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Thermal comfort refers to the process of determining a pleasant working temperature by considering various environmental, occupational, and personal factors. With the widespread adoption of Internet of Things (IoT) technologies, there is a potential to develop an IoT-based embedded system that directly integrates sensor data with building information to collect the necessary environmental factors for thermal comfort. This paper outlines the development and implementation of an embedded system for remote temperature monitoring, which gathers data such as temperature and humidity using sensors installed within the building and transmits this information to the cloud in real-time via a private Message Queuing Telemetry Transport (MQTT) server. An IoT-based embedded system makes the data collection process more efficient, automated, and integrated, which ultimately leads to a faster and smoother experience in gathering and processing the necessary information to optimize thermal comfort. Following data collection, a machine learning model may be trained using the acquired data to automatically adjust the thermal comfort level in the building, improving overall comfort and energy efficiency. &nbsp

Thermal comfort refers to the process of determining a pleasant working temperature by considering various environmental, occupational, and personal factors. With the widespread adoption of Internet of Things (IoT) technologies, there is a potential to develop an IoT-based embedded system that directly integrates sensor data with building information to collect the necessary environmental factors for thermal comfort. This paper outlines the development and implementation of an embedded system for remote temperature monitoring, which gathers data such as temperature and humidity using sensors installed within the building and transmits this information to the cloud in real-time via a private Message Queuing Telemetry Transport (MQTT) server. An IoT-based embedded system makes the data collection process more efficient, automated, and integrated, which ultimately leads to a faster and smoother experience in gathering and processing the necessary information to optimize thermal comfort. Following data collection, a machine learning model may be trained using the acquired data to automatically adjust the thermal comfort level in the building, improving overall comfort and energy efficiency. &nbsp

This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model (\"Ust\"un et al., 2024), focusing on pairing a highly performant pre-trained model with the recently released Aya collection (Singh et al., 2024). The result is a powerful multilingual large language model serving 23 languages, expanding state-of-art language modeling capabilities to approximately half of the world's population. The Aya model covered 101 languages whereas Aya 23 is an experiment in depth vs breadth, exploring the impact of allocating more capacity to fewer languages that are included during pre-training. Aya 23 outperforms both previous massively multilingual models like Aya 101 for the languages it covers, as well as widely used models like Gemma, Mistral and Mixtral on an extensive range of discriminative and generative tasks. We release the open weights for both the 8B and 35B models as part of our continued commitment for expanding access to multilingual progress.

BackgroundPrescription medication labels (PMLs) predominantly dispensed in English, are an important adjunct to medication counselling. PMLs are routinely used by pharmacy staff to counsel older adults about their medications. This study sought to identify challenges that pharmacy staff observe older adults face in using their PMLs, and to identify and quantify solutions employed by pharmacy staff during medication counselling to address such challenges.MethodsTen in-depth interviews were done with primary care pharmacy staff to gather the range of challenges and solutions. Subsequently, a quantitative survey, informed by the qualitative findings, was administered to 121 pharmacy staff to assess if the reported solutions were commonly used.ResultsThe two main challenges were incongruity between PML language (English) and older adults' language proficiency, and poor PML legibility. The solutions, classified under three themes, were simplifying medication information on PMLs, supplementing PMLs with additional medication information and mitigating poor readability.ConclusionsPharmacy staff observed challenges faced by older adults in using PMLs during medication counselling. Ad-hoc improvisations by pharmacy staff to PMLs were pervasive. System-level PML improvements, such as provision of legible bilingual medication instructions, pharmaceutical pictograms and additional medication information, through patient information leaflets or using quick response (QR) codes on PMLs, should be considered. This will facilitate patient-provider communication, especially in settings with language dissonance between PMLs and patients.

ObjectiveIn an earlier study, several tested International Pharmaceutical Federation (FIP) pictograms did not achieve validity among older adults in Singapore. In this study, for 27 unvalidated FIP pictograms, we (1) developed variants of each pictogram, (2) elicited the most-preferred variant, and (3) assessed the validity of the most-preferred variant among older Singaporeans.MethodsIn phase 1, up to three variants of the 27 pictograms were developed, based on older adults' feedback from a previous study. In phase 2, the most-preferred variant of 26 pictograms, which had two or three variants, was selected by 100 older participants. In phase 3, the 27 most-preferred variants (including the pictogram with only one variant) were assessed for validity - transparency and translucency - among 278 older participants (10 pictograms per participant). To evaluate transparency, participants were first asked: "If you see this picture on a medicine label, what do you think it means?" for each assigned pictogram. If they responded, they were asked, "How do you know?", and if not, they were told, "Tell me everything you see in this picture". Then, participants were shown their assigned pictograms again, one by one, and the pictogram's intended meaning was revealed to evaluate translucency. Pictograms were classified as valid (≥66% participants interpreted its intended meaning correctly [transparency criterion] and ≥85% participants rated its representativeness as ≥ 5 [translucency criterion]), partially valid (only transparency criterion fulfilled) or not valid.ResultsIn phase 1, 77 variants of the 27 pictograms were developed. In phase 2, a majority of the most-preferred variants were selected by >50% participants. In phase 3, 10 (37.0%) of the 27 pictograms tested were considered valid, and five (18.5%) were partially valid. A higher proportion of pictograms portraying dose and route of administration and precautions were valid or partially valid, versus those depicting in

