The concept of short-range order(SRO)—the arrangement of atoms over small distances—in metallic alloys has been underexplored in materials science and engineering.But the past decade has seen renewed interest in quantifying it,since decoding SRO is acrucial step toward developing tailored high-performing alloys,such as stronger or heat-resistant materials.Understanding how atoms arrange themselves is no easy task and must be verified using intensive lab experiments or computer simulations based on imperfect models.These hurdles have made it difficult to fully explore SRO in metallic alloys.But Killian Sheriff and Yifan Cao,graduate students in MIT’s Department of Materials Science and Engineering(DMSE),are using machine learning to quantify,atom-by-atom,the complex chemical arrangements that make up SRO.Under the supervision of Assistant Professor Rodrigo Freitas,and with the help of Assistant Professor Tess Smidt in the Department of Electrical Engineering and Computer Science,their work was recently published in The Proceedings of the National Academy of Sciences.Interest in understanding SRO is linked to the excitement around advanced materials called high-entropy alloys,whose complex compositions give them superior properties.Typically,materials scientists develop alloys by using one element as abase and adding small quantities of other elements to enhance specific properties.The addition of chromium to nickel,for example,makes the resulting metal more resistant to corrosion.Unlike most traditional alloys,high-entropy alloys have several elements,from three up to 20,in nearly equal proportions.This offers avast design space.“It’s like you’re making arecipe with alot more ingredients,”says Cao.The goal is to use SRO as a“knob”to tailor material properties by mixing chemical elements in high-entropy alloys in unique ways.This approach has potential applications in industries such as aerospace,biomedicine,and electronics,driving the need to explore permutations and combinations of elements,Cao says. 查看详细>>

DALLAS–July 18,2024–Cancer cells salvage purine nucleotides to fuel tumor growth,including purines in foods we eat,an important discovery with implications for cancer therapies from research by Children’s Medical Center Research Institute at UT Southwestern published in Cell.CRI Assistant Professor Gerta Hoxhaj,Ph.D.,and her team have challenged the long-standing belief that tumors primarily acquire purine nucleotides–building blocks for DNA,which is required for cellular growth and function–by constructing them from scratch via de novo synthesis.The Hoxhaj Lab’s newest research shows tumors also significantly use the more efficient salvage,or recycling,pathway to acquire purines.“For more than 70 years,drugs targeting purine nucleotides have been acornerstone of cancer treatment,but these treatments have limitations,and drug resistance often develops,”Dr.Hoxhaj said.“Our research illuminates the contributions of both pathways–de novo and salvage–and highlights the crucial,yet previously overlooked,role the salvage pathway plays in tumor growth.”Dr.Hoxhaj,with co-authors Diem Tran,Ph.D.,Rushendhiran Kesavan,Ph.D.,and Dohun Kim,B.S.,used isotope tracing to follow the de novo and salvage purine pathways across normal mouse tissues and avariety of cancer types,including breast,kidney,colon,and liver cancers.Normal tissue analyses showed the kidney salvaged the most purines,which could explain why people with kidney disease are at higher risk for gout.Gout,a type of arthritis linked to uric acid buildup,may be caused by the kidney’s inability to process uric acid,a purine byproduct.When conducting the same analyses on tumors,CRI researchers discovered cancer cells use both de novo and salvage pathways to fulfill their constant need for purines.Additionally,tumors grew faster in mice given ahigh dose of oral nucleotides,indicating purines from the diet contribute to cancer growth. 查看详细>>

