bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period
Authors: Vosshenrich Jan , Nesic Ivan , Boll Daniel T. , Heye Tobias
Published: August 2023

Objectives

To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and distinguishability.

Methods

A total of 747,393 radiology reports dictated between January 2011 and June 2020 were retrospectively analyzed. The body and cardiothoracic imaging divisions introduced a reporting concept using standardized language and structured reporting templates in January 2016.

Reports were segmented by a natural language processing algorithm and converted into a 20-dimension document vector. For analysis, dimensionality was reduced to a 2D visualization with t-distributed stochastic neighbor embedding and matched with metadata. Linguistic standardization was assessed by comparing distinct report types’ vector spreads (e.g., run-off MR angiography) between reporting standards. Changes in report type distinguishability (e.g., CT abdomen/pelvis vs. MR abdomen) were measured by comparing the distance between their centroids.

Results

Structured reports showed lower document vector spread (thus higher linguistic similarity) compared with free-text reports overall (21.9 [free-text] vs. 15.9 [structured]; − 27.4%; p < 0.001) and for most report types, e.g., run-off MR angiography (15.2 vs. 1.8; − 88.2%; p < 0.001) or double-rule-out CT (26.8 vs. 10.0; − 62.7%; p < 0.001). No changes were observed for reports continued to be written in free text, e.g., CT head reports (33.2 vs. 33.1; − 0.3%; p = 1).

Distances between the report types’ centroids increased with structured reporting (thus better linguistic distinguishability) overall (27.3 vs. 54.4; + 99.3 ± 98.4%) and for specific report types, e.g., CT abdomen/pelvis vs. MR abdomen (13.7 vs. 37.2; + 171.5%).

Conclusion

Structured reporting and the use of factual language yield more homogenous and standardized radiology reports on a linguistic level, tailored to specific reporting scenarios and imaging studies.

Clinical relevance

Information transmission to referring physicians, as well as automated report assessment and content extraction in big data analyses, may benefit from standardized reporting, due to consistent report organization and terminology used for pathologies and normal findings.

Key Points

• Natural language processing and t-distributed stochastic neighbor embedding can transform radiology reports into numeric vectors, allowing the quantification of their linguistic standardization.

• Structured reporting substantially increases reports’ linguistic standardization (mean: − 27.4% in vector spread) and distinguishability (mean: + 99.3 ± 98.4% increase in vector distance) compared with free-text reports.

• Higher standardization and homogeneity outline potential benefits of structured reporting for information transmission and big data analyses.
Comprehensive analysis of CPSF4-related alternative splice genes in hepatocellular carcinoma
Authors: Yuemaierabola Anwaier , Guo Jun , Sun Lili , Yeerkenbieke Buerlan , Liu Fuzhong , Ye Dilinaer , Zhai Xiaoyi , Guo Wenjia , Cao Yan
Published: August 2023

Background

An important stage in controlling gene expression is RNA alternative splicing (AS), and aberrant AS can trigger the development and spread of malignancies, including hepatocellular carcinoma (HCC). A crucial component of AS is cleavage and polyadenylation-specific factor 4 (CPSF4), a component of the CPSF complex, but it is unclear how CPSF4-related AS molecules describe immune cell infiltration in the total tumor microenvironment (TME).

Methods

Using RNA-sequencing data and clinical data from TCGA-LIHC from the Cancer Genome Atlas (TCGA) database, the AS genes with differential expression were found. The univariate Cox analysis, KM analysis, and Spearman analysis were used to identify the AS genes related to prognosis. Screening of key AS genes that are highly correlated with CPSF4. Key genes were screened using Cox regression analysis and stepwise regression analysis, and prognosis prediction models and the topography of TME cell infiltration were thoroughly analyzed.

Results

A model consisting of seven AS genes (STMN1, CLSPN, MDK, RNFT2, PRR11, RNF157, GHR) was constructed that was aimed to predict prognostic condition. The outcomes of the HCC samples in the high-risk group were considerably worse than those in the lower risk group (p < 0.0001), and different risk patient groups were formed. According to the calibration curves and the area under the ROC curve (AUC) values for survival at 1, 2, and 3 years, the clinical nomogram performs well in predicting survival in HCC patients. These values were 0.76, 0.70, and 0.69, respectively. Moreover, prognostic signature was markedly related to immune infiltration and immune checkpoint genes expression.