Yi Zhen Tan,1 Lucy R Thomsen,1 Nensi Shrestha,1 Adalberto Camisasca,2 Silvia Giordani,2 Rhonda Rosengren1 1Department of Pharmacology and Toxicology, University of Otago, Dunedin, 9016, New Zealand; 2School of Chemical Sciences, Dublin City University, Glasnevin, Dublin, D09 NA55, IrelandCorrespondence: Rhonda Rosengren, Department of Pharmacology and Toxicology, 18 Frederick Street, Dunedin, 9016, New Zealand, Tel +64 3 479 9141, Fax +64 3 479 9140, Email rhonda.rosengren@otago.ac.nzBackground: A nanoscale drug carrier could have a variety of therapeutic and diagnostic uses provided that the carrier is biocompatible in vivo. Carbon nano-onions (CNOs) have shown promising results as a nanocarrier for drug delivery. However, the systemic effect of CNOs in rodents is unknown. Therefore, we investigated the toxicity of CNOs following intravenous administration in female BALB/c mice.Results: Single or repeated administration of oxi-CNOs (125, 250 or 500 μg) did not affect mouse behavior or organ weight and there was also no evidence of hepatotoxicity or nephrotoxicity. Histological examination of organ slices revealed a significant dose-dependent accumulation of CNO aggregates in the spleen, liver and lungs (p< 0.05, ANOVA), with a trace amount of aggregates appearing in the kidneys. However, CNO aggregates in the liver did not affect CYP450 enzymes, as total hepatic CYP450 as well as CYP3A catalytic activity, as meased by erythromycin N-demethylation, and protein levels showed no significant changes between the treatment groups compared to vehicle control. CNOs also failed to act as competitive inhibitors of CYP3A in vitro in both mouse and human liver microsomes. Furthermore, CNOs did not cause oxidative stress, as indicated by the unchanged malondialdehyde levels and superoxide dismutase activity in liver microsomes and organ homogenates.Conclusion: This study provides the first evidence that short-term intravenous administration of oxi-CNOs is non-toxic to femal

The pre-trained speech encoder wav2vec 2.0 performs very well on various spoken language understanding (SLU) tasks. However, on many tasks, it trails behind text encoders with textual input. To improve the understanding capability of SLU encoders, various studies have used knowledge distillation to transfer knowledge from natural language understanding (NLU) encoders. We use a very simple method of distilling from a textual sentence embedder directly into wav2vec 2.0 as pre-training, utilizing paired audio-text datasets. We observed that this method is indeed capable of improving SLU task performance in fine-tuned settings, as well as full-data and few-shot transfer on a frozen encoder. However, the model performs worse on certain tasks highlighting the strengths and weaknesses of our approach., Comment: Interspeech 2023

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

The recent advances in Convolutional Neural Networks (CNNs) and Vision Transformers have convincingly demonstrated high learning capability for video action recognition on large datasets. Nevertheless, deep models often suffer from the overfitting effect on small-scale datasets with a limited number of training videos. A common solution is to exploit the existing image augmentation strategies for each frame individually including Mixup, Cutmix, and RandAugment, which are not particularly optimized for video data. In this paper, we propose a novel video augmentation strategy named Selective Volume Mixup (SV-Mix) to improve the generalization ability of deep models with limited training videos. SV-Mix devises a learnable selective module to choose the most informative volumes from two videos and mixes the volumes up to achieve a new training video. Technically, we propose two new modules, i.e., a spatial selective module to select the local patches for each spatial position, and a temporal selective module to mix the entire frames for each timestamp and maintain the spatial pattern. At each time, we randomly choose one of the two modules to expand the diversity of training samples. The selective modules are jointly optimized with the video action recognition framework to find the optimal augmentation strategy. We empirically demonstrate the merits of the SV-Mix augmentation on a wide range of video action recognition benchmarks and consistently boot the performances of both CNN-based and transformer-based models.

BackgroundPrescription medication labels (PMLs) are an important source of written medication information for patients. However, real-world PMLs do not entirely conform with available labelling best practices and guidelines. Given this disconnect, older adults remain particularly at risk of misinterpreting their PMLs. Past studies have commonly assessed hypothetical PMLs, warranting studies that explore the lived experiences of older adults with real-world PMLs. Furthermore, the perspective of family caregivers of older adults is yet to be studied.ObjectivesThis qualitative study documented the challenges faced by older adults and their family caregivers in using real-world PMLs, their strategies to cope with these challenges, and their suggestions to improve existing PMLs.MethodsWe conducted two focus group discussions (n = 17) and 30 in-depth interviews with older adults (n = 20; including those who can read in English and those with limited English proficiency) and caregivers (n = 10) in Singapore. The data were systematically assigned to codes that were continuously refined to accommodate emergent themes.ResultsChallenges, coping strategies and suggested improvements were related to the comprehensibility, availability, readability and consistency of medication information on PMLs.ConclusionsReal-world PMLs continue to pose challenges for older adults and their caregivers, necessitating them to seek unique and personal coping strategies. The identified PML improvements, desired by older adults and their caregivers, urge healthcare systems to implement improved PMLs. Future research should explore system-level logistical, financial, and administrative barriers (or opportunities) that hinder (or facilitate) this implementation.

