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Objectives: This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework., Materials and Methods: Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance., Results: Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance., Discussion: This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study., Conclusion: The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness., Materials and Methods: Our study was conducted in two phases. Initially, we analyzed a range of demonstration components vital for LLMs' biomedical data capabilities, including task descriptions and examples, experimenting with various combinations. Subsequently, we introduced the LLM instruction-example adaptive prompting (LEAP) framework, including instruction adaptive tuning, example adaptive tuning, and instruction-example adaptive tuning methods. This framework aims to systematically investigate both adaptive task descriptions and adaptive examples within the demonstration. We assessed the performance of the LEAP framework on the DDI, ChemProt, and BioRED datasets, employing LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B., Results: Our findings indicated that Instruction + Options + Example and its expanded form substantially improved F1 scores over the standard Instruction + Options mode for zero-shot LLMs. The LEAP framework, particularly through its example adaptive prompting, demonstrated superior performance over conventional instruction tuning across all models. Notably, the MedLLAMA_13B model achieved an exceptional F1 score of 95.13 on the ChemProt dataset using this method. Significant improvements were also observed in the DDI 2013 and BioRED datasets, confirming the method's robustness in sophisticated data extraction scenarios., Conclusion: The LEAP framework offers a compelling strategy for enhancing LLM training strategies, steering away from extensive fine-tuning towards more dynamic and contextually enriched prompting methodologies, showcasing in biomedical relation extraction., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Importance: Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings., Objectives: To systematically explore the applications of RL in NLP, focusing on its effectiveness in acquiring optimal strategies, particularly in healthcare settings, and provide a comprehensive understanding of RL's potential in NLP tasks., Materials and Methods: Adhering to the PRISMA guidelines, an exhaustive literature review was conducted to identify instances where RL has exhibited success in NLP applications, encompassing dialogue systems, machine translation, question-answering, text summarization, and information extraction. Our methodological approach involves closely examining the technical aspects of RL methodologies employed in these applications, analyzing algorithms, states, rewards, actions, datasets, and encoder-decoder architectures., Results: The review of 93 papers yields insights into RL algorithms, prevalent techniques, emergent trends, and the fusion of RL methods in NLP healthcare applications. It clarifies the strategic approaches employed, datasets utilized, and the dynamic terrain of RL-NLP systems, thereby offering a roadmap for research and development in RL and machine learning techniques in healthcare. The review also addresses ethical concerns to ensure equity, transparency, and accountability in the evolution and application of RL-based NLP technologies, particularly within sensitive domains such as healthcare., Discussion: The findings underscore the promising role of RL in advancing NLP applications, particularly in healthcare, where its potential to optimize decision-making and enhance patient outcomes is significant. However, the ethical challenges and technical complexities associated with RL demand careful consideration and ongoing research to ensure responsible and effective implementation., Conclusions: By systematically exploring RL's applications in NLP and providing insights into technical analysis, ethical implications, and potential advancements, this review contributes to a deeper understanding of RL's role for language processing., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance., (© 2024. The Author(s).)

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering., (© 2024. The Author(s).)

Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets. To address this, we introduce MedChatZH, a dialogue model optimized for Chinese medical QA based on transformer decoder with LLaMA architecture. Continued pre-training on a curated corpus of Chinese medical books is followed by fine-tuning with a carefully selected medical instruction dataset, resulting in MedChatZH outperforming several Chinese dialogue baselines on a real-world medical dialogue dataset. Our model, code, and dataset are publicly available on GitHub (https://github.com/tyang816/MedChatZH) to encourage further research in traditional Chinese medicine and LLMs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic has been accompanied by a proliferation of online misinformation and disinformation about the virus. Combating this 'infodemic' has been identified as one of the top priorities of the World Health Organization, because false and misleading information can lead to a range of negative consequences, including the spread of false remedies, conspiracy theories, and xenophobia. This paper aims to combat the COVID-19 infodemic on multiple fronts, including determining the credibility of information, identifying its potential harm to society, and the necessity of intervention by relevant organizations. We present a prompt-based curriculum learning method to achieve this goal. The proposed method could overcome the challenges of data sparsity and class imbalance issues. Using online social media texts as input, the proposed model can verify content from multiple perspectives by answering a series of questions concerning the text's reliability. Experiments revealed the effectiveness of prompt tuning and curriculum learning in assessing the reliability of COVID-19-related text. The proposed method outperforms typical text classification methods, including fastText and BERT. In addition, the proposed method is robust to the hyperparameter settings, making it more applicable with limited infrastructure resources., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier Ltd. All rights reserved.)