Conclusion

By shedding light on the function of CPSF4 and the seven AS genes in the formation and progression of HCC, this research analysis contributes to the development of more useful prognostic, diagnostic, and possibly therapeutic biomarkers.
Implementing precision oncology for sarcoma patients: the CCCLMUmolecular tumor board experience
Authors: Berclaz Luc M. , Burkhard-Meier Anton , Lange Philipp , Di Gioia Dorit , Schmidt Michael , Knösel Thomas , Klauschen Frederick , von Bergwelt-Baildon Michael , Heinemann Volker , Greif Philipp A. , Westphalen C. Benedikt , Heinrich Kathrin , Lindner Lars H.
Published: August 2023

Purpose

Due to poor outcomes and limited treatment options, patients with advanced bone and soft tissue sarcomas (BS/STS) may undergo comprehensive molecular profiling of tumor samples to identify possible therapeutic targets. The aim of this study was to determine the impact of routine molecular profiling in the setting of a dedicated precision oncology program in patients with BS/STS in a German large-volume sarcoma center.

Methods

92 BS/STS patients who received comprehensive genomic profiling (CGP) and were subsequently discussed in our molecular tumor board (MTB) between 2016 and 2022 were included. Patient records were retrospectively reviewed, and the clinical impact of NGS-related findings was analyzed.

Results

89.1% of patients had received at least one treatment line before NGS testing. At least one molecular alteration was found in 71 patients (82.6%). The most common alterations were mutations in TP53 (23.3% of patients), followed by PIK3CA and MDM2 mutations (9.3% each). Druggable alterations were identified, and treatment recommended in 32 patients (37.2%). Of those patients with actionable alterations, ten patients (31.2%) received personalized treatment and six patients did benefit from molecular-based therapy in terms of a progression-free survival ratio (PFSr) > 1.3.

Conclusion

Our single-center experience shows an increasing uptake of next-generation sequencing (NGS) and highlights current challenges of implementing precision oncology in the management of patients with BS/STS. A relevant number of patients were diagnosed with clinically actionable alterations. Our results highlight the potential benefit of NGS in patients with rare cancers and currently limited therapeutic options.
Acupuncture improves immunity and fatigue after chemotherapy in breast cancer patients by inhibiting the Leptin/AMPK signaling pathway
Authors: Li Jinxia , Fu Ruiyang , Guo Xiaoqing , Pan Zhongqiang , Xie Jingjun
Published: August 2023

Objective

Acupuncture has become a popular complementary treatment in oncology. This study is based on RNA-Seq transcriptome sequencing technology to investigate the molecular mechanisms underlying the effect of acupuncture-mediated regulation of the Leptin/AMPK signaling pathway on mitochondrial dysfunction-induced fatigue in breast cancer patients after chemotherapy.

Methods

Peripheral blood samples from 10 patients with post-operative chemotherapy for breast cancer were selected for transcriptome sequencing to screen the key molecular pathways involved in fatigue after chemotherapy in breast cancer patients. Besides, peripheral blood samples were collected from 138 post-operative chemotherapy patients with breast cancer to study the composite fatigue and quality of life scores. Flow cytometry was used to detect T lymphocyte subsets in peripheral blood-specific immune cells. In addition, a blood cell analyzer was used to measure peripheral blood leukocyte counts, and MSP-PCR was used to detect mitochondrial DNA mutations in peripheral blood leukocytes.

Results

Transcriptome bioinformatics analysis screened 147 up-regulated mRNAs and 160 down-regulated mRNAs. Leptin protein was confirmed as the key factor. Leptin was significantly higher in the peripheral blood of breast cancer patients who developed fatigue after chemotherapy. Acupuncture treatment effectively improved post-chemotherapy fatigue and immune status in breast cancer patients, suppressed the expression of Leptin/AMPK signaling pathway-related factor and leukocyte counts, and significantly reduced the rate of mitochondrial DNA mutations in peripheral blood leukocytes.

Conclusion

The Leptin/AMPK signaling pathway may be the key molecular pathway affecting the occurrence of fatigue after chemotherapy in breast cancer patients. Leptin may improve post-chemotherapy fatigue in breast cancer patients by activating AMPK phosphorylation and alleviating mitochondrial functional impairment.
Coefficients of Multilinear Forms on Sequence Spaces
Authors: Raposo Anselmo , Serrano-Rodríguez Diana M.
Published: August 2023

The investigation of regularity/summability properties of the coefficients of bilinear forms in sequence spaces was initiated by Littlewood in 1930. Nowadays, this topic has important connections with other fields of Pure and Applied Mathematics. In this paper we explore a regularity technique to obtain optimal parameters for several results in this framework, extending/generalizing theorems of Osikiewicz and Tonge (Linear Algebra Appl 331:1–9, 2001), Albuquerque et al. (J Funct Anal 266:3726–3740, 2016), Aron et al. (Linear Algebra Appl 531:399–422, 2017), Albuquerque and Rezende (Commun Contemp Math 20:1750087, 2018), Paulino (Rend Circ Mat Palermo 69:369–380, 2020), among others.
Molecular signature of excessive female aggression: study of stressed mice with genetic inactivation of neuronal serotonin synthesis
Authors: Strekalova Tatyana , Moskvin Oleg , Jain Aayushi Y. , Gorbunov Nikita , Gorlova Anna , Sadovnik Daria , Umriukhin Aleksei , Cespuglio Raymond , Yu Wing Shan , Tse Anna Chung Kwan , Kalueff Allan V. , Lesch Klaus-Peter , Lim Lee Wei
Published: August 2023

