Effect of microbial inoculum on composting efficiency in the composting process of spent mushroom substrate and chicken manure.

This work aimed to investigate the microbial mechanisms for the improvement of composting efficiency driven by the compound microbial inoculum (MI) (Bacillus subtilis SL-44, Enterobacter hormaechei Rs-189 and Trichoderma reesei) during co-composting of spent mushroom substrate (SMS) and chicken manure (CM). The treatments used in the study were as follows: 1) MI (inoculation with microbial inoculum), 2) CI (inoculation with commercial microbial inoculum), and 3) CK (without inoculation). The results demonstrated that MI increased the seed germination index (GI) by 25.11%, and contents of humus, humic acid (HA) and available phosphorus (AP) were correspondingly promoted by 12.47%, 25.93% and 37.16%, respectively. The inoculation of MI increased the temperature of the thermophilic stage by 3-7 °C and achieved a cellulose degradation rate of 52.87%. 16S rRNA gene analysis indicated that Actinobacteria (11.73-61.61%), Firmicutes (9.46-65.07%), Proteobacteria (2.86-32.17%) and Chloroflexi (0.51-10.92%) were the four major phyla during the inoculation composting. Bacterial metabolic functional analysis revealed that pathways involved in amino acid and glycan biosynthesis and metabolism were boosted in the thermophilic phase. There was a positive correlation between bacterial communities and temperature, humification and phosphorus fractions. The average dry weight, fresh weight and seedling root length in the seedling substrates adding MI compost were 1.13, 1.23 and 1.06 times higher than those of the CK, respectively. This study revealed that biological inoculation could improve the composting quality and efficiency, potentially benefiting the resource utilization of agricultural waste resources.

Exploration of street space architectural color measurement based on street view big data and deep learning-A case study of Jiefang North Road Street in Tianjin.

Urban space architectural color is the first feature to be perceived in a complex vision beyond shape, texture and material, and plays an important role in the expression of urban territory, humanity and style. However, because of the difficulty of color measurement, the study of architectural color in street space has been difficult to achieve large-scale and fine development. The measurement of architectural color in urban space has received attention from many disciplines. With the development and promotion of information technology, the maturity of street view big data and deep learning technology has provided ideas for the research of street architectural color measurement. Based on this background, this study explores a highly efficient and large-scale method for determining architectural colors in urban space based on deep learning technology and street view big data, with street space architectural colors as the research object. We conducted empirical research in Jiefang North Road, Tianjin. We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. Based on K-Means clustering model, we identified the colors of the architectural elements in the street view. The accuracy of the building color measurement results was cross-sectionally verified by means of a questionnaire survey. The validation results show that the method is feasible for the study of architectural colors in street space. Finally, the overall coordination, sequence continuity, and primary and secondary hierarchy of architectural colors of Jiefang North Road in Tianjin were analyzed. The results show that the measurement model can realize the intuitive expression of architectural color information, and also can assist designers in the analysis of architectural color in street space with the guidance of color characteristics. The method helps managers, planners and even the general public to summarize the characteristics of color and dig out problems, and is of great significance in the assessment and transformation of the color quality of the street space environment.

The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005-2050) of Tianjin City, China.

Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city's land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area's temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis.

BACKGROUND: In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. OBJECTIVE: The aim of this study was to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. METHODS: Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). RESULTS: We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people's attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. CONCLUSIONS: Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.

Natural language processing applied to mental illness detection: a narrative review.

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people's life with impact on society's health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

DREAM: Drug-drug interaction extraction with enhanced dependency graph and attention mechanism.

Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently, graph neural networks are applied on dependency graph to promote the performance of DDI extraction with better semantic representations. However, current method concentrates more on first-order dependency relations and cannot discriminate the connected nodes properly. To better incorporate the dependency relations and improve the representations, we propose a novel DDI extraction method named Drug-drug Interactions extRaction with Enhanced Dependency Graph and Attention Mechanism in this work. Specifically, the dependency graph is enhanced with some potential long-range words to complete the semantic information and fit the aggregation process of graph neural networks. And graph attention mechanism is adopted to further improve word representation by discriminating the connected nodes according to the specific task. Numerical experiments on DDIExtraction 2013 corpus, the benchmark corpus for this domain, demonstrate the superiority of our proposed method.

Plant Functional Groups Dominate Responses of Plant Adaptive Strategies to Urbanization.

Urbanization causes alteration in atmospheric, soil, and hydrological factors and substantially affects a range of morphological and physiological plant traits. Correspondingly, plants might adopt different strategies to adapt to urbanization promotion or pressure. Understanding of plant traits responding to urbanization will reveal the capacity of plant adaptation and optimize the choice of plant species in urbanization green. In this study, four different functional groups (herbs, shrubs, subcanopies, and canopies, eight plant species totally) located in urban, suburban, and rural areas were selected and eight replicated plants were selected for each species at each site. Their physiological and photosynthetic properties and heavy metal concentrations were quantified to reveal plant adaptive strategies to urbanization. The herb and shrub species had significantly higher starch and soluble sugar contents in urban than in suburban areas. Urbanization decreased the maximum photosynthetic rates and total chlorophyll contents of the canopies (Engelhardtia roxburghiana and Schima superba). The herbs (Lophatherum gracile and Alpinia chinensis) and shrubs (Ardisia quinquegona and Psychotria rubra) species in urban areas had significantly lower nitrogen (N) allocated in the cell wall and leaf δ15N values but higher heavy metal concentrations than those in suburban areas. The canopy and subcanopy (Diospyros morrisiana and Cratoxylum cochinchinense) species adapt to the urbanization via reducing resource acquisition but improving defense capacity, while the herb and shrub species improve resource acquisition to adapt to the urbanization. Our current studies indicated that functional groups affected the responses of plant adaptive strategies to the urbanization.