BackgroundPrescription medication labels (PMLs) are an important source of written medication information for patients. However, real-world PMLs do not entirely conform with available labelling best practices and guidelines. Given this disconnect, older adults remain particularly at risk of misinterpreting their PMLs. Past studies have commonly assessed hypothetical PMLs, warranting studies that explore the lived experiences of older adults with real-world PMLs. Furthermore, the perspective of family caregivers of older adults is yet to be studied.ObjectivesThis qualitative study documented the challenges faced by older adults and their family caregivers in using real-world PMLs, their strategies to cope with these challenges, and their suggestions to improve existing PMLs.MethodsWe conducted two focus group discussions (n = 17) and 30 in-depth interviews with older adults (n = 20; including those who can read in English and those with limited English proficiency) and caregivers (n = 10) in Singapore. The data were systematically assigned to codes that were continuously refined to accommodate emergent themes.ResultsChallenges, coping strategies and suggested improvements were related to the comprehensibility, availability, readability and consistency of medication information on PMLs.ConclusionsReal-world PMLs continue to pose challenges for older adults and their caregivers, necessitating them to seek unique and personal coping strategies. The identified PML improvements, desired by older adults and their caregivers, urge healthcare systems to implement improved PMLs. Future research should explore system-level logistical, financial, and administrative barriers (or opportunities) that hinder (or facilitate) this implementation.

Squamous cell carcinoma (SCC) and malignant pleural mesothelioma (MPM) are thoracic malignancies with very poor prognosis and limited treatment options. It is an established fact that most of the solid tumors have overexpression of EPHA2 receptor tyrosine kinase. EPHA2 is known to exhibit opposing roles towards cancer progression. It functions in inhibiting cancer survival and migration via a ligand and tyrosine kinase dependent signaling (Y772). Whereas it is known to promote tumor progression and cell migration through a ligand-independent signaling (S897). We analyzed the expression profile and mutational status of the ephrin receptor A2 (EPHA2) in SCC and MPM cell lines and primary patient specimens. The EPHA2 receptor was found to be either overexpressed, mutated or amplified in SCC and MPM. In particular, the EPHA2 mutants A859D and T647M were interesting to explore, A859D Y772 dead mutant exhibited lower levels of phosphorylation at Y772 compared to T647M mutant. Molecular Dynamics simulations studies suggested that differential changes in conformation might form the structural basis for differences in the level of EPHA2 activation. Consequently, A859D mutant cells exhibited increased proliferation as well as cell migration compared to controls and T647M mutant. Kinomics analysis demonstrated that the STAT3 and PDGF pathways were upregulated whereas signaling through CBL was suppressed. Considered together, the present work has uncovered the oncogenic characteristics of EPHA2 mutations in SSC and MPM reinstating the dynamics of different roles of EPHA2 in cancer. This study also suggests that a combination of doxazosin and other EPHA2 inhibitors directed to inhibit the pertinent signaling components may be a novel therapeutic strategy for MPM and Non-small cell lung cancer patients who have either EPHA2 or CBL alterations.

Squamous cell carcinoma (SCC) and malignant pleural mesothelioma (MPM) are thoracic malignancies with very poor prognosis and limited treatment options. It is an established fact that most of the solid tumors have overexpression of EPHA2 receptor tyrosine kinase. EPHA2 is known to exhibit opposing roles towards cancer progression. It functions in inhibiting cancer survival and migration via a ligand and tyrosine kinase dependent signaling (Y772). Whereas it is known to promote tumor progression and cell migration through a ligand-independent signaling (S897). We analyzed the expression profile and mutational status of the ephrin receptor A2 (EPHA2) in SCC and MPM cell lines and primary patient specimens. The EPHA2 receptor was found to be either overexpressed, mutated or amplified in SCC and MPM. In particular, the EPHA2 mutants A859D and T647M were interesting to explore, A859D Y772 dead mutant exhibited lower levels of phosphorylation at Y772 compared to T647M mutant. Molecular Dynamics simulations studies suggested that differential changes in conformation might form the structural basis for differences in the level of EPHA2 activation. Consequently, A859D mutant cells exhibited increased proliferation as well as cell migration compared to controls and T647M mutant. Kinomics analysis demonstrated that the STAT3 and PDGF pathways were upregulated whereas signaling through CBL was suppressed. Considered together, the present work has uncovered the oncogenic characteristics of EPHA2 mutations in SSC and MPM reinstating the dynamics of different roles of EPHA2 in cancer. This study also suggests that a combination of doxazosin and other EPHA2 inhibitors directed to inhibit the pertinent signaling components may be a novel therapeutic strategy for MPM and Non-small cell lung cancer patients who have either EPHA2 or CBL alterations.

The drainage system is an important part of civil infrastructure. However, the underground sewage pipe will gradually suffer from defects over time, such as tree roots, deposits, infiltrations and cracks, which heavily affect the performance of sewage pipes. Therefore, it is significant to timely inspect the condition of sewage pipes. Closed-circuit television (CCTV) inspection is a commonly employed underground infrastructure inspection technology requiring engineering experience that can be subjective and inefficient. Nowadays, object detection based on convolutional neural network (CNN) can automatically detect defects, showing high potential for improving inspection efficiency. This paper proposed an improved CNN-based You Only Look Once version 3 (YOLOv3) method for automatic detection of sewage pipe defects, where the improvements are mainly involved in loss function, data augmentation, bounding box prediction and network structure. Experiment results demonstrate that the improved model outperforms Faster R-CNN and YOLOv3, achieving a mean average precision (mAP) value of 92%, which is higher than the existing research on automatic detection of sewage pipe defects. © 2021

