dataesr/scientific-paragraphs-categorization huggingface dataset:
When FER-1 was identified and sequenced, no other known proteins had strong resemblance to it. Subsequently, homologs were found by sequence similarity in mammals, forming a family of similar proteins now called ferlins. In humans, a first C. elegans fer-1 homolog gene was discovered and the protein encoded by this gene was named dysferlin  [3]. Shortly after, a second human FER-1-Like gene was identified. The product of the gene was named otoferlin  [4]. The human EST database mining revealed a dysferlin paralog called myoferlin  [5, 6]. Three new members joined the ferlin gene family: FER1L4, a pseudogene; FER1L5; and FER1L6. The main features of ferlins are summarized in Table  1. The dysferlin mutations were involved in Limb-Girdle muscular dystrophy 2B (LGMD2B), a autosomal recessive degenerative myopathy, and in Miyoshi muscular dystrophy 1 (MMD1), a late-onset muscular dystrophy  [3, 7].
Ferlin's Structure and Localization Caenorrhabditis elegans FER-1 is a large protein rich in charged residues. Charged amino acids are distributed throughout the whole protein length such that no particularly acidic or basic domains are observed. The hydrophobicity plot described a 35 amino acid long hydrophobic region at the C-terminal end  [2]. To the authors' knowledge, it has never been experimentally demonstrated. Similarity studies suggest that this region might be a transmembrane domain. FER-1 sequence analysis with Pfam protein families database  [14]  revealed the existence of 4 C2 domains and several other domains. Ferlins are proteins harboring multi-C2 domains. These structural domains are ~130 amino acid long independently folded modules found in several eukaryotic proteins. They were identified in classical Protein Kinase C (PKC) as the second conserved domain out of four.
The typical C2 domain is composed of a beta-sandwich made of 8 beta-strands coordinating calcium ions, participating to their ability to bind phospholipids (for review  [15] ). However, some C2 domains have lost their capacity to bind calcium but still bind membranes  [16]. A large variety of proteins containing C2 domains have been identified, and most of them are involved in membrane biology, such as vesicular transport (synaptotagmin), GTPase regulation (Ras GTPase activating protein) or lipid modification (phospholipase C) (for review  [17] ). Human ferlin proteins harbour 5 to 7 C2 domains as described in the Pfam database (Figure  1A ). According to this database, in humans, 342 proteins harbour C2 domains. However, the occurrence of multiple tandem C2 domains is uncommon. Only three vertebrate protein families contain more than two C2 domains: The multiple C2 domain and transmembrane region proteins (MCTP)  [18] , the E-Syt (extended synaptotagmins)  [19] , and the ferlins.
Ferlins in Cancer, Potential Targets to Kill Cancer It is clear from the data above that ferlins are consistently involved in membrane processes requiring membrane fusion, including endocytosis, exocytosis, membrane repair, recycling and remodelling. Membrane processes are of extreme importance for cell survival and signalling, making them core machinery for cancer cell adaptation to hostile environments. Considering that ferlins have been only scarcely investigated in cancer, the authors next sought to mine publicly available databases and gain information regarding ferlin's expression or mutation in tumors. Using the FireBrowse gene expression viewer (firebrowse. org), The Cancer Genome Atlas (TCGA) RNAseq data of all ferlin's genes in neoplastic tissues were investigated in order to obtain a differential expression in comparison to their normal counterparts.
Interestingly, a recent publication points out specific single nucleotide polymorphisms in dysferlin genes as significantly associated with pancreas cancer patient survival  [91]. Mining the TCGA database, a high Fer1L4 expression was reported as a predictor of a poor prognosis in glioma  [92, 93]  and as an oncogenic driver in several human cancers  [94]. However, several other publications pointed it out as a predictor of good prognosis in osteosarcoma  [95] , gastric cancer  [96] , endometrial carcinoma  [97].
Statistical Methods The multivariate Cox regressions (Table  3 ) were performed with the coxph function from the R survival library. For each cancer and data type, OncoLnc attempted to construct a model with gene expression, sex, age, and grade or histology as multivariates  [90]. The clinical information was obtained from TCGA and only patients who contained all the necessary clinical information were included in the analysis. The patients were split into low and high expressing according to the median gene expression.
