bluuebunny/crossref_metadata_2025 huggingface dataset:
Distinct Clinical Implications of Patient‐ Versus Clinician‐Rated Motor Symptoms in Parkinson's Disease
Authors: Kikuya Akihiro , Tsukita Kazuto , Sawamura Masanori , Yoshimura Kenji , Takahashi Ryosuke
Published: August 2024

Background

Patient‐rated motor symptoms (PRMS) and clinician‐rated motor symptoms (CRMS) often differ in Parkinson's disease (PD).

Objective

Our goal was to investigate the determinants and clinical implications of PRMS compared with CRMS in PD.

Methods

This retrospective, observational cohort study analyzed the cross‐sectional associations and longitudinal impacts of PRMS as assessed by the Movement Disorders Society‐sponsored Unified PD Rating Scale (MDS‐UPDRS) part 2, while controlling for CRMS measured by MDS‐UPDRS part 3. Longitudinal analyses used Cox proportional hazards models and multiple linear mixed‐effects random intercepts/slope models, adjusting for many clinical predictors. We conducted propensity score matching (PSM) to reinforce our analyses' robustness and surface‐based morphometry to investigate neural correlates.

Results

We enrolled 442 patients with early‐stage PD. At baseline, regardless of CRMS, PRMS were associated with the severity of postural instability and gait disturbance (PIGD). Notably, PRMS independently and more accurately predicted faster long‐term deterioration in motor function than CRMS (Hoehn and Yahr 4, adjusted hazard ratio per +1 point = 1.19 [95% confidence intervals, 1.08–1.32]), particularly in PIGD (PIGD subscore, β‐interaction = 0.052 [95% confidence intervals, 0.018–0.086]). PSM confirmed these findings' robustness. Surface‐based morphometry suggested that enhanced sensory processing was distinctively associated with PRMS.

Conclusions

In early‐stage PD, PRMS weighed different aspects of symptoms and more effectively predicted motor deterioration compared to CRMS, with distinctive brain structural characteristics. The superior sensitivity of PRMS to subtle declines in drug‐refractory symptoms like PIGD likely underlie our results, highlighting the importance of understanding the differential clinical implications of PRMS to prevent long‐term motor deterioration. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Chaotic behaviors and stability analysis of pure‐cubic nonlinear Schrödinger equation with full nonlinearity
Authors: Li Yaxi , Kai Yue
Published: August 2024

This paper explores the pure‐cubic nonlinear Schrödinger equation (PC‐NLSE) with different nonlinearities. According to qualitative analysis, we get the dynamic systems and show that solitons and periodic solutions exist. The corresponding traveling wave solutions of these equations are constructed to demonstrate the correctness of qualitative analysis, and some solutions are initially given. In particular, a special kind of soliton solution, the Gaussian soliton, is constructed, which is rarely identified in non‐logarithmic equation. Next, the solitons stability and modulation instability (MI) of PC‐NLSE with two types of nonlinearity are discussed. Finally, by adding perturbed terms to the dynamic systems, we obtain the largest Lyapunov exponents and the phase diagrams of the equation, which proves there are the chaotic behaviors in PC‐NLSE. To the best of our knowledge, the Gaussian solitons, stability analysis and chaotic behaviors we obtained are first presented, which improves the study and proposes a new direction for the future researches on PC‐NLSE.
Identification of anN‐valued heterogeneous conductivity profile in an inverse heat conduction problem
Authors: Ciarbonetti Angel A. , Idelsohn Sergio , Mazzieri Gisela L. , Spies Ruben D.
Published: August 2024

In this article we deal with the problem of determining a non‐homogeneous ‐valued heat conductivity profile in a steady‐state heat conduction boundary‐value problem with mixed Dirichlet‐Neumann boundary conditions over a bounded domain in , from the knowledge of the temperature field over the whole domain. In a previous work we developed a method based on a variational approach of the PDE leading to an optimality equation which is then projected into a finite dimensional space. Discretization of the optimality equation then yields a linear although severely ill‐posed equation which is then regularized via appropriate ad‐hoc penalizers based upon a‐priori information about the conductivities of all materials present. This process results in a generalized Tikhonov‐Phillips functional whose global minimizer yields our approximate solution to the inverse problem. In our previous work we showed that this approach yields quite satisfactory results in the cases of two different conductivities. We considered here an appropriate extension of that approach for the materials case and show a few numerical examples for the case in which the method is able to produce very good reconstructions of the exact solution.
Effect of A118G (rs1799971) single‐nucleotide polymorphism of the μ‐opioid receptor OPRM1 gene on intraoperative remifentanil requirements in Japanese women undergoing laparoscopic gynecological surgery
Authors: Zou Ruying , Nishizawa Daisuke , Inoue Rie , Hasegawa Junko , Ebata Yuko , Nakayama Kyoko , Hara Atsuko , Sumikura Hiroyuki , Kitade Mari , Hayashida Masakazu , Ikeda Kazutaka , Kawagoe Izumi
Published: August 2024

Aim

Abundant data are available on the effect of the A118G (rs1799971) single‐nucleotide polymorphism (SNP) of the μ‐opioid receptor OPRM1 gene on morphine and fentanyl requirements for pain control. However, data on the effect of this SNP on intraoperative remifentanil requirements remain limited. We investigated the effect of this SNP on intraoperative remifentanil requirements.

