dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Marina Siponen  from the  BIAM ProteinTec platform  for providing α-GFP nanobody: Halo:His6  and  Julia Bartoli  for providing nucleotide standards. Nucleotide measurements were performed on the IJPB Plant Observatory technological platform that is supported by  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ), microscopy experiments were performed on the PiCSL-FBI core faculty member of the  France-BioImaging national research infrastructure  ( ANR-10-INBS-04 ). We acknowledge funding form the  Erasmus+ International Credit Mobility for MM  and funding from the  Agence National de la Recherche  ( ANR-22-CE20-0033 ,  ANR-17-CE13-0005 ).
Lung allograft standardized histologic analysis template To improve the standardized pathology reporting and terminology a multispecialty panel of lung transplant experts recently published the lung allograft standardized histologic analysis (LASHA) template. 6  It is designed to serve as a comprehensive construct for reporting pathology findings in lung allograft specimens. As such, LASHA represents a crucial step forward in standardization, with an emphasis toward research endeavors. The LASHA template has been recently updated and formulated in an electronic design of Research Electronic Data Capture (https://www. redcap. link/lasha), a secure web application for building and managing online databases. An important next step will be the validation of the parameters of the scoring system used to grade different lesions.
Annotations of A-grade (A1/ A2) rejection and normal lung tissue were used to create a digitalized slide training set and then evaluated using the convolutional neural network. The algorithm distinguished ACR from normal alveolated lung tissue with 95% validation accuracy. 18  The implementation of digital pathology requires basic resources for whole slide imaging as well as image management. The larger hurdle to overcome is high-quality, multicenter data set studies with annotations from expert pathologists, which will allow for the advancement of the field through simple diagnostic error reduction or more complex deep learning with integration of multidimensional clinical data sets and digital spatial transcriptomics for prognostication and tailored therapy. 19.
47  This paradigm provides an opportunity for enhanced molecular understanding and diagnosis of these challenging phenotypes. Future multicenter studies are the next step to validate these molecular archetypes against a multidimensional data set, including histology findings from concurrent lung transplant biopsies. It is not yet possible to transition clinical practice and therapeutic decision-making from tissue-guided to molecular archetypal-guided without understanding the alignment of the two. Parallel work has been performed utilizing the NanoString nCounter platform with formalin-fixed paraffin-embedded lung transplant samples. In collaboration with NanoString, the Banff Molecular Diagnostics Working Group recently developed a commercially available 770-gene Banff Human Organ Transplant (B-HOT) panel based on previously published gene expression signatures from kidney, liver, heart, and lung transplant biopsies, supplemented by transcripts representing relevant cellular pathways and cell types.
10, 11  LVOT-G is also the primary driver of exertional dyspnea, chest pain, fatigue, and exercise intolerance, which are heart failure symptoms that severely impact functional capacity. Peak oxygen uptake (pVO 2 ), measured by cardiopulmonary exercise testing (CPET), can be used to quantify functional capacity. Studies have demonstrated that pVO 2 in HCM has important prognostic implications and is able to independently predict heart failure death and total mortality (Table  1 ). [12] [13] [14] [15] [16] [17] [18]  Here we consider the current treatments for oHCM and the available tools for quantifying clinically meaningful changes in symptom burden and patient outcomes. We describe the use of these tools, with a particular focus on pVO 2 , to assess the safety and efficacy of aficamten in patients with symptomatic oHCM enrolled in SEQUOIA-HCM (Safety, Efficacy, and Quantitative Understanding of Obstruction Impact of Aficamten in HCM; NCT05186818), the phase 3 pivotal trial of this drug candidate.
STATISTICAL CONSIDERATIONS AND SAMPLE SIZE CALCULATION. Sample size was calculated based on an assumption of a difference in change from baseline in pVO 2 of 1. 5 mL/kg/min for aficamten compared with placebo, an SD of 3. 5 mL/kg/min, and w10% missing data for the primary endpoint. A sample size of 270 patients at a randomization ratio of 1:1 (w135 randomized to aficamten and w135 to placebo) will provide >90% power to detect the assumed difference in pVO 2 change from baseline to week 24 with a 2-sided type I error of 0. 05. 54, 55  Unless otherwise specified, efficacy analyses will be performed for all randomized patients. The primary analysis will test the null hypothesis that there is no treatment difference in the primary endpoint between patients randomized to placebo and those randomized to aficamten.