Most discriminating ones were conidial shape, mycelial form, colony color, and culture smell. e molecular identification of our fungal strains was performed by sequencing the ITS 1 regions of rDNA and compared NCBI GenBank nucleotide database. e BLAST analyses showed that the ITS sequences of all sequenced strains were at least 99% similar to the corresponding GenBank sequences. All belonged to Trichoderma, with only two species identified: five strains were affiliated to the species Trichoderma asperellum (G7, G18, G3, G11, and G9) while three proved to be Trichoderma longibrachiatum (G13, G14, and G17). One of the T. asperellum strains (G7) showed the best 6-PP production with an average of 86. 69 μg•g•DM -1 of 6-PP (Table  1 ). Two other strains, T. asperellum G18 and T. asperellum G3, produced 80.
In vivo recordings of parasympathetic activity Parasympathetic activity was recorded at the thoracic branch of the vagus nerve, along the carotid artery as described previously  [43]. Unipolar nerve activity was recorded continuously for 15 min during fasting state and after an intraperitoneal injection of glucose (2 mg/kg) during 15 min. Data were digitized with PowerLab/4sp digitizer (ADinstruments, Paris, France). Signals were amplified 105 times and filtered using low-and high-frequency cut-offs of 100 and 1000 Hz and monitored using the Chart 4 computer program (ADinstruments, Paris, France).
Islet isolation, cell culture, electrophysiology and signal analysis, immunofluorescence Electrophysiological recordings were performed according to published methods [44e46]. Mice islets were prepared and seeded on microelectrode arrays (MEA, 60 electrodes; MCS, Reuttlingen, Germany). Four days later, signals were recorded (10 kHz) and analyzed offline in terms of frequency with commercial software (MC_Rack, MCS). Immunofluorescence was performed on whole islets and dispersed islet cells using either a polyclonal rabbit anti-GluK2 antibody (Synaptic Systems, Goettingen, Germany)  [47]  or a rabbit monoclonal antibody (anti-GluR6/7 clone NL9, Merck Millipore)  [48]  and appropriate secondary antibodies.
FUNDING MA and FL hold PhD scholarships from the French Research Agency (ANR-17-CE09-0015 MULTISPOT to JL) or the French Ministry of Education (to JL). We acknowledge institutional funding from the CNRS and the University of Bordeaux (JL). C Magnan received funding from the French Research Agency (ANR PRCI-15-CE14-0027-01 BetaDiamark) and the French Society for Study of Diabetes (SFD-MSD). JL designed the project; C Mulle provided transgenic animals, conceptual help and valuable discussions; MA, JG, FL, RP, MJ and C Magnan performed experiments; MA, JG, FL, RP, MJ, C Magnan, MR, and JL analyzed data; JL drafted the article; all authors approved the version submitted.
Electrophysiology studies The brain slices preparation is described in the Supplementary Methods. Patch-clamp recordings. Patch electrodes were pulled (micropipette puller P-97, Sutter instrument, USA) from borosilicate glass (O. D. 1. 5 mm, I. D. 0. 86 mm, Sutter Instrument) to a resistance of 2e4 mU. Electrophysiological data were recorded using a Multiclamp 700B amplifier (Molecular devices, UK), low-pass filtered at 4 kHz and digitized at 10 Hz (current clamp) or 4 Hz (voltage clamp) (Digidata 1440A, Molecular devices, UK). Signals were analyzed offline (Clampfit software, pClamp 10, Molecular devices, UK). The pipette internal solution used of mEPSCs acquisition contained [in mM: 125 potassium gluconate, 5 KCl, 10 Hepes, 0. 6 EGTA, 0. 3 CaCl 2 , 2 MgCl 2 , 7 Phosphocreatine, 3 adenosine-5 0 -triphosphate (magnesium salt), 0.
ACKNOWLEDGEMENTS We thank the animal facility and the genotyping platform of the  INSERM U1215 NeuroCentre Magendie , funded by  INSERM and Labex Brain  ( ANR-10-LABX-43 ), for animal care and mouse genotyping. In particular, help of  Delphine Gonzales ,  Fiona Corailler ,  Helizabeth Huc ,  Philippe Alzieu  and  Florian Monteuil  is acknowledged. We also thank  Dr. Francis Chaouloff  and  Geoffrey Terral  ( INSERM U1215 ) for useful suggestions and helpful discussion. The microscopy was done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging. The help of  Fabrice Cordelières  is acknowledged. This work was supported by  INSERM (DC, GM)  and  Nouvelle Aquitaine Region  (DC,  GM ),  ANR-13-BSV4-0006  Neuronutrisens (DC,  GM ),  ANR-18-CE14-0029  Mitobesity (DC,  GM ),  ANR-2010-1414-01  (DC),  Labex BRAIN   ANR-10-LABX-43  (DC, GM),  Fondation Francophone pour la Recherche sur le Diabète , which is sponsored by  Fédération Française des Diabétiques (AFD) ,  AstraZeneca ,  Eli Lilly ,  Merck Sharp & Dohme ,  Novo Nordisk , and  Sanofi (DC) ,  Inserm/Region  and FRM PhD fellowship FDT20150532545 (NS), PhD fellowship from the  French Ministry of Higher Education  and FRM PhD fellowship FDT201805005371 (VS),  European Research Council (Endofood ,  ERCe2010eStGe260515  and  CannaPreg ,  ERC-2014-PoC-640923 , MiCaBra, ERC-2017-AdG-786467, GM),  Fondation pour la Recherche Medicale (FRM ,  DRM20101220445 , GM).