Methods

We investigated 333 Japanese women, aged 21–69 years, who underwent laparoscopic gynecological surgery for benign gynecological disease under total intravenous anesthesia at Juntendo University Hospital. Average infusion rates of propofol and remifentanil during anesthesia and the average bispectral index (BIS) during surgery were recorded. Associations among genotypes of the A118G and phenotypes were examined with the Mann–Whitney U test.

Results

The average propofol infusion rate was not different between patients with different genotypes. The average remifentanil infusion rate was significantly higher in patients with the AG or GG genotype than the AA genotype (p = 0.028). The average intraoperative BIS was significantly higher in patients with the GG genotype than the AA or AG genotype (p = 0.039).

Conclusions

The G allele of the A118G SNP was associated with higher intraoperative remifentanil requirements and higher intraoperative BIS values but was not associated with propofol requirements. Given that remifentanil and propofol act synergistically on the BIS, these results suggest that the G allele of the A118G SNP is associated with lower effects of remifentanil in achieving adequate intraoperative analgesia and in potentiating the sedative effect of propofol on the BIS.
A fine‐tuning workflow for automatic first‐break picking with deep learning
Authors: Mardan Amir , Blouin Martin , Fabien‐Ouellet Gabriel , Giroux Bernard , Vergniault Christophe , Gendreau Jeremy
Published: August 2024

First‐break picking is an essential step in seismic data processing. For reliable results, first arrivals should be picked by an expert. This is a time‐consuming procedure and subjective to a certain degree, leading to different results for different operators. In this study, we have used a U‐Net architecture with residual blocks to perform automatic first‐break picking based on deep learning. Focusing on the effects of weight initialization on first‐break picking, we conduct this research by using the weights of a pre‐trained network that is used for object detection on the ImageNet dataset. The efficiency of the proposed method is tested on two real datasets. For both datasets, we pick manually the first breaks for less than 10 of the seismic shots. The pre‐trained network is fine‐tuned on the picked shots, and the rest of the shots are automatically picked by the neural network. It is shown that this strategy allows to reduce the size of the training set, requiring fine‐tuning with only a few picked shots per survey. Using random weights and more training epochs can lead to a lower training loss, but such a strategy leads to overfitting as the test error is higher than the one of the pre‐trained network. We also assess the possibility of using a general dataset by training a network with data from three different projects that are acquired with different equipment and at different locations. This study shows that if the general dataset is created carefully it can lead to more accurate first‐break picking; otherwise, the general dataset can decrease the accuracy. Focusing on near‐surface geophysics, we perform traveltime tomography and compare the inverted velocity models based on different first‐break picking methodologies. The results of the inversion show that the first breaks obtained by the pre‐trained network lead to a velocity model that is closer to the one obtained from the inversion of expert‐picked first breaks.
Generation of two‐level representative volume element model for uncertainty analysis of composite materials
Authors: Peng Xiang , Jiang Haohao , Li Jiquan , Jia Weiqiang , Yi Bing , Wu Huaping , Jiang Shaofei
Published: August 2024

This work presents a new two‐level representative volume element model for generating random fiber distributions in continuous fiber composite materials. A representative volume element (RVE) model with random geometric boundaries is generated, and the adaptive connections between neighboring RVEs are realized to eliminate unreasonable connections that may occur at the boundaries of RVEs. Subsequently, a novel adaptive generation and construction algorithm for larger RVE (LRVE) is proposed to consider more micro‐scale geometric and material uncertainties. A computational framework is established to implement the adaptive construction of LRVE and determine the statistical uncertainties of continuous fiber composite materials. The uncertain statistical results of the proposed algorithm are compared with those of the random sequential expansion algorithm and the complete spatial random pattern, showing good agreements among them. Finally, the uncertainty of macro‐scale linear elastic anisotropic material parameters is evaluated by using the integrated software framework. The maximum relative error of the predicted macro‐scale elastic properties is only 8.70%, which outperforms other numerical analysis results in literatures. The proposed algorithm is helpful for further research on multi‐scale linear elastic uncertainty analysis and reliability design of continuous fiber composite materials.
Reinforcing and toughening poly (lactic acid) using cellulose nanocrystal grafted with epoxidized soybean oil
Authors: Xie Zhongyuan , Li Xinyu , Zhang Qiyuan , Jiang Hua
Published: August 2024

Currently, toughening poly(lactic acid) (PLA) without reducing its strength and modulus is still challenging. Fortunately, this problem could be addressed by incorporating cellulose nanocrystals grafted with epoxidized soybean oil (CNCs‐g‐ESO) into the PLA matrix. The percolation threshold for uniform dispersion of the CNCs‐g‐ESO in the PLA matrix was about 0.8 wt%. The loading made the elongation at break and tensile fracture energy of the PLA composite film increase to 26.5 and 11.26 from 3.7% and 0.96 MJ.m−3, respectively, concerning the neat PLA. Meanwhile, it also resulted in a 4.7% and 12.4% increase in the tensile strength and Young's modulus, respectively. Additionally, the initial decomposition temperature (T5%) of the PLA composite increased to 334.9 from 316.1°C after incorporating the CNCs‐g‐ESO. Therefore, the CNCs‐g‐ESO, acting as an ESO‐like plasticizer and a CNCs‐like nucleating agent, is a potential nanofiller for reinforcing and toughening PLA.

