dataesr/scientific-paragraphs-categorization huggingface dataset:
(iii) All flagged incorrect targeting sequences were double-checked through additional blastn searches against the database: "Homo sapiens (taxid:9606)," optimized for "Somewhat similar sequences (blastn)," using an expect threshold 1,000, in February 2021. Nucleotide sequence reagents that were verified to have been wrongly identified were assigned to one of three previously described error categories  (11, 12) : (i) Reagents claimed to represent targeting reagents but verified to target a human gene or target other than that claimed within the text. This error category included miR-targeting reverse RT-PCR primers with incorrect gene targeting descriptions, as supported by sequence verification (https://www. protocols. io/view/seekamp-blastn-standard-operating-procedure-bjhpkj5n), and by having been used to analyse gene(s) other than the claimed miR and/or as a claimed universal primer according to Google Scholar searches  (11, 13).
Additional publication analyses For all articles subjected to S&B analysis, publication titles were visually inspected to identify human gene identifiers, human cancer types, and drug identifiers which were confirmed through Google searches. Human genes were categorized as either protein-coding or ncRNA's (miRs, lncRNA, or circRNA) according to Gene-Cards (https://www. genecards. org/). Journal publishers were identified via the SCImago database (https://www. scimagojr. com/). The Journal Impact Factor corresponding to the (closest) publication year of each article was obtained from the Clarivate Analytics Journal Citation Reports database (40). Numbers of original articles published per year by Gene and Oncology Reports were obtained from Clarivate InCites (https://incites. clarivate. com), under Entity type = "Publication Sources," Publication Date = 2007-2018, DT = include only "Article.
Acknowledgements JA Byrne  and  C Labb  é gratefully acknowledge funding from the  US Office of Research Integrity , grant ID  ORIIR180038-01-00. JA Byrne ,  C Labb  é, and  A Capes-Davis  gratefully acknowledge grant funding from the  National Health and Medical Research Council of Australia, Ideas  grant  ID APP1184263. T Stoeger  gratefully acknowledges funding from the  National Science Foundation , 1956338, SCISIPBIO: A data-science approach to evaluating the likelihood of fraud and error in published studies;  K99AG068544 , National Institutes on Aging, Integrative Multi-Scale Systems Analysis of Gene-Expression-Driven Aging Morbidity ;  National Institute of Allergy and Infectious Diseases ,.
Manual verification of S&B results revealed that most (31/50, 62%) flagged miR-145 articles described at least one wrongly identified sequence, with a median of one (range 1-5) incorrect sequence/article (Table1and Supplemental Data 2). The 31 problematic miR-145 articles were published from 2009 to 2019 across 25 journals, with the highest proportion published by Wiley (Supplemental Data 2). The 31 problematic miR-145 articles analysed 12 human cancer types, most frequently colorectal or lung cancer, and described 49 wrongly identified sequences, most of which (38/49, 78%) targeted a different gene or target from that claimed (Fig 2 and Table and keyword searches of the Google Scholar database (see the Materials and Methods section). A total of 163 miR-145 articles were then screened by S&B to flag 50 miR-145 articles for further analysis (Fig1).
Introduction Circular data are collected when the topic of interest is a direction or a time of day. These particular data appear in many applications: earth sciences (e. g. wind directions), medicine (e. g. circadian rhythm), ecology (e. g. animal movements), forensics (crime incidence). Different surveys on statistical methods for circular data can be found:  Mardia and Jupp (2000) ,  Jammalamadaka and SenGupta (2001) ,  Ley and Verdebout (2017)  or more recently  Pewsey and García-Portugués (2020). In the present work, we consider a mixture model with two components equal up to a rotation. We observe X 1 ,. , X n a sample of data on S 1 with probability distribution function: g(x) = p 0 f (R -1 0 x) + (1 -p 0 )f (Q -1 0 x) = p 0 f (x -α 0 ) + (1 -p 0 )f (x -β 0 ). (1. 1) In the right hand side we have identified f : S 1 → R and its periodized version on R.
