1 return self.findBestValue(arr[i:], target) Reference https://leetcode.com/problems/sum-of-mutated-array-closest-to-target
高级别浆液性卵巢癌(HGSOCs)是免疫检查点抑制剂(ICIs)效果不理想的肿瘤之一。因此必须开发可行的生物标志物,用于鉴定响应候选者并指导HGSOC患者的精确免疫疗法。在这里,我们分析了HGSOC患者的基因组数据,以描述他们的肿瘤微环境(TME)的免疫表型,并找出免疫原性的主要决定因素。与其他实体肿瘤相比,我们观察到HGSOCs中PD-L1,总突变负荷(TMB)和溶细胞分子的最低水平。令人惊讶的是,TMB与肿瘤免疫反应无关,因为它无法预测以前临床试验中相当一部分患者对ICI的反应。通过机器学习方法寻找生物标志物对HGSOCs的免疫治疗意义,我们确定了决定HGSOCs免疫原性的十大最主要因素。有趣的是,我们发现BRCA1突变肿瘤呈现出一种独立于TMB的强效免疫原性表型,符合我们的主要因素和之前建立的免疫原性决定因素的标准。我们的研究结果提供证据表明,BRCA1突变可作为指导IGS治疗HGSOC患者的预测生物标志物。
mutated) { mutated = true; // todo 创建拷贝值 } // todo 把写操作代理到拷贝值上 } }) mutated) { draftState.mutated = true; // 下一层有修改时才往父级 draftValue 上挂 if (onWrite) { onWrite mutated)才将原值上的其余属性拷贝到draftValue上 特殊的,浅拷贝时需要注意属性描述符、Symbol属性等细节: // 跳过target身上已有的属性 function copyProps draftValue.mutated) { hostDraftState.mutated = true; // 拷贝host所有属性 copyProps(draftValue } = draft[INTERNAL_STATE_KEY]; // 将改过的新值patch上去 const next = mutated ?
- If the two hashes are different, shows a Cached Object Mutated warning. 的情况下,再刷新页面一次,因为a/b没变,所以就没有重新计算expensive_computation(a, b) 就会造成res['output'] = "result was manually mutated ", 这个时候就有问题,会报错提醒: CachedObjectMutationWarning: Return value of expensive_computation() was mutated between 21 res = expensive_computation(a, b) st.write("Result:", res) res["output"] = "result was manually mutated " # Mutated cached value st.write("Mutated result:", res)
hands,mutated fingers,deformed,bad anatomy,disfigured,poorly drawn face,extra limb,ugly,poorly drawn hands,mutated fingers,deformed,bad anatomy,disfigured,poorly drawn face,extra limb,ugly,poorly drawn hands,mutated fingers,deformed,bad anatomy,disfigured,poorly drawn face,extra limb,ugly,poorly drawn hands,mutated fingers,deformed,bad anatomy,disfigured,poorly drawn face,extra limb,ugly,poorly drawn hands,mutated fingers,deformed,bad anatomy,disfigured,poorly drawn face,extra limb,ugly,poorly drawn
context_call func()) { if len(new_values) == 0 { context_call() return } mutated_keys = state } m.mtx.Unlock() for key, new_val := range new_values { mutated_keys = append(mutated_keys, key) if old_val, ok := state[key]; ok { mutated_vals m.mtx.Unlock() return } for _, key := range mutated_keys { if val, ok := mutated_vals[key]; ok { state[key] = val
return child1, child2 else: return parent1, parent2# 变异操作def mutation(individual): mutated_chromosome = individual.chromosome.copy() for i in range(len(mutated_chromosome)): if random.random() < MUTATION_RATE: mutated_chromosome[i] = 1 - mutated_chromosome[i] return Individual(mutated_chromosome = individual.chromosome.copy() for i in range(len(mutated_chromosome)): if random.random() < MUTATION_RATE: mutated_chromosome[i] = random.randint(0, 3) return Individual(mutated_chromosome
frame, worst quality, low quality, jpeg artifacts, pgly, duplicate, morbid, mutilated, extra fingers, mutated extra legs, fused fingers, gross proportions, long neck, malformed limbs, missing arms, missing legs, mutated drawn face, too many fingers, ugly, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated
missing arms, long neck, Humpbacked, deformed, bad anatomy, disfigured, poorly drawn face, mutation, mutated feet, poorly drawn face, out of frame, extra limbs, missing limb, floating limbs, disconnected limbs, (mutated limbs, extra arms, extra legs, malformed limbs, fused fingers, too many fingers, long neck, cross-eyed, mutated extra limbs,extra arms,extra legs,malformed limbs,fused fingers,too many fingers,long neck,cross-eyed,mutated feet, poorly drawn face, out of frame, extra limbs, missing limb, floating limbs, disconnected limbs, (mutated