Aggression is a complex social behavior, critically involving brain serotonin (5-HT) function. The neurobiology of female aggression remains elusive, while the incidence of its manifestations has been increasing. Yet, animal models of female aggression are scarce. We previously proposed a paradigm of female aggression in the context of gene x environment interaction where mice with partial genetic inactivation of tryptophan hydroxylase-2 (Tph2+/− mice), a key enzyme of neuronal 5-HT synthesis, are subjected to predation stress resulting in pathological aggression. Using deep sequencing and the EBSeq method, we studied the transcriptomic signature of excessive aggression in the prefrontal cortex of female Tph2+/− mice subjected to rat exposure stress and food deprivation. Challenged mutants, but not other groups, displayed marked aggressive behaviors. We found 26 genes with altered expression in the opposite direction between stressed groups of both Tph2 genotypes. We identified several molecular markers, including Dgkh, Arfgef3, Kcnh7, Grin2a, Tenm1 and Epha6, implicated in neurodevelopmental deficits and psychiatric conditions featuring impaired cognition and emotional dysregulation. Moreover, while 17 regulons, including several relevant to neural plasticity and function, were significantly altered in stressed mutants, no alteration in regulons was detected in stressed wildtype mice. An interplay of the uncovered pathways likely mediates partial Tph2 inactivation in interaction with severe stress experience, thus resulting in excessive female aggression.
A cross-platform application for the ecological and remote assessment of memory impairment in aging: ECO-MEMORY
Authors: Bruni Francesca , Mancuso Valentina , Greci Luca , Arlati Sara , Cavallo Marco , Riva Giuseppe , Goulene Karine , Stramba-Badiale Marco , Pedroli Elisa
Published: August 2023

This work aims to present the first step of a creation of an instrument to assess memory deficits responding to the needs imposed by the inability to access clinical care, such as physical or geographical constraints or still limitations imposed during the pandemic era. The older population, who would benefit from these services, may be at risk as access to services that support psychological and neuropsychological needs, which are not considered essential, has frequently been restricted in recent years. Moreover, because deficits are commonly mistaken for the effects of physiological aging, the early signs of cognitive decline might be ignored. On these bases, we used the potential of 360-degree media to create an application for memory assessment without the physical presence of clinicians: ECO-MEMORY. Firstly, we developed the application and evaluated its usability. ECO-MEMORY is divided into four sections, each addressing a different memory task: recognizing objects and faces, learning a path, and creating an allocentric map. Thirteen older adults who used the tablet application provided usability data as well as qualitative feedback on their experience. After the performance, the System Usability Scale, the Senior Technology Acceptance Model, and the Independent Television Commission Sense of Presence were administered. We performed a qualitative analysis and descriptive statistics, which showed that ECO-MEMORY is a usable instrument. Also, it was enjoyable for users who generally accepted technology in their life. ECO-MEMORY may therefore offer a promising approach to memory evaluation by including real-world scenarios.
Integrated multiomics analysis of chromosome 19 miRNA cluster in bladder cancer
Authors: Ware Akshay Pramod , Satyamoorthy Kapaettu , Paul Bobby
Published: August 2023

With 46 microRNAs (miRNAs) embedded tandemly over a distance of ~100 kb, chromosome 19 microRNA cluster (C19MC) is the largest miRNA cluster in the human genome. The C19MC is transcribed from a long noncoding genomic region and is usually expressed simultaneously at a higher level. Hence, we performed an integrative multiomics data analysis to examine C19MC regulation, expression patterns, and their impact on bladder cancer (BCa). We found that 43 members of C19MC were highly expressed in BCa. However, its co-localization with recurrent copy number variation (CNV) gain was not statistically significant to implicate its upregulation. It has been reported that C19MC expression is regulated by a well-established CpG island situated 17.6 kb upstream of the transcription start site, but we found that CpG probes at this island were hypomethylated, which was not statistically significant in the BCa cohort. In addition, the promoter region of C19MC is strongly regulated by a group of seven transcription factors (NR2F6, SREBF1, TBP, GATA3, GABPB1, ETV4, and ZNF444) and five chromatin modifiers (SMC3, KDMA1, EZH2, RAD21, and CHD7). Interestingly, these 12 genes were found to be overexpressed in BCa patients. Further, C19MC targeted 42 tumor suppressor (TS) genes that were downregulated, of which 15 were significantly correlated with patient survival. Our findings suggest that transcription factors and chromatin modifiers at the promoter region may regulate C19MC overexpression. The upregulated C19MC members, transcription regulators, and TS genes can be further exploited as potential diagnostic and prognostic indicators as well as for therapeutic management of BCa.
Development and verification of a physiologically motivated internal controller for the open-source extended Hill-type muscle model in LS-DYNA
Authors: Martynenko Oleksandr V. , Kempter Fabian , Kleinbach Christian , Nölle Lennart V. , Lerge Patrick , Schmitt Syn , Fehr Jörg
Published: August 2023

