dataesr/scientific-paragraphs-categorization huggingface dataset:
Motion capture to assess the risk of developing MSDs. Participants were equipped with seventeen motion sensors of MVN Biomech Awinda wireless inertial unit type, recording at 60Hz (Xsens, Enschede, the Netherlands). These sensors were placed on the head, shoulders, sternum, pelvis, arms, forearms, hands, thighs, legs, and feet using scratch strips  [34]. The sensors provide positional coordinates in the three planes of space and quaternion orientation. Using a biomechanical model integrated into Xsens MVN Animate Pro software 2021. 0. 0 and a MATLAB program, joint angle data were calculated in the three axes. From this data and with a MATLAB program, a RULA assessment per side (i. e. , left and right) was conducted. The method for performing the RULA assessment is explained in the article by McAtamney and Corlett  [5].
To start a trial, participants placed their right hand on the central base and did not move it. The author initiated a video recording and a motion capture recording, and then participants quickly moved their right hand to press a remote control on their right side to trigger the cobot co-worker program (they simulated the same action with the human co-worker). Simultaneously, the author triggered the auditory task. Overall, participants were present on the premises for approximately two and a half hours. Data analysis. For all variables, tests were conducted to compare the four conditions (COB2s, COB5s, HUM2s, and HUM5s), the two modalities of the motor task (with the cobot co-worker versus with the human co-worker), and the two modalities of the auditory task (with a two-second ISI versus a five-second ISI).
All statistical tests were performed using the STATISTICA software. For performance in both tasks, the number of manufactured products (motor task) and the absolute error (auditory task) were measured. The four conditions were compared using a non-parametric repeated measures Friedman's ANOVA. When the test was significant, a Wilcoxon matched pairs test was conducted to observe significant differences between the conditions. Wilcoxon matched pairs tests were also conducted to test the differences between the two modalities of the motor task and auditory task. For the success rate in the auditory task trials, which is a bimodal qualitative variable (success or fail), a chi-square test was performed to compare the different conditions and modalities. To assess a possible learning effect with the co-worker, the last trial and the first trial of both modalities of the motor task were compared using Wilcoxon matched pairs tests.
The discovery of molecular subtypes has corroborated the heterogeneity of breast cancers. But in clinical routine, treatment selection relies on measuring the aggressiveness of the tumor via histopathology, which is routinely based on grading, a prognostic yet laborious, poorly reproducible, and subjective procedure de ned more than 30 years ago, before molecular subtypes existed. In this paper, we present an interpretable deep learning-based computer-aided diagnosis method to automate mitotic count, one of the components of grading; the method complies with current guidelines and automates hot-spot nding on full digital whole-slide images of routinely prepared pathologic slides stained with hematoxylin and eosin. We use the computer model to query the current "one-size-ts-all" mitotic count procedure by investigating subtype-speci c prognostic value of mitotic count using an external independent large-scale multicentric dataset of 1,709 breast cancers.
Automatic assessment of the mitotic count has been studied in a large cohort of TNBC, providing indications that mitotic counting does not add prognostic value for TNBC, regardless of the applied cut-off  19, 20. The application of AI for mitotic count assessment thereby allows for an e cient, timesaving and highly reproducible assessment of the mitotic count across a large number of cases. Our contribution. The aim of this study was twofold. The primary aim was to investigate the prognostic value of fully-automated AI-based mitotic density at scale, clinically validating a mitosis detection algorithm  15, 19, 21  on large, external, independent multi-centric datasets of breast cancer cases. In particular, we focused on human epidermal growth factor receptor 2 positive (HER2+) and hormonalreceptor positive-HER2 negative (HR+/HER2-) breast cancer subtypes.