Acknowledgments Our laboratory is funded by ' Agence Nationale pour la Recherche ' ( ANR-17-CE12-0025-01 ,  ANR-20-COVI-0078 ,  ANR-21-CE12-0028-01 ), by ' Fondation pour la Recherche M edicale ' (project  CoronaIRES and program   ALZHEIMER 2019 ALZ201912009641 ), by the  University of Strasbourg  and by the  Centre National de la Recherche Scientifique. We would also like to thank  Paulo Da Costa  for critical reading of the manuscript.
Sample Most datasets represent a static combination of resources at a particular point in time, as conducting longitudinal surveys for potential dynamic analysis is even more costly and time-intensive. Self-reported data from firms is likely to be more biased  (Gonzalez et al. , 2012)  than objective data, especially if managers in growing firms cannot gather enough data on markets, customers, and competitors or underreport data for accounting reasons. The data agency Beauhurst collects firm-level data for all high-growth firms in the UK  (Beauhurst, 2022). This data provides information (daily, monthly, and annually) on all high-growth firms in the UK, including all university spinoffs from 2011 onwards. Beauhurst employs an artificial intelligence algorithm to extract information from websites and annual reports about existing and potential high-growth firms, including university spinoffs.
We obtained a comprehensive sample of university (academic) spin-offs identified by Beauhurst as academic spinoffs and officially registered in the UK. This sample comprised 1194 academic spinoffs, including 1047 academic spinoffs in non-natural science fields and 147 in natural science fields. These 1194 academic spinoffs were observed from 2015 to 2019 as a panel. However, not all academic spinoffs are associated with a university or its staff  (Klofsten et al. , 2021). To distinguish intrapreneurial academic spinoffs from the broader cohort of academic spinoffs, we drew on previous research on university spinoffs  (Mustar et al. , 2008; Guerrero et al. , 2016 Guerrero et al. , , 2021;; Klofsten et al. , 2021; Valka et al. , 2020). We manually examined data from the Link-edIn accounts of a CEO and (co)founder(s) of an academic spinoff (provided within the Beauhurst data).
Consequently, having a full-time professional position at a UK university was a crucial criterion for inclusion in our sample as an intrapreneurial academic spinoff. Therefore, our final sample consisted of 89 natural science intrapreneurial spinoffs and 660 non-natural science intrapreneurial spinoffs from 2015 to 2019. The number of observations remained consistent across years, allowing us to analyze how independent characteristics influenced the dependent variables consistently. Please refer to Table for sample descriptions of both natural science and non-natural science intrapreneurial spinoffs. Drawing on OECD (2015) Frascati Manual we describe our sample construction as follows. Intrapreneurial spinoffs in our data which represent natural sciences belong to physical sciences and astrophysics, mathematics, chemical sciences, earth and related environmental sciences.
Using micro-level data from 89 intrapreneurial spin-offs in natural sciences and 660 in other fields from the UK (2015-2019), we analyzed their financial access and profitability across different growth stages. Our findings suggest that early-stage intrapreneurial spin-offs in natural science are perceived as riskier, with more uncertain investments than those in other science fields. Consequently, natural science spin-offs secure fewer public grants and raise less debt finance on average. Despite their innovative nature, the innovations often demand extra investment and validation. This prolonged period from invention to market introduction limits fundraising during the initial stages  (Sørheim et al. , 2011; Bock et al. , 2018). Our empirical results confirm that intrapreneurial spin-offs in natural sciences secure funding at later growth stages compared to their counterparts.
Acknowledgements Research was supported by  Cagliari University  ( 2018e2020 FIR  grant to  L. G. Costamagna ) and  Calabria University  ( S. Criniti ). Prof. Emilia Le Pera  and an anonymous reviewer are thanked for contributing to the improvement of the manuscript with suggestions and constructive criticisms. Prof. S. Critelli  is also thanked for his constant scientific and financial support.