outdoor:1.6), backlight,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated outdoor:1.6), backlight,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated outdoor:1.6), backlight,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated outdoor:1.6), backlight,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated
child2) 变异策略:创新的源泉 自适应变异动态调整强度: def adaptive_mutation(individual, gen, max_gen, mutation_rate): mutated = individual.copy() for i in range(len(mutated)): if random.random() < mutation_rate: # 随着代数增加减小变异幅度 delta = (1 - gen/max_gen)**2 * random.gauss(0, 1) mutated[i] + = delta # 边界处理 mutated[i] = max(min(mutated[i], upper_bound), lower_bound) return mutated 进化计算的高级范式 多目标优化:帕累托前沿探索 NSGA-II算法实现: def nsga2(population, fitness_values): # 快速非支配排序
""" 函数功能:对个体进行位变异操作 参数:child - 待变异的个体 mutation_rate - 变异概率 返回值:变异后的个体 """ mutated_child = child.copy() for i in range(len(mutated_child)): if random.random() < mutation_rate: mutated_child[i] = 1 - mutated_child[i] # 将0变为1,将1变为0 return mutated_child# 示例child = [0, 1, 0, 1 , 0, 1]mutation_rate = 0.1mutated_child = mutation(child, mutation_rate)print(mutated_child) # 输出可能为
selected_parents = select_best(population) offspring = crossover(selected_parents) mutated_offspring = mutate(offspring) population = selected_parents + mutated_offspring evaluate_population # 进化算法示例 def mutate_architecture(architecture): # 随机修改架构中的某个层 mutated_architecture = architecture.copy () layer_to_mutate = random.choice(mutated_architecture.layers) mutated_architecture.modify_layer (layer_to_mutate) return mutated_architecture 3.3 基于梯度的NAS 一种更高效的NAS方法是基于梯度的DARTS(Differentiable
disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, amputation, (extra
out of frame,worst quality,low quality,jpeg artifacts,pgly,duplicate,morbid,mutilated,extra fingers,mutated out of frame,worst quality,low quality,jpeg artifacts,pgly,duplicate,morbid,mutilated,extra fingers,mutated out of frame,worst quality,low quality,jpeg artifacts,pgly,duplicate,morbid,mutilated,extra fingers,mutated
mutate(KRAS_status = ifelse(Sample %in% c("p018t", "p023t", "p030t", "p031t", "p032t", "p033t"), "mutated ", "wildtype")) %>% mutate(TP53_status = ifelse(Sample %in% c("p023t", "p027t"), "mutated", "wildtype ")) %>% mutate(PIK3CA_status = ifelse(Sample %in% c("p031t"), "mutated", "wildtype")) %>% mutate( KRAS_status = factor(TP53_status, levels = c("wildtype", "mutated"))) %>% mutate(TP53_status = factor (TP53_status, levels = c("wildtype", "mutated"))) ggplot(summarized_progeny_scores_mutations, aes(x
,acnes,skin blemishes,age spot,(ugly:1.3),(duplicate:1.3),(morbid:1.2),(mutilated:1.2),(tranny:1.3),mutated extra limbs,extra arms,extra legs,malformed limbs,fused fingers,too many fingers,long neck,cross-eyed,mutated
分享的万用的负向关键字,可以防止出现断手断脚 (((deformed))), blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated hands), fused fingers, messy drawing, broken legs censor, censored, censor_bar, multiple breasts, (mutated mutation, poorly drawn :1.2), black-white, bad anatomy, liquid body, liquidtongue, disfigured, malformed, mutated mosaic, futa, testis, (((deformed))), blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated hands), fused fingers, messy drawing, broken legs censor, censored, censor_bar, multiple breasts, (mutated
'].system('powershell -e <Base64EncodedRevShellHere>') for Word in [ orgTypeFun( 'Word', (str,), { 'mutated ': 1, 'startswith': lambda self, x: False, '__eq__': lambda self, x: self.mutate() and self.mutated < 0 and str(self) == x, 'mutate': lambda self: {setattr(self, 'mutated', self.mutated - 1)}, '__hash__
= 5; // 定义一个不可变的静态变量 static CORRECT: i32 = 1; fn main() { // 错误: note: mutable statics can be mutated races will cause undefined behavior LEVEL = LEVEL + 1; // 错误: note: mutable statics can be mutated