In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Q: Context: To assess, in a retrospective cohort, urinary tract urothelial carcinoma (UT-UC) in patients with various stages of chronic kidney disease (CKD) and their clinicopathological features, as patients with end-stage renal disease (ESRD) have a higher incidence of UT-UC, but the relationship between early stages of CKD and characteristics of UT-UC are less well known.', 'The study included 267 patients with pathologically confirmed UT-UC from January 1994 to December 2006; all had a physical examination (blood pressure), and measurements of laboratory data (serum creatinine, serum haemoglobin) and pathological data. The glomerular filtration rate (GFR) was calculated using the Modification of Diet in Renal Disease equation. Patients were divided into three groups by individual GFR (mL/min), i.e. >60 (no/mild CKD), 30-60 (CKD stage 3) and <30 (CKD stage 4/5).', 'The CKD stages included 81 (30.3%) patients with none/mild CKD, 121 (45.3%) with CKD stage 3 and 65 (24.3%) with CKD stage 4/5. There was a significant and parallel increase in the frequency of UT-UC as CKD severity increased from none/mild CKD to stage 3 (11% vs 55%), and from CKD stage 3 to 4/5 (55% vs 71%; P < 0.05). Pathologically, the frequency of high-grade and high T stage UT-UC in patients with CKD stage 3 (90% and 35%, respectively) and CKD stage 4/5 (91% and 29%, respectively) were significantly greater than in the group with none/mild CKD (P < 0.001). Advanced age and more distant metastasis were independent risk factors for patient survival.\Question: Does the aggressiveness of urinary tract urothelial carcinoma increase with the severity of chronic kidney disease?

A: The aggressiveness of UT-UC increased with the severity of CKD, and this might have important clinical consequences.
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Q: Context: Animals control the speed of motion to meet behavioral demands. Yet, the underlying neuronal mechanisms remain poorly understood. Here we show that a class of segmentally arrayed local interneurons (period-positive median segmental interneurons, or PMSIs) regulates the speed of peristaltic locomotion in Drosophila larvae.', 'PMSIs formed glutamatergic synapses on motor neurons and, when optogenetically activated, inhibited motor activity, indicating that they are inhibitory premotor interneurons. Calcium imaging showed that PMSIs are rhythmically active during peristalsis with a short time delay in relation to motor neurons. Optogenetic silencing of these neurons elongated the duration of motor bursting and greatly reduced the speed of larval locomotion.\Question: Does a group of segmental premotor interneurons regulate the speed of axial locomotion in Drosophila larvae?

A: Our results suggest that PMSIs control the speed of axial locomotion by limiting, via inhibition, the duration of motor outputs in each segment. Similar mechanisms are found in the regulation of mammalian limb locomotion, suggesting that common strategies may be used to control the speed of animal movements in a diversity of species.
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Q: Context: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.', 'Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.\Question: Does comparison of gene expression microarray data with count-based RNA measurements inform microarray interpretation?

A:
Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.
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