The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
High density polyethylene(HDPE)pyrolysis and in-line oxidative steam reforming was carried out in a two-step reaction system consisting of a conical spouted bed reactor and a fluidized bed reactor.Continuous plastic p...High density polyethylene(HDPE)pyrolysis and in-line oxidative steam reforming was carried out in a two-step reaction system consisting of a conical spouted bed reactor and a fluidized bed reactor.Continuous plastic pyrolysis was conducted at 550℃ and the volatiles formed were fed in-line to the oxidative steam reforming step(space-time 3.12 gcat min gHDPE−1;ER=0.2 and steam/plastic=3)operating at 700℃.The influence Ni based reforming catalyst support(Al_(2)O_(3),ZrO_(2),SiO_(2))and promoter(CeO_(2),La_(2)O_(3))have on HDPE pyrolysis volatiles conversion and H_(2) production was assessed.The catalysts were prepared by the wet impregnation and they were characterized by means of N_(2) adsorption-desorption,X-ray fluorescence,temperature-programmed reduction and X-ray powder diffraction.A preliminary study on coke deposition and the deterioration of catalysts properties was carried out,by analyzing the tested catalysts through temperature programmed oxidation of coke,transmission electron microscopy,and N_(2) adsorption-desorption.Among the supports tested,ZrO_(2) showed the best performance,attaining conversion and H_(2) production values of 92.2% and 12.8 wt%,respectively.Concerning promoted catalysts,they led to similar conversion values(around 90%),but significant differences were observed in H_(2) production.Thus,higher H_(2) productions were obtained on the Ni/La_(2)O_(3)-Al_(2)O_(3) catalyst(12.1 wt%)than on CeO_(2) promoted catalysts due to La_(2)O_(3) capability for enhancing water adsorption on the catalyst surface.展开更多
The use of electronic currency for transactions,denoting a cashless paradigm,has become increasingly common.However,this financial innovation is not prevalent in all countries.This study aims to explain the discrepanc...The use of electronic currency for transactions,denoting a cashless paradigm,has become increasingly common.However,this financial innovation is not prevalent in all countries.This study aims to explain the discrepancies across countries,including individual and country factors.It may be superficially posited that this lag in development stems from individual or microlevel usage challenges.However,the application of the Technology Acceptance Model highlights the presence of overarching characteristics conducive to extensive adoption.Thus,an additional stratum,the multilevel perspective,needs to be examined.This analytical framework incorporates not only individual attributes but also the sociotechnical framework or mesolevel factors in which they operate.A multilevel econometric model is used.The results of these analyses show that the impact on the adoption of cashless payments extends beyond individual factors(attitude to technology use,perceived usefulness,and perceived ease of use).Our primary contribution,conceptually and empirically,is to broaden the analysis vision.A comprehensive multilevel analysis revealed that broader contextual elements,such as infrastructure and national skills,exert a significant influence on the adoption of cashless transactions.Consequently,the widespread acceptance of cashless payment methods is not only contingent on individual choices but is also a collective phenomenon in which the surrounding environment plays a crucial role as a catalyst for the end users in the cashless economy.展开更多
Comprehensive studies identify motor neuron spectrum disorders including amyotrophic lateral sclerosis(ALS)as globally rising fatal disorders with the highest prevalence in aging populations,influenced by ethnicity an...Comprehensive studies identify motor neuron spectrum disorders including amyotrophic lateral sclerosis(ALS)as globally rising fatal disorders with the highest prevalence in aging populations,influenced by ethnicity and ancestry(GBD 2016 Motor Neuron Disease Colla borators,2018).While~10% of diagnoses involve a family history(fALS),most cases are considered sporadic(sALS).However,population-based studies suggest that even cases without a common index mutation impart heritability(Ryan et al.,2019),indicating a crucial role of rare and as yet unknown genetic denominators.展开更多
Monte Carlo(MC) simulations have been performed to refine the estimation of the correction-toscaling exponent ω in the 2D φ^(4)model,which belongs to one of the most fundamental universality classes.If corrections h...Monte Carlo(MC) simulations have been performed to refine the estimation of the correction-toscaling exponent ω in the 2D φ^(4)model,which belongs to one of the most fundamental universality classes.If corrections have the form ∝ L^(-ω),then we find ω=1.546(30) andω=1.509(14) as the best estimates.These are obtained from the finite-size scaling of the susceptibility data in the range of linear lattice sizes L ∈[128,2048] at the critical value of the Binder cumulant and from the scaling of the corresponding pseudocritical couplings within L∈[64,2048].These values agree with several other MC estimates at the assumption of the power-law corrections and are comparable with the known results of the ε-expansion.In addition,we have tested the consistency with the scaling corrections of the form ∝ L^(-4/3),∝L^(-4/3)In L and ∝L^(-4/3)/ln L,which might be expected from some considerations of the renormalization group and Coulomb gas model.The latter option is consistent with our MC data.Our MC results served as a basis for a critical reconsideration of some earlier theoretical conjectures and scaling assumptions.In particular,we have corrected and refined our previous analysis by grouping Feynman diagrams.The renewed analysis gives ω≈4-d-2η as some approximation for spatial dimensions d <4,or ω≈1.5 in two dimensions.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
