bluuebunny/crossref_metadata_2025 huggingface dataset:
The clock drawing test is an important contribution to the Mini Mental State Examination in screening for cognitive impairment
Authors: Claus Caroline C. , Staekenborg Salka S. , Verweij Kim H. W. , Schuur Jacqueline , van der Werf Sieberen P. , Scheltens Philip , Claus Jules J.
Published: April 2023

Background

The clock drawing test (CDT) and the Mini Mental State Examination (MMSE) are frequently used screening instruments for cognitive impairment, however, the precise contribution of the CDT to the MMSE is largely unknown.

Methods

We studied patients with subjective cognitive impairment (SCI, n = 481), mild cognitive impairment (MCI, n = 628) and Alzheimer's disease (AD, n = 1099). Discrimination between patients was examined with multiple logistic regression, adjusted for age, sex, and education. Four groups were constructed based on a normal/abnormal MMSE (cut‐off <24/30) versus normal/abnormal CDT (cut‐off ≤2/3). Visually rated medial temporal lobe atrophy (MTA) on CT was used as parameter of neurodegeneration.

Results

The CDT significantly contributed to the MMSE in discriminating SCI from both MCI and AD patients. Our four group analyses showed that of those patients with a normal MMSE and incorrectly classified as SCI, an abnormal CDT could significantly identify 10.0% as MCI and 13.2% as AD. Among those with an abnormal MMSE, the percentage AD patients shifted from 53.1% to 82.1% due to an abnormal CDT. Presence of an abnormal CDT was significantly related to MTA increase, regardless of the MMSE score.

Conclusion

The CDT is an important additional screening tool to the MMSE. An abnormal CDT with a normal MMSE is an indicator for cognitive impairment. An abnormal CDT in combination with an abnormal MMSE can be considered as an indicator of disease progression.
R&D alliances and SMEs post‐entry internationalization speed: The impact of alliance management capability and co‐innovation ambidexterity
Authors: Zahoor Nadia , Al‐Tabbaa Omar , Khan Zaheer
Published: April 2023

Research Summary

This study delves into the relationship between alliance management capability (AMC) and the post‐entry internationalization speed of small‐sized and medium‐sized enterprises (SMEs). We develop a novel theoretical framework that illuminates the effect of co‐innovation ambidexterity as a mechanism that unlocks AMC value in speeding up SME internationalization following entry into foreign markets. To validate our framework, we conduct an empirical investigation using a sample of 278 UK‐based manufacturing SMEs. Our findings support the proposition that co‐innovation ambidexterity is a crucial mediating mechanism through which AMC boosts the post‐entry internationalization speed of SMEs. Therefore, this research sheds new light on the critical roles of AMC and co‐innovation ambidexterity during the post‐entry stage, which has far‐reaching implications for the fields of international entrepreneurship and international strategic alliances.

Managerial Summary

Internationalizing SMEs are increasingly forming alliances with the aim of accessing and leveraging external knowledge to tackle the challenges they typically encounter during post‐entry phase. However, these alliances are difficult to establish and manage, which results in high failure rates. To address this lacuna, we argue that AMC and co‐innovation ambidexterity facilitate the post‐entry internationalization speed of SMEs. Using data from 278 UK‐based manufacturing SMEs, we show that AMC is an important capability that allows SMEs to leverage and harness the knowledge of their alliances for co‐innovation ambidexterity, which, in turn, increases their post‐entry internationalization speed. Together, these findings provide important insights for those SMEs' managers who aim to develop effective strategies for rapid internationalization via strategic alliances.
Photoinduced Electron Transfer in Clicked Ferrocene‐BODIPY‐Fullerene Conjugates
Authors: Rabah Jad , Fatima Anam , Fensterbank Hélène , Wright Karen , Vallée Anne , Gueye Maïssa , Burdzinski Gotard , Clavier Gilles , Miomandre Fabien , Pham Julie , Sliwa Michel , Méallet‐Renault Rachel , Steenkeste Karine , Pino Thomas , Ha‐Thi Minh‐Huong , Allard Emmanuel
Published: April 2023

Ferrocene‐BODIPY (Fc‐BDP) conjugates in which one or two ferrocene entities are linked to the β‐positions of the BODIPY core by an ethynyl bridge have been developed. These derivatives were easily and efficiently grafted onto a dual‐clickable fullerene platform using CuAAC reactions, leading to a clickable Fc‐BDP‐C60 triad and a clickable [Fc]2‐BDP‐C60 tetrad which can be used for further derivatization with complex structures. Due to the extended π‐conjugation and the presence of an intramolecular charge transfer band from Fc to BDP, all these conjugates display a broad absorption in the visible region, which is bathochromically shifted when two Fc are appended to the BDP core. Ultrafast multistep electron transfers leading to charge stabilization were demonstrated in the Fc‐BDP‐C60 triad and [Fc]2‐BDP‐C60 tetrad by femtosecond transient absorption studies.
2D‐Quantitative structure–activity relationship modeling for risk assessment of pharmacotherapy applied during pregnancy
Authors: Karaduman Gul , Kelleci Çelik Feyza
Published: April 2023

