dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the database retrieves the last value of this property and returns it to the DH which will then send the response to the user. The choice of these two operations is not accidental. The first is a write operation, while the second is a read operation. It is essential to realize that only write operations require communication with the device. In contrast, read operations use the database as a kind of cache for the data requested by the IoT device. When we are talking about TD, we see that the possible interactions of an IoT device according to this schema are actions, properties, and events. The invocation of an action will always be a write operation while listening to an event is always a read operation, and in the case of properties, these can be both read and write operations. Reducing the number of requests executed by the IoT device is very important, especially when talking about industrial environments.
Physical device strategies Reflection is the only point of connection with the physical device and prevents the device from collapsing; it is the primary decision regarding device communication strategies. As we have previously said, many IoT devices have notable limitations in terms of performance since they are often governed by micro-controllers of the ESP32 type or similar, which are designed above all to prioritize energy saving. Furthermore, in some IoT environments, such as the bus-based KNX or Modbus installations, we find limitations in the number of connections we can establish. When many users want to request data simultaneously, the bus can collapse and then all the requests are denied. The use of Reflection, as well as using the database as an intermediate system, lead to the reading operations not having to reach the physical device, which already avoids many requests to the said devices.
In addition, having a single point of connection with the device ensures that the writing operations are on a first-come, first-served basis. The Reflection operation is based on subscribing to the reading interactions of the device. If the device does not allow the subscription to interact, Reflection is responsible for making long-polling requests to the device every X seconds, X being a configurable time. Furthermore, Reflection listens to the writing operations related to the controlled device. When the microservice detects that a given operation requests changes on the device, it will execute it. The choice of MongoDB means that the Reflection does not need to be continuously requesting data from the database. MongoDB allows us to subscribe to particular queries through a reactive pattern, making the database responsible for notifying the subscriber when new data meets the parameters established in the query.
Communication strategies When discussing communication strategies in DD, we have to divide them into two different viewpoints, namely, how DD communicates with the end-user and what happens with the communication between the microservices. As we have already mentioned, DD uses the standard proposed by the WoT. This standard establishes a definition file, the Thing Description, which encapsulates all the interactions DD can manage. The Thing Description aims to be to things or virtual things (Digital Twins -Digital Dice) what Open API is to a service. One of the Digital Dice premises is to facilitate its use by any web or mobile developer, so from the inception of DD, we decided that end-users should be able to interact with DD through an API. However, the use of an API in the field of IoT brings with it a series of limitations when we talk about data in real-time, so in the end, our DD uses what we could call a hybrid connection pattern.
In this figure, we mean by errors those requests where the response is part of the 400 Series Client Error Status Codes, like the responses 404 Not found or 408 Request Timeout. To perform the measurement, we used two different platforms for the synthetic generation of requests on our system, Locust and JMeter. In the upper-left chart of Fig. 4a , we see that errors appear from a relatively low number of requests per second in the different physical devices we used as a baseline. Interestingly, in bus-based connections, like KNX, we find that after eight simultaneous reading operations, time-outs begin to surface since the data buffer of this system collapses. Conversely, in the upper-right chart of Fig. 4b , we can see how our Digital Dice performs as far as reading operations are concerned.
This point is reached when we get to the maximum amount of requests that can be handled by the database simultaneously. At that time, the database becomes the limiting factor. After increasing the database resources, we observed that the number of requests increased as expected. Furthermore, suppose we increase the number of replicas of the microservices and the number of replicas of the database in our deployment to more than five. In that case, we reach another limiting factor, the bandwidth from where we launch the synthetic requests. We suspect that if we used a network of nodes to launch the requests in a parallelized way, our system would continue to scale the requests as expected, but due to the cost it would entail, we have not yet been able to confirm this hypothesis. The same happens with config#2, as DD is not the limiting factor.