They are more likely to contract infections caused by unusual bacteria  [7] ; particular attention should thus be paid to these patients  [8]. We aimed to outline immunological evolution in elderly people presenting with frailty syndrome, screening options for these at-risk patients, and management modalities. We performed a literature analysis using the following MeSH terms on the PubMed and Google Scholar databases: "inflammaging", "immunosenescence", "frail elderly", and "undernutrition". We mainly focused on meta-analyses and systematic reviews.
1% SDS, 10% methanol, pH 9. 2). To control the equal protein charge in each line, the PVDF membranes were stained with 0. 2% Red Ponceau S solution (Sigma-Aldrich) and washed a few times with water. After blocking in 5% skimmed milk in TBS-T (50 mM Tris/HCl pH 7. 6, 150 mM NaCl supplemented with 0. 05% Tween-20) for 1 h at room temperature or overnight at 4 °C, membranes were incubated with primary antibodies (3 µg/mL for anti-ABCA1 clone 3A1-891. 3 or 1 µg/mL for anti-ABCG1) in 1% skimmed milk in TBS-T overnight at 4 °C or for 3 h at room temperature. Excess of primary antibodies was removed by washing the membrane three times in 1% skimmed milk in TBS-T before incubation with horseradish peroxidase-labeled secondary antibody (0. 1 μg/ mL) for 1 h. After several washes with TBS-T, the presence of protein was revealed using Western Lightning Plus-ECL (PerkinElmer) on a ChemiDoc MP System with ImageLab software (Bio-Rad).
Microscopy imaging A1G and MMG cells were seeded at 1 × 10 4 cells/well in Lab-Tek chambers (Nunc) in complete Ham's F-12 medium and incubated for 48 h at 37 °C. Cells were then washed three times with HBSS (Gibco) supplemented with 10 mM HEPES, pH 7. 4 (Gibco), and were imaged using a 63 × oil immersion objective on a LEICA SP8 confocal microscope (LEICA) with LAS X software.
Flow cytometry CHO-K1, A1G and MMG cells were harvested at 5 × 10 5 cells/condition and washed twice with PBS buffer (Gibco) then analyzed on a NovoCyte Flow Cytometer (ACEA Biosciences) using NovoExpress software. Mock and GW3965treated Raw 264. 7 macrophages were harvested at 5 × 10 5 cells/condition, washed twice with PBS buffer and blocked with PBS containing 2. 5% BSA for 30 min at 4 °C. Cells were then labeled with 1 µg/mL of anti-ABCA1 coupled directly with Alexa Fluor-647 (1422-AF647) for 1 h at 4 °C, washed three times with PBS buffer and analyzed.
03. Fluorescence lifetime and fluorescence anisotropy values were obtained with the application of SymPhoTime 64 v. 2. 3 software (Picoquant GmbH, Germany). Fluorescence emission spectra of the imaged cells were recorded with the application of a Shamrock 163 spectrograph and a single-photon-sensitive camera (Newton EMCCD DU970P BUF, Andor Technology cooled to minus 50 °C) both coupled to the MT 200 microscope. Application of this system enables detection of fluorescence emission spectra on microscale.
Mathematical and statistical analysis All mathematical and statistical analysis was performed using the GraphPad Prism 7 software package (GraphPad). For IC 50 value determination, a nonlinear fit (inhibitor vs. normalized response with variable slope) of cell viability in the function of AmB decreased concentrations was applied. The statistical significance of differences was assessed either by two-way ANOVA or by t test. For all tests, the significance level (alpha, α) was set to 0. 05. Significant differences are indicated in individual graphs with p values as follows: ****p < 0. 0001, ***p < 0. 001, **p < 0. 01, *p < 0. 1. If the differences were not significant, nothing is stated. For each result, an appropriate description of the analysis appears in the figure legend.
