In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Example Input: Viroporins are small, hydrophobic trans-membrane viral proteins that oligomerize to form hydrophilic pores in the host cell membranes. These proteins are crucial for the pathogenicity and replication of viruses as they aid in various stages of the viral life cycle, from genome uncoating to viral release. In addition, the ion channel activity of viroporin causes disruption in the cellular ion homeostasis, in particular the calcium ion. Fluctuation in the calcium level triggers the activation of the host defensive programmed cell death pathways as well as the inflammasome, which in turn are being subverted for the viruses' replication benefits. This review article summarizes recent developments in the functional investigation of viroporins from various viruses and their contributions to viral replication and virulence. Recent categorization places viroporins into two major classes based on the number of TMD that are then further classified into A or B subclasses based on their membrane topology [2] . Single TMD viroporins in subclass A have their N terminus facing the ER lumen while those in subclass B have their C-terminal tails in the ER lumen. For Class IIA and IIB viroporins, both N-and C-terminus are inside the ER lumen or the cytoplasmic matrix, respectively (Figure 1 ). An additional third class of viroporins may be necessary as viroporins with three-pass TMD have been proposed, such as the non-structural protein 4 (NSP4) of rotavirus [49] and 3a of severe acute respiratory syndrome SARS-CoV [31]. Due to their high structural variability under different conditions, solving the architecture of viroporins under physiological environment has been difficult. However, recent advancement in technology such as the ability to characterize protein structure at the atomic resolution using nuclear magnetic resonance (NMR)
Example Output: Viral Membrane Channels: Role and Function in the Virus Life Cycle

Example Input: Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets.
Example Output: Concept learning via granular computing: A cognitive viewpoint

Example Input: Therapeutic resistance remains the principal problem in acute myeloid leukemia (AML). We used area under receiver operator characteristic curves (AUC) to quantify our ability to predict therapeutic resistance in individual patients where AUC=1.0 denotes perfect prediction and AUC=0.5 denotes a coin flip, using data from 4,601 patients with newly diagnosed AML given induction therapy with 3+7 or more intense standard regimens in MRC/NCRI, HOVON, SWOG, and MD Anderson Cancer Center studies. Age, performance status, white blood cell count, secondary disease, cytogenetic risk, and FLT3-ITD/NPM1 mutation status were each independently associated with failure to achieve complete remission despite no early death ("primary refractoriness"). However, the AUC of a bootstrap-corrected multivariable model predicting this outcome was only 0.78, indicating only fair predictive ability. Removal of FLT3-ITD and NPM1 information only slightly decreased the AUC (0.76). Prediction of resistance, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:defined as primary refractoriness or short relapse-free survival (RFS), was even more difficult. Our ability to forecast resistance based on routinely available pre-treatment covariates provides a rationale for continued randomization between standard and new therapies and supports further examination of genetic and post-treatment data to optimize resistance prediction in AML. Acute myeloid leukemia; area under the receiver operator characteristic curve; prediction; therapeutic resistance Walter et al. [x10 3 /L], median (range) 15 (0-559) 19 (1-510) 14 (0-370) 8 (1-340) 15 (0-559) Platelets 1 [x10 3 /L], median (range) 56 (2-983) 66 (3-998) 54 (2-9,300) 41 (3-1,069) 58 (2-9,300) Bone marrow blasts 1 , median% (range) 70 (0-100) 60 (0-98) 67 (3-100) 47 (6-96) 65 (0-100) Performance status 1 , n (%) 0-1 2,081 (80) 1,051 (93) 368 (83) 374 (91) 3,874 (84)
Example Output:
Resistance Prediction in AML: Analysis of 4,601 Patients from MRC/NCRI, HOVON/SAKK, SWOG, and MD Anderson Cancer Center HHS Public Access