Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
Designing fast and accurate neural networks is becoming essential in various vision tasks.Recently,the use of attention mechanisms has increased,aimed at enhancing the vision task performance by selectively focusing o...Designing fast and accurate neural networks is becoming essential in various vision tasks.Recently,the use of attention mechanisms has increased,aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input.In this paper,we concentrate on squeeze-and-excitation(SE)-based channel attention,considering the trade-off between latency and accuracy.We propose a variation of the SE module,called squeeze-and-excitation with layer normalization(SELN),in which layer normalization(LN)replaces the sigmoid activation function.This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention.In addition,we propose a latency-efficient model named SELNeXt,where the LN typically used in the ConvNext block is replaced by SELN to minimize additional latency-impacting operations.Through classification simulations on ImageNet-1k,we show that the top-1 accuracy of the proposed SELNeXt outperforms other ConvNeXt-based models in terms of latency efficiency.SELNeXt also achieves better object detection and instance segmentation performance on COCO than Swin Transformer and ConvNeXt for small-sized models.Our results indicate that LN could be a considerable candidate for replacing the activation function in attention mechanisms.In addition,SELNeXt achieves a better accuracy-latency trade-off,making it favorable for real-time applications and edge computing.The code is available at https://github.com/oto-q/SELNeXt(accessed on 06 December 2024).展开更多
Paediatric liver transplantation(PLT)is a life-saving procedure for children with advanced liver disease or hepatoblastoma.The number of available grafts is limited in relation to the number of children on PLT waiting...Paediatric liver transplantation(PLT)is a life-saving procedure for children with advanced liver disease or hepatoblastoma.The number of available grafts is limited in relation to the number of children on PLT waiting list.This graft shortage has led transplant societies and healthcare organizations to explore ways to investigate possible options and expand the donor pool.The safe use of grafts from obese donors has always been a subject of debate among PLT specialists.Donors’obesity is strongly associated with hepatic steatosis which can affect graft function by impairing microcirculation and maximizing the potential of ischemiareperfusion injury.Donor body mass index consideration should go hand in hand with the workup for hepatic steatosis which is an independent predictor for early graft dysfunction.New strategies to optimize the grafts before PLT such as normothermic regional perfusion and ex vivo liver perfusion can potentially mitigate the risk of using grafts from obese donors.This review summarizes the available evidence about the impact of donor obesity on PLT and highlights the current policies to widen the graft pool and suggest future research directions to improve donor selection and patient outcomes.展开更多
The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pos...The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense mechanisms.While AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and decision-makers.Literature review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant concern.However,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach.Researchers have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for detection.Hence,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors.While the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset 1.Additionally,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct datasets.Using the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction.Feature engineering presented four critical APT LM intrusion detection(ID)indicators(features)across the three datasets,namely,the source port number,the destination port number,the packets,and the bytes.This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach.It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration,thus providing an additional layer of organizational defense.展开更多
AIM: To establish the potential of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) as a material for tendon repair. METHODS: The biocompatibility of PHBHHx with both rat tenocytes (rT) and human mesenchymal ste...AIM: To establish the potential of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) as a material for tendon repair. METHODS: The biocompatibility of PHBHHx with both rat tenocytes (rT) and human mesenchymal stem cells (hMSC) was explored by monitoring adhesive characteristics on films of varying weight/volume ratios coupled to a culture atmosphere of either 21% O2 (air) or 2% O2 (physiological normoxia). The diameter and stiffness of PHBHHx films was established using optical coherence tomography and mechanical testing, respectively. RESULTS: Film thickness correlated directly with weight/volume PHBHHx (r2 = 0.9473) ranging from 0.1 mm (0.8% weight/volume) to 0.19 mm (2.4% weight/volume). Film stiffness on the other hand displayed a biphasic response which increased rapidly at values > 1.6% weight/volume. Optimal cell attachment of rT required films of ≥ 1.6% and ≥ 2.0% weight/volume PHBHHx in 2% O2 and 21% O2 respectively. A qualitative adhesion increase was noted for hMSC in films ≥ 1.2% weight/volume, becoming significant at 2% weight/volume in 2% O2. An increase in cell adhesion was also noted with ≥ 2% weight/volume PHBHHx in 21% O2. Cell migration into films was not observed. CONCLUSION: This evaluation demonstrates that PHBHHx is a suitable polymer for future cell/polymer replacement strategies in tendon repair.展开更多
The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI ...The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.展开更多
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2021R1A6A1A03039493.
