Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare. Complications due to clinical deterioration and medical errors are often caused by human error, either due to forgetfulness, inattention, or inexperience and are far greater than technical failures , 2. FurtIn the context of medical AI, the two major disciplines of Medicine and AI need to come together. Recent discoveries in medicine and medical technology as well as new advancement in AI modeling and computational power increased the application of ML-based methodologies in healthcare domains, such as disease diagnosis, prognosis and treatment planning , and oveDespite the large body of evidence illustrating the promising relevance of AI methodologies in medical domains, there are some common challenges which limit the integration of AI-based methodologies in daily routines. For instance, trained classifiers may make biased predictions due to various sources of bias, such as gender bias, present in medical datasets , 23. AnoThere are already a number of impactful articles which closely relate to the current systematic review. Fleuren et al.  conductePerforming a systematic review over patient monitoring articles following PRISMA and PICOS guidelinesCovering the technical foundations according to the medical AI life cycle Providing an extensive factual and narrative analysis of the selected articlesProviding expertise from both data science and medical science points of viewDiscussing limitations and insights for the successful integration of AI-driven methods for decision making in cardiac ICUsRecommending additional standardization and risk of bias criteria applicable to novel medical AI tools with regards to generalization and external validation aspects.In this systematic review, following the PRISMA  and PICOIn the next sections, first, we discuss the basic concepts which are fundamental to be able to follow the reported findings from the selected articles. The Methods section provides the details on the screening and selection criteria of the papers followed by the Results and Discussion sections which provide a comprehensive outline of the findings from the selected contributions. Finally, a short conclusion of the findings is given.According to the best practices , the lifFrom a high-level perspective, machine learning (ML) techniques can be categorized in three main groups: supervised, unsupervised and reinforcement learning. If ground truth labels are available and used to train and fit the model , the model corresponds to supervised ML paradigm. Otherwise, if the model is trained without prior knowledge on the target variable , the model corresponds to unsupervised ML paradigm. Another ML paradigm that has been frequently used for clinical decision support is Reinforcement Learning (RL).Reinforcement learning: In RL, a computational agent is trained to maximize the cumulative reward it receives over a series of time-steps by taking observations of the current state of the environment and by evaluating the feedback it receives after taking an action in that state  and r, and exploiting its knowledge of the environment represented by V_pi or Q_pi and y.at state . More foat state , where tThere is a long history of clinical decisions being formulated as an MDP. Initial efforts in this direction focused on dynamic programming solutions, while in recent work, variations of the Q-Learning algorithm have become more prominent, such as fitted-Q-iteration (FQI)  or deep Logistic regression: As a supervised ML algorithm, logistic regression (LR) (ion (LR)  is a preSupport vector machines: Support vector machines (SVMs) (s (SVMs)  is a supDecision trees and ensemble algorithms: Decision trees employ tree-structured flowcharts of decisions based on the values of the input features to solve classification problems , natural language processing (NLP) comes into action. NLP [Allen2003] denotes the set of AI based approaches which are capable of identifying underlying patterns in the textual data, hence understanding human languages. Taking the examples of EHRs and temporal textual patient information stored in medical databases such as Medical Information Mart for Intensive Care (MIMIC) ( (MIMIC) , 54, cli (MIMIC) .Alongside continuous monitoring of patients by the intensivists and medical staff during patients stays at ICUs, several scoring systems are widely used in critical care units to monitor and manage patients states such as the acute physiologic and chronic health evaluation (APACHE), the sequential organ failure assessment (SOFA), and the mortality prediction model (MPM) . Such scMortality Prediction Models (MPMs) (and APACHEs (s (MPMs)  and APAC APACHEs  are mathSequential Organ Failure Assessment (SOFA) (t (SOFA)  is a scoFrom a general perspective, one can subdivide medical data modalities into the following subgroups: structured data (with and without timestamp) and unstructured data such as medical image modalities and electronic health records (EHR). Like other fields of data science, numerical tabular information such as patient demographic information  can be used to form feature vectors for AI- and ML-based methods. In case of time-dependent measurements such as lab values and vital signs, the dimension of time  should be integrated in the corresponding analysis pipeline, hence the clinical time series data. This section provides a brief overview of different data modalities used in the scope of this systematic review.Numerous kinds of data in diverse modalities are processed by medical experts and intelligent systems for patient monitoring in ICUs. Clinical time series and electrocardiograms (ECGs) are among the most common types of data applied in this domain. Furthermore, open access databases facilitate objective performance analyses of the implemented AI methods.Clinical time series data: Continuous patient monitoring leads to a magnitude of measurements captured and stored at discrete timestamps. Regardless of the disease type, a variety of temporal datasets such as Electronic health records (EHR), lab values, vital signs, diagnoses and treatments records can be used for patient monitoring (nitoring .Electrocardiograms (ECGs): First invented by William Eindhoven in 1902, electrocardiograms (ECGs) (s (ECGs)  are recoEnsuring that methodology can be replicated is a key consideration in data science, which typically necessitates the sharing of data. However, in the medical and clinical field, there are often additional ethical limitations and considerations when it comes to sharing patient data, which is considered highly sensitive and confidential. These ethical concerns must be balanced with the need for reproducibility in research. This highlights the importance of open access datasets for medical and clinical research. This subsection briefly introduces some of the most applied publicly available datasets for intensive patient care.One of the majorly used information platforms in biomedical research and education is PhysioNet which offers free access to large collections of physiological and clinical data and related open-source software, and educational tutorials . Among tMIMIC is a public database of de-identified electronic health records of over 60,000 adult patients admitted to the intensive care units at the Beth Israel Deaconess Medical Center. It contains information on demographics, diagnoses, laboratory tests, medications, and clinical notes collected from various sources such as bedside monitors, clinical documentation, and hospital information systems. The database has been widely used in clinical research and machine learning applications to develop predictive models, identify risk factors, and improve clinical outcomes. Access to the database requires an application process and approval from the Institutional Review Board at BIDMC, but it is publicly available through PhysioNet, a repository of physiological data and clinical information maintained by MIT.Intensive care units (ICU) are a prominent source of time series data, as the nature of intensive care usually requires close and regular monitoring of patients and thereby produce a high density of measurements. Instances of time-dependent measurement data that can be found in publicly available ICU datasets include time-stamped nurse-verified physiological measurements such as hourly documentation of heart rate, arterial blood pressure, or respiratory rate. Other examples include documented progress notes by care providers, continuous intravenous drip medications, and fluid balances .post hoc explanations. This AI transparency is crucial in medical AI, especially in the use case of patient monitoring  and SHapley Additive exPlanations (SHAP) methods are commonly applied also in the medical AI domain. RFE takes an ML classifier and the desired number of features as input and starts from the entire input feature set. Then at each recursion step, the features are ranked based on an importance metric and the least relevant variables are removed. This procedure continues until the desired number of features are chosen . InspireWe followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA)  and the In the screening phase, original studies focusing on clinical decision support for adult subjects (age ≥17 years) visiting cardiovascular ICUs were analyzed. Thus, studies focusing on pediatric cohorts and review articles were removed from the results of search in the screening process. The summary of PICOS scheme containing the inclusion as well as exclusion criteria is outlined in All the papers collected as results of search engine queries were assessed whether they held enough significance and relevance from both data science and medical points of view. First, each of the papers underwent qualitative reviews by two independent reviewers which were selected randomly from a group of reviewers with data science and AI background. In case of agreement about selecting the manuscript between the two assigned reviewers, the manuscript would be short-listed or eliminated from the systematic review accordingly. On the contrary, in case of a mismatch between the assessments carried out by the first two reviewers, a third reviewer with higher qualification would decide whether to select or reject the manuscript. Consecutively, the selected papers underwent another assessment step by a group of medical experts whether they fit within the scope of this study: patient monitoring in cardiovascular ICUs. The technical criteria to assess the manuscripts qualitatively include proper research concept, representative train/test cohorts, and proper cross-validation either within the dataset or against external cohorts.To visualize the risk of bias assessment results, the robvis package  is used.In this section, the results of the systematic review are elaborated. First, a summary of the screening step is given followed by narrative reviews of the selected papers. Afterwards, a comprehensive analysis of the papers is provided which comprise a risk of bias analysis and assessments of studies outcomes, used datasets, and applied algorithms. Furthermore, if existing, relevant discussions on the integration of AI in cardiovascular ICUs are reported.The search engine queries have resulted in 89 papers in total. Out of these papers, 60 were from PubMed database and 25 were from Google Scholar. Another four papers were selected from subjective literature research from most relevant related articles. In the screening phase, 12 papers were excluded due to not available full text and three studies were excluded because of being review articles. In the eligibility assessment step, 11 papers were eliminated as they analyzed non-adult cohorts, 27 studies were excluded as considered not to be of proper significance from data science point of view, and 15 papers eliminated because they did not particularly focus on cardiovascular ICU cohorts see . As a reZhao et al.  integratJentzer et al.  used mulGandin et al.  investigAndersson et al.  took advParsi et al.  took advYu et al.  evaluateTo predict noninvasive ventilation (NIV) failure in cardiac ICU patients, Wang et al.  took advChen et al.  analyzedDutra et al.  applied Bodenes et al.  applied Moazemi et al.  evaluateBaral et al.  applied Qin et al.  applied Nanayakkara et al.  took advZeng et al.  also appIn another study leveraging reinforcement learning methodologies, Peine et al.  introducApplying regularized logistic regression and random forest algorithms to vital signs from MIMIC-II dataset, Yoon et al.  suggest Meyer et al.  applied Kim et al.  introducAushev et al.  applied Akrivos et al.  took advAs illustrated in Conventionally, patients visiting different care units undergo continuous examinations and interventions during their stays at the corresponding units. Thus, the physicians and medical staff are required to proactively monitor all the patients’ critical signs and examination results regardless of their types and frequencies. In particular, for cardiovascular patients who are subject to higher complication rates and longer stays at intensive care units (ICUs) , 14, theAdvancements in AI are taking place continuously. Their presence in medicine is ever growing, and they could soon be present in cardiac ICUs. AI has the ability to assist clinicians in diagnosing arrhythmias, as shown in Parsi et al. where they were able to detect atrial fibrillation with a sensitivity and specificity >96% . Atrial As illustrated in To provide an overview of the results of this systematic review, most of the selected studies focused on critical cardiac and respiratory complications resulting in mortality of patients visiting cardiac ICUs . To thisOur findings further highlight the importance of utilizing open access datasets to provide AI-assisted clinical decision support in cardiovascular ICUs. While there are clear benefits to using open access datasets such as MIMIC in the field of critical care, it is important to consider the potential limitations of such datasets. Open access datasets may not fully capture the nuances of specific healthcare systems or populations in certain regions, which may impact the generalizability of the AI models trained on them. Therefore, researchers and clinicians should carefully evaluate the suitability of open access datasets for their particular use case and consider supplementing them with domestic datasets if necessary. Nonetheless, open access datasets can facilitate collaboration and knowledge sharing, which are essential for advancing the field of AI-assisted clinical decision making. Also, open access datasets are often rigorously curated and annotated by experts, ensuring the data is of high quality and can be used reliably. On the other hand, domestic datasets may not have the same level of diversity and may be limited in size, leading to suboptimal AI models. Nevertheless, regardless of the fact that which kind of data is used to fit AI agents, a proper cross-validation scheme should be applied to account for generalizability.As illustrated in the analysis results, logistic regression (LR), SVM, decision trees, random forests, neural networks, and recurrent deep learning models are all popular machine learning algorithms used for various tasks in the field. Each of these algorithms has its own strengths and weaknesses, and the choice of algorithm depends on the specific task at hand and the available data. Most of the time, LR, SVM, and often tree-based methods are used as baseline methods to complement other more complex methodologies such as deep or recurrent neural networks (RNNs). Furthermore, decision trees and random forests are good choices when dealing with small to medium-sized datasets that have both categorical and numerical features. They work well when the data has a clear and interpretable structure, and when the decision-making process can be represented as a sequence of simple if-then-else rules. Decision trees are also good when there is a need to explain the reasoning behind a model’s decision-making process. Neural networks, including deep learning models, are ideal for large and complex datasets with many features, such as image, speech, and text data. They are especially powerful when the relationships between input and output data are highly nonlinear and difficult to capture with simple models. However, neural networks can be computationally expensive to train and require a lot of data to generalize well. Recurrent deep learning models are a type of neural network that are well-suited for sequential and longitudinal data, such as time series, speech, and text data. They can capture long-term dependencies and patterns in the data and are especially useful when the output depends on past inputs. However, they can be more difficult to train than linear or tree-based models and require more specialized expertise. In summary, it’s important to evaluate the strengths and weaknesses of each machine learning algorithm carefully and select the one that is best suited to the specific needs.The findings from the selected articles have shown the predictive potential of different AI approaches including RNNs and RL. While many of the included studies integrated supervised ML classifiers like SVMs or RNNs for continuous patient monitoring in cardiac ICUs, one general advantage reinforcement learning provides over other paradigms of ML is that this way of defining the problem allows RL to take into account long-term rewards. This characteristic makes it especially appealing for clinical applications since, in numerous healthcare issues, the response to treatment decisions is frequently delayed . AdditioBased on the findings of the included literature, the most critical limitations towards integration of AI-driven methods in routine clinical decision making are generalizability and explainability issues. As illustrated in Furthermore, despite all the promising achievements of AI in the medical domain, the medical experts are still responsible for patients’ lives. Therefore, to reduce the burden of responsibility and to provide further support, it is of critical importance to build trust in decisions made by the AI-assisted agents. As discussed in the related work , interprAnother concern which affects the effective integration of AI methodologies in the healthcare domain is the certification of the established models and products upon proper evaluations and clinical trials. Although an increasing number of approved AI/ML products has been traceable since 2015 in the united states and Europe in domains such as radiology, related works urge for more transparency on the criteria for the approval of AI/ML-based products facilitated through publicly accessible databases from authorities such as the food and drug association (FDA) of united states of America (United States) and Conformité Européene (CE) of Europe . As an iThe current study presents a comprehensive overview of the most widely used AI-related methodologies as reported in recent literature, which were selected in a systematic and objective manner. As a result, the majority of the methodology employed is based on modern machine learning solutions. However, as per some other studies such as Roller et al. , there iAs a limitation of current study, due to diverse datasets and algorithms used in the selected cohort of studies, it was not feasible to conduct comprehensive meta-analysis covering comparison of all the methods across all the databases. Nonetheless, we reported performance results from all the articles in In this study, we included studies from PubMed and Google Scholar databases alongside additional papers chosen from subjective search queries within impactful related works. Also, we focused on the studies written in the English language. Thus, our findings might be biased with regard to the choices of search engines and text language and might not be fully comprehensive. However, we covered the application oriented, model-driven, and data-driven aspects of AI-assisted methodologies utilized for patient monitoring and medical intervention in cardiovascular ICUs, following the PRISMA  and mediTechnical conclusion: Recent advancements in AI-driven methodologies in intensive patient monitoring open up new horizons for the integration of clinical decision support in practice. However, regardless of being totally automated or requiring an expert’s input or annotation, AI assisted methodologies for clinical decision support are meant to operate as a complementary aid to physicians and intensivists’ subjective decisions rather than acting in complete autonomy. To achieve this, certain limitations should be mitigated. Most importantly, to address the generalizability issue which has been highlighted by our findings to be a common source of bias, proper validation against independent unseen sets of data should be taken care of. This becomes more critical as the medical datasets attribute high levels of confidentiality, affecting multicentric and federated learning scenarios.Medical conclusion: AI has the potential to simplify part of the decision making in intensive patient monitoring by reducing the burden of processing huge amounts of information available from different sources of vital signs and critical patient parameters. However, still efforts need to be made to enhance interpretability of state-of-the-art AI methods for clinicians. In addition, proper training and understandable insights should be provided for the medical staff to enhance the level of trust in AI decisions. Moreover, AI algorithms should be tested in prospective clinical trials similar to other new medical devices under observation of legal instances such as FDA in the United States and CE in Europe.Future work: For the future, we plan to conduct studies on the integration of eXplainable AI (XAI) best practices for patient monitoring in cardiac ICUs, focusing on federated learning scenarios in which data from multiple hospitals are processed.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.SM, SV, MT, HA, and FS: conceptualization and study design. SM, SV, JL, PS, SK, RB, and BD: identification of papers through search engine queries. SM, SV, SK, and PS: technical review of searched papers. LC, HA, RAB, and AL: medical review of searched papers. JL, BD, RB, and PS: conducting visual analysis and preparing figures. SM, SV, PS, and LC: risk of bias analysis. SM, SV, PS, SK, BD, and RB: summarizing selected papers’ contents. SM, SV, MT, RAB, HA, and FS: narrative discussion on the findings. All authors contributed to the article and approved the submitted version.The project is funded by the German Federal Ministry of Education and Research (BMBF) under the grant number 16SV8601.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The artificial MMS construction by simplifying the community structure, clarifying the labor division, and streamlining the metabolic flux has shown tremendous efficiency. This review describes the construction principles, influencing factors, and enhancement strategies of artificial MMS for PAH degradation. In addition, we identify the challenges and future opportunities for the development of MMS toward new or upgraded high-performance applications.Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants with major risks to human health. Biological degradation is environmentally friendly and the most appealing remediation method for a wide range of persistent pollutants. Meanwhile, due to the large microbial strain collection and multiple metabolic pathways, PAH degradation  Graphical Abstract With the rapid development of the global economy, a large number of non-renewable fossil-based fuels  are always needed for mining and refining, in order to provide various types of functional compounds to improve industrial processes and living quality  have achieved very gratifying results  and metabolic pathways  were used under anaerobic and aerobic conditions to degrade and transform PAHs into harmless substances (such as carbon dioxide and water)  and the nidA+nidB gene (from Mycobacterium) into E. coli HY1. Compared to the wild type , PHE oxidation and ring cleavage were performed by E. coli HY1 and Cupriavidus sp. PH2, respectively, thus the degradation rates of PHE were significantly increased  was introduced into artificial MMS, and the biodegradation rates of PHE were significantly increased from 61.15% to 73.86%  and hydrocarbon degradation pathways could be introduced into core strain by molecular engineering strategies. Some studies have indicated that the biodegradation of PHE and PYR in soil could be enhanced by over-expression of the nahH gene  in Stenotrophomonas maltophilia  , and multi-functional MMS is crucial for PAHs bioremediation. For a suitable microbial strategy, it is necessary to assess the types and concentrations of hydrocarbons, mineral element contents , and environmental factors  at contaminated sites Varjani, . In the For traditional MMS, the relative abundance of strain and substrate-dependent competition of dominant bacteria was higher than that of non-dominant bacteria, meanwhile, the growth of non-dominant bacteria was maintained by intermediate metabolites. In contrast, a rational simplified and optimized artificial MMS displayed enhanced PAH degradation and bioremediation potential, but the degradation efficiency of different functional microorganisms was inhibited by high substrate competition and poor compatibility . As it is well known, a single strain or artificial MMS directly introduced into a new environment would result in an obvious decrease of exogenous microorganism richness or even quickly disappeared in the pollution sites.The addition of supplemental nutrients to contaminated sites can enhance the activity of microbial strains, and it will provide a buffer for exogenous MMS to adapt to the local environment. Moreover, the combined biostimulation with the bioaugmentation process is more effective than bioaugmentation alone in increasing microbial community diversity, maintaining structural balance, and improving remediation efficiency.Recently, the artificial MMS has exhibited high efficiency toward hydrocarbon conversion, but there is still space for further improvement in the remediation potential for PAH pollutants. The existence of high microbial diversity and complex metabolic networks in traditional MMS results in high substrate competition and metabolic interference among microbes.S. pyogenes Cas9-derived base editor, Prime Editor), which provide robust support for precisely modifying and reconstructing core genes and metabolic pathways in the artificial MMS , nitrogen (N), and phosphorus (P) (C: N: P = 100: 10: 1) has displayed a dominant role in the biodegradation of PAHs during microbial metabolism, while an increase in carbon concentrations led to an imbalance in the ratio of nutrients at contaminated sites (water or soil), thus the aromatic hydrocarbon conversion potential of artificial MMS should be enhanced by nutrients' exogenous addition.The biodegradation ability of indigenous microorganisms was improved through the biostimulation of marine sediments by supplementation of digestate  are not bioavailable to microorganisms, and their absorptivity decreases with increasing molecular weight, thus significantly declining the bioavailability of PAHs. Surfactants with hydrophilic heads and hydrophobic tails were applied to the solubilization and exploitation of petroleum. Basically, surfactants are mainly divided into chemical and biological surfactants .K. rosea + A. sydowii) was improved by tween-80 addition, where tween-80 was preferable to be utilized by microorganism with increasing concentrations, while the carbohydrate bioconversion level was decreasing  have been widely utilized in the degradation of PAHs. It was reported that the biodegradation efficiency of MMC , the zeta-potential and hydrophobicity of cell surface decrease, which lead to retardation of absorption and degradation ability of Pseudomonas for PHE  has been widely used to enhance the bioavailability of PAHs; the cell surface zeta-potential, hydrophobicity of As the second largest biomass in nature, lignin is a type of aromatic polymer mainly present in the cell walls of plants. The presence of hydrophilic and lipophilic groups in lignin makes it a potential precursor for lignin-based surfactants  and nutrients . Recently, microbial immobilization technology (MIT) , which pThe carriers are divided into inorganic carriers  and organic carriers , and the metal nanoparticles (size <100 nm) are the late model multifunction carriers. Due to the unique physical and chemical properties imparted by their nanoscale size, metal nanoparticles have received substantial attention in the field of pollution remediation, particularly for the removal of PAHs   exhibit low bioavailability and poor bioaccessibility, and they are difficult to be used as a carbon source and degraded by microorganisms. In previous studies, the PAHs' biodegradation efficiency was increased by co-metabolized substrate (glucose) in petroleum hydrocarbon-polluted groundwater or soil, while the metabolic activity of microorganisms may be inhibited for some PAHs (BaA) by the aforementioned substrate. Thus, it is a key point to explore and study more suitable co-metabolic substrates in the field of bioremediation.B. subtilis ZL09-26 was enhanced by the addition of citric acid, glutaric acid, and oxalic acid , low-molecular weight PAHs , and lignin are regarded as potential co-metabolic substrates for PAH biodegradation  Zhang e. In natuThe key factors affecting co-metabolism are the functional enzymes expressed by microorganisms and their substrate specificity. The degradation of multiple substrates is dependent on the enzymes produced by microorganisms, which are influenced by the structure of the substrates being degraded. For example, the biodegradation rates of PYR and PHE were improved by the addition of NAP, while that of NAP was decreased, which was caused by the competitive inhibition of PYR and PHE on NAP , and gene engineering tools  would provide a large number of supports for modifying artificial MMS (Lapinaite et al., To ensure the effectiveness of artificial MMS for remediating PAH contaminated sites, it is necessary to conduct small-scale experiments in the laboratory, to evaluate their degradation capabilities. However, these experiments are often time-consuming and may not fully simulate real wastewater or soil environments. In recent years, deep learning has made significant advances in the field of structural biology (Jumper et al., J-QC and Z-QH performed and prepared the manuscript. SN contributed to data collection. B-ZL and Z-HL guided the writing and editing of this manuscript. All authors contributed to the article and approved the submitted version.


The start of 2023 witnessed a disruptive development in Conversational AI: ChatGPT. Large language model technology suddenly became available to millions of users. The underlying GPT-3.5 language model, with 175 billion parameters, trained on 300 billion words and finetuned with human feedback, displayed baffling fluidity, style transfer, and emergent behavior like chain-of-thought reasoning. Moreover, its context window of thousands of tokens enabled a form of conversational training: on-the-fly supervised  training through prompting. From a conversational perspective, ChatGPT has session-spanning conversational memory, enabling it to pick up on previous interactions in a dialogue. In March 2023, GPT-3.5 was succeeded by GPT-4, with a larger context window, reportedly better accuracy in handling factual questions, and connecting image analysis to language model-based communicative interaction.Given these significant developments, one could be tempted to think that Conversational AI has come of age. Yet, a full slate of unresolved problems and research questions remains. Crucial debates surround the societal impact of large language models and the future of NLP, the environmental impact of training regimes as well as mass adoption, the impact and prevention of bias, and possible copyright infringement of training data. Central Research Topics in the field of Conversational AI are to a large extent orthogonal to the underlying technology, including large language models. This Research Topic of Frontiers addresses a number of such topics: the human perception of conversational agents and the effects of social cues exhibited by conversational agents on humans, the role of information presentation in hybrid conversational systems, the usage of carefully annotated data in addition to raw textual observational data, and the emergence of communicative patterns between humans and machines.Blomsma et al. addresses the perception by human interlocutors of personality traits displayed by embodied conversational agents. These authors demonstrate through a comparison of human-human and human-AI interaction that dynamic social feedback cues, in particular head nodding, correlate with human-perceived personality traits. With Conversational AI becoming increasing multimodal and embedded, these findings will be of practical interest to industry, and may contribute to more natural interaction modes between humans and AI.The paper by Wieland et al. ties in with these results—these authors investigate the applicability of chatbots for the generation of ideas through brainstorming, taking into account static social cues  presented by the chatbot. They find that brainstorming with a chatbot enables participants to generate more and more diverse ideas than through brainstorming with a human, and that the presence of static social cues further reinforces this process. Adding dynamic social cues like the ones identified by Blomsma et al. could actually be an interesting next step.The paper of Kamoen and Liebrecht also relates to the topic of conversational agent representation and its effect on user interaction. Addressing the topic of Voting Advice Applications (VAAs)- systems that inform humans about political parties and programs during election times-, these authors first assess that chatbot-assisted VAAs lead to higher user satisfaction, and that a hybrid setup  leads to higher user satisfaction, as opposed to pure textual interaction modes. This finding has potential ramifications for the design of task-oriented conversational agents and raises interesting questions about supplementary information presentation modes.The paper of Bunt and Petukhova underlines the importance of carefully annotated conversational data in addition to raw textual data for finetuning and training conversational AI models.The paper by The authors show that the pragmatic and semantic precision performance of conversational agents benefits from (ISO standardized) formal representations of semantic content, domain-specific communicative functions, and explicit representations of affect. The paper further demonstrates how such annotated data can be used to automatically generate dialogue data in simulation scenarios. A possible implementation of this idea in the current context of Large Language Models would be to impose semantic and pragmatic constraints in annotated data onto a language model through relevance feedback learning, in the same spirit as the human feedback-based reinforcement learning that was applied to finetune ChatGPT.Kouwenhoven et al. addresses the topic of emergence in conversational AI: the spontaneous appearance of qualitative traits from the interplay of certain factors  in communicative settings. Specifically, this position paper focuses on the emergence of grounded shared vocabularies between humans and machines, drawing on insights from language evolution, and pleading for human-assisted reinforcement learning for implementing the preconditions for such emergence.Finally, the paper by In summary, this collection of papers addresses important Research Topics on the intersection of humans and conversational agents that eventually will help shape, evaluate and deploy next generations of conversational AI systems.SR wrote the initial text. EK, AC, and MW delivered comments, corrections, and reformulations. All authors contributed to the article and approved the submitted version.


