Genomic signatures of selection,local adaptation and production type characterisation of East Adriatic sheep breeds

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.

Selection of treatment modality for hepatocellular carcinoma according to the modified Japan Integrated Staging score

AIM： To compare the prognosis of patients who underwent hepatectomy and ablation using the modified Japan Integrated Staging score （mJIS）. METHODS： We examined the clinicopathologic records and patient outcomes in 278 HCC patients including 226 undergoing hepatectomy and 52 undergoing ablation therapy. RESULTS： Cirrhosis was more frequent in the ablation group. Tumor size, number and presence of vascular invasion were significantly higher in the operation group compared to the ablation group. The local recurrence rate adjacent to treated lesions was significantly higher in the ablation group compared to the operation group （P 〈 0.05）. The 3- and 5-year survival rates in the ablation and the operation group were 66% and 78%, and 50% and 63%, respectively, but not significantly different. Over 50% survival rates were observed in patients with a m.lIS score of 0-2 in both groups. However, survival rates with a score of 3-5 in both groups were significantly lower. CONCLUSION： According to the mJIS system, both local treatments could be selected for patients with a score of 0-2. However, for patients with a score more than 3, liver transplantation might be a better option in patients with HCC.

Relationship between microvessel count and post-hepatectomy survival in patients with hepatocellular carcinoma

AIM: To elucidate the relationship between the microvessel count (MVC) by CD34 analyzed by immunohistochemical method and prognosis in hepatocellular carcinoma (HCC) patients who underwent hepatectomy based on our preliminary study. METHODS: We examined relationships between MVC and clinicopathological factors in 128 HCC patients. The modifi ed Japan Integrated Staging score (mJIS) was applied to examine subsets of HCC patients. RESULTS: Median MVC was 178/mm^2, which was used as a cut-off value. MVC was not signif icantly associated with any clinicopathologic factors or postoperative recurrent rate. Lower MVC was associated with poor disease-free and overall survivals by univariate analysis (P = 0.039 and P = 0.087, respectively) and lower MVC represented an independent poor prognostic factor in disease-free survival by Cox’s multivariateanalysis (risk ratio, 1.64; P = 0.024), in addition to tumor size, vascular invasion, macroscopic fi nding and hepatic dysfunction. Signifi cant differences in disease-free and overall survivals by MVC were observed in HCC patients with mJIS 2 (P = 0.046 and P = 0.0014, respectively), but not in those with other scores. CONCLUSION: Tumor MVC appears to offer a useful prognostic marker of HCC patient survival, particularly in HCC patients with mJIS 2.

Density peaks clustering based integrate framework for multi-document summarization

We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].

Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects

Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects.This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic(2014),the stable balance weighting procedure by Zubizarreta(2015),the calibration balance weighting approach by Chan,et al.(2016),and the integrated propensity score technique by Sant’Anna,et al.(2020).Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods.Simulation results show that in general,the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes.Finally,the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.