinese Bays is largely elusive. The current study aims to systematically highlight factors intricating MP contamination as well as their geographic distribution, interactions, risk evaluation, and abundance prediction in bays. MPs' abundance was varied in different bays, at concentrations ranging between 0.26 ± 0.14-89, 500 ± 20, 600 items/m3 in water, 15 ± 6-6433.5 items/kg dry weight in sediment and 0.21 ± 0.10-103.5 items/individual in biota. Redundancy analysis, Permannova, and GeoDetector model revealed that the sampling and extraction/identification methods, and geographical locations were the major drivers affecting MP distribution and characteristics. The Mantel test highlighted that the MP characteristics changed with geographic distance, higher in water than that in sediment and biota. ANOSIM results showed that the different environmental media exhibit significant differences in MP characteristics (e.g., color, shape, and polymer). The ARIMA model predicted that Sanggou Bay and Hangzhou Bay have a higher potential for significantly increasing MP contamination in the future. The highest hazard index (HI) values for water, sediment, and biota were respectively reported at Jiaozhou Bay (18,844.16), Bohai Bay (11,485.37), and Dongshan Bay (48,485.11). The highest values for the ecological risk index (RI) in water, sediment, and biota were detected at Beibu Gulf (6,129,559.02), Haikou Bay (2229.14), and Dongshan Bay (561,563.05), respectively. Overall, this framework can be used at different scales and in different environments, which makes it useful for understanding and controlling MP pollution in the ecosystem.

Liu Shulin, Junaid Muhammad, Sadaf Mamona, Ai Wenjie, Lan Xue, Wang Jun

2022-Nov-21

Bays, Ecological impacts, Microplastics, Multivariate analysis, Polymer risks, Spatiotemporal distribution