文摘Designing fast and accurate neural networks is becoming essential in various vision tasks.Recently,the use of attention mechanisms has increased,aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input.In this paper,we concentrate on squeeze-and-excitation(SE)-based channel attention,considering the trade-off between latency and accuracy.We propose a variation of the SE module,called squeeze-and-excitation with layer normalization(SELN),in which layer normalization(LN)replaces the sigmoid activation function.This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention.In addition,we propose a latency-efficient model named SELNeXt,where the LN typically used in the ConvNext block is replaced by SELN to minimize additional latency-impacting operations.Through classification simulations on ImageNet-1k,we show that the top-1 accuracy of the proposed SELNeXt outperforms other ConvNeXt-based models in terms of latency efficiency.SELNeXt also achieves better object detection and instance segmentation performance on COCO than Swin Transformer and ConvNeXt for small-sized models.Our results indicate that LN could be a considerable candidate for replacing the activation function in attention mechanisms.In addition,SELNeXt achieves a better accuracy-latency trade-off,making it favorable for real-time applications and edge computing.The code is available at https://github.com/oto-q/SELNeXt(accessed on 06 December 2024).
文摘Paediatric liver transplantation(PLT)is a life-saving procedure for children with advanced liver disease or hepatoblastoma.The number of available grafts is limited in relation to the number of children on PLT waiting list.This graft shortage has led transplant societies and healthcare organizations to explore ways to investigate possible options and expand the donor pool.The safe use of grafts from obese donors has always been a subject of debate among PLT specialists.Donors’obesity is strongly associated with hepatic steatosis which can affect graft function by impairing microcirculation and maximizing the potential of ischemiareperfusion injury.Donor body mass index consideration should go hand in hand with the workup for hepatic steatosis which is an independent predictor for early graft dysfunction.New strategies to optimize the grafts before PLT such as normothermic regional perfusion and ex vivo liver perfusion can potentially mitigate the risk of using grafts from obese donors.This review summarizes the available evidence about the impact of donor obesity on PLT and highlights the current policies to widen the graft pool and suggest future research directions to improve donor selection and patient outcomes.
基金Rabdan Academy for funding the research presented in the paper.
文摘The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense mechanisms.While AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and decision-makers.Literature review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant concern.However,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach.Researchers have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for detection.Hence,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors.While the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset 1.Additionally,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct datasets.Using the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction.Feature engineering presented four critical APT LM intrusion detection(ID)indicators(features)across the three datasets,namely,the source port number,the destination port number,the packets,and the bytes.This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach.It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration,thus providing an additional layer of organizational defense.
基金Supported by EPSRC Doctoral Training Centre in Regenerative Medicine and the HYANJI Scaffold Project (European Commission Framework 7 program)
文摘AIM: To establish the potential of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) as a material for tendon repair. METHODS: The biocompatibility of PHBHHx with both rat tenocytes (rT) and human mesenchymal stem cells (hMSC) was explored by monitoring adhesive characteristics on films of varying weight/volume ratios coupled to a culture atmosphere of either 21% O2 (air) or 2% O2 (physiological normoxia). The diameter and stiffness of PHBHHx films was established using optical coherence tomography and mechanical testing, respectively. RESULTS: Film thickness correlated directly with weight/volume PHBHHx (r2 = 0.9473) ranging from 0.1 mm (0.8% weight/volume) to 0.19 mm (2.4% weight/volume). Film stiffness on the other hand displayed a biphasic response which increased rapidly at values > 1.6% weight/volume. Optimal cell attachment of rT required films of ≥ 1.6% and ≥ 2.0% weight/volume PHBHHx in 2% O2 and 21% O2 respectively. A qualitative adhesion increase was noted for hMSC in films ≥ 1.2% weight/volume, becoming significant at 2% weight/volume in 2% O2. An increase in cell adhesion was also noted with ≥ 2% weight/volume PHBHHx in 21% O2. Cell migration into films was not observed. CONCLUSION: This evaluation demonstrates that PHBHHx is a suitable polymer for future cell/polymer replacement strategies in tendon repair.
文摘The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.