2025年诺贝尔物理学奖授予约翰·克拉克(John Clarke)、米歇尔·德沃雷(Michel H. Devoret)和约翰·马蒂尼斯(John M. Martinis),以表彰他们在宏观电路中首次实验发现“量子隧穿”与“能量量子化”的奠基性贡献。他们的研...2025年诺贝尔物理学奖授予约翰·克拉克(John Clarke)、米歇尔·德沃雷(Michel H. Devoret)和约翰·马蒂尼斯(John M. Martinis),以表彰他们在宏观电路中首次实验发现“量子隧穿”与“能量量子化”的奠基性贡献。他们的研究将著名的“薛定谔的猫”思想实验变为现实。通过厘米尺度的超导电路,他们成功捕捉到由数十亿电子对协同形成的宏观量子叠加态。实验不仅观测到该宏观系统如拥有“穿墙术”般穿越经典势垒(量子隧穿),并揭示其能量如楼梯台阶般是分立的(能量量子化)。这项工作彻底打破了量子现象仅存在于微观世界的界限,为超导量子计算技术奠定了坚实的物理与实验基础。如今,以他们开创的约瑟夫森结为核心构建的量子比特,正驱动着谷歌、IBM等公司的量子处理器,标志着人类从“观察量子”迈向“建造量子”的新时代。展开更多
Over recent decades,carbon-based chemical sensor technologies have advanced significantly.Nevertheless,significant opportunities persist for enhancing analyte recognition capabilities,particularly in complex environme...Over recent decades,carbon-based chemical sensor technologies have advanced significantly.Nevertheless,significant opportunities persist for enhancing analyte recognition capabilities,particularly in complex environments.Conventional monovariable sensors exhibit inherent limitations,such as susceptibility to interference from coexisting analytes,which results in response overlap.Although sensor arrays,through modification of multiple sensing materials,offer a potential solution for analyte recognition,their practical applications are constrained by intricate material modification processes.In this context,multivariable chemical sensors have emerged as a promising alternative,enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition,while utilizing a single sensing material.Among various carbon-based materials,carbon nanotubes(CNTs)and graphene have emerged as ideal candidates for constructing high-performance chemical sensors,owing to their well-established batch fabrication processes,superior electrical properties,and outstanding sensing capabilities.This review examines the progress of carbon-based multivariable chemical sensors,focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition.The discussion encompasses fundamental aspects of these sensors,including sensing materials,sensor architectures,performance metrics,pattern recognition algorithms,and multivariable sensing mechanism.Furthermore,the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.展开更多
The development of an analytical method for determining the properties of quantum dots(QDs)is crucial for improving the optical performance of QD-based displays.Therefore,synchrotron-based X-ray photoelectron spectros...The development of an analytical method for determining the properties of quantum dots(QDs)is crucial for improving the optical performance of QD-based displays.Therefore,synchrotron-based X-ray photoelectron spectroscopy(XPS)is designed here to accurately characterize the chemical and structural differences between different QDs.This method enables the determination of the reason for the minimal differences between the optical properties of different QDs depending on the synthesis process,which is difficult to determine using conventional methods alone.Combined with model simulations,the XPS spectra obtained at different photon energies reveal the internal structures and chemical-state distributions of the QDs.In particular,the QD synthesized under optimal conditions demonstrates a relatively lower degree of oxidation of the core and more uniformly stacked ZnSe/ZnS shell layers.The internal structures and chemical-state distributions of QDs are closely related to their optical properties.Finally,the synchrotron-based XPS proposed here can be applied to compare nearly equivalent QDs with slightly different optical properties.展开更多
The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed confl...The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique.The results show that between 2000 and 2021,armed conflicts slowed overall SDG progress by 3.43%,equivalent to a setback of 18 years.The Middle East was the most affected region,with a 6.10%slowdown in progress.The impact of different types of conflict varies across specific goals:interstate conflicts primarily affect SDG 5(Gender Equality)and SDG 7(Affordable and Clean Energy),while intrastate conflicts have a larger impact on SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).Additionally,SDG 15(Life on Land)is severely affected by both types of conflict,with long-term consequences.As armed conflicts increase,the development progress would regress rapidly in a non-linear manner.To achieve the SDGs by 2030,it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.展开更多
Alzheimer's disease(AD),the leading cause of dementia,remains a formidable challenge to neurology.Despite decades of research focused on amyloid-β(Aβ)and tau pathologies,most clinical trials targeting these mole...Alzheimer's disease(AD),the leading cause of dementia,remains a formidable challenge to neurology.Despite decades of research focused on amyloid-β(Aβ)and tau pathologies,most clinical trials targeting these molecules failed,highlighting the need for alternative strategies[1].Recent attention has turned to neuroinflammation,particularly the role of microglia,the brain's resident immune cells[1].Microglia are central to AD progression.They can degrade Aβplaques and protect neurons,but may also exacerbate neurotoxicity through chronic inflammation[1].展开更多