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based r

Platform decommissioning decision making is a major issue as many of the existing offshore oil and gas platforms (OOGPs) require to be decommissioned. The cost and environmental impact are two main criteria for OOGP decommissioning. This paper reviews OOGP decommissioning factors and the current estimation methodologies of decommissioning cost and environmental impact. Six factors are identified: (1) platform types and complexity, (2) decommissioning options, (3) technical approaches, (4) circumstances, (5) regulations, and (6) strategies, followed by a detailed review of the estimation methodologies of cost and environmental impact. Descriptive statistics are common cost estimation, together with standard-based and index-based cost estimations. For environmental impact estimation, qualitative methods like ranking decommissioning options based on expert option, and quantitative methods like emission calculation based on equipment utilization, duty cycles, and emission factors are commonly used. The findings of this paper indicate that current OOGP decommissioning databases are incomplete, leading to inaccurate and subjective decommissioning cost estimation, and incomplete environmental impact estimation. Recommendations include integrating 3D information modeling with cost indexing to estimate cost, and with life cycle assessment to estimate environmental impact. OOGP decommissioning regulations should include strategies and rules for estimation, and formulating a roadmap with a long-term perspective is necessary. © 2020 Elsevier Inc.

Many modern commercial buildings involve complex shaped façades, resulting in increasing complexity as well as challenges in façade manufacturing and assembly processes. Currently, the keys to manufacture façade panels are fabrication drawings. However, generating fabrication drawings for façade panels using conventional approaches is time-consuming and error-prone, especially when the number of façade panels gets large. Computer-aided automation can significantly improve the efficiency and accuracy of the manufacturing and assembly process, while conventional drawing automation processes are not able to integrate information from various data sources and optimize drawing layouts. In this paper, a BIM-based framework is proposed to facilitate the automatic generation of fabrication drawings for façade panels. The framework integrates both graphical and non-graphical information from BIM models and other external data sources. Specific algorithms are applied to obtain proper drawing views and optimize drawing layouts based on the BIM geometric models. Complete fabrication drawings as well as a tabulated file with essential graphical information on similar components are generated automatically. The framework is demonstrated using an illustrative example and it shows that time and human effort can be significantly reduced. As a result, all the fabrication drawings of similar façade panels follow a consistent drawing format with explicit layout, leading to higher readability. © 2021 Elsevier B.V.

Many modern commercial buildings involve complex shaped façades, resulting in increasing complexity as well as challenges in façade manufacturing and assembly processes. Currently, the keys to manufacture façade panels are fabrication drawings. However, generating fabrication drawings for façade panels using conventional approaches is time-consuming and error-prone, especially when the number of façade panels gets large. Computer-aided automation can significantly improve the efficiency and accuracy of the manufacturing and assembly process, while conventional drawing automation processes are not able to integrate information from various data sources and optimize drawing layouts. In this paper, a BIM-based framework is proposed to facilitate the automatic generation of fabrication drawings for façade panels. The framework integrates both graphical and non-graphical information from BIM models and other external data sources. Specific algorithms are applied to obtain proper drawing views and optimize drawing layouts based on the BIM geometric models. Complete fabrication drawings as well as a tabulated file with essential graphical information on similar components are generated automatically. The framework is demonstrated using an illustrative example and it shows that time and human effort can be significantly reduced. As a result, all the fabrication drawings of similar façade panels follow a consistent drawing format with explicit layout, leading to higher readability. © 2021 Elsevier B.V.

Platform decommissioning decision making is a major issue as many of the existing offshore oil and gas platforms (OOGPs) require to be decommissioned. The cost and environmental impact are two main criteria for OOGP decommissioning. This paper reviews OOGP decommissioning factors and the current estimation methodologies of decommissioning cost and environmental impact. Six factors are identified: (1) platform types and complexity, (2) decommissioning options, (3) technical approaches, (4) circumstances, (5) regulations, and (6) strategies, followed by a detailed review of the estimation methodologies of cost and environmental impact. Descriptive statistics are common cost estimation, together with standard-based and index-based cost estimations. For environmental impact estimation, qualitative methods like ranking decommissioning options based on expert option, and quantitative methods like emission calculation based on equipment utilization, duty cycles, and emission factors are commonly used. The findings of this paper indicate that current OOGP decommissioning databases are incomplete, leading to inaccurate and subjective decommissioning cost estimation, and incomplete environmental impact estimation. Recommendations include integrating 3D information modeling with cost indexing to estimate cost, and with life cycle assessment to estimate environmental impact. OOGP decommissioning regulations should include strategies and rules for estimation, and formulating a roadmap with a long-term perspective is necessary. © 2020 Elsevier Inc.

