dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Lung Cancer May 2022 Although symptoms are usually low grade, proactive management should be considered to minimize the impact on quality of life. and corticosteroids might reduce severity. To manage edema while retaining treatment effectiveness, lymphatic drainage (manual or mechanical) may be required. MET TKI dose reduction, interruption or intermittent dosing schedules should be considered; data from patients with dose reductions of tepotinib show that they remained on treatment for prolonged periods. 48 poalbuminemia. Treatment-related hypoalbuminemia was reported in patients with MET ex14 skipping NSCLC treated with MET TKIs. 21 , 32 Most cases of hypoalbuminemia reported with tepotinib were mild-to-moderate in severity (all grades: 16%; grade ≥ 3: 2%); with savolitinib, all cases were mild-to-moderate (all grades: 23%; grade ≥ 3: 0%).
Acknowledgments Medical writing assistance for the development of this manuscript was provided by  Syneos Health Medical Communications , and funded by the healthcare business of  Merck KGaA, Darmstadt, Germany. Novartis ,  Pfizer ,  Roche ,  Takeda , receiving research funding from  Novartis  and the healthcare business of  Merck KGaA, Darmstadt, Germany , and receiving honoraria and/or travel expenses from  AstraZeneca ,  BMS ,  Novartis ,  MSD ,  Pfizer ,  Roche  and  Takeda. X. L. reports acting in an advisory role for  AstraZeneca ,  Eli Lilly ,  EMD Serono , and receiving research funding from  Eli Lilly and Boehringer Ingelheim. E. S. reports acting in an advisory role for  Eli Lilly ,  AstraZeneca ,  Boehringer Ingelheim ,  Roche/Genentech ,  Bristol-Myers Squibb, MSD Oncology, Takeda ,  Bayer ,  Regeneron ,  Novartis ,  Daiichi Sankyo, Seattle Genetics , and the healthcare business of  Merck KGaA, Darmstadt, Germany , and receiving research funding from  Boehringer Ingelheim ,  Bayer ,  Roche/Genentech ,  AstraZeneca  and  Bristol-Myers Squibb.
In a LCA clinical trial (NCT00999609, Phase 3), mild inflammation was observed in 2 of the 21 patients who received the RPE65 gene packaged in AAV2 vectors  [45]. Transient and low levels of anti-AAV2 neutralizing antibodies (NAbs) were detected in two out of three patients, one at day 14 and the other at day 90, which decreased to baseline 1-year post-injection  [46]. In another LCA clinical trial (NCT02781480, Phase 1/2) delivering the RPE65 gene via the AAV2/5 vector, uveitis developed in one out of three patients in the low dose group, all three patients in the intermediate dose group and in two out of three patients in the high dose group. In NCT00749957 (Phase 1/2), three out of six patients who received a high dose of AAV showed ocular inflammation, even though the T-cell responses to capsid and the transgene product were not observed by ELISpot  [47].
In another LCA trial (NCT00643747, Phase 1/2), 12 patients received either a low or high dose of AAV carrying the RPE65 gene. To investigate the long-term effect of AAV-mediated gene therapy, clinical measures of vision such as visual acuity, contrast sensitivity, color vision, and spectral sensitivities were evaluated. In this study, prednisolone was provided before and up to three years after injection. A deleterious response was observed in one participant in whom an episode of mild anterior uveitis was followed by focal pigmentary changes at the macula and a persistent reduction in visual acuity by 15 letters on the Early Treatment of Diabetic Retinopathy Study (ETDRS) chart. NAbs against AAV2 increased in this participant at week 4 and T cells specific to AAV2 capsid were also detected.
