Outcomes and efficacy of magnetic resonance imaging-compatible sacral nerve stimulator for management of fecal incontinence: A multi-institutional study

BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a well-established treatment for FI.Given the increased need of magnetic resonance imaging(MRI)for diagnostics,the In-terStim which was previously used in sacral nerve stimulation was limited by MRI incompatibility.Medtronic MRI-compatible InterStim was approved by the United States Food and Drug Administration in August 2020 and has been widely used.AIM To evaluate the efficacy,outcomes and complications of the MRI-compatible InterStim.METHODS Data of patients who underwent MRI-compatible Medtronic InterStim placement at UPMC Williamsport,University of Minnesota,Advocate Lutheran General Hospital,and University of Wisconsin-Madison was pooled and analyzed.Patient demographics,clinical features,surgical techniques,complications,and outcomes were analyzed.Strengthening the Reporting of Observational studies in Epidemiology(STROBE)cross-sectional reporting guidelines were used.RESULTS Seventy-three patients had the InterStim implanted.The mean age was 63.29±12.2 years.Fifty-seven(78.1%)patients were females and forty-two(57.5%)patients had diabetes.In addition to incontinence,overlapping symptoms included diarrhea(23.3%),fecal urgency(58.9%),and urinary incontinence(28.8%).Fifteen(20.5%)patients underwent Peripheral Nerve Evaluation before proceeding to definite implant placement.Thirty-two(43.8%)patients underwent rechargeable InterStim placement.Three(4.1%)patients needed removal of the implant.Migration of the external lead connection was observed in 7(9.6%)patients after the stage I procedure.The explanation for one patient was due to infection.Seven(9.6%)patients had other complications like nerve pain,hematoma,infection,lead fracture,and bleeding.The mean follow-up was 6.62±3.5 mo.Sixty-eight(93.2%)patients reported significant improvement of symptoms on follow-up evaluation.CONCLUSION This study shows promising results with significant symptom improvement,good efficacy and good patient outcomes with low complication rates while using MRI compatible InterStim for FI.Further long-term follow-up and future studies with a larger patient population is recommended.

Geometry Flow-Based Deep Riemannian Metric Learning

Deep metric learning(DML)has achieved great results on visual understanding tasks by seamlessly integrating conventional metric learning with deep neural networks.Existing deep metric learning methods focus on designing pair-based distance loss to decrease intra-class distance while increasing interclass distance.However,these methods fail to preserve the geometric structure of data in the embedding space,which leads to the spatial structure shift across mini-batches and may slow down the convergence of embedding learning.To alleviate these issues,by assuming that the input data is embedded in a lower-dimensional sub-manifold,we propose a novel deep Riemannian metric learning(DRML)framework that exploits the non-Euclidean geometric structural information.Considering that the curvature information of data measures how much the Riemannian(nonEuclidean)metric deviates from the Euclidean metric,we leverage geometry flow,which is called a geometric evolution equation,to characterize the relation between the Riemannian metric and its curvature.Our DRML not only regularizes the local neighborhoods connection of the embeddings at the hidden layer but also adapts the embeddings to preserve the geometric structure of the data.On several benchmark datasets,the proposed DRML outperforms all existing methods and these results demonstrate its effectiveness.

关节镜和保守疗法治疗冻结肩的临床效果比较

目的:比较关节镜结合麻醉下手法松解和保守疗法治疗冻结肩的临床效果。方法:将60例冻结肩患者分为两组,关节镜组30例采用关节镜结合麻醉下手法松解治疗,保守组30例采用药物加功能锻炼的方法治疗。采用国际通行的VAS疼痛评分,Constant评分,UCLA评分以及前屈、外展、内旋、外旋活动度测量分别对两组进行疗效评定。结果:两组60例均获得随访,平均随访时间14个月,两组患者在治疗期间均无严重并发症产生。治疗前关节镜组Constant评分为31.1±4.2,UCLA评分为10.1±1.5;保守组Constant评分为30.7±4.6,UCLA评分为10.1±1.7,两组间差异无统计学意义。治疗2周后关节镜组Constant评分为72.5±4.60,UCLA评分为25.03±2.77;保守组Constant评分为32.47±5.37,UCLA评分为10.83±2.05,关节镜组明显优于保守组(P均<0.001)。治疗后12个月关节镜组Constant评分为89.93±4.48,UCLA评分为30.43±1.85;保守组Constant评分为76.97±22.77,UCLA评分为26.2±7.74,关节镜组仍明显优于保守组(P均<0.05)。术后VAS评分和关节活动度关节镜组也明显优于保守组。结论:关节镜结合麻醉下手法松解和保守疗法治疗冻结肩相比,前者比后者恢复时间更快,在2周时就有明显疗效,在术后12个月时两组都有明显疗效,但关节镜组功能恢复仍优于保守组。

