dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS In this report, we studied a consanguineous Tunisian family including an affected individual with clinical features suggestive of Leigh-like syndrome. Next-Generation Sequencing of a panel of 281 nuclear genes encoding mitochondrial proteins was performed in the indexed case (II. 1) and variant ltering led to the identi cation of the homozygous variant c. 1264A > G (p. T422A) in exon 5 of SLC19A3. Family segregation by Sanger sequencing disclosed the presence of the variant in the heterozygous state in the parents, and its absence in the healthy brothers (Fig. 1. II). This variant was predicted to be pathogenic according to the ACMG classi cation (PP3/PP5/PM2). Furthermore, analyses using I-Mutant and Mutpred softwares suggested a decrease in protein stability and an alteration of the transmembrane protein surface.
Methods developed to augment automated cell tracking and fate classification While several examples of automated cell tracking software have been reported  23, 35, 36  , none has provided a solution that allows the identification of cell fate in real time. To overcome this challenge, we built upon our previously described and well-validated Baxter Algorithm  23, 37. We augmented the existing Baxter Algorithm by adding fluorescence analysis and cell division type classifier scripts used for the automated tracking of a single MuSC colony. As validation, we performed test runs with manual correction on every tracked well to train the algorithm, an essential parameter in the workflow sequence to quantify the fluorescence signal generated by cells. The resulting Baxter Algorithm allowed for complete lineage tree reconstruction coupled with fluorescent reporter expression dynamics and division type classification (Fig.
Therefore, the signals were subsequently classified into 5 quintiles to allow visual separation of distinct phases and still maintain the characteristics of the expression of each tracked clone (Supplementary Fig. 4A ). To enable classification for the type of cell division that had occurred using the processed fluorescent images, we generated an additional script that enables analysis of the binary fluorescent signals from the time lapse image sequences. This analysis yielded the automated generation of a lineage tree with a heatmap overlay representing the expression of the MyoG reporter over time (Fig. 2f ). Furthermore, this appended script also generated the division types for each cell division event without user input by implementing the 1-nearest neighbor (1-NN) classifier applied to more than 220 k-means-derived and manually-validated training samples (Supplementary Fig.
Time lapse setup The plates containing the single cells in hydrogel microwells were positioned in the environmental chamber of an inverted microscope with motorized stage and temperature-controlled chamber (Axio Observer Z1 with PM S1 incubator, Carl Zeiss AG, Oberkochen, Germany). The chamber was equipped with a gas exchanger (CO 2 module S, Carl Zeiss AG) and was kept at 37. 5 °C and 5% CO 2. The images were acquired with an AxioCam MRm camera (Carl Zeiss AG) at 1388 × 1040 (brightfield) or 4 × 4 binning 346 × 260 (fluorescent channels) pixel resolution. The microscope software AxioVision (Carl Zeiss AG) was set up to acquire brightfield images in different positions at an interval of 5 min, and a custom-made script was written to acquire fluorescence images of the same positions at an interval of 20 min using Filter Set 44 (Carl Zeiss AG) for GFP and Filter Set 20 (Carl Zeiss AG) for mCherry.
The acquisition was conducted for a period of 3 days (72 h). The exposure times for the different channels (15-50 ms for brightfield, 80-160 ms for GFP and 100-200 ms for mCherry) and the camera parameters were carefully chosen to reduce phototoxicity. Each single-cell colony was manually selected, and its position was recorded by the software before starting the time lapse, with an equal number of wells imaged for each condition (drug treatment and control). Each time lapse experiment produced about 45 image sequences in total. The resulting images were analyzed with an augmented version of the Baxter Algorithm in Matlab (MathWorks, Natick, MA)  23. After the time lapse acquisition, differentiation medium was added to the well plate to induce myogenic commitment to validate that the tracked clones were successfully transduced with the MyoG reporter.
