dataesr/scientific-paragraphs-categorization huggingface dataset:
5G-J ). No change in any Th subset was also in agreement with our CyTOF results (Supplementary Table  5 ). We also did not observe any significant difference in the Th1 and Th2 cytokines, such as GM-CSF, IFN-γ, IL-5 and IL-13 measured in the sera of our cohort (Supplementary Fig. 5K ). Furthermore, since IL-33 plays a crucial role in regulating the ILC2 response and serves as an alarmin in CNS  42  , we quantified the circulating IL-33 level and found it was similar between PD and HC (Supplementary Fig. 5K ). We further measured several CNS-homing relevant chemokines, such as IL-8, IP10 and MCP-1. None of them showed any significant difference (Supplementary Fig. 5K ). Together, our data from various viewpoints firmly suggest that the CD4 composition is less affected in early-tomid stage iPD.
Reprogrammed CD8 Tn & TCM to favor terminal differentiation in early-to-mid stage iPD Since the ratios between CD8 TEMRA and TCM were substantially enhanced in iPD, while total CD8 T cells were kept intact, we reasoned that the CD8 differentiation process might be affected. To unbiasedly quantify the CD8 differentiation process, we performed pseudotime trajectory analysis in our CD8 scRNA-seq datasets using the tool Slingshot  69. Among the most significantly-differentially-expressed genes along the CD8 differentiation trajectory, the marker genes known to change over the CD8 differentiation stages, such as IL7R, LEF1 and GZMA, were confirmed by our unsupervised analysis (Fig. 7a ). Encouragingly, the mean pseudotime increased from pre-sorted CD8 Tn to TCM, TEM and TEMRA (Fig. 7b ). The relative fractions of cells for   several mid-to-later pseudotime windows (>60) were much higher in CD8 T cells of iPD vs HC, while those in earlier windows (20-50) were lower (Fig.
Although CCR7 + GZMK + cells have been identified as exhausted-like CD8 memory T-cell progenitors  61  , none of the analyzed exhaustion markers (TIGIT, TIM3/HAVCR2, LAG3 and CTLA4) showed an expression in GZMK + CCL5 + GZMA + C0 in our TCM dataset (Supplementary Fig. 7A ). We further examined whether GZMA, CCL5 and GZMK are co-expressed within the same cells of CD8 TCM. The scRNA-seq confirmed that CCL5 and GZMK were co-expressed in the CD8 TCM cluster area (Fig. 7h, i ). GZMK and GZMA were also co-expressed, although only in a small fraction of CD8 TCM (Fig. 7h, i ). Consistent with our FCM data, the expression between GZMK and GZMB was largely mutually exclusive (Supplementary Fig. 7B ). In short, our unbiased scRNA-seq from different angles demonstrate that CD8 TCM were already more ready, which might promote the terminal differentiation in early-to-mid stage iPD.
Cohort design We followed the ethical regulation of Luxembourg and obtained ethical approval [Luxembourg National Research Ethics Committee (CNER) approval No. 20140713-SU1; two amendment notifications on February 20, 2023 and on April 12, 2023]. Informed consent was obtained before each participant was recruited by the clinical team in Luxembourg. All study participants were recruited from the Luxembourg Parkinson's Study (https://clinicaltrials. gov/study/NCT05266872), a nation-wide, monocentric, observational longitudinal study with parallel HC. There is no financial compensation for the participants. We recruited the participants from the ongoing nation-wide Luxembourg Parkinson's study with more than 800 PD and 800 HC  22  (https://parkinson. lu/ research-participation) and controlled for several major confounding factors, medications and comorbidities, known to affect the immune system, to ensure that our observations are PD-specific (Fig.
030 mL per min. Generated *. fcs files were normalized with the HELIOS acquisition software (v. 7. 0. 8493) by using EQ beads as a standard. The frequency among living CD45 + cells well recapitulated the absolute number of cells for the given subset, as we standardized the analyzed whole blood volume (300 uL) from each participant, and no difference in the number of total living CD45 + cells was observed between PD and HC (Supplementary Table  5 ). Of note, due to notable staining issues, we excluded CD25, CD16 and CD127 from the relevant subset analyses as specified below. First, we did not gate and analyze Treg in our CyTOF panel (Supplementary Fig. 1 ). Second, for nonclassic monocyte and ILC gating, we alternatively used CD38 instead of CD16 as employed elsewhere 117. For NK subset gating, we used CD56 and CD57 to distinguish immature, mature and terminallydifferentiated NK subsets 118.
