dataesr/scientific-paragraphs-categorization huggingface dataset:
The parenchyma cells were divided into two types: those with and without direct contact with stone cells, and a second Image J software (V. 2. 1. 0) macro was established for measuring parenchyma cell size and circularity index. The internal size of each cell was identified, measured, and average cell size calculated. A circularity shape index ranging from 0 to 1 was used to assess the roundness of the cells, in which values closer to 1 indicate more circular shape and closer to 0 a more elliptical shape. To obtain this value, the largest and smallest diameters of each parenchyma cell were automatically measured, and the following equation was used: Mesocarp cell circularity = Largest diameter/Smallest diameter.
Fruit tissue sampling and preparation All the data reported were tested for homogeneity of variance and normal distribution, and then analyzed using analysis of variance (ANOVA) followed by Tukey test, or Student's t-test to determine the significant differences (p≤0. 05). In two analyses which considered percentages, that is, 1) the percentage area occupied by SCCs and 2) the ratio of isolated stone cell number to the numbers of stone cells in SCCs, were subjected to arcsine transformation before ANOVA. Statistical analysis was performed by SPSS V24. 0.
1 μL of the metabolite solution and 1 μL of the matrix solution were mixed, deposited on the MALDI plate or the silanized surfaces, and let crystallized in air before measurement. In the case of functionalized samples, an imagery of the evaporated droplet was performed using the FlexImaging 3. 0 software (Bruker Daltonics) with a spatial resolution of 200 μm (40 spots per droplet). For each spot, 500 satisfactory laser shots were averaged to obtain a mass spectrum. The spectra were analyzed using the FlexAnalysis 3. 4 software (Bruker Daltonics) for peak detection and for measuring the signal-to-noise ratios. Statistical Analysis of MALDI-ToF MS Spectra. Each type of sample was done in triplicate. For each replicate of a given triplicate, the mean S/N ratio of each metabolite at [M + H] + was calculated from 40  ToF-SIMS Profiling.
High-resolution mass depth profiles were acquired by ToF-SIMS (ToF-SIMS V from ION-TOF). A bunched 15 keV Bi 3+ ions was used as the primary probe for analysis (rastered area 80 × 80 μm 2 ) and sputtering was obtained using Cs + ions at low energy (500 eV, rastered area 400 × 400 μm 2 ). The sputter and analysis beams were turned on alternatively, giving in result a series of spectra acquired all along the depth of the sample. XPS Analysis. Spectra were obtained using a VSW spectrometer equipped with a monochromatized X-ray source (Al Kα 1486. 6 eV) in which the angle between the incident beam and the detector was the magic angle. The angular resolution was 3°. The take-off angle was 30°relative to the substrate surface. The energetic resolution was 0. 2 eV. The data analysis was performed with CasaXPS software.
The downhill simplex optimization (DSO) method is a heuristic search method that is used for non-linear problems for optimizing 1-or multi-dimensional cost-functions. This method allows for finding the maximum or minimum of the objective function  [147]. DSO does not always lead to an optimal solution  [148, 149]. MATLAB ® environment provides an optimization algorithm that is called fminsearch based on the Nelder-Mead technique. However, this technique is highly computationally complex to find the feature with the highest performance. To overcome this limitation, the Newton-Raphson algorithm (NRA) was proposed in  [117]. It is an iterative algorithm allowing the maximization of the objectivefunction to find the optimum solution. This algorithm uses the tangent knowledge of the curve that is close to the root.
It includes a user interface that shows how the information is sent to the system, i. e. , the output of PQ monitoring. The classification results present the outputs of the system. Reasoning between rules and information is performed by the interface engine. An explanation system allows for defining the reasoning to the user. The knowledge base editor allows updating the knowledge base and rules. Finally, the user can stock all data that has additional information in case-specific data. In the literature, several classifiers-based on expert systems to classify the PQ disturbances were proposed  [126, 137, 155, [202] [203] [204] [205] [206] [207] [208] [209] [210] [211]. In  [202] , the authors proposed an events classification technique during the RES integration in the power system. This technique is based on the data mining tool and the expert system.