Nowadays, active human body models are becoming essential tools for the development of integrated occupant safety systems. However, their broad application in industry and research is limited due to the complexity of incorporated muscle controllers, the long simulation runtime, and the non-regular use of physiological motor control approaches. The purpose of this study is to address the challenges in all indicated directions by implementing a muscle controller with several physiologically inspired control strategies into an open-source extended Hill-type muscle model formulated as LS-DYNA user-defined umat41 subroutine written in the Fortran programming language. This results in increased usability, runtime performance and physiological accuracy compared to the standard muscle material existing in LS-DYNA. The proposed controller code is verified with extensive experimental data that include findings for arm muscles, the cervical spine region, and the whole body. Selected verification experiments cover three different muscle activation situations: (1) passive state, (2) open-loop and closed-loop muscle activation, and (3) reflexive behaviour. Two whole body finite element models, the 50th percentile female VIVA OpenHBM and the 50th percentile male THUMS v5, are used for simulations, complemented by the simplified arm model extracted from the 50th percentile male THUMS v3. The obtained results are evaluated additionally with the CORrelation and Analysis methodology and the mean squared error method, showing good to excellent biofidelity and sufficient agreement with the experimental data. It was shown additionally how the integrated controller allows simplified mimicking of the movements for similar musculoskeletal models using the parameters transfer method. Furthermore, the Hill-type muscle model presented in this paper shows better kinematic behaviour even in the passive case compared to the existing one in LS-DYNA due to its improved damping and elastic properties. These findings provide a solid evidence base motivating the application of the enhanced muscle material with the internal controller in future studies with Active Human Body Models under different loading conditions.
Clinical performance comparison between lithium disilicate and hybrid resin nano-ceramic CAD/CAM onlay restorations: a two-year randomized clinical split-mouth study
Authors: Hassan Amr , Hamdi Kareem , Ali Ashraf I. , Al-Zordk Walid , Mahmoud Salah Hasab
Published: August 2023

A total of 20 lithium disilicate glass–ceramics (IPS e.max CAD, Ivoclar Vivadent) and 20 resin nano-ceramic (Voco Grandio Blocks) onlay restorations were performed in 20 patients using a split-mouth design to compare the two-year clinical performance of lithium disilicate and resin nano-ceramic onlay restorations. Both restorations were evaluated at baseline, one-year, and two-year clinical follow-ups based on the modified United States Public Health Service (USPHS) criteria. Chi-square and Fisher’s exact tests showed no statistically significant difference between Voco Grandio and IPS e.max ceramic restorations for all evaluated parameters during the different follow-up periods (p > 0.05). Cochrane’s and MC-Nemar’s tests indicated statistically significant differences regarding color match within the Voco Grandio group. They also indicated statistically significant differences in marginal discoloration, marginal adaptation, surface texture, and postoperative hypersensitivity within both ceramic material groups (p < 0.05). Kaplan–Meier curve indicated that the survival rate of both ceramic materials was 90%. After two years of clinical service, IPS e.max CAD and Voco Grandio onlay restorations exhibited similar clinical performance.
Differences in healthcare service utilization between older adults with and without dementia: a cross-sectional study in Shandong, China
Authors: Gao Yu , Sun Jingjie , Zheng Wengui , Cai Weiqin , Gao Qianqian , Lyu Juncheng , Zheng Xiaomeng , Gao Runguo , Ji Lihong , Jing Qi
Published: August 2023

Aim

Dementia is characterized by a decline in cognitive functioning and is the main cause of disability, mortality, and care dependence among older adults. This study compared healthcare utilization in older adults with and without dementia and explored factors influencing service use in the former.

Subject and methods

A total of 8847 seniors (≥ 60 years) were chosen from the Health Service Survey of Shandong Province conducted in 2018. The chi-square test was used for baseline characteristics and healthcare utilization for older adults with and without dementia. Multivariate logistic regression analysed service utilization factors.