“There is great interest in reducing senescence to slow or reverse aging or aging-associated diseases.We discovered anoncoding RNA that when inhibited strongly impairs senescence,suggesting that it could be atherapeutic target for conditions associated with aging,”said Joshua Mendell,M.D.,Ph.D.,Professor of Molecular Biology and amember of the Harold C.Simmons Comprehensive Cancer Center at UT Southwestern.He is also aHoward Hughes Medical Institute Investigator.Dr.Mendell led the study with co-first authors Yujing Cheng,Ph.D.,recent graduate of the Genetics,Development,and Disease graduate program,and Siwen Wang,M.D.,a former postdoctoral researcher,both in the Mendell Lab.Cellular senescence is a“double-edged sword,”Dr.Mendell explained.Cells sometimes undergo senescence when acancer-causing mutation arises,halting uncontrolled cell division and preventing tumors from developing.On the other hand,too much senescence contributes to aging and degenerative diseases.The Mendell Lab has long studied noncoding RNAs,finding new roles for these molecules in both health and disease.In this newest study,he and his colleagues used atechnique for regulating gene activity called CRISPR interference to individually inactivate thousands of noncoding RNAs in human cells that carried acancer-causing mutation.Usually,this mutation prompts cells to become senescent;however,inactivating anoncoding RNA involved in senescence caused the cells to continue dividing.These experiments quickly revealed apreviously unrecognized regulator of senescence called SNORA13,a member of afamily of noncoding RNAs known as small nucleolar RNAs that are thought largely to function as guides for chemical modification of other RNA molecules.A series of additional experiments showed that SNORA13 plays another important and unexpected role:slowing down the construction of ribosomes,cellular machines that synthesize proteins.Dr.Mendell explained that cellular stress–prompted by acancer-causing mutation,for example–can perturb ribosome assembly and push cells into senescence.However,removing SNORA13 caused cells to ramp up ribosome assembly,blocking the quality control that would normally trigger senescence and allowing cells to continue dividing. 查看详细>>

A research team from The Chinese University of Hong Kong(CUHK)’s Faculty of Medicine(CU Medicine)has conducted alarge cohort study among 1,627 children with and without autism spectrum disorder(ASD)and found alterations in four kingdoms of the gut microbial species including archaea,bacteria,fungi and viruses in children with ASD.Using machine learning,they developed apanel of 31 multikingdom and functional markers that showed high diagnostic performance for ASD and has great potential as aclinical diagnostic tool.The findings were published in Nature Microbiology.In apilot study,the researchers also showed that modulation of the gut microbiome alleviated symptoms of anxiety in children with ASD,introducing the possibility of anew therapeutic paradigm for the condition.Multikingdom gut microbial markers facilitate ASD diagnosis ASD is aneurodevelopmental condition characterised by impairment in social communication,and restrictive and repetitive behaviour.Genetic and environmental factors contribute to the pathogenesis of ASD but emerging evidence suggests that impaired cross-talk between the gut microbiome and central nervous system,dubbed the gut-brain axis,may contribute to the development of ASD.According to the latest estimation from CU Medicine,approximately 2.54%of children in Hong Kong has ASD,and the incidence has been rising in recent years.The CU Medicine research team performed metagenomic sequencing on faecal samples from 1,627 children with or without ASD,aged one to 13 years old from five independent cohorts.Subjects were recruited from the Child and Adolescent Psychiatric Services of the Department of Psychiatry at Alice Ho Miu Ling Nethersole Hospital of the Hospital Authority’s New Territory East Cluster from 2021 to 2023.The team analysed faecal samples and clinical data including diet,medication and co-morbidities.The researchers identified apanel of novel gut microbiome markers including 14 archaea,51 bacteria,seven fungi,18 viruses,27 microbial genes and 12 metabolic pathways that were altered in children with ASD.Using machine learning approaches,they then developed anovel,non-invasive diagnostic model based on apanel of 31 multikingdom and functional markers that showed ahigh diagnostic accuracy for ASD.Dr Su Qi,Research Assistant Professor in the Department of Medicine and Therapeutics at CU Medicine,said,“Bacterial composition has been shown to be altered in ASD but the contribution of other components of the microbiome including the archaea,fungi,viruses,microbial genes or functional pathways remains unexplored.We found that the 31-microbiome panel has asensitivity of 94%and specificity of 93%for the diagnosis of ASD,and maintained asensitivity of 91%in children from an independent hospital cohort and ayounger community cohort from one to six years old.”Professor Siew Ng,Croucher Professor in Medical Sciences at CU Medicine,Director of the Microbiota I-Center(MagIC),and New Cornerstone Investigator added,“The diagnosis of ASD is challenging and requires regular developmental assessment in children who show signs of atypical social and language development.Diagnosis is often delayed especially in younger children who may only have mild symptoms and this could lead to delayed intervention.This,to our knowledge,is the first study to demonstrate the robustness and utility of anon-invasive biomarker to diagnose and predict ASD across different ages,gender and settings.” 查看详细>>