Objective To evaluate the prevalence and one-year prognosis associated with frailty in a contemporary cohort of older patients with non-ST-elevation acute coronary syndrome(NSTEACS).Methods The IMPACT-TIMING-GO regist...Objective To evaluate the prevalence and one-year prognosis associated with frailty in a contemporary cohort of older patients with non-ST-elevation acute coronary syndrome(NSTEACS).Methods The IMPACT-TIMING-GO registry(IMPACT of Time of Intervention in patients with Myocardial Infarction with Non-ST seGment elevation.ManaGement and Outcomes)prospectively included 1020 patients with NSTEACS undergoing invasive coronary angiography between April and May 2021.For this sub-study,patients≥65 years were selected.Frailty was assessed according to FRAIL scale.We studied all-cause mortality and the composite of all-cause mortality or all-cause hospitalizations at one-year follow-up after discharge.Results Five hundred and sixty seven patients(mean age:75.8±6.7 years,28.2%women)were included:316(55.7%)were robust,183(32.3%)prefrail,and 68(12.0%)frail.Frail patients were significantly older,more often women,and presented a worse baseline clinical profile.There were no differences among groups regarding pretreatment with a P2Y12 inhibitor.An urgent angiography(<24 h)was less frequently performed in frail patients,with no differences regarding revascularization approach or in main in-hospital adverse events,although acute kidney disease occurred more frequently in frail patients.At 1-year follow-up,20 patients died(3.6%).Chronic kidney disease was independently associated with 1-year all-cause death,although a trend towards higher mortality was observed in frail patients(HR=3.01;95%CI:0.93-9.78;P=0.065).Frailty was independently associated with higher 1-year all-cause mortality or all-cause rehospitalizations(HR=2.23;95%CI:1.43-3.46;P<0.001)Conclusions In older patients with NSTEACS,frailty independently associates higher all-cause mortality or all-cause hospital admissions at one-year follow-up.展开更多
A systematic study of the magnetic and structural properties dependence on the particle size was realized.For this,commercial NdFeB powder was separated into five different mean particle sizes using sieves.Besides,fro...A systematic study of the magnetic and structural properties dependence on the particle size was realized.For this,commercial NdFeB powder was separated into five different mean particle sizes using sieves.Besides,from the original powder,eleven samples were also produced by mechanical milling assisted by surfactant,using various milling times.A total of sixteen samples were studied by scanning electron microscopy(SEM),X-ray diffraction(XRD),vibrating sample magnetometry(VSM),and Mdssbauer spectrometry(MS).The particle sizes of the samples vary from the micrometer to the nanometer scale.The crystallite size decreases with decreasing particle size.XRD result indicates that the Nd2Fe14B phase is found in all the samples,and the presence of this phase is also corroborated by MS using six sextets for fitting their spectra,with an additional singlet corresponding to the Nd1.1Fe4B4 phase.The mean hyperfine magnetic field increases with increasing particle size because the magnetic dipolar interaction between the magnetic moment of the particles increases with particle size.From the VSM measurements the magnetic energy density(BH)max values were calculated for different particle sizes,and their maximum value of 34.45 MGOe is obtained for the sample with the particle size of 60μm.展开更多
With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided e...With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided efficient solutions to assist radiologists for medical imaging analysis.For instance,automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions.In this paper,we propose an end-to-end system,which is based on You-Only-Look-Once(YOLO)model,to simultaneously localize and classify suspicious breast lesions from entire mammograms.The proposed system first preprocesses the raw images,then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification.We evaluated the model on two publicly available datasets,with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography(CBIS-DDSM)and 235 mammograms from INbreast database.We also used a privately collected dataset with 487 mammograms.Furthermore,we suggested a fusion models approach to report more precise detection and accurate classification.Our best results reached a detection accuracy rate of 95.7%,98.1%and 98%for mass lesions and 74.4%,71.8%and 73.2%for calcification lesions,respectively on CBIS-DDSM,INbreast and the private dataset.展开更多
