An optimization algorithm for maximum quasi-clique problem based on information feedback model.

The maximum clique problem in graph theory is a well-known challenge that involves identifying the complete subgraph with the highest number of nodes in a given graph, which is a problem that is hard for nondeterministic polynomial time (NP-hard problem). While finding the exact application of the maximum clique problem in the real world is difficult, the relaxed clique model quasi-clique has emerged and is widely applied in fields such as bioinformatics and social network analysis. This study focuses on the maximum quasi-clique problem and introduces two algorithms, NF1 and NR1. These algorithms make use of previous iteration information through an information feedback model, calculate the information feedback score using fitness weighting, and update individuals in the current iteration based on the benchmark algorithm and selected previous individuals. The experimental results from a significant number of composite and real-world graphs indicate that both algorithms outperform the original benchmark algorithm in dense instances, while also achieving comparable results in sparse instances.

Postoperative Antiosteoporotic Treatment with Zoledronic Acid Improves Rotator Cuff Healing but Does Not Improve Outcomes in Female Patients with Postmenopausal Osteoporosis: A Prospective, Single-Blinded, Randomized Study.

PURPOSE: To investigate the effect of the antiosteoporotic agent zoledronic acid (ZA) on rotator cuff healing and clinical outcomes in patients with postmenopausal osteoporosis. METHODS: We prospectively enrolled 138 female patients with postmenopausal osteoporosis who were scheduled to undergo arthroscopic rotator cuff repair (ARCR) from March 2020 to March 2021. Patients were randomly allocated to the ZA group (ARCR followed by intravenous ZA infusions at postoperative Day 1 and 1 year later) and the control group (ARCR alone). All patients were followed up for 24 months. Tendon healing was evaluated by ultrasonography at 6 weeks and 24 months after surgery. The American Shoulder and Elbow Surgeons (ASES) score, Western Ontario Rotator Cuff (WORC) index, and Numeric Rating Scale (NRS) for pain were recorded at each follow-up, and the minimal clinically important difference (MCID) was calculated. RESULTS: A total of 124 patients were included in the final analysis, 61 in the ZA group and 63 in the control group. There was no statistically significant difference in participant characteristics between the 2 groups. The ZA group had a significantly higher tendon healing rate than the control group at 2 years after surgery (odds ratio = 5.0; 95% confidence interval [CI], 1.4-18.7; P = .014). Regarding clinical outcomes, 100% of patients exceeded the MCID in both groups, and no significant differences were found at 2 years after surgery between the 2 groups (ASES: 2.5 [95% CI, -2.2 to 7.2; P = .291]; WORC index: 4.5 [95% CI, -0.117 to 9.117; P = .056]; NRS: -0.1 [95% CI, -0.3 to 0.1; P = .394]). CONCLUSIONS: Antiosteoporotic treatment with ZA reduced the retear rate but did not significantly influence the clinical outcomes after ARCR in female patients with postmenopausal osteoporosis. Outcomes of ARCR showed good results in both groups and exceeded the MCID. LEVEL OF EVIDENCE: Level I, randomized controlled trial.

A Simple Algorithm for Population Classification.

A single-nucleotide polymorphism (SNP) is a variation in the DNA sequence that occurs when a single nucleotide in the genome differs across members of the same species. Variations in the DNA sequences of humans are associated with human diseases. This makes SNPs as a key to open up the door of personalized medicine. SNP(s) can also be used for human identification and forensic applications. Compared to short tandem repeat (STR) loci, SNPs have much lower statistical testing power for individual recognition due to the fact that there are only 3 possible genotypes for each SNP marker, but it may provide sufficient information to identify the population to which a certain samples may belong. In this report, using eight SNP markers for 641 samples, we performed a standard statistical classification procedure and found that 86% of the samples could be classified accurately under a two-population model. This study suggests the potential use of SNP(s) in population classification with a small number (n ≤ 8) of genetic markers for forensic screening, biodiversity and disaster victim controlling.