Highlights

Cellulose nanocrystal grafted with epoxidized soybean oil (CNCs‐g‐ESO)

The CNCs‐g‐ESO acting as both plasticizer and nucleating agent

The CNCs‐g‐ESO decreasing the spherulite size, but increasing the CrI of PLA

The CNCs‐g‐ESO enlarging the window for thermal process of PLA

Simultaneous reinforcement and toughness of PLA
Solutions and applications of fracture properties for clamped single edge notched tensile specimen of orthotropic materials
Authors: Wang Jinghui , Jin Pengfei , Chen Haoruo , Liu Zheng , Zhang Zhe , Wang Xin , Chen Xu
Published: August 2024

Clamped single edge notched tensile (SENT) specimens are widely used in fracture toughness test and fatigue crack growth experiment of structural materials. However, the existing fracture parameters of clamped SENT specimens are all based on isotropic conditions, which limits the application of anisotropic materials. In this study, a systematic finite element analysis of orthotropic clamped SENT specimens was carried out to determine the stress intensity factor (K) and compliance (C). A wide range of orthotropic and geometric parameters, including crack length over width ratio (a/W), daylight to width ratio (H/W), and material parameters λ and ρ, were considered. The coupling effects of material parameters and geometrical dimensions on fracture properties were then investigated. Results showed that the K and C increase with the increase of a/W and H/W, and decrease with the increase of λ and ρ. The value of C is more sensitive to changes of orthotropic and geometric parameters than the K. Moreover, the accurate solutions for normalized K, C and compliance‐based crack length prediction were developed. Applications of the proposed solutions on experimental data were presented and discussed. The current results will help to accurately calculate the fracture toughness or FCG rate of composites, further advancing the standardization process of composites testing based on SENT specimens.

Highlights

Evaluate the K and Compliance for orthotropic single edge notched tensile (SENT) specimens using 2D finite element analysis;

The coupling effects of orthotropic parameters and geometric sizes are studied;

Develop prediction equations for K and C of clamped SENT specimen;

Propose a universal crack length estimation method based on the compliance;

Discuss the application of the proposed equations to the tested data of UD CFRP.
The Comparison of Newly Diagnosed Invasive Breast Patient Cohorts in Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE‐BPC) and Other Real‐World Databases
Authors: Liu Xinyue , Alipour Golnoosh Haris , Shao Changxia , Burcu Mehmet , Bortnichak Edward , Vo Thao , Yu Chu‐Ling
Published: August 2024

Background

Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed.

Objectives

To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE‐BPC and three other databases.

Methods

This study utilized GENIE‐BPC, SEER, SEER‐Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD‐9/10‐CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death.

Results

Of female breast cancer patients in GENIE‐BPC (n = 775), SEER (n = 548 336), SEER‐Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE‐BPC, compared to SEER/SEER‐Medicare, had higher nuclear grades (%III–%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1‐, 3‐, and 5‐year overall survival rates were lower in GENIE‐BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER‐Medicare (91.6%, 81.4%, 75.0%).

Conclusion

Breast cancer patients in GENIE‐BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE‐BPC to the larger breast cancer population.
Pregnancy and Infant Outcomes After Prenatal Exposure to Golimumab in Denmark, Finland, and Sweden 2006–2019
Authors: Karlsson Pär , Gembert Karin , Esslinger Suzan , Geldhof Anja , Gissler Mika , Leinonen Maarit K. , Otero‐Lobato Marijo , Pedersen Lars , Cesta Carolyn E.
Published: August 2024

Purpose

To present the main findings of a post‐authorization safety study assessing pregnancy and infant outcomes after prenatal golimumab exposure in a real‐world setting.

Methods

This observational population‐based cohort study included data from pregnancies ending in 2006–2018 (Finland) or 2019 (Denmark, Sweden). Infants born to women with rheumatic diseases or ulcerative colitis diagnoses were identified. Based on prescription fills from 90 days prior to pregnancy until delivery, infants were assigned to one of the four drug‐exposure cohorts: golimumab, other anti‐TNF biologics, other biologics, and nonbiologic systemic therapy, and the general population. Prevalence of adverse pregnancy outcomes, mortality, diagnoses of major congenital anomalies (MCA), and inpatient infections in the infants' first year of life were assessed. Odds ratios and 95% CIs were calculated for MCA and infection.

