dataesr/scientific-paragraphs-categorization huggingface dataset:
Tissue-specific quantification of global transcriptomic alterations across age To assess the global transcriptomic impact of each of the three modelled effects in each tissue across age, we then analyzed the progression over age (i. e. over consecutive linear models) of the percentage of genes whose expression is significantly altered (t-statistic's pvalue ≤ 0. 01) by each effect (Figure  3B ). To evaluate the significance of each percentage and see if high ones can be confidently associated with major transcriptomic alterations, we controlled for their false discovery rate (FDR) by randomly permuting the samples' ages within each age window one million times and performing ShARP-LM on each randomised dataset. We were then able to associate an FDR to each percentage of differentially expressed genes by comparing it with the distribution of those randomly generated (Figure  3B ).
Gene Set Enrichment Analysis (GSEA) For each Peak of significant gene expression modifications, we performed GSEA  (48)  on the ordered (from the most positive to the most negative) t-statistics of differential expression for the respective tissue and age, using R package fgsea  (73)  and the Reactome database  (47). The resulting normalized enrichment score (NES) for each pathway was used in subsequent analyses as a metric of its over-or down-representation in the Peak.
Gene co-expression modules Gene co-expression modules were defined with R package WGCNA  (51). For each tissue, genes were first filtered based on the variance of their expression across samples (variable A), with lowly variant ones being discarded from subsequent co-expression analyses. Then, an adjacency matrix was computed with the adjacency function. To this end, we first computed the matrix of Pearson's correlation between all selected genes' expressions and then raised all of its values to a power β (soft thresholding) to emphasise stronger correlations. The soft threshold was determined with the pickSoftThreshold function. The thereby generated matrix (|correlation| β ) was then converted into a dissimilarity matrix based on the topological overlap measure, determined with function TOMsimilarity.
Finally, the 40%, 30%, 35%, and 40% most variant genes, the tree cut at a height of 0. 96, and a minimum number of 20, 18, 11, and 16, were respectively chosen for the three variables in those four tissues. Each module is then represented by a set of genes and an eigengene, the first principal component obtained by singular-value decomposition of the expression of the module's genes and representative of its own expression profile. The enrichment of modules in cell types, Reactome pathways and diseases from DisGeNET  (55) ,  (56)  was quantified with Fisher's tests. Disease enrichment was also calculated using the DOSE package  (57).
Editors Reviewing voyAGEr implements many kinds of interesting R-based tools such as pathway overrepresentation analysis and gene co-expression module analysis, in a way that makes these approaches accessible to non-bioinformaticist aging researchers. As the tidal wave of publicly available large, high-dimensional datasets such as transcriptomes continues to grow exponentially, the usefulness of tools such as voyAGEr will only increase. While test users may be able to imagine features or refinements they wish were already present, due to the open source approach they or anyone else including but not limited to the present authors can implement additional features in the future. I look forward to using this tool and to staying abreast of its future development. Overall, this study describes a new tool of interest to the field.
Reviewer #2 (Public Review): The purpose of this study is to develop a tool that serves as a starting point for investigating and uncovering genes and pathways associated with aging. The tool utilizes information from the GTEx public database, which contains post-mortem human data. It focuses on identifying age-related gene expression changes across different age range, biological sexes, and medical histories, with a focus on specific tissues. Additionally, the authors envision the platform as continuously evolving, with ongoing development and expansion to include new data and features, ensuring it remains a cuttingedge resource for researchers studying aging. # Strengths voyAGEr presents a tool for exploring gene expression changes across multiple tissues in the context of aging. One of the main strengths of the tool is its intuitive and user-friendly interface, which allows for easy navigation and exploration of gene expression patterns for biologists.