Results

Of the respondents, 261 (3.0%) had dementia. The proportions of respondents who used outpatient care and inpatient care were 40.6% and 76.9%, respectively. The rates of outpatient and inpatient care use among seniors with dementia were slightly higher than those among seniors without dementia (44.8% vs 40.5% and 80.8% vs 76.7%, respectively). Older adults with dementia who were married and had experienced non-communicable diseases within the past 6 months were less likely to use outpatient care. No factors associated with the utilization of inpatient care were found.

Conclusion

Health publicity and a combination of community care and medical assistance targeting older adults with dementia is essential to increase healthcare service utilization.
Machine and deep learning for longitudinal biomedical data: a review of methods and applications
Authors: Cascarano Anna , Mur-Petit Jordi , Hernández-González Jerónimo , Camacho Marina , de Toro Eadie Nina , Gkontra Polyxeni , Chadeau-Hyam Marc , Vitrià Jordi , Lekadir Karim
Published: August 2023

Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthcare digitisation, the application of machine learning techniques for longitudinal biomedical data may enable the development of new tools for assisting clinicians in their day-to-day medical practice, such as for early diagnosis, risk prediction, treatment planning and prognosis estimation. However, due to the heterogeneity and complexity of time-varying data sets, the development of suitable machine learning models introduces major challenges for data scientists as well as for clinical researchers. This paper provides a comprehensive and critical review of recent developments and applications in machine learning for longitudinal biomedical data. Although the paper provides a discussion of clustering methods, its primary focus is on the prediction of static outcomes, defined as the value of the event of interest at a given instant in time, using longitudinal features, which has emerged as the most commonly employed approach in healthcare applications. First, the main approaches and algorithms for building longitudinal machine learning models are presented in detail, including their technical implementations, strengths and limitations. Subsequently, most recent biomedical and clinical applications are reviewed and discussed, showing promising results in a wide range of medical specialties. Lastly, we discuss current challenges and consider future directions in the field to enhance the development of machine learning tools from longitudinal biomedical data.
Hydrogen Jet Flame Control by Global Mode
Authors: Wawrzak Agnieszka , Wawrzak Karol , Boguslawski Andrzej , Tyliszczak Artur , Geurts Bernard J.
Published: August 2023

In this study, we employ the potential of the global instability phenomenon to qualitatively alter the dynamics of a turbulent flame. We adopt the large eddy simulation method and an in-house numerical code. For the combustion process we do not include explicit sub-filter modeling. The accuracy of this ‘no combustion model’ approach is verified by comparison against the Eulerian Stochastic Fields method. We include several test cases representing different flow regimes, including convective and absolutely unstable conditions. The focus is on a counter-current configuration, which includes a central jet nozzle supplying hydrogen fuel surrounded by a somewhat larger co-axial nozzle sucking fluid from the surroundings of the central nozzle. The suction generates a counterflow where global instability is found to be triggered if sufficiently strong suction is applied. The critical suction value $$I_{GI}$$

I

GI

is larger if the level of the inlet turbulence intensity $$T_i$$

T
i

is increased. Depending on the suction strength, the flame quickly stabilizes, either as being attached to the nozzle or as a lifted flame hovering at a height of a few fuel nozzle diameters. Additionally, it is shown that increasing $$T_i$$

T
i

can assist an initially lifted flame to attach to the nozzle. Flame lifting is the result of self-induced strong toroidal vortices that lead to leaner combustion conditions of the mixture upstream of the flame base. The toroidal vortices prevent upstream flame propagation and lead to a very intense mixing process that gives rise to the lifted reaction zone. This ensures almost complete fuel combustion at a short distance from the inlet. On the contrary, when the suction strength is below $$I_{GI}$$

I

GI

, the flames are attached and a high temperature near the fuel inlet causes an increase in molecular viscosity. This tends to laminarize the mixing layer between the fuel and oxidizer. The flames appear as if they were laminar and non-premixed over a long distance from the inlet. In such a situation, a significant amount of fuel remains unburned.
Soil nitrous oxide and methane fluxes from a land-use change transition of primary forest to oil palm in an Indonesian peatland
Authors: Swails Erin , Drewer Julia , Hartill Jodie , Comeau Louis-Pierre , Verchot Louis V. , Hergoualc’h Kristell A.
Published: August 2023

