兰州地区枣树新害虫——枣绮夜蛾

枣绮夜蛾力兰州地区枣树的毁灭性害虫。据在西固泉枣产区的调查访问,1956年开始发现有此虫,以后逐年增多,为害甚烈。该地共有3,250株枣树,平均年产量20万斤左右,最高达50多万斤。自1956年后枣产量大幅度下降,1960年以后每年几乎没有收成。在其他枣区本种夜蛾也有不同程度的为害,实为目前枣树生产上急待解决的突出问题。为此,自1963年以来,我们对该虫进行了初步惆查与观察,以便摸清其发生规律与为害习性,初步提出防治办法。现将惆查及观察资料整理于后,以供参考。     

Acid-sensing ion channel 3 mediates peripheral anti-hyperalgesia effects of acupuncture in mice inflammatory pain

Background  

Peripheral tissue inflammation initiates hyperalgesia accompanied by tissue acidosis, nociceptor activation, and inflammation mediators. Recent studies have suggested a significantly increased expression of acid-sensing ion channel 3 (ASIC3) in both carrageenan- and complete Freund's adjuvant (CFA)-induced inflammation. This study tested the hypothesis that acupuncture is curative for mechanical hyperalgesia induced by peripheral inflammation.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

昆虫不育性药剂的研究——Ⅳ.昆虫不育剂的筛选

本文继续报告了另一批化合物(102种)作为家蝇成虫不育性药剂的筛选结果,仍用Mitlin(1958)或Labrecque(1960)的方法。结果指出:1)在取代嘌呤和嘧啶类中,5-氟乳清酸(5-Fo)(北昆-137)和5-氟尿嘧啶(5-Fu)(北昆 134)的效果为最好,不育性效果的级别是第一级(用 0.5%的剂量,处理24小时,不产卵,或卵不孵化或产卵量及孵化率抑制达90%以上);2)低剂量(0.01%)秋水仙素(北昆 139)长期处理效果极好,高剂量(1%)短期处理(24小时)造成全部死亡,用0.5%饲喂24小时,不能达到全部不育的效果,因此不育级别在1-2级之间;3)乙胺嘧啶类抗叶酸剂都有—定的效果,能减少家蝇的产卵数量,但对于孵化率没有影响;4)氨基甲酸酯类有几种(北昆56、45、57、59)具有较高或极高的毒性及击倒性,但是没有不育性作用,北昆58为北昆57、59的同分异构体,它的毒性极低,而有—定的不育性效果(三级);5)又试验了一批新的氮芥类化合物,作用也不显著,其中之一带有二个乙烯亚胺基(北昆73)效果较高,但由于该化合物已有些变质,尚待进一步试验证实;6)N-甲基羟基脲素(北昆74)的效果不高,但用1%浓度,处理96小时,产卵数为对照的40%,估计在长期饲喂时可能有效;7)结构类似 DDT的对二氯苯三氟甲基甲醇(北昆136),与文献上的报导相反,没有不育作用,但具有极高的毒性;8)喹啉类及其他类型化合物一般均无效。     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

多胺及其合成抑制剂MGBC对稀脉萍成花反应的影响

腐胺、亚精胺和精胺对稀脉萍的成花均有一定的抑制作用,这种作用随多肢的浓度增加而增强。多胺合成抑制剂MGBG强烈抑制稀脉萍群体的增殖速率,并使稀脉萍群体在非诱导光周期下开花。这种由MGBG引起的增殖速率的降低及成花诱导作用均可被多胺逆转。稀脉萍成花诱导过程中,内源腐胺含量显著升高,亚精胺则下降。     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.