Numerical results All computations are performed with Matlab software and the Optimization Toolbox. We shall implement our statistical procedure to both estimate the parameter θ 0 and the density f. We consider three popular circular densities namely, the von Mises density, the wrapped Cauchy and the wrapped normal densities. We remind their expression (see  Ley and Verdebout (2017) ). The von Mises density is given by: f V M (x) = 1 2πI 0 (κ) e κ cos(x-µ) , with κ ≥ 0, I 0 (κ) the modified Bessel function of the first kind and of order 0. The wrapped Cauchy density has density: f W C (x) = 1 2π 1 -γ 2 1 + γ 2 -2γ cos(x -µ) , with 0 ≤ γ ≤ 1. The wrapped normal density expression is: f W N (x) = 1 σ √ 2π ∞ k=-∞ e -(x-µ+2kπ) 2 2σ 2 , σ > 0. For more clarity, we set σ 2 =: -2 log(ρ). Hence, we have 0 ≤ ρ ≤ 1.
All these densities are characterized by a concentration parameter κ, γ or ρ and a location parameter µ. Remind that values κ = 0, γ = 0 and ρ = 0 correspond to the uniform density on the circle. To meet symmetry assumptions of Theorem 1, we consider in the sequel that the location parameter is set to µ = 0. First, let us focus on the parametric part. We set θ 0 = (p 0 , α 0 , β 0 ) = ( 1 4 , π 8 , 2π 3 ). Obtaining the estimate θn of θ 0 (see (3. 2)) requires to solve a nonlinear minimization problem. To this end, we resort to the function fmincon of the Matlab Optimization toolbox. The function fmincon finds a constrained minimum of a function of several variables. Two parameters are to be specified: the domain over which the minimum is searched and an initial value. We consider the domain {(0, 1 2 ) × [0, π) × [0, π)}.
A C T Ensemble pre-processing is emerging as a potential tool to avoid the tiring pre-processing selection and optimization task in near-infrared (NIR) spectral modelling. Furthermore, differently pre-processed data may carry complementary information, hence, ensemble pre-processing may represent the best suited modelling option to extract all the useful information from differently pre-processed data. Recently, multi-block techniques such as sequential (SPORT) and parallel (PORTO) orthogonalized partial least squares regression were proposed to extract complementary information present in differently pre-processed data. Although such multi-block techniques allowed efficient modelling of differently pre-processed data blocks, depending on the approach, challenges related to choosing block order, parameter tuning, block scaling and optimization time requirements still must be dealt with.
To cope with such issues, the present study proposes the use of a recently developed faster, block order independent and scale independent, multi-block data modelling technique called response-oriented sequential alternation (ROSA) to process the multi-block data generated by differently pre-processing the same NIR data. This new method is called PROSAC, i. e. , pre-processing ensembles with ROSA calibration. The potential of the approach is demonstrated on five real NIR spectral datasets. Furthermore, as baselines for comparison, partial least squares regression was done on individually pre-processed data sets, and using two multi-block pre-processing fusion approaches, i. e. , SPORT and PORTO. The ensemble pre-processing with ROSA achieved either better performance compared to the baseline methods or achieved comparable performance without the need to worry about the pre-processing order, the scaling of data after pre-processing and optimization time requirements.
Data sets Five real NIR datasets were used to demonstrate the potential of PROSAC for ensemble pre-processing modelling as well for its comparison with the baseline techniques. The five datasets were related to the prediction of dry matter (DM %), soluble solids content (SSC %) and moisture content (MC %) in five different fresh fruits. All data sets were measured with Felix fresh fruit quality meter (Camas, WA, USA). The Felix fruit quality meter is a hand-held spectrometer that uses the interaction mode to measure the spectral signature of samples. The Felix spectrometer covers the spectral range of 310-1135 nm with a ~3 nm spectral sampling interval. The spectrometer has a Xenon Tungsten Lamp for illumination and a built-in white painted reference standard for estimating the reflectance.
