Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
667
result(s) for
"Zhang, Ailing"
Sort by:
Naked earth
\"After leaving the Mainland for Hong Kong in 1952, Eileen Chang was commissioned by the United States Information Service to write two books, one of which was her magnificent novel Naked Earth. Far from being a simplistic exercise in anti-Communist propaganda (two previous novels Chang wrote were pro-Communist), Naked Earth is a powerfully moving, Balzacian tale that follows two young students, Liu Ch'uen and Su Nan, who fall in love at a time when, as Chang writes, \"the whole country lay stretched out like an open palm, ready to close around any one person at any minute.\" Mao's land reform movement is in full force, and Liu and Su Nan are sent to a farm to help the peasants take over the fields. The work is hard, the nights long, and slowly it becomes clear that spies abound. Both Liu and Su Nan harbor festering secrets that are pulling them apart and Liu is eventually imprisoned by his enemies and sent to fight on the Korean front. A romance, a thrilling drama, a tragedy, Naked Earth is a stunning work of twentieth-century fiction by one of China's most revered modern novelists\"-- Provided by publisher.
Book
Application of big data and artificial intelligence in visual communication art design
In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications’ reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.
Journal Article
Half a lifelong romance : a novel
\"Shanghai, 1930s. Shen Shijun, a young engineer, has fallen in love with his colleague, the beautiful Gu Manzhen. He is determined to resist his family's efforts to match him with his wealthy cousin so that he can marry the woman he truly loves. But dark circumstances--a lustful brother-in-law, a treacherous sister, a family secret--force the two young lovers apart. As Manzhen and Shijun go on their separate paths, they lose track of one another, and their lives become filled with feints and schemes, missed connections and tragic misunderstandings. At every turn, societal expectations seem to thwart their prospects for happiness. Still, Manzhen and Shijun dare to hold out hope--however slim--that they might one day meet again. A glamorous, wrenching tale set against the glittering backdrop of an extraordinary city, Half a Lifelong Romance is a beloved classic from one of the essential writers of twentieth-century China.\"-- Provided by publisher.
Book
Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data
•Early identification of depressive symptoms may reduce associated societal costs.•Machine Learning selects predictors of subclinical depression from vast MRI data.•Generated models demonstrated varied but overall good performance for prediction.•Functional abnormalities in brain areas like the Orbital Gyrus predicted depression.•The best predictive model achieved 85 % accuracy and an AUC of 0.80 in healthy people.
Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depression Inventory, structural MRI (sMRI), and resting-state functional MRI (rs-fMRI). Feature selection methods, including the least absolute shrinkage and selection operator (LASSO), Boruta, and VSURF, were applied to identify MRI features associated with depression. Support vector machine and random forest algorithms were then used to construct prediction models. Eight MRI features were identified as predictive of depression, including brain regions in the Orbital Gyrus, Superior Frontal Gyrus, Middle Frontal Gyrus, Parahippocampal Gyrus, Cingulate Gyrus, and Inferior Parietal Lobule. The overlaps and the differences between selected features and brain regions with significant between-group differences in t-tests suggest that ML provides a unique perspective on the neural changes associated with depression. Six pairs of prediction models demonstrated varying performance, with accuracies ranging from 0.68 to 0.85 and areas under the curve (AUC) ranging from 0.57 to 0.81. The best-performing model achieved an accuracy of 0.85 and an AUC of 0.80, highlighting the potential of combining sMRI and rs-fMRI features with ML for early depression detection while revealing the potential of overfitting in small-sample and high-dimensional settings. This study necessitates further research to (1) replicate findings in independent larger datasets to address potential overfitting and (2) utilize different advanced ML techniques and multimodal data fusion to improve model performance.