Automatic identification of suicide notes with a transformer-based deep learning model.

Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media.

Soil microbial community dynamics mediate the priming effects caused by in situ decomposition of fresh plant residues.

Extreme climate events always leave numerous fresh plant materials (FOM) in forests, thus increasing the input of carbon (C) resources to soil system. The input of exogenous C may accelerate or inhibit the decomposition of soil organic carbon (SOC), which is defined as the positive or negative priming effect (PE), respectively. However, the characteristics and microbial mechanisms of PE caused by FOM remain unknown. A 110-day in situ soil incubation experiment was conducted in a subtropical forest, with 13C-labeled fresh leaves from four native species (Castanopsis fissa, CF; Pinus massoniana, PM; Machilus chekiangensis, MC; and Castanopsis chinensis, CC) serving as the FOM respectively. We measured the CO2 effluxes derived from 13C-labeled FOM and soil, and the composition and diversity of soil bacterial and fungal communities throughout the incubation to explore the correlations between PE and microbial attributes. As a result, the PE caused by FOM inputs were negative initially but became positive after 61 d. The FOM decomposition rate was positively related to PE intensity, and there was a significant difference between coniferous and broadleaved species in the middle period of the study. More than 77% of the total C lost from FOM was emitted as CO2, indicating that FOM-C serves as an energy resource for soil microbes. The α-diversity of the bacterial community at genus-level showed significantly positive correlation with PE at 24 d, and the composition of bacterial community at OTU-level had a marked relationship with the PE between 24-110 d. The relationship between fungal community diversity and composition with PE was only observed at 7 and 110 d, respectively. This study firstly investigated the patterns of PE resulted from numerous FOM input, and the results suggested that soil bacterial community, in particular the Actinobacteria phyla, played a more important role in triggering such PEs than fungal community.

Deep learning for drug-drug interaction extraction from the literature: a review.

Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

Design of encapsulated hosts and guests for highly efficient blue and green thermally activated delayed fluorescence OLEDs based on a solution-process.

The molecular aggregation and exciton-polaron interaction of the host-guest system were successfully restricted by efficient molecular encapsulation. The solution-processed blue and green TADF OLEDs have been realized with external quantum efficiencies above 23% by employing the encapsulated TADF host and guest as emission layers.

Highly Efficient All-Solution-Processed Fluorescent Organic Light-Emitting Diodes Based on a Novel Self-Host Thermally Activated Delayed Fluorescence Emitter.

Here, we conveniently designed and synthesized a self-host thermally activated delayed fluorescence (TADF) emitter, which can not only form a uniform thin film through wet-process, but also allow the subsequently deposition of electron transporting layer (ETL) by orthogonal solvent. By using this self-host material as emitter, the all-solution-processed multilayer TADF organic light emitting diodes (OLEDs) was successfully fabricated. The maximum current, power and external quantum efficiencies of this nondoped device are 46.3 cd A-1, 39.3 lm W1- and 15.5%, respectively, which are much higher than the values of all-solution-processed OLEDs based on tranditional fluorescence and even comparable to the TADF devices with vacuum-deposited ETL. Moreover, the device maintains the high efficiency of 42.9 cd A-1 and 39.0 cd A-1 at the luminance of 100 cd m-2 for display and 1000 cd m-2 for practical lighting. The high efficiency and small efficiency roll-off of the all-solution-processed fluorescent OLEDs can be attributed to the superiority of the newly designed self-host TADF emitter, which possesses the perfect electroluminescent property and sufficient solvent resistance at the same time.

Mechanical properties of RF magnetron sputtering ZnO thin film by nanoindentation.

The mechanical properties of radio frequency (RF) magnetron sputtering epitaxial ZnO thin film on aluminum and diamond substrates were investigated by nanoindentation. Comparing the different substrates, we are able to assess the mechanical properties of the film on nanoindentation response. Though the elastic modulus and hardness values of the film are consistent on different substrates, the experimental results are distributed with a range of E (approximately 20-55 GPa) and H (approximately 0.4-2.5 GPa) due to amorphous structure even the indentation depth less than half of film thickness(1 microm).

[Studies on the reactions of unsaturated chloride polyether polyol with trimethyl phosphite by FTIR].

The reactions of unsaturated chloride polyether polyol with trimethyl phosphite have been studied by FTIR, which was prepared by copolymerization of ethanediol, allyl glycidyl ether and epoxychloropropane. The experimental results showed that the reaction included ester-exchange reaction, ester-exchange polymerization and Arbuzov rearrangement. The process conditions such as catalyst, mass rate and reaction time were determined through the experiments. The analytic results indicated that the reaction temperature was the key controlling the process and the reaction products are suitable for reactive multifunctional flame retarders.

[Study on effect of thermal process on the morphology and mechanical properties of RIM PUU by in-situ FTIR].

The samples of PUU polymers were characterized by In-situ FTIR in a temperature-controlled cell. The spectra of FTIR showed that the absorbance of ordered hydrogen-bonded urea carbonyl group (1643 cm(-1)) became stronger with increasing thermal process time (at 100 degrees C), then changed little after a certain time. The rate of change for absorbance of ordered hydrogen-bonded urea link, attributed to microphase separation kinetics, was enhanced with increasing thermal process temperature. In carbonyl region of FTIR spectra, absorbance for various carbonyls available in PUU polymers, associated with micro-hard domain, was observed before process. However, after the process, only the absorbance of free urethane, hydrogen-bonded urethane and ordered urea became obvious. At 100 degrees C, the longer the process time (beyond 8 h), the worse the mechanical properties obtained. The PUU polymers processed at 100 degrees C with the identical process time exhibited the best mechanical properties.