A model co-designed by aprofessor at Anglia Ruskin University is expected to reduce the need for chemotherapy in up to 38%of breast cancer patients who would previously have been advised to consider the treatment.The latest version of the PREDICT Breast model,published in the npj Nature journal and launched this week,uses the latest breast cancer survival data as well as taking into account the benefits and harms of chemotherapy and radiotherapy.PREDICT Breast was initially launched in 2010 by Gordon Wishart,Professor of Cancer Surgery at ARU and then Director of the Cambridge Breast Unit at Cambridge University Hospitals NHS Foundation Trust,and Paul Pharoah who at the time was Professor of Cancer Epidemiology at University of Cambridge.They brought together ateam of leading clinicians and scientists to develop and validate the PREDICT Breast model,which was based on Cancer Registry data from the UK.PREDICT Breast has been continuously updated since its launch and allows estimation of 10 and 15-year survival,as well as the absolute benefits of chemotherapy,trastuzumab,hormone therapy and bisphosphonates,to allow appropriate use of these therapies.The model is currently used worldwide by over 40,000 clinicians and their patients each month.The new version has been largely unfunded,but the recently published data is now being followed up by afurther study in the United States,using data from the SEER(Surveillance,Epidemiology,and End Results programme)database.Professor Wishart,now Chief Medical Officer at Check4Cancer alongside his visiting role at ARU,said:“Chemotherapy can cause significant physical effects such as nausea,weight loss,fatigue,bleeding,bruising and increased risk of infection.The data from the new model shows that for asignificant number of women with breast cancer,chemotherapy can be safely avoided. 查看详细>>

A newly discovered hormone that keeps the bones of breastfeeding women strong could also help bone fractures heal and treat osteoporosis in the broader population.Researchers at UC San Francisco and UC Davis showed that in mice,the hormone known as Maternal Brain Hormone(CCN3)increases bone density and strength.Their results,published July 10 in Nature,solve along-standing puzzle about how women’s bones remain relatively robust during breastfeeding,even as calcium is stripped from bones to support milk production.“One of the remarkable things about these findings is that if we hadn’t been studying female mice,which unfortunately is the norm in biomedical research,then we could have completely missed out on this finding,”said Holly Ingraham,PhD,the senior author of the new paper and aprofessor cellular molecular pharmacology at UCSF.“It underscores just how important it is to look at both male and female animals across the lifespan to get afull understanding of biology.”More than 200 million people worldwide suffer from osteoporosis,a severe weakening of the bones that can cause frequent fractures.Women are at particularly high risk of osteoporosis after menopause because of declining levels of the sex hormone estrogen,which normally promotes bone formation.Estrogen levels are also low during breastfeeding,yet osteoporosis and bone fractures are much rarer during this time,suggesting that something other than estrogen promotes bone growth. 查看详细>>

近日，中国科学院近代物理研究所的科研人员与来自法国、芬兰、南非和英国等国家的合作者首次成功测量了β缓发质子核镧-120的激发态结构，在质子滴线原子核的质子中子相互作用和形状演化的研究中取得重要进展，相关成果于近期发表在Physics Letters B上。理论预言，当位于中重质量区的原子核靠近N="Z线时，质子-中子相互作用会增强，并对激发态的结构产生重要影响。同时，原子核可能伴随形状的演化，呈现出“橄榄球”（长椭球），甚至是稀有的“南瓜形”（扁椭球）、“梨形”（八极形变）和“猕猴桃形”（三轴形变）。因此，通过实验测量奇特核的激发态性质对于检验相关理论模型至关重要。为了探索极端丰质子镧原子核的结构演化及其背后的物理机制，近代物理所和法国巴黎萨克雷大学的研究人员主导开展了寻找镧-120激发态的实验。镧-120是一种稀有的β缓发质子核，于1984年首次发现。由于熔合蒸发反应生成镧-120的截面极小，反应产物十分复杂，因此分离及鉴别镧-120极其困难。在过去的40年中，实验物理学家一直未能成功测量到镧-120的激发态。研究团队利用芬兰于韦斯屈莱大学重离子加速器上的质量分析谱仪和伽马探测器阵列，结合多种时间空间关联测量技术，首次在实验上建立了镧-120的激发态能级结构，发现镧-120的奇偶能级劈裂符合系统性，但是它的电磁跃迁比显著不同。结合理论模型，研究团队发现镧-120展现出一种稀有的三轴形变，并且质子-中子相互作用在描述质子滴线奇奇核的结构中扮演着重要角色。该研究得到了国家自然科学基金、中法科研伙伴交流计划项目和中国科学院未来伙伴网络专项的支持。 查看详细>>