Later, the samples were dried in a hot-air oven (FD 56, Binder GmbH, Tuttlingen, Germany) at 80 C for 60 h and the final weights (g) of the aluminium cups were recorded with an analytical balance (Mettler-Toledo GmbH, Giessen, Germany). A summary of all data sets is provided in Table  1. It can be noted that the wavelength ranges for different data sets varied slightly but all were in the 3rd overtones range of OH and CH and relevant for prediction of parameters such as dry matter, soluble solids content and moisture content. The reason for the slight variation in the wavelength range was because the datasets were measured with similar but different instruments and had spectral ranges that had been reduced by the suppliers of the original data. A key point to note is that all the data sets were pre-partitioned into calibration (60%) and test (40%) sets using the Kennard-Stone (KS) algorithm  [51].
Finally, the ability of PROSAC to model many pre-processed data blocks was also explored and the time requirements were reported. The PLS analysis on single block data were performed using the 'plsregress' function in MATLAB's 'Statistics and machine learning' toolbox and using a 10-fold CV as used for the PROSAC modelling. SPORT was implemented using the freely available MBA-GUI  [52] , and the number of latent variables for each block was optimized by exploring all combinations in the range of 0-10 to find the lowest RMSECV. PORTO modelling was done using the PO-PLS multi-block data analysis codes from NOFIMA (https://nofimamodeling. org/software-downloads-list/multiblock-regr ession-by-poso-pls/). After a preliminary data compression (which also provides noise-filtering) operated by retaining, for each block, the scores resulting by individual PLS modelling, several local cross-validation (CV) steps were performed in sequence, as discussed in Ref.
[53] , to find the optimal number of common and distinct components for building the PORTO model. All data analyses were performed using MATLAB (Release 2018b; The Mathworks, Natick, MA) on a workstation equipped with a NVidia GPU (GeForce RTX 2080 Ti), an Intel® Core™ i7-4770k @3. 5 GHz and 64 Gb RAM, running Microsoft Windows 10 OS.
The PROSAC model (Fig. 4C ) for the Mango data set performs better than the models made on normalised data, and a combination of normalisation followed by derivative (Table  2 ). However, the PROSAC model performed more poorly than the PLS model built solely on derivative pre-processed data (Table  2 ). In Table  2 , it can be noted that the normalisation deteriorates the model. Looking at the order of the selected blocks per component (Fig. 4D ), it can be noted that the PROSAC model for Mango selected the first component from the SNV normalised data block. Such a selection of the first component from the normalised data block makes it impossible, due to the block deflation step in ROSA, to retrieve a good model as the nonnormalised space gets eliminated during the deflation. Hence, a reason for the failure of the PROSAC model here may be found in the limitations associated to the heuristic of the ROSA algorithm which does not allow already selected model components to be updated during the selection of future model components.
Basically, the ROSA model heuristic involves a forward stepwise selection but without any backward elimination step to rejudge the selection of the model components. Currently, there is room for improvement of the ROSA algorithm as its current heuristic for model component selection may in some cases lead to poor models, although this was observed for only 1 out of the 5 data sets in this study. In the case of the Olive (Fig. 5 ) data set, PROSAC modelling selected just a single data block, i. e. , SNV þ2nd derivative. Such a selection of a single data block suggests that PROSAC modelling does not only perform ensemble modelling but can also identify an individual pre-processing as the best one, when only a single pre-processing is sufficient to achieve the optimal model. For the Olive (Fig.
A comparison of ensemble pre-processing using ROSA with SPORT and PORTO The performance of PROSAC was also benchmarked with the two popular multi-block ensemble techniques SPORT and PORTO. The analysis was carried out on the Pear data set and on four data blocks in the order: Raw, SNV, SNV þ1st Derivative, SNV þ2nd derivative. The analysis was reduced to one data set and from six to four blocks due to the high optimization time cost for SPORT and PORTO. The effect of block order on SPORT, PORTO and PROSAC is examined later in this study. In Fig. 6 , it can be noted that SPORT achieved the lowest RMSEP, and outperformed PROSAC and PORTO, even if a close look at the model statistics shows that the differences in RMSEP's of SPORT, PORTO and PROSAC were minimal. This ranking is quite logical, since SPORT comprehensively searches for the best LV combination, whilst PROSAC uses a stepwise heuristic, and can be affected by local minima.