Emotion has an important impact on human behavior and mental health in people's daily interpersonal communication and behavior decision-making. Recently, with the rapid development of the brain-computer interface (BCI), the emotional and intelligence demand of human-computer interaction is increasing, and the ability of computers to recognize human emotions has become a research hotspot. Affective intelligence realizes the emotional interaction between humans and computers, making computers more intelligently handle human instructions, and has a wide range of applications in the fields of education, medicine, and criminal investigation. Affective BCI is a branch of emotional intelligence and an important way to achieve the goal of affective intelligence research  signals can fully and accurately reflect human emotions. An affective BCI extracts brain features related to emotional states based on neurophysiological signals. By recognizing, processing, and responding to users' affection (such as emotions or moods), it can identify users' internal states and adjust them in real time to improve brain-computer interaction  speller based on a time-space variable frequency SSVEP stimulation mode using dry electrodes. By combining electrooculography (EOG) and SSVEP signals and using time and space coding, the spelling paradigm not only improves the flexibility of BCI spellers, but also greatly increases the number of output instructions of BCI spelling systems in dry-electrode environments, paving the way for portable and comfortable dry-electrode BCI systems in the future. The brain-controlled switch in the study incorporated EOG signals for arousal stimuli because electrical eye signals are easy to detect. However, the system needs to collect blink data of the previous round of subjects in advance, because different subjects have different blink ranges. Therefore, subsequent work may need to combine more robust algorithms to classify the blinking of different subjects to improve the performance of the system.EEG is the most widely used physiological signal for emotional BCIs. Currently, two types of EEG acquisition equipment are commonly used: wet and dry electrodes. Wet electrodes have been widely used in BCI systems because of their improved signal quality; however, wet electrode operation is complicated, time-consuming, labor-intensive, and provides a poor experience for patients. Dry electrodes are easy to operate and wear, provides a comfortable experience for patients, and is suitable for ordinary individuals  for the small-sample characteristics of MRI data and the properties of thick-layer scanning. HC-Net fully uses the spatial information between adjacent sections of the MRI image sequence and combines the continuous sections of three axes for three-dimensional (3D) convolution, which reduces the number of calculations and improves accuracy. The key to the effectiveness of this method is that the use of 3D convolution to fully extract spatial information between data and the reduction of computational load and parameters through 2D convolution overcomes the problems of 3D convolution overfitting and 2D volume underfitting on small samples. Compared with other serial convolutional neural network models, the training complexity is reduced.A limitation of EEG signals is their low spatial resolution. In contrast, oxygen-based brain imaging technology, such as functional magnetic resonance imaging (fMRI), has better spatial resolution and has been widely used in neuroimaging research, which is an important tool for researchers to explore the mechanisms of brain activity  and recorded the event-related potentials (ERPs) elicited by these stimuli, focusing on the measurement and statistical analysis of anterior N400 and centroparietal late positive potentials. The results showed that ERPs were smaller and more anteriorly distributed during imagery than perception, but showed some similarity in terms of lateralization, distribution, and category response, thus indicating some overlap in neural processing. As for the affective states, regardless of condition, the N400 was of greater amplitude in response to positive states (such as cheerfulness) than negative ones (such as fear or sadness).The absence of joyfulness is a central symptom of major depressive disorder and may be more than a single component defect of decreased pleasurable response to reward or positive stimuli or a diminished pursuit of reward (Renner et al., Guo et al. developed a multi-scale brain network modeling analysis method based on topology data analysis and persistent homology theory, which can coherently quantify various persistent topological features at different scales. In this method, the related algorithm and parameters in the data processing of the model were analyzed and some key problems were investigated, including the construction of nodes and the edge weight matrix and the selection of the filtering threshold in this network. Compared to existing methods, this method extracts the topological features of the brain network more accurately and improves the accuracy of diagnosis and classification. In addition, the experimental results of applying the model to task-based patients with schizophrenia suggest that it can be used as a stable biological reference standard for stability and noise immunity.The brain comprises complex structures. By representing brain function as measured by EEG, magnetoencephalography, and fMRI as an abstract network, it can be applied to methods of studying complex systems. Brain network analysis methods have been extensively studied in explaining psychiatric disorders whose neural mechanisms are not well understood (Gonuguntla and Kim, Affective brain-computer interface in emotion artificial intelligence and medical engineering” provide favorable technical support for the further development of emotional BCIs.From the perspective of artificial intelligence and neurocognitive science, emotional intelligence has been widely studied by academics, but research on emotional BCIs started late. Studies presented in the topic about “ZL prepared an original draft of the manuscript. ZL, TW, and RZ critically reviewed and edited the manuscript. All authors have reviewed and approved the final manuscript.


Age-related hearing loss, or presbycusis, is a common cause of hearing loss in elderly people worldwide. It typically presents as progressive, irreversible, and usually affects the high frequencies of hearing, with a tremendous impact on the quality of life. Presbycusis is a complex multidimensional disorder, in addition to aging, multiple factors including exposure to noise, or ototoxic agents, genetic susceptibility, metabolic diseases and lifestyle can influence the onset and severity of presbycusis. With the aging of the body, its ability to clean up deleterious substances produced in the metabolic process is weakened, and the self-protection and repair function of the body is reduced, which in turn leads to irreversible damage to the cochlear tissue, resulting in the occurrence of presbycusis. Presently, oxidative stress (OS), mitochondrial DNA damage, low-grade inflammation, decreased immune function and stem cell depletion have been demonstrated to play a critical role in developing presbycusis. The purpose of this review is to illuminate the various mechanisms underlying this age-related hearing loss, with the goal of advancing our understanding, prevention, and treatment of presbycusis. Age-related hearing loss (ARHL), or presbycusis, is the most common sensory deficit affecting aging adults . ARHL tyAccording to a 2012 estimate by the World Health Organization, about 164.5 million people over the age of 65 suffer from hearing impairment worldwide, accounting for about 33% worldwide in adults older than 65 years . The prePresbycusis is a complex chronic aging disease that results from the gradual accumulation of deleterious biological lesions and auditory system dysfunction . With thThe cochlea is a spiral-shaped cavity composed of three liquid-filled compartments, scala media, scala tympani and scala vestibuli, which can convert the mechanical vibration caused by sound waves into electrical signals during sound transmission and is an important target organ for presbycusis. Scala media contains endolymph with high potassium and low sodium and is sandwiched by scala tympani and scala vestibuli containing perilymph with low potassium and high sodium. The endolymph in the scala media contains a positive voltage of +80 to +100 mV , which is generated by the potential difference between the endolymph in scala media and the perilymph in scala tympani . The strAuditory information transmission is a complex process, and the cochlea is an important organ in this process. The shear motion caused by acoustic vibration causes the cilia of hair cells to bend or deflect, which in turn opens potassium channels at the top of hair cells, allowing potassium ions in the endolymph to flow into the hair cells to produce depolarization. Depolarization of hair cells causes calcium channels to open in cells and calcium ions to influx, which in turn prompts hair cells to release neurotransmitters into the synaptic cleft of the hair cells and auditory neurons . Damage According to postmortem histopathological studies, a variety of pathological changes occur in the inner ear of patients with presbycusis, such as the atrophy of the stria vascularis (SV) and loss of fibrocytes of the spiral ligament (SL) in the lateral wall of the cochlea , decrease of sensory hair cells (sensory presbycusis) and degeneration of the auditory nerve  . The cocHowever, in reality, affected by various pathogenic factors, presbycusis can cause lesions in multiple parts of the cochlea, showing a “mixed” pathology . In addiPresbycusis is a disease with genetic susceptibility. According to twins studies and longitudinal studies of family cohorts, its heritability is small to moderate, with a heritability indices of between 0.35 and 0.55 . Unlike GRM7 may lead to the accumulation of neurotransmitters in synaptic connections, thereby altering the susceptibility to presbycusis (UCP2), Superoxide dismutase 2 (SOD2) and N-acetyltransferase 2 (NAT2) (Genome-wide association study (GWAS) is widely used for genetic composition analysis of presbycusis, and many candidate genes have been found to be associated with presbycusis . These gsbycusis . Genetic2 (NAT2) . Additio2 (NAT2) .TMC1 variant was previously thought to be associated with progressive postlingual hearing loss and profound prelingual deafness. However, in a recent study, in vitro transfection and in vivo animal models that the heterozygous pathogenic variants of TMC1 can cause presbycusis in a single-gene form, which updates our understanding of the inheritance pattern of presbycusis and provides a basis for potential inner ear treatments.Monogenic