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五年撤资审查
1 As of 30 Nov 2020 2 Return impact analysis completed by Wilshire, presented at the November 2020 Investment Committee meeting 3 Predicted Tracking Error based on cap-weighted benchmark completed by Wilshire Associates 4 Relative to divestment-unconstrained benchmark, annualized based on monthly performance for 10-year window as of 11/30/20 5 Calculated by dividing divested portfolio's annualized active return by its active risk, both calculated over a截至2020年11月30日的5年期间使用每月观察值6少于五年期
机器学习辅助测量Lepton-Jet方位角角度不对称在HERA的深弹性散射中
在深度无弹性正面散射中，使用与HERA的H1检测器收集的数据测量Lepton-Jet方位角不对称性。When the average transverse momentum of the lepton-jet sys- tem, lvert ⃗ P ⊥ rvert , is much larger than the total transverse momentum of the system, lvert⃗q ⊥ rvert , the asymmetry between parallel and antiparallel configurations, ⃗ P ⊥ and ⃗q ⊥ , is expected to be gener- ated by initial and final state soft gluon radiation and can be predicted using perturbation theory.量化不对称的角度特性提供了对强力的额外测试。研究不对称性对于通过横向动量依赖（TMD）Parton分布函数（PDFS）产生的固有不对称的未来测量很重要，其中这种不对称构成了主要背景。方位角不对称的力矩是使用机器学习方法来测量不需要归安宁的。
SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction
Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.
肠道菌群丰度与宫颈癌的发病机理之间的因果关系：双向孟德尔随机研究
Results: In primary outcomes, we found that a higher abundance of class Clostridia, family Family XI, genus Alloprevotella, genus Ruminiclostridium 9, and order Clostridiales predicted higher risk of CC, and a higher abundance of class Lentisphaeria, family Acidaminococcaceae, genus Christensenellaceae R7 group, genus Marvinbryantia, order维多利亚菌，肌动杆菌和小扁豆门预测CC的风险较低。在可验证的结果中，我们发现甲甲基类，家族放线菌科，家族甲状腺杆菌科，lachnospiraceae ucg属010，甲苯基菌科属，甲苯基逆葡萄菌属，命令放线菌和甲基甲基甲基菌属越高的风险和cccccund ccccccc，链球菌科，属媒介物和细菌植物属预测CC的风险较低，反之亦然。
旋转大厅效应：对称性破坏，扭曲和巨大障碍重归于
基于过渡金属二色元和石墨烯基于原子上的薄材料，提供了有前途的途径，以解锁异性峰中旋转厅效应（SHA）的机制。在这里，我们为扭曲的范德华异质结构开发了一个微观理论，该理论完全融合了扭曲和混乱效应，并说明了对称性破坏在自旋霍尔电流产生中的关键作用。我们发现，对顶点校正的准确处理与从流行的iη和梯子近似获得的定性和定量不同。A pronounced oscillatory behavior of skew-scattering processes with twist angle θ is predicted, reflecting a nontrivial interplay of Rashba and valley-Zeeman effects and yields a vanishing SHE for θ = 30 ◦ and, for graphene-WSe 2 heterostructures, an optimal SHE for θ ≈ 17 ◦ .我们的发现揭示了障碍和对称性破裂，作为重要的旋钮，以优化界面。
深海：大型语言模型揭示了全球海洋微生物组的隐藏生物合成潜力
图2：在隐性BGC的验证数据集（n = 940）的验证数据集上的现有方法的比较。a，BGC的数量通过每种方法预测至少一个化学有效的结构，并以每种方法（成功率）的至少一个为每种BGC预测的化学有效结构的百分比（成功率）。b，每种方法预测的化学有效SM结构的数量，并用每种方法预测的独特结构的百分比（唯一性）。c，通过每种方法的预测SM结构的化学空间。d，通过每种方法的分子量的分子量分布。e，通过每种方法的综合可访问性（合成可访问性得分）的分布。f，通过每种方法对预测的SM结构的QED分布（药物的定量估计值）。源数据在源数据文件中提供。