We have provided the Dual-FLIT analysis algorithm as an open-source code in the GitHub repository; the algorithm and a video tutorial can be obtained at (https://github. com/klasma/ BaxterAlgorithms) and (https://www. youtube. com/playlist? list=PLWNt_wdNK0Rp3RoAuUG3SK2B-CejdDrW6), respectively. Fluorescence analysis Image retrieval. A Python-based script was written to rename and resize all the binned images obtained from the acquisition so they could match the existing requirements of the Baxter Algorithm. Signal treatment. In order to counter uneven fluorescence illumination present in each image sequence, pre-processing was applied to all fluorescence images and their respective extracted signal. An initial homogenization step was applied to the image sequence to limit large fluctuations of signal between time-adjacent images (flickering): the average signal of every fluorescence image was computed and used to fit the sequence average intensity throughout time.
Building on top of that, features corresponding to each division (i. e. , reflecting the parent and daughter cells behaviors) were extracted and normalized so as to have similar variances and values across features. Weights were then assigned to each feature to match their importance in characterizing a cellular division. Weights were decided empirically and validated with ground truth lineage trees. In total, 38 features were collected from every division. A complete list can be found in Supplementary Table  1. K-means clustering. Divisions of cells were classified using the k-means clustering algorithm in Matlab ® combined with the 1 NN classifier. K-means clustering is a method used in unsupervised or semi-supervised learning to partition n observations in k clusters. The method finds k centroids so as to minimize the distance of every observation to its cluster centroid.
It allows regrouping of similar observations together without the need for labeled samples (i. e. , samples with explicit, manually-assigned ground truth). According to the elbow method-commonly used to identify the best suited number of clusters for a dataset-4 clusters would represent the overall data better than 3, but the only further distinction of a fourth class would be to indicate new divisions that do not yield dividing cells at the end of a time lapse (where it is not possible to know whether cells will undergo further divisions). The clusters were therefore obtained on a specific set of sequences (N = 12 sequences, n = 229 total divisions). Division classification. All new divisions that were not used in the initial clustering were then classified using a 1 NN algorithm that assigned every division to its closest cluster centroid.
The features for these new divisions underwent the same normalization steps that the features of the division used in the clustering part. It is important to note that this classification is not perfectly accurate for borderline cases but generates comparable results across clones. User interface. All the above-mentioned image processing and data analysis steps were integrated in a new analysis window under the Baxter Algorithm software. Seamless integration of the different software components was ensured in Matlab ®. New settings were added to the list of settings in the Baxter Algorithms to accommodate parameters such as mismatching intervals between brightfield and fluorescent image acquisition and measurements after differentiation. The new user interface allows for direct output of statistics about single cell lineage tree or overall population statistics.
ACKNOWLEDGEMENTS The authors would like to thank  Dr. Matthias Lutolf  for helpful discussions about the Dual-FLIT approach. M. T. was supported by a  Zeno Karl Schindler  grant and a  Swiss Study Foundation Scholarship. A. T. V. H. was supported by a  Muscular Dystrophy Association (MDA)   Career Development Award  ( MDA 217821 ). C. M. M. was supported by a  Life Sciences Research Foundation   Postdoctoral Fellowship  and a  U. S. National Institutes of Health (NIH)   K99  award ( K99 AG071738 ). Y. X. W. was supported by the  Canadian Institutes of Health Research  ( MFE-152457 ) and an  NIH   K99 award  ( K99 NS120278 ). This study was supported by funding from the  Baxter Foundation , the  Li Ka Shing Foundation , and the  NIH  ( R01 AG069858  and  R01 AG075436  to H. M. B. ).
CODE AVAILABILITY The Dual-FLIT algorithm and a video tutorial can be obtained at (https:// github. com/klasma/BaxterAlgorithms) and (https://www. youtube. com/playlist? list=PLWNt_wdNK0Rp3RoAuUG3SK2B-CejdDrW6), respectively. Correspondence and questions should be addressed to H. M. B.