Despite the documented increase in greenhouse gas (GHG) emissions from Southeast Asian peat swamp forest degradation and conversion to oil palm over recent decades, reliable estimates of emissions of nitrous oxide (N2O) and methane (CH4) are lacking. We measured soil fluxes of N2O and CH4 and their environmental controls along a peatland transition from primary forest (PF) to degraded drained forest (DF) to oil palm plantation (OP) over 18 months in Jambi, Sumatra, Indonesia. Sampling was conducted monthly at all sites and more intensively following two fertilization events in the OP. Mean annual emissions of N2O (kg N ha−1 yr−1) were 1.7 ± 0.2 for the PF, 2.3 ± 0.2 for the DF and for the OP 8.1 ± 0.8 without drainage canals (DC) and 7.7 ± 0.7 including DC. High N2O emissions in the OP were driven by peat decomposition, not by N fertilizer addition. Mean CH4 annual fluxes (kg C ha−1 yr−1) were 8.2 ± 1.9 for the PF, 1.9 ± 0.4 for the DF, and 1.6 ± 0.3 for the OP with DC and 1.1 ± 0.2 without. Considering their 20-year global warming potentials (GWP), the combined non-CO2 GHG emission (Mg CO2-equivalent ha−1 yr−1) was 3.3 ± 0.6 for the PF and 1.6 ± 0.2 for the DF. The emission in the OP (3.8 ± 0.3 with or without DC) was similar to the PF because reductions in CH4 emissions offset N2O increases. However, considering 100-year GWP, the combined non-CO2 GHG emission was larger in the OP (3.4 ± 0.3 with DC and 3.5 ± 0.3 without) compared to both the PF and the DF (1.5 ± 0.2 and 1.2 ± 0.1, respectively). The increase in peat N2O emissions associated with the land-use change transition from primary forest to oil palm plantation at our sites provides further evidence of the urgent need to protect tropical peat swamp forests from drainage and conversion.
Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes
Authors: Di Francesco Valentina , Boso Daniela P. , Moore Thomas L. , Schrefler Bernhard A. , Decuzzi Paolo
Published: August 2023

The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters – flow rates and mixing configurations, type and concentrations of the reagents – contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles.
Effect of neighborhood deprivation index on breast cancer survival in the United States
Authors: Roy Arya Mariam , George Anthony , Attwood Kristopher , Alaklabi Sabah , Patel Archit , Omilian Angela R. , Yao Song , Gandhi Shipra
Published: August 2023

Purpose

To analyze the association between the Neighborhood Deprivation Index (NDI) and clinical outcomes of locoregional breast cancer (BC).

Methods

Surveillance, Epidemiology and End Results (SEER) database is queried to evaluate overall survival (OS) and disease-specific survival (DSS) of early- stage BC patients diagnosed between 2010 and 2016. Cox multivariate regression was performed to measure the association between NDI (Quintiles corresponding to most deprivation (Q1), above average deprivation (Q2), average deprivation (Q3), below average deprivation (Q4), least deprivation (Q5)) and OS/DSS.

Results

Of the 88,572 locoregional BC patients, 27.4% (n = 24,307) were in the Q1 quintile, 26.5% (n = 23,447) were in the Q3 quintile, 17% (n = 15,035) were in the Q2 quintile, 13.5% (n = 11,945) were in the Q4 quintile, and 15.6% (n = 13,838) were in the Q5 quintile. There was a predominance of racial minorities in the Q1 and Q2 quintiles with Black women being 13–15% and Hispanic women being 15% compared to only 8% Black women and 6% Hispanic women in the Q5 quintile (p < 0.001). In multivariate analysis, in the overall cohort, those who live in Q2 and Q1 quintile have inferior OS and DSS compared to those who live in Q5 quintile (OS:- Q2: Hazard Ratio (HR) 1.28, Q1: HR 1.2; DSS:- Q2: HR 1.33, Q1: HR 1.25, all p < 0.001).

Conclusion

Locoregional BC patients from areas with worse NDI have poor OS and DSS. Investments to improve the socioeconomic status of areas with high deprivation may help to reduce healthcare disparities and improve breast cancer outcomes.
Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models
Authors: Chandra Rohitash , Sharma Yash Vardhan
Published: August 2023

Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggle. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges; however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply to a geoscientifc model that features landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computationally time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in better understanding of paleoclimate and geomorphology.
Predicting Gun Violence in Stockholm, Sweden, Using Sociodemographics, Crime and Drug Market Locations
Authors: Magnusson Mia-Maria
Published: August 2023

The well-being of neighbourhoods in terms of socioeconomic conditions constitutes an important element in analyses focused on the explanation of crime trends and public safety. Recent developments in Sweden concerning gun violence and open drug scenes are worrying and the police are under a great deal of pressure to resolve the situation in many neighbourhoods, which is in turn affecting Swedish society as a whole. This study focuses on micro areas in terms of sociodemographic factors and the presence of drug markets and gun violence. The aim is to explore the relationship between these factors and what characterises areas that are experiencing the greatest difficulties. The study develops an index for the prediction of gun violence in micro areas, in this study portrayed by vector grids. The findings show an overlap between gun violence and drug markets and that micro areas in that overlap share harsh sociodemographic conditions. The study produces an index indicating the probability that a grid cell would experience gun violence. The index was then validated using recent gun incidents, and was found to have high accuracy. The resulting grids constitute a suitable target for resource allocation by police and other actors. This could facilitate a more accurate and precise focus for measures to prevent areas from becoming—or to disrupt already existing—hot spots for gun violence.
Using AI and BES/MFC to decrease the prediction time of BOD5 measurement
Authors: Medvedev Ivan , Kornaukhova Mariya , Galazis Christoforos , Lóránt Bálint , Tardy Gábor Márk , Losev Alexander , Goryanin Igor
Published: August 2023