Journal Article
Transcriptome analysis of heat stress and drought stress in pearl millet based on Pacbio full-length transcriptome sequencing
Background
Heat and drought are serious threats for crop growth and development. As the sixth largest cereal crop in the world, pearl millet can not only be used for food and forage but also as a source of bioenergy. Pearl millet is highly tolerant to heat and drought. Given this, it is considered an ideal crop to study plant stress tolerance and can be used to identify heat-resistant genes.
Results
In this study, we used Pacbio sequencing data as a reference sequence to analyze the Illumina data of pearl millet that had been subjected to heat and drought stress for 48 h. By summarizing previous studies, we found 26,299 new genes and 63,090 new transcripts, and the number of gene annotations increased by 20.18%. We identified 2792 transcription factors and 1223 transcriptional regulators. There were 318 TFs and 149 TRs differentially expressed under heat stress, and 315 TFs and 128 TRs were differentially expressed under drought stress. We used RNA sequencing to identify 6920 genes and 6484 genes differentially expressed under heat stress and drought stress, respectively.
Conclusions
Through Pacbio sequencing, we have identified more new genes and new transcripts. On the other hand, comparing the differentially expressed genes under heat tolerance with the DEGs under drought stress, we found that even in the same pathway, pearl millet responds with a different protein.
Journal Article
Thermal Conductivity of Aluminum Alloys—A Review
Aluminum alloys have been extensively used as heatproof and heat-dissipation components in automotive and communication industries, and the demand for aluminum alloys with higher thermal conductivity is increasing. Therefore, this review focuses on the thermal conductivity of aluminum alloys. First, we formulate the theory of thermal conduction of metals and effective medium theory, and then analyze the effect of alloying elements, secondary phases, and temperature on the thermal conductivity of aluminum alloys. Alloying elements are the most crucial factor, whose species, existing states, and mutual interactions significantly affect the thermal conductivity of aluminum. Alloying elements in a solid solution weaken the thermal conductivity of aluminum more dramatically than those in the precipitated state. The characteristics and morphology of secondary phases also affect thermal conductivity. Temperature also affects thermal conductivity by influencing the thermal conduction of electrons and phonons in aluminum alloys. Furthermore, recent studies on the effects of casting, heat treatment, and AM processes on the thermal conductivity of aluminum alloys are summarized, in which processes mainly affect thermal conductivity by varying existing states of alloying elements and the morphology of secondary phases. These analyses and summaries will further promote the industrial design and development of aluminum alloys with high thermal conductivity.
Journal Article
Ultrahigh-activity immune inducer from Endophytic Fungi induces tobacco resistance to virus by SA pathway and RNA silencing
Background
Plant viruses cause severe economic losses in agricultural production. An ultrahigh activity plant immune inducer (i.e., ZhiNengCong, ZNC) was extracted from endophytic fungi, and it could promote plant growth and enhance resistance to bacteria. However, the antiviral function has not been studied. Our study aims to evaluate the antiviral molecular mechanisms of ZNC in tobacco.
Results
Here, we used
Potato X virus
(PVX), wild
-
type tobacco and
NahG
transgenic tobacco as materials to study the resistance of ZNC to virus. ZNC exhibited a high activity in enhancing resistance to viruses and showed optimal use concentration at 100–150 ng/mL. ZNC also induced reactive oxygen species accumulation, increased salicylic acid (SA) content by upregulating the expression of phenylalanine ammonia lyase (PAL) gene and activated SA signaling pathway. We generated transcriptome profiles from ZNC-treated seedlings using RNA sequencing. The first GO term in biological process was positive regulation of post-transcriptional gene silencing, and the subsequent results showed that ZNC promoted RNA silencing. ZNC-sprayed wild-type leaves showed decreased infection areas, whereas ZNC failed to induce a protective effect against PVX in
NahG
leaves.
Conclusion
All results indicate that ZNC is an ultrahigh-activity immune inducer, and it could enhance tobacco resistance to PVX at low concentration by positively regulating the RNA silencing via SA pathway. The antiviral mechanism of ZNC was first revealed in this study, and this study provides a new antiviral bioagent.