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier Ltd. All rights reserved.)

Introduction: Nighttime crashes account for 74% of pedestrian fatalities in the United States, and reduced visibility is a significant cause of nighttime pedestrian crashes. Maintaining sufficient and uniform roadway lighting is an effective countermeasure to improve pedestrian visibility and prevent nighttime pedestrian crashes and injuries. Previous studies have not quantified the safety effects of roadway photometric patterns (i.e., average lighting level and uniformity) on nighttime pedestrian crashes on roadway segments., Method: This study investigated the association between two roadway photometric criteria (horizontal illuminance mean representing average lighting level and horizontal illuminance standard deviation representing lighting uniformity) and nighttime pedestrian crash occurrence in Florida roadway segments. The matched case-control method was used to decouple the confounding effects between the illuminance mean and standard deviation. Statistically-significant crash modification factors (CMFs) were developed to quantify the safety effects of the mean and standard deviation of horizontal illuminance on nighttime pedestrian crashes., Results: The results show that if the average lighting level on a roadway segment is increased from a low illuminance mean (<0.2 foot-candle [fc]) to a medium illuminance mean [0.2 fc, 0.5 fc], a medium-high illuminance mean (0.5 fc, 1.0 fc], and a high illuminance mean (>1.0 fc), the relative likelihood of nighttime pedestrian crashes on midblock segments in Florida tends to be reduced by 77.5% (CMF = 0.225), 81.2% (CMF = 0.188), and 85.5% (CMF = 0.145), respectively., Practical Applications: A poor uniformity (illuminance standard deviation ≥ 0.52 fc) is likely to increase the relative likelihood of nighttime pedestrian crashes on midblock segments in Florida by 80.3% (CMF = 1.803) compared to good uniformity (illuminance standard deviation < 0.52 fc)., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Zhenyu Wang reports financial support was provided by The Center for Transportation, Equity, Decisions and Dollars (CTEDD) at the University of Texas at Arlington.]., (Copyright © 2023 National Safety Council and Elsevier Ltd. All rights reserved.)

The reuse of recycled waste plastics has long been attempted in pavement engineering as bitumen modifier. It was revealed that waste plastics can significantly enhance the high-temperature performance of bitumen and bitumen mixtures. Even so, the application of waste plastics as a bitumen modifier is still not widespread. This is attributable to the generally poor low-temperature performance of plastic-modified bitumen, which often fails to meet specification requirements. For this purpose, styrene butadiene rubber (SBR) was selected to improve the low-temperature performance of plastic-modified bitumen. However, due to the long-term aging process, the composite and structure of the modified bitumen will change, which negatively impacts its performance. The objective of this study is to investigate the long-term aging behavior of plastic/SBR composite-modified bitumen. For this purpose, waste polyethylene was used as a plastic modifier and was mixed with base bitumen and 3% SBR at ratios 4.5%, 6% and 7.5%. The rheological properties and molecular weight distribution of base bitumen, plastic and plastic/SBR-modified bitumen before and after long-term aging were measured. Results show that the incorporation of plastic can improve the complex modulus, rutting factor and percent recovery of bitumen and reduce the non-recoverable creep compliance of the bitumen, indicating the modification process enhances the high-temperature performance of bitumen. The enhancement effect is more pronounced with the increase of plastic content. For modified bitumen with 7.5% plastic modifier, the complex modulus of modified bitumen is increased by 1127.55% compared to base bitumen. The addition of 3% SBR modifier can further improve the high-temperature performance of the modified bitumen. In addition, the modification process also increases the large molecule size percentage (LMSP) and weight average molecular weight of bitumen. Compared with weight average molecular weight, the LMSP correlates well with the rheological properties of modified bitumen. In accordance with the complex modulus, using the LMSP and weight average molecular weight of bitumen before and after aging, the corresponding aging index was calculated. The quantitative results showed that the addition of plastic modifier can improve the aging resistance of bitumen, but the enhancement effect is not as obvious as that of SBR modifier.

