dataesr/scientific-paragraphs-categorization huggingface dataset:
All audio samples from the three benchmark datasets were either originally sampled or resampled at a 16 kHz frequency. Before inputting the audio signal into the x-vectors  [20]  architecture, an MFCC  [31]  transform with a 25 ms frame length and a 10 ms overlap was applied. The cepstrum coefficients were mean normalized across the word duration, instead of the 3 s mentioned in the reference article  [20] , resulting in a feature vector X a ∈ R 512. For training x-vectors  [20]  on ultrashort utterances from VoxCeleb2  [17] , a batch size of 32 was used, along with the ADAMAX optimizer and a learning rate of 5 × 10 -3.
Data Collection To evaluate our proposed methodology, we used three well-known benchmark datasets: KinectsDigits  [3] , VoxCeleb2  [17] , and TCD-TIMIT  [30]. KinectsDigits  [3]  is a valuable resource for multimodal speaker recognition research. The dataset, captured using Microsoft's Kinect sensor, consists of RGB, depth, and audio modalities. It features recordings of individuals articulating digits from zero to nine under various environmental conditions. We only kept one situation to prevent repeated words. The videos had a resolution of 104 × 80 pixels, a frame rate of 30 fps, and a voice signal sampling rate of 16 kHz. VoxCeleb2  [17]  is an essential resource for speaker recognition research, containing RGB and audio modalities from a diverse range of speakers. The dataset features over a million utterances from thousands of speakers with various accents, languages, and speech contents.
Videos in VoxCeleb2 have a 25 fps and a low resolution of 224 × 224, while audio signals are sampled at 16 kHz. As we used a very low resolution, we predicted that the depth estimation network  [33]  would give less details about the lip region, as shown on Figure  3 ; therefore, we only selected 1000 random speakers from this dataset. TCD-TIMIT  [30]  is a valuable resource for multimodal speech recognition research, comprising continuous speech in both RGB and audio modalities. The dataset features individuals delivering sentences from the TIMIT corpus. Videos in the TCD-TIMIT database have a resolution of 1920 × 1080 pixels, a frame rate of 29 fps, and a voice signal sampling rate of 48 kHz. Only audio signals were re-sampled to 16 kHz. While KinectsDigits  [3]  includes RGB, depth, and audio modalities, VoxCeleb2  [17]  and TCD-TIMIT  [30]  only provide RGB and audio data.
Depth Estimation The primary aim of this research was to demonstrate the synergistic value of depth and audio data for multimodal person identification. However, most publicly accessible datasets suitable for this purpose only include RGB video and audio modalities. To address this limitation, we utilize a pretrained face depth estimation network (employing an encoder-decoder architecture)  [33]  to generate depth maps from full RGB facial images. This method, trained on an extensive synthetic face dataset with varying head poses, expressions, backgrounds, and image resolutions, outperforms state-of-the-art models on real face depth datasets, such as Pandora  [34] , Eurecom Kinect Face  [35] , and the Biwi Kinect Head Pose dataset  [36]. Consequently, this model can produce a depth modality for RGB faces in datasets.
The depth estimation network was independently applied to each facial frame within a given video for Voxceleb2 and TCD-TIMIT. For the KinectsDigits dataset, we retrained the depth estimation network on the Pandora  [34]  lip regions to enable a fair comparison between the estimated and actual depth in the person identification task. Although the resulting depth estimation may be considered low resolution due to some missing depth information, it remains a valuable data source for our analysis, as evidenced in the results section. The Figure  3  presents a sample of real or estimated depth mouth crop frames for the three benchmark datasets.
Lip extraction In our approach to data preparation, we employed an existing method for lip detection and applied it to two supplementary datasets, namely VoxCeleb2  [17]  and TCD-TIMIT  [30]. The aim was to accurately identify and extract the lip region of speakers in RGB videos for further analysis. To accomplish this, we utilized Google's Mediapipe tool. By leveraging the power of Mediapipe, we were able to identify the key landmarks associated with the mouth region. This allowed us to isolate the lower part of the face on the depth video and focus our attention on the lips.