The authors proposed in  [203]  a techniques-based on neuro-fuzzy classifier and principle component analysis (PCA). The authors proposed in  [204] , a classifier uses the neuro-fuzzy and theorem of Parseval for PQ disturbances recognition systems. This technique was tested for several disturbances using noise-riding signals. An algorithm-based fuzzy decision tree for the classification of various disturbances was proposed in  [205]. However, the above-presented techniques require a good knowledge of the disturbances or training database and their performance depends on the learning stage. Database size must be large to cover all types of PQ disturbances which is a difficult task in practice. Moreover, their performance also depends on that of the extraction stage. In addition, the learning step results in a high computational time to perform the task.
However, the above-published estimation techniques are sub-optimal and most of these techniques do not use the information of the three-phase signals. Moreover, their performances lead to low performance under off-nominal conditions. Regarding the classification stage, the extracted feature is used as input for the PQDs classifier. Pattern recognition-based techniques (i. e. , ANN, SVM, etc. ) require a good knowledge of disturbances, and a large training database and their performance critically rely upon the extraction, selection and learning stages. Moreover, these techniques have a high computation complexity. To overcome these limitations, the development of advanced SP algorithms has become essential. It is still necessary to develop robust and reliable techniques under noisy and off-nominal environments and for short-length signals.
In addition, the mentioned techniques achieve low estimation performance under high noisy and off-nominal conditions. Furthermore, they require a long signal length to achieve high estimation performance, where various fast disturbances occur. For the classification stage, the studied classical techniques achieve low performance under highly noisy environments and for high variations in frequency. In addition, these techniques are not able to classify all sag and swell types. Pattern recognition-based techniques require a good knowledge of disturbances, a large training database and their performance critically relies on the extraction, selection and learning stages. The disturbances extraction based on SP tools is required and is considered of great importance to accelerate the evolution of future electric grids.
Data and methods With a cross-sectional approach, we used data from the first five waves  (1993, 1997, 2000, 2007, and 2014-15)  of the IFLS, which is representative of 83 per cent of the Indonesian population. A sample of 7,730 households was selected in the first wave. Multiple individuals were selected within each household using the following criteria: all heads of households and their spouses, two randomly selected children aged 0-14, an individual aged 50+ randomly selected from the remaining members and their spouses. Households and household members were added in subsequent waves, to ensure IFLS households are always representative of the Wave 1 (IFLS1) households and their descendants. The strength of this survey lies in the wide range of topics included, the low attrition (92 per cent of IFLS1 households were re-contacted in Wave 5 (IFLS5))  (Strauss et al.
2016) , and data on a range of life-course events and health indicators. The sample was increased at each wave to arrive at a total of 16,204 households in IFLS5. Although the IFLS data are longitudinal, we used data mainly from the fifth wave, as many of the AL questions were included only in this wave. For the outcome variable we used only IFLS5 data, as we explain shortly. We did reconstruct some of the independent variables using more than one wave in cases with high item missingness or where some of the reproductive history questions were asked at certain ages (e. g. only women aged <60 were asked their age at first birth): these variables covered parity, miscarriages, employment, union status, age, hospitalization, and child death. We found higher missingness at older ages (90+), which is negligible given the very small size of this age group (less than 1 per cent of the sample); see also Tables  1  and 2  for further information on missingness.
Outcome: Allostatic load We used a total of nine biomarkers to calculate AL: two from the anthropometric system (WHR and body mass index (BMI)); two from the metabolic system (haemoglobin (to test for anaemia) and glycosylated haemoglobin (A1C blood sugar test for diabetes)); one from the immune system (mean C-  6 Tiziana Leone et al. reactive protein concentration, measuring the level of inflammation in the body); and four from the cardiovascular and respiratory system (pulse rate, systolic and diastolic blood pressure, and lung function). Table  1  reports the level of missingness for each item. Our measure therefore included all the main groups within the secondary biomarkers mentioned earlier but did not include any primary biomarkers. This is also common in other data sets widely used to study AL in HICs, for example the English Longitudinal Study of Ageing (ELSA) and Health and Lifestyle Survey in England.