Transition metal dichalcogenides (TMDs) have attracted great attention in valleytronics. Owing to the giant valley coherence at room temperature, valley pseudospin of TMDs open a new degree of freedom to encode and process binary information. The valley pseudospin only exists in non-centrosymmetric TMDs (e.g., monolayer or 3R-stacked multilayer), which is prohibited in conventional centrosymmetric 2H-stacked crystals. Here, we propose a general recipe to generate valley-dependent vortex beams by using a mix-dimensional TMD metasurface composed of nanostructured 2H-stacked TMD crystals and monolayer TMDs. Such an ultrathin TMD metasurface involves a momentum-space polarization vortex around bound states in the continuum (BICs), which can simultaneously achieve strong coupling (i.e., form exciton polaritons) and valley-locked vortex emission. Moreover, we report that a full 3R-stacked TMD metasurface can also reveal the strong-coupling regime with an anti-crossing pattern and a Rabi splitting of 95 meV. The Rabi splitting can be precisely controlled by geometrically shaping the TMD metasurface. Our results provide an ultra-compact TMD platform for controlling and structuring valley exciton polariton, in which the valley information is linked with the topological charge of vortex emission, which may advance valleytronic, polaritonic, and optoelectronic applications.

Due to the highly viscous characteristics of high viscosity modified bitumen (HVMB), the commonly used short-term aging schemes are not suitable for it. As such, the objective of this study is to introduce a suitable short-term aging scheme for HVMB by increasing the aging period and temperature. For this purpose, two kinds of commercial HVMB were aged via rolling thin-film oven test (RTFOT) and thin-film oven test (TFOT) at different aging periods and temperatures. At the same time, open-graded friction course (OGFC) mixtures prepared using HVMB were also aged via two aging schemes to simulate the short-term aging of bitumen at the mixing plant. With the aid of temperature sweep, frequency sweep, and multiple stress creep recovery tests, the rheological properties of short-term aged bitumen and the extracted bitumen were tested. By comparing the rheological properties of TFOT- and RTFOT-aged bitumen with those of extracted bitumen, suitable laboratory short-term aging schemes for HVMB were determined. Comparative results showed that aging the OGFC mixture in a 175 °C forced-draft oven for 2 h is suitable to simulate the short-term aging process of bitumen at the mixing plant. Compared with RTOFT, TFOT was more preferable for HVMB. Additionally, the recommended aging period and temperature for TFOT was 5 h and 178 °C, respectively.

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein., (© 2023. The Author(s).)