To provide a detailed analysis of an innate and adaptive immune response to clinical-grade AAV8 in NHP and make a comparison with preliminary clinical data from three patients from a retinal gene therapy trial for CNGA3based achromatopsia (NCT02610582, Phase 1/2), 34 NHPs received subretinal injections of AAV8 at low or high doses. The results revealed that three animals from the high dose groups developed mononuclear infiltration in the retina and an upregulation in the IFNγ-mediated cytokines of the pro-inflammatory Th1 pathway four weeks after subretinal injection, as well as CD8 + T cells in the retina and serum antibodies. To have a better understanding of the parameters that may contribute to the immune responses induced by ocular AAV delivery, ELISA and NAb assays were performed on serum from 41 NHPs that received AAV in low, medium or high doses.
CD4 + and CD8 + T-cell infiltration was observed in retinal sections, and anti-Ad antibodies were also measured in the immunocompetent mice. The transgene expression in BALB/c mice after the transient depletion of T-cells stayed longer than in the immunocompetent mice. The data demonstrated that the transgene expression decreased with time due to the immune response elicited by Ad, whereas when T-cells were deleted transiently, the expression could stay longer but would be eliminated once the transient depletion reagent delivery was stopped  [63]. This study provided evidence of the pivotal role played by pro-inflammatory effector T-cells in the long-term expression of the transgene. Currently, there is one active clinical trial recruiting to evaluate the safety of Ad (VCN-01) to treat refractory retinoblastoma (RTB) (NCT03284268, Phase 1).
VLPs were injected subretinally in rd12 mouse, which is a LCA model harboring a mutation in the Rep65 gene. Five weeks post-injection, they reported 21% editing in the RPE cells and the rescue of visual function, as measured by ERG. This study provided compelling evidence for the use of VLPs for in vitro and in vivo applications. Although analyses of immune responses were not performed, they reported low toxicity based on the data from liver delivery  [83]. Another study developed murine-leukemia-based VLPs carrying the Cas9-gRNA RNP complex (named nanoblades) and showed 67% editing efficiency in human-induced pluripotent stem cells (hiPSCs) targeting the EMX1 gene. They further tested nanoblades on mouse zygotes to demonstrate that they can be used to generate transgenic animals. Here, the editing efficiency showed large variability, ranging from 11% to 78%.
Acknowledgments:This work was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  University of Evry Val d'Essonne (UEVE). Duohao Ren  is supported by the  China Scholarship Council (CSC)  ( 202108070132 ). The authors would like to thank  Oudom Kem  for his comments and corrections of this review.
Introduction: Dolutegravir is being scaled up globally as part of antiretroviral therapy (ART), but for people with HIV and tuberculosis co-infection, its use is complicated by a drug-drug interaction with rifampicin requiring an additional daily dose of dolutegravir. This represents a disadvantage over efavirenz, which does not have a major drug-drug interaction with rifampicin. We sought to describe HIV clinic practices for prescribing concomitant dolutegravir and rifampicin, and characterize virologic outcomes among patients with tuberculosis co-infection receiving dolutegravir or efavirenz. Methods: Within the four sub-Saharan Africa regions of the International epidemiology Databases to Evaluate AIDS consortium, we conducted a site survey (2021) and a cohort study (2015)(2016)(2017)(2018)(2019)(2020)(2021).
M E T H O D S The data for this study came from the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium, which collects routine clinical data from HIV care and treatment sites in 44 countries across seven geographical regions. This study, which focused on the four sub-Saharan Africa regional cohorts (Central, East, Southern and West; Appendix S1: members of the contributing IeDEA regions), consisted of a cross-sectional site survey of HIV clinics and a patient-level cohort study. Institutional review boards in each country and regional data management centre provided ethical oversight and approved the use of de-identified patient data. Consent requirements for study participation were deferred to local institutional review boards.
Inclusion and exclusion criteria We included sites that had begun to prescribe dolutegravir and were in countries with national guidelines that recommended dolutegravir as part of the preferred first-line ART regimen, including among patients with tuberculosis coinfection. We included patients who were ≥15 years old at HIV care enrolment and had a diagnosis of active tuberculosis from 24 months before their site began to use dolutegravir until 12 months before their site's database closure date. A diagnosis of tuberculosis was defined as documentation of a clinician's diagnosis or a proxy diagnosis based on documentation of initiating the standard regimen of isoniazid, rifampicin, pyrazinamide and ethambutol (HRZE) for drug-susceptible tuberculosis  [21]. Additional inclusion criteria were for patients already on ART to be receiving efavirenz or dolutegravir at the time of tuberculosis diagnosis and for ARTnaïve patients to initiate ART with efavirenz or dolutegravir within 2 months after tuberculosis diagnosis.