Stillbirths are likely under-reported due to the challenge in distinguishing them from neonatal deaths  (Jones and Kost 2007; Lawn et al. 2011). For women aged <60, we constructed a binary variable age at first pregnancy, showing whether the respondent first became pregnant before age 18 or aged 18+. The majority of these pregnancies (94 per cent) resulted in live births and thus the variable indicates early childbearing. These data were largely unavailable for women aged 60+ in the IFLS. This variable has previously been found to be significantly associated with later-life health  (Grundy and Read 2015). To measure significant stressors, we included whether the woman had ever experienced child death. Child deaths negatively impact the mental and physical health of parents and their ageing process  (Rogers et al.
These variables were later removed due to high missingness. We did not include alcohol consumption, as data were unavailable. Socio-demographics. We created a range of variables measuring respondents' current socio-demographic characteristics. Education was measured in IFLS5 (2014-15) as an ordinal variable for highest grade achieved (no schooling, up to elementary, up to junior high, up to high school / junior college, and university). If educational level was missing at Wave 5 but available for Wave 4, we used the latter value (n = 127) as, given the age of those in our sample, we would not expect it to change between the waves. Wealth in adulthood was measured as an index variable constructed with principal component analysis using assets (e. g. electricity, type of floor and wall material, ownership of various household goods) at residence level in the respondent's household.
Some of the youngest cohorts of women in our study might not yet have completed their reproductive histories. However, we expected only a few of them to experience more pregnancies, given the low median age at childbearing in Indonesia (22. 4 years) and the low age-specific fertility rates for ages 40+ (20 per 1,000 women for ages 40-44; 4 per 1,000 for ages 45-49)  (BKKBN 2018). In order to include a wider range of fertilityrelated variables (i. e. age at first pregnancy and death of child), we conducted some models separately for parous women only. Finally, we analysed these data using path analysis within a structural equation framework using Mplus software (version 8)  (Muthén and Muthén 1998-2017). The advantage of path models is their detailed information about the direct and indirect pathways both from and via fertility events to AL.
We constructed paths reflecting plausible chains of association, based on previous literature, data availability, timing (when the variables were recorded in the IFLS), and the endogeneity of the variables. We created four models mirroring the regression modelling strategy: two including all women (for cohorts aged 40-59 and 60 + at IFLS5); and two including only parous women in each cohort but with a wider range of fertility variables. Models for both cohorts together were also conducted but not reported here (available on request), as they failed to converge or reach a good model fit due to the pathways being quite different for the two cohorts. We calculated direct, indirect, and total effects of key variables on AL based on the results of the path models. We report standardized coefficients, allowing us to compare the magnitude of the impact of each variable on the outcome.
Strengths and limitations This study's strengths and limitations relate mainly to the data used, in terms of both timing and availability of information. The inclusion of nine biomarkers, including all the key secondary marker groups, provided a wealth of information which allowed the calculation of AL with very low missingness compared with HIC settings. On the limitations side, we could not explore in depth the full lifecourse information, including childhood wealth, age at menarche, and breastfeeding among others. Nor could we include more information on adverse childhood experiences, which can, for example, increase the risk of preterm births and miscarriages  (Christiaens et al. 2015). However, the IFLS provides five waves of data for a wider-than-usual age group (40-101), allowing the analysis of adults at both midlife and older ages.
Conclusions This paper is the first to estimate the effect of reproductive burden on AL in a high-fertility LMIC setting. It is also the first to explore AL data in an older population in such a context. On the application side, the study highlighted the following points: (1) AL should be a major research agenda topic; (2) existing sources such as the IFLS can, with many technical adjustments and further assumptions, be used to outline why and what is at issue; and (3) there are still several limitations that need to be overcome, and for this reason we call for further research. Studies in LMICs have often been apologetic about the limitations of the data. However, a closer look at the biomarkers showed that response was particularly high and attrition relatively low. The challenges of the data when analysing AL in LMICs are therefore similar to those in HICs.