Acknowledgements AW, NF, ORJ and TT acknowledge financial support from the  National Science Center of Poland (NCN)  under project no. 2016/21/B/NZ3/00343. TT acknowledges  Wroclaw Biotechnology Center (KNOW)  for financial support. EG, RL, WG and WIG acknowledge financial support from the  National Science Center of Poland (NCN)  under project no. 2015/19/B/NZ7/02159. We acknowledge  Richard Ashcroft  and  Ewa Blaszczak  for critical reading and correction of this manuscript.
Moreover, due to large heterogeneity of inclusion criteria and study populations, intervention types and outcome assessments, the pooling of individual patient data and performing meta-analyses is currently seriously hampered  [47, 57]. Applying a fixed definition for malnutrition in future studies could importantly increase comparability between studies. The field could also benefit from establishing a minimum dataset (MDS) for clinically relevant outcome variables, per setting when deemed necessary, and defining the preferred method for assessing these outcome variables. This MDS would stimulate the incorporation of standardised outcome variables in future trials, and would enable the pooling of data and the performance of meta-analyses in order to obtain the highest level of evidence regarding the effect of nutritional interventions on relevant clinical outcomes.
Study population In this retrospective study, we analyzed data of 234 consecutive women treated for an invasive breast cancer between January 2009 and December 2011 in our Breast Cancer Care unit. Patients with invasive breast cancer treated between January 2009 and December 2011 with an available breast adipose tissue sample stored in liquid nitrogen were included in the analysis. Patients diagnosed with ductal carcinoma in situ (DCIS) alone were excluded. Data about baseline patients' characteristics were retrieved from the electronic medical records at our Institution.
25mm, SGE, Courtaboeuf, France). Hydrogen was used as carrier gas at constant pressure (130kPa). The oven program started with initial temperature 60°C held for 5min, increased to 140°C (rate of 5°C/min) that it was held for 25min, increased to 145°C (0. 4°C/min) held for 10min, increased to 180°C (1. 5°C/min) held for 10 min, increased 190°C (0. 5°C/min) held for 4 min, then increased to 220°C (rate of 10°C/min) held for 6min. FAME were identified by comparison of their retention time with those of authentic standards (Supelco, USA). Using GCSolution software (Shimadzu, France), peaks were integrated and each fatty acid was expressed as % of total peak area. The percentage of identified peaks was superior to 96%.
2018 (Ditzler et al. , 2019)). FarmDESIGN is a farm-household model developed to represent the farm-household and the flow of resources among the farm components (crops, soil, animals, and manure) within and outside the farm (e. g. soil nutrient losses) (Fig. 1 )  (Groot et al. 2012). We expanded the FarmDESIGN model to calculate various farm-household performance indicators in order to capture more accurately farm-household budget, labour and nutrition (farm-household budget and labour modules explained in detail in  Ditzler et al. 2019 ; Fig. 1  and Table  1 ). The 'Human nutrition' module integrates various performance indicators that have been proposed to monitor progress towards NSA interventions (e. g. Gustafson et al. 2015; Herforth et al. 2016; Melesse et al. 2019 ) and to measure different aspects related to nutrition such as food consumption patterns, diet diversity, nutrient supply and nutrient adequacy.
For instance, FarmDESIGN facilitates conducting farmhousehold scenario analysis through optimization routines  (Ditzler et al. 2019; Groot et al. 2012). Consequently, the model has the potential to contribute valuable information to the design and guide of NSA interventions by jointly quantifying performance indicators across socio-economic, productive, environmental and nutrition farm-household domains. Our study tested the expanded FarmDESIGN farmhousehold model to provide guidance and planning on NSA interventions, and tested the performance of the incorporated metrics in the 'Human nutrition' module. We used a case study in Vietnam, where diversifying home gardens with nutritious crops selected through a participatory approach was undertaken as an NSA intervention. Our overall aim was to test the impact of a specific nutrition-sensitive intervention, which, in our study's case, is crop diversification targeted to home gardens for improving diets and livelihoods.