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD5 is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD5 measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD5 measurements were carried out within 6–24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD5 sensor in real-life scenarios.
Fluid-flow effects in the reactive decontamination of porous materials driven by chemical swelling or contraction
Authors: Geng Y. , Kamilova A. A. , Luckins E. K.
Published: August 2023

Following the release of a chemical warfare agent, it is crucial for public health that the affected environment is entirely decontaminated. If the agent has seeped into a porous building material, the decontamination is achieved by applying a cleanser solution to the surface of the porous material, and allowing it to react in, neutralising the agent. Typically, the agent and cleanser solution are immiscible fluids and so the reaction occurs at the fluid–fluid interfaces within the pores. Previous studies have shown that the rate of decontamination of the porous material can depend on both the chemical reaction rate and the transport of cleanser to the reacting interface. These studies have all assumed that the two fluids have the same densities, so that diffusion is the only cleanser-transport mechanism. In this paper, we relax this assumption and investigate the effect of a fluid flow—generated by a change in density of the material (a swelling, or contraction) during the chemical reaction—on the decontamination process. This flow of fluid results in advection as well as diffusion of chemicals. Buoyancy effects are neglected. In particular, we show that when the agent is more dense than the reaction product, the decontamination process is slower, due to the adverse advection effect.
Sustainability and specifics of fire water sources in new climatic conditions using the example of the Czech Republic
Authors: Kavan Štěpán , Kročová Šárka
Published: August 2023

Sufficient fire water for fire protection of an area is of fundamental importance in the ​​security of the state and its infrastructure. Fires cannot be completely prevented, but the consequent damages can be minimized provided that there are enough extinguishing agents to eliminate them. The main and most important current and future extinguishing agent is water. Due to the fact that climate change is ongoing, it can be assumed that many existing natural and multipurpose sources of fire water may not meet the capacity requirements for firefighting, or they will not be available at all. The aim of the scientific approach is to evaluate and specify the availability of fire water in the new climatic conditions using the example of the Czech Republic. Another aim of the article is to characterize the possible shortage of fire water and to propose means by which the maximum effect can be achieved with minimum investment costs, and to ensure the long-term sustainability of natural and multipurpose fire water sources. The analysis of strengths and weaknesses, opportunities and threats was used for the evaluation of the availability of fire water in new climatic conditions. It arises from the results of the article and its broader concept that, despite the current satisfactory situation in fire protection in the Czech Republic regarding fire water, the risks of its shortage are increasing when the climate changes significantly. The lack of fire water will not be nationwide, but most likely only regional.
Genetic variation, correlation, and association mapping of seed yield and its component traits in sesame
Authors: Wang Cuiying , Niu Jiaojiao , Miao Hongmei , Li Chun , Duan Yinghui , Ju Ming , Cao Hengchun , Wei Libin , Wang Huili , Zhang Haiyang
Published: August 2023

Sesame is an important and specific oilseed crop with high quality. Here, we systematically investigated the relationships between plant seed yield (PSY) and nine related traits in 369 sesame core accessions under five environments. The PSY and related traits had general heritability ranging from 14% for PSY to 72% for height to the first capsule (FCH) and final flowering stage (FFS). Correlation analysis suggested that seven yield-related traits, including capsule number per plant (CNP, r = 0.67), capsule stem length (CSL, 0.56), thousand-seed weight (TSW, 0.26), plant height (PH, 0.24), FFS (0.17), initial flowering stage (IFS, − 0.16) and FCH (− 0.14) were the key factors associated with seed yield in sesame (P < 0.01). For the uniculm and branching groups, CNP, CSL, and TSW were the common key factors. Path analysis showed similar direct effects of these key factors on PSY. Association mapping identified 13 simple sequence repeat (SSR) markers significantly associated with the nine seed yield-related traits (P < 0.001). Among these, three SSR markers were repeatedly detected in two environments. Marker Hs635 was associated with the seed number per capsule (SNC). Hs345 was associated with the CNP. Hs618 was associated with the IFS in uniculm accessions. These results provide insights into the traits that influence PSY and their genetics in sesame.
Therapeutic Competence in Parenting Programs: A Focus Group Study
Authors: Lundström Susanna , Lundström Markus
Published: August 2023