得出一个出发点，用于评估具有体外方法的可穿戴设备成分的皮肤敏化风险
可穿戴设备长时间与皮肤接触。因此，应评估设备成分的皮肤敏化潜力，并应得出一个出发点（POD）以进行适当的风险评估。没有历史的体内数据，必须使用新的方法方法（NAM）得出POD。To accomplish this, regression models trained on LLNA data that use data inputs from OECD-validated in vitro tests were used to derive a predicted EC3 value, the LLNA value used to classify skin sensitization potency, for three adhesive monomers (Isobornyl acrylate (IBOA), N, N- Dimethylacrylamide (NNDMA), and Acryl oylmorpholine （ACMO）和一个染料（溶剂橙60（SO60））这些化学物质可以用作可穿戴设备的成分，并且与引起过敏性接触性皮炎（ACD）有关。使用动力学DPRA和角质素™数据，使用回归模型获得的POD为180、215、1535和8325μg/cm 2
2024研讨会
结果：该研究对全球队列中HPDL相关的神经退行性疾病的自然历史进行了定量模拟，从而阐明了该疾病的分子和表型谱系，并鉴定出三个不同的患者亚组，其特征在于，以临床表型，发育轨迹和存活率的临床表型，临床表型，发育型和存活率的年龄差异显着差异。It also establishes genotype-phenotype associations, finding that presence of a predicted moderately pathogenic missense variant in at least one allele typically leads to a milder, predominantly spastic paraplegic phenotype (OR = 12.4, p < 0.0001) with later disease onset (11 years [IQR = 11] vs. 6 months [IQR = 11], p < 0.0001), whereas双重，高度致病的错义或蛋白质截断的变体与更严重的表型和预期寿命降低有关（中位生存期= 11.0岁）。
聚谷氨酸疾病蛋白：相互作用曲线和病理效应的共同性和差异
图1 Polyq疾病蛋白的αFOLD结构。 (A) Predicted AlphaFold protein model of full-length ATXN1 (Human; AF-P54253), (B) ATXN2 (Human; AF-Q99700), (C) ATXN3 (Human; AF-P54252), (D) ATXN7 (Human; AF-O15265), (E) CACNA1A (Human; AF-O00555), (F) TBP（人类； AF-P20226），（G）AR（人类； AF-P10275）和（H）ATN1（Human; AF-P54259）。 （i）预测氨基酸残基1至413的Alphafold蛋白模型HTT（HTTQ21（1-414）），其中包含21个聚谷氨酰胺。 预测的HTTQ21（1-414）AlphaFold模型叠加在灰色（蛋白质数据库ID 6x9O，2.60Å分辨率[99]中显示的Cryo-EM确定的HTT-HAP40蛋白结构[99]，其中未在Cryo-Em结构中确定PolyQ区域。图1 Polyq疾病蛋白的αFOLD结构。(A) Predicted AlphaFold protein model of full-length ATXN1 (Human; AF-P54253), (B) ATXN2 (Human; AF-Q99700), (C) ATXN3 (Human; AF-P54252), (D) ATXN7 (Human; AF-O15265), (E) CACNA1A (Human; AF-O00555), (F) TBP（人类； AF-P20226），（G）AR（人类； AF-P10275）和（H）ATN1（Human; AF-P54259）。（i）预测氨基酸残基1至413的Alphafold蛋白模型HTT（HTTQ21（1-414）），其中包含21个聚谷氨酰胺。预测的HTTQ21（1-414）AlphaFold模型叠加在灰色（蛋白质数据库ID 6x9O，2.60Å分辨率[99]中显示的Cryo-EM确定的HTT-HAP40蛋白结构[99]，其中未在Cryo-Em结构中确定PolyQ区域。HTTQ21（1-414）模型高度对齐冷冻结构。由黑色矩形构建的残基代表野生型Polyq区域。比例尺表示源自AlphaFold预测的PLDDT值，并表示每日置信度度量[97]：PLDDT> 90，高精度； 90> plddt> 70建模良好； 70> PLDDT> 50低置信度； PLDDT <50差精度。ar，雄激素受体； ATN1，Atrophin 1; atxn1，ataxin 1; atxn2，ataxin 2; atxn3，ataxin 3; atxn7，ataxin 7; Cacna1a，钙电源门控通道亚基Alpha1 A（Cav2.1）;冷冻电子，冷冻电子显微镜； HTT，亨廷顿； PLDDT，每个保留模型置信度评分； Polyq，聚谷氨酰胺； TBP，TATA结合蛋白。
补充信息响应曲面...
ipred ：为模型预测的第i个浓度数据点对应的值；为平均实验测得浓度值；为平均预测浓度值。 ，和 分别表示第i个和第i个输入变量的平均值（k=SV,T,Rt）。 和 分别表示第i个预测组分浓度和平均预测组分浓度。