Apoptosis is a widespread phenomenon that occurs in the brain in both physiological and pathological conditions. Dead ceils must be quickly removed to avoid the further toxic effects they exert in the pa- renchyma, a ...Apoptosis is a widespread phenomenon that occurs in the brain in both physiological and pathological conditions. Dead ceils must be quickly removed to avoid the further toxic effects they exert in the pa- renchyma, a process executed by microglia, the brain professional phagocytes. Although phagocytosis is critical to maintain tissue homeostasis, it has long been either overlooked or indirectly assessed based on microglial morphology, expression of classical activation markers, or engulfment of artificial phagocytic targets in vitro. Nevertheless, these indirect methods present several limitations and, thus, direct obser- vation and quantification of microglial phagocytosis is still necessary to fully grasp its relevance in the diseased brain. To overcome these caveats and obtain a comprehensive, quantitative picture of microglial phagocytosis we have developed a novel set of parameters. These parameters have allowed us to identify the different strategies utilized by microglia to cope with apoptotic challenges induced by excitotoxicity or inflammation. In contrast, we discovered that in mouse and human epilepsy microglia failed to find and engulf apoptotic ceils, resulting in accumulation of debris and inflammation. Herein, we advocate that the efficiency of microglial phagocytosis should be routinely tested in neurodegenerative and neuro- logical disorders, in order to determine the extent to which it contributes to apoptosis and inflammation found in these conditions. Finally, our findings point towards enhancing microglial phagocytosis as a novel therapeutic strategy to control tissue damage and inflammation, and accelerate recovery in brain diseases.展开更多
The general objective of this work is to analyze energy input in a vacuum process with the incorporation of microwave heating. Thus, necessary criteria for designing an efficient freeze-drying operation are considered...The general objective of this work is to analyze energy input in a vacuum process with the incorporation of microwave heating. Thus, necessary criteria for designing an efficient freeze-drying operation are considered through the analysis of strategies based on the combination of different intensities of radiant and microwave heating.The other aim of this research topic is to study the kinetics of drying in relation to mass transfer parameters.Five freeze-drying strategies using both heating systems were used. Consequently, energy input could be related to diffusivity coefficients, temperature and pressure profiles during dehydration of the product and analyzed in comparison to a conventional freeze-drying process.展开更多
Breast cancer(BCa)and prostate cancer(PCa)are the two most common types of cancer.Various factors play a role in these cancers,and discovering the most important ones might help patients live longer,better lives.This ...Breast cancer(BCa)and prostate cancer(PCa)are the two most common types of cancer.Various factors play a role in these cancers,and discovering the most important ones might help patients live longer,better lives.This study aims to determine the variables that most affect patient survivability,and how the use of different machine learning algorithms can assist in such predictions.The AURIA database was used,which contains electronic healthcare records(EHRs)of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland.In total,there were 178 features for BCa and 143 for PCa.Six feature selection algorithms were used to obtain the 21 most important variables for BCa,and 19 for PCa.These features were then used to predict patient survivability by employing nine different machine learning algorithms.Seventy-five percent of the dataset was used to train the models and 25%for testing.Cross-validation was carried out using the StratifiedKfold technique to test the effectiveness of the machine learning models.The support vector machine classifier yielded the best ROC with an area under the curve(AUC)=0.83,followed by the KNeighborsClassifier with AUC=0.82 for the BCa dataset.The two algorithms that yielded the best results for PCa are the random forest classifier and KNeighborsClassifier,both with AUC=0.82.This study shows that not all variables are decisive when predicting breast or prostate cancer patient survivability.By narrowing down the input variables,healthcare professionals were able to focus on the issues that most impact patients,and hence devise better,more individualized care plans.展开更多
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
文摘High density polyethylene(HDPE)pyrolysis and in-line oxidative steam reforming was carried out in a two-step reaction system consisting of a conical spouted bed reactor and a fluidized bed reactor.Continuous plastic pyrolysis was conducted at 550℃ and the volatiles formed were fed in-line to the oxidative steam reforming step(space-time 3.12 gcat min gHDPE−1;ER=0.2 and steam/plastic=3)operating at 700℃.The influence Ni based reforming catalyst support(Al_(2)O_(3),ZrO_(2),SiO_(2))and promoter(CeO_(2),La_(2)O_(3))have on HDPE pyrolysis volatiles conversion and H_(2) production was assessed.The catalysts were prepared by the wet impregnation and they were characterized by means of N_(2) adsorption-desorption,X-ray fluorescence,temperature-programmed reduction and X-ray powder diffraction.A preliminary study on coke deposition and the deterioration of catalysts properties was carried out,by analyzing the tested catalysts through temperature programmed oxidation of coke,transmission electron microscopy,and N_(2) adsorption-desorption.Among the supports tested,ZrO_(2) showed the best performance,attaining conversion and H_(2) production values of 92.2% and 12.8 wt%,respectively.Concerning promoted catalysts,they led to similar conversion values(around 90%),but significant differences were observed in H_(2) production.Thus,higher H_(2) productions were obtained on the Ni/La_(2)O_(3)-Al_(2)O_(3) catalyst(12.1 wt%)than on CeO_(2) promoted catalysts due to La_(2)O_(3) capability for enhancing water adsorption on the catalyst surface.