Word segmentation In our investigation of the ultrashort utterance problem, we segmented words from the audio in both the VoxCeleb2 and TCD-TIMIT datasets (while the KinectsDigits audio only contained digit words from zero to nine). We removed duplicate instances of the same word for each speaker to concentrate on the text-independent speaker identification task. Subsequently, we interpolated the resulting timestamps on the video to extract the lip video section corresponding to the spoken word. We utilized models from the Kaldi toolkit for word segmentation, which are available for various languages. It is worth noting that, for some words, we obtained a single frame, which the spatiotemporal network processed as 3D data, just like longer videos. Table  1  presents pertinent information regarding each dataset and its usage, with an 80%/20% training/testing split.
Results This section addresses the experimental results of the proposed architecture in Figure  2  and its components applied on the three datasets. We investigate the performance of our spatiotemporal fusion system, the importance of adding depth information to audio, and the complementary information added by each modality. To understand how the fusion of modalities can improve the performance of the system, we first examine each individual modality and its identification abilities. We begin our discussion by exploring the impact of depth video encoding. The fusion of all three views, as depicted in Figure  1 , led to a noticeable improvement of approximately 2% in performance. This improvement validates the significant advantage of integrating both spatial and temporal information into a single modality.
Its performance improvement is quite significant: 5% for the KinectsDigits dataset trained on the real or estimated depth, 9. 27% for TCD-TIMIT, and 8. 08% for VoxCeleb2. Following the (XY) view, the horizontal view (XT), which covers the horizontal movements of the lip, showed remarkable results on the real depth KinectsDigits dataset  [3]. In fact, it surpassed the (TY) view by 8. 33%, indicating the importance of temporal dynamics. Furthermore, the performance improvement from depth estimated video reveals an interesting prospect of considering the valuable information provided by the estimated depth. Diving into another critical aspect of our analysis, the audio component of videos, we discovered its pronounced significance in the sphere of speaker identification tasks. We used the x-vectors architecture, a powerful method for dealing with audio data.
However, our results, as depicted in Table  3 , highlight a considerable drop in performance, primarily attributed to the short duration of the voice samples. This brevity tended to trigger substantial overfitting, depending on the specificities of the dataset. We found that the audio-based identification performed well in controlled recording environments, such as those present in the KinectsDigits  [3]  and TCD-TIMIT  [30]  datasets, where the identification accuracy surged beyond 75%. Nonetheless, in scenarios that are more reflective of real-world conditions, as is the case with the VoxCeleb2  [17]  dataset, the accuracy nosedived to nearly half of its initial value. Such a drastic dip in accuracy raises concerns about the reliability of the system for accurate speaker recognition in practical applications.
In speaker identification tasks, our spatiotemporal fusion method (illustrated in Figure  1 ) is an efficient way to process depth videos while deliberately avoiding the use of resource-intensive 3D CNNs. The logic behind steering clear of 3D CNNs is based on their inherent drawbacks, such as the high computational demand and memory requirements that can impede their use in real-time applications or on devices with limited resources. Furthermore, the greater number of parameters in 3D CNNs has potential implications for longer training times and increased susceptibility to overfitting, especially when working with datasets of limited size. In contrast, our chosen approach, which relies on a more efficient spatiotemporal fusion method, has the advantage of pulling out distinctive features from real or estimated depth videos, thus ensuring a comparably strong performance in speaker identification tasks without facing the challenges tied to the use of 3D CNNs, such as a high computational complexity and prolonged training periods.
This spatiotemporal fusion architecture consists of multiple 2D views, and an investigation into the contribution of each view is required. For the real depth data, specifically the KinectsDigits  [3]  dataset, the horizontal view of the mouth (XT) outperformed the vertical view (TY). This could be attributed to the mouth's greater horizontal length, which enhances discriminant dynamic features. However, the (TY) view exhibited more pronounced and rapid overfitting, suggesting its susceptibility to overfitting in this context. It is crucial to select an appropriate mouth crop resolution, as the speaker identification performance can be significantly impacted by the resolution or depth estimation, as seen in the VoxCeleb  [17]  dataset. Considering the spatial view (XY), it outperformed other fusions for both the estimated and real depths.