The lower-left chart of Fig. 4c  represents the write requests that our system can handle. Again, the behavior is similar to read operations, but collapse occurs much earlier in the two configurations. The collapse happens because Reflection establishes a queue with the write requests that arrive at the database, requests that will be sent sequentially to the physical device. This behavior occurs when the number of requests that reach the database exceeds the number of requests that our physical device can accept, and the queue in Reflection gets filled up. As such, we run into the limitation of writing operations onto the physical device. Finally, the result displayed in the lower-right chart of Fig. 4d  allows us to see the behavior of server-sent events (SSE). This type of request is quite challenging to test since practically none of the load testing tools can work with these.
To test SSE, we must generate custom test scripts using a Java Sampler. Once launched, we see that the behavior differs from the usual read operations. First, fewer requests can be processed at the same time. In addition, we can observe that the error spikes, even though they appear to be in sync with the replication of a service, are more pronounced and seem to affect the number of requests processed by the system. Furthermore, using five as the maximum replication factor, the system does not find any limitations in the database, unlike with the read operations. In config#2, where the replication factor for the microservices is set at seven, we can see how we start to find the same limiting factors again as with the read operations. The replication becomes erratic with SSE using the CPU limit as a replication metric because the containers are almost idle until a message is triggered.
We are studying how to establish other custom replication metrics in future work, such as the number of requests per second our system receives. With this metric, microservices will anticipate the need for replication, making our system even more resilient. Augmenting the number of replicas or giving more resources to the database certainly improves the performance of the different operations in Digital Dice. However, due to the cost of the operation using more replicas, we decided that the configuration used was good enough to see the behavior of Digital Dice for all the different types of operations.
In this scenario, the virtualization services are responsible for imitating the behavior of containers and trucks. The latter's movements are established from the generated route, which is defined by the containers to be collected, whose optimal route is calculated thanks to Google Directions API. Unless the user indicates how much to fill a particular container manually, the containers can execute a virtualization behavior, which fills each container every x seconds with a semi-random weight depending on the container we are dealing with. In the ACG WoT-Lab you can find an interface for the management and monitoring of this scenario, specifically in this case, the interface to follow the data from truck 2. Furthermore, WoT-Lab shows a scenario related to smart homes and gives access to Thing Descriptions that represent physical devices, virtual devices, and Digital Dice.
Introduction: data-driven decision requires causal inference Medicine increasingly relies on data, with the promise of better clinical decision-making. Machine learning is central to this endeavor. On medical images, it achieves human-level performance to diagnose various conditions  (Aggarwal et al. , 2021; Esteva et al. , 2021; Xiaoxuan Liu et al. , 2019). Using Electronic Health Records (EHRs) or administrative databases, it outperforms traditional rule-based clinical scores to predict a patient's readmission risk, mortality, or future comorbidities  (Rajkomar, Oren, et al. , 2018; Y. Li et al. , 2020; Beaulieu-Jones et al. , 2021). And yet, there is growing evidence that machine-learning models may not benefit patients equally. They reproduce and amplify biases in the data  (Rajkomar, Hardt, et al.
, 2022) , statistics  (Belloni et al. , 2014; Chernozhukov et al. , 2018) , machine learning  (Shalit and Sontag, 2016; Sharma, 2018; Moraffah et al. , 2021)  or econometrics (Guido W  Imbens and Wooldridge, 2009). Time-varying features of EHR data, however, raise particular challenges that call for an adapted framework. We focus on single interventions: only one prescription during the study period, e. g. , a patient either receives mechanical ventilation or not during admission to an intensive care unit compared to, e. g. blood transfusion which may be given repeatedly. Section 2 details our proposed step-by-step analytic framework on EHR data. Section 3 instantiates the framework by emulating a trial on the effect of albumin on sepsis using the Medical Information Mart for Intensive Care database (MIMIC-IV) database (A.