Study design and population This retrospective, observational, and multicenter study included patients who consulted in the neurology departments of specialized neuromuscular centers in Lyon, Paris, Marseille, Strasbourg, and Saint-Etienne between 1983 and 2021 for proximal muscle weakness, predominantly affecting the lower limbs, with motor axonal neuropathy on nerve conduction study and electromyography (EMG). The latter was defined by the presence of neurogenic motor unit potentials and/or a reduction of compound motor action potentials (CMAP), with a normal or <30% reduction of sensory nerve action potentials (SNAP). After exclusion of other diagnoses, and in the absence of a homozygous deletion in the SMN1 gene, all patients had a diagnosis of non-5q proximal SMA. All clinical data were collected anonymously from the study units' medical files.
Clinical, laboratory, and electrophysiological data The demographic data collected were age at onset, family history of SMA, and parents' consanguinity. The clinical data of interest included the predominant site of muscle weakness in the lower limbs, the presence of muscle weakness in upper limbs, and if present, the weakness was classified as proximal (predominantly involving deltoid, biceps, or triceps) or distal (predominantly involving flexor digitorum, extensor digitorum, interosseous, or abductor pollicis brevis). Other clinical data were notified: axial muscle weakness (defined as a muscle weakness ≤ 4/5 according to the Medical Research Council (MRC) scale on neck flexion, and/or as an impossibility to rise from a lying position without hands), osteoarticular deformities (scoliosis, pes cavus, pes planus, pes equinovarus, pes planovalgus, clubfoot and/or articular retractions), tremor (classified as a postural or resting tremor), pyramidal signs (brisk reflexes, Babinski or Hoffman's signs, or spasticity), and scapular winging.
Genetic analyses Overall, 22 of the 24 patients first benefited from the same next-generation sequencing (NGS) gene panel analysis, composed of 103 genes involved in hereditary peripheral neuropathy using Capture Roche KAPA HyperCap v3. 0 (Pleasanton, CA, USA), and sequenced on Illumina ® NextSeq500 (San Diego, CA, USA; Supplementary Table  1 ). In patients with no causative variant identified using the gene panel analysis, WES were performed using Capture Roche MedExome (Pleasanton, CA, USA), and sequenced on Illumina ® NextSeq500 as previously described  [6]. Reads were aligned to the human reference genome hg37/GRCh37 (Genome Reference Consortium Human GRCh37). The following filters were applied: heterozygosity threshold of 0. 20, read depth >14 for homozygous variants and >7 for each allele for heterozygous variants, frequency in 1000 Genomes database <0.
01, heterozygous occurrence <250 in ExAc database, maximum occurrence of 6/12 in the run, and introns usually covered at +/-60 base pairs (bp). A total of 16,582 variants remained after the application of these filters. Genes known to be associated with non-5q proximal SMA  [7, 8]  or amyotrophic lateral sclerosis (ALS)  [9] , another disease affecting motoneurons, were individually checked (Supplementary Table  2 ). The two other patients benefited either directly from WES (patient 10) or from WGS (patient 4) after a negative panel analysis of genes associated with myopathy (initial suspicion of myopathy). Finally, identified variants were searched in the ClinVar and gnomAD databases, and their pathogenicity was evaluated according to the American College of Medical Genetics and Genomics (ACMG) guidelines, and in silico analyses of variants were performed using three softwares (PolyPhen2, SIFT, and MutationTaster).
Four disease-causing variants were identified in DYNC1H1 (NM_001376. 5), affecting five patients (from four families). The variants c. 1792C>T and c. 2327C>T were already classified as pathogenic on ClinVar and gnomAD databases, and were classified as likely-pathogenic according to the ACMG criteria  [10, 11]. Two other variants detected in DYNC1H1 were absent from the ClinVar and gnomAD databases (c. 596A>C and c. 1427T>C)  [10, 11]. The c. 596A>C variant in DYNC1H1 occurred de novo. These two variants (c. 596A>C and c. 1427T>C) were also classified as likely-pathogenic according to the ACMG guidelines. Three variants were identified in BICD2 (NM_001003800. 2), with one variant not reported in the ClinVar and gnomAD databases (c. 380A>G), while the two others (c. 1922T>C and c. 2042C>T) were classified as variants of unknown significance (VUS)  [10, 11].