从大数据到大知识

从大数据获取大知识是未来人工智能要解决的重要科学问题。本文从大数据(BD)概念诞生谈起,回顾了大数据知识工程(BDKE)的提出和大知识概念的出现。然后从(1)大知识(BK)、大知识系统(BKS)、大知识工程(BKE)等概念的基本定义、(2)大知识规模及其演化、(3)大知识建模、(4)大知识计算、(5)大知识支撑平台、(6)大知识编程语言等六个视角对大知识进行了探讨。最后给出了有关未来研究方向的想法。

A Laboratory Investigation into the Fuel Atomization Process in a Diesel Engine for Different Configurations of the Injector Nozzles and Flow Conditions

This paper reports a laboratory investigation of the fuel injection process in a diesel engine.The atomization process of the considered fuel(a hydrocarbon liquid)and the ensuing mixing with air is studied experimentally under high-pressure conditions.Different types of injector nozzles are examined,including(two)new configurations,which are compared in terms of performances to a standard injector manufactured by the Bosch company.For the two alternate configurations,the intake edges of one atomizing hole(hole No.1)are located in the sack volume while for the other(hole No.2)they are located on the locking cone of the needle valve.The injection process,the fuel atomization fineness and fuel supply speed characteristics are studied as functions of high-pressure fuel pump camshaft speed and rotation angle.The results obtained show that a decrease in the high-pressure fuel pump camshaft speed can produce fuel redistribution depending on the injector operation.In general,however,the hole No.1 can ensure fuel flow with higher speed with respect to the hole No.2 for all the operation modes of the injector.Based on such an analysis,we conclude that the use of certain injectors can enable a fine tuning of the propagation process of fuel sprays into various areas of the diesel engine combustion chamber.

The time‑varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

Microstructure of Carbon Fiber and Carbon Reinforced Plastic

This study is the investigation of the microstructure of different types of carbon fiber. They were compared with the carbonized and graphitized fibers. Results of structural researches have been presented. It was found that the damage varies from different pollution and the damage of the monofibers. The effect of the pollution of the monofiber was determined.

Modification of Steel Surface Using the Laser Energy Olga Chudina

The article is devoted to surface hardening of steels by alloying with the use of laser energy. Two combined technologies were proposed: first—laser alloying by nitride-forming elements followed by nitriding, and second—the local laser alloying followed by metallization in atmosphere of ammonia. It is shown that laser alloying in continuous radiation forms a layer with a homogeneous fine-grained structure with thickness of 600 microns. The subsequent nitriding increases the microhardness of the surface layer of low-carbon steels to 20,000 MPa, increases wear-resistance in a 3 - 15 times and crack resistance in a 1.5 times. Two-stage technology of metallization allows getting diffusion layer on the surface of steels with the thickness, which is 1.5 - 2 times higher than after traditional metallization. In addition, this method of surface modification can significantly reduce the temperature of diffusion metallization and reduce the processing time to 3 hours. The optimal regimes of both technologies, which provide homogeneous multiphase diffusion layers with high hardness and wear resistance, were determined.

About the Conflicts between the Unitary Quantum Theory and the Special and General Relativity Theories

The authors discuss contradictions between the principal branches of the modern physical picture of the universe. Space and time have been shown in the Unitary Quantum Theory (UQT) not to be connected one with the other, unlike in the Special Theory of Relativity. In UQT, time becomes Newtonian again, and the growth of the particle’s mass with growing speed proceeds from other considerations of physics. Unlike the quantum theory, the modern gravitation theory (the general theory of relativity) is not confirmed by experiments and needs to be considerably revised.