The risk evaluation for pharmacological therapy during pregnancy is critical for maternal and fetal health. The initial risk assessment stage, the risk measurement, begins with pregnancy‐labeling categories (A, B, C, D, and X) for pharmaceuticals defined by the US Food and Drug Administration (FDA). Recently, in silico methods have been preferred in toxicology studies to eliminate ethical issues before conducting clinical toxicology studies and animal experiments. Quantitative structure–activity relationship (QSAR) modeling is one of the in silico methodologies. The research focuses on creating a QSAR model that predicts the five FDA pregnancy categories of medications. Our dataset included 868 pharmaceuticals, containing nearly every pharmacological group collected from the FDA. 2D‐molecular descriptors were calculated using PaDEL software. Twenty‐four QSAR models were developed, and the best four models were discussed. The results of the models were compared according to sensitivity, accuracy, F‐score, specificity, receiver operating characteristic (ROC) values, and Matthews correlation coefficient. Considering the statistical results, random forest is the best model for determining the pregnancy risk category of drugs. The accuracy of the model was 76.49% for internal and 93.58% for external validation. According to the kappa statistics, there is an average agreement of 0.583 for internal validation and a perfect agreement of 0.893 for external validation. Because the error rates of the model are very close to 0, the model is highly accurate. Consequently, our novel QSAR model gives guidance on the safe use of pharmaceuticals during pregnancy without requiring animal tests or clinical trials on pregnant women.
AQP8 may affect glioma proliferation and growth by regulating GSK‐3β phosphorylation and nuclear transport of β‐catenin
Authors: Cai Ziling , Shen Zihao , Zhao Jing , Zhang Hao , Guo Zhen , Xia Qingqian , Liang Hang , Liu Junnan , Tan Lihao , Sheng Huajun , Zhu Shujuan
Published: April 2023

Purpose

The purpose of this work is to examine the impact of AQP8 on the proliferation and development of human glioma cell lines A172 and U251 and to determine if aquaporin 8 (AQP8) is associated with GSK‐3β phosphorylation and nuclear transport of β‐catenin in the Wnt signaling pathway.

Methods

AQP8 knockdown cell lines were constructed using a CRISPR/Cas9 double vector lentivirus infection. SAM/dCas9 was used to construct AQP8 overexpression cell lines and the CV084 lentivirus vector was used to construct AQP8 rescue cell lines. AQP8 and its mRNA, and phosphorylated GSK‐3β, β‐catenin, and other related proteins, were detected using western blot and qRT‐PCR. Glioma cell apoptosis was detected using Hoechst 33342 dye. The migration of glioma cells was discovered using a wound healing assay. β‐catenin localization in cells was detected using immunofluorescence staining.

Results

The proliferative and migratory capacities of A172 and U251 cells were significantly enhanced after AQP8 overexpression. The Wnt signaling pathways appeared to have higher levels of phosphorylated GSK‐3β and β‐catenin, and a rise in the fluorescence intensity ratio of β‐catenin in the nucleus and cytoplasm, which suggests that β‐catenin translocated into the nucleus, while AQP8 knockdown produced the opposite effect. Further, overexpression of AQP8 in AQP8 knockdown cell lines rescued the reduction of related protein levels caused by AQP8 knockdown.

Conclusion

High AQP8 expression promotes proliferation and growth of glioma cells, a process associated with phosphorylation of GSK‐3β and nuclear translocation of β‐catenin.
Artificial Intelligence Applications in Glioma With 1p/19q Co‐Deletion: A Systematic Review
Authors: Zhang Simin , Yin Lijuan , Ma Lu , Sun Huaiqiang
Published: April 2023

As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co‐deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co‐deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta‐analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies‐2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71–0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75–0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre‐operatively and noninvasively, which can possibly help clinical decision‐making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting.

Evidence Level

2.

Technical Efficacy

2.
Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction
Authors: Vanstrum Erik B. , Choi Janet S. , Bensoussan Yael , Bassett Alaina M. , Crowson Matthew G. , Chiarelli Peter A.
Published: April 2023

Background

Machine learning (ML) analysis of biometric data in non‐controlled environments is underexplored.

Objective

To evaluate whether ML analysis of physical activity data can be employed to classify whether individuals have postural dysfunction in middle‐aged and older individuals.

Methods

A 1 week period of physical activity was measured by a waist‐worn uni‐axial accelerometer during the 2003–2004 National Health and Nutrition Examination Survey sampling period. Features of physical activity along with basic demographic information (42 variables) were paired with ML models to predict the success or failure of a standard 30 s modified Romberg test during which participants had their eyes closed and stood upon a 3‐inch compliant surface. Model performance was evaluated by area under the receiver operating characteristic curve (AUC‐ROC), balanced accuracy, and F1‐score.