基金Euskal Herriko Unibertsitatea(UPV/EHU)ECRI Ethics in Finance&Social Value GIU22/003Fundacion Emilio Soldevilla para la Investigacion y el Desarrollo en Economia de la Empresa(FESIDE)BOPV2020.
文摘The use of electronic currency for transactions,denoting a cashless paradigm,has become increasingly common.However,this financial innovation is not prevalent in all countries.This study aims to explain the discrepancies across countries,including individual and country factors.It may be superficially posited that this lag in development stems from individual or microlevel usage challenges.However,the application of the Technology Acceptance Model highlights the presence of overarching characteristics conducive to extensive adoption.Thus,an additional stratum,the multilevel perspective,needs to be examined.This analytical framework incorporates not only individual attributes but also the sociotechnical framework or mesolevel factors in which they operate.A multilevel econometric model is used.The results of these analyses show that the impact on the adoption of cashless payments extends beyond individual factors(attitude to technology use,perceived usefulness,and perceived ease of use).Our primary contribution,conceptually and empirically,is to broaden the analysis vision.A comprehensive multilevel analysis revealed that broader contextual elements,such as infrastructure and national skills,exert a significant influence on the adoption of cashless transactions.Consequently,the widespread acceptance of cashless payment methods is not only contingent on individual choices but is also a collective phenomenon in which the surrounding environment plays a crucial role as a catalyst for the end users in the cashless economy.
基金The lab of AK obtained support from the Interdisciplinary Center for Clinical Research(IZKF)Jena(MSPProject ID:MSP09)+2 种基金DG and MJA B were supported by the Circular Vision project,which has received funding from the European Union's Horizon 2020 research and innovation program(Grant agreement No.899417)the Ministerio de Ciencia e Innovoción,Spain(Grant No.PID2020-119715GB-I00/AEI/10.13039/501100011033)the Instituto de Salud CarlosⅢ,Infrastructure of Precision Medicine associated with Science and Technology(IMPaCT)of the Strategic Action in Health(iDATAMP)(to MJAB)。
文摘Comprehensive studies identify motor neuron spectrum disorders including amyotrophic lateral sclerosis(ALS)as globally rising fatal disorders with the highest prevalence in aging populations,influenced by ethnicity and ancestry(GBD 2016 Motor Neuron Disease Colla borators,2018).While~10% of diagnoses involve a family history(fALS),most cases are considered sporadic(sALS).However,population-based studies suggest that even cases without a common index mutation impart heritability(Ryan et al.,2019),indicating a crucial role of rare and as yet unknown genetic denominators.