Patient and tumor characteristics We collected a unique multi-centric digital pathology WSI and clinical data set of n=2603 breast cancer patients from three different data sources: n=1093 cases from public data available from the multicentric TCGA-BRCA dataset  22  ; n=1010 cases from the multi-centric (n=17 centers) phase-III clinical trial FinHer 23 , and n=500 clinical diagnostic cases from the MVZ Pathologie Hamburg-West (Germany). Collected cases included a variety of morphological and molecular subtypes (see Table  1 ). In the collected cohorts, we de ned mutually exclusive subgroups of patients based on clinical reports provided by clinical partners, namely 1) HR+/HER2-, which are estrogen receptor (ER) positive and/or progesterone receptor (PR) positive, and HER2 negative; 2) HER2+, independent of HR status.
Digitized whole slides images (WSIs) of hematoxylin and eosin (H&E) stained sections prepared from breast cancer surgical resections were digitized using a variety of scanners for automated analysis (see Material and Methods section for details). For the FinHER cohort, only one WSI per patient was made available by the data providers, collected as described in  22, 23  , respectively. For both the TCGA and Hamburg cohort, the slide with the largest tumor diameter (and adjacent resection margins), was selected, being considered as the one carrying the highest information content.
This study underlines the large potential of such AI based breast cancer grading, by assessing mitotic density in over 1700 tumors with very little human expert involvement. To the best of our knowledge, this is the rst study investigating the prognostic value of AI-assisted mitotic count in a large, multicenter independent cohort of breast cancer cases. Our study has a few limitations. As described, despite the large sample size used in this study, subdivision of cases into multiple subgroups generates small cohorts. Especially for the HER2+ cases, the analysis of the prognostic value has limitations. From a technical point of view, we encountered limitations when applying a pre-trained AI model for mitosis detection to entire WSI in a large-scale dataset. Despite the robustness in detecting mitosis in multi-centric external data, our algorithm suffered from the presence of false positives (FPs) when applied at WSI level.
TCGA The rst cohort was obtained by downloading diagnostic whole-slide images and clinical data of 1093 breast cancer cases from the TCGA-BRCA cohort of the public The Cancer Genome Atlas (TCGA) repository  22. All slides were scanned at 40X magni cation (0. 25 µm/px) using an Aperio scanner. From this cohort, next to clinical exclusion criteria (e. g. , TNBC patients, male patients, patients with stage 4 disease at initial presentation), we excluded 411 cases because of incomplete data (the identi er in the clinical entry did not match any of the images in the data set), and 104 WSIs could not be either read or processed by our mitosis pipeline. As a result, 578 cases were included from this cohort. The images and clinical data in the TCGA repository are freely available to use for scienti c purposes.
Hamburg The second cohort consisted of 500 cases, randomly collected from the clinical pathology archive of the Medizinische Versorgungszentre (MVZ) Pathologie Hamburg-West (Hamburg, Germany). H&E slides were produced and scanned at the pathology laboratory using a Pannoramic P1000 scanner (3DHistech, Hungary) at a resolution of 0. 24 µm/px. From this cohort, no TNBC or patients with stage 4 disease at initial presentation were present; we excluded 65 cases because of incomplete data (mismatch between the clinical entry and the image data). As a result, 435 cases were included from this cohort. Ethical approval was obtained from the ethics commission of the Ärztekammer Hamburg (PV 2946).
FinHer The third data set consisted of n=1010 cases from the FinHer trial  23. Slides were stained with H&E and scanned at the Jules Bordet institute (Belgium) using a NanoZoomer 2. 0-RS C10730 series (Hamamatsu) at a resolution of 0. 23 µm/px. From this cohort, next to clinical exclusion criteria (e. g. , TNBC patients, patients with stage 4 disease at initial presentation), we excluded 269 cases because of incomplete data (no match between image data and clinical data) and 45 images because they could either not be read or processed by our mitosis pipeline. As a result, 696 cases were included from this cohort. For the Finher trial, an ethics committee at Helsinki University Central Hospital approved the study and study participants provided written informed consent (trial identi er: ISRCTN76560285).