Results

The cohort was comprised of 1625 participants ≥40 years (median age 61, IQR 51–71). Approximately half (47%) were diagnosed with postural dysfunction having failed the binarized (pass/fail) scoring mechanism of the modified Romberg exam. Five ML models were trained on the classification task, achieving AUC values ranging from 0.67 to 0.73. The support vector machine (SVM) and a gradient‐boosted model, XGBoost, achieved the highest AUC of 0.73 (SD 0.71–0.75). Age was the most important variable for SVM classification, followed by four features that evaluated accelerometer counts at various thresholds, including those delineating total, moderate, and moderate‐vigorous activity.

Conclusions

ML analysis of accelerometer‐derived physical activity data to classify postural dysfunction in middle‐aged and older individuals is feasible in real‐world environments such as the home.

Level of Evidence

3 Laryngoscope, 133:3529–3533, 2023
Organic Phosphoric Acid Doped Polyaniline‐Coupled g‐C3N4 for Enhancing Fire Safety of Intumescent Flame‐Retardant Epoxy Resin
Authors: Chen Xilei , Piao Junxiu , Dong Huixin , Ou Mingyu , Lian Richeng , Guan Haocun , Cui Jiahui , Jiao Chuanmei
Published: April 2023

Two kinds of polyaniline coupled graphitized carbon nitride nanosheets doped with different organic phosphoric acids (CP@PA, with phytic acid; CP@NP, with amino trimethyl phosphonic acid) are developed by in‐situ polymerization. According to the analysis of the section morphology and element distribution of epoxy resin (EP) composites, although CP@PA and CP@NP show completely different morphology, they can significantly enhance the dispersion of graphitized carbon nitride nanosheets in EP. Moreover, the different oxidation states of phosphorus contained in the CP@PA and CP@NP lead to varying effects on the fire safety of EP. The flame retardancy Index (FRI) is a dimensionless index to evaluate the performance of flame retardants. When used as a flame retardant, CP@NP (FRI = 3.22) is better than CP@PA (FRI = 1.29) in flame retardant, especially in suppressing thermal hazards. As a synergist of intumescent flame retardants (IFR), CP@PA (FRI = 26.12) is most effective in improving the comprehensive fire safety property of EP and achieves an “Excellent” rating. Therefore, two different flame‐retardant mechanisms of CP@PA and CP@NP are summarized by analyzing the combustion behavior and changes of condensed phase. In summary, this research may be helpful to the design of nano synergies for IFR systems.
A computational stochastic dynamic model to assess the risk of breakup in a romantic relationship
Authors: de la Cruz Jorge Herrera , Rey José‐Manuel
Published: April 2023

We introduce an algorithm to find feedback Nash equilibria of a stochastic differential game. Our computational approach is applied to analyze optimal policies to nurture a romantic relationship in the long term. This is a fundamental problem for the applied sciences, which is naturally formulated in this work as a stochastic differential game with nonlinearities. We use our computational model to analyze the risk of marital breakdown. In particular, we introduce the concept of “love at risk,” which allows us to estimate the probability of a couple breaking up in the face of possible unfavorable scenarios.
RNA sequencing and expression analysis reveal a role for Lhx9 in the haploinsufficient adult mouse ovary
Authors: Workman Stephanie , Wilson Megan J.
Published: April 2023

Understanding the molecular pathways that underpin ovarian development and function is vital for improving the research approaches to investigating fertility. Despite a significant improvement in our knowledge of molecular activity in the ovary, many questions remain unanswered in the quest to understand factors influencing fertility and ovarian pathologies such as cancer. Here, we present an investigation into the expression and function of the developmental transcription factor LIM Homeobox 9 (LHX9) in the adult mouse ovary. We have characterized Lhx9 expression in several cell types of the mature ovary across follicle stages. To evaluate possible LHX9 function in the adult ovary, we investigated ovarian anatomy and transcription in an Lhx9+/− knockout mouse model displaying subfertility. Despite a lack of gross anatomical differences between genotypes, RNA‐sequencing found that 90 differentially expressed genes between Lhx9+/− and Lhx9+/+ mice. Gene ontology analyses revealed a reduced expression of genes with major roles in ovarian steroidogenesis and an increased expression of genes associated with ovarian cancer. Analysis of the ovarian epithelium revealed Lhx9+/− mice have a disorganized epithelial phenotype, corresponding to a significant increase in epithelial marker gene expression. These results provide an analysis of Lhx9 in the adult mouse ovary, suggesting a role in fertility and ovarian epithelial cancer.
Experimental study on the bond behavior of polymer impregnated basalt textile embedded in a fine‐grained concrete
Authors: Zhang Xiaofei , Wang Xin , Zhou Jingyang , Wu Zhishen
Published: April 2023