In this case, the key benefit of the PROSAC approach compared to SPORT and PORTO is that it is independent of the block order. This is important for ensemble pre-processing modelling as the user does not need to worry about arranging the blocks in an optimal order. To show the effect of changing block order on PORTO, SPORT and PROSAC analyses, the analysis presented in Fig. 6  was repeated for different block order combinations as explained in Table  2. The results of the SPORT, PORTO and PROSAC analyses performed on the different block orders (Table  3 ) are presented in Fig. 7  and Fig. 8. It can be noted that with change in block order, the number of components corresponding to the different data blocks changes for both SPORT (Fig. 7A ) and PORTO (Fig. 7B ). In the case of PORTO, it is the total number of common and distinct components that changes.
On the other hand, for PROSAC (Fig. 7C ), the number of components from the blocks are always the same and for the same data blocks. Such a uniform selection of components and blocks by PROSAC shows that it is indeed an order-independent technique, which can be of great use for ensemble pre-processing modelling. The effect of such changes in block order can also be seen in the RMSEP of the SPORT and PORTO models (Fig. 8A ), while the RMSEP for the PROSAC stayed the same. Please note that the good performance of SPORT and PROSAC than the PORTO could be related to the number of LVs extracted by SPORT and PROSAC being higher (Fig. 8B ). Again, PROSAC maintained its performance despite the changing block orders.
Further comments on PROSAC for pre-processing exploration In the earlier section, the potential of PROSAC for ensemble modelling and its comparison with other multi-block ensemble approaches was presented. In this section the capabilities of PROSAC to handle several blocks are highlighted. To show this, a ROSA model was used for 24 data blocks (Mango data set) containing the Savitzky-Golay derivative over the window ranges of from 5 to 51 in steps of 2. This means using PROSAC to explore 24 blocks. The results for the 1st and the 2nd derivatives are shown as the top and bottom rows in Fig. 9. It can be noted that out of the wide range of intervals explored, only a subset of window intervals was found to be the most explanatory. Learning from such an analysis can allow the selection of a subset of the window sizes for further exploration in combination with other pre-processings such as normalisation or scatter corrections.
As noted in the analysis presented in Fig. 9 , a total of 24 data blocks were modelled with PROSAC to evaluate the possible interplay of different window sizes for the SavGol derivatives. Handling such a substantial number of blocks is challenging with the traditional methods such as SPORT and PORTO which rely on a more complex heuristic, due to the sequential or parallel nature of the analyses. To have an insight into the time requirements, plots of the computational cost as a function of the number of data blocks are shown in Fig. 10. It can be noted that with increasing number of blocks, the model optimization (10-fold CV) time increased; however, optimising PROSAC on the largest block size of 96, took just ~85 s. The model selection process in this case involved exploring a total of 10 Â 50 Â 96 ¼ 48000 total candidate components for data of dimensions 300 Â 103.
Conclusions This study proposed the PROSAC multi-block analysis approach for ensemble pre-processing modelling of NIR spectral data. The analysis of five different real NIR data sets showed that in four out of five data sets, PROSAC ensemble modelling either achieved better performance by using an ensemble of information from differently pre-processed NIR data or achieved performance like PLS analyses performed on individually pre-processed NIR data. A key point to note is that the single block PLS requires training several models for each pre-processing, while for PROSAC it is done in a single run, thus saving a substantial amount of time for model exploration. Furthermore, PROSAC converges to a single block PLS analysis when only a single block of data is selected as the winning block. In such a way, PROSAC chooses the optimal pre-processing rather than an ensemble of pre-processings.
PROSAC, PORTO, and SPORT are all ensemble learning methods based on multiple blocks. They differ primarily in the heuristics they use to explore how to combine the latent variables extracted from the blocks. Because they use different heuristics, they produce different results and have different advantages and disadvantages. On one data set, PROSAC modelling did not perform as well as some single block models. The reason for this result is related to the heuristic used by PROSAC, based on forward stepwise selection. In some cases, if a particular component is selected first for the model, the subsequent deflation phase could alter the data set and compromise the rest of the selections. This shows that the PROSAC algorithm could be further improved by adding a backward step to re-evaluate the usefulness of previously selected model components.