The K-means algorithm, routinely used in many scientific fields, generates clustering solutions that depend on the initial cluster coordinates. The number of solutions may be large, which can make locating the global minimum challenging. Hence, the topography of the cost function surface is crucial to understanding the performance of the algorithm. Here we employ the energy landscape approach to elucidate the topography of the K-means cost function surface for Fisher's Iris dataset. For any number of clusters we find that the solution landscapes have a funnelled structure that is usually associated with efficient global optimisation. An analysis of the barriers between clustering solutions shows that the funnelled structures result from remarkably small barriers between almost all clustering solutions.
I. INTRODUCTION Clustering is an important component of data analysis, where the aim is to partition a given dataset into groups whose members have high similarity. It can help to reveal the principles that determine the underlying organisation, providing insight into the phenomena that govern the data. Many clustering methods exist,  1  and the K-means algorithm 2 is amongst the most popular, with previous applications to chemical modelling,  3  bioinformatics,  4, 5  image compression  6  and finance. 7 r a given number of clusters, and initial cluster positions, a K-means solution is obtained by minimising the sum-of-squares cost function until a local minimum is attained. The number of local minima, which correspond to clusterings of varying quality, can be large. Consequently, the quality of K-means solutions can be highly dependent on the scheme for generating initial cluster positions, and location of the optimal solution is an NP-hard problem.
8, 9 [15]  For any initialisation scheme the topography of the cost function surface governs our ability to locate good K-means solutions. The surface topography, described by the K-means minima and the intermediate behaviour of the cost function, depends upon both the number of clusters and the dataset. Analysis of the convergence properties of Lloyd's algorithm can be used to describe behaviour of the cost function around local minima,  16  and its geometrical properties. 17 The organisation of the minima themselves can be understood through the distinct types of K-means clustering solution,  18  but existing methods that determine the relation between minima for a given dataset, rely on their statistical properties,  19  without directly accessing the topography of the K-means cost function surface.
The present contribution provides a new approach to this analysis. The K-means cost function defines a surface analogous to the potential energy surfaces (PESs) routinely studied in physics and chemistry. The PES is a fundamental object of interest in molecular science and, consequently, many methods have been devised for exploring these high-dimensional landscapes. The computational tools developed in the energy landscape approach enable us to locate stationary points, minima, and the transition states between them, on the PES. 20 The application of this framework to the K-means cost function can explain the success, or failure, of K-means on given datasets, and aid location of.
C. Building the network Using the methods described above it is possible to locate K-means minima, transition state analogues, and determine their connectivity. Beginning from a database containing all known K-means minima (Sec. II A), we identify the transition states that connect them (Sec. II B). 6] [37] [38]  The database can be represented as a weighted graph, where all minima are nodes and all pairs of minima connected by a transition state analogue are joined by an edge weighted by a function of the barrier height. For connection attempts between minima in the stationary point database we select pairs using a distance-based criterion. 39 This criterion finds the lowest-valued minimum not currently in the subset containing the global minimum, and selects the nearest member of this subset in Euclidean distance.
Provided the number of images is large, the two images differing in cluster assignment will be close to the intersection seam, so that fewer minimisation steps are required. For every valid MECP we subsequently evaluate the Hessian at F -and find the connected minima, Fig. 4. After evaluating all candidates, the valid transition state analogues are added to the stationary point database, along with their connectivity, and the connected minima, if not already present in the database. Minima with unassigned clusters were discarded, apart from the K = 7 and K = 8 landscapes, where two minima with unassigned clusters were included to allow connection between higher partitionings that proved difficult to connect directly. Two new minima are selected from the database using the distance-based criterion, and the process is repeated until all minima are connected.