The bond properties between polymer impregnated basalt textile and fine‐grained concrete were studied by pull‐out test considering the effects of bond length, impregnation polymer type, surface condition, and transverse yarn. The experimental results showed the pull‐out load increased with the bond length increasing, and the slight decrease in bond strength was attributed to the nonlinear distribution of bond stress. Owing to the high viscosity and good permeability of epoxy resin, the bond strength of epoxy resin‐impregnated textile was 238% and 592% higher than that of textile impregnated with acrylic emulsion and polyvinyl chloride, respectively. Moreover, the bond properties were improved significantly for impregnated textile with sticking sand or fiber by increasing the interfacial friction between textile and concrete, and the highest bond strength was obtained for yarn sticking sand with particle sizes of 0.09 ~ 0.18 mm. The transverse yarn of epoxy resin‐impregnated textile can generate an interlocking effect to concrete due to the semi‐rigid node, which increased the bond strength by 48.6%, whereas this did not happen for textile impregnated with flexible material. Furthermore, the anchorage lengths of textile were calculated, and the proposed simple bond slip model can be used to describe the pull‐out response of polymer impregnated basalt textile embedded in concrete.
Bivalent monomer bearing styryl and five‐membered cyclic dithiocarbonate moieties for orthogonal radical and cationic ring‐opening polymerizations
Authors: Yoshida Yoshiaki , Krishnamurthy Suvratha , Asakura Naoya , Endo Takeshi
Published: April 2023

We synthesized a novel bivalent monomer 1 bearing 4‐vinylbenzyl group and five‐membered cyclic dithiocarbonate. This monomer 1 was polymerized orthogonally by free radical polymerization to afford polystyrene 1a bearing a five‐membered cyclic dithiocarbonate moiety in the side chain. On the other hand, cationic ring‐opening polymerization of five‐membered cyclic dithiocarbonate in the monomer 1 successfully proceeded, resulting in a narrow molecular weight distribution of polydithiocarbonate 1b bearing a 4‐vinylbenzyl group in the side chain. Furthermore, post‐polymerizations of 1a and 1b maintaining the polymerizable functionality were carried out respectively by suitable polymerization methods. The five‐membered cyclic dithiocarbonate of 1a underwent cationic ring‐opening polymerization with a high molar ratio of methyl trifluoromethanesulfonate at a high temperature compared to the case of monomer 1, whereas radical polymerization of the 4‐vinylbenzyl group of 1b successfully proceeded under the almost same conditions as the case of monomer 1. These post‐polymerizations achieved the synthesis of corresponding networked polymers 2a and 2b.
Changes in ventilation modes in the last decade and their impact on the prevalence of bronchopulmonary dysplasia in preterm infants
Authors: Adler‐Haltovsky Tehila , Gileles‐Hillel Alex , Erlichman Ira , Eventov‐Friedman Smadar
Published: April 2023

Background

Less invasive forms of ventilation have evolved aiming to decrease bronchopulmonary dysplasia (BPD) morbidity. It is unclear whether changes in ventilation practices have been associated with improvements in respiratory outcomes.

Objective

To examine the changes in ventilation modes in preterm neonates between two periods during the last decade and their impact on BPD prevalence.

Methods

A retrospective chart review of very low birth weight infants and those born at less than 32 weeks gestation hospitalized during two periods: the years 2012–2013 and 2018–2019. The primary outcome was the prevalence of BPD. Study variables included the mode and duration of ventilation, duration of oxygen need, and perinatal clinical parameters.

Results

Four hundred eighty‐one infants were enrolled. Between the two study periods, a significant increase was observed in invasive (33%–47%, p = 0.002), and noninvasive ventilation rates (44%–72%, p < 0.001). The average duration of noninvasive ventilation increased significantly (from 9.24 to 14.08 days, p = 0.016). The total duration of respiratory support remained unchanged. The overall prevalence of moderate and severe BPD at 36 weeks corrected age remained approximately 40% in preterm infants born at less than 28 weeks gestation.

Conclusion

The increasing use of non‐invasive ventilation was not accompanied by a reduction in the use of invasive ventilation, nor by a reduced prevalence of BPD. The high prevalence of BPD remains a significant problem in extreme prematurity. Other interventions, in addition to less aggressive ventilation, need to be explored.
Lung function after SARS‐CoV‐2 infection: A prospective cohort study in children
Authors: Iovine Elio , Di Mattia Greta , Nenna Raffaella , La Regina Domenico Paolo , Mancino Enrica , Petrarca Laura , Conti Maria Giulia , Matera Luigi , Virgili Fabrizio , Midulla Fabio ,   and on behalf of the Long COVID Research Group
Published: April 2023

Introduction

Although impaired lung function after severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection has been described in adults, it is unclear whether lung function might be altered in children, especially among asymptomatic or mildly symptomatic patients. In this study, we report the results of lung function testing performed after SARS‐CoV‐2 infection in a large pediatric population.

Methods

The study included 589 patients with previous confirmed SARS‐CoV‐2 infection aged 0–18 years. Both symptomatic and asymptomatic patients during acute infection were enrolled in the study. A spirometry was performed in all cooperating patients.

Results

The mean age of enrolled patients was 9.6 years and the mean time from infection to enrollment was 171 days. Spirometry was performed and deemed evaluable in 433 patients. No patient had reduced forced vital capacity (FVC) and only 14 patients (3.2%) had a forced expiratory volume in the First second (FEV1) < 80%. The mean spirometry values recorded were in the normal range. There were no statistically significant differences in spirometry values between patients with respiratory symptoms during infection and those without. Similarly, there were no differences in spirometry parameters according to the time elapsed between infection and enrollment.