Bankati et al. [27]  improved fuel cell deterioration model by integrating calendar aging and operation cycling aging. Then, an online RUL-based EMS is developed for a MFC system to minimize system fuel consumption and deterioration, obtaining 30% improved lifetime compared to the classic average load split method. Few studies have been conducted to address the development of strategies considering maintenance cost optimization. Colombo et al. [28]  performed a reliability analysis of a MFC system with strict constant power supply requirements. The system failure probability function is built based on the experimental data and physics-based deterioration model. Reliability analysis of a MFC from a system engineering perspective is presented by Colombo and Kharton  [29]. They systematically demonstrate the methodology of combining physical modeling and experimental data to construct system failure probability which is inspiring for fuel cell-based system reliability studies.
Additionally, as the stacks are supposed to be identical, the fresh stacks have the same model. The shape parameter (ν), scale parameter (β 0 ), and failure threshold (FT ) are the main parameters of fuel cell stack deterioration models. The fuel cell physical parameters are taken from a fuel cell experimental dataset  [41]. The maximum output power of the studied three-stack fuel cell is computed as 1. 16 kW. The shape and scale parameters are estimated from the same dataset, using the method of moments approach. Besides the parameters specific to fuel cells, the hyper-parameters k 1 and k 2 shown in Equation (  17 ) are selected based on the grid search results presented in Figure  7  and the values of k 1 and k 2 maximizing the estimated system lifetime are selected. The parameter ℓ is set for tuning the variance of MFC deterioration trajectories v ′ = v 0 /ℓ, β ′ = β 0 • ℓ (19) where v ′ , β ′ are perturbed parameters that are used as the shape and scale parameters of the Gamma process used in the simulations.
In the following work, experimental works will be conducted on the studied MFC system to collect the deterioration data for constructing the MFC deterioration model; and further, validate the proposed strategy. Efforts will also be made on developing a maintenance-based EMS by integrating stack replacement scheduling into the proposed strategy. For simulation purposes, the MC model can be used to generate a random power demand profile X t as follows: • Draw an initial state X 0 from a specified initial distribution. • For each t = 0, 1, • • • , draw X t+1 from the transition probability matrix P(x, y). Assuming that the load profile imposed to the MFC system L d (t) can be described as an MC model with Markov transition matrix P tr and all possible states L ds , we can generate random dynamic load profiles for MFC systems.
The time reversal algorithm was run for 100 iterations and stopped when the error did not decrease. We reconstructed the PA image and then divided it by the fluence map to generate a time-reversal (TR) DeAC map. Fig. 6 (a) utilizes a homogeneous SOS = 1500 m/s and there is no adjustment for acoustic attenuation. Comparing the DeAC map from our proposed algorithm (Fig. 5(i) ) and those extracted using the iterative time reversal algorithm (Fig. 6(b) ), shows our proposed algorithm can recover the DeAC map nearly perfectly, while the iterative time reversal algorithm, using nearly the same starting information, poorly recovers the DeAC map, especially in deeper regions. We next assess the robustness of our method against random noise in the data. We added different amounts of noise level to the raw PA data (noise level =1 to 5 (Fig.
V. DISCUSSION Many commonly-used ray-based PA image reconstruction algorithms (e. g. , universal back-projection  [85] , delay and sum  [86]  and multiply delay and sum  [9] ) assume that SOS and fluence maps are homogeneous across the entire imaging area and there is no acoustic attenuation. Therefore, to estimate DeACs, insufficient information is available, leading to large error. Prior information from a different imaging modality can be used to decrease DeAC estimation error  [30, 87, 88]. Because of the use of US transducers in PA data collection, US-PA dual modality is a natural choice to obtain such prior information. On the other hand, in wave-based methods, the full information contained in the recorded wavefields, i. e. , amplitudes and phases, is considered without any assumption about level of heterogeneity  [21, 22].
The method operates by first calculating the SOS and acoustic attenuation maps using the acquired US data through the IR-WRI algorithm (with BM3D regularization). It should be noted that the calculated acoustic attenuation map (error < 14%) is less accurate than the calculated SOS map (error < 5%). This is because the US recorded data is less responsive to acoustic attenuation compared to SOS. Subsequently, the IR-WRI algorithm is used again (with TV regularization) to solve a source estimation problem on the PA data (using the obtained SOS, acoustic attenuation and fluence maps). The PA problem is then solved by segmenting the SOS map to compensate for the trade-off between not knowing the pixelwise DeACs and the reduced size of the null space  (11). Some of the limitations of the proposed method are as follows.