deafness-causing genes, especially those that cause delayed-onset deafness, may also be associated with presbycusis. Noise is the second most common cause of hearing loss other than old age, and noise-induced hearing loss (NIHL) is considered a common occupational disease, with a high incidence in occupational workers who have been exposed to noise for a long time, such as workers in textile, mining, and heavy engineering industries . Both agNoise damage to the auditory system is affected by intensity and duration of noise exposure and can cause permanent threshold shifts (PTS) or temporary threshold shift (TTS) . Both loAminoglycoside antibiotics and chemotherapy drugs such as cisplatin and carboplatin can cause degenerative changes in the cochlea and hearing loss . AdditioMetabolic diseases are a cluster of diseases or disorders that disrupt normal metabolism, including high blood sugar (hyperglycemia), increased blood pressure (hypertension), excess fat around the waist (obesity), and abnormal levels of cholesterol or triglycerides (dyslipidemia). In recent years, under the influence of unhealthy diet and lifestyle, the incidence of metabolic diseases and its components are on the rise, and it is most common in the elderly . PresbycLifestyle effects on hearing are diverse, with studies showing that both smoking and passive smoking increase the risk of hearing loss, while moderate alcohol consumption has a protective effect on hearing . Diet anReactive oxygen species (ROS) are highly reactive chemicals produced during mitochondrial respiration or cellular response to endogenous and exogenous factors, mainly including superoxide anion (O2-), hydrogen peroxide (H2O2), hydroxyl radical (OH-) and nitric oxide (NO-) . ROS canThe cochlea is an energy-intensive organ in which mitochondria provide energy for their sodium-potassium pump activity and ion transport through oxidative phosphorylation, while producing large quantities of ROS. The aggregation of ROS in the cochlea can lead to mutations in the mitochondrial genome, resulting in mitochondrial DNA (mtDNA) damage . MitochoHowever, as the body ages, the production and function of antioxidant enzymes decrease, and ischemic and hypoxic damage from local vascular lesions in the cochlea leads to increased production of ROS . As a reInflammaging is chronic and low-grade inflammation of tissues and organs that occurs during aging and can lead to conditions as diverse as cardiovascular disease, diabetes, and neurodegenerative diseases . VariousMacrophages are a key part of the innate immune system, presented in the cochlear spiral ligament, the auditory nerve, and the organ of Corti. Activated macrophages transform from highly branched morphology to amoeboid shape and can phagocytose cellular debris or pathogens, which are critical for maintaining the homeostasis of the cochlear microenvironment . In addiThe intestinal tract contains a large number of microorganisms and their genetic material, and gut dysbiosis may also contribute to inflammaging. In addition to the high-fat diet can lead to the development of cochlear inflammation . As the The spiral ligament is a component of the potassium ion transport system in the lateral wall of the cochlea, which contains five types of fibrocytes, and its normal function is crucial for the maintenance of the EP . Unlike The management of presbycusis should first focus on prevention and avoid exposure to risk factors. While we cannot prevent aging, nor can we change our genetic background, we can minimize noise exposure, wear earplugs in noisy environments, and avoid ototoxic medications . ElderlyTreatment of presbycusis still relies clinically on hearing amplification and cochlear implantation. Air conduction hearing aids are commonly worn in patients with mild to moderate hearing loss, active middle ear implants can be used in patients with moderate to severe hearing loss, and cochlear implants should be considered in patients with severe to profound hearing loss . HoweverIn recent years, some researchers have begun to explore the treatment of presbycusis with antioxidants, anti-inflammatories, neurotrophins and other drugs . StudiesPresbycusis is a common chronic disease occurring in the process of aging, which is the result of the interaction of various extrinsic and intrinsic factors under the genetic background. With the aging of the body, the function or number of immune functions, antioxidant enzymes and self-repairing stem cells that are closely related to the body’s self-protection decline, resulting in the accumulation of inflammatory cytokines, reactive oxygen species and tissue cell damage in the body. Therefore, under the action of chronic inflammation and oxidative stress, irreversible damage occurs to the cochlear stria vascularis, spiral ligament, sensory hair cells and auditory nerve fibers, and then lead to the occurrence of presbycusis.Mitigating exposure to risk factors is essential for the prevention of presbycusis. The clinical treatment of presbycusis is still mainly based on wearing hearing aids and cochlear implants, but various drugs launched according to its pathogenesis are also undergoing clinical research. The in-depth exploration of the pathogenesis of presbycusis provides a reference for potential drug treatment and clinical intervention.WY: Writing – original draft, Methodology. XZ: Writing – original draft, Methodology. RC: Visualization, Writing – review & editing. JF: Validation, Writing – review & editing.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Lantibiotics are believed to have a conceivable potential to be used as therapeutics, especially against clinically resistant bacterial strains. However, their low solubility and poor stability under physiological conditions limit their availability for clinical studies and further pharmaceutical commercialization. Nisin is a readily available and cheap lanthipeptide and thus serves as a good model in the search for the tools to engineer lantibiotics with improved pharmacological properties. This review aims to address technologies that can be applied to alter and enhance the antimicrobial activity, antibacterial spectrum and physicochemical properties  of nisin. There are basically two general means to obtain nisin analogs—protein engineering and chemical functionalization of this antibiotic. Although bioengineering techniques have been well developed and enable the creation of nisin mutants of variable structures and properties, they are lacking spectacular effects so far. Chemical modifications of nisin based on utilization of the reactivity of its free amino and carboxylic moieties, as well as reactivity of the double bonds of its dehydroamino acids, are in their infancy. Bacterial infections are among the most important diseases and they still account for a substantial proportion of deaths worldwide. For decades, multiple varieties of antibiotics have not only been used for therapeutic purposes but applied prophylactically in human medicine and across agriculture and animal husbandry ,2,3. ThiAmong strategies to tackle antibiotic resistance that have been under investigation, are the use of prophylactic vaccines, therapeutic monoclonal antibodies, phage therapy and phage enzymes, the use of plasmid curing agents, quorum sensing inhibitors, nanomedicines, drug repurposing and, finally, antimicrobial peptides ,9,10.In the latter respect promising candidates appeared to be peptides produced by bacteria and targeting peptidoglycans in pathogenic bacterial cell walls. Due to this mechanism of action, they usually show little or no cross-resistance and a lack of harm to humans ,12. AmonDespite these promising attributes, there are also a number of limitations that have prevented their more widespread use so far. These include poor pharmacokinetics, instability and/or insolubility at physiological pH, as well as some susceptibility to proteolytic digestion ,16. To oThis review outlines some of the more recent developments on the implementation of in vivo and in vitro engineering technologies to alter, and even enhance, the antimicrobial activity, antibacterial spectrum, solubility, stability and protease resistance of nisin. These studies are designed to supplement and update several reviews published recently ,18,19.Lactococcus lactis strains. Low toxicity and safety of use in food contributed to the worldwide success of nisin as a natural food preservative (E234), especially in dairy and canned products . De. De26]. It is an antibiotic of dual action, which is a consequence of the presence of two distinct structural domains located at the N- and C-termini of the peptide  27,28,2,228,29. At the same time, the C-terminus of nisin flips into the membrane and enables the formation of pores, thus causing cell leakage, namely immediate efflux of nutrients and small compounds, resulting in cell death . The fleThe activity of nisin against Gram-negative bacteria is much lower than that against Gram-positive bacteria, mainly because lipid II is located at the inner membrane, and the rather impermeable outer membrane in Gram-negative bacteria prevents nisin from reaching lipid II .The structural features and mode of action make nisin suitable for its structure engineering. These modifications are performed in order to generate nisin variants with enhanced antimicrobial activity, better solubility, and ability to evade resistance. Enhancement of activity against Gram-negative bacteria is one of the major driving forces of these studies. Thus, the obtained results should allow progress to be made in overcoming the inherent shortcomings of nisin. The two major means that have been used for this purpose are bioengineering and chemical modifications of the structure of this antibiotic.Due to the complex nature of nisin’s structure, chemical synthesis of its analogues is challenging, especially in economic terms. This leaves a huge space for the efforts of bioengineering, especially if considering possible industrial preparation of the analogs by fermentation. Recent years have seen a growing number of studies on the use of genetic tools (engineering within the cell) and the use of synthetic biology-based (in vitro engineering) approaches ,18,19,31Bioengineering could be performed either by applying genome mining, design and preparation of hybrid-peptides  and especially by utilization of mutation techniques. As a result, the number of identified nisin analogs have increased tremendously in the last decades .Streptococcus agalactiae and Straptococcus uberis. Because of their gene-encoded nature they could easily undergo bioengineering procedures, which resulted in obtaining libraries of analogs suitable for elucidating structure–function relationships [Escherichia coli with the nisin biosynthesis pathways or the expression of nisin variant genes in a wild type nisin producing strains, from which the nisin