Conclusion

Lung function, according to spirometry values, does not appear to be impaired long after infection in the pediatric population. The presence of respiratory symptoms during SARS‐CoV‐2 infection would not represent a risk factor for impaired lung function in this cohort of patients.
Comparison of hydrogen and hydrocarbon fuels hazards and practical risk management strategies
Authors: MacNguyen Richard
Published: April 2023

The use of hydrogen as a fuel and energy carrier is increasing globally. Existing processes are being expanded, and hydrogen is also being incorporated into novel technologies and processes. As new hydrogen applications and processes are being developed, it is important for industry professionals to understand the associated hazards and how they differ from the hazards of hydrocarbon fuels. While best practices and risk management strategies for hydrocarbon fuel processes are mature and generally known within thre industry, the hazards of hydrogen processes, such as liquid storage tanks and fuel dispensers, are less commonly understood by those who may be planning to enter this industry. The objective of this paper is to explain the hazards of hydrogen and to provide a comparison of these hazards with those of commonly used hydrocarbon fuels through a review of the flammable hazards, consequence modeling, and select hydrogen‐release incidents. The analysis will provide current industry risk practitioners with useful guidance to understand the hazards associated with this increasingly popular fuel. This paper provides information in three main sections. The first section provides a review and comparison of fire and explosion hazards for hydrogen as compared to hydrocarbon fuels. Second section provides consequence modeling results comparing hydrogen and hydrocarbon fuels for similar release scenarios using a common software model (DNV Phast version 8.71). Third section provides a review of select hydrogen incidents.
Q‐Whex: A simple and highly mobile quasi‐wheeled hexapod robot
Authors: Zhang Guoteng , Ma Shugen , Liu Jinguo , Zeng Xianwu , Kong Lingyu , Li Yibin
Published: April 2023

This paper presents the design and control of Q‐Whex, an untethered, quasi‐wheeled hexapod robot. Q‐Whex has only six actuators—one motor located at each hip—achieving mechanical simplicity that promotes reliable and robust operation in real‐world tasks. All of the robot's mechanical parts are simply fabricated with carbon fiber plates, which makes the robot very easy to make. Q‐Whex is capable of performing wheeled‐like smooth rides over flat ground with a tripod gait, and thereby prevent the common problem of trunk fluctuations in legged‐wheel robots. It is also able to traverse height variations well exceeding its trunk clearance. The performance of Q‐Whex is evaluated in various scenarios, including driving and turning over flat terrains, ramp‐riding, step‐crossing, stair‐climbing, and irregular terrain‐traversing.
Seed banks and seed survival of submersion for an emerging plant invader in the Colorado River system, USA
Authors: Jackson S. Ka‐Voka , Pilkington Lonnie H. , Berghel Kevin M. , Chiquoine Lindsay P. , Abella Scott R.
Published: April 2023

Soil seed banks along shorelines and the ability of seeds to survive submersion in water are among potential factors that can influence seed supply, a frequent driver of riparian plant invasions. We investigated germinable soil seed banks and the ability of seeds to germinate after different durations of submersion in water for non‐native ravennagrass (Saccharum ravennae), an emerging but poorly understood invader of riparian habitats in the American Southwest, including along the Lake Powell section of the Colorado River where we performed the study. We assessed soil seed bank composition in 27 ravennagrass‐invaded plots using the emergence technique and evaluated effects on ravennagrass germination of submersion in river water for up to 15 months in a laboratory experiment. Ravennagrass was sparse in soil seed banks, detected in samples from only one of the 27 plots. Instead, soil seed banks were dominated by the non‐native annual cheatgrass (Bromus tectorum) and a mixture of native early colonizers (e.g., Pseudognaphalium stramineum) and shrubs (e.g., Baccharis emoryi). Although ravennagrass seed germination was low (<9%) in the submersion experiment, some seeds retained germinability after 15 months of continuous submersion in water. Results suggest that while ravennagrass may have limited on‐site, readily germinable soil seed banks, potential implications of its protracted seed viability in water for dispersal along waterways warrants further attention.
How different amino acid scoring patterns recommended by FAO/WHO can affect the nutritional quality and protein claims of lentils
Authors: Sá Amanda G. A. , Hang Jiayi , Jardine Laura , Bett Kirstin E. , House James D.
Published: April 2023