文摘Monte Carlo(MC) simulations have been performed to refine the estimation of the correction-toscaling exponent ω in the 2D φ^(4)model,which belongs to one of the most fundamental universality classes.If corrections have the form ∝ L^(-ω),then we find ω=1.546(30) andω=1.509(14) as the best estimates.These are obtained from the finite-size scaling of the susceptibility data in the range of linear lattice sizes L ∈[128,2048] at the critical value of the Binder cumulant and from the scaling of the corresponding pseudocritical couplings within L∈[64,2048].These values agree with several other MC estimates at the assumption of the power-law corrections and are comparable with the known results of the ε-expansion.In addition,we have tested the consistency with the scaling corrections of the form ∝ L^(-4/3),∝L^(-4/3)In L and ∝L^(-4/3)/ln L,which might be expected from some considerations of the renormalization group and Coulomb gas model.The latter option is consistent with our MC data.Our MC results served as a basis for a critical reconsideration of some earlier theoretical conjectures and scaling assumptions.In particular,we have corrected and refined our previous analysis by grouping Feynman diagrams.The renewed analysis gives ω≈4-d-2η as some approximation for spatial dimensions d <4,or ω≈1.5 in two dimensions.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
文摘2025年诺贝尔物理学奖授予约翰·克拉克(John Clarke)、米歇尔·德沃雷(Michel H. Devoret)和约翰·马蒂尼斯(John M. Martinis),以表彰他们在宏观电路中首次实验发现“量子隧穿”与“能量量子化”的奠基性贡献。他们的研究将著名的“薛定谔的猫”思想实验变为现实。通过厘米尺度的超导电路,他们成功捕捉到由数十亿电子对协同形成的宏观量子叠加态。实验不仅观测到该宏观系统如拥有“穿墙术”般穿越经典势垒(量子隧穿),并揭示其能量如楼梯台阶般是分立的(能量量子化)。这项工作彻底打破了量子现象仅存在于微观世界的界限,为超导量子计算技术奠定了坚实的物理与实验基础。如今,以他们开创的约瑟夫森结为核心构建的量子比特,正驱动着谷歌、IBM等公司的量子处理器,标志着人类从“观察量子”迈向“建造量子”的新时代。
基金supported by National Natural Science Foundation of China(92263109,52305607 and 61904188)the Shanghai Rising-Star Program(22QA1410400)+1 种基金the Natural Science Foundation of Shanghai(23ZR1472200)the Medical Innovation Research Program of Shanghai Science and Technology Innovation Action Plan(Grant No.24DX2800100)。
文摘Over recent decades,carbon-based chemical sensor technologies have advanced significantly.Nevertheless,significant opportunities persist for enhancing analyte recognition capabilities,particularly in complex environments.Conventional monovariable sensors exhibit inherent limitations,such as susceptibility to interference from coexisting analytes,which results in response overlap.Although sensor arrays,through modification of multiple sensing materials,offer a potential solution for analyte recognition,their practical applications are constrained by intricate material modification processes.In this context,multivariable chemical sensors have emerged as a promising alternative,enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition,while utilizing a single sensing material.Among various carbon-based materials,carbon nanotubes(CNTs)and graphene have emerged as ideal candidates for constructing high-performance chemical sensors,owing to their well-established batch fabrication processes,superior electrical properties,and outstanding sensing capabilities.This review examines the progress of carbon-based multivariable chemical sensors,focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition.The discussion encompasses fundamental aspects of these sensors,including sensing materials,sensor architectures,performance metrics,pattern recognition algorithms,and multivariable sensing mechanism.Furthermore,the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.
基金Advanced Light Source,which is a DOE Office of Science User Facility under contract no.DE-AC02-05CH11231the Basque Government for funding through a PhD Fellowship(Grant no.PRE_2018_2_0285)+1 种基金through Egonlabur Travel Fellowship(Grant no.EP_2018_1_0004)partially supported by an Early Career Award in the Condensed Phase and Interfacial Molecular Science Program,in the Chemical Sciences Geosciences and Biosciences Division of the Office of Basic Energy Sciences of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘The development of an analytical method for determining the properties of quantum dots(QDs)is crucial for improving the optical performance of QD-based displays.Therefore,synchrotron-based X-ray photoelectron spectroscopy(XPS)is designed here to accurately characterize the chemical and structural differences between different QDs.This method enables the determination of the reason for the minimal differences between the optical properties of different QDs depending on the synthesis process,which is difficult to determine using conventional methods alone.Combined with model simulations,the XPS spectra obtained at different photon energies reveal the internal structures and chemical-state distributions of the QDs.In particular,the QD synthesized under optimal conditions demonstrates a relatively lower degree of oxidation of the core and more uniformly stacked ZnSe/ZnS shell layers.The internal structures and chemical-state distributions of QDs are closely related to their optical properties.Finally,the synchrotron-based XPS proposed here can be applied to compare nearly equivalent QDs with slightly different optical properties.
基金supported and funded by the Special Research Assistant Program of Chinese Academy of Sciences(Grant No.E3S30015Y5)Maria de Maeztu excellence accreditation 2023–2026(Ref.CEX2021-001201-M),funded by MCIN/AEI/10.13039/501100011033。
文摘The Sustainable Development Goals(SDGs)represent a solemn commitment by United Nations member states,but achieving them faces numerous challenges,particularly armed conflicts.Here,we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique.The results show that between 2000 and 2021,armed conflicts slowed overall SDG progress by 3.43%,equivalent to a setback of 18 years.The Middle East was the most affected region,with a 6.10%slowdown in progress.The impact of different types of conflict varies across specific goals:interstate conflicts primarily affect SDG 5(Gender Equality)and SDG 7(Affordable and Clean Energy),while intrastate conflicts have a larger impact on SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).Additionally,SDG 15(Life on Land)is severely affected by both types of conflict,with long-term consequences.As armed conflicts increase,the development progress would regress rapidly in a non-linear manner.To achieve the SDGs by 2030,it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.