# Weaknesses ## Underlying data analysis In this tool/resource paper, it is crucial that the data used is up-to-date to provide the most comprehensive and relevant information to users. However, the authors utilized GTEx v7, which is an outdated (2016) version of the dataset. It is worth noting that GTEx v8 includes over 940 individuals, representing a 35% increase in individuals, and a 50% increase in the total number of samples. The authors should check the newer versions of GTEx and update the data. The authors did not address any correction for batch effects or RNA integrity numbers, which are known to affect transcriptome profiles. For instance, our analysis of GTEx v8 Cortex tissue revealed that after filtering out lowly expressed genes, in the same way authors did, PC1 (which accounts for 24% of the variation) had a Spearman's correlation value of 0.
48 (p<6. 1e-16) with RNA integrity number. The data analyzed in the GTEx dataset is not filtered or corrected for the cause of death, which can range from violent and sudden deaths to slow deaths or cases requiring a ventilator. As a result, the data may not accurately represent healthy aging profiles but rather reflect changes in the transcriptome specific to certain diseases due to the age-related increase in disease risk. While the authors do acknowledge this limitation in the discussion, stating that it is not a healthy cohort and disease-specific analysis is not feasible due to the limited number of samples, it would be useful for users to have the option to analyze only cases of fast death, excluding ventilator cases and deaths due to disease. This is typically how GTEx data is utilized in aging studies.
The authors should clearly elaborate on how to interpret the NES from their results. 6. In the Modules of co-expressed genes section, the authors did not explain how or why they selected the four tissues: brain, skeletal muscle, heart (left ventricle), and whole blood. This should be elaborated on. 7. In the modules of the co-expressed genes section, the authors did not provide an explanation of the "diseases-manual" sub-tab of the "Pathway" tab of the voyAGEr tool. It would be helpful for readers to understand how the candidate disease list was prepared and what the results represent.
This fascinating paper by A. L. Schneider et al. describes voyAGEr, a shiny-based interface for easy exploration of the GTEx dataset by non-or novice programmers. Importantly, voyAGEr is open source and available from github, which could greatly accelerate additional development and further uses of this interesting tool. The authors developed a pipeline for modeling age-related changes in gene expression in the GTEx data called ShARP-LM, fitting a linear model for age, sex, and age&sex interaction terms. This pipeline underlies the later analyses that can be applied within voyAGEr. These analyses are labeled by tissue so that users can easily begin a query based on a tissue or a gene of possible interest. Editor Bérénice Benayoun University of Southern California, United States of America Senior Editor Carlos Isales Augusta University, United States of America Reviewer #1 (Public Review):.
Acknowledgments To  Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports de Barcelona  for granting computational resources in the cluster Titani, which contributed to the results presented in this article. The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska -Curie  grant agreement no. 814066  ( Managed Aquifer Recharge Solutions Training Network -MARSoluT)  and project  GRA-DIENT  (reference no. PID2021-127911OB-I00 ) from the  Ministry of Science and Innovation of Spain.
Once the multicast Fig. 3. Illustration of delivery methods in 5G NR  [43]. session expires the transport provided by MBS is released and can be utilized for another session. From the 5GC perspective, two delivery methods are defined  [43] , as illustrated in Fig. 3. In both cases, only one copy of the data packet is delivered to the 5G Core Network (5G CN). Then, the procedure differs for individual and shared MBS delivery options. In the former case, a separate content copy is delivered by 5G CN to all the participating UEs via separate Protocol Data Unit (PDU) sessions. When instead the shared mode is utilized, a content copy is first delivered to RAN nodes, and then these nodes are responsible for the delivery of the data traffic to UEs. As the shared mode potentially allows a more efficient radio resource utilization, we concentrate on this option.
Furthermore, enhanced-QoE multicast applications require ultra-low latency data transfer capability, high dependability, extended coverage, and blockage mitigation to provide appropriate signal quality to UEs in unfavorable locations. Intelligent environments: One of the main purposes of the platforms that exploit MTC in 5G systems is the possibility of supporting stakeholders and users in using services in everyday environments at reduced costs, of contributing to the improvement of their quality of life and to optimize their work activities by providing them with real-time data on which to base their decisions. A prime example of the benefits deriving from multicast MTC applications is provided by users who, through their UEs, transmit messages outside their residences to a series of actuators to turn on/off electrical equipment (such as the heating/cooling system).