In Yb-Er co-doped upconversion (UC) nanomaterials, upconversion luminescence (UCL) can be modulated to generate multiband UCL emissions by changing the concentration of activator Er 3+ . Nonetheless, the effect of the Er 3+ concentrations on the kinetics of these emissions is still unknown. We here study the single β -NaYF 4 :Yb 3+ /Er 3+ microcrystal (MC) doped with different Er 3+ concentrations by nanosecond time-resolved spectroscopy. Interestingly, different Er 3+ doping concentrations exhibit different UCL emission bands and UCL response rates. At low Er 3+ doping concentrations (1 mol%), multiband emission in β -NaYF 4 :Yb 3+ /Er 3+ (20/1 mol%) MCs could not be observed and the response rate of UCL was slow (5-10 μs) in β -NaYF 4 :Yb 3+ /Er 3+ . Increasing the Er 3+ doping concentration to 10 mol% can shorten the distance between Yb 3+ ions and Er 3+ ions, which promotes the energy transfer between them. β -NaYF 4 :Yb 3+ /Er 3+ (20/10 mol%) can achieve obvious multiband UCL and a quick response rate (0.3 µs). However, a further increase in the Er doping concentration (80 mol%) makes MCs limited by the CR process and cannot achieve the four-photon UC process ( 4 F 5/2 → 2 K 13/2 and 2 H 9/2 → 2 D 5/2 ). Therefore, the result shows that changing the Er 3+ doping concentration could control the energy flow between the different energy levels in Er 3+ , which could affect the response time and UCL emission of the Yb/Er doped rare earth materials. Our work can facilitate the development of fast-response optoelectronics, optical-sensing, and display industries., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Huang, Zhong, Li, Cui, Yu, Zhao, Xing, Guo and Han.)

The development of transparent electronics has advanced metal-oxide-semiconductor Thin-Film transistor (TFT) technology. In the field of flat-panel displays, as basic units, TFTs play an important role in achieving high speed, brightness, and screen contrast ratio to display information by controlling liquid crystal pixel dots. Oxide TFTs have gradually replaced silicon-based TFTs owing to their field-effect mobility, stability, and responsiveness. In the market, n-type oxide TFTs have been widely used, and their preparation methods have been gradually refined; however, p-Type oxide TFTs with the same properties are difficult to obtain. Fabricating p-Type oxide TFTs with the same performance as n-type oxide TFTs can ensure more energy-efficient complementary electronics and better transparent display applications. This paper summarizes the basic understanding of the structure and performance of the p-Type oxide TFTs, expounding the research progress and challenges of oxide transistors. The microstructures of the three types of p-Type oxides and significant efforts to improve the performance of oxide TFTs are highlighted. Finally, the latest progress and prospects of oxide TFTs based on p-Type oxide semiconductors and other p-Type semiconductor electronic devices are discussed.

Banxia Baizhu Tianma decoction (BBTD), a six-herb Chinese medicine formula first described approximately 1732 AD, is commonly prescribed for Hypertension with Phlegm-dampness Stagnation (HPDS) as an adjuvant therapy in China. Obesity is an important risk factor for the increasing prevalence of hypertension year by year in China. In Traditional Chinese medicine, obesity is often differentiated as the syndrome of excessive phlegm-dampness.Vascular endothelial cell injury plays an important role in the development and occurrence of HPDS. In this study, the protective effects of 18 batches of BBTD samples from different origins on HUVEC cells were evaluated, including antioxidant and anti-inflammatory activities. Ultrahigh performance liquid chromatography (UPLC) was used to establish fingerprints, and combined with pharmacodynamic indexes, the protective components of BBTD on endothelial cells were analyzed. Antioxidant and anti-inflammatory activities were evaluated by ROS and Hs-CRP models, respectively. Hierarchical cluster analysis (HCA) and Bivariate correlation analysis (BCA) were used to investigate the potential correlation between chemical components and endothelial cell protection. The results indicated that BBTD could reduce ROS and hs-CRP levels in HUVEC cells, and the pharmacological activities in 18 batches of BBTD samples were significantly different. The results of BCA indicated that Gastrodin, Liquiritin, Hesperidin, Isoliquiritin, Hesperetin, and Isoliquiritigenin might be the active constituents to activate ROS and suppress hs-CRP as determined by spectrum-effect relationships. The antioxidant and anti-inflammatory activities of the 6 components at different concentration were verified, and the results showed that all of them had good antioxidant and anti-inflammatory activities in a concentration-dependent manner. This study showed that activity determination and spectral correlation can be used to search for active substances in Chinese medicine formula and provide data support for quality control of Traditional Chinese medicine (TCM)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Li, Wang, Wang and Shi.)