III. RESULTS Fisher's Iris dataset has a simple structure, consisting of two clearly-separated clusters of differing sizes. The smaller cluster contains data points from the species Iris setosa, and the larger cluster contains data from both Iris virginica and Iris versicolor. Separation of the larger cluster into the constituent species is not possible using the K-means algorithm. K-means, at K ≥ 3, is forced to distribute its clusters between the two underlying clusters of the Iris dataset in varying numbers. These different partitionings of the K clusters give rise to distinct groups of clustering solutions. Within a given partitioning the minima have only small variations in data point assignment relative to those in different partitionings. The K-means landscapes are visualised using disconnectivity graphs in Fig.
5 , revealing the presence of a well-defined global minimum for all K. The landscapes contain no minima with similar values of the cost function that are separated by large barriers. 3] [44]  All global minima are accompanied by a set of low-lying solutions, similar in structure and separated by small barriers, which can be viewed as equivalent clusterings. Any member of this set provides a suitable substitute for the global minimum clustering, as they retain the same partitioning and only differ in cluster boundaries. In all cases the global minimum clustering, and its associated set of minima, corresponds to the optimal partitioning of K-means clusters between the underlying clusters of the dataset. However, the gap between partitionings decreases with K, as fewer data points are assigned to each cluster, which leads to alternative partitionings becoming more competitive with the global minimum.
In glassy landscapes there is no clear global minimum if the crystal is excluded, as there are a large number of almost equivalent amorphous minima, corresponding to funnels that are separated by large barriers. 64, 65 For these surfaces global optimisation is very challenging and minima from more than one of these funnels would be needed to fully describe the optimal clustering solutions. All the cost function landscapes for the Iris dataset exhibit a well-defined funnel for the global minimum. However, the growing number of kinetic traps, and their competition with the global minimum, leads to more multifunnelled character at larger K. Therefore, as the number of clusters increases the cost function landscape changes, such that global optimisation becomes more challenging, and the cost functions of alternative partitionings approach the value for the global minimum.
The ability of subfunnels to act as kinetic traps can be described in more detail using kinetic analysis, 66-68 which provides transition rates or mean first passage times in physical systems. Here, we apply the same framework to the networks of the cost function landscapes. The density of states, which appears in the partition function for each minimum and the transition state theory rate constant formulation,  69, 70  was set to unity in the present calculations. Hence we do not attempt to include the analogue of a vibrational density of states, but simply use the appropriate Boltzmann factor to assign relative occupation probabilities for the local minima. Overall rates between selected minima in the database, accounting for all possible paths between them, were calculated using the graph transformation method.
Additionally, we observe that the rates for reaching the global minimum, from all other subfunnels, decrease with increasing K, and the reverse rates become more competitive. Therefore, as the number of kinetic traps increases, so does the difficulty in escaping from them, illustrating the likely performance of global optimisation algorithms. The lower rates between partitionings at higher K is due to the increased number of steps, rather than the barriers that have to be overcome on the pathways, as shown in Table  I. The barriers remain small for all the landscapes, but the number of steps increases due to the greater number of minimal clusterings. We observe that, as expected, rates for the conversion between partitionings are significantly faster for adjacent partitionings, namely those that correspond to a difference of one in the number of clusters assigned to the smaller of the underlying clusters of the dataset.
These barriers Every node represents a minimum and is labelled by its partitioning, the number of clusters in the smaller of the two underlying clusters of the dataset, and its cost function value. Rates are given in both directions between all different minima. will occur when the transition state analogues are MECPs between two cluster assignments in a higher-valued partitioning, but both steepest-descent paths end at minima of a lowervalued partitioning. The distributions indicate that most transition state analogues lie within the same partitioning as one of the connected minima. The small barriers indicate that the vast majority of transition state analogues lie at, or close to, an intersection directly involving the higher of the two connected minima, Table  II. Therefore, transition state analogues often correspond to the exchange of a single data point from the higher connected minimum.