The drainage system is an important part of civil infrastructure. However, the underground sewage pipe will gradually suffer from defects over time, such as tree roots, deposits, infiltrations and cracks, which heavily affect the performance of sewage pipes. Therefore, it is significant to timely inspect the condition of sewage pipes. Closed-circuit television (CCTV) inspection is a commonly employed underground infrastructure inspection technology requiring engineering experience that can be subjective and inefficient. Nowadays, object detection based on convolutional neural network (CNN) can automatically detect defects, showing high potential for improving inspection efficiency. This paper proposed an improved CNN-based You Only Look Once version 3 (YOLOv3) method for automatic detection of sewage pipe defects, where the improvements are mainly involved in loss function, data augmentation, bounding box prediction and network structure. Experiment results demonstrate that the improved model outperforms Faster R-CNN and YOLOv3, achieving a mean average precision (mAP) value of 92%, which is higher than the existing research on automatic detection of sewage pipe defects. © 2021

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based r

OBJECTIVE: Major depressive disorder (MDD) is associated with deficits in working memory. Several cognitive subprocesses interact to produce working memory, including attention, encoding, maintenance and manipulation. We sought to clarify the contribution of functional deficits in these subprocesses in MDD by varying cognitive load during a working memory task.METHODS: 41 depressed participants and 41 age and gender-matched healthy controls performed the n-back working memory task at three levels of difficulty (0-, 1-, and 2-back) in a pregistered study. We assessed response times, accuracy, and event-related electroencephalography (EEG), including P2 and P3 amplitudes, and frontal theta power (4-8 Hz).RESULTS: MDD participants had prolonged response times and more positive frontal P3 amplitudes (i.e., Fz) relative to controls, mainly in the most difficult 2-back condition. Working memory accuracy, P2 amplitudes and frontal theta event-related synchronisation did not differ between groups at any level of task difficulty.CONCLUSIONS: Depression is associated with generalized psychomotor slowing of working memory processes, and may involve compensatory hyperactivity in frontal and parietal regions.SIGNIFICANCE: These findings provide insights into MDD working memory deficits, indicating that depressed individuals dedicate greater levels of cortical processing and cognitive resources to achieve comparable working memory performance to controls.

Although Southeast Asia is a hotspot of global seagrass diversity, there are considerable information gaps in the distribution of seagrass beds. Broad-scale seagrass distribution has not been updated in the global seagrass database by UNEP-WCMC since 2000, although studies on seagrasses have been undertaken intensively in each region. Here we analyze the recent distribution of tropical seagrass beds, their temporal changes, causes of decline and conservation status in Southeast Asia (plus southern mainland China, Taiwan and Ryukyu Island of Japan) using data collected after 2000. Based on the 195 literature published since 2000, we identified 1,259 point data and 1,461 polygon data showing the distribution of seagrass beds. A large discrepancy was found in the seagrass bed distribution between our updated data and the UNEP-WCMC database, mostly due to inaccurate and low resolution location information in the latter. Temporal changes in seagrass bed area analyzed for 68 sites in nine countries/regions demonstrated that more than 60% of seagrass beds declined at an average rate of 10.9% year–1, whereas 20% of beds increased at an average rate of 8.1% year–1, leading to an overall average decline of 4.7% year–1. Various types of human-induced threats were reported as causes for the decline, including coastal development, fisheries/aquaculture, and natural factors such as typhoons and tsunamis. The percentage of seagrass beds covered with existing marine protected areas (MPAs) varied greatly among countries/regions, from less than 1% in Brunei Darussalam and Singapore to 100% in southern Japan. However, the degree of conservation regulation was not sufficient even in regions with higher MPA coverage. The percentage of seagrass beds within EBSAs (Ecologically and Biologically Significant Area determined by the Convention of Biological Diversity) was higher than that within MPAs becau

Few-shot classifiers have been shown to exhibit promising results in use cases where user-provided labels are scarce. These models are able to learn to predict novel classes simply by training on a non-overlapping set of classes. This can be largely attributed to the differences in their mechanisms as compared to conventional deep networks. However, this also offers new opportunities for novel attackers to induce integrity attacks against such models, which are not present in other machine learning setups. In this work, we aim to close this gap by studying a conceptually simple approach to defend few-shot classifiers against adversarial attacks. More specifically, we propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering, to flag out adversarial support sets which destroy the understanding of a victim classifier for a certain class. Our extended evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance, across three different few-shot classifiers and across different attack strengths, beating baselines. Our observed results allow our approach to establishing itself as a strong detection method for support set poisoning attacks. We also show that our approach constitutes a generalizable concept, as it can be paired with other filtering functions. Finally, we provide an analysis of our results when we vary two components found in our detection approach., Comment: arXiv admin note: text overlap with arXiv:2012.06330

OBJECTIVE: Major depressive disorder (MDD) is associated with deficits in working memory. Several cognitive subprocesses interact to produce working memory, including attention, encoding, maintenance and manipulation. We sought to clarify the contribution of functional deficits in these subprocesses in MDD by varying cognitive load during a working memory task.METHODS: 41 depressed participants and 41 age and gender-matched healthy controls performed the n-back working memory task at three levels of difficulty (0-, 1-, and 2-back) in a pregistered study. We assessed response times, accuracy, and event-related electroencephalography (EEG), including P2 and P3 amplitudes, and frontal theta power (4-8 Hz).RESULTS: MDD participants had prolonged response times and more positive frontal P3 amplitudes (i.e., Fz) relative to controls, mainly in the most difficult 2-back condition. Working memory accuracy, P2 amplitudes and frontal theta event-related synchronisation did not differ between groups at any level of task difficulty.CONCLUSIONS: Depression is associated with generalized psychomotor slowing of working memory processes, and may involve compensatory hyperactivity in frontal and parietal regions.SIGNIFICANCE: These findings provide insights into MDD working memory deficits, indicating that depressed individuals dedicate greater levels of cortical processing and cognitive resources to achieve comparable working memory performance to controls.