Step 2: identification -List necessary information to answer the causal question The identification step builds a causal model to answer the research question (Figure  6 ). Indeed, the analysis must compensate for differences between treated and non-treated that are not due to the intervention (Pearl and Mackenzie, 2018, chapter 1, Hernàn and James M Robins, 2020, chapter 1). Causal Assumptions Not every question can be answered from a given dataset: valid causal inference requires assumptions  (Rubin, 2005)  -detailed in Appendix D. The analyst should thus review the plausibility of the following: 1) Unconfoundedness: after adjusting for the confounders as ascertained by domain expert insight, treatment allocation should be random; 2) Overlap -also called positivity-the distribution of confounding variables overlaps between the treated and controls -this is the only assumption testable from data (Austin and Stuart, 2015)-; 3) No interference between units and a constant version of the treatment, a reasonable assumption in most clinical questions.
Estimation Confounder aggregation: We tested multiple aggregations such as the last value before the start of the follow-up period, the first observed value, and both the first and last values as separated features. Causal estimators: We implemented multiple estimation strategies, including Inverse Propensity Weighting (IPW), outcome modeling (G-formula) with T-Learner, Augmented Inverse Propensity Weighting (AIPW) and Double Machine Learning (DML). We used the python packages dowhy  (Sharma, 2018)  for IPW implementation and EconML  (Battocchi et al. , 2019)  for all other estimation strategies. Confidence intervals were estimated by bootstrap (50 repetitions). Appendices E. 1 and E. 3 detail the estimators and the available Python implementations. Outcome and treatment estimators: To model the outcome and treatment, we used two common but different estimators: random forests and ridge logistic regression implemented with scikit-learn  (Pedregosa et al.
g. via billing codes, the time-wise dimension requires aggregations (subsection 2. 3). These challenges can create subtle causal biases (subsection 2. 1). To ensure that our framework addresses all aspects of EHR analysis and to expose it in a didactic way, we detailed a complete analysis of a publicly-available EHR dataset, supported by open code. A well-framed target trial can be validated Assessing the validity of an analysis is challenging even for experts  (Ioannidis, 2005; Breznau et al. , 2022). Our framework recommends using a well-specific target trial to establish a valid pipeline because it helps confronting the resulting average treatment effect to other evidence  (Hernán and James M. Robins, 2016; S. V. Wang, Schneeweiss, et al. , 2023). Our resuscitation-fluid analysis matches well published findings: Pooling evidence from high-quality RCTs, no effect of albumin in severe sepsis was demonstrated for both 28-day mortality (odds ratio (OR) 0.
, 2021) , safer for clinical use  (Richens et al. , 2020) , and more fair  (Plecko and Bareinboim, 2022). Capturing individualized effects via machine-learning models does require to probe many diverse individuals. EHRs and claims data are well suited for these models, as they easily cover much more individuals than a typical clinical study. But EHRs cannot inform on trade-offs that have not been explored in the data. No matter how sophisticated, causal inference cannot conclude if there is no data to support an apple-to-apple comparison between treated and non-treated individuals. For example, treatment allocation is known to be influenced by race-and gender-concordance between the patient and the care provider. Yet, if the EHR data does no contain this information, it cannot nourish evidencebased decisions on such matter.
The all-stay model performance markedly decreases in the pre-treatment only dataset. low hypoxemia. Authors concluded that their model was heavily dependent on clinical context and baseline caracteristics. There might be some starting-time bias in this study since it is really close • D. J. Hsu et al. , 2015  studied the effect of indwelling arterial catheters (IACs) vs non-IAC for 1,776 patients who are mechanically ventilated and did not require vasopressor support on 28-day mortality. They used propensity score matching and found no effect. A notebook based on google cloud access to MIMIC-IV replicating the study is available here. •  Feng et al. , 2018  studied the effect of transthoracic echocardiography vs no intervention for 6,361 patients with sepsis on 28-day mortality. They used IPW, PSM, g-formula and a doubly robust estimation.