Single-domain antibody (sdAb) holds the promising strategies for diverse research and translational applications. Here, we describe a method for the adaptation of the in situ proximity ligation assay ( is PLA) followed by sequencing ( is PLA-seq) to facilitate screening of a high-sensitive, high-throughput sdAb library for a given protein at subcellular and single-cell resolution. Based on the sequence of complementarity-determining region 3 (CDR3), the recombinant sdAb can be produced for in vitro and in vivo utilities. This method provides a general means to identify the functional measure of sdAb and its complementary epitopes and its potential applications to investigate cellular processes., (© 2021 Yin et al.)

Background: This study aimed to explore the effect of nesfatin-1 on cobalt chloride (CoCl 2 )-induced hypoxic injury in cardiomyocyte H9c2 cells., Methods: H9c2 cardiomyocytes were induced by different concentrations of CoCl 2 to mimic the hypoxia condition. Cell viability was detected by MTT assay. Cell apoptosis was detected by TUNEL staining and flow cytometry. ROS production was detected using the fluorescence probe DCFH-DA. The mitochondrial membrane potential (MMP) was detected using the TMRE method. The levels of released lactate dehydrogenase (LDH), malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT) were detected using the commercial kits. The protein levels of MAPK signaling members (p-JNK1/2, p-ERK1/2, and p-p38) and Notch1 signaling members (Notch1, Hes 1, and Jagged 1) were detected by Western blot., Results: CoCl 2 significantly promoted cell apoptosis, increased LDH leakage, MDA concentration, and decreased cell viability, SOD activity, GSH production, and CAT activity. CoCl 2 -induced hypoxic injury in H9c2 cells was partially restored by nesfatin-1 treatment. Moreover, nesfatin-1 treatment attenuated CoCl 2 -induced increase in ROS production and mitochondrial dysfunction, decreased mitochondrial membrane potential, Bax/Bcl-2 imbalance, as well as c-caspase-9 and c-caspase-3 levels. Moreover, nesfatin-1 treatment inhibited the activation of MAPK and Notch1 signaling pathways., Conclusions: Nesfatin-1 could effectively protect H9c2 cells against CoCl 2 -induced hypoxic injury by blocking MAPK and Notch1 signaling pathways, suggesting that nesfatin-1 might be a promising therapeutic agent for hypoxic cardiac injury., (© 2021. The Author(s).)

Roadway lighting is used to ensure nighttime safety and security for multimodal road users. However, the absence of reliable quantitative analyses of the safety effects of roadway lighting photometric characteristics prevents accurate assessment of street lighting maintenance and retrofitting projects. This study aimed to investigate the relationship between nighttime crash risk and two critical photometric criteria, i.e., average lighting level and uniformity. To achieve this goal, high-resolution horizontal illuminance data were collected in Florida for 300 + center-miles from 2011 to 2014. Based on the data, a matched case-control study was conducted to address two major issues existing in previous studies: (1) the confounding effects of illuminance standard deviation on illuminance mean and (2) spatially-unrelated extreme values for ratio-based uniformity. By eliminating the confounding effects through a random matching strategy (one case, a segment with nighttime crashes, to one control, a segment without nighttime crashes at 1,046 strata), this study successfully isolated the negative effects of the standard deviation and developed more significant crash modification factors (CMFs) for average lighting levels: 0.679 for increasing the average lighting level from [0 fc, 0.5 fc] to (0.5 fc, 1.0 fc] and 0.581 for increasing the average lighting from [0 fc, 0.5 fc] to higher than 1.0 fc. Additionally, a CMF of 1.391 for a max-min ratio greater than 10 was identified by controlling the segment length at a short uniform unit (1,200 ft). The developed CMFs overcame the underestimation issue in previous studies and are implementable in current street lighting design and safety management., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Cancer cells at the invasive front directly interact with stromal tissue that provides a microenvironment with mechanical, nutrient, and oxygen supply characteristics distinct from those of intratumoral tissues. It has long been known that cancer cells at the invasive front and cancer cells inside the tumor body exhibit highly differentiated functions and behaviors. However, it is unknown whether cancer cells at different locations exhibit a variety of autophagic flux, an important catabolic process to maintain cellular homeostasis in response to environmental changes. Here, using transmission electron microscopy (TEM), we found that invading cancer cells at the invasive front, which show mesenchymal transcriptomic traits, exhibit higher autophagic flux than cancer cells inside the tumor body in human primary non-small cell lung cancer (NSCLC) tissues. This autophagic feature was further confirmed by a live cell autophagic flux monitoring system combined with a 3D organotypic invasion coculture system. Additionally, the increased autophagic flux endows cancer cells with invasive behavior and positively correlates with the advanced tumor stages and the reduced survival period of lung cancer patients. These findings expand the understanding of autophagic dynamics during cancer invasion.