基金supported by the National Natural Science Foundation of China(32170980)Guangdong Basic and Applied Basic Research Foundation(2022B1515020012)+7 种基金Shenzhen Fundamental Research Program(RCJC20231211090018040,ZDSYS20220606100801003)the 2023 Key Support Project of the Liaoning Provincial Department of Science and Technology([2023]61-7)the Ciberned(CB06/05/0076)the Spanish MICINN grant(PID2022-143020OB-I00)the Basque Government grant(IT1551-22)the Slovenian Research Agency grant J4-60077the Science and Technology Planning Project of Guangdong Province(2021B1212040006)the Sanming Project of Medicine in Shenzhen(SZSM202411023,SZSM202411013).
文摘Alzheimer's disease(AD),the leading cause of dementia,remains a formidable challenge to neurology.Despite decades of research focused on amyloid-β(Aβ)and tau pathologies,most clinical trials targeting these molecules failed,highlighting the need for alternative strategies[1].Recent attention has turned to neuroinflammation,particularly the role of microglia,the brain's resident immune cells[1].Microglia are central to AD progression.They can degrade Aβplaques and protect neurons,but may also exacerbate neurotoxicity through chronic inflammation[1].
文摘Objective To evaluate the prevalence and one-year prognosis associated with frailty in a contemporary cohort of older patients with non-ST-elevation acute coronary syndrome(NSTEACS).Methods The IMPACT-TIMING-GO registry(IMPACT of Time of Intervention in patients with Myocardial Infarction with Non-ST seGment elevation.ManaGement and Outcomes)prospectively included 1020 patients with NSTEACS undergoing invasive coronary angiography between April and May 2021.For this sub-study,patients≥65 years were selected.Frailty was assessed according to FRAIL scale.We studied all-cause mortality and the composite of all-cause mortality or all-cause hospitalizations at one-year follow-up after discharge.Results Five hundred and sixty seven patients(mean age:75.8±6.7 years,28.2%women)were included:316(55.7%)were robust,183(32.3%)prefrail,and 68(12.0%)frail.Frail patients were significantly older,more often women,and presented a worse baseline clinical profile.There were no differences among groups regarding pretreatment with a P2Y12 inhibitor.An urgent angiography(<24 h)was less frequently performed in frail patients,with no differences regarding revascularization approach or in main in-hospital adverse events,although acute kidney disease occurred more frequently in frail patients.At 1-year follow-up,20 patients died(3.6%).Chronic kidney disease was independently associated with 1-year all-cause death,although a trend towards higher mortality was observed in frail patients(HR=3.01;95%CI:0.93-9.78;P=0.065).Frailty was independently associated with higher 1-year all-cause mortality or all-cause rehospitalizations(HR=2.23;95%CI:1.43-3.46;P<0.001)Conclusions In older patients with NSTEACS,frailty independently associates higher all-cause mortality or all-cause hospital admissions at one-year follow-up.
基金the financial support of the grant PID2019-107357RB-I00 funded by MCIU/AEI/10.13039/501100011033 and "ERDF,a way of making Europe"the grants TED2021-132056B-I00 and PLEC2021-008062 funded by MCIN/AEI/10.13039/ 501100011033 and"European Union NextGenerationEU/ PRTR"+1 种基金the grant IT1645-22 funded by the Basque Governmentfunding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.823745。
基金Project supported by Colciencias,Colombian Agency,ColombiaUniversidad del Valle,Colombia(110671250407)+1 种基金the projects 691235-INAPEM of the H2020 ProgramW911NF-17-S-0003 US Army。
文摘A systematic study of the magnetic and structural properties dependence on the particle size was realized.For this,commercial NdFeB powder was separated into five different mean particle sizes using sieves.Besides,from the original powder,eleven samples were also produced by mechanical milling assisted by surfactant,using various milling times.A total of sixteen samples were studied by scanning electron microscopy(SEM),X-ray diffraction(XRD),vibrating sample magnetometry(VSM),and Mdssbauer spectrometry(MS).The particle sizes of the samples vary from the micrometer to the nanometer scale.The crystallite size decreases with decreasing particle size.XRD result indicates that the Nd2Fe14B phase is found in all the samples,and the presence of this phase is also corroborated by MS using six sextets for fitting their spectra,with an additional singlet corresponding to the Nd1.1Fe4B4 phase.The mean hyperfine magnetic field increases with increasing particle size because the magnetic dipolar interaction between the magnetic moment of the particles increases with particle size.From the VSM measurements the magnetic energy density(BH)max values were calculated for different particle sizes,and their maximum value of 34.45 MGOe is obtained for the sample with the particle size of 60μm.