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based r

Platform decommissioning decision making is a major issue as many of the existing offshore oil and gas platforms (OOGPs) require to be decommissioned. The cost and environmental impact are two main criteria for OOGP decommissioning. This paper reviews OOGP decommissioning factors and the current estimation methodologies of decommissioning cost and environmental impact. Six factors are identified: (1) platform types and complexity, (2) decommissioning options, (3) technical approaches, (4) circumstances, (5) regulations, and (6) strategies, followed by a detailed review of the estimation methodologies of cost and environmental impact. Descriptive statistics are common cost estimation, together with standard-based and index-based cost estimations. For environmental impact estimation, qualitative methods like ranking decommissioning options based on expert option, and quantitative methods like emission calculation based on equipment utilization, duty cycles, and emission factors are commonly used. The findings of this paper indicate that current OOGP decommissioning databases are incomplete, leading to inaccurate and subjective decommissioning cost estimation, and incomplete environmental impact estimation. Recommendations include integrating 3D information modeling with cost indexing to estimate cost, and with life cycle assessment to estimate environmental impact. OOGP decommissioning regulations should include strategies and rules for estimation, and formulating a roadmap with a long-term perspective is necessary. © 2020 Elsevier Inc.

Many modern commercial buildings involve complex shaped façades, resulting in increasing complexity as well as challenges in façade manufacturing and assembly processes. Currently, the keys to manufacture façade panels are fabrication drawings. However, generating fabrication drawings for façade panels using conventional approaches is time-consuming and error-prone, especially when the number of façade panels gets large. Computer-aided automation can significantly improve the efficiency and accuracy of the manufacturing and assembly process, while conventional drawing automation processes are not able to integrate information from various data sources and optimize drawing layouts. In this paper, a BIM-based framework is proposed to facilitate the automatic generation of fabrication drawings for façade panels. The framework integrates both graphical and non-graphical information from BIM models and other external data sources. Specific algorithms are applied to obtain proper drawing views and optimize drawing layouts based on the BIM geometric models. Complete fabrication drawings as well as a tabulated file with essential graphical information on similar components are generated automatically. The framework is demonstrated using an illustrative example and it shows that time and human effort can be significantly reduced. As a result, all the fabrication drawings of similar façade panels follow a consistent drawing format with explicit layout, leading to higher readability. © 2021 Elsevier B.V.

The drainage system is an important part of civil infrastructure. However, the underground sewage pipe will gradually suffer from defects over time, such as tree roots, deposits, infiltrations and cracks, which heavily affect the performance of sewage pipes. Therefore, it is significant to timely inspect the condition of sewage pipes. Closed-circuit television (CCTV) inspection is a commonly employed underground infrastructure inspection technology requiring engineering experience that can be subjective and inefficient. Nowadays, object detection based on convolutional neural network (CNN) can automatically detect defects, showing high potential for improving inspection efficiency. This paper proposed an improved CNN-based You Only Look Once version 3 (YOLOv3) method for automatic detection of sewage pipe defects, where the improvements are mainly involved in loss function, data augmentation, bounding box prediction and network structure. Experiment results demonstrate that the improved model outperforms Faster R-CNN and YOLOv3, achieving a mean average precision (mAP) value of 92%, which is higher than the existing research on automatic detection of sewage pipe defects. © 2021

Shuangchi Liu,1 Zhiduan Xu,1 Zhuling Fang,2 Dengyong Zhang,1 Zhongqiang Qin,1 Longfei Fan,1 Jiakang Duan,1 Hongxiang Yin,1 Yigang Zhang,1 Qing Pang,1,3 Yi Tan1 1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, 233000, Anhui, People’s Republic of China; 2Department of Medical Laboratory, Bengbu Medical College, Bengbu, Anhui, 233000, People’s Republic of China; 3Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, People’s Republic of ChinaCorrespondence: Yi Tan; Qing PangDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, 233000, People’s Republic of ChinaEmail doctortanyi2007@126.com; portxiu2@126.comBackground: Advanced liver fibrosis can lead to cirrhosis, portal hypertension and liver failure. Besides, advanced liver fibrosis and cirrhosis are the major risk factors for hepatocellular carcinoma (HCC). Almost all patients with HCC also have liver cirrhosis. This study aims to predict the survival rate of hepatitis B-related hepatocellular carcinoma (HCC) by age, international standardized ratio, albumin and γ-glutamyl transpeptidase (AIAG), an indicator measuring the degree of cirrhosis.Methods: A total of 501 hepatitis B-related HCC patients experiencing radical surgery were analyzed, retrospectively. General data about demographics and labs were collected at the date of diagnosis to calculate AIAG [age, international standardized ratio (INR), albumin and gamma-glutamyl transferase (GGT)]. The Kaplan–Meier curves and Cox analysis were used to evaluate overall survival (OS) and recurrence-free survival (RFS). The C-index was calculated in R software (version 4.0.3) to evaluate the accuracy of the prognostic model.Results: During a median follow-up period of 30 months, 31.1% (156/501) of the patients died, and 34.3% (172/501) experienced the recurrence of HCC. Compared with