gene has been deleted.There are at least five naturally occurring variants of nisin A, which differ by up to 10 amino acids . The mosionships . Nisin vStreptococcus agalactiae [The simplest examples are mutations of the hinge region, in which flexibility is crucial for C-terminal membrane translocation. Additionally, the three residues, which form this region, affect antibiotic stability and solubility. The first approach was concentrated on studying the replacement of one of its three amino acid  by randomly introduced proteinogenic ones using site-saturation mutagenesis. These studies initially brought quite promising results, providing variants which displayed enhanced activity against Gram-negative bacteria . The genalactiae .Mutations, consisting of changes in the length of hinge region, did not dramatically affect nisin production in host bacteria . DeletioThe high activity of nisin also results from formation of stable pores in bacterial membranes. Pores are formed by the C-terminal fragment of this antibiotic  binds to the bacterial cell wall precursor—lipid II. The two lanthionine rings A and B  form a pEarly engineering attempts were targeted at the N-terminal ring A (amino acids 3–7). Amino acids in positions 4, 5 and 6, namely in a region that is at the border of the pyrophosphate cage , suggestListeria monocytogenes [Advanced, highly sensitive solid-state NMR study on binding of nisin to lipid II present in a model membrane consisting of mixture of artificial phospholipids brought identification of Ile4, Lys12, Ser 29 and the whole hinge region as functional hotspots that are critical for the cellular adaptability of nisin . Indeed,ytogenes , a Gram-Although nisin remains the only lantibiotic that is so extensively modified by application of mutagenesis, the full use of any variant as a therapeutic entity has not yet been fulfilled. The progress of mutagenetic studies, although quite slow and cumbersome, is clear. However, the successful clinical development of nisin analogs requires more detailed understanding of the mechanism of nisin pharmacology and creative improvement of the discovered modification procedures. Many knowledge gaps are still waiting for closure.Logic extension of the studies described above was introduction of nonproteinogenic  amino acids into discrete positions of nisin. Generally, there are two complementary approaches for the incorporation of non-canonical amino acids into the peptide chain of interest. The first is residue-specific and exploits the translational machinery of the host, which accepts the structural analog of the amino acid as a surrogate of a classic one. The second is site-specific, which is far more complex and requires the additional presence an orthogonal aminoacyl-tRNA-synthetase-tRNA pair, which do not cross-react with the endogenous expression machinery. This orthogonal pair uses a stop codon as a coding one to incorporate a chosen non-canonical amino acid into the protein of interest. Thus, the first step to biosynthetic incorporation of nonproteinogenic amino acids into lantipeptides was the elaboration of novel molecular biology tools ,51,52,53Analogs of proline and methionine have been incorporated into nisin at positions of their parent amino acids without gene manipulation A, by feeN-chloroacetylamino)phenylalanine, which replaced cysteine in position 3 of the nisin precursor and thus disturbed the formation of the ring A. Spontaneous reactions of the chloroacetyl fragment with nucleophilic amino acids of this mutant provided several analogs with novel macrocyclic ring topologies. Unfortunately, none of the variants exhibited promising activity.A site-specific procedure has been used for modification of the structure of nisin with analogs of phenylalanine ,53 and dCombining of nisin, or its fragments, with other antimicrobial peptides is performed on the premise of receiving potent and novel antimicrobial drug candidates of multiple modes of action. So far, only single examples of the use of bioengineering approach for that purpose have been described. N-terminal fragments, with short antimicrobial peptides that combine different functionalities. These include peptides whose designs are based on statistical analyses and natural antimicrobials produced by various organisms . The obtained hybrids are able to pass the outer membrane of Gram-negative organisms while retaining as much nisin antimicrobial function as possible at the cytoplasmic membrane, which is documented by their up to 12-fold activity if compared with nisin [The outer membrane of Gram-negative bacteria constitutes an efficient protective barrier that prevents nisin from reaching the cellular membrane and then exerting its antimicrobial action. This inconvenience was addressed by fusion of nisin, or its th nisin ,60,61.Enterococcus faecium, a bacterial strain used as probiotics, but also known to produce various complicated infections . In a similar study the same fragment of nisin was conjugated with variants of small insect defense peptides yielding a series of hybrids [Podisus maculiventris) and rip-thanatin (produced by Riptortus pedestris) were obtained by replacing their disulfide bridges with just a single sulfur. Some of these novel antibiotics  and the distinct small C-terminal lipid II binding motifs derived from other lantibiotics—haloduracin or lacticin . One of  hybrids . The varChemical modifications have attracted far less interest and are quite scarce. Due to the complex nature of lanthipeptide structures, chemical synthetic strategies are challenging. This is well documented by a total synthesis of nisin and mutacin rings A and B performed by solid state peptide synthesis. The study was carried out in order to understand the important structural factors underlying highly selective molecular recognition between these fragments of the antibiotics and lipid II by using NMR . The conMost of the described procedures of nisin modification consider the use of nisin variants carrying acetylenic or azide moieties and are thus suitable for application as substrates in a click reaction . The firN-terminal fragment and of its C-terminal fragment mimics, in which ring structure is built up by the replacement of lanthionines by double bonds , containing additional lysines at the C-termini, provided another interesting class of nisin mimics . LysinesMicrococcus luteus. One of these peptides, a hybrid of nisin ABC fragment and a 22-amino-acid antibiotic derived from magainin—pexiganan—is shown in In addition, nisin AB fragment was conjugated with variable linear peptidoids, which are known to have increased in vivo stability compared to the corresponding peptides . These hFunctionalization of nisin with fluorescent reporter molecules was designed for studies on the mode of action of this antibiotic. They were obtained from nisin C-terminally substituted with propargylamine and a series of functionalized fluorescent probe azides , and three dehydroamino acids. In addition, the possibility of modifications of two side chains of methionine could be considered , though Especially surprising is the practical lack of the modifications of nisin by the Michael reaction. This reaction is quite commonly used for the modification of peptides and proteins containing dehydroamino acids  and resuA palladium-mediated cross-coupling reaction of nisin with a series of phenylboronic acids gave mixtures of the Heck reaction  and conjugated addition to double bonds  . The reaReactivity of the other groups of nisin, except for synthesis of substrates using the click reaction, are scarcely used for functionalization of this antibiotic. Thus, a variety of hydrophobic amines were coupled with the C-terminal AB fragment to generate semisynthetic lipopeptides that display potent inhibition of bacterial growth . A repreThere are a number of reports of structural and chemical modification of proteins with reducing sugars, following the Maillard reaction, which is demonstrated by changes such as fluorescence and browning. The Maillard reaction is associated with the development of UV-absorbing intermediate compounds, prior to generation of brown pigments. These intermediates are characterized by a high proportion of carbonyl groups that can react with the amino moieties of proteins. Thus, Maillard products induced by gamma irradiation (40 kGy) of solutions of glucose or dextran reacted with nisin—providing mixtures of novel conjugates with extended activity towards Gram-negative bacteria along with improved antioxidant activity . This maSphingomonas paucimobilis possessing many advantages such as biocompatibility and stability against most microbes, enzymes, and many chemicals. The idea on its use was to protect nisin against deactivation; the antibacterial duration of nisin against Staphylococcus epidermidis increased 2–4 times in comparison with the mother antibiotic, lasting for several days.Free amino groups of nisin were also used for its functionalization with gellan by formation of amides with its carboxylic moieties . The reaBacteriocins can be considered unconventional antimicrobial molecules which have attracted great interest because they possess many of the attributes essential for the treatment of infections caused by multi-drug-resistant bacteria and to be alternatives to traditional antibiotics. Nisin is the most commercially important member of this group and is approved as a food protectant in over 50 countries. Being readily available and cheap, it is also the most widely studied and exploited. However, its clinical potential and possible use in human and animal therapy has not yet been fulfilled, in part due to its low solubility and stability in body fluids—thus the intensive research focused on enhancement of its functionality in terms of specific activity, spectrum of activity, solubility and/or temperature and pH stability. The broad range of technologies that has been developed for the engineering of nisin has resulted in expanding the knowledge of the factors vital for nisin’s mode of action and provided many variable tools for producing and modifying lantibiotic peptides, as well as enabling numerous nisin variants of variable structures to be obtained. However, nisin is quite a complex molecule and it is not surprising that these studies do not allow us to draw meaningful relation between nisin analog structures and their antibacterial activity. Thus, further multifaceted studies are required.The studies also reinforced the belief that bioengineered lantibiotics can contribute to a solution of antibiotic resistance across a broad range of bacterial pathogens. The most promising seem to be the results on the introduction of non-canonical amino acids into the structure of this antibiotic, as well as the studies on conjugates of nisin (or nisin fragments) with variable antibacterials. These studies, however, are in their infancy.