Compared to SPORT and PORTO, PROSAC was unaffected by the order in which the pre-processing blocks were arranged. Furthermore, the test of PROSAC on a substantial number of data blocks showed that it is a fast approach to perform a multi-block ensemble pre-processing. It was found that optimising a 96 data block PROSAC model took ~80 s, which is difficult to achieve with any other multi-block ensemble pre-processing approach. However, it should be stressed that the speed of PROSAC will always depend on the implementation and data dimensions, as with any PLS based algorithm. The ability of PROSAC to perform fast orderand scale-independent ensemble pre-processing and converge to a traditional PLS when only a single pre-processing is sufficient, makes it a useful tool for NIR calibration. Note that PROSAC is an application of the ROSA multi-block modelling approach which, in turn, is an extension of PLS regression, and therefore, PROSAC supplies all the relevant parameters for model interpretability, such as regression coefficients, scores and loadings.
On the other hand, it should be stressed that, in principle, when selecting components from multiple versions of the same dataset, there could always be the risk of overfitting. However, due to its algorithmic nature, this is not the case for PROSAC (as well as for SPORT or PORTO), as is shown by the closeness of the values of the RMSEC, RMSECV and RMSEP in all the reported examples. Based on the findings from this study, it can be concluded that ROSA ensemble pre-processing gives promising directions to end the era of exhaustive pre-processing search and optimization for modelling NIR data.
Each test was performed with the provided control samples and the raw optical densities were processed using the EarlyCDT-Lung test kit result calculation software (Oncimmune). The software reported each autoantibody as either "high," "moderate" or "no-significant" levels based on marker-specific cut-off values. Based on the manufacturer's recommendations, research participants with one or more autoantibodies above the high cut-off value were defined as high risk, whereas research participants with at least one autoantibody above the moderate cut-off (but none above the high cut-off) were defined as moderate risk. Research participants with all autoantibodies below the moderate threshold were categorized as no significant test result.
ACKNOWLEDGEMENTS Our study was supported by an early detection of cancer development grant from  Swedish Department of Health Ministry. The participation of IARC personnel was supported by a programme award from the  US National Cancer Institute  ( NCI U19 CA203654 ). Part of the study was supported by a generous grant from the  Cancer Research Foundation of Northern Sweden  ( AMP19-962 ). We thank the  Biobank Research Unit at Umeå Universitet, Västerbotten Intervention Programme , the  Northern Sweden MONICA study , the  Mammography Study  and  Region Västerbotten  for providing data and samples and acknowledge the contribution from  Biobank Sweden , supported by the  Swedish Research Council  ( VR 2017-00650 ). The coordination of EPIC is financially supported by  International Agency for Research on Cancer (IARC)  and also by the  Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London  which has additional infrastructure support provided by the  NIHR Imperial Biomedical Research Centre (BRC).
The angle adjustments are usually conducted by the corresponding servo motors that are linked with the rudder and the sail. There are also auxiliary sensors within autonomous sailboats such as communication modules for the purpose of remote control and online monitoring  [5]. Depending on the budget and the dimension of the autonomous sailboat, there are many combinations of these sensors, actuators and controllers in the literature  [6]. Early autonomous sailboat platforms usually use a mechanical anemometer that can provide the direction and the speed of the wind  [7]. Ultrasonic wind sensors are increasingly used in autonomous sailboats due to its higher resolution and greater reliability with no moving parts  [8]. The controllers used in autonomous sailboats can be Arduino-based microcontrollers for easy access of sensor data or Raspberry Pi-based computers to implement Robot Operating System (ROS) based software architecture for obstacle avoidance and computer vision.
Traditional line following and station keeping algorithms can be tested on an Arduino-based control system while obstacle avoidance sailing algorithms can be tested on a Raspberry Pi-based control system with computer vision. Advanced sailing algorithms such as machine learning-based sailing can also be tested on a Raspberry Pi-based or a NVIDIA-based control system  [10]. The main challenge or hurdle to conduct autonomous sailing research is to have an agile and compact platform in terms of hardware and software configurations since most of existing sailboat platforms are rather complex to build and to program. This is mainly due to the existing combinations of sensors and microcontrollers are often hybrid involving multiple types of microcontrollers and programming languages. Focusing on the core need of testing autonomous sailing algorithms, radio controlled (RC) model sailboats can provide a credible test platform since nowadays model sailboats are very agile.