Shorter utterances provide a limited temporal context, and the brevity of these utterances can lead to the omission of certain speaker-specific cues, thus causing the model to underperform in these cases. This issue is compounded in the VoxCeleb2 dataset, where the utterances are notably brief and the recording conditions are significantly more varied than in the other datasets, thus challenging the model's performance. Additionally, another observed pattern is the decrease in identification accuracy as the environmental noise increases, particularly with the VoxCeleb2 dataset. This dataset features recordings in diverse and often challenging conditions, such as outdoor recordings or instances with significant background noise. These factors can mask or distort speakerspecific cues, making it harder for the model to correctly identify the speaker.
In our examination of the depth video modality, we found that errors frequently stemmed from the low resolution of the RGB videos, which was particularly noticeable in the VoxCeleb2 dataset. This resulted in less accurate depth estimation which, in turn, affected the precision of the spatial representation of lip movements, subsequently lowering the identification performance. It is worth noting that the depth estimation algorithm is specifically trained on facial features and thus performs optimally with high-resolution RGB videos, as is the case with the KinectsDigits and TCD-TIMIT datasets. Therefore, when applied to lower-resolution videos, such as those in the VoxCeleb2 dataset, the depth estimation's accuracy diminishes. The lower-resolution videos also struggle to capture the finer details of lip movements, limiting the depth modality's discriminatory ability and potentially leading to identification errors.
Conclusions This study demonstrates the effectiveness of a multimodal approach for speaker identification that incorporates both audio and depth information to achieve more accurate and reliable results. Through the examination of three benchmark datasets-KinectsDigits, VoxCeleb2, and TCD-TIMIT-we showed that the fusion of these modalities leads to significant improvements in identification performance. The proposed spatiotemporal architecture effectively extracts spatial and temporal features from depth information, while the x-vectors architecture processes the audio modality. Our findings highlight the importance of integrating multiple modalities to overcome the limitations posed by short utterances and variable recording environments. By fusing audio and depth data, we achieved an enhanced performance in a range of scenarios, including clean recording environments and more realistic, constrained situations.
Unlike Figure  3(a) , there is a significant difference in lateral position signal change between 2. 5 % and 5 % PBS, which proved that our device possessed higher sensitivity. Moreover, our device can distinguish even lower PBS concentration (1. 25 %), which corresponds to a lower detection limit compared to the commercial one. It is also noticeable that the standard deviations of data acquired by our device were much smaller than the commercial one, which revealed the higher stability and signal-to-noise ratio of our device. To further enhance the detection sensitivity, we have utilized MXene nanosheets to tune the absorption of our plasmonic sensing substrate. To validate the reliability of our device, we measured the reflectivity of the MXene-on-Au substrate in air and compared the experimental results with our simulation analysis.
We randomly selected two points on the substrate and plotted the results in Figure  4(a). Two sets of experimental data showed no significant variations with each other and were both in good accordance with our simulation results, demonstrating the stability of our device and the capability of achieving comparable results with simulation calculations. Next, as a standard calibration test, we measured the change of lateral position shift signal with respect to glycerol solutions with different concentrations. As depicted in Figure  4 (b), a much higher signal response upon injection and larger signal change between different concentration levels was achieved using our optimized MXene-on-Au substrate compared with the traditional Au-only substrate. The bulk sensitivity of our device can reach the level of 10 4 μm/RIU.