Drone multicasts may also assist terrestrial multicast systems in relaying information to group members in nLOS conditions. Drones often relocate to acquire data from a variety of angles and to adjust the Lineof-Sight (LOS) states between sky and ground UEs. In conclusion, multicast transmissions can provide more efficient delivery than unicasting every time a given data from an application must be delivered to multiple UEs simultane-ously. Thus, multicasting is expected to be vital for 5G/6G applications in many verticals and applications  [60].
To this aim, ITU-R in Rec. P. 1410 offers an alternative approach specifying the LOS probability as an explicit function of environmental parameters in the following form P IT U L = m n=0   1 -exp   [h A - (n+ 1 2 )(h A -h U ) m+1 ] 2 2ϵ 2 3     , (11) where m = ⌊r (ϵ 1 ϵ 2 )⌋ -1 is the mean number of buildings in between UE and BS, h A and h U are BS and UE heights. The parameters ϵ 1 , ϵ 2 , and ϵ 3 are the input model parameters representing the deployment specifics, including building dimensions, density, and height. These parameters can be related to the different parts of the cities, such as high-rise urban, urban, and suburban, by utilizing ITU-R typical districts data provided in ITU-R Rec. P. 1410 as demonstrated in  [27].
If there is the need to account for molecular absorption, one has to complement the propagation model with an additional factor, L A (f, y), obeying the Beer-Lambert law  [96]   L A (y) = 1 τ (f T,c , y) , (17) where τ (f T,c , y) is the transmittance of the medium following the Beer-Lambert law, τ (f T,c , y) ≈ e -K A y , K A is the absorption coefficient calculated on the base of the HITRAN database  [97] , as demonstrated in  [96]. A much more pronounced effect is produced by weather conditions (e. g. , fog, rain) and trees. In the latter case, the impairments can be as high as 0. 5 -2 dB/m 3. Detailed values of these impairments are provided in  [19]. Finally, the absorption phenomena may also lead to the molecular noise theoretically predicted in  [96]. The theoretical model for molecular noise has been proposed in  [98].
Below, we illustrate three classes of algorithms based on their complexity: (i) classification/regression models, (ii) decision trees and forests, and (iii) neural networks. Running the optimal solution for a multicast group with a small number of UEs can provide data that can be used to develop a fast algorithm that can solve the multicast subgrouping problem even in the case of a multicast group with many more UEs. Thus, supervised learning is the suitable class of ML algorithms. In supervised ML techniques, the algorithm leverages labeled training on a prepared dataset (i. e. , data provided by the optimal solution) and returns outcomes (i. e. , a class or a predicted value), which can be evaluated in terms of accuracy using labels from the training dataset. Both an online and offline learning platform based on recent International Telecommunication Union (ITU) standards for 5G ecosystems  [109] -  [111]  can implement the supervised algorithm.
If ML and optimization outcomes match perfectly, the ratio of occupied to available resources, ρ, coincide for both metrics, i. e. , ρ opt = ρ M L. Moreover, due to the discrete nature of resource allocation and MCSs/spectral efficiency mapping, the investigated metrics ρ opt and ρ M L may be close even when the number of subgroups and UEs assigned to these subgroups varies. Considering that resource usage is the primary metric of interest, one may also examine a second metric based on the resulting resource utilization matching: γ = ρ M L ρ opt × 100%. Due to the availability of the training datasets, supervised learning algorithms, whose most common learning task is classification, can be utilized. For the problem at hand, one may consider the following supervised learning algorithms: • Decision Tree learning splits the dataset based on different conditions and can be used for classification and regression predictive models.