Artificial intelligence(AI)could be used to identify drug resistant infections,significantly reducing the time taken for acorrect diagnosis,Cambridge researchers have shown.The team showed that an algorithm could be trained to identify drug-resistant bacteria correctly from microscopy images alone.Antimicrobial resistance is an increasing global health issue that means many infections are becoming difficult to treat,with fewer treatment options available.It even raises the spectre of some infections becoming untreatable in the near future.One of the challenges facing healthcare workers is the ability to distinguish rapidly between organisms that can be treated with first-line drugs and those that are resistant to treatment.Conventional testing can take several days,requiring bacteria to be cultured,tested against various antimicrobial treatments,and analysed by alaboratory technician or by machine.This delay often results in patients being treated with an inappropriate drug,which can lead to more serious outcomes and,potentially,further drive drug resistance.In research published in Nature Communications,a team led by researchers in Professor Stephen Baker’s Lab at the University of Cambridge developed amachine-learning tool capable of identifying from microscopy images Salmonella Typhimurium bacteria that are resistant to the first-line antibiotic ciprofloxacin–even without testing the bacteria against the drug.S.Typhimurium causes gastrointestinal illness and typhoid-like illness in severe cases,whose symptoms include fever,fatigue,headache,nausea,abdominal pain,and constipation or diarrhoea.In severe cases,it can be life threatening.While infections can be treated with antibiotics,the bacteria are becoming increasingly resistant to anumber of antibiotics,making treatment more complicated.The team used high-resolution microscopy to examine S.Typhimurium isolates exposed to increasing concentrations of ciprofloxacin and identified the five most important imaging features for distinguishing between resistant and susceptible isolates.They then trained and tested machine-learning algorithm to recognise these features using imaging data from 16 samples.The algorithm was able to correctly predict in each case whether or not bacteria were susceptible or resistant to ciprofloxacin without the need for the bacteria to be exposed to the drug.This was the case for isolates cultured for just six hours,compared to the usual 24 hours to culture asample in the presence of antibiotic. 查看详细>>

Everyday objects such as metal chains,handcuffs,and key rings are examples that demonstrate aunique combination of properties where hard,rigid rings are interlocked together to exhibit flexibility and strength as awhole,and as such enabling them to perfect their intended functions(Figure 1).At the molecular level,molecules composed of interlocked,nano-sized rings are known as catenanes,which are promising candidates for developing molecular switches and machines.Yet,due to their challenging synthesis,applications of catenanes in other areas are largely unexplored.Recently,a research team led by Professor Ho Yu AU-YEUNG from the Department of Chemistry at The University of Hong Kong(HKU)has synthesised acatenane composed of two freely-rotating rigid macrocycles and showed that the catenane can bind strongly and selectively to either copper(I)cation or sulfate anion despite their opposite charge and different geometry.The ability to detect and differentiate these specific ions has important implications for applications in areas like environmental monitoring and medical diagnostics.As same charges repel and opposite charges attract each other,a binding site that attracts apositively charged cation will normally experience arepulsive interaction with the negatively charged anion and vice versa,which made designing ahost that is suitable for both cation and anion very challenging.To overcome this challenge,the team installed both cation and anion binding sites on each of the interlocked rings,and by virtue of the rotatory motions of the catenane,the host can efficiently adjust the relative position of the binding sites and freely adapt aspecific form favourable for the spherical copper(I)cation or the tetrahedral sulfate anion,resembling achameleon that can change its appearance to fit in specific environments(Figure 2).This work has recently been published in the leading scientific journal Nature Communications.Apart from their industrial and environmental significance,both copper(I)and sulfate ion are essential for proper cell growth and organism development.The strong and selective binding to these ions by the catenane host could hence be exploited for the extraction and recycling of these ions from environmental samples.Also,just as the measurement of sodium ions,chloride and other electrolytes in blood samples can be aroutine test for blood pressure monitoring and general health,new technologies for selective recognition and binding of ions and minerals will be useful for diagnostic and therapeutic purposes.‘This work highlights catenane as an efficient candidate for potent molecular receptors with versatile structures,switchable properties and guest binding behaviours.’stated Professor Au-Yeung.In terms of future plans,Professor Au-Yeung and his group are developing more sophisticated catenane hosts for the simultaneous binding of multiple cations,anions and ion pairs. 查看详细>>