Statistical analysis The prognostic value of the mitotic count was assessed via survival analysis performed on the pooled data from the three data sources, which were merged and uni ed. RFS and OS were used as endpoints. RFS was de ned as the interval between date of diagnosis of the index breast cancer and date of clinically and/or pathologically detected metastasis (either local or distant metastasis) or last date of follow up. OS was de ned as the interval between date of diagnosis of breast cancer and the date of death or last date of follow up. To overcome missing values, multiple imputation was performed using predictive mean matching with 20 iterations and 20 imputations. Imputations were predicted by including all available variables in the model. The performance of the imputations was evaluated by descriptive statistics and convergence plots.
All subsequent analyses were performed on the imputed datasets. We performed separate Cox regression analyses for HER2+ (either HR+ or HR-) and for HR+ (HER2-) cases, as well as for PIK3CA wildtype and PIK3CA mutated tumors. Univariable analyses for all four groups were conducted to investigate the potential prognostic value of the mitotic count. For this, the effect of the mitotic count on RFS and OS was explored using mitotic count as a) a continuous variable, b) dichotomized using the median and c) by using the prevailing Nottingham scoring system. For the HR+ group, additional analyses were conducted to nd the most optimal cut-off point for the mitotic count. We performed univariable Cox regression analyses for a range of cut-offs of the mitotic count. To evaluate the performance of the model, Hazard Ratios were calculated.
Funding This project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  945358. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA. The  Knut and Alice Wallenberg foundation  is acknowledged for generous support. RS is supported by the  Breast Cancer Research Foundation (BCRF , grant nr. 17-194 ).
, 2018). The Nano interfaces with a real-time clock (RTC) for accurate time keeping and a thermistor to enable internal temperature to be measured during sampling. Sample date, time, and temperature are then stored on an SD card using an openlog data logger over the serial line. A 7 ah lead acid battery supplies 12 v, which is either directly switched to the pump and stepper motor using a relay switch (5 v logic) or stepped down to ~9 v for the Nano using a voltage regulator. The sample tube holder is constructed from two polycarbonate plastic sheets with 3. 4 cm diameter, drilled circular holes (Supplementary Figure  S1 ), and two supporting plastic sheets 9 cm high. The drilled sheets are attached to the support sheets using eight assembly joints (modesty blocks). The stepper motor is mounted centrally, with a distributor arm attached to the shaft.
A flowchart illustrating the operation of our autosampler is displayed in Figure  2. The sketch developed for this autosampler (see https://github. com/ kkhamis/Autobot-WQ) has a number of variables that users can edit to meet their specific monitoring (Table  1 ). These can account for differences in pump tube length, head (i. e. , height of the sampler above the water), potential for cross-contamination, and flashy flow regimes (e. g. , enabling control over sampling frequency to capture stormwater first flush and hydrograph recession). Bench testing of the sensor was conducted at the University of Birmingham. Given the small sample size the potential for crosscontamination between high and low concentration samples was of immediate concern. A lab experiment was conducted to assess the potential for cross-contamination by alternating sampling between Bourn Brook River water (Dissolved Organic Carbon [DOC] = 3.
Rise The number of samples to collect for the rising limb (i. e. , those at higher frequency) 6 varies with depth. Monitoring was conducted during summer 2019 (August 27), when a large storm was experienced. Samplers were trigged manually at the storm's onset to ensure synchronicity between the two autosamplers, and samples were collected samples every 30 min (n = 14). All samples were processed as outlined in the previous section (i. e. , filtered through Whatman GF/F and analyzed for fluorescence). All lab data were pre-processed using the staRdom package  (Pucher et al. , 2019)  to account for inner filtering and instrument specific artifacts. Fluorescence peak picking was undertaken following  Khamis et al. (2020) , and linear regression was used to assess the relationship between the ISCO and low-cost autosampler.