文摘With recent breakthroughs in artificial intelligence,the use of deep learning models achieved remarkable advances in computer vision,ecommerce,cybersecurity,and healthcare.Particularly,numerous applications provided efficient solutions to assist radiologists for medical imaging analysis.For instance,automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions.In this paper,we propose an end-to-end system,which is based on You-Only-Look-Once(YOLO)model,to simultaneously localize and classify suspicious breast lesions from entire mammograms.The proposed system first preprocesses the raw images,then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification.We evaluated the model on two publicly available datasets,with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography(CBIS-DDSM)and 235 mammograms from INbreast database.We also used a privately collected dataset with 487 mammograms.Furthermore,we suggested a fusion models approach to report more precise detection and accurate classification.Our best results reached a detection accuracy rate of 95.7%,98.1%and 98%for mass lesions and 74.4%,71.8%and 73.2%for calcification lesions,respectively on CBIS-DDSM,INbreast and the private dataset.
基金supported by grants from the Spanish Ministry of Economy and Competitiveness with FEDER funds to AS(BFU2015-66689,RYC-2013-12817)OA is recipient of a predoctoral fellowship from the Basque GovernmentIDA is recipient of a predoctoral fellowship from the University of the Basque Country EHU/UPV
文摘Apoptosis is a widespread phenomenon that occurs in the brain in both physiological and pathological conditions. Dead ceils must be quickly removed to avoid the further toxic effects they exert in the pa- renchyma, a process executed by microglia, the brain professional phagocytes. Although phagocytosis is critical to maintain tissue homeostasis, it has long been either overlooked or indirectly assessed based on microglial morphology, expression of classical activation markers, or engulfment of artificial phagocytic targets in vitro. Nevertheless, these indirect methods present several limitations and, thus, direct obser- vation and quantification of microglial phagocytosis is still necessary to fully grasp its relevance in the diseased brain. To overcome these caveats and obtain a comprehensive, quantitative picture of microglial phagocytosis we have developed a novel set of parameters. These parameters have allowed us to identify the different strategies utilized by microglia to cope with apoptotic challenges induced by excitotoxicity or inflammation. In contrast, we discovered that in mouse and human epilepsy microglia failed to find and engulf apoptotic ceils, resulting in accumulation of debris and inflammation. Herein, we advocate that the efficiency of microglial phagocytosis should be routinely tested in neurodegenerative and neuro- logical disorders, in order to determine the extent to which it contributes to apoptosis and inflammation found in these conditions. Finally, our findings point towards enhancing microglial phagocytosis as a novel therapeutic strategy to control tissue damage and inflammation, and accelerate recovery in brain diseases.
文摘The general objective of this work is to analyze energy input in a vacuum process with the incorporation of microwave heating. Thus, necessary criteria for designing an efficient freeze-drying operation are considered through the analysis of strategies based on the combination of different intensities of radiant and microwave heating.The other aim of this research topic is to study the kinetics of drying in relation to mass transfer parameters.Five freeze-drying strategies using both heating systems were used. Consequently, energy input could be related to diffusivity coefficients, temperature and pressure profiles during dehydration of the product and analyzed in comparison to a conventional freeze-drying process.
基金funding from the European Union’s Horizon 2020 CATCH ITN project under the Marie Sklodowska-Curie grant agreement no.722012,website https://www.catchitn.eu/.
文摘Breast cancer(BCa)and prostate cancer(PCa)are the two most common types of cancer.Various factors play a role in these cancers,and discovering the most important ones might help patients live longer,better lives.This study aims to determine the variables that most affect patient survivability,and how the use of different machine learning algorithms can assist in such predictions.The AURIA database was used,which contains electronic healthcare records(EHRs)of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland.In total,there were 178 features for BCa and 143 for PCa.Six feature selection algorithms were used to obtain the 21 most important variables for BCa,and 19 for PCa.These features were then used to predict patient survivability by employing nine different machine learning algorithms.Seventy-five percent of the dataset was used to train the models and 25%for testing.Cross-validation was carried out using the StratifiedKfold technique to test the effectiveness of the machine learning models.The support vector machine classifier yielded the best ROC with an area under the curve(AUC)=0.83,followed by the KNeighborsClassifier with AUC=0.82 for the BCa dataset.The two algorithms that yielded the best results for PCa are the random forest classifier and KNeighborsClassifier,both with AUC=0.82.This study shows that not all variables are decisive when predicting breast or prostate cancer patient survivability.By narrowing down the input variables,healthcare professionals were able to focus on the issues that most impact patients,and hence devise better,more individualized care plans.