The data were analyzed using the FlowJo v. 10 software. Of note, with our hypothesis-free approach, we could not foresee the CD8 TEMRA results. We never used CD45RA, CCR7 and CD27 in the same panel, and this is why we had to demonstrate the CD8 TEMRA results by combining different gating strategies from different panels. Of note, in our cytometry analysis, we manually inspected the gates of each immune subset for each donor and might slightly adjust the gate positions across different donors whenever needed. Two PD samples were excluded for CD183 related results where CD183 abs was missing during staining in one panel. Furthermore, one female PD sample was excluded as the same patient visited twice within a short period.
Finally, the cells were washed twice in cold permeabilization buffer and resuspended in 100 μL of a freshlyprepared 4% Formaldehyde solution (Fisher Scientific, 10751395) for 30 min at 4 °C. After centrifugation, the pellet was resuspended with 200 μL of cold FCM buffer. All the stained PBMC were acquired on a BD FACSymphony TM A6 (BD Biosciences) and the FCS files were analyzed using the FlowJo v. 10. 8. 0 software. The detailed gating strategy is illustrated in Fig. 5. CD8 subset sorting and scRNA-seq experimental procedures FACS was performed using a BD FACSymphony TM S6 six-way cell sorter with a 100 μm nozzle. After counting, PBMC were stained with the protocol above used to characterize the cellular phenotype, but without proceeding to the intracellular staining steps. To preserve cell integrity to a maximum degree, the 15-mL Falcon collection tubes were extemporaneously pre-coated with a cold solution of 20% BSA in PBS for at least one hour.
scRNA-seq computational preprocessing and analysis The initial quality control involved eliminating low-quality reads. This preprocessing task was performed separately for each dataset (each subset of the group is defined as one dataset, resulting eight in total). To ensure only dataset with the highest quality was selected, we retained cells with more than 100 genes and genes that were expressed in more than 10 cells. Moreover, we excluded cells with more than 20% mitochondrial counts. In droplet-based single-cell RNA-seq experiments, there is often background contamination resulting from a certain amount of background mRNAs. This contamination arises not only from the cells enclosed within a droplet, but also from the solution containing the cells, created through cell lysis. To correct this type of contamination, we utilized SoupX (v.
1. 6. 2) to estimate the cell-specific contamination fraction and adjust the expression matrix. Subsequently, we employed scDblFinder (v. 1. 12. 0) to identify and filter out doublets. In this process, mitochondrial (number = 13), ribosomal (number = 94), and hemoglobin (number = 1) genes were removed. The integration of the four subsets was conducted using sctransform (v. 2), with 3000 integration features. The integrated object comprised 34,792 cells, with a median count of genes equal to 2, 252. We further filtered the cells based on CCR7 expression. We excluded cells that were CCR7 + in the CD45RO + CCR7 -and CD45RO -CCR7 -datasets, while retaining only the CCR7+ cells in the CD45RO-CCR7 + and CD45RO + CCR7 + datasets. The remaining cell count was then reduced to 24,832, with a median count of detected genes equal to 2, 210 and a median number of detected unique molecular identifiers (UMIs) equal to 4, 805 per cell.
Subsequently, DEG analysis was carried out separately for each of the four CD8 subsets between HC and PD. The FindMarkers function from the Seurat package (v. 4. 3. 0) 120 was employed. Only genes detected with a fraction value (pct) of ≥0. 3 in at least one group were tested. Then we calculated two metrics for each gene, the enrichment ratio and the gene. score. The enrichment ratio was calculated for two scenarios: (i), if pct. 1 > pct. 2, the ratio was the fraction (pct. 1) in the first group divided by that in the second group (pct. 2); if pct. 2 > pct. 1, the ratio showing pct. 2 divided by pct. 1. The gene. score is a metric by multiplying the average log2 fold change and the enrichment ratio. The volcano plots of the DEGs were illustrated using ggplot2 (v. 3. 4. 1) and ggrepel (v. 0.