We explore the impact of choosing different sets of Time-Delay Interferometry (TDI) variables for detecting and reconstructing Stochastic Gravitational Wave Background (SGWB) signals and estimating the instrumental noise in LISA. Most works in the literature build their data analysis pipelines relying on a particular set of TDI channels, the so-called AET variables, which are orthogonal under idealized conditions. By relaxing the assumption of a perfectly equilateral LISA configuration, we investigate to which degree these channels remain orthogonal and compare them to other TDI channels. We show that different sets of TDI variables are more robust under perturbations of the perfect equilateral configuration, better preserving their orthogonality and, thus, leading to a more accurate estimate of the instrumental noise.
This critical noise source needs to be suppressed by several orders of magnitude to allow any GW detections with LISA. This noise suppression will be achieved using an on-ground data processing technique called Time-Delay Interferometry (TDI)  [15]. The results of the TDI algorithm are synthesized data streams representing several laser-noise-free virtual interferometers. As shown in  [16] [17] [18] [19] [20] [21] [22] [23] , it is possible to form several TDI channels which can have different sensitivities to GW signals and instrumental noise. The most commonly used TDI channels for LISA data analysis are the three Michelson like-variables, typically dubbed X, Y, and Z, often re-combined into the (quasi-)orthogonal channels, typically dubbed A, E, and T  [17]. Note that there are several motivations to advocate the search for orthogonal channels.
For example, working in a diagonal TDI basis is convenient for data analysis algorithms. Indeed, since a diagonal matrix is trivial to invert, this avoids problems related to numerical inversion, thus improving the numerical stability and possibly significantly speeding up likelihood evaluations. The AET basis is exactly orthogonal if LISA is a perfect equilateral triangle and if all secondary noise sources, i. e. , noises that dominate the LISA data stream after laser noise suppression through TDI, such as Test Mass (TM) acceleration and Optical Metrology System (OMS) noise, are perfectly equal in all three spacecraft. In fact, for realistic motion of the satellites, the arm-lengths will be unequal (and time-varying) at the percent level  [24] , and LISA will not be a perfectly equilateral triangle.
Moreover, the noises appearing in the different spacecraft will not be equal, breaking another assumption underlying the idealized derivation of the A, E, and T variables. Therefore the standard expressions for these channels will not be perfectly orthogonal when computed using actual LISA data  2. In this work, we first quantify the impact that a LISA configuration with unequal fixed armlengths and different noise levels among the satellites would have on the orthogonality of different sets of TDI variables by an explicit calculation of the noise and signal power spectral density (PSD) and cross-spectral density (CSD). For simplicity, we focus on the so-called first-generation TDI variables  [16] , which only fully suppress laser noise for a static LISA constellation, i. e. , with arm-lengths that do not evolve in time.
While both sets of variables are expected to be orthogonal sets and perfectly equivalent in the idealized situation of equal LISA arms and equal noise amplitudes, differences arise when the assumptions underlying the construction of the orthogonal channels are broken. The paper is organized as follows. In section II, we describe the data model employed and derive the signal response of LISA to a SGWB for unequal LISA arms. We also show how the two dominant noise sources left after TDI (TM and OMS) appear in the LISA measurements. We then introduce the TDI formalism and derive a general formula for the noise and signal PSD and CSD for all the TDI channels considered in this work. In section III, we discuss the noise and signal PSDs and CSDs when the noise levels are assumed to be the same for all TM and OMS components, or when each TM and OMS component is assumed to be different.
In either case, we compare the signal and noise correlations for equal and unequal LISA arms. In section IV, as mentioned above, for a specific set of noise parameters and for a power-law SGWB signal, we produce simulated LISA data and perform PE using MCMC to compare the performance of two sets of TDI variables, namely AET and AEζ, when the cross-correlations in the TDI matrix are neglected. Using FIM, we also study the impact of including or neglecting those cross-correlations on the PE results. We do so for both equal and unequal noise levels and report on the results obtained for the SGWB in the main text, while those for the noise parameters can be found in an appendix. We then conclude in section V. Our paper includes four appendices. Appendix A discusses the relationships among the different sets of TDI variables considered.