Results

Among 134 infants in the golimumab cohort, none were stillborn or died in the first year of life. MCA were diagnosed in 4.5% of the infants in the golimumab cohort, versus 6.8%, 10.9%, 5.5%, and 4.6% in the other anti‐TNF biologics, other biologics, nonbiologic systemic therapy and general population cohorts, respectively. Inpatient infections were diagnosed in 11% of golimumab‐exposed infants, compared with 9%–11% of infants in the other cohorts. Unadjusted and selected adjusted comparisons showed no association between prenatal golimumab exposure and MCA or infection compared with the other exposure cohorts or general population.

Conclusions

The number of infants with prenatal golimumab exposure was low, but results are reassuringly consistent with the evidence available for other anti‐TNF biologics. Continued monitoring is needed.
Disparity in Guideline‐Based Antidiabetic Drugs Prescribing for Type 2 Diabetes Patients in Primary Healthcare Facilities Across China, 2017–2019
Authors: Fu Mengyuan , Gong Zhiwen , Zhu Yuezhen , Li Can , Li Huangqianyu , Shi Luwen , Guan Xiaodong
Published: August 2024

Purpose

The purpose of this study is to evaluate the pattern, appropriateness, and cost of antidiabetic drugs prescribed for patients with Type 2 diabetes at primary healthcare facilities (PHFs) in China.

Methods

We collected outpatient‐visit prescriptions from 363 PHFs in 31 cities covering eastern, central, and western regions of China. The visits of adult patients with Type 2 diabetes diagnosis were collected and classified the antidiabetic medication pattern of each patient use as recommended or non‐recommended according to Chinese guidelines. We then calculated the proportion of guideline‐recommended patterns and the average monthly cost for each pattern, overall and by region.

Results

Of 33 519 prescriptions for Type 2 diabetes, most (73.9%) were for guideline‐recommended antidiabetic treatments. The proportion of guideline‐recommended prescriptions varied by region (eastern [75.9%], central [87.5%], and western [59.7%]). Metformin monotherapy was the most common guideline‐recommended treatment in all three regions (eastern [20.1%], central [28.0%], and western [24.6%]). The most common non‐guideline‐recommended treatments were monotherapy of insulin (eastern [16.5%], central [5.1%], and western [25.7%]) and traditional Chinese antidiabetic medicines (eastern [5.6%], central [5.7%], and western [11.1%]). The average monthly costs were lower for guideline‐recommended treatments compared to non‐recommended treatments in all regions (eastern [13.6 ± 15.4 USD vs. 28.1 ± 22.0 USD], central [9.8 ± 10.9 USD vs. 28.7 ± 19.4 USD], and western [17.9 ± 21.4 USD vs. 30.3 ± 23.6 USD]).

Conclusions

The majority of patients with Type 2 diabetes received guideline‐recommended antidiabetic medications at PHFs in China, with only half of the prescriptions containing guideline‐recommended metformin. Utilization of guideline‐recommended therapies differed across regions. Tailored interventions to promote evidence‐based antidiabetic prescribing are urgently needed, especially in the undeveloped western region.
Empowering Antimicrobial Peptides: Harnessing Nanotechnology and Engineering Strategies to Combat Microbial Resistance
Authors: Avinash Dubey , Bisht Anjali , Kumar Manish , Thakur Shubham , Patel Preeti , Kurmi Balak Das
Published: August 2024

Antimicrobial peptides (AMPs) are being studied for their potential in addressing microbial resistance, a significant issue in treating infections. More than 22,500 AMPs are listed in the database, but their clinical use faces challenges like instability and selectivity. Nanodrug delivery systems are now seen as a beneficial method to improve the effectiveness of AMPs. These systems can shield AMPs from degradation, enhance their availability, and deliver them to specific infection sites, enhancing their ability to combat resistance. One example is liposomal nanoparticles loaded with a mix of AMPs, targeting drug‐resistant bacteria directly. Strategies to develop novel AMPs through engineering methods, including design approaches, computational techniques, and modifications for stability, are discussed in this review. Combinatorial approaches with antibiotics, patents, and clinical trials involving AMPs against infections are also considered, highlighting the potential of AMPs in battling microbial resistance.
Spatial and Size Distributions of Ti(C5H7O2)2[(CH3)2CHO]2 Mist Particles in a Tubular Furnace for Conformal and Uniform Deposition of Amorphous TiO2 Thin Films
Authors: Kuddus Abdul , Sato Tomomasa , Yokoyama Kojun , Shirai Hajime
Published: August 2024