The expanded FarmDESIGN model The previous version of the FarmDESIGN model was unable to capture off-farm data for the household's employment, leisure activities and food use  (Ditzler et al. 2019). The model therefore could not capture the different livelihood strategies or the food availability of the farm-household. We overcame this limitation by adding the household and its members to the model as entities and placing the family enterprise (i. e. the farm) within the farm-household. This change facilitated calculating diverse farm-household performance indicators for the productive, socio-economic, environmental, and nutritional domains. Therefore, the addition of three new farmhousehold modules 'Household budget,' 'Household labour' and 'Household nutrition' widens the applicability of FarmDESIGN for modelling farming systems  (Ditzler et al.
The 'human nutrition' module in FarmDESIGN The new 'Human nutrition' module assesses the potential contribution of a household's on-farm annual production (i. e. plants, livestock and fish) or off-farm food acquisition (e. g. purchased from the market) to meet the dietary requirements of the household. The new module calculates several metrics that capture production diversity, diet diversity and nutrient adequacy (see Fig. 2 , Appendix 1 for detailed metrics description, equations, assumptions, input data and limitations). The 'Human nutrition' module calculates diet diversity metrics from household diet assessments, whereas production diversity is calculated using farming systems characterization (Diet -D ; Production -P ). Nutrient adequacy metrics assess the sufficiency of nutrient intake from a given diet and consumption pattern.
The nutrient adequacy metric in FarmDESIGN considers dietary requirements of all household members by using dietary reference intakes, either as recommended dietary allowances or as estimated average requirements depending on data availability; whereas food composition tables are used to estimate the nutrient intakes (see Table  2 ). The new module also accounts for food consumption patterns, or food patterns at the food group level, and nutrient loss due to processing and cooking procedures through nutrient retention factors. Diet diversity, production diversity and nutrient adequacy metrics are divided into two groups of count-based and abundance-based metrics (Fig. 2 ). Count-based metrics are estimated using the presence of unique food items or species. For example, the species richness of a diet is measured as the number of species consumed in the diet from on-and off-farm sources (SR D ; e.
2 , Appendix 1). FarmDESIGN also incorporates other commonly used metrics for assessing food items' abundance in diets and Fig. 2  Metrics included in this modelling study calculated with the new 'Human Nutrition' module within the FarmDESIGN model (see Appendix 1 for a detailed description of each metric). The metrics capture the different components of species diversity (i. e. species richness and abundance) and their nutritional contribution. Green and blue areas indicate count-and abundance-based metrics, respectively. The metrics are calculated for on-farm production set aside for household consumption (superscript P ) and for the diet (superscript D ) by accounting for the on-and off-farm food set aside for household consumption. The nutrient adequacy metric is calculated by considering the combined nutritional demand based on the age and gender of each household member.
Nutrition data We calculated the dietary reference intakes through the revised recommended dietary allowances for the Vietnamese population  (Khan and Hoan 2008). We chose the revised recommended dietary allowances, since they were estimated for the Vietnamese population, whereas the available estimated average requirements were available for humans in general. The household nutrient requirement is the sum of each household member's requirement given their age group, gender and reproductive status (i. e. , pregnant, lactating). The nutrients included in the modelling study were the dietary energy for moderate work category, vitamins (A retinol activity equivalent, C, Thiamine, Riboflavin, Niacin, B6, Folate, B12) and minerals (calcium, magnesium, iron [5% bioavailability] and zinc [poor zinc absorption]).
Discussion The global commitment to end malnutrition through nutritionsensitive agriculture (NSA) requires the use of robust methods, models, and metrics that disentangle the complex relationship between agriculture-and nutrition  (Herforth and Ballard 2016). The use of whole farm-household models enables ex-ante assessments of the potential trade-offs and challenges that NSA interventions could pose to a farm-household. Farm-household models also enabled us to estimate the potential contribution of agricultural interventions towards satisfying household dietary requirements and improving household livelihoods. In this study we showed the applicability of the new 'Household Nutrition' module included in the expanded FarmDESIGN model for estimating several metrics linked to diet and nutrition.
Similarly, the available food consumption data for Doan Ket was particularly limited since it is based on one weekly dietary recall, which is more subjective to recall error  (Kennedy et al. 2011). Nonetheless, our comparison with the regional averages indicates similar consumption levels at the food group level (See Appendix 3). The 'Human Nutrition' module is potentially overestimating consumption or nutrient intake since food waste is currently not being captured by FarmDESIGN. Another inherent limitation of the model is the yearly analysis, which underestimates issues related to seasonal food availability, food price seasonality and intra-household food distributions. Despite the considerable efforts in collecting accurate data, the modelling effort is potentially underestimating the species richness in on-farm production and household diets.