While research on parenting programs typically focuses on program-specific evaluations, this article considers therapeutic competence as a transversal feature across different programs. It draws empirically on focus group interviews with practitioners of two separate parenting programs in Sweden: Circle of Security (COS-P) and Communication Method (COMET). The qualitative data analysis shows how the practitioners of both programs used their therapeutic competence to tailor the program manuals for each parent group; they combined the techniques of positive reinforcement and intentional self-disclosure with an attuned flexibility that was enabled by mentalization. The article concludes that therapeutic competence is a crucial aspect of parenting programs that needs further attention from scholars and practitioners alike.
Long-Term Disability Outcomes for Migrants (and Non-migrants) 12 Years Post-injury: Results from the Prospective Outcomes of Injury Study in New Zealand
Authors: Anglemyer A. , Wyeth E. H. , Derrett S.
Published: August 2023

To understand, and identify predictors of, long-term post-injury (i.e. 12 years post-injury) disability outcomes for migrants and non-migrants. This 12-year longitudinal study followed participants with entitlement claim injuries registered with New Zealand’s universal no-fault injury insurer between 2007 and 2009. Information was collected about migrant status, other sociodemographic, health and disability characteristics, and injury characteristics. Disability outcome information was collected 12 years later. Of 1543 people interviewed 12 years post-injury, 1497 had disability and migrant status data available; 20% were migrants (n = 301). Migrants reporting inadequate pre-injury household income or those who perceived their injury as a threat to life at the time of injury were more likely to experience disability 12 years post-injury (aRR 2.08; 95% CI 1.09–4.03, aRR 2.93; 95%CI 1.17–6.69, respectively). Hospitalised injured migrants were significantly less likely to have long-term disability (aRR 0.18; 95%CI 0.04–0.55) than those not hospitalised. We found sociodemographic and injury-related characteristics were independently associated with long-term disability among migrants. We highlight that some characteristics, ascertained early in the injury pathway, predict risk of long-term disability. Early post-injury interventions focused on improving disability outcomes for migrants may also have long-term impacts.
Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy
Authors: Azzolina Danila , Lanera Corrado , Comoretto Rosanna , Francavilla Andrea , Rosi Paolo , Casotto Veronica , Navalesi Paolo , Gregori Dario
Published: August 2023

The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.

Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.

This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.

The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.

In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.
New cardiovascular disease prediction approach using support vector machine and quantum-behaved particle swarm optimization
Authors: Elsedimy E. I. , AboHashish Sara M. M. , Algarni Fahad
Published: August 2023

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Early detection of CVD reduces the risk of a heart attack and increases the chance of recovery. The use of angiography to detect CVD is expensive and has negative side effects. In addition, existing CVD diagnostic methods usually achieve low detection rates and reach the best decision after many iterations with low convergence speeds. Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk. First, the data preprocessing was performed by transforming nominal data into numerical data and applying effective scaling techniques. Next, the SVM fitness equation is expressed as an optimization problem and solved using the QPSO to determine the optimal features. Finally, a self-adaptive threshold method for tuning the QPSO-SVM parameters is proposed, which permits it to drop into local minima, and balances between exploration and exploitation in the solution search space. The proposed model is applied to the Cleveland heart disease dataset and compared with state-of-the-art models. The experimental results show that the proposed QPSO-SVM model achieved the best heart-disease-prediction accuracies of 96.31% on the Cleveland heart data set. Furthermore, QPSO-SVM outperforms other state-of-the-art prediction models considered in this research in terms of sensitivity (96.13%), specificity (93.56%), precision (94.23%), and F1 score (0.95%).
Fast elitist ABC for makespan optimisation in interval JSP
Authors: Díaz Hernán , Palacios Juan José , González-Rodríguez Inés , Vela Camino R.
Published: August 2023

This paper addresses a variant of the Job Shop Scheduling Problem with makespan minimisation where uncertainty in task durations is taken into account and modelled with intervals. A novel Artificial Bee Colony algorithm is proposed where the classical layout is simplified, increasing the algorithm’s speed and reducing the number of parameters to set up. We also take into account the fundamental principles of exploration around a local solution and attraction to a global solution to improve diversity in the hive. The increase on speed and diversity allows to include a Local Search phase to better exploit promising areas of the search space. A parametric analysis is conducted and the contribution of the new strategies is analysed. The results of the new approach are competitive with those obtained with previous methods in the literature, but taking less runtime. The addition of Local Search improves the results even further, outperforming the best-known ones from the literature. An additional sensitivity study is conducted to assess the advantages of considering uncertainty and how increasing it affects the solution’s robustness.