Genes that were significantly different following the trajectory were found using the testPseudotime function of the TSCAN R package (v. 1. 36. 0). The method fitted a natural spline to the expression of each gene with respect to pseudotime and to test whether any of the spline coefficients are non-zero with an ANOVA method. The top 20 up-and downregulated genes were selected based on log2 fold change (if p-value < 0. 01). In parallel, we performed cell clustering within each CD8 subset using the Seurat function "FindNeighbors" (dims = 1:20) and "FindCluster". As each sorted subset was already highly purified, we did not aim to generate a high number of clusters. The clustering levels were visually inspected using the functions "clustree" and UMAP. Additionally, cluster-specific marker genes were identified using the Seurat function "FindAllMarkers" for each cluster.
Furthermore, to determine a meaningful cluster resolution for each subphenotype, additional cluster merging was performed on the subsets CD45RO+CCR7 -and CD45RO -CCR7+ between cluster 0 and 1 as these clusters were not further distinguishable based on the identified marker genes. To identify DEGs between the conditions HC and PD within each cluster of each subset, the function "FindAllMarkers" (log2FC. threshold = 0. 5, test. use = "wilcox", min. pct = 0. 3) was used. Selected DEGs of interest were visualized in heatmaps or violin plots. Of note, the preprocessing for cell clustering was slightly different from that of the pathway analyses aforementioned and the dedicated scripts were provided.
Statistical analysis Statistics analysis of cytometry datasets was performed in Graphpad Prism v. 9. 0 using an unpaired two-tailed Student's t test or the Mann-Whitney nonparametric unpaired test. ROC analysis, volcano plot and PCA analysis were also performed using GraphPad Prism v. 9. 0. The precise test method used for the different figures is also specified in the corresponding figure legends. P-value ≤0. 05 was considered significant. The error bars in the related types of figures represent the standard deviation (s. d. ). For a few participants, some of the clinical information was unavailable. In that case, the related analysis did not interpolate any values to replace those empty cells in the data tables. For scRNA-seq, the precise statistical methods used for different results were provided in the sections above or the corresponding figure legends.
Acknowledgements We thank all the anonymous participants of the  Luxembourg Parkinson  's Study for their support of our research and acknowledge the joint effort of the  National Centre of Excellence in Research on Parkinson 's Disease ( NCER-PD ) Consortium members generally contributing to the  Luxembourg Parkinson 's Study (the full list of consortium members are provided in Supplementary Note 1). We thank the  CIEC of LIH  (especially  Daniela Valoura Esteves ), the processing and biorepository teams of IBBL (especially  Wim Ammerlann  and  Sabrina Saracino ) and the LuxGen sequencing platform (especially  Arnaud Muller  and  Nathalie Nicot ) for their support. This study was initially supported by the  Luxembourg Personalized Medicine Consortium (PMC)  ( CoPImmunoPD ,  PMC/2018/ 01 , F.
Data availability The raw scRNA-seq data generated in this study have been uploaded in NCBI GEO database under the accession number GSE237254. The raw flow-cytometry and mass cytometry (CyTOF) dataset generated in this work are available via a Zenodo repository (https://doi. org/10. 5281/ zenodo. 8382970) 121. The GRCh38-2020-A human reference genome used by CellRanger was downloaded from 10x Genomics website. Source data are provided with this paper. Only the relevant clinical data (i. e. , disease duration) that were displayed in figures are provided in Source Data. The disaggregated sex information is also available in Source Data. Source data are provided with this paper.
A. ADR for static end-devices The ADR algorithm is defined in the LoRaWAN specification  [4]. Its goal is to adapt the SF and the transmission power of end-devices depending on the current channel conditions. It is enabled only for static end-devices. It uses two modules: one run by the network server, and one run by the end-devices. The network-server module stores the received signal strength indicator (RSSI) of the last 20 frames, for each end-device that has the ADR enabled. The network server computes the maximum RSSI of these frames, denoted RSSI max , as well as the required demodulation threshold RSSI req based on the current datarate of the end-device. Then, it computes N step = round(RSSI max -RSSI req -margin)/3, where margin is a configurable margin equal to 10 dBm by default. If N step is negative, the network server increases the transmission power by 3 × N step , up to the maximum allowed transmission power.