As a nutritious pulse and protein source, lentils play an important role in the plant‐based protein market. Pulses' nutritional quality is influenced by their protein content and amino acid composition. Recommended scoring patterns by FAO/WHO can estimate protein quality for dietary assessment, but different guidelines for protein content in food labeling exist in North America. This study determined the in vitro protein digestibility (IVPD) and amino acid score (AAS) for the protein quality assessment of lentils. The impact of different recommended amino acid scoring patterns by FAO/WHO (1991, 2013) on AAS and AAS corrected for in vitro protein digestibility (AAS‐IVPDC) were evaluated. The impact of AAS‐IVPDC for determining protein content claims for lentils using USA standards was also evaluated. Sulfur AA and tryptophan were the most limiting amino acids. From this work, estimates of lentil protein quality vary with different recommended amino acid scoring patterns. IVPD in lentils was 82.6%, while mean AAS‐IVPDC values ranged from 37.5% to 64.0%. Regarding the protein content claims, if considering a similar interpretation to the protein digestibility–corrected amino acid score (PDCAAS) system (i.e., corrected content is ≥ 5.0 g per RACC), all lentil samples were considered a “good source of protein.” However, if considering a similar interpretation to digestible indispensable amino acid score (DIAAS) system (i.e., corrected content is ≥ 5.0 g per RACC and a claim threshold of 75%), no samples met these protein claims due to the arbitrary cut‐off. The criteria set for making protein content claims should be revised.
A robust and fast two‐sample test of equal correlations with an application to differential co‐expression
Authors: He Liang , Philipp Ian , Webster Stephanie , Hjelmborg Jacob v. B. , Kulminski Alexander M.
Published: April 2023

A robust and fast two‐sample test for equal Pearson correlation coefficients (PCCs) is important in solving many biological problems, including, for example, analysis of differential co‐expression. However, few existing methods for this test can achieve robustness against deviation from normal distributions, accuracy under small sample sizes, and computational efficiency simultaneously. Here, we propose a new method for testing differential correlation using a saddlepoint approximation of the residual bootstrap (DICOSAR). To achieve robustness, accuracy, and efficiency, DICOSAR combines the ideas underlying the pooled residual bootstrap, the signed root of a likelihood ratio statistic, and a multivariate saddlepoint approximation. Through a comprehensive simulation study and a real data analysis of gene co‐expression, we demonstrate that DICOSAR is accurate and robust in controlling the type I error rate for detecting differential correlation and provides a faster alternative to the bootstrap and permutation methods. We further show that DICOSAR can also be used for testing differential correlation matrices. These results suggest that DICOSAR provides an analytical approach to facilitate rapid testing for the equality of PCCs in large‐scale analysis.
Microfluidic‐Assisted Self‐Assembly of 2D Nanosheets toward in situ Generation of Robust Nanofiber Film
Authors: Zhou Liangliang , Han Jingtao , Xiao Jijun , Yang Xiaoning , Chen Su
Published: April 2023

Methods allow the enhancement of nanofibers via self‐assembly are potentially important for new disciplines with many advantages, including multi‐anchor interaction, intrinsic mechanical properties and versatility. Herein, a microfluidic‐assisted self‐assembly process to construct hydroxyl functionalized boron nitride nanosheets (OH−BNNS)/graphene oxide (GO)/thermoplastic polyurethane (TPU) composite nanofiber film, in which stable and precisely controlled self‐assembly is fulfilled by the confined ultra‐small‐volume chip is demonstrated. Multiple fine structural analyses alongside with the density‐functional theory (DFT) calculations are implemented to confirm the synergistic effect of noncovalent interactions (hydrogen bonding interaction, π – π stacking interaction, and van der Waals attraction) plays a critical role in the robust micro‐structure and a massive 700% enhancement of mechanical strength via adding only 0.3 wt% OH−BNNS and GO. Importantly, profiled from broadband optical absorption ability, robust mechanical properties and outstanding flexibility, the self‐assembled 3D OH−BNNS/GO/TPU nanofiber film reveals an adorable evaporation rate of 4.04 kg m−2 h−1 under one sun illumination with stable energy transfer efficiency (93.2%) by accompanying hydrogen bonding interaction. This microfluidic‐assisted self‐assembly strategy will provide a constructive entry point for the rational design of nanofibers and beyond.
Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean
Authors: Izquierdo Paulo , Kelly James D. , Beebe Stephen E. , Cichy Karen
Published: April 2023

Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta‐analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL‐YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL‐YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL‐YC identified were co‐located with 210 SNP peak positions reported via genome‐wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL‐YC with MQTL‐YC reported in soybean and pea allowed the identification of 14 orthologous MQTL‐YC shared across species. The integration of MQTL‐YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.
Neural Network-Guided Synthesis of Recursive List Functions
Authors: Kobayashi Naoki , Wu Minchao
Published: April 2023

Kobayashi et al. have recently proposed NeuGuS, a framework of neural-network-guided synthesis of logical formulas or simple program fragments, where a neural network is first trained based on data, and then a logical formula over integers is constructed by using the weights and biases of the trained network as hints. The previous method was, however, restricted the class of formulas of quantifier-free linear integer arithmetic. In this paper, we propose a NeuGuS method for the synthesis of recursive predicates over lists definable by using the left fold function. To this end, we design and train a special-purpose recurrent neural network (RNN), and use the weights of the trained RNN to synthesize a recursive predicate. We have implemented the proposed method and conducted preliminary experiments to confirm the effectiveness of the method.
Stack-Aware Hyperproperties
Authors: Bajwa Ali , Zhang Minjian , Chadha Rohit , Viswanathan Mahesh
Published: April 2023