No observable signal change was detected during the whole process. The two entirely different binding curves in Figure  5 (a) indicated that only perfectly matched miRNA can cause a large lateral position shift signal, which demonstrated the high specificity of our device. Both experiments (detection of miRNA-21 and single base mismatched miRNA) were repeated three times. A summary of these multiple sets of experiments including the average lateral displacement signal value and standard deviations is shown in Figure  5 (b). We can see that the single base mismatched miRNA barely induced any signal change in the system while the miRNA-21 can cause an average signal increase of 13. 93 μm. The dataset also presented a high consistency between different sets of experiments, which proved the stability and reliability of our device.
In this condition, the position occupied by RGD is at the interface between αIIb and β3, in the same region where fibrinogen binds (as shown in the crystal structure PDB ID: 2VDO  [56] ). Next, we also investigated the possible interaction between sEng and the closed conformation of inactive αIIbβ3. When looking at the sEng+inactive integrin complex, we found structures with just 1 arm of sEng engaged in interaction with αIIbβ3, whereas the other arm remained free of contacts (Figure  4B , right structure). Overall, our in silico predictions of a more stable complex between sEng and the activated αIIbβ3 compared to the complex formed with the inactivated αIIbβ3, are in agreement with the strong vs lack of interaction, respectively, detected by SPR (Figure  4A ). after 10 minutes (magnification, ×50).
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research was funded by , LABEX LIFESENSES (reference ANR-10-LABX-65 ), IHU FOReSIGHT (ANR-18-IAHU-0001) supported by French state funds managed by the Agence Nationale de la Recherche within the 24 Investissements d'Avenir program, Retina France and Foundation Fighting Blindness center grant (C-CMM-0907-0428-INSERM04) Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10. 1016/j. scr. 2023. 103166.
Kidney pathology Formalin or Carnoy fixed kidneys were embedded in paraffin, sectioned in 4 μm thick slices and stained with filtered Sirius red 1% (BDH laboratories), Masson's Trichrome, and Von Kossa staining. For immunohistochemistry, rabbit anti-myoglobin (1/100, Invitrogen, Waltham, United States) and rat anti-uromodulin (1/100, Fisher Scientific, Hampton, United States) antibodies were incubated for 1 h at room temperature. Following this, the specimens were washed twice with TBS 0. 1% Tween 20 and incubated with Histofine simple stain MAX-PO (Nichirei) for 30 min. For detection of signals the Dako Envision system (K4010; Dako) was used. Finally, sections were counterstained with hematoxylin, dehydrated and mounted. Sections were scanned using a Nanozoomer 2. 0 RS (Hamamatsu) and analyzed with image J software.
Fluorescence-activated cell sorting (FACS) Kidneys were decapsulated, minced, and incubated with collagenase (2 mg/mL, Sigma-Aldrich) and DNAse (1KU/µl, Qiagen). After red blood cell lysis, cells were passed through a 40-µm mesh and stained with Viobility (Biolegend). Then cells were incubated with anti-CD16/32 (Biolegend) and stained with anti-CD45-PE violet 770 (BD), anti-CD11b-PE violet 615 (BD), anti-F4/80-APC (BD), anti-Ly6C-PerCP violet 700 (BD), anti-Ly6G Alexa Fluor700 (Biolegend), anti-CD3-PE (BD), anti-CD4-APC Violet 770 (BD), anti-CD8a viogreen 520 (BD), anti-CD19 viobright 515 (BD), and anti-NK 1. 1-Billant violet 605 (Biolegend). A known quantity of Countbright beads (Molecular Probes) was added. Acquisition was performed on a BD LSR-Fortessa cytometer. Standard analyses were performed on FACS Diva Software (Becton Dickinson, Franklin Lakes, NJ).
Statistical analysis Quantitative variables are shown as mean ± SEM and compared with the Mann-Whitney test (2 groups) or Kruskal-Wallis test, as appropriate. Qualitative variables are shown as number and percentages and compared with the Fischer exact test. Correlation between BUN and calcium phosphate deposits was assessed with the Spearman test. A p-value below 0. 05 was considered significant. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
A C Previous studies using immunohistochemistry reported that Abcc6 is expressed in circulating leukocytes and some node lymphocytes  14  , suggesting that ABCC6 may have direct effect on immune cell activation or polarization. Indeed, we performed qPCR of FACS-sorted CD45 + immune cells extracted from kidneys and identified Abcc6 transcripts in these cells. Alternatively, baseline liver ABCC6 deficiency and subsequent changes in systemic metabolism may increase the ability of the kidney to resist to injury. These findings deserve further attention. The lack of a specific ABCC6 inhibitor currently precludes the confirmation of our data by a pharmacological approach but our findings now point to ABCC6 as a new potential molecular target to prevent RAKI and its consequences on deterioration of kidney function in the long term.