The foremost benefit of this approach consists of the construction of an interpretable model. Hence, these algorithms are often referred to as white-box implementations  [112]. Increasing the number and size of connected layers in NN models boosts their flexibility. Table  VII  provides the characteristics of considered algorithms in terms of interpretability, which allows for a better understanding of obtained solutions. This feature makes it easy to avoid solution mistakes or errors and compensate for them. With higher interpretability, one can understand how an ML model makes its decision. All the above-mentioned algorithms can solve the required classification problem, namely, assigning an observation (UE) to one of the classes (multicast subgroups). In general, the following set of model's features can be considered as parameters that form the training dataset: (i) UE's coordinates, (ii) number of UEs, (iii) service area radius, (iv) available bandwidth, (v) session data rate, and (vi) number of subgroups (classes).
VI. LEARNED LESSONS AND MAJOR TAKEAWAYS In this section, based on the performance optimization and modeling frameworks presented in the previous section, we will report the learned lessons and major takeaways for multicast operation in mmWave/sub-THz systems with directional antennas. A. Single-RAT Optimized Performance 1) Execution Times: We start with the comparison of the computation complexity of the algorithms  4  , reported in Table  VIII. The simulations are performed in MATLAB on a standard laptop PC with an Intel Core i7-1260P CPU running at a frequency of 2. 10 GHz and equipped with 16 GB of RAM. First, one can see that in the category of single-RAT multicast solutions, the heuristic solution offers a lowcomplexity scheduling scheme but at the expense of optimally (described in sub-sections below).
Finally, we comment on the complexity of ML solutions. We note that the run time depends on the size of the testing dataset, H 2 , and is not affected by the number of UEs. This proves that ML algorithms represent a good tool to work with a high number of UEs in the case of optimal multicast grouping. We emphasize that time constraints for multicast group formations are not precisely defined in current standards and are likely to vary due to operators' resource management implementations. It is evident that the constraints do not apply at the sub-slot or frame level and, in practice, may have even longer durations. Still, to avoid the infeasibility of applying a brute-force solution with exponential complexity, we have chosen to utilize simulated annealing and ML models. This decision offers a polynomial time complexity, enabling us to efficiently address the problem while maintaining computational tractability.
5) Machine Learning: Regarding the ML algorithms implementation, Fig. 9  depicts the accuracy of UEs allocations to subgroups. As anticipated, the UE allocation to subgroups accuracy, σ, grows with the training dataset size H 1. However, starting from approximately H 1 = 1000, the accuracy essentially plateaus and no longer increases. At the same time, we note that, as per our additional investigation, the perfect resource matching with the optimal solution is seen for this considered distance of R = 250 m, even for very small values of H 1. Therefore, the fact that the accuracy of all examined algorithms (with the exception of Random Forest)  remains almost stable when the training sample size increases from H 1 = 1000 to larger values, permits us to consider H 1 = 1000 as the lowest limit on the training set size for practical implementations.
The inability to learn particular UE allocations to different subgroups, as shown by σ, does not imply that the investigated ML algorithms cannot learn other UE classification characteristics. To demonstrate it, Table XI provides resource matching accuracy, γ, for various BS service area distances, R. As may be noticed, several algorithms offer excellent performance. Specifically, the tree algorithms are also showing excellent extrapolation capabilities. Thus, Random Forest and Fine Trees show almost 100% accuracy in terms of resource utilization, γ, over all the considered distances. Considering the relatively small computational effort required for trees, one may regard them as the best candidate for subgroup formation. 6) ML Predictors' Importance: Recall that in order to collect a dataset, several variables of interest were chosen.
However, the algorithms may or may not use these variables for classifications. Now, we will investigate which factors have the most impact on the performance of the algorithms. To this end, we present the predictor importance for the classification ensemble of decision trees in Fig. 11. It computes the estimated predictor importance for the dataset by summing these estimates over all weak learners in the ensemble. Here, a high value corresponds to a high importance of the variable for the model. Fig. 11 (a) presents the importance of the whole dataset, where the model's behavior is analyzed as a function of the service area radius, R. Therefore, variable R is expected to be of great importance. However, we anticipated the locations of UEs to be the most important model features. In contrast, the number of clusters derived from the solution of the optimization problem and the cell radius are the two most influential predictors of the learning process, followed by the coordinates of the UEs.