For patient 10, a WES was performed first, and biallelic variants (c. 77C>G and c. 125 + 1G>A) were found in ASAH1 (NM_177924. 3), classified as pathogenic according to the ACMG guidelines. Analyses in the family confirmed the bi-parental segregation of the ASAH1 variants  [12]. Patient 4 benefited from a WGS first, which identified a c. 751C>T variant in DYNC1H1, the pathogenicity of which had conflicting interpretations in the ClinVar database. It occurred de novo and was classified herein as likely-pathogenic according to the ACMG criteria. The Sanger sequencings of the de novo and/or not previously reported variants, which were identified through the gene panel, are available in Supplementary Fig. 1. Then, predictive factors for informative genetic testing were determined by comparing two groups: one (n = 10) with an  identified variant, and the other one (n = 14) without an identified variant.
21). The clinical pattern of weakness in the lower limbs, the presence of tremor, axial muscle weakness, scapular winging, and pyramidal signs were not significantly different between groups. Regarding complementary exams, neither the CK elevation in blood samples nor the presence of decreased CMAP or fibrillations was significantly different between patients with and without identified variants (Table  2 ). The precise clinical description of the cohort is available in Table  3. Tremor was mainly postural tremor, observed in five patients, while resting tremor was observed in only two patients. Pyramidal signs (i. e. brisk reflexes in the lower limbs) were found in only three patients, including one with a BICD2 variant. No cerebellar syndrome was found. Data regarding walking aid were not compared between groups because of heterogenous followup durations between individuals.
All the other variants of the cohort were dominant. This woman, whose lower limb weakness started at 14 years old, presented a pure motor phenotype without epileptic myoclonic seizures, contrary to the classical phenotype associated with ASAH1 mutations named SMA with progressive myoclonic epilepsy (SMA-PME)  [17]. This woman has already been reported in a previous case-report  [12]. Overall, this cohort shows that two genes, DYNC1H1 and BICD2, account for nearly 40% of the non-5q-SMA French patients. These data also confirm that CHCHD10 mutations, which are frequent in the Finnish population of non-5q proximal SMA patients, and the previously described TRPV4 and VAPB mutations are not a major feature in the French population  [18, 19]. A major issue in hereditary neuropathy is the identification and interpretation of VUS.
Indeed, misclassification of a variant can lead to clinical or legal consequences, especially concerning genetic counseling. Here, by adding new reports, we give new arguments for the pathogenicity of three variants initially classified as VUS or with conflicting interpretations in the ClinVar database  [10] ; one in the DYNC1H1 gene (c. 751C>T) and two in the BICD2 gene (c. 2042C>T and c. 1922T>C). Among the three variants not previously described that we report here, two were classified as likely-pathogenic according to the ACMG criteria (c. 596A>C and c. 1427T>C in DYNC1H1) while the last one (c. 380A>G in BICD2) was classified as a VUS. This variant was predicted to be pathogenic by three in silico algorithms. However, a genetic analysis of BICD2 in the patient's parents would certainly help determine the inheritance of this variant and thus, confirm or refute its pathogenicity.
Second, we increased the sample size per species, aiming for many samples per species. We profiled 348 species from 25 of the 26 mammalian taxonomic orders. This comprehensive dataset enables examination of phylogenetic relationships, comethylation relationships between cytosines, and their associations with maximum life span and other species characteristics. We profiled 15,456 samples (Fig. 1A  and table  S1 ) using a methylation array platform that provides effective sequencing depth at highly conserved CpGs across mammalian species  (5). This dataset is the product of the multinational Mammalian Methylation Consortium. In previous studies, we applied supervised machine learning methods to generate DNAm-based predictors of age called epigenetic clocks for numerous species. Here, we performed a large-scale cross-species unsupervised analysis of the entire dataset to reveal the relationship of DNAm with mammalian phylogeny.