Our novel algorithm, CParty, unambiguosly decomposes each possible density-2 structure, ensuring a one-to-one mapping to calculating the partition function. CParty computes the CPF with respect to the input structure, which can be used to obtain conditional base pair probabilities and unveil RNA kinetic structure formation paths. Although a 'full' partition function is ultimately desirable, our focus on a CPF algorithm for density-2 pseudoknotted structures allows us to achieve cubic run time complexity. A CPF is particularly useful in case partial information is available for a given sequence. For example, different structure reactivity datasets may only align for specific subparts of a functional structure e. g. initial non-crossing 'stems', while the rest of the structure is unknown  [24].
CParty for analyzing pseudoknot motifs in SARS-CoV-2 There has been widespread research focusing on structures for the SARS-CoV-2 frameshift site, including multiple viral genome reactivity probing experiments  [27, 11, 30, 7]. Based on the available SHAPE datasets and thermodynamic-based predictions  [2] , multiple candidates exist for initial non-crossing structure at the frameshifting site of SARS-CoV-2  [24]. It was shown previously  [24, 18]  that suboptimal structures (i. e. structures close to the MFE structure in the energy landscape) may play important roles in function of the viral sequence. Finding folding paths from original base pairs to the MFE as well as suboptimal structures can be essential for designing effective therapeutics. While multiple implementations of partition function calculations exist that can estimate base pair probabilities in pseudoknotfree structures, there are few partition function algorithms that can handle pseudoknotted structure and none that can provide conditional partition function to shed light on folding paths.
The FPGA internal memory buffer allows it to store up to 16,384 samples per channel. The acquisition can be manually triggered from software, from an internal trigger, or from an external trigger. On the analog waveform generation path, the AD2 module is equipped with a 2channel DAC (AD5643, Analog Devices, USA), generating arbitrary waveforms at a sample rate up to 100MS=s, and with a 14-bit resolution. Both channels can generate signals with a 12MHz bandwidth and AE5V voltage range. A buffer of 16,384 samples per channel is dedicated to storing custom arbitrary waveforms. Analog specifications are summarized in Table  1. Choosing the AD2 platform over a custom solution has been dictated by several aspects. The data rate and memory requirement for both the waveform generation and acquisition, as well as the need for parallelism and accurate timing, make microcontrollers not relevant for the application.
Python library We developed a Python3 library to enable simple and fast user-friendly scripting for measurements. The library is objectoriented and measurement boards are declared as unique boards, each one having a specific ID number. Boards are identified according to their Analog Discovery 2 ID and serial number. The global architecture of the library relies on two classes described in the UML class diagram provided in Fig. 15. We developed a class dedicated to the Analog Discovery 2 hardware, overloading Digilent's existing code to get simple methods for generic signal generation and acquisition, as well as digital I/O communication to reach the embedded microcontrollers of our design. An instance of this class, Andi, is contained in the class to be instantiated by the final user, BIMMS.
For programming, two options are possible. Programming via the MBED Studio software, or just by copying the bin file into the USB device folder corresponding to the ST-link. MBED project and bin files are provided in the BIMMS_STM32_V0. tgz archive. Alternatively, CubeMx IDE from STMicroelectronics can be used, with the BIMMS_STM32_cu-beIDE. zip archive. The board should be powered during the flashing of the microcontroller. Once programmed, D1 should start blinking at a rate of 1 Hz. Ultimately, the system can be tested with a computer. The python API can be installed with the pip packet-management system. The Waveform software from Digilent needs to be installed separately. The board can be tested via test scripts available on the Zenodo depository. The test_all. py will call all the scripts located in the directory unitary_test.
Python API The API handles the low-level communication with the BIMMS platform to configure the AD2 and the microcontroller. Basic methods allow to set the board state (idle, locked, off, error), setting the excitation front-end configuration (current or voltage mode, single-ended or differential, DC or AC coupling, etc. ), and the readout front-end configuration (IAs gains, coupling, etc. ). Higher-level methods are also provided to facilitate the usage of the board and to make its configuration transparent to the end user. High-level methods include a galvanostatic EIS measurement method, a potentiostatic EIS measurement method, and a cyclic voltammetry measurement method. The API facilitates scripting for automated measurement setups. Detailed examples are provided with the API. The API is open source (under CeCILL license), referenced in Pypi (https://pypi.