Perosa et al,  16  used duodenal cuff biopsies for prospective graft surveillance but also obtained concurrent PTxBx in patients with graft dysfunction. In the latter group, there was 70% concordance of rejection in duodenal and pancreatic samples, but the grade of rejection was only in agreement in the more severe cases. Both Perosa et al  16  , and Nordheim et al 123 , identified cases of isolated pancreas or duodenal rejection. Duodenal biopsies have also been used to identify patients with cytomegalovirus infection (unpublished data presented at the 2022 Banff meeting). In summary, the most accurate diagnosis of rejection grade and type (TCMR vs AMR) is achieved with PTxBx, but a surrogate (C) Type 2 DM after pancreas transplantation often presents with islet amyloid deposition. These amorphous, acellular deposits are highlighted using the Congo red stain.
Such approaches pave the way for InP DHBTs to penetrate industrial applications, with the ultimate goal of a direct integration of InP-based high-performance analog front ends with silicon digital/mixed-signal functions (data converters and DSP). Filipe Jorge (Member, IEEE) received the Ph. D. degree in electronic engineering from the University of Lille 1, Villeneuve d'ascq, France, in 1999. In 2000, he joined OPTO+, an Alcatel Research and Innovation unit, as a Research and Development Engineer. He is currently involved in module design and characterization of high-speed circuits for optical communication systems within III-V Lab. Dr. Jorge is a member of the IEEE. Bernadette Duval, photograph and biography not available at the time of publication. Fabrice Blache (Member, IEEE) received the Ph.
2, 3  Histopathological findings are nonspecific and definitive diagnosis of ADTKD requires identification of a pathogenic variant in one of the underlying genes 1 -i. e. those encoding uromodulin (UMOD), also known as uromucoid or Tamm-Horsfall protein; mucin 1 (MUC1); hepatocyte nuclear factor 1ˇ (HNF1ˇ); renin (REN); or the ␣1 subunit of SEC61 translocon (SEC61A1). 2, 3  The most prevalent form of ADTKD is ADTKD-UMOD, which is typically associated with early-onset hyperuricemia and gout. [2] [3] [4]  More than 225 deleterious UMOD variants are reported at The Human Gene Mutation Database (https://www. hgmd. cf. ac. uk/ac/gene. php?gene=UMOD; accessed on 15. 08. 23), of which the large majority (95. 3%) are missense single base-pair substitutions, most of them clustered in exons 3 and 4.
10 g. (20335481 20333375) ? - Novel (20333051 ?)del SLC8A1 NM 021097. 5 d c. 16C>T p. (Arg6*) rs767111343 f 1/152076 alleles (genomes) h PKD1 NM 001009944. 3 e c. 9170T>C p. (Val3057Ala) rs758627119 g 1/160674 alleles (exomes) i a Reference sequence database. b i https://gnomad. broadinstitute. org/variant/16-2152413-A-G?dataset=gnomad r2 1 [accessed on 15. 01. 23].
Here, "raw" histology (and other) data are entered into a software program, and a classification is produced following Banff rules or other algorithms. Presentations included the "Software-assisted sign out" (SAS) developed at Pittsburgh, the Banff automation system published by the Paris Transplant Institute,  8  and machine-learning classifiers that interpret kidney graft scoring together with clinical data. 9  Important requirements for such tools are the commitment to continuously update the algorithms and the need for adjudication of discrepancies between the algorithms and the pathologist, to help pinpoint areas of difficulty for either. Further discussion is needed around validation, clinical use, and the need for regulatory approval of such tools.
Data The main source for our data on mutual funds is Morningstar Direct. We download observations for all surviving as well as nonsurviving funds from Morningstar Direct, therefore, the data is survivorship bias free. Our sample consists of monthly observations on fund flows and performance for the period 2001-2017. We include only actively managed U. S. domestic equity mutual funds in our analysis. In particular, a fund is included if it is domiciled in the United States, its US_Broad_Asset_Class is "U. S. Stock", and its Morningstar Category is one of the following: "Large Blend", "Large Growth", "Large Value", "Mid Blend", "Mid Growth", "Mid Value", "Small Blend", "Small Growth", and "Small Value". We focus on actively managed funds, therefore we exclude index funds. We also exclude funds that have total net assets under $10 million and funds that are younger than one year.