Use of food additives, such as colorants and preservatives, is highly regulated because of their potential health risks to humans. Therefore, it is important to detect these compounds effectively to ensure conformance with industrial standards and to mitigate risk. In this paper, we describe the preparation and performance of an ultrasensitive electrochemiluminescence (ECL) sensor for detecting a key food additive, sunset yellow. The sensor uses graphene quantum dots (GQDs) as the luminescent agent and potassium persulfate as the co-reactant. Strong and sensitive ECL signals are generated in response to trace amounts of added sunset yellow. A detection limit (signal-to-noise ratio = 3) of 7.6 nM and a wide linear range from 2.5 nM to 25 μM are demonstrated. A further advantage of the method is that the luminescent reagents can be recycled, indicating that the method is sustainable, in addition to being simple and highly sensitive., (Copyright © 2020 Niu, Yang, Wang, Li, Zhang, Pan, Qi, Yang, Wang and Liao.)

In this study, the microbial fuel cell (MFC) was combined with the Fenton-like technology to simultaneously generate electricity and degrade refractory contaminants in both anode and cathode chambers. The maximum power density achieved was 15.9 W/m(3) at an initial pH of 3.0 in the MFC. In the anode chamber, approximately 100% of furfural and 96% COD were removed at the end of a cycle. In the cathode chamber, the Fenton-like reaction with FeVO(4) as a catalyst enhanced the removal of AO7 and COD. The removal rates of AO7 and COD reached 89% and 81%, respectively. The optimal pH value and FeVO(4) dosage toward degrading AO7 were about 3.0 and 0.8 g, respectively. Furthermore, a two-way catalyst mechanism of FeVO(4) and the contaminant degradation pathway in the MFC were explored., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Veratryl alcohol (VA) is a product from the biodegradation of lignocellulosic biomass. The objective of this study was to explore the possibility whether VA could be used as the fuel of the microbial fuel cell (MFC) to generate power. Two types of MFCs, a two-chamber MFC and a single-chamber air-cathode MFC, were set up for experiments. In the two-chamber MFC, average maximum current outputs higher than 700 microA were obtained using various mixtures of glucose and VA as the fuel. The highest power density of 35.17 W m(-3) was achieved using the mixture of 1000 mg L(-1) glucose and 50 mg L(-1) VA as the fuel. With 500 mg L(-1) VA as the fuel in the MFC, we obtained an average maximum current output of 181 microA. In the single-chamber MFC, the maximum current output reached up to 178 microA with 500 mg L(-1) VA in the fed-batch mode and the maximum CE reached 23.77% with 100 mg L(-1) VA. At the end of all operation cycles of the MFCs, the glucose and VA were undetectable in the solution, and the removal efficiencies of COD were between 75% and 88%. The denaturing gradient gel electrophoresis profiles of 16S rRNA gene indicated that the dominant species on the anode biofilm did not change significantly with the different substrates, but the abundance of some species increased greatly. The scanning electron micrographs showed that the most abundant bacteria on the electrode were bacilli. The dominant species belonged to bacteroidetes and proteobacterium.