Physico-Chemical and Microbiological Study for the Stability of Phenytoin Sodium Extemporaneously Compounded Suspension in Saudi Arabia Hospitals

Epilepsy is a chronic and the fourth most common neurological disorder which affects people of all age groups. Recently research and awareness on epilepsy-related deaths have rapidly grown over the past two decades. Many previous studies are attributed to the guidelines that apprise health care professionals in handling these deaths, but there is a relative scarcity of information accessible for clinicians and pharmacists who are responsible for manufacturing or preparing the extemporaneous anti-epileptic suspensions in the hospitals. Mostly in partial seizures, phenytoin is one of the first-choice drugs. In Saudi Arabian hospitals, the extemporaneous preparation of phenytoin suspension is common, but the hot climatic weather in Saudi Arabia possesses stability problems that should be tackled as the prepared suspension should pass all the stability tests to ensure uniform dosage of the extemporaneous formulation. In the current study, the commercial capsules were used to prepare the oral phenytoin sodium extemporaneous suspension. The physical, chemical and microbiological stability of phenytoin sodium suspension is analyzed at various temperatures.

Association of electrocardiographic markers with myocardial fibrosis as assessed by cardiac magnetic resonance in different clinical settings

BACKGROUND Cardiac magnetic resonance(CMR)is a unique tool for non-invasive tissue characterization,especially for identifying fibrosis.AIM To present the existing data regarding the association of electrocardiographic(ECG)markers with myocardial fibrosis identified by CMR-late gadolinium enhancement(LGE).METHODS A systematic search was performed for identifying the relevant studies in Medline and Cochrane databases through February 2021.In addition,we conducted a relevant search by Reference Citation Analysis(RCA)(https://www.referencecitationanalysis.com).RESULTS A total of 32 studies were included.In hypertrophic cardiomyopathy(HCM),fragmented QRS(fQRS)is related to the presence and extent of myocardial fibrosis.fQRS and abnormal Q waves are associated with LGE in ischemic cardiomyopathy patients,while fQRS has also been related to fibrosis in myocarditis.Selvester score,abnormal Q waves,and notched QRS have also been associated with LGE.Repolarization abnormalities as reflected by increased Tp-Te,negative Twaves,and higher QT dispersion are related to myocardial fibrosis in HCM patients.In patients with Duchenne muscular dystrophy,a significant correlation between fQRS and the amount of myocardial fibrosis as assessed by LGE-CMR was observed.In atrial fibrillation patients,advanced inter-atrial block is defined as P-wave duration≥120 ms,and biphasic morphology in inferior leads is related to left atrial fibrosis.CONCLUSION Myocardial fibrosis,a reliable marker of prognosis in a broad spectrum of cardiovascular diseases,can be easily understood with an easily applicable ECG.However,more data is needed on a specific disease basis to study the association of ECG markers and myocardial fibrosis as depicted by CMR.

γ-Butyrolactone Synthesis from Allylic Alcohols Using the CO_(2)Radical Anion

γ-Butyrolactone structures are commonly found in various natural products and serve as crucial building blocks in organic synthesis.Consequently,the development of methods for synthesizingγ-butyrolactones has garnered significant interest within the organic synthesis community.In this study,we present a direct and highly efficient approach for the synthesis ofγ-butyrolactones from allylic alcohols.Notably,this study represents the first instance ofγ-butyrolactone synthesis initiated by radical hydrocarboxylation using CO_(2)^(•–),generated from metal formates,followed by cyclization.This two-step process is achieved through the synergistic interaction of photoredox and hydrogen atom transfer(HAT)catalysis,resulting in the production ofγ-butyrolactones with exceptional efficiency.Additionally,when employingα,α-diaryl allylic alcohol derivatives as substrates,the reaction involves 1,2-aryl migration,which occurs concomitantly with CO_(2)^(•–)addition,leading to the formation of 4,5-substituted lactones in a good yield.The artificial force induced reaction(AFIR)method identified the preferred 1,2-aryl migration pathway along with potential byproduct pathways,in which the targeted 1,2-migration was found to be the most plausible pathway.