Methods: We conducted a quasi-experimental, nonrandomized study. Individuals with MetS were recruited from the health promotion center of a tertiary hospital in Zhejiang province, China. The study involved 138 adults with MetS, comprising a control group of 69 participants and an intervention group of 69 participants. All participants received health education regarding diet and physical activity. The intervention group additionally received a 12-week individualized intervention through a WeChat mini program and a telephone follow-up in the sixth week of the intervention. Primary outcomes included diet, physical activity behaviors, and ASCVD risk. Secondary outcomes included diet self-efficacy, physical activity self-efficacy, knowledge of MetS, quality of life, and the quality and efficiency of health management services.
The Mann-Whitney U test and Wilcoxon signed rank test were primarily used for data analysis. Data analysis was conducted based on the intention-to-treat principle using SPSS (version 25. 0; IBM Corp). Results: Baseline characteristics did not differ between the 2 groups. Compared with the control group, participants in the intervention group showed statistically significant improvements in diet behavior, physical activity behavior, diet self-efficacy, physical activity self-efficacy, knowledge of MetS, physical health, and mental health after a 12-week intervention (P=. 04, P=. 001,.
A WeChat mini program grounded in WeChat has diversified functions, supports developers to implement individualized functions, and can provide customized significant and meaningful experiences according to the needs of patients. It does not need to log in to a specific website  [25]  and avoids the limitations of a long development cycle, high cost, and troublesome installation of mobile apps  [26, 27]. Hence, in this case, a WeChat mini program provides solutions for meeting the diversified individual needs of patients with MetS.
Study Design The study was a quasi-experimental, non-randomized controlled trial (RCT) and was reported according to the Transparent Reporting of Evaluations with Nonrandomized Designs Statement Checklist  [36]. A quasi-experimental design was chosen because the WeChat mini program is easily accessible to everyone. To prevent contamination between the intervention and control groups, the WeChat mini program was released after a 12-week observation period in the control group, and patients with MetS were recruited to the intervention group. Therefore, we chose to conduct a quasi-experimental study and not a full RCT.
Intervention Before the intervention, patients in the control and intervention groups received health education about diet and physical activity during their examination day at the health promotion center, which lasted 30 to 40 minutes. The content focused on specific diet and physical activity recommendations for patients with MetS, mainly including the type and amount of diet and the type, frequency, and intensity of physical activity. We selected patients who were hospitalized for physical examinations, which provided ample time for them to receive health education. The intervention group additionally received a 12-week individualized intervention through a WeChat mini program and telephone follow-up in the sixth week of the intervention. Specifically, the WeChat mini program was the primary delivery mode for the intervention.
The team members stayed connected with each other through WeChat to discuss and solve problems and to report the progress of the intervention. On the day of the health examination, our team member guided patients to use the WeChat mini program named "Metabolic Syndrome Management Assistant" and provided detailed answers to any questions from patients, ensuring patient familiarity with the program. The WeChat mini program includes 10 modules: (1) "Health assessment" primarily provides individualized diet and physical activity recommendations; (2) "Daily record" records patients' physical activity and diet on a daily basis; (3) "Last 7 days report" module provides physical activity and dieting reports for the past 7 days in the form of a chart; (4) "Suggestions for behavior barriers" provide individualized solutions to obstacles to performing behaviors; (5) "Knowledge encyclopedia" introduces diagnosis, etiology, clinical manifestations, complications, physical activity guidance, dietary guidance, and other textual and video information about MetS; (6) "Health consultation and reminders" provides individualized feedback according to patients' questions and provides physical activity and diet reminders; (7) "Questionnaires" assists users in completing web-based assessments, including health promotion lifestyle, knowledge of MetS prevention and treatment, self-efficacy of health behaviors, and quality of life; (8) "Operation guide" introduces the rules for using the program; (  9 ) "Disclaimer" introduces the conditions under which the program is exempt from liability; and (10) "Privacy statement" introduces the content of the users' privacy protection.
