Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.

It is largely unknown how frequently low-abundance HIV drug-resistant variants at levels under limit of detection of conventional genotyping (<20% of quasi-species) are present in antiretroviral-experienced persons experiencing virologic failure. Further, the clinical implications of low-abundance drug-resistant variants at time of virologic failure are unknown.', "Plasma samples from 22 antiretroviral-experienced subjects collected at time of virologic failure (viral load 1380 to 304,000 copies/mL) were obtained from a specimen bank (from 2004-2007). The prevalence and profile of drug-resistant mutations were determined using Sanger sequencing and ultra-deep pyrosequencing. Genotypes were interpreted using Stanford HIV database algorithm. Antiretroviral treatment histories were obtained by chart review and correlated with drug-resistant mutations. Low-abundance drug-resistant mutations were detected in all 22 subjects by deep sequencing and only in 3 subjects by Sanger sequencing. In total they accounted for 90 of 247 mutations (36%) detected by deep sequencing; the majority of these (95%) were not detected by standard genotyping. A mean of 4 additional mutations per subject were detected by deep sequencing (p<0.0001, 95%CI: 2.85-5.53). The additional low-abundance drug-resistant mutations increased a subject's genotypic resistance to one or more antiretrovirals in 17 of 22 subjects (77%). When correlated with subjects' antiretroviral treatment histories, the additional low-abundance drug-resistant mutations correlated with the failing antiretroviral drugs in 21% subjects and correlated with historical antiretroviral use in 79% subjects (OR, 13.73; 95% CI, 2.5-74.3, p = 0.0016).
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Chronic pancreatitis is characterized by inflammation, atrophy, fibrosis with progressive ductal changes, and functional changes that include variable exocrine and endocrine insufficiency and multiple patterns of pain. We investigated whether abdominal imaging features accurately predict patterns of pain.', 'We collected data from participants in the North American Pancreatitis Study 2 Continuation and Validation, a prospective multicenter study of patients with chronic pancreatitis performed at 13 expert centers in the United States from July 2008 through March 2012. Chronic pancreatitis was defined based on the detection of characteristic changes by cross-sectional abdominal imaging, endoscopic retrograde cholangiopancreatography, endoscopic ultrasonography, or histology analyses. Patients were asked by a physician or trained clinical research coordinator if they had any abdominal pain during the year before enrollment, those who responded "yes" were asked to select from a list of 5 pain patterns. By using these patterns, we\xa0classified patients\' pain based on timing and severity. Abnormal pancreatitis-associated features on abdominal imaging were recorded using standardized case report forms.', 'Data were collected from 518 patients (mean age, 52 ± 14.6 y; 55% male; and 87.6% white). The most common physician-identified etiologies were alcohol (45.8%) and idiopathic (24.3%); 15.6% of patients reported no abdominal pain in the year before enrollment. The most common individual pain pattern was described as constant mild pain with episodes of severe pain and was reported in 45% of patients. The most common imaging findings included pancreatic ductal dilatation (68%), atrophy (57%), and calcifications (55%). Imaging findings were categorized as obstructive for 20% and as inflammatory for 25% of cases. The distribution of individual imaging findings was similar among patients with different patterns of pain. The distribution of pain patterns did not differ among clinically relevant groups of imaging findings.
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Mass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed "frequent itemset mining" can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks.', 'First a model is proposed that describes the origin of the observed peaks in a mass spectrum. For this purpose we use the classical correlative database search algorithm. Peaks that support a positive identification of the spectrum are termed explained peaks. Next, frequent itemset mining techniques are introduced to infer which unexplained peaks are associated in a spectrum. The method is validated on two types of experimental proteomic data. First, peptide mass fingerprint data is analyzed to explain the unassigned peaks in a full scan mass spectrum. Interestingly, a large numbers of experimental spectra reveals several highly frequent unexplained masses, and pattern mining on these frequent masses demonstrates that subsets of these peaks frequently co-occur. Further evaluation shows that several of these co-occurring peaks indeed have a known common origin, and other patterns are promising hypothesis generators for further analysis. Second, the proposed methodology is validated on tandem mass spectrometral data using a public spectral library, where associations within the mass differences of unassigned peaks and peptide modifications are explored. The investigation of the found patterns illustrates that meaningful patterns can be discovered that can be explained by features of the employed technology and found modifications.

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