On group structures of strategic-form games

There are two recognized classes of strategic-form symmetric games,both of which can be conveniently defined through the corresponding player symmetry groups.We investigate the basic properties of these groups and several related concepts.We generalize the notion of coveringness and adapt their results to characterize these player symmetry groups.We study the relationships between the coveringnesses of various symmetry groups.Our results demonstrate that these symmetry groups have rich mathematical structures that are of game theoretical and economic interests.

How to Estimate the Mortality Risk of COVID-19:A New Approach with a Three-Factor Decomposition

Statistical analysis of COVID-19 mortality is challenging due to its non-stationarity and cross-sectional instability.In this paper,the authors introduce a unified method to evaluate the fatality rate of COVID-19 across countries,whose method provides more reliable information for cross-country comparison than the traditional case-fatality rate(CFR).It emerges that the new method,the blockwise case-fatality rate(BCFR),varies for different countries and in different periods.The authors also decompose the COVID-19 fatality data by three factors:1)The virus infection dynamics over population in different countries,2)pure distribution and evolution of instantaneous death rate attributed to different individual’s physical characteristics such as age and health,and 3)individual countries’variations affecting interactions between the virus infection and the instantaneous mortality due to individual’s physical characteristics.Based on the new three-factor model,the authors obtain six key findings of the COVID-19 fatality rate.Our study suggests that,on average,the estimated instantaneous fatality rate contributes about 57.0%to the global BCFR while the time-varying weight contributes about 41.5%in December 2020.The country-specific contribution of instantaneous fatality rate is significantly higher than that of the time-varying weight.Besides,the country-specific characteristics in demographical,social,and economic aspects would affect the relative severity of the disease.

Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers

Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large datasets.Here,we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical space.For constructing a dataset,four polymers were precipitated from 416 solvent/nonsolvent combinations,and the morphology of the resulting precipitates were collected.The dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector machine.Thereby,we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation behavior.The predicted appropriate solvents are those with low hydrogen bonding capability,low polarity,and small molecular volume.Furthermore,we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space,which is the origin of the conventional difficulty in identifying the trend.The extraordinary axis points toward a void region,indicating the potential value of synthesizing novel solvents located therein.

基于投入产出优化方法的行业节能潜力和节能目标分析

本文基于投入产出优化方法,提出了行业实际节能潜力和行业实际结构调整潜力两个概念,并将全国总节能目标按照各个不同的行业进行优化分解。同时分析了产业结构调整前后不同情景下,行业能源效率优化的结果。得到三点主要结论:(1)产业结构调整情况下,行业可以制定更小的节能目标;(2)行业能源利用效率的提高,有利于高耗能行业产业结构减小,对产业结构长期良性调整有利;(3)节能潜力大的行业,对实现全国目标的贡献大,但能实现的节能目标不一定大,因此要给予政策扶持。

Towards a Mathematical Theory of Knowledge

A typed category theory is proposed for the abstract description of knowledge and knowledge processing. It differs from the traditional category theory in two directions： all morphisms have types and the composition of morphisms is not necessary a morphism. Two aspects of application of typed category theory are discussed： cones and limits of knowledge complexity classes and knowledge completion with pseudo-functors.

A WEIGHTED PRODUCT METHOD FOR BIDDING STRATEGIES IN MULTI-ATTRIBUTE AUCTIONS

To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method to present a new scoring function that converts each bid into a score.Twokinds of multi-attribute auction models are introduced in terms of scoring rules and bidding objectivefunctions.Equilibrium bidding strategies,procurer's revenue comparisons and optimal auction designare characterized in these two models.Finally,this article discusses some improvement of robustnessof our models,with respect to the assumptions.

Predicting interfacial thermal resistance by machine learning

Various factors affect the interfacial thermal resistance(ITR)between two materials,making ITR prediction a high-dimensional mathematical problem.Machine learning is a cost-effective method to address this.Here,we report ITR predictive models based on experimental data.The physical,chemical,and material properties of ITR are categorized into three sets of descriptors,and three algorithms are used for the models.Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%.Over 80,000 material systems composed of 293 materials were inputs for predictions.Among the top-100 high-ITR predictions by the three different algorithms,25 material systems are repeatedly predicted by at least two algorithms.One of the 25 material systems,Bi/Si achieved the ultra-low thermal conductivity in our previous work.We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications.This study proposed a strategy for material exploration for thermal management by means of machine learning.

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.