44H, NIH, USA). Images of randomly selected bronchi were extracted from the digital images of the entire lung sections and deleted manually from their surrounding parenchymal tissue and vessels using Gimp 2. 8 software (Free Software Foundation Inc. ). Software image analysis was then performed on these cleaned digital images (ImageJ 1. 44H, NIH, USA). Bronchial wall thickness was measured at five different equidistant points along the bronchial wall allowing the estimation of the mean bronchial thickness. As a confirmation method, a dedicated software program (Biocellvia, France) has been especially developed and allowed automatical delineating and measurement of the bronchial wall thickness of selected bronchi, with a pixel size of 0. 454 µm allowing both a high-resolution visualization of morphological structure and an accurate measurement of bronchi morphometric parameters.
The fluorescent signal was detected using a goat anti-mouse secondary antibody (1:500, conjugated with Alexa 488, Life technologies). Slides were mounted using ProLong DAPI (Life Technologies). A fluorescence microscope coupled to a digital camera utilizing the Axiovision ® software was used to view and acquire images (Zeiss, Jena, Germany). The quantification of signal was performed by using Image J software. Telomerase activity. Telomerase activity was quantified with a TeloTAGGG Telomerase PCR ELISA PLUS kit as per the manufacturer instructions (Roche Diagnostics, Meylan, France).
Information sources and search strategy Studies were identified by searching in the PubMed, Science Direct, and Web of Science databases. These databases were selected because they represent a broad spectrum of disciplines related to motor behavior. The final search was performed on 4 July 2022. There was no restriction on the year of publication; all articles present in the databases at this time point were searched. The search was first conducted in all languages, and then only English and French studies were selected for screening. As the term "spontaneous motor tempo" is not exclusively used, we searched a broad spectrum of synonyms for this term. Filters were also used to identify relevant research depending on the database (Table  1 ).
Selection of studies and eligibility criteria We only selected articles and reviews before screening by excluding congress papers, chapters, books, and theses. Reviews identified in databases were just used to find missing original articles about SMT, and they have not been included in the systematic review (reviews not included:  Provasi et al. , 2014a; Poeppel and Assaneo, 2020; Van Wassenhove, 2022). For greater specificity in the selection of the studies, inclusion criteria were based on the PICO (population, intervention, comparator, and outcome) strategy (Table  2 ). For this, we selected studies carried out on human samples producing rhythmic tasks. A control factor or control group was identified as a comparator. Spontaneous motor tempo was identified as the Outcome. Moreover, we selected other exclusion criteria: (1) studies that did not present (3) studies that did not report data on SMT (a SMT task is produced by the participants, but variables studied assess, for example, brain data or relative phases); (4) studies that did not focus on intentional SMT (studies on cardiorespiratory rhythms like breath or heart rate); and (5) studies that focus on walking with displacement (locomotion).
Data collection process For tabulation and extraction of data referring to the selected studies, Excel R software spreadsheets were used. After screening the selected studies, we classified them into two categories, i. e. , those measuring the SMT values (in general, as a prerequisite for a subsequent rhythmic sensorimotor synchronization task) and those examining the effect of factor(s) on the SMT values. For studies measuring the SMT values, we extracted study characteristics, demographic variables, methodological variables, and outcome indicators from each study. The extracted characteristics included the authors, the year of publication, and the sample size. Demographic variables included sex, age, and laterality. Methodological variables included the instruction, the task, the effector(s), and the measurement recording.