近日，清华大学交叉信息研究院邓东灵研究组与浙江大学物理学院王浩华、宋超研究组等合作，在超导系统中首次制备了斐波那契非阿贝尔拓扑态并实现了斐波那契任意子的编织操作。自然界中常见的基础粒子分为玻色子和费米子两种，交换两个基础粒子的位置会导致系统波函数产生+1（玻色子，如光子）或-1（费米子，如电子）的相位。这是由于在三维空间中，粒子A绕粒子B一圈（等价于交换位置两次）的环路可以在不经过粒子B的情况下连续变形至消失。这限制了系统在粒子交换两次后必须回到最初的量子态，因此每交换一次系统波函数只能产生+1或-1的相因子，相应的粒子被称为玻色子或费米子，满足玻色-爱因斯坦或费米-狄拉克统计规律。而在二维空间中，粒子A绕粒子B一圈的环路无法在不经过粒子B的情况下连续变形至消失，因此没有粒子交换两次后必须回到最初的量子态的限制。在此情形下，粒子的交换可以产生任意的相位，这样的粒子被称作阿贝尔任意子（Anyon），其交换位置的过程被称作编织（braiding）。更一般地，如果系统基态存在简并，交换两个粒子甚至可以改变系统波函数的振幅，导致系统整体的幺正演化而非仅获得一个全局相位。这种粒子被称为非阿贝尔任意子。非阿贝尔任意子的研究具有重要基础理论意义和潜在应用价值。此类粒子满足非阿贝尔统计规律，是与传统玻色子和费米子有着根本不同的奇异粒子。非阿贝尔任意子也是拓扑量子计算的基石。在拓扑量子计算中，量子门由非阿贝尔任意子的编织实现，计算结果的测量则由任意子的融合（fusion）完成。任意子的拓扑性质使得这种量子计算机天生对局域错误免疫，提供了硬件层面的容错量子计算方案。尽管存在多种理论方案，非阿贝尔任意子的实验实现十分困难，直到近年来才出现在量子处理器上模拟非阿贝尔任意子的工作。然而之前所有模拟的非阿贝尔任意子其编织操作所对应的量子门均不具备通用量子计算的能力。而斐波那契任意子则拥有更加复杂的统计性质，其实验实现更为困难。斐波那契任意子量子维度为黄金分割率1.618，与数学中的斐波那契序列息息相关（图1）。其编织能实现任意量子门，可以用于构建通用的容错量子计算机。实验制备斐波那契非阿贝尔拓扑态以及实现斐波那契任意子的编织操作被广泛认为极为困难。该实验采用弦网凝聚模型，通过几何变换使得超导量子芯片方形格子上的量子比特与弦网模型中蜂窝形状的“弦”相吻合（图2）。在该模型中，系统哈密顿量由所有涡旋算符Qv和所有块算符Bp之和构成，基态中所有的弦均为闭合，而激发态中斐波那契任意子分布在开弦的两端（图2）。该实验使用了27个超导量子比特，单（双）比特门精度为99.96%（99.5%），通过115层量子线路制备了系统基态。在制备基态之后，实验通过将系统分成不同区域的方法测量了拓扑纠缠熵，所得结果与理论预言吻合。在此基础上，实验通过弦算符操作产生了两对斐波那契任意子并展示了其编织操作（图3）。实验设计了多种不同的编织次序来测试斐波那契任意子的特性（图3a），分别为：（i）斐波那契任意子与其反粒子湮灭；（ii）编织改变融合结果；（iii）和（iv）融合结果相同验证Yang-Baxter方程；（v）测量斐波那契任意子的量子维度。实验所得结果均与理论预测吻合得很好（图3b），其中根据编制次序（v）的实验结果所得的斐波那契任意子量子维度为1.598，十分接近理论预言的黄金分割率1.618。作为拓扑量子计算领域重要的基础模型，斐波那契任意子的成功模拟与编织是实现通用拓扑量子计算的基础。该研究首次制备了斐波那契非阿贝尔拓扑态并实现了斐波那契任意子的编织操作，向最终实现通用拓扑量子计算迈出了重要一步。 查看详细>>