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<b><i>Background:</i></b> Colorectal cancer (CRC) is one of the common malignant tumors, with high mortality and poor prognosis. Our study aimed to determine the association between the long noncoding RNA (LncRNA) <i>C5orf66</i> polymorphism and CRC risk in southern Chinese Han population. <b><i>Method:</i></b> Using the experimental design of “case-control” study (512 cases and 513 controls), we selected 4 candidate single-nucleotide polymorphisms (SNPs) of <i>C5orf66</i>. All candidate SNPs were genotyped by Agena MassARRAY. Logistic regression was used to analyze the association between SNPs and CRC risk. Then, we used false-positive report probability analysis to detect whether the significant result is just a chance or noteworthy observation. Multi-factor dimensionality reduction was used to analyze the interaction of “SNP-SNP” in CRC risk. <b><i>Results:</i></b> Our results showed that <i>C5orf66</i> SNPs rs4976270 (odds ratio [OR] = 1.69, <i>p</i> = 0.021) and rs639933 (OR = 1.67, <i>p</i> = 0.024) were, respectively, associated with increasing CRC risk in the southern Chinese Han population. Stratified analysis showed that rs4976270 and rs639933 were significantly associated with an increased risk of CRC in subgroups (&#x3e;60 years, body mass index ≤24 and drinking) under multiple genetic models. In addition, rs254563 and rs647161 also had potential association with CRC risk in subgroups (BMI ≤24 and drinking). Finally, haplotype analysis results showed that haplotype “TA” was significantly associated with increased CRC risk (OR = 1.21, confidence interval = 1.47–2.02, <i>p</i> = 0.043). <b><i>Conclusion:</i></b> Our study provides a new idea for the risk assessment of CRC. LncRNA C5orf66 SNPs have a certain association with CRC risk in the southern Chinese Han population.

Modeling Crew Assignments for Urban Transport Services Using Differentiated Flows Public transit agencies need to judiciously deploy their limited crew members to operate numerous daily scheduled services, while meeting duty and working time regulations for each crew member. Since crew costs account for a large portion of the organizations’ operating expenses, minimizing the total crew and transfer costs is very important. But, with hundreds of daily trips and millions of possible crew itineraries, optimizing trip-to-crew assignment decisions is challenging. In “Crew Assignment with Duty Time Limits for Transport Services: Tight Multicommodity Models,” Balakrishnan, Mirchandani, and Lin propose a novel integer optimization model that represents itineraries as multicommodity flows, differentiated by first trip and depot, to capture the duty time limits and incorporate additional requirements such as selecting equitable schedules. The authors show that this compact model can be tighter than previous formulations, further strengthen the model, and propose a restricted optimization approach combined with an optimality test to generate near-optimal solutions quickly. Extensive computational tests using well-known and real-life problem instances show that the proposed model and solution approach can be very effective in practice.