In the realm of basketball, refining shooting skills and decision-making levels using intelligent agents has garnered significant interest. This study addresses the challenge by introducing an innovative framework that combines multi-modal perception and deep reinforcement learning. The goal is to create basketball robots capable of executing precise shots and informed choices by effectively integrating sensory inputs and learned strategies.The proposed approach consists of three main components: multi-modal perception, deep reinforcement learning, and end-to-end architecture. Multi-modal perception leverages the multi-head attention mechanism (MATT) to merge visual, motion, and distance cues for a holistic perception of the basketball scenario. The deep reinforcement learning framework utilizes the Deep Q-Network (DQN) algorithm, enabling the robots to learn optimal shooting strategies over iterative interactions with the environment. The end-to-end architecture connects these components, allowing seamless integration of perception and decision-making processes.The experiments conducted demonstrate the effectiveness of the proposed approach. Basketball robots equipped with multi-modal perception and deep reinforcement learning exhibit improved shooting accuracy and enhanced decision-making abilities. The multi-head attention mechanism enhances the robots' perception of complex scenes, leading to more accurate shooting decisions. The application of the DQN algorithm results in gradual skill improvement and strategic optimization through interaction with the environment.The integration of multi-modal perception and deep reinforcement learning within an end-to-end architecture presents a promising avenue for advancing basketball robot training and performance. The ability to fuse diverse sensory inputs and learned strategies empowers robots to make informed decisions and execute accurate shots. The research not only contributes to the field of robotics but also has potential implications for human basketball training and coaching methodologies. Basketball is one of the most popular and influential sports globally, characterized by its competitive, recreational, and fitness aspects, as well as its rich cultural and social values  to enhance the shooting skills of a basketball robot. Utilizing the DQN (Deep Q-Network) algorithm, the robot learns and refines its shooting strategy through interactions with the environment, leading to gradual improvements in shooting proficiency. The DQN algorithm effectively handles this complexity using a deep neural network structure. This empowers basketball robots to efficiently learn and master intricate shooting techniques.The logical structure of this paper is as follows: In the second section, the current research on the improvement of shooting skills of basketball robots is reviewed from three aspects: traditional methods, end-to-end learning methods and multi-modal perception methods, and the advantages and disadvantages of various methods are analyzed. An end-to-end architecture will be adopted, combined with methods of multi-modal perception and deep reinforcement learning to improve shooting skills. In the third section, the key methods used in this research, such as end-to-end architecture, multi-head attention mechanism, and DQN algorithm, are introduced in detail, and the calculation formula and flow chart of each method are given, and the design ideas, principles, and target. In the fourth section, describe the experimental environment, including hardware configuration and software environment; introduce the data set used in the experiment; define the evaluation indicators used in the experiment; outline the ideas of comparative analysis of the experiment. In the fifth section, the significance, contribution and limitations of the experimental results are summarized and discussed; the implications of the results for related research fields are discussed; the future research directions and application prospects are prospected. In the sixth section, summarize the research content, innovations and contributions of the full text; emphasize the significance of the research and its impact on related fields; look forward to the future development direction and application prospects.Basketball has always been a sport filled with charm, and the emergence of basketball robots has brought new possibilities to this field. With the advancement of modern artificial intelligence technology, we can continuously improve the shooting skills of basketball robots through methods like reinforcement learning. This study aims to explore an innovative approach by combining end-to-end architecture, multi-modal perception, and deep reinforcement learning, enabling basketball robots to achieve more efficient and precise shooting actions in real-world scenarios. In the past, enhancing the shooting skills of basketball robots has been a challenging task, as traditional methods often required complex manual design and processing, limiting their applications in complex environments  is a machine learning technology that combines deep learning and reinforcement learning, which enables the agent to learn the optimal behavior strategy autonomously in the interaction with the environment, so as to achieve complex task goals . This data set comes from the collection and arrangement of the game and training data of the ASU basketball team in the past few seasons. The data set covers a large amount of basketball data, including player performance, technical statistics, position information, shooting data, scoring, and other aspects. At the same time, the data set also includes relevant information such as team records, opponent team information, and game dates, providing researchers with a comprehensive basketball game background. In the ASU basketball dataset, the data is presented in a structured form, which is convenient for researchers to perform data analysis and modeling. The data set is rich in content and can be used to explore the relationship between player performance and game results in basketball games, study the impact of team tactics and strategies on game results, and explore trends and laws in basketball.• NCAA basketball datasetNCAA basketball dataset  is a comprehensive data set covering college basketball game data, including statistical information and game results of NCAA basketball games in various universities in the United States, as well as personal data of players. The data covers multiple seasons of play, from the regular season to the playoffs, and includes both men's and women's basketball. The sources of this data set mainly include official game statistics, media reports, professional basketball websites, etc. After strict collation and verification, the accuracy and reliability of the data are guaranteed. The data is rich in content, including statistical data such as team points, rebounds, assists, steals, blocks, etc., as well as personal data such as players' points, assists, rebounds, free throw percentage, and three-point shooting percentage. It also records the date of the game, location, score, and other details.• NBA datasetNBA dataset is an important basketball dataset that brings together rich statistics and game results in the American Professional Basketball League (NBA). This data set comes from multiple reliable channels such as the official NBA website, media reports, and professional basketball websites. It has been strictly sorted and verified to ensure the accuracy and credibility of the data. The NBA dataset includes data from multiple seasons, including regular seasons, playoffs, and All-Star games. The data covers various important indicators of the team and players, such as points, rebounds, assists, blocks, steals, etc., and also records the personal information of the players, such as age, height, weight, etc. In addition, the data set also records key information such as the date, location, and score of the game in detail.• SportVU datasetSportVU dataset is an important sports data set collected and provided by SportVU, which is widely used in basketball, football and other sports. The data set tracks and records the entire game by installing high-performance cameras and sensor systems inside the stadium, and captures sports data such as the position, speed, and acceleration of athletes, as well as information such as the position and trajectory of the ball. This highly intelligent data processing and calculation enables SportVU dataset to provide accurate and detailed game data, including various precise indicators such as player position changes, passing, shooting, and running trajectories. SportVU provides a wealth of basketball game data and information for our experiments, which can help us deeply study the rules and tactics of basketball games, design more intelligent and efficient basketball robot shooting models, and evaluate the robot's shooting ability.In this study, in order to comprehensively evaluate the basketball robot's shooting skills and performance, we adopted several key indicators to quantify its shooting accuracy, speed, and interaction efficiency with the environment. These metrics not only help measure the robot's shooting performance, but also provide us with important references for algorithm optimization and performance improvement.Shooting accuracy: in this study, we focus on the indicator shooting accuracy (SA), which is an important measure for evaluating the shooting accuracy of basketball robots. In basketball, accurate shooting technique is crucial to winning the game. In our experiment, we hope to use this indicator to evaluate the shooting accuracy of the basketball robot, and then measure the pros and cons of its shooting skills. Calculated as follows:Number of Successful Shots indicates the number of successful shots made by the robot, and Total Number of Shots indicates the total number of shots attempted by the robot.Among them, Through the shooting accuracy, we can objectively understand the accuracy of the basketball robot in the shooting process. A high accuracy rate means that the robot can hit the target more stably and has better shooting skills and movements. On the contrary, a lower accuracy rate may imply that there is room for improvement in the shooting of the robot, and its shooting action or algorithm needs to be further optimized.• Shooting recall: next, we will introduce the evaluation metric shooting recall (SR), which is an important metric to measure whether a basketball robot can successfully capture and shoot all feasible targets in a shooting task. This indicator focuses on the robot's coverage of shooting targets, that is, the proportion of successful shooting targets to all possible shooting targets. Calculated as follows:Number of Successfully Covered Targets indicates the number of targets that the robot successfully shot and covered, and Total Number of Possible Targets indicates the total number of targets that can be shot.Among them, Through the shooting recall rate, we can evaluate whether the basketball robot can fully cover the targets of different positions and difficulties in the actual shooting task. A high recall rate means that the robot has better shooting planning and decision-making capabilities, and can make accurate shooting actions for various situations. The lower recall rate may indicate that the robot has certain limitations in the shooting process, and it needs to further improve the shooting strategy or enhance its perception and understanding of different targets.• Shooting form score: in this study, we also used an important evaluation index—shooting form score (SFS), which is used to evaluate the quality and accuracy of the shooting action of the basketball robot in the shooting task. This indicator mainly focuses on whether the shooting action of the robot meets the correct shooting posture and technical requirements, as well as the excellence and stability of its shooting form. Calculated as follows:Total Score of Shooting Form indicates the sum of form scores obtained by the robot in all shooting actions, and Number of Shots Attempted indicates the number of shots attempted by the robot.Among them, Through shooting form scoring, we can evaluate the basketball robot's shooting skill level and action specification. A high score means that the robot's shooting movements are accurate, standardized, and stable, and it has a good level of shooting skills. The lower score may indicate that there are certain problems in the shooting action of the robot, and further optimization and improvement of shooting skills are needed.• Shooting success time: this evaluation indicator, which is used to measure the average time spent by the basketball robot in the shooting task. shooting success time (SST) can help evaluate the speed and efficiency of the robot's shooting, which is of great significance to measure the practical application value of its shooting skills. Calculated as follows:N represents the number of successful shots made by the robot, and Timei represents the time it takes for the i-th successful shot.Among them, It reflects the speed and efficiency of the robot during the actual shooting process. The short time to successful shooting indicates that the robot reacts quickly and moves smoothly when shooting, and can complete the shooting task efficiently. On the contrary, a longer successful shooting time may indicate that the robot has deficiencies in the shooting action, which needs further optimization and improvement.