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Rice, one of the world's most important food plants, has important syntenic relationships with the other cereal species and is a model plant for the grasses. Here we present a map-based, finished quality sequence that covers 95% of the 389 Mb genome, including virtually all of the euchromatin and two complete centromeres. A total of 37,544 nontransposable-element-related protein-coding genes were identified, of which 71% had a putative homologue in Arabidopsis. In a reciprocal analysis, 90% of the Arabidopsis proteins had a putative homologue in the predicted rice proteome. Twenty-nine per cent of the 37,544 predicted genes appear in clustered gene families. The number and classes of transposable elements found in the rice genome are consistent with the expansion of syntenic regions in the maize and sorghum genomes. We find evidence for widespread and recurrent gene transfer from the organelles to the nuclear chromosomes. The map-based sequence has proven useful for the identification of genes underlying agronomic traits. The additional single-nucleotide polymorphisms and simple sequence repeats identified in our study should accelerate improvements in rice production.

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OBJECTIVE Patients with obesity are at increased risk of exacerbations from viral respiratory infections. However, the association of obesity with the severity of coronavirus disease 2019 (COVID-19) is unclear. We examined this association using data from the only referral hospital in Shenzhen, China. RESEARCH DESIGN AND METHODS A total of 383 consecutively hospitalized patients with COVID-19 admitted from 11 January 2020 to 16 February 2020 and followed until 26 March 2020 at the Third People’s Hospital of Shenzhen were included. Underweight was defined as a BMI <18.5 kg/m2, normal weight as 18.5–23.9 kg/m2, overweight as 24.0–27.9 kg/m2, and obesity as ≥28 kg/m2. RESULTS Of the 383 patients, 53.1% were normal weight, 4.2% were underweight, 32.0% were overweight, and 10.7% were obese at admission. Obese patients tended to have symptoms of cough (P = 0.03) and fever (P = 0.06) compared with patients who were not obese. Compared with normal weight patients, those who were overweight had 1.84-fold odds of developing severe COVID-19 (odds ratio [OR] 1.84, 95% CI 0.99–3.43, P = 0.05), while those who were obese were at 3.40-fold odds of developing severe disease (OR 3.40, 95% CI 1.40–2.86, P = 0.007), after adjusting for age, sex, epidemiological characteristics, days from disease onset to hospitalization, presence of hypertension, diabetes, cardiovascular disease, chronic obstructive pulmonary disease, liver disease, and cancer, and drug used for treatment. Additionally, after similar adjustment, men who were obese versus those who were normal weight were at increased odds of developing severe COVID-19 (OR 5.66, 95% CI 1.80–17.75, P = 0.003). CONCLUSIONS In this study, obese patients had increased odds of progressing to severe COVID-19. As the severe acute respiratory syndrome coronavirus 2 may continue to spread worldwide, clinicians should pay close attention to obese patients, who should be carefully managed with prompt and aggressive treatment.

Surgery is an essential component in the treatment of brain tumors. However, delineating tumor from normal brain remains a major challenge. Here we describe the use of stimulated Raman scattering (SRS) microscopy for differentiating healthy human and mouse brain tissue from tumor-infiltrated brain based on histoarchitectural and biochemical differences. Unlike traditional histopathology, SRS is a label-free technique that can be rapidly performed in situ. SRS microscopy was able to differentiate tumor from non-neoplastic tissue in an infiltrative human glioblastoma xenograft mouse model based on their different Raman spectra. We further demonstrated a correlation between SRS and H&E microscopy for detection of glioma infiltration (κ=0.98). Finally, we applied SRS microscopy in vivo in mice during surgery to reveal tumor margins that were undetectable under standard operative conditions. By providing rapid intraoperative assessment of brain tissue, SRS microscopy may ultimately improve the safety and accuracy of surgeries where tumor boundaries are visually indistinct.

This is the accepted version of the paper.This version of the publication may differ from the final published version. sub-sample analyses we identify differences between entering developed and developing host countries in terms of the impact of home country government support and quality of host country institutions. Our findings help explain the puzzle concerning why EE firms have rapidly internationalized in a short period of time and do not follow the pattern predicted by classical IB theories. In comparison with studies from developed country contexts, our findings also highlight that the effect of home country support may be context specific. Permanent repository link

Combining international business research with the knowledge-based view, this paper examines factors affecting the export orientation and export performance of high-technology Small and medium enterprises (SMEs) in an emerging economy. Using a unique, hand-collected dataset of 711 SMEs from Zhongguancun Science Park in China, it argues that export orientation and performance depend not only on the development of capabilities through R&D and technology transfer, but also on entrepreneurial characteristics, such as the founder's international background and global networks. It is also shown that both export orientation and performance are positively associated with the presence of a “returnee” entrepreneur. Journal of International Business Studies (2009) 40, 1005–1021. doi:10.1057/jibs.2008.105

Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO). Most current EMO algorithms perform well on problems with two or three objectives, but encounter difficulties in their scalability to many-objective optimization. This paper proposes a Gridbased Evolutionary Algorithm (GrEA) to solve many-objective optimization problems. Our aim is to exploit the potential of the grid-based approach to strengthen the selection pressure towards the optimal direction while maintaining an extensive and uniform distribution among solutions. To this end, two concepts-grid dominance and grid difference-are introduced to determine the mutual relationship of individuals in a grid environment. Three grid-based criteria, i.e., grid ranking, grid crowding distance, and grid coordinate point distance, are incorporated into the fitness of individuals to distinguish them in both the mating and environmental selection processes. Moreover, a fitness adjustment strategy is developed by adaptively punishing individuals based on the neighborhood and grid dominance relations in order to avoid partial overcrowding as well as guide the search towards different directions in the archive. Six state-of-the-art EMO algorithms are selected as the peer algorithms to validate GrEA. A series of extensive experiments is conducted on 52 instances of 9 test problems taken from 3 test suites. The experimental results show the effectiveness and competitiveness of the proposed GrEA in balancing convergence and diversity. The solution set obtained by GrEA can achieve a better coverage of the Pareto front than that obtained by other algorithms on most of the tested problems. Additionally, a parametric study reveals interesting insights of the division parameter in grid and also indicates useful values for problems with different characteristics.