The spatial and size distributions of titanium diisopropoxide bisacetylacetonate [(C5H7O2)2[(CH3)2CHO]2, also known as Ti(acac)2(OiPr)2] mist, diluted in CH3OH, are investigated in a tubular furnace using atmospheric‐pressure mist chemical vapor deposition (mist CVD). The focus is on the deposition of amorphous (a)‐TiO2 films with tubular furnace temperature and mesh bias as variables. When the furnace temperature reaches 350 °C, the number density of mist particles increases without significant changes in their size distribution, leading to a higher film deposition rate. Further, the deposition rate and average size of the mist particles with lower adhesion coefficient decrease with increasing spatial distance from the furnace inlet. Furthermore, applying a mesh bias results in an increase in the maximum number density of mist particles with a narrower size distribution; however, the overall film deposition rate decreases. These variations are attributed to the chemical reactivity of the mist precursors produced by pyrolysis and mesh bias. The fine mist precursors, which are strongly charged, coordinate with CH3OH and CHO groups through solvation, enhancing their chemical stability and lifetime. This process yields a dense and rigid a‐TiO2 network, improving the junction properties at the a‐TiO2/c‐Si interface.
Effects of pre‐treatment, historical age, and sample characteristics on the stable isotope analyses of killer whale (Orcinus orca) bone
Authors: Bowen Kelly R. , Kurle Carolyn M.
Published: August 2024

Rationale

Stable isotope analysis of bone provides insight into animal foraging and allows for ecological reconstructions over time, however pre‐treatment is required to isolate collagen. Pre‐treatments typically consist of demineralization to remove inorganic components and/or lipid extraction to remove fats, but these protocols can differentially affect stable carbon (δ13C) and nitrogen (δ15N) isotope values depending on the chemicals, tissues, and/or species involved. Species‐specific methodologies create a standard for comparability across studies and enhance understanding of collagen isolation from modern cetacean bone.

Methods

Elemental analyzers coupled to isotope ratio mass spectrometers were used to measure the δ13C and δ15N values of powdered killer whale (Orcinus orca) bone that was intact (control) or subjected to one of three experimental conditions: demineralized, lipid‐extracted, and both demineralized and lipid‐extracted. Additionally, C:N ratios were evaluated as a proxy for collagen purity. Lastly, correlations were examined between control C:N ratios vs. historical age and control C:N ratios vs. sample characteristics.

Results

No significant differences in the δ15N values were observed for any of the experimental protocols. However, the δ13C values were significantly increased by all three experimental protocols: demineralization, lipid extraction, and both treatments combined. The most influential protocol was both demineralization and lipid extraction. Measures of the C:N ratios were also significantly lowered by demineralization and both treatments combined, indicating the material was closer to pure collagen after the treatments. Collagen purity as indicated via C:N ratio was not correlated with historical age nor sample characteristics.

Conclusions

If only the δ15N values from killer whale bone are of interest for analysis, no pre‐treatment seems necessary. If the δ13C values are of interest, samples should be both demineralized and lipid‐extracted. As historical age and specimen characteristics are not correlated with sample contamination, all samples can be treated equally.
Management of Refractory/Resistant Herpes Simplex Virus Infections in Haematopoietic Stem Cell Transplantation Recipients: A Literature Review
Authors: Sallée Léo , Boutolleau David
Published: August 2024

Herpes simplex virus (HSV) infections in allogeneic haematopoietic stem cell transplantation (HSCT) recipients pose significant challenges, with higher incidence, severity, and risk of emergence of resistance to antivirals due to impaired T‐cell mediated immunity. This literature review focuses on acyclovir‐refractory/resistant HSV infections in HSCT recipients. The review addresses the efficacy of antiviral prophylaxis, the incidence of acyclovir‐refractory/resistant HSV infections, and the identification of risk factors and potential prognostic impact associated with those infections. Additionally, alternative therapeutic options are discussed. While acyclovir prophylaxis demonstrates a significant benefit in reducing HSV infections in HSCT recipients and, in some cases, overall mortality, concerns arise about the emergence of drug‐resistant HSV strains. Our systematic review reports a median incidence of acyclovir‐resistant HSV infections of 16.1%, with an increasing trend in recent years. Despite limitations in available studies, potential risk factors of emergence of HSV resistance to acyclovir include human leucocyte antigen (HLA) mismatches, myeloid neoplasms and acute leukaemias, and graft‐versus‐host disease (GVHD). Limited evidences suggest a potentially poorer prognosis for allogeneic HSCT recipients with acyclovir‐refractory/resistant HSV infection. Alternative therapeutic approaches, such as foscarnet, cidofovir, topical cidofovir, optimised acyclovir dosing, and helicase‐primase inhibitors offer promising options but require further investigations. Overall, larger studies are needed to refine preventive and therapeutic strategies for acyclovir‐refractory/resistant HSV infections in allogeneic HSCT recipients and to identify those at higher risk.
Reachable set estimation for switched homogeneous positive systems of degree greater than one
Authors: Zhang Na , Sun Yuangong
Published: August 2024

This article estimates the reachable set for a class of switched nonlinear positive systems with disturbance. The systems we discussed are homogeneous of degree greater than one and cooperative. Criteria are derived for the existence of a ball where all the solutions of the system converge asymptotically within. Then the results are extended to switched homogeneous positive time‐varying system of degree greater than one and general switched time‐varying system. Finally numerical examples are proposed to illustrate the theoretical results.
Chance, probability, and uncertainty at the edge of human reasoning: What is Knightian uncertainty?
Authors: Townsend David M. , Hunt Richard A. , Rady Judy
Published: August 2024

Research Summary

For more than a century, Frank Knight's Risk, Uncertainty, and Profit has significantly influenced entrepreneurship theory development by exploring the nature of uncertainty and the epistemic limits of entrepreneurial action. Knight's work highlights how economic actors cannot fully predict the consequences of their actions. Despite its broad influence, debates persist regarding the nature of Knightian uncertainty. This study addresses these debates through a comprehensive analysis of RUP and Knight's other published and unpublished writings to offer new insights into the nature and meaning of Knightian uncertainty, revealing Knight's holistic theory that integrates “real indeterminism,” “partial knowledge,” and “subjective beliefs.” This analysis provides much needed construct clarity to advance contemporary theories of entrepreneurial action and the role of uncertainty in business venturing processes.

