IndraLab

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NET binds CALM1. 61 / 61
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"In the recent phase IV, prospective, open-label, exploratory study “Circulating Tumour Cells in Somatuline Autogel Treated NeuroEndocrine Tumors Patients” (CALM-NET) investigating the effect of the administration of 120 mg lanreotide AG every 28 days on CS symptoms, 70.6% of treated patients had an at least 50% reduction in diarrhea episodes and 77.1% in flushing episodes at follow-up [ xref ]."
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"To overcome these challenges, we developed a platform named Branched CALM-Net that aims to predict students’ stress levels through dynamic clustering in a personalized manner."
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"In the cold-start online learning setting, Branched CALM-Net outperforms other models, attaining an average F1 score of 87% with just 1 week of training data for a new student, which shows it is reliable and effective at predicting stress levels from mobile data."
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"The standard version of our proposed method is called the Cross-personal Activity LSTM Multitask Network (CALM-Net), a system designed specifically to deal with inter-subject variability."
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"An effect of CTC presence on the effectiveness of somatostatin analog (SSA) treatment has been evaluated in the CALM-NET trial; patients with no detectable CTC might be more likely to respond positively to the treatment [ xref ]."
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"CALM-Net learns personalized parameters by considering each subject as a task in a multitask learning structure."
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"With shared and individual parts, CALM-Net can learn both the shared dynamics of the data between all subjects as well as personalized characteristics."
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"We also propose Branched CALM-Net, a variant of CALM-Net designed to find and share parts of the network among similar subjects."
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"Branched CALM-Net combines multitask learning with the Learn-to-Branch technique to efficiently model both personal and population-level characteristics."
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"Here we present CAM-Net, a context-aware microbial correlation framework that fundamentally departs from pairwise approaches."
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"We show that Branched CALM-Net achieves state-of-the-art performance in student stress prediction from as little as 1 week of training data for an individual subject."
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"Specifically, Branched CALM-Net achieves an F1 score of around 0.67 for 3-class stress prediction for a new subject who has only the first week of training data available."
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"With more training data included for a subject, Branched CALM-Net remains the top performer among the tested methods."
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"Thus, Branched CALM-Net enables the accurate detection of high levels of stress from passively collected smartphone data."
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"We evaluate the baselines and our model, CALM-Net (Fig.  xref ) with Learn-to-Branch (Fig.  xref ), in two scenarios."
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"In the cold-start-with-online-learning setting we also compare our results against another baseline: a neural network model that uses student clusters as tasks (named Clustered CALM-Net )."
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"CALM-Net and Branched CALM-Net attain AUC scores of more than 0.8 on student stress detection."
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"Table  xref also shows the performance of Branched CALM-Net, which is the version of our method that uses Learn-to-Branch."
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"In Fig.  xref we show the ROC curve along with the AUC score for Location MLP, GBDT, LSTM, Transformer, CATrans-Net, and CALM-Net models."
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"Empirical results, indicate that our proposed CALM-Net and CATrans-Net significantly outperforms Location MLP, GBDT, Transformer and LSTM, which do not have personalized parameters."
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"Instead of treating associations in isolation, CAM-Net dynamically constructs a ‘local environment’ around a target microbe and infers relational propagation cliques that reflect ecologically meaningful interactions."
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"Branched CALM-Net improves upon the SOTA on 3-class stress level prediction."
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"In this context, we also compare our results to the baseline approach, referred to as Clustered CALM-Net, which explicitly forms student groups."
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"Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over"."
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"According to Table xref , Branched CALM-Net achieves the best performance, which demonstrates the benefit of dynamic clustering."
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"This consistency reinforces the robustness and applicability of our Branched CALM-Net approach in real-world settings."
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"With 1 week of data, Branched CALM-Net achieves 17.24% boost in performance over SOTA."
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"By explicitly controlling for community background, CAM-Net reduces false positives and captures context-dependent ecological dynamics such as facilitation and competition."
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"CALM-Net still outperforms Location MLP which is our baseline model."
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"[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model]."
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"In this case, since there are no training data to train the personalized layers contained in CALM-Net and Branched CALM-Net, we will use the generic layer, which is trained with data from other students to make the predictions."
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"Since branched CALM-Net has more trainable parameters, it would need more data points to achieve good performance, and it can be seen that with zero included data, CALM-Net and clustered CALM-Net outperform this model."
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"When only 1 week of data is included from a student—meaning that the student has been using the application for at least 1 week—Branched CALM-Net outperforms other models."
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"We then inspect the performance of CALM-Net models on predicting binary stress labels (not stressed vs. stressed)."
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"The Branched CALM-Net achieves F1 scores ofandwith one and 2 weeks of data included, respectively, from left-out students."
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"CALM‐NET was a phase IV exploratory study in the UK that aimed to evaluate if the presence of circulating tumour cells (CTCs) at baseline predicted symptomatic response in patients with midgut neuroendocrine tumours (NETs) treated with lanreotide autogel (LAN)."