IV. CONCLUSIONS In this paper we present a methodology for determining, and visualising, the topography of K-means cost function surfaces. We applied the methodology to Fisher's Iris dataset, varying the number of clusters, to produce these first examples of K-means landscapes. The topography of the cost function surfaces underpins the performance of K-means clustering, and visualisation of K-means landscapes is a crucial step to understanding its successes and failures. The landscape classification for different types of dataset, and cluster number, can be used to predict the performance of K-means for novel datasets, and which variant of the algorithm may be most successful. 6] [77]  Many alternative schemes for K-means attempt to locate the global minimum through multiple initialisation attempts, with no further perturbations to the located minima, and may also produce low-lying solutions for funnelled landscapes.
In general, we recommend basin-hopping, especially for more complex landscapes. As K increases, there are more subfunnels in the landscapes that correspond to alternative partitionings. The number of such subfunnels will depend on the exact nature of the dataset, but it is clear that the number increases with K. The corresponding reduction in the cost function gap between the global minimum funnel and subfunnels, as K increases, leads to more multifunnelled character in the landscapes. It is expected that as K continues to increase the landscapes will progress through multifunnelled and perhaps to glassy character. The growth in subfunnels leads to more challenging global optimisation, which we have illustrated by an analogue of kinetic analysis for a molecular landscape. The effect of subfunnels in impeding global optimisation was quantified using transition rates from subfunnels to the global minimum funnel.
towards a well-defined global minimum. The organisation of these landscapes arises due to distinct partitionings of the K clusters amongst the two underlying clusters of the Iris dataset. Clear partitionings arise in this dataset because it contains well-defined clusters with no outliers. Partitionings, as a method of understanding the organisation of K-means landscapes, will be present in other datasets, but may be less instructive as the number of outliers grows and the underlying clusters are less clearly separated. The clustered nature of the dataset leads to funnelled landscapes. This feature of the Iris dataset is characteristic of many other datasets, and we expect all datasets that have clearly-defined underlying clusters to fall into the single-funnel paradigm at low values of K.
14 FIG. 1. 141 FIG. 1. The K-means cost function surface for two clusters (K = 2), and a one-dimensional dataset (N f = 1) containing seven data items (N = 7). Intersections between different clusterings are denoted by red lines, and each clustering is labelled and visualised below. The data points are distributed along the one feature, x 1 , and assignment to the two clusters is indicated by red or blue.
| Outcome measures The primary endpoint was to analyse the influence of demographic, tumour type and intraoperative results in the growth of the FLR. Data on patient characteristics (age, gender, BMI and comorbidities), tumour aetiology, tumour stage (TNM), neoadjuvant chemotherapy, liver disease in histology (steatohepatitis, fibrosis, macrosteatosis, sinusoid obstruction, cirrhosis and CASH), operative time, Pringle manoeuvre, blood transfusions, surgical technique, complications after stage 1 surgery and length of hospital stay after stage 1 were extracted. The Clavien-Dindo classification was used to assess 90-day morbidity. 16 Post hepatectomy liver failure (PHLF) was analysed according to the International Study Group of Liver Surgery (ISGLS) definition. 17 pending on the time required to reach sufficient volume for the second stage, we divided our cohort of patients into three groups (less than 7 days, between 7 and 14 days and more than 14 days).
| Volumetric data The volume of the FLR was calculated in millilitre and percentage using computed-tomography (CT) scans before both stages. The inter-stage interval was defined as the time period between the first and second stage. For both, prior stage 1 and stage 2 the following liver volumes were measured: total liver volume, tumour volume (in the deportalized liver and in the FLR), functional liver volume (FLV = total liver volume - total tumour volume) of deportalized liver and FLR. Standardized future liver remnant (sFLR) was calculated according to Vauthey formula. 18 The percent (%) growth of the FLR between the first and second stage was also analysed. A FLR was considered insufficient when it was less than 25% in patients with a healthy liver and less than 35% in those who had received chemotherapy or steatosis.