Managerial Summary

This article revisits Frank Knight's foundational work, Risk, Uncertainty, and Profit, a cornerstone in entrepreneurship research for over a century. We highlight Knight's holistic approach to uncertainty, which integrates the concepts of real indeterminism (the inherent unpredictability of future events), partial knowledge (the incomplete understanding of the present and future), and subjective beliefs (individual perceptions and interpretations). The study offers new perspectives on how Knightian uncertainty influences entrepreneurial decision‐making and action, highlighting how this unique type of uncertainty plays a critical role in the business venturing process. These insights provide valuable contributions to contemporary theories of entrepreneurship, emphasizing the complexity and multifaceted nature of navigating uncertainty in business.
Selective Segregation of Thermo‐Responsive Microgels via Microfluidic Technology
Authors: Sharma Anjali , Rohne Fabian , Vasquez‐Muñoz Daniela , Jung Se‐Hyeong , Lomadze Nino , Pich Andrij , Santer Svetlana , Bekir Marek
Published: August 2024

Separation of equally sized particles distinguished solely by material properties remains still a very challenging task. Here a simple separation of differently charged, thermo‐responsive polymeric particles (for example microgels) but equal in size, via the combination of pressure‐driven microfluidic flow and precise temperature control is proposed. The separation principle relies on forcing thermo‐responsive microgels to undergo the volume phase transition during heating and therefore changing its size and correspondingly the change in drift along a pressure driven shear flow. Different thermo‐responsive particle types such as different grades of ionizable groups inside the polymer matrix have different temperature regions of volume phase transition temperature (VPTT). This enables selective control of collapsed versus swollen microgels, and accordingly, this physical principle provides a simple method for fractioning a binary mixture with at least one thermo‐responsive particle, which is achieved by elution times in the sense of particle chromatography. The concepts are visualized in experimental studies, with an intend to improve the purification strategy of the broad distribution of charged microgels into fractioning to more narrow distribution microgels distinguished solely by slight differences in net charge.
Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks
Authors: Wu Wensi , Daneker Mitchell , Turner Kevin T. , Jolley Matthew A. , Lu Lu
Published: August 2024

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.
Systemic Brain Delivery of Oligonucleotide Therapeutics Enhanced by Protein Corona‐Assisted DNA Cubes
Authors: Kim Kyoung‐Ran , Kang Ji Hee , Thai Hien Bao Dieu , Back Ji Hyun , Mao Chengde , Lee Ji Eun , Ko Young Tag , Ahn Dae‐Ro
Published: August 2024

The systemic delivery of oligonucleotide therapeutics to the brain is challenging but highly desirable for the treatment of brain diseases undruggable with traditional small‐molecule drugs. In this study, a set of DNA nanostructures is prepared and screened them to develop a protein corona‐assisted platform for the brain delivery of oligonucleotide therapeutics. The biodistribution analysis of intravenously injected DNA nanostructures reveals that a cube‐shaped DNA nanostructure (D‐Cb) can penetrate the brain‐blood barrier (BBB) and reach the brain tissue. The brain distribution level of D‐Cb is comparable to that of other previous nanoparticles conjugated with brain‐targeting ligands. Proteomic analysis of the protein corona formed on D‐Cb suggests that its brain distribution is driven by endothelial receptor‐targeting ligands in the protein corona, which mediate transcytosis for crossing the BBB. D‐Cb is subsequently used to deliver an antisense oligonucleotide (ASO) to treat glioblastoma multiforme (GBM) in mice. While free ASO is unable to reach the brain, ASO loaded onto D‐Cb is delivered efficiently to the brain tumor region, where it downregulates the target gene and exerts an anti‐tumor effect on GBM. D‐Cb is expected to serve as a viable platform based on protein corona formation for systemic brain delivery of oligonucleotide therapeutics.
High‐Performance Hematite Photoanodes for Unassisted Recharging of Solar Redox Flow Battery
Authors: Ma Jiaming , Pan Ziyan , Tagliabue Giulia
Published: August 2024