Wang-Jia Li,1,* Fa-Jin Lv,1,* Yi-Wen Tan,2 Bin-Jie Fu,1 Zhi-Gang Chu1 1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhi-Gang ChuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of ChinaTel +86 18723032809Fax +86 23 68811487Email chuzg0815@163.comBackground: Some pulmonary ground-glass nodules (GGNs) are benign and frequently misdiagnosed due to lack of understanding of their CT characteristics. This study aimed to reveal the CT features and corresponding pathological findings of pulmonary benign GGNs to help improve diagnostic accuracy.Patients and Methods: From March 2016 to October 2019, patients with benign GGNs confirmed by operation or follow-up were enrolled retrospectively. According to overall CT manifestations, GGNs were classified into three types: I, GGO with internal high-attenuation zone; II, nodules lying on adjacent blood vessels; and other type, lesions without obvious common characteristics. CT features and pathological findings of each nodule type were evaluated.Results: Among the 40 type I, 25 type II, and 14 other type GGNs, 24 (60.0%), 19 (76.0%), and 10 (71.4%) nodules were resected, respectively. Type I GGNs were usually irregular (25 of 40, 62.5%) with only one high-attenuation zone (38 of 40, 95.0%) (main pathological components: thickened alveolar walls with inflammatory cells, fibrous tissue, and exudation), which was usually centric (24 of 40, 60.0%), having blurred margin (38 of 40, 95.0%), and connecting to blood vessels (32 of 40, 80.0%). The peripheral GGO (main pathological component: a small amount o

Recent research suggests α-synuclein (α-Syn)-induced alterations in neuronal excitability contribute to cell death in Parkinson's disease (PD). The Fragile X Mental Retardation Protein (FMRP) controls the expression of numerous genes related to cell excitability. Here, we found a reduced FMRP expression in PD cell culture and mouse models. In addition, FMRP expression was also lost in the brain of PD patients. Overall, our results reveal a new role of FMRP in PD., Neuere Forschungen legen nahe, dass α-Synuclein (α-Syn)-induzierte Veränderungen der neuronalen Erregbarkeit zum Zelltod bei der Parkinson-Krankheit (PD) beitragen. Das Fragile X Mental Retardation Protein (FMRP) kontrolliert die Expression zahlreicher Gene, die die Erregbarkeit von Nervenzellen kontrollieren. Wir fanden eine reduzierte FMRP-Expression in PD-Zellkultur- und Mausmodellen. Darüber hinaus war die FMRP-Expression auch im Gehirn von PD-Patienten reduziert. Insgesamt zeigen unsere Ergebnisse eine neue Rolle von FMRP in der Parkinson-Krankheit.

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient.

To effectively control and prevent air pollution, it is necessary to study the influential factors of air quality. A number of previous studies have explored the relationships between air pollution and related factors. However, the methods currently used either cannot well address the multicollinearity problem or fail to explain the importance of the influential factors. Moreover, most of the existing literature limited their studied area in a city or a small region and studied factors in one aspect. There is a lack of studies that analyze the influential factors from the perspective of a country or take into consideration multiple variables. To fill the research gap, this paper proposes a multivariate analysis in the national scale to investigate the most important factors of air quality. In order to study as much influential factors as possible, 171 features ranging from environmental, demographical, economic, meteorological, and energy, were collected and analyzed. To tackle such a “big data” problem, a non-linear machine learning algorithm namely Extreme Gradient Boosting (XGBoost) is utilized to model the relationship and measure the variable importance. Geographical Information System (GIS) is employed to preprocess the diversified variables and visualize the results. Performance of XGBoost is compared with other models and its parameters are tuned using Bayesian Optimization. Experimental results of a case study in the U.S. show that our methodology framework can effectively uncover the important factors of air quality. Six kinds of factors are found to have the largest impact on air quality. Practical suggestions are also proposed from the six aspects to control and prevent air pollution. © 2019 Elsevier Ltd

To effectively control and prevent air pollution, it is necessary to study the influential factors of air quality. A number of previous studies have explored the relationships between air pollution and related factors. However, the methods currently used either cannot well address the multicollinearity problem or fail to explain the importance of the influential factors. Moreover, most of the existing literature limited their studied area in a city or a small region and studied factors in one aspect. There is a lack of studies that analyze the influential factors from the perspective of a country or take into consideration multiple variables. To fill the research gap, this paper proposes a multivariate analysis in the national scale to investigate the most important factors of air quality. In order to study as much influential factors as possible, 171 features ranging from environmental, demographical, economic, meteorological, and energy, were collected and analyzed. To tackle such a “big data” problem, a non-linear machine learning algorithm namely Extreme Gradient Boosting (XGBoost) is utilized to model the relationship and measure the variable importance. Geographical Information System (GIS) is employed to preprocess the diversified variables and visualize the results. Performance of XGBoost is compared with other models and its parameters are tuned using Bayesian Optimization. Experimental results of a case study in the U.S. show that our methodology framework can effectively uncover the important factors of air quality. Six kinds of factors are found to have the largest impact on air quality. Practical suggestions are also proposed from the six aspects to control and prevent air pollution. © 2019 Elsevier Ltd

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient.