A hyperproperty relates executions of a program and is used to formalize security objectives such as confidentiality, non-interference, privacy, and anonymity. Formally, a hyperproperty is a collection of allowable sets of executions. A program violates a hyperproperty if the set of its executions is not in the collection specified by the hyperproperty. The logicHyperCTL*has been proposed in the literature to formally specify and verify hyperproperties. The problem of checking whether a finite-state program satisfies aHyperCTL*formula is known to be decidable. However, the problem turns out to be undecidable for procedural (recursive) programs. Surprisingly, we show that decidability can be restored if we consider restricted classes of hyperproperties, namely those that relate only those executions of a program which have the same call-stack access pattern. We call such hyperproperties,stack-aware hyperproperties.Our decision procedure can be used as a proof method for establishing security objectives such as noninference for recursive programs, and also for refuting security objectives such as observational determinism. Further, if the call stack size is observable to the attacker, the decision procedure provides exact verification.
Unsatisfiability Proofs for Distributed Clause-Sharing SAT Solvers
Authors: Michaelson Dawn , Schreiber Dominik , Heule Marijn J. H. , Kiesl-Reiter Benjamin , Whalen Michael W.
Published: April 2023

Distributed clause-sharing SAT solvers can solve problems up to one hundred times faster than sequential SAT solvers by sharing derived information among multiple sequential solvers working on the same problem. Unlike sequential solvers, however, distributed solvers have not been able to produce proofs of unsatisfiability in a scalable manner, which has limited their use in critical applications. In this paper, we present a method to produce unsatisfiability proofs for distributed SAT solvers by combining the partial proofs produced by each sequential solver into a single, linear proof. Our approach is more scalable and general than previous explorations for parallel clause-sharing solvers, allowing use on distributed solvers without shared memory. We propose a simple sequential algorithm as well as a fully distributed algorithm for proof composition. Our empirical evaluation shows that for large-scale distributed solvers (100 nodes of 16 cores each), our distributed approach allows reliable proof composition and checking with reasonable overhead. We analyze the overhead and discuss how and where future efforts may further improve performance.
Active Learning for SAT Solver Benchmarking
Authors: Fuchs Tobias , Bach Jakob , Iser Markus
Published: April 2023

Benchmarking is a crucial phase when developing algorithms. This also applies to solvers for the SAT (propositional satisfiability) problem. Benchmark selection is about choosing representative problem instances that reliably discriminate solvers based on their runtime. In this paper, we present a dynamic benchmark selection approach based on active learning. Our approach predicts the rank of a new solver among its competitors with minimum runtime and maximum rank prediction accuracy. We evaluated this approach on the Anniversary Track dataset from the 2022 SAT Competition. Our selection approach can predict the rank of a new solver after about 10 % of the time it would take to run the solver on all instances of this dataset, with a prediction accuracy of about 92 %. We also discuss the importance of instance families in the selection process. Overall, our tool provides a reliable way for solver engineers to determine a new solver’s performance efficiently.
Correct Approximation of Stationary Distributions
Authors: Meggendorfer Tobias
Published: April 2023

A classical problem for Markov chains is determining their stationary (or steady-state) distribution. This problem has an equally classical solution based on eigenvectors and linear equation systems. However, this approach does not scale to large instances, and iterative solutions are desirable. It turns out that a naive approach, as used by current model checkers, may yield completely wrong results. We present a new approach, which utilizes recent advances in partial exploration and mean payoff computation to obtain a correct, converging approximation.
Robust Almost-Sure Reachability in Multi-Environment MDPs
Authors: van der Vegt Marck , Jansen Nils , Junges Sebastian
Published: April 2023

Multiple-environment MDPs (MEMDPs) capture finite sets of MDPs that share the states but differ in the transition dynamics. These models form a proper subclass of partially observable MDPs (POMDPs). We consider the synthesis of policies that robustly satisfy an almost-sure reachability property in MEMDPs, that is,onepolicy that satisfies a propertyfor allenvironments. For POMDPs, deciding the existence of robust policies is an EXPTIME-complete problem. We show that this problem is PSPACE-complete for MEMDPs, while the policies require exponential memory in general. We exploit the theoretical results to develop and implement an algorithm that shows promising results in synthesizing robust policies for various benchmarks.
Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning
Authors: Hahn Ernst Moritz , Perez Mateo , Schewe Sven , Somenzi Fabio , Trivedi Ashutosh , Wojtczak Dominik
Published: April 2023

Mungojerrie is an extensible tool that provides a framework to translate linear-time objectives into reward for reinforcement learning (RL). The tool provides convergent RL algorithms for stochastic games, reference implementations of existing reward translations for $$\omega $$
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-regular objectives, and an internal probabilistic model checker for $$\omega $$
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-regular objectives. This functionality is modular and operates on shared data structures, which enables fast development of new translation techniques. Mungojerrie supports finite models specified in PRISM and $$\omega $$
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-automata specified in the HOA format, with an integrated command line interface to external linear temporal logic translators. Mungojerrie is distributed with a set of benchmarks for $$\omega $$
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-regular objectives in RL.
Model Checking Linear Dynamical Systems under Floating-point Rounding
Authors: Lefaucheux Engel , Ouaknine Joël , Purser David , Sharifi Mohammadamin
Published: April 2023

We consider linear dynamical systems under floating-point rounding. In these systems, a matrix is repeatedly applied to a vector, but the numbers are rounded into floating-point representation after each step (i.e., stored as a fixed-precision mantissa and an exponent). The approach more faithfully models realistic implementations of linear loops, compared to the exact arbitrary-precision setting often employed in the study of linear dynamical systems.