Solar redox flow batteries (SRFB) have attracted increasing interest for simultaneous capture and storage of solar energy by integrating a photoelectrochemical cell with a redox flow battery. Herein, a scalable, nanostructured α‐Fe2O3 photoanode exhibiting a high photovoltage of 0.68 V in a fully integrated Na4Fe(CN)6/AQDS SRFB is demonstrated. Thanks to its optimal band alignment, it uniquely enables stable, unassisted photocharging of the SRFB up to a state‐of‐charge (SOC) higher than 50%. Concurrently, its improved charge transfer results in a record unbiased photocurrent density of 0.22 mA cm−2, with a sixfold increase at zero SOC compared to α‐Fe2O3 film. Through an in‐depth optical and photoelectrochemical characterization of different α‐Fe2O3 morphologies, the impact of nanostructuring on charge transfer is quantified. Most interestingly, an increase in unbiased photocurrent is observed at 10% SOC (0.31 mA cm−2) and attributed to adsorption of ferricyanide, which enhances charge transfer. Importantly, it is demonstrated that the superior performance is retained after device scale‐up to 5.72 cm2. Overall, the demonstrated unassisted device is on par with previously reported dye‐sensitized solar cell‐assisted hematite‐based SRFBs. More broadly, this work contributes to the real‐world deployment of cost‐effective SRFBs based on Earth‐abundant materials.
Combining Social–Emotional Learning Competencies and Contemporary Concerns Picturebooks to Foster Early Literacy Practices: An Interdisciplinary Approach
Authors: Deliman Amanda , Robertson Marla K. , Turner Rachel K.
Published: August 2024

There is value in integrating social–emotional learning with academic development as a way to support children's abilities to effectively communicate ideas, collaboratively solve problems, and strengthen early literacy practices. In this article, we discuss suggestions for using picturebook read‐alouds as stepping stones for integrating social–emotional learning (SEL) competencies and core standards (ELA and SS) to address topics related to social justice, equity, and inclusion. We connect each SEL competency with a related topic for discussion and highlight lesson ideas for early grades to examine these themes, including potential prompts a teacher may use to foster curiosity and help students develop questions about each topic.
Hey, look over here! Perspectives on the role of monitoring during fetal surgery
Authors: Rychik J.
Published: August 2024

Linked article: This Editorial comments on the article by Backley et al. Clickhereto view the article.
Specification-Compliant Motion Planning of Cooperative Vehicles Using Reachable Sets
Authors: Liu Edmond Irani , Althoff Matthias
Published: August 2024

Automated vehicles must comply explicitly with specifications, including traffic-based and handcrafted rules, in order for them to safely and effectively participate in mixed traffic. In addition to driving individually, there are many traffic situations in which cooperation between vehicles maximizes their collective benefits, including preventing collisions. To realize these benefits, we compute specification-compliant reachable sets for vehicles, i.e., sets of states which can be reached by vehicles over time that are constrained by a set of considered specifications. We summarize and combine our previous works on computing specification-compliant reachable sets and negotiating conflicting reachable sets within a group of cooperating vehicles. As a result, conflicts between specification-compliant reachable sets of vehicles are resolved, and specification-compliant trajectories can be individually planned for each vehicle within the negotiated reachable sets using arbitrary motion planners.
Cooperation Between Vehicle and Driver: Predicting the Driver’s Takeover Capability in Cooperative Automated Driving Based on Orientation Patterns
Authors: Herzberger Nicolas , Usai Marcel , Schwalm Maximilian , Flemisch Frank
Published: August 2024

This chapter first describes central development steps of cooperative vehicle control before focusing on the cooperation within the vehicle, between driver and co-system. To enable smooth transitions within this internal cooperation, both agents (driver and co-system) need a mutual understanding of the current capabilities for safely executing the driving task. For this purpose, first the model of confidence horizons is briefly introduced, which represents these mutual capability assessments. In the following, the focus of this chapter is on the assessment of the driver’s ability to take over. First, the state of the art of Driver State Monitoring Systems (DSMS) as well as current challenges are presented. Here it is shown that a prediction based purely on driver observation is not yet possible. Therefore, an alternative approach, the diagnostic takeover request (TOR), is presented, which predicts the takeover capability based on the driver’s initial orientation reaction. In the following, two driving simulator studies are presented in which the diagnostic TOR was used for the first time and thereafter the results are presented and discussed. Finally, a brief outlook is given on how both the diagnostic TOR and the concept of confidence horizons will be further developed.
Confidence Horizons: Dynamic Balance of Human and Automation Control Ability in Cooperative Automated Driving
Authors: Usai Marcel , Herzberger Nicolas , Yu Yang , Flemisch Frank
Published: August 2024

This chapter presents the concept of confidence horizon for cooperative vehicles. The confidence horizon is designed to let the automation predict its own and the human’s abilities to control the vehicle in the near future. Based on the pattern approach originating from Alexander et al. [1], the confidence horizon concept is instantiated with a pattern framework. In case of a necessary takeover of the driving task by the human, a mode transition pattern is initiated. In order to determine when the takeover is required, which pattern to start and when to omit the takeover attempt and directly start a minimum risk maneuver, the confidence horizon for both human and co-system is an important parameter. A visual representation of the confidence horizon for the driver in different scenarios prior to a takeover request was explored. Intermediate results of a simulator study are presented, which assess the confidence horizon in automation safety-critical takeover scenarios involving an intersection and a broken-down vehicle on a highway.
Prediction of Cyclists’ Interaction-Aware Trajectory for Cooperative Automated Vehicles
Authors: Raeck Dominik , Pech Timo , Trommler Daniel , Mößner Klaus
Published: August 2024