The effect of nitrobenzene (NB) on electricity generation and simultaneous biodegradation of NB were studied with two types of microbial fuel cells (MFCs): a ferricyanide-cathode MFC with NB as the anodic reactant and a NB-cathode MFC. Compared to controls without NB, the presence of NB in the anode of the first MFC decreased maximum voltage outputs, maximum power densities and Coulombic efficiencies. No electricity was generated from the first MFC using NB as the sole fuel; however, the second MFC using NB as the electron acceptor generated electricity successfully with a maximum voltage of 400mV. NB was degraded completely within 24h in both anode and cathode chambers. Denaturing gradient gel electrophoresis (DGGE) profiles demonstrated that the presence of NB caused changes in relative abundance of the dominant bacterial species and emergence of new bacteria on the anodes., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Indole is a typical refractory and inhibitory compound present in coking wastewater. The aim of this study was to investigate possible electricity generation with indole degradation in the microbial fuel cell (MFC). Experiments were conducted in two types of the MFC: a continuous-fed MFC (C-MFC) and a batch-fed MFC (B-MFC). In the C-MFC, the maximum power densities reached 45.4, 51.2, and 2.1 W/m(3), respectively, from using 1000 mg/L glucose, a mixture of 1000 mg/L glucose and 250 mg/L indole, and 250 mg/L indole as the fuel. When using 250 mg/L indole as the fuel, the removal efficiency of indole was up to 88% within 3 h. Increasing indole concentrations from 250 to 1500 mg/L resulted in decrease of the maximum power densities from 2.1 to 0.8 W/m(3), and average degradation rates from 41.7 to 8.9 mg/(Lh). Compared with the C-MFC, the B-MFC increased the maximum power densities from 2.1 to 3.3 W/m(3) and the coulombic efficiencies from 0.7% to 81.5%. Microbial community analyses showed that the addition of indole obviously changes the microbial community of the anode electrode, including the changes of relative abundance and emergence of new species. The results should be useful for treatment of wastewater containing indole., (2009 Elsevier B.V. All rights reserved.)

The objective of this study was to investigate the feasibility of pyridine biodegradation in the microbial fuel cell (MFC), from which electricity was generated. Experiments were initially conducted in a graphite-packed MFC (G-MFC) using a pyridine concentration of 500 mg/L combined with different glucose concentrations. Pyridine of 500 mg/L only used as the G-MFC fuel resulted in a maximal voltage of 116 mV and a maximal power density of 1.7 W/m(3). The maximal voltage reached within 12 h when pyridine was totally depleted. The glucose supplement with concentrations of 500, 250, and 100 mg/L resulted in the maximum voltages of 623, 538, and 349 mV, respectively, correspondingly the maximal volumetric power densities were 48.5, 36.2, and 15.2 W/m(3). Pyridine biodegradation rates reached 95% within 24h using the G-MFC. Interestingly, after 90 d of acclimation, the biodegradation rates of pyridine in the G-MFC using pyridine only as the fuel were higher than those using the glucose-pyridine mixtures. Further experiments were conducted using a graphite fiber brush MFC (B-MFC). Compared to the G-MFC, the B-MFC enhanced the electrical charges by 89, 186, and 586% for the mixtures with ratios of glucose-to-pyridine of 1:1, 1:2, and 1:5, respectively. GC/MS analyses of the anode solution indicated that the metabolism of pyridine in the MFC was initiated by ring reduction and NH3-N production. The results suggest that pyridine may be used as the MFC fuel in practical applications of wastewater treatment.