Evidence acquisition The Population, Intervention, Control, Outcomes, and Study Design (PICOS) method was used to define literature inclusion criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Metaanalysis of Observational Studies in Epidemiology reporting guideline methodologies were followed. 36  A comprehensive search was conducted in PubMed/MEDLINE, Embase, and the Cochrane Database of Systematic Reviews between January 1989 and November 2021. The search strategy applied concepts of RN, SRS/radiation therapy, which was defined as the stereotactic delivery of conformal doses with 1 to 5 fractions, and treatments of RN, which included bevacizumab, LITT, surgery, and HBOT (with corticosteroids and/or anticoagulants being used as a control). The detailed search strategy is summarized in the Data Supplement 1.
Outcome measures and data extraction The primary outcome measure was the proportion of patients with symptom improvement/symptom stability and/or radiologic improvement/stability postintervention. Secondary outcomes included: (1) proportion of patients with recurrent RN requiring salvage treatment; (2) overall survival (defined as time from RN intervention to death); (3) proportion of patients with severe intervention-related toxicity (grade 3 and above); and (4) proportion of patients who were able to reduce steroid use. Data on study design, number of patients, number of lesions, details of intervention applied, primary and secondary outcomes, and median follow-up of the cohort were extracted. Individual study effect sizes were modelled as proportions in which the denominator was the total number of patients for which the outcome was available, and the numerator was the number of patients experiencing the particular outcome measure of interest.
Quality (risk of bias) assessment There is no single recommended instrument to assess the risk of bias for systematic reviews that includes both prospective and retrospective observational studies. We chose to evaluate the risk of bias by using a previously published system based on a 9-item assessment (Data Supplement 2). 38, 39  The system was developed to evaluate bias and completeness of information. In particular, items 1 to 2 assess selection bias, items 3 to 5 reporting bias, item 6 attrition bias, and items 7 to 10 assess the extensiveness of available information on intervention and reported outcomes. We used a 3-point scale where "yes" indicates a low risk of bias, "no" indicates a high risk of bias, and "unclear" indicates uncertainty regarding the risk of bias.
Access to HBOT requires specialized facilities and requires multiple sessions over a prolonged period. An updated Cochrane meta-analysis by Bennett et al  71  did not find HBOT to be beneficial for late radiation injury to neural tissues. The only randomized trial comparing HBOT to steroid therapy for RN is currently inactive with no reported results (NCT00087815). Therefore, we conclude that there is insufficient evidence to recommend this modality for RN but acknowledge the historic use of this treatment for late radiation injury. Ultimately, proper evidence is needed to inform the role of HBOT in the care pathway (Table  5 ). Our study addresses an unmet need in assessing the evidence for the treatment of RN. We chose to further refine this analysis by focusing on studies where patients had a brain neoplasm treated with SRS techniques.
Acknowledgements https://doi. org/10. 26434/chemrxiv-2023-prlgn ORCID: https://orcid. org/0000-0003-3162-3318 Content not peer-reviewed by ChemRxiv. License:  CC BY-NC-ND 4. 0 This work was supported by the  French National Research Agency ANR  through contracts Single Nuclei on Chip via  Institut Pierre-Gilles de Gennes (IPGG)  ( laboratoire d'excellence, Investissements d'avenir program   ANR-10-IDEX-0001-02 PSL ,  ANR-10-LABX-31 ),  SCDiag  ( ANR-21-CE18-0051 ). This work has benefited from the technical contribution of the joint service unit  CNRS   UAR 3750  (IPGG). The authors thank  Drs. Nathalie Delgehyr  ( Institut de Biologie de l'École Normale Supérieure ),  Julie Plastino ,  Christine Gourier  ( Laboratoire de Physique de l'Ecole normale supérieure ), and  Saikat Saha  ( Department of Chemistry, Ecole Normale Supérieure ) fruitful discussions.