Cooperative behaviour is one of the most crucial factors for safety and comfort in shared traffic spaces. While a human driver might be able to automatically identify behavioural indicators of other traffic participants to predict their movement, an automated vehicle is not. This is especially important in interaction situations with vulnerable road users (VRU), such as cyclists. The focus of this work is to implement, evaluate and compare different possible methods of trajectory forecasts for cyclists in order to estimate their behavioural intention. With accurate trajectory information of the VRU, an automated vehicle might be able to plan a cooperative reaction ahead in time and guarantee a comfortable traffic flow. In sum, three different neural network architectures have been tested with the main focus on a CNN, which is capable of incorporating map data into the trajectory forecast. The results showed, that including external influencing factors, like the infrastructure of a traffic scene, can have a beneficial effect on the accuracy of the cyclist’s predicted movement.
Total gluon helicity from lattice without effective theory matching
Authors: Pang Zhuoyi , Yao Fei , Zhang Jian-Hui
Published: August 2024

Abstract

We propose two approaches for extracting the total gluon helicity contribution to proton spin from lattice QCD, one from local operator matrix elements in a fixed gauge accessible on lattice with feasible renormalization, and the other from gauge-invariant nonlocal gluon correlators. Neither of these approaches requires a matching procedure when converted to the $$ \overline{\textrm{MS}} $$

MS
¯

scheme. Our proposal resolves a long-standing inconsistency in the literature regarding lattice calculations of the total gluon helicity, and has the potential to greatly facilitate these calculations.
Green’s functions in the presence of a bubble wall
Authors: Kubota Takahiro
Published: August 2024

Abstract

Field theoretical tools are developed so that one can analyze quantum phenomena such as transition radiation that must have occurred during the Higgs condensate bubble expansion through plasma in the early universe. Integral representations of Bosonic and Fermionic propagators are presented for the case that particle masses are varied continuously during the passage through the bubble wall interface between symmetry-restored and symmetry-broken regions. The construction of propagators is based on the so-called eigenfunction expansion method associated with self-adjoint differential operators, developed by Weyl, Stone, Titchmarsh, Kodaira and several others. A novel method of field quantization in the presence of the bubble wall is proposed by using the spectral functions introduced in constructing the two-point Green’s functions.
On Density and Bishop–Phelps–Bollobás-Type Properties for the Minimum Norm
Authors: García Domingo , Maestre Manuel , Martín Miguel , Roldán Óscar
Published: August 2024

We study the set $${\text {MA}}(X,Y)$$

MA
(
X
,
Y
)

of operators between Banach spaces X and Y that attain their minimum norm, and the set $${\text {QMA}}(X,Y)$$

QMA
(
X
,
Y
)

of operators that quasi attain their minimum norm. We characterize the Radon–Nikodym property in terms of operators that attain their minimum norm and obtain some related results about the density of the sets $${\text {MA}}(X,Y)$$

MA
(
X
,
Y
)

and $${\text {QMA}}(X,Y)$$

QMA
(
X
,
Y
)

. We show that every infinite-dimensional Banach space X has an isomorphic space Y, such that not every operator from X to Y quasi attains its minimum norm. We introduce and study Bishop–Phelps–Bollobás type properties for the minimum norm, including the ones already considered in the literature, and we exhibit a wide variety of results and examples, as well as exploring the relations between them.
AI and mental health: evaluating supervised machine learning models trained on diagnostic classifications
Authors: van Oosterzee Anna
Published: August 2024

Machine learning (ML) has emerged as a promising tool in psychiatry, revolutionising diagnostic processes and patient outcomes. In this paper, I argue that while ML studies show promising initial results, their application in mimicking clinician-based judgements presents inherent limitations (Shatte et al. in Psychol Med 49:1426–1448. https://doi.org/10.1017/S0033291719000151, 2019). Most models still rely on DSM (the Diagnostic and Statistical Manual of Mental Disorders) categories, known for their heterogeneity and low predictive value. DSM's descriptive nature limits the validity of psychiatric diagnoses, which leads to overdiagnosis, comorbidity, and low remission rates. The application in psychiatry highlights the limitations of supervised ML techniques. Supervised ML models inherit the validity issues of their training data set. When the model's outcome is a DSM classification, this can never be more valid or predictive than the clinician’s judgement. Therefore, I argue that these models have little added value to the patient. Moreover, the lack of known underlying causal pathways in psychiatric disorders prevents validating ML models based on such classifications. As such, I argue that high accuracy in these models is misleading when it is understood as validating the classification. In conclusion, these models will not will not offer any real benefit to patient outcomes. I propose a shift in focus, advocating for ML models to prioritise improving the predictability of prognosis, treatment selection, and prevention. Therefore, data selection and outcome variables should be geared towards this transdiagnostic goal. This way, ML can be leveraged to better support clinicians in personalised treatment strategies for mental health patients.