The propensity score was modeled with boosting and the outcome model with a logistic regression. They found a significant positive reduction of mortality (odd ratio 0. 78, 95% CI 0. 68-0. 90). Study code is open source. •  Gani et al. , 2023  studied the effect of liberal -target SpO2 greater than 96%-vs conservative oxygenation -target SpO2 between 88-95%-in 4,062 mechanically ventilated patients on 90-day mortality. They found an advantage of the liberal strategy over liberal (ATE=0. 13) by adjusting on age and apsii. This is not consistent with previous RCTs where no effects have been reported  (Panwar et al. , 2016; Mackle et al. , 2019). •  Shahn et al. , 2020  studied the effect of fluid-limiting treatment -caped between 6 and 10 L-vs no cap on fluid administration strategies for 1,639 sepsis patients on 30 day-mortality.
3, 4 Thus, treatment options for many TNBC patients remain limited to regimens using cytotoxic chemotherapies, which themselves lack predictive biomarkers for guided agent selection. 5  recent years, there have been some advances for treating TNBC more effectively. [8] [9] [10] [11] [12] [13] [14] [15] [16]  Atezolizumab is given with nab-paclitaxel,  11  and pembrolizumab is given with a broader range of chemotherapeutics. 10 Pembrolizumab has also been approved with chemotherapy (various) in high-risk primary TNBC in the neoadjuvant setting. 17 In addition to immunotherapies, PARP inhibitors olaparib and transcriptomics (deep RNAseq $200M reads/sample), and mass-spectrometry-based total proteomics) and compared these data with large patient datasets to demonstrate the range of representation of the PDX models.
We then aggregated single-agent responses to docetaxel or carboplatin across three recently completed PDX-based preclinical trials designed to approximate human equivalent dosing and scheduling in the mouse (to be described in full elsewhere) for analysis. Because of the sequencing depth, and the availability of both PDX-based and clinical datasets with which to validate our observations, we ultimately selected mRNA for generation of differential response signatures. The transcriptomic data contain a wealth of information, but as with other ''omics'' studies, these data are high dimensional, and the number of features (genes) vastly exceeds the number of observations (PDXs). Because there are many more features than observations, feature selection is crucial in identifying informative genes, and in avoiding overfitting, to develop robust models that are generalizable across datasets.
Results may also be associated with inter-sample biological heterogeneity rather than to actual response. Furthermore, the use of module eigengenes to determine if a module is associated with response may miss more highly informative genes in an otherwise less informative module. This happens when a large module contains a small, more highly informative, submodule whose signal is diluted by the presence of the large number of less informative genes. To test our hypothesis that PDX-based cohorts can yield translationally relevant predictive information, and to address one of the limitations of WGCNA, we adapted a newly developed network algorithm, CTD,  65  ''Connect The Dots,'' in our molecular data relative to single-agent chemotherapy responses. Although this method has been used previously to identify transcriptomic differences between breast cancer subtypes, this is the first time that CTD has been used as a feature selection method to identify informative genes relative to a phenotype of interest.
CTD is used in addition to WGCNA to identify highly connected sets of analyte nodes and to assign an upper-bounded p value to sets of nodes. Although other networkbased algorithms also estimate p values, they use permutation testing, which is not feasible for large gene co-expression graphs with thousands of genes without prior feature selection. Our ability to assign p values without the need for permutation testing allows the identification of small sets of nodes that are significantly associated with a phenotype. This algorithmic advancement allowed for the identification of a specific set of genes that were differentially associated with docetaxel and carboplatin response/resistance in our PDX cohort. These PDX-derived informative panels were validated for their predictive power in other PDX collections, as well as in two clinical datasets.