Journal Article
Research on English semantic modeling and understanding algorithm based on the SemBERT model of natural language processing
English machine reading comprehension (MRC) is a pivotal and challenging task in the field of natural language understanding, which requires a model to comprehend a given passage and then answer corresponding questions or infer textual implications. Prevailing deep learning models typically employ word or character levels as fundamental input units. However, the simple concatenation of word and character vectors has been proven suboptimal for semantic representation. To address this limitation, this paper conducts an in-depth investigation and proposes a novel framework centered on fine-grained language unit segmentation and fusion. Our research focuses on hybrid character-word modeling to achieve a more effective mixed-granularity representation. We explore advanced fusion methods for character and word embeddings and introduce a shortlist mechanism based on word frequency filtering, which significantly enhances the training for low-frequency and out-of-vocabulary words. Furthermore, we propose leveraging subwords and specialized uncommon character embeddings to enrich word representations, all within a general subword segmentation framework. Recognizing that many leading MRC models lack genuine semantic understanding and often focus on semantically irrelevant components, we argue that the core of MRC aligns with the goal of semantic role labeling (SRL). Consequently, we innovatively integrate SRL into the MRC and reasoning pipeline to provide richer and more accurate semantic prompts. Extensive experimental evaluations demonstrate that our proposed methods consistently improve the benchmark model across a range of language understanding tasks, including natural language inference, question answering, reading comprehension, semantic similarity, and text classification, achieving competitive performance.
Journal Article
Composite Hydrogels with Rapid Self-Healing, Stretchable, Moldable and Antibacterial Properties Based on PVA/ε-Poly-l-lysine/Hyaluronic Acid
Self-healing, stretchable, and moldable hydrogels have a great potential application in tissue engineering and soft robotics. Despite great success in reported hydrogels, it is still a great challenge to construct the moldable hydrogels with an ultrafast self-healing performance. Herein, the composite hydrogels (PBLH) with ultrafast self-healing, stretchable, and moldable properties were successfully constructed by poly (vinyl alcohol) (PVA), borate (B), ε-poly-l-lysine (EPL), and hyaluronic acid (HA) based on an efficient one-pot method. Fourier transform infrared spectroscopy, X-ray diffraction, and rheological measurements confirmed the formation of a dynamic network among PVA, B, EPL, and HA through the cross-linking of dynamic borate bonds, electrostatic interaction, and hydrogen bonding. Having fabricated the dynamic network structure, the damage gap of the composite hydrogels can heal within 1 min, presenting an excellent self-healing ability. Simultaneously, the composite hydrogels can be molded into various shapes, and the length of the composite hydrogels can be stretched to 15 times their original length. In addition, the composite hydrogels exhibited an excellent antibacterial property against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Our results illustrated that the composite hydrogels not only retain the advantages of traditional hydrogels but also possess ultrafast self-healing, outstanding stretchable and antibacterial properties, presenting a prospective candidate for constructing biomedical materials.
Journal Article
Metabolic Controls on Epigenetic Reprogramming in Regulatory T Cells
Forkhead box protein 3 (Foxp3 + )-expressing regulatory T (Treg) cells are a unique CD4 + T cell subset that suppresses excessive immune responses. The epigenetic plasticity and metabolic traits of Treg cells are crucial for the acquisition of their phenotypic and functional characteristics. Therefore, alterations to the epigenetics and metabolism affect Treg cell development and function. Recent evidence reveals that altering the metabolic pathways and generation of metabolites can regulate the epigenetics of Treg cells. Specifically, some intermediates of cell metabolism can directly act as substrates or cofactors of epigenetic-modifying enzymes. Here, we describe the metabolic and epigenetic features during Treg cell development, and discuss how metabolites can contribute to epigenetic alterations of Treg cells, which affects Treg cell activation, differentiation, and function.
Journal Article