Multimedia Appendix 2 shows screenshots of the WeChat mini program. The specific application of the WeChat mini program is shown in Figure  2. The WeChat mini program was used as the primary intervention delivery method. However, according to experts' suggestions, relying solely on the program is insufficient to promote health behavior change due to the different initiative levels of patients. Moreover, referring to previous studies  [19, 21] , this study conducted a telephone follow-up in the sixth week of the intervention to ensure that all patients received health education. All the contents of the telephone follow-up were determined by our prior study  [30]. Specifically, the health care professionals provided patients with knowledge of healthy diets. They also highlighted the importance of reasonable family dietary arrangements to ensure a balanced nutritional intake.
Quality Control Our team member guided the intervention group to search for "Metabolic Syndrome Management Assistant" on WeChat and provided detailed instructions on how to use the WeChat mini program to ensure that patients could master the methods of using the program. In addition, when entering data, 2 members of our research team were required to double-enter and cross-check to ensure the accuracy of data entry. When conducting the data analysis, other team members who did not collect data performed the analysis to reduce subjective bias.
Ethical Considerations This study followed the Declaration of Helsinki and was approved by the Hospital Institutional Review Board (number 20210220-32). This study was registered with the Chinese Clinical Trial Registry (ChiCTR2100043877). Informed consent was obtained from all participants, and all data were guaranteed to be confidential. Participants were informed of their right to voluntarily withdraw from the study. All participants with MetS underwent free blood biochemical examinations for participation in our study.
Patient Flow and Baseline Characteristics This study included 138 adults with MetS. The intervention group comprised 69 patients; 6 patients refused data collection, and 1 patient withdrew voluntarily, resulting in a final sample of 62 patients with a dropout rate of 10. 1%. Similarly, a total of 69 patients were recruited for the control group. During the 12-week observation period, 7 patients refused data collection and 1 patient withdrew voluntarily, resulting in a final sample of 61 patients with a dropout rate of 11. 2%. The flowchart of the study is shown in Figure  3. Of the 138 participants, the average age was 51. 50 (range 45. 75-56. 00) years. Most participants were male (111/138, 80. 4%), lived in cities (86/138, 62. 3%), had no religious affiliation (81/138, 58. 7%), had junior high school education (37/138, 26.
In our study, the main reasons for the improvement in health behaviors were that the WeChat mini program provides a solution for providing individualized care services to patients, which helps improve patients' health behaviors  [48]. Specifically, the program can provide individualized diet and physical activity recommendations for patients with MetS based on their biochemical indicators and work life types. The program can record health behaviors and automatically provide feedback based on patients' daily and weekly records. To address barriers to adopting healthy behaviors, the program provides individualized suggestions according to patients' options. At the same time, the program can provide individualized health consultations for patients. These individualized measures contribute to enhancing patients' adherence to healthy behaviors.
Therefore, more comprehensive consideration is required when interpreting the findings. Our research determined that the individualized mHealth-based intervention was an effective approach for enhancing health behavior self-efficacy, which aligned with previous research. The app-based intervention group showed an improvement in exercise self-efficacy from baseline to 24 weeks  [20]. Zheng et al  [18]  found that statistically significant improvements in health behavior self-efficacy were observed when patients followed a lifestyle intervention program consisting of 30-to 40-minute face-to-face health education sessions, reading health education manuals, and 6 follow-up telephone consultations. Our study attributes the effects of the intervention to some contributing factors. Health care professionals can help patients resolve queries and provide encouragement toward continued healthy behaviors through the WeChat mini program and a telephone follow-up.
Moreover, the program presents knowledge and skills related to diet and physical activity in videos, pictures, and texts, which may be attractive for patients with MetS to learn. These measures enhanced patients' confidence in performing health behaviors, statistically significantly improving their health behavior self-efficacy. The results of this study showed that, after the individualized mHealth-based intervention, the intervention group demonstrated an increase in knowledge of the MetS score compared with the control group. The baseline survey of this study found that patients lacked knowledge of MetS. A contributing factor is that MetS is largely asymptomatic  [55] , and many individuals with this condition may fail to perceive its harm, resulting in a lack of preventive measures. Therefore, this study embedded specific content on the diagnosis, etiology, complications, diet, and physical activity recommendations for MetS into the WeChat mini program to enhance patients' knowledge of MetS.