To effectively control and prevent air pollution, it is necessary to study the influential factors of air quality. A number of previous studies have explored the relationships between air pollution and related factors. However, the methods currently used either cannot well address the multicollinearity problem or fail to explain the importance of the influential factors. Moreover, most of the existing literature limited their studied area in a city or a small region and studied factors in one aspect. There is a lack of studies that analyze the influential factors from the perspective of a country or take into consideration multiple variables. To fill the research gap, this paper proposes a multivariate analysis in the national scale to investigate the most important factors of air quality. In order to study as much influential factors as possible, 171 features ranging from environmental, demographical, economic, meteorological, and energy, were collected and analyzed. To tackle such a “big data” problem, a non-linear machine learning algorithm namely Extreme Gradient Boosting (XGBoost) is utilized to model the relationship and measure the variable importance. Geographical Information System (GIS) is employed to preprocess the diversified variables and visualize the results. Performance of XGBoost is compared with other models and its parameters are tuned using Bayesian Optimization. Experimental results of a case study in the U.S. show that our methodology framework can effectively uncover the important factors of air quality. Six kinds of factors are found to have the largest impact on air quality. Practical suggestions are also proposed from the six aspects to control and prevent air pollution. © 2019 Elsevier Ltd

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart., Comment: NAACL 2021

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at https://github.com/salesforce/esprit., Comment: ACL 2020

Few-shot classifiers excel under limited training samples, making them useful in applications with sparsely user-provided labels. Their unique relative prediction setup offers opportunities for novel attacks, such as targeting support sets required to categorise unseen test samples, which are not available in other machine learning setups. In this work, we propose a detection strategy to identify adversarial support sets, aimed at destroying the understanding of a few-shot classifier for a certain class. We achieve this by introducing the concept of self-similarity of a support set and by employing filtering of supports. Our method is attack-agnostic, and we are the first to explore adversarial detection for support sets of few-shot classifiers to the best of our knowledge. Our evaluation of the miniImagenet (MI) and CUB datasets exhibits good attack detection performance despite conceptual simplicity, showing high AUROC scores. We show that self-similarity and filtering for adversarial detection can be paired with other filtering functions, constituting a generalisable concept., Comment: Accepted in the International Workshop on Safety and Security of Deep Learning 2021

We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG) induced from existing text-to-SQL datasets. We pre-train our model on the synthetic data using a novel text-schema linking objective that predicts the syntactic role of a table field in the SQL for each question-SQL pair. To maintain the model's ability to represent real-world data, we also include masked language modeling (MLM) over several existing table-and-language datasets to regularize the pre-training process. On four popular fully supervised and weakly supervised table semantic parsing benchmarks, GraPPa significantly outperforms RoBERTa-large as the feature representation layers and establishes new state-of-the-art results on all of them., Comment: 16 pages; Accepted to ICLR 2021

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient., SCOPUS: ar.j, info:eu-repo/semantics/published

Casitas B-lineage lymphoma (CBL) is an E3 ubiquitin ligase and a molecule of adaptor that we have shown is important for non-small-cell lung cancer (NSCLC). We investigated if MET is a target of CBL and if enhanced in CBL-altered NSCLC. We showed that CBL wildtype cells have lower MET expression than CBL mutant cells. Ubiquitination of MET was also decreased in CBL mutant cells compared to wildtype cells. Mutant cells were also more sensitive to MET inhibitor SU11274 than wild-type cells. sh-RNA-mediated knockdown of CBL enhanced cell motility and colony formation in NSCLC cells, and these activities were inhibited by SU11274. Assessment of the phospho-kinome showed decreased phosphorylation of pathways involving MET, paxillin, EPHA2, and VEGFR. When CBL was knocked down in the mutant cell line H1975 (erlotinib-resistant), it became sensitive to MET inhibition. Our findings suggest that CBL status is a potential positive indicator for MET-targeted therapeutics in NSCLC.

Casitas B-lineage lymphoma (CBL) is an E3 ubiquitin ligase and a molecule of adaptor that we have shown is important for non-small-cell lung cancer (NSCLC). We investigated if MET is a target of CBL and if enhanced in CBL-altered NSCLC. We showed that CBL wildtype cells have lower MET expression than CBL mutant cells. Ubiquitination of MET was also decreased in CBL mutant cells compared to wildtype cells. Mutant cells were also more sensitive to MET inhibitor SU11274 than wild-type cells. sh-RNA-mediated knockdown of CBL enhanced cell motility and colony formation in NSCLC cells, and these activities were inhibited by SU11274. Assessment of the phospho-kinome showed decreased phosphorylation of pathways involving MET, paxillin, EPHA2, and VEGFR. When CBL was knocked down in the mutant cell line H1975 (erlotinib-resistant), it became sensitive to MET inhibition. Our findings suggest that CBL status is a potential positive indicator for MET-targeted therapeutics in NSCLC.

Traffic accidents have been one of the most important global public problems. It has caused a severe loss of human lives and property every year. Studying the influential factors of accidents can help find the reasons behind. This can facilitate the design of effective measures and policies to reduce the traffic fatality rate and improve road safety. However, most of the existing research either adopted methods based on linear assumption or neglected to further evaluate the spatial relationships. In this paper, we proposed a methodology framework based on XGBoost and grid analysis to spatially analyze the leading factors on traffic fatality in Los Angeles County. Characteristics of the collision, time and location, and environmental factors are considered. Results show that the proposed method has the best modeling performance compared with other commonly seen machine learning algorithms. Eight factors are found to have the leading impact on traffic fatality. Spatial relationships between the eight factors and the fatality rates within the Los Angeles County are further studied using the grid-based analysis in GIS. Specific suggestions on how to reduce the fatality rate and improve road safety are provided accordingly.