Our results are twofold: We show that for non-negative matrices there is a special structure to the sequence of vectors generated by the system: the mantissas are periodic and the exponents grow linearly. We leverage this to show decidability of $$\omega $$
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-regular temporal model checking against semialgebraic predicates. This contrasts with the unrounded setting, where even the non-negative case encompasses the long-standing open Skolem and Positivity problems.

On the other hand, when negative numbers are allowed in the matrix, we show that the reachability problem is undecidable by encoding a two-counter machine. Again, this is in contrast with the unrounded setting where point-to-point reachability is known to be decidable in polynomial time.
Parameterized Verification under TSO with Data Types
Authors: Abdulla Parosh Aziz , Atig Mohamad Faouzi , Furbach Florian , Godbole Adwait A. , Hendi Yacoub G. , Krishna Shankara N. , Spengler Stephan
Published: April 2023

We consider parameterized verification of systems executing according to the total store ordering (TSO) semantics. The processes manipulate abstract data types over potentially infinite domains. We present a framework that translates the reachability problem for such systems to the reachability problem for register machines enriched with the given abstract data type.
Verifying Learning-Based Robotic Navigation Systems
Authors: Amir Guy , Corsi Davide , Yerushalmi Raz , Marzari Luca , Harel David , Farinelli Alessandro , Katz Guy
Published: April 2023

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies are known to be susceptible to bugs. Despite significant progress in DNN verification, there has been little work demonstrating the use of modern verification tools on real-world, DRL-controlled systems. In this case study, we attempt to begin bridging this gap, and focus on the important task of mapless robotic navigation — a classic robotics problem, in which a robot, usually controlled by a DRL agent, needs to efficiently and safely navigate through an unknown arena towards a target. We demonstrate how modern verification engines can be used for effective model selection, i.e., selecting the best available policy for the robot in question from a pool of candidate policies. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior, such as collisions and infinite loops. We also apply verification to identify models with overly conservative behavior, thus allowing users to choose superior policies, which might be better at finding shorter paths to a target. To validate our work, we conducted extensive experiments on an actual robot, and confirmed that the suboptimal policies detected by our method were indeed flawed. We also demonstrate the superiority of our verification-driven approach over state-of-the-art, gradient attacks. Our work is the first to establish the usefulness of DNN verification in identifying and filtering out suboptimal DRL policies in real-world robots, and we believe that the methods presented here are applicable to a wide range of systems that incorporate deep-learning-based agents.
ALASCA: Reasoning in Quantified Linear Arithmetic
Authors: Korovin Konstantin , Kovács Laura , Reger Giles , Schoisswohl Johannes , Voronkov Andrei
Published: April 2023

Automated reasoning is routinely used in the rigorous construction and analysis of complex systems. Among different theories, arithmetic stands out as one of the most frequently used and at the same time one of the most challenging in the presence of quantifiers and uninterpreted function symbols. First-order theorem provers perform very well on quantified problems due to the efficient superposition calculus, but support for arithmetic reasoning is limited to heuristic axioms. In this paper, we introduce the $$\textsc {Alasca}$$

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calculus that lifts superposition reasoning to the linear arithmetic domain. We show that $$\textsc {Alasca}$$

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is both sound and complete with respect to an axiomatisation of linear arithmetic. We implemented and evaluated $$\textsc {Alasca}$$

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using the Vampire theorem prover, solving many more challenging problems compared to state-of-the-art reasoners.
Symbolic Model Checking for TLA+ Made Faster
Authors: Otoni Rodrigo , Konnov Igor , Kukovec Jure , Eugster Patrick , Sharygina Natasha
Published: April 2023

The need to provide formal guarantees about the behaviour of the algorithms underpinning modern distributed systems became evident in recent years. This interest made apparent the complexities involved in applying verification techniques in a distributed setting, with significant effort being made in both academia and industry to aid in this endeavour. Many formalisms have been proposed to tackle the difficulties faced by practitioners, with one that has seen widespread use in industry being TLA$$^+$$+, adopted, for instance, by Amazon Web Services. TLA$$^+$$+provides engineers with a way of specifying both systems and desired properties, and is supported by a number of verification tools. Despite their extensive use, such tools suffer considerably from lack of scalability. To solve this, we propose a novel encoding of TLA$$^+$$+into SMT constraints to improve symbolic model checking efficiency. Our insight is the need to provide the SMT solver with structural information about the TLA$$^+$$+specification encoded, i.e., how data structures and their component elements interact, which we do by relying on the SMT theory of arrays. We implemented our approach by modifying the SMT-based model checkerApalacheand evaluated it against comparable tools. Our results show that our approach outperforms existing ones on a number of benchmarks, with an order of magnitude improvement in checking time.