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"We applied CAM-Net to whole-genome sequencing (WGS) and 16S rRNA gene sequencing datasets encompassing over 30,000 human gut microbiomes, focusing on two microbes: Akkermansia muciniphila and Lactobacillus acidophilus ."
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"CALM‐NET was a phase IV exploratory study designed to evaluate if the presence of circulating tumour cells (CTCs) could be used to predict symptomatic response in patients with midgut neuroendocrine tumours (NETs) treated with lanreotide autogel (LAN)."
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"Strengths of the CALM‐NET study include the prospective design, and adequately powered evaluation."
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"Despite these limitations, CALM‐NET is one of only a few studies evaluating the prognostic significance of CTCs in patients with NETs, and the only study to date assessing disease outcome in relation to CTCs in patients with functional NETs."
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"If we train CALM-Net directly on the binary stress labels, the precision in recall of is aroundwith an AUC of, worse than the performance shown in Table  xref and Fig.  xref where the models are trained with 3 distinct stress labels."
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"The experiments conducted in the cold-start online learning setting, the results of which are summarized in Fig.  xref , indicate that CALM-Net and Branched CALM-Net can quickly adapt to new subjects, with Branched CALM-Net outperforming the other methods as long as the subject has at least 1 week of training data."
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"Notably, Branched CALM-Net employs a learning-to-branch method that dynamically clusters students during the training process."
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"Overall, Branched CALM-Net is able to cluster new students into a closer group, providing accurate stress level predictions after the first week of data collection as shown in Fig.  xref ."
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"Although most patients do not have uniform high expression of SSTRs throughout their tumors, the expression of these SSTRs has been measured in circulating tumor cells from midgut NEN patients ( xref ), and a recent clinical trial called the “CALM-NET” study has tested if the effectiveness of SSTR targeting therapies can be predicted from circulating tumor cells ( xref )."
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"Following the solid empirical evidence of the performance of CALM-Net and Branched CALM-Net under both the warm-start and cold-start settings, we summarize our contributions as follows: To sum up, our study demonstrates the ability of our system to effectively address the complex task of predicting student stress levels using mobile phone data."
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"In contrast, for L. acidophilus , an exogenous species of the human gut, CAM-Net recovered only a weakly correlated clique, consistent with prior knowledge of its transient colonization."
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"In this study, we introduced CALM-Net and its branched version, Branched CALM-Net, for stress level prediction in the StudentLife dataset."
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"LAN has been shown to improve symptoms and PFS in patients with functional NETs when administered at doses of up to 120 mg every 28 days. xref , xref , xref , xref LAN prolongs PFS in those with nonfunctional, locally advanced or metastatic enteropancreatic NETs. xref  The aim of the CALM‐NET exploratory study was to determine whether pretreatment CTC status can help predict the clinical symptomatic response to LAN in patients with functional midgut NETs; CTCs were also enumerated during the study."
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"The results from the CALM‐NET study demonstrate that a clinical symptomatic response was achieved for the majority of patients with midgut NETs treated with LAN."
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"This paper also proposes a novel multitask learning model with dynamic clustering called Branched CALM-Net, which aims to provide accurate stress prediction using data collected from smartphones."
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"The experimental results presented in “ xref ” section demonstrate that CALM-Net and Branched CALM-Net outperform the state-of-the-art methods xref , xref in both binary diagnosis and stress level prediction tasks."
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"For A. muciniphila , an autochthonous bacterium, CAM-Net identified a relational propagation clique that was reproducible across independent datasets, underscoring its symbiotic ecological role."
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"These findings show CAM-Net as a robust, context-aware alternative to conventional correlations, moving beyond sparse pairwise comparisons to infer microbial interactions and decode microbial ecology."
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"Both CALM-Net and Branched CALM-Net achieved peak performance with an F1-score of 0.602 and 0.605, respectively, in the five-fold cross-validation setting in the 3-level stress classification task."
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"Branched CALM-Net outperformed all other models with just 1 week of training data available under leave-one-subject-out."
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"In the binary diagnosis task, Branched CALM-Net maintained a precision of over 84% and 82%, while recovering 90% and 95% of positive cases, respectively."
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"With this in mind, this work presents a novel meta-learning-based semantic segmentation method, MetaSeg, that comprises a primary content-aware meta-net (CAM-Net) to serve as a noise indicator for an arbitrary segmentation model counterpart."
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"The subsequent CALM-NET clinical trial evaluated the potential use of CTCs to monitor disease progression."
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"Specifically, CAM-Net learns to generate pixel-wise weights to suppress noisy regions with incorrect pseudo-labels while highlighting clean ones by exploiting hybrid strengthened features from image content, providing straightforward and reliable guidance for optimizing the segmentation model."
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"Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies."
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