Further, by examining Fig. 11 (b), one can see that the importance of the predictors varies with the dataset. Here, fixing the radius R leads to the UEs' coordinates being the most important predictors. This trend may be explained by the fact that in directional multicast systems, the service area radius influences the type of transmission used for service (i. e. , multicast for multiple UEs or unicast for each multicast UE). The numerical results demonstrate that the solution mainly depends on the cell radius. When varying the number of UEs in the system, one may notice, for instance, that a single subgroup is chosen for the 100-225 m radius range. Then, for the range 275 m and beyond, only unicast transmissions are used to serve multicast UEs, whereas the considered multicast group formation solutions can be utilized for the radii around 250 m.
C. Summary of Key Points 1) Single-RAT Deployment: The summary presents the conclusions on the complexity and performance of different optimization solutions, the optimal number of beams, multicast subgroup size, and the importance of predictors in ML algorithms for optimal multicast grouping in the case of the single-RAT deployment: • Computation complexity: The complexity of single-RAT multicast solutions varies, mainly depending on the number of multicast UEs. The optimal solution becomes impractical for a high number of multicast UEs. Simulated annealing significantly reduces computational time and at the same time closely approximates the optimal solution. • ML solutions: ML algorithms are a valuable tool for achieving optimal multicast grouping with a large number of UEs. The runtime depends on the testing dataset's size and remains unaffected by the number of UEs.
The importance of ML predictors varies with the dataset, but influential predictors include the number of clusters derived from the optimization solution and the cell radius. • Solution performance and water-filling comparison: When comparing solutions designed for single-RAT systems, the ratio of occupied to available resources grows with increased cell radius. The difference between power water-filling schemes is negligible. • Beam configuration: For practical cell sizes and the evaluated number of multicast UEs, the optimal approach typically utilizes no more than 2-3 beams. • Multicast subgroup size: As the cell radius reaches approximately 250m, practically all schemes employ the unicast mode to serve multicast UEs. A single beam (multicasting) is utilized for smaller cell radii. 2) Single-RAT Deployment: The summary analyzes resource utilization and RAT selection in a dual-mode mmWave/µWave system, considering factors, such as cell radius, beam configuration, RAT priority, and weight parameters.
Moreover, in  [202] , the use of D2D links carrying additional CSIs is proposed for determining the pseudo-range estimates between UEs that might be helpful for, e. g. , position estimation and relay selection. While it has been demonstrated that sidelink communications can enhance point-to-multipoint transmissions through efficient relay selection, direct D2D connections present even greater security challenges since data exchange occurs directly between nodes in close proximity. In  [127] , a mechanism to effectively deliver trustworthy multicast/broadcast traffic in 5G-oriented networks is introduced to address this issue. In the same vein, a protocol for effectively managing multicast services with a particular emphasis on security in a 5Goriented IoT environment is proposed in  [128].
D-MBAF leverages multicast subgrouping and simultaneously serves subsets of users at different data rates. Albeit the approaches mentioned above work with 5G NR systems where traditional multicasting is used, it does not limit the applicability of mmWave multicasting over integrated terrestrial/non-terrestrial networks. Recently, a number of surveys were published to underline the role of multicasting in future NTN 5G systems. In  [143] , a comprehensive overview of the NTN evolution with an emphasis on the role of NTN within the 5G NR system is provided. Multicasting is presented as the primary enabler of network scalability for Enhanced Mobile Broadband (eMBB) service in 5G NTN. In  [144] , the evolution of NTNs and the solutions to close the gap between 5G and 6G ecosystems are considered.