II. MEASUREMENT CHARACTERIZATION In this section, we describe the signal and noise components of the LISA data stream. After writing down a general model for the data as a superposition of signal and noise in section II A, we derive the instrument response for an isotropic SGWB signal and the propagation of the different noise components in a single LISA link in section II A 1 and section II A 2, respectively. In section II B we introduce the TDI variables that we will use in this paper. After providing the definitions of the base variables in section II B 1, we introduce, in section II B 2, a general method to compute the TDI PSDs and CSDs using the single link spectra computed in section II A 1 and section II A 2.
4) reduces to S GW (f ) = 2R(f )P h (f ). Absorbing the factor 2 into R and assuming the signal to be parity even (i. e. , P λ ( k) = P (k)) leads to S GW (f ) = R(f ) P h (f ). (2. 5) We conclude our discussion of the signal by recalling the expression of the signal power spectrum in units of the energy density parameter: Ω GW h 2 ≡ 4π 2 3(H 0 /h) 2 f 3 P h (f ) , (2. 6) where H 0 ≈ 3. 24 × 10 -18 h 0 Hz is the Hubble constant today and h 0 = 0. 6766 ± 0. 0042 is its dimensionless value  [30]. 3 This choice would only impact the high-frequency part of the data, where the frequency gets close to the Nyquist frequency. Since for LISA, the sampling rate is expected to be 2Hz, and since in the analysis we only consider data up to. 5Hz, this assumption should not impact the main results of this work.
(2. 26) These channels are designed to be orthogonal for both signal and noise, at least in the idealized case of equal arm and equal and symmetric noise levels in the three base variables. Having orthogonal channels drastically simplifies the computation of the inverse of the covariance matrix appearing in the likelihood, which makes these channels attractive for the practical application of LISA data analysis. We will discuss in section III the extent to which the orthogonality of these channels survives if one relaxes some of the assumptions used to derive them. Note finally that the different TDI variables discussed here are not independent, see appendix A for more details.
The MCMC runs are performed considering only the diagonal terms of the AET and AEζ TDI matrices 12. We proceed by comparing the results obtained with pure Fisher analyses, including, or neglecting, the off-diagonal terms in the TDI matrices, in order to assess their impact on the precision with which the determination of signal parameters can be made. A similar analysis for the noise parameters is presented in appendix D. We conclude by comparing the MCMC posteriors for the signal parameters obtained with the different levels of complexity for the LISA configuration we have discussed throughout this work. For all the analyses discussed in this section, we assume the LISA frequency range to be 3 × 10 -5 ≤ f ≤ 5 × 10 -1 11 The reader is referred to appendix C for the technical details of the MCMC data analysis.
While some previous analyses, e. g. ,  [39] [40] [41] , have already considered different levels for the TM and OMS noises, we also studied and quantified their impact on signal parameter reconstruction for different sets of TDI variables, going beyond the usual XYZ and AET bases. It is particularly noteworthy that the signal parameter reconstruction does not vary significantly among the different configurations considered, see fig. 10 , and that it is not sensitive to the inclusion of the off-diagonal terms of the TDI matrix, see fig. 9a  and fig. 9b. For all these reasons, the results presented in this work represent an important contribution to the community's understanding of cosmological SGWB data analysis for LISA. We conclude by commenting on some of the assumptions of our work and on the possibility of relaxing them in future analyses.
Similarly, while we have restricted our study to isotropic SGWBs, the signal might have a non-trivial angular structure, whose reconstruction should be one of the targets to be included in an SGWB data analysis pipeline. Existing works in this direction range from more theoretical studies  [44]  to numerical techniques involving the decomposition of the LISA response function in pixel space  [45]  / in spherical harmonics  [46]. Finally, in this work, we have considered a simplified scenario where a single SGWB of cosmological origin, described by a simple power-law mode with zero tilt, is present in the LISA band. In reality, as already mentioned in this work, at least two SGWB of astrophysical origin will be present, implying SGWB measurement will require component separation techniques to disentangle the different components contributing to the observed signal.