67 This PDX collection shows a wide range of responses to docetaxel and carboplatin, with some PDX being cross-resistant or cross-responsive, but others showing differential responses to these two agents, suggesting that molecular signatures of differential treatment response could be generated that might also have predictive power in clinical datasets. 40  evaluate the quality of our TNBC PDX multi-omic data and to determine if our collection of TNBC PDX reflected a significant cross section of TNBC clinical samples represented in TCGA (genome and transcriptome) and CPTAC (proteome) to be considered a representative preclinical cohort, we compared DNA, RNA, and protein-based assays. At the DNA level, we first compared genomic copy number alteration patterns in our cohort relative to TCGA samples using whole-exome sequencing (WES) data.
With respect to gene expression analysis at the transcriptomic level, deep RNAseq ($200M reads per sample) GSE183187 data from untreated PDX models (n = 93) were hierarchically clustered with patient tumors represented in TCGA that have been classified with PAM50 (n = 849) using the PAM50 signature (Figure  3A ). 68 Although the PAM50 gene set does not perform well for subtyping PDX relative to its patient-matched tumor of origin, it is useful for demonstrating that the PDX models do not cluster together as a single group but are interspersed among basal-like TCGA samples (88%) and luminal TCGA samples (12%), suggesting transcriptomic heterogeneity in PDX TNBC cohort that models primary human tumors. With respect to gene expression analysis at the proteomic level, we detected 8,346 human proteins in at least half of the PDX models by mass spectrometry (MSV000088233, Table  S4  which is related to Figure  3 ).
As with the RNAseq analysis, PDX models were again distributed across the samples represented in the CPTAC proteomic dataset using the top 1,000 most variable proteins (Figure  3B ). The clustering pattern was remarkably like that derived from mRNA data, again with two main groups clustering with luminal tumors (16%), and the majority clustering in the basal-like group (84%), with PDX models distributed across the full spectrum of basal-like tumors. For PDXs that had both proteomics and expression data available, we found that 89% of the PDXs were assigned to the same group by clustering with proteomics and expression data from primary TCGA tumors.
Identification of multigene biomarker panels predictive of differential chemotherapy response Given the high degree of concordance in the treatment response analysis (at least for taxanes), we hypothesized that the PDX could be used to develop differential taxane/platinum response predictors. Here, we used the RNAseq transcriptomic data, as this was the most robust ''omic'' type. Widely used tools such as Xenome  79  allow for the separation of murine and human genes. In this study we performed deep RNA-seq ($200M paired end reads per sample), which allowed us to identify far more unique genes (25,384 human genes and 21,552 murine) than proteins (8,346 human proteins) that were identified by our proteomics analysis. Finally, transcriptomic datasets are more widely available for the validation of candidate biomarkers.
In fact, there are no relevant proteomics datasets in existence by which we could evaluate a protein-derived signature. For each PDX, epithelial gene expression and stromal gene expression were evaluated by separating the RNAseq reads by species using Xenome  79  This separation allowed us to evaluate both epithelial and stromal genes that may be associated with response for each approach. For both the WGCNA and combined CTD/WGCNA approaches, we first built a pair of weighted genegene co-expression networks for each cell type (stromal or epithelial)/response combination using WCGNA only (biweight midcorrelation). For each condition, one of these graphs was built over all the PDX models, whereas the other was built only over the responsive PDXs (CR and PR). We first removed all weak edges (edge weight <0.
2). Because gene-gene correlation could be caused by heterogeneity in the samples that is not related to the responsiveness of the samples to the agents in question, we then pruned or removed edges with the CTD graph naive pruning function, 80 that were found in the graph built over just the responsive PDXs to ensure that we eliminated noise not related to response. By removing these edges, we both eliminated gene-gene variance that may not be associated with response, and made the networks more sparse, and thus easier to evaluate. This approach produced four pruned networks that were specific to both a tissue compartment and chemotherapy treatment (i. e. , human epithelial carboplatin, human epithelial docetaxel, murine stromal carboplatin, murine stromal docetaxel). Pruned networks were then broken into modules with WGCNA using the standard approach (described in detail at https://horvath.