• Average interaction count: this indicator is used to measure the average number of interactions between the basketball robot and the environment. For reinforcement learning or multi-agent approaches, the average interaction count (AIC) is an important performance metric that reflects how much the robot interacts with the environment while learning the shooting skill. An efficient algorithm should usually learn good shooting skills with as few interactions as possible. Calculated as follows:N represents the total number of shooting tasks, and Interactionsi represents the number of interactions between the robot and the environment in the i-th shooting task.Among them, It reflects the average number of interactions required for the robot to learn the shooting skill. The lower average number of interactions indicates that the robot can effectively learn and master shooting skills with fewer interactions, and has the ability to learn efficiently. On the contrary, a higher average number of interactions may mean that more trial-and-error and interaction are required in the robot learning process, and there is room for improvement.In this study, we have successfully designed a deep reinforcement learning method combined with multi-modal perception in an end-to-end architecture for improving the shooting skills of a basketball robot. In order to comprehensively evaluate the performance and effectiveness of the method, we conducted a series of experiments and collected rich data, including key metrics such as shooting accuracy, recall rate, points, and time to shot success.In the experimental comparison and analysis section, we will conduct a detailed analysis of the performance of different schemes and models on the above indicators in the following order. First, we will compare the difference between our proposed end-to-end architecture and traditional methods in terms of shooting accuracy, recall and score, and verify the advantages of our method in realizing the improvement of shooting skills of basketball robots. We will show the significant improvement in various indicators of the end-to-end architecture compared with traditional methods in the experimental results, and explain the reasons behind these improvements. Second, we will delve into the role of the multi-head attention mechanism on metrics such as shooting accuracy, recall, and points. The multi-head attention mechanism is to make the basketball robot more comprehensively perceive the surrounding environment and basketball state, thereby improving the accuracy of shooting decisions. We will demonstrate the contribution of the multi-head attention mechanism to the improvement of basketball robot shooting skills through experimental results, and explore its applicability in different scenarios. At the same time, we will also compare the experimental results under different hardware environments to verify the stability of our method under different computing resources. This analysis will help us understand whether our proposed method has good generality and scalability in practical applications.Through these comparisons and analyzes, we aim to deeply study the performance of our proposed deep reinforcement learning method for multi-modal perception in improving the shooting skills of basketball robots, and provide useful references for further improvement and optimization methods in the future. At the same time, we will continue the introduction of the data set and evaluation indicators in the previous article to ensure the credibility and practicability of the experiment.According to the data in According to the data in According to the data in According to the data in Through the detailed analysis and comparison of each experimental index, it can be seen that our proposed framework shows obvious advantages compared with other methods. From the key indicators of shooting, such as the average number of interactions, shooting accuracy, recall rate, and action score, our method has achieved a substantial improvement. This shows that in complex environments, our framework can quickly master shooting skills with less trial and error, and learn shooting strategies with higher accuracy and stronger generalization. At the same time, from the perspective of model size, calculation amount, training and inference time, and our framework also shows better efficiency, which not only ensures performance, but also controls complexity. The introduced end-to-end learning mechanism can complete direct learning from multi-source heterogeneous perception to precise motion control, which avoids the error accumulation of traditional step-by-step methods, and also accelerates model training and prediction. The multi-modal attention module enhances the ability to understand and express complex environments. The combination of the two forms a meta-architecture that is both efficient and powerful. The experimental results fully verify the advantages of the meta-architecture in improving the ability of the agent's motion control.In the previous chapters, we introduced a deep reinforcement learning approach that combines multi-modal perception within an end-to-end architecture to enhance the shooting skills of a basketball robot. We conducted a comprehensive evaluation of this method in the experimental section. In this chapter, we delve into a thorough discussion of the research results, summarizing the key findings and contributions of the experiments, and explore the significance, advantages, limitations, and future prospects of these results.By comparing the experimental results, we observed that our proposed approach, which integrates multi-modal perception within an end-to-end architecture using deep reinforcement learning, exhibited remarkable performance in metrics such as shooting accuracy, recall rate, and scores, surpassing traditional methods significantly. This indicates that the combination of multi-modal perception and deep reinforcement learning enables the basketball robot to comprehensively perceive its environment and basketball state, and make more efficient and precise shooting decisions. This finding holds practical importance in improving the shooting skills of basketball robots and advancing research in intelligent agent control. Additionally, our research results validated the effectiveness of the multi-head attention mechanism in metrics like shooting accuracy and recall rate. By simultaneously incorporating various perceptual information, such as vision, motion, and distance, the multi-head attention mechanism assists the basketball robot in better understanding its surrounding environment and basketball state, thereby enhancing the accuracy of shooting decisions. This provides valuable experience and guidance for introducing multi-modal perception and attention mechanisms into other intelligent agent tasks. Furthermore, we compared the experimental results under different hardware environments. The results showed that our method demonstrated good stability and scalability across various computational resources. This indicates that our proposed approach exhibits strong generalizability and possesses high reliability and efficiency in practical applications.Despite achieving significant achievements in our research, there are also some limitations. Firstly, as the shooting skills of the basketball robot are influenced by multiple factors, our method may still have room for further improvement. Secondly, our experiments primarily took place in simulated environments, and the complexity and uncertainty of real-world scenarios could influence the results. Therefore, in future research, we aim to optimize the algorithms and include testing in real-world settings to validate the feasibility and stability of our approach in practical applications.The deep reinforcement learning and multi-modal perception-based approach holds vast potential for applications in the field of intelligent agent control. In the future, we will continue to explore the application of multi-modal perception and deep reinforcement learning in other robot tasks, such as motion planning for soccer robots and tactical decision-making in soccer matches. Simultaneously, we will further investigate how to apply these methods to real-world environments, addressing challenges and issues encountered in real-world scenarios, and advancing the development and application of intelligent agents in the real world.In conclusion, this study successfully improved the shooting skills of a basketball robot by integrating an end-to-end architecture, multi-modal perception, and deep reinforcement learning. We achieved a series of beneficial experimental results, providing valuable insights for research in the field of intelligent agent control.This research aims to enhance the shooting skills of a basketball robot by combining an end-to-end architecture, multi-modal perception, and deep reinforcement learning, resulting in more efficient and precise shooting behavior. In this chapter, we will provide a summary of the entire study, emphasize its significance and contributions, and highlight the research's innovations and implications. Finally, we will provide an outlook for future developments.In this study, we first proposed a novel approach based on an end-to-end architecture and multi-modal perception using deep reinforcement learning to improve the shooting skills of the basketball robot. By integrating perception and decision-making into a unified learning system, we enabled the robot to comprehensively perceive its environment and basketball state and learn better shooting strategies through deep reinforcement learning. Experimental results showed that our method outperformed traditional approaches significantly in metrics such as shooting accuracy, recall rate, and scores, thus confirming the effectiveness and advantages of our approach. Additionally, we introduced the multi-head attention mechanism, which simultaneously fused various perceptual information, such as vision, motion, and distance, thereby enhancing the robot's perception ability in complex scenes and the accuracy of shooting decisions. The multi-head attention mechanism exhibited strong performance in the experiments, providing valuable insights and guidance for introducing multi-modal perception and attention mechanisms into other intelligent agent tasks.The significance and contributions of this research lie in the novel approach we proposed to improve the shooting skills of a basketball robot, offering valuable insights for research in the field of intelligent agent control. Our method combines end-to-end architecture, multi-modal perception, and deep reinforcement learning, enabling the robot to comprehensively perceive and understand scenes in complex environments and make optimal shooting decisions through learning. This opens up new avenues and methods for the application and development of intelligent agents in the real world. To summarize the research's innovations and implications, this study effectively improved the basketball robot's shooting skills by fusing perception and decision-making into a unified learning system. We introduced the multi-head attention mechanism, enhancing the robot's perception ability and improving the accuracy of shooting decisions. Moreover, our approach demonstrates good stability and scalability across different hardware environments, offering significant practical application value.In the future, we will continue exploring the application of multi-modal perception and deep reinforcement learning in other robot tasks, such as motion planning and tactical decision-making for soccer robots. Additionally, we will further investigate how to apply these methods to real-world environments and address challenges and issues encountered in real scenarios. We hope that through continued research and exploration, we can make further contributions to the development and application of intelligent agent control. Future work can focus on advancing model capabilities, optimizing for practical use, and expanding the approach to new application areas. Explore application of end-to-end deep learning in more basketball skills like passing, dribbling, shooting under pressure. Integrate more sensing modalities like motion capture to enable more fluid autonomous dribbling and shooting motions.In conclusion, this research successfully improved the shooting skills of a basketball robot using an end-to-end architecture and multi-modal perception with deep reinforcement learning. Our work provides new insights and methods for the application and development of intelligent agents in complex environments, demonstrating significant research significance and practical value. We are confident that through ongoing efforts and exploration, intelligent agents will achieve more remarkable progress and breakthroughs in the near future.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.JZ: Conceptualization, Formal analysis, Methodology, Resources, Writing—original draft. DT: Data curation, Funding acquisition, Investigation, Project administration, Writing—review and editing.