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Sacred lotus (Nelumbo nucifera) has been cultivated as a crop in Asia for thousands of years. An ∼1300-yr-old lotus fruit, recovered from an originally cultivated but now dry lakebed in northeastern China, is the oldest germinated and directly (14)C-dated fruit known. In 1996, we traveled to the dry lake at Xipaozi Village, China, the source of the old viable fruits. We identified all of the landmarks recorded by botanist Ichiro Ohga some 80 yr ago when he first studied the deposit, but found that the fruits are now rare. We (1) cataloged a total of 60 lotus fruits; (2) germinated four fruits having physical ages of 200-500 yr by (14)C dating; (3) measured the rapid germination of the old fruits and the initially fast growth and short dormancy of their seedlings; (4) recorded abnormal phenotypes in their leaves, stalks, roots, and rhizomes; (5) determined γ-radiation of ∼2.0 mGy/yr in the lotus-bearing beds; and (6) measured stratigraphic sequences of the lakebed strata. The total γ-irradiation of the old fruits of 0.1-3 Gy (gray, the unit of absorbed dosage defined as 1 joule/kg; 1 Gy = 100 rad), evidently resulting in certain of the abnormal phenotypes noted in their seedlings, represents the longest natural radiobiology experiment yet recorded. Most of the lotus abnormalities resemble those of chronically irradiated plants exposed to much higher irradiances. Though the chronic exposure of the old fruits to low-dose γ-radiation may be responsible in part for the notably weak growth and mutant phenotypes of the seedlings, it has not affected seed viability. All seeds presumably repair cellular damage before germination. Understanding of repair mechanisms in the old lotus seeds may provide insight to the aging process applicable also to other organisms.

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This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

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