Gui A Graphical User Interface (GUI) application is provided to the user to easily perform EIS measurements. The GUI is based on the developed python API and uses the PyQt package for the graphical aspect. The PyQt package needs to be installed separately with the pip command for example. The GUI can be run on a Windows, macOS, or Linux computer. The GUI can be executed by running the script named EIS_GUI. py located in the examples folder of the python API. A window like the one in Fig. 17  should appear. The GUI allows the user to configure the system, run the measurement, plot the result, and save it in a CSV file.
01 %. Open-Short-Load calibration must be executed for each different measurement configuration as the introduction of different components on the signal path influences the high-frequency behavior. Both DC calibration and Open-Short-Load calibration data are stored in specific data files using the JSON format. Those files are stored in the CalibrationData directory, located in the same directory as BIMMS. py. Those files are automatically loaded when calling the BIMMS object in the python library. Each file contains the ID of the board used during the calibration. During the loading of the files, the API checks that the ID in the files matches the ID of the board under use, ensuring consistency between the calibration files and the hardware.
System characterization The AD2, excitation front-end, and readout front-end have been characterized. Bandwidth, Total Harmonic Distortion plus Noise (THD + N), DC-offset, and power consumption are measured. Mismatch and crosstalk between the two inputs of the AD2 and the two-channel of the readout-front end are also evaluated. Output impedance is measured for the voltage source and the current source. All measurement plots are available in Supplementary Materials, in the document entitled SuppMaterials. pdf. This document is available on the Zenodo deposit. Results are summarized and discussed in this section. The AD2 is measured using a loopback setup, i. e. the AD2 AWG output is connected to the AD2 scope inputs. Table  10  summarizes the measures. The À3dB bandwidth of the AD2 is above 10 MHz and high-frequency performances are mainly limited by the slew rate: the gain is about À1.
Impedance spectroscopy accuracy on a complex load To measure the accuracy of the system on a complex impedance, a measurement was performed on a test load (Fig. 20 ). This load presents an inflection point around 80kHz: Measurement parameters are identical to the previous setup, and both the galvanostatic EIS and potentiostatic EIS configurations are tested. Ground-truth impedance is measured with a calibrated Keysight E4990A Impedance Analyzer (USA). Error is evaluated for un-calibrated and calibrated measurement, with the exact same calibration data as the previous setup. Data are depicted in Fig. 21. At 1MHz, the absolute magnitude error is about 1 % for the un-calibrated potentiostat EIS and about 1. 5 % for the un-calibrated galvanostat EIS. The absolute phase error is 2°for both configurations.
Conclusion The proposed BIMMS platform can perform accurate spectroscopy measurements as well as cyclic voltammetry. Performances are summarized in Table  15. A large variety of measurement configurations is possible and automatically handled in software. The low cost and compact form factor in comparison to commercially available systems make it well suited for lab experiments as well as a useful tool for teaching. Scripting capability offers the possibility of long-term autonomous experiments. In combination with a low-cost Raspberry Pi4 mini-computer, remote measurements are possible when direct connection is not favorable. A large number of available IOs offer the possibility of connecting the platform to extension boards, such as an electrode multiplexing board. From the hardware point of view, the presented design is currently limited by the possibilities offered by the AD2 platform.
Despite offering a wide range of possibilities, the lack of access to the FPGA internal functionalities limits the possibilities of time-sensitive applications, such as real-time fast impedance spectroscopy. Replacing the AD2 with a custom data acquisition/generation circuitry would greatly improve the possibilities of the platform but at the cost of long-time development and prototyping. The system could be made more compact by using smaller passives and with a tighter design but would make the assembly of the board more challenging, and possibly more expensive. Components on both sides of the PCB would also help reduce the size of the design. Replacing the electromechanical relays with solid-state switches would also greatly help save space. The currently provided python library implements all the basic functionalities for using BIMMS.