Limitations This study has some limitations. A limitation of our study is that it was not an RCT. However, it prevented contamination caused by accessibility of the WeChat mini program to everyone. A standardized RCT design is necessary to further validate the effects of the intervention. Meanwhile, the data used in this study were obtained from a tertiary hospital, which may have introduced sampling bias despite meeting the statistical requirements. Hence, the generalizations of the research findings should be interpreted with caution. In the future, multicenter studies with a large sample size should be conducted. Furthermore, this study did not use an objective tool to measure physical activity and evaluate the quality and efficiency of health management services because of limited costs and time.
Conclusions In conclusion, the mHealth-based behavior intervention with individualized assessments, strategies, feedback, and suggestion components and using the BCW, including a WeChat mini program and a telephone follow-up, effectively increased patients' dietary behavior, physical activity behavior, diet self-efficacy, physical activity self-efficacy, MetS knowledge, quality of life, and quality and efficiency of health management services. Nurses and other health care professionals may incorporate the intervention into their health promotion programs. Future studies will optimize and upgrade the WeChat mini program using artificial intelligence technology to provide more personalized and intelligent services. Studies with larger samples and a longer follow-up period are also needed to evaluate the effect of the individualized mHealth-based behavior intervention on behavior change and ASCVD risk among people with MetS.
2023; O'Connor et al. 2023; Shrestha et al. 2023; Williams et al. 2023 ). These data paint a complex picture of a burst with multiple components not readily subsumed within standard afterglow models. In addition, there are apparent detections of the associated SN 2022ixw  (Fulton et al. 2023 ) and excess emission in the radio regime  (Laskar et al. 2023 ). Here we present a set of space-based, high spatial resolution observations of GRB 221009A obtained with the James Webb Space Telescope (JWST) and Hubble Space Telescope (HST). These minimize impacts from crowding, extend redward of the limit of ground-based observations, and provide the necessary spatial resolution to identify the host galaxy. Although they do not sample the temporal evolution of the event as well as the extensive observations from the ground, they are, in principle, substantially cleaner because of their high resolution and signal-to-noise ratio (S/N) and provide a well-sampled spectral energy distribution (SED).
James Webb Space Telescope On 2022 October 22, we obtained observations of the afterglow of GRB 221009A with JWST (program GO 2782,  PI Levan). A single, uninterrupted set of observations were obtained with the Near Infrared Spectrograph (NIRSPEC;  Jakobsen et al. 2022)  and Mid-Infrared Instrument (MIRI;  Rieke et al. 2015). NIRSPEC observations began at 17:13 UT and MIRI at 18:12, corresponding to times since burst of 13. 16 and 13. 20 days, respectively. An image of the field at the time is shown in Figure  1 , and the resulting spectra are shown in Figure  2. For NIRSPEC, we utilized the prism, spanning a spectral range from 0. 5-5. 5 μm at a low (and variable) spectral resolution. The MIRI observations were undertaken in lowresolution mode and span the 5-12 μm range. For both NIRSPEC and MIRI observations, we reprocessed the data with the most up-to-date calibrations from 2022 December and obtained 1D extractions.
Comparing these products with those obtained from the archive processing shows good agreement; however, our re-reduction of the NIRSPEC data is ∼10% brighter beyond 5 μm than the archival data. This reprocessing provides more consistent spectral fits between MIRI and NIRSPEC (see below), although it also introduces a small disjoint at the overlap region (see Figure  2 ). In addition to spectroscopy, a short (11 s) acquisition image was also obtained in F560W with MIRI, as shown in Figure  1. This provides a photometric measurement at this epoch of F560W(AB) = 17. 9 ± 0. 1. For NIRSPEC data, we used nearsimultaneous observations taken with the 3. 6 m Italian Telescopio Nazionale Galileo (TNG) to check the calibration in the J, H, and K bands. The match appears excellent, so we adopt the flux calibration direct from the pipeline without additional scaling.