Development of a rhodamine-based fluorescent probe for ATP detection for potential applications in meat freshness assessment.

Adenosine triphosphate (ATP) plays a vital role in physiological processes and is an essential indicator of microbial content in food. Herein, a new sensitive, rapid and water-soluble probe for ATP detection was developed. Rhodamine B and pentaethylenehexamine were employed to design and synthesise the probe rhodamine-pentaethylenehexamine (RP) for selective ATP detection. The synthesised probe RP was characterized using Fourier transform infrared, NMR and dynamic light scattering size distributions. Upon the addition of ATP, the probe exhibited a distinct change in fluorescence intensity, with fluorescence emission at 580 nm. A linear relationship was observed between fluorescence intensity and ATP concentrations at 0-50 µmol/L, with a limit of detection of 10.97 × 10-9 mol/L. The results of the zeta potential and molecular dynamics simulation demonstrated that the detection mechanism of the probe RP is associated with the electrostatic adsorption interaction between the multi-positively charged sites of RP and the negatively charged triphosphate structure of ATP. Our study provides new insights into improving charge site identification in small molecule detection. Furthermore, the successful detection of ATP on meat surfaces indicates that RP has the potential to assess meat freshness.

The features of multidrug-resistant organisms between 2016 and March 2023 and its change after the end of zero-COVID-19 policy in a teaching hospital in Shenzhen, China.

BACKGROUND: To investigate the clinical distribution and changing trend of antibiotic resistance profiles of multidrug-resistant organisms (MDROs), a retrospective study was undertaken. METHODS: The characteristics of MDROs isolated from 2016 to March 2023 were retrospectively analysed. The detection rate of these MDROs was compared prior to COVID-19 (Period 1, 2016-2019), during the COVID-19 pandemic with restrictions (Period 2, 2020-2022), and after the end of zero-policy (Period 3, Jan-March, 2023). Antibiotic-resistant genes were detected. RESULTS: The overall detection rates of CRPA, CRAB, CREC, CRKP, MRSA, and VREfm were 22.6%, 22.6%, 1.3%, 4.0%, 19.5%, and 3.1%, respectively. The detection rate of CRAB was significantly lower in Period 2 and 3 compared with Period 1 (P < 0.0001). The detection rate of CRPA and VREfm was significantly increased in Period 3 compared with Period 1 and 2 (P < 0.0001). The resistance rate to ticarcillin/clavulanic acid (TIM) and piperacillin/tazobactam (TZP) has gradually increased in CRPA since 2018. NDM and KPC were the most common carbapenemase genes identified in CREC (60.0%) and CRKP isolates (47.8%), respectively. All the 10 VREfm isolates carried the vanA gene. CONCLUSIONS: The detection rate of CRAB has decreased since 2018, but a significantly increased prevalence of CRPA and VREfm was seen after the end of zero-policy. An increasing resistance rate to TIM and TZP was seen in CRPA. NDM, KPC, and vanA were the common genes harboured by CREC, CRKP, and VREfm, respectively. Ongoing surveillance after the COVID-19 era is suggested.

A hydrogel-based ratiometric fluorescent sensor relying on rhodamine B labelled AIE-featured hyperbranched poly(amido amine) for heparin detection.

The fluorescent flexible sensor for point-of-care quantification of clinical anticoagulant drug, Heparin (Hep), is still an urgent need of breakthrough. In this research, a hyperbranched poly(amido amine) (HPA) was decorated with tetraphenylethene (TPE) and Rhodamine B (RhB), constructing a ratiometric fluorescent sensor (TR-HPA) for Hep. When the sensor was exposed to Hep, the TPE units within the probe skeleton would aggregate, resulting in an increasing fluorescent emission at 483 nm. The 580 nm of fluorescence came from RhB enhance, simultaneously, due to the fluorescence resonance energy transfer. As a result, there are two good linear correlation between the fluorescence emission ratio (E483/E580) of TR-HPA and the Hep concentration over a range of 0-1.0 µM, with a low limit of detection of 3.0 nM. Furthermore, we incorporate the TR-HPA probe into a polyvinyl alcohol (PVA) hydrogel matrix to create a flexible fluorescent sensing system platform, denoted as TR-HPA/PVA. This approach offers a straightforward visual detection method by causing a fluorescence color change from pink to blue when trace amounts of Hep are present. The hydrogel-based fluorescent sensor streamlines the detection procedures for Hep in biomedical applications. It shows great potential in rapid and point-of-care human blood clotting condition monitoring, making it suitable for next-generation wearable medical devices.

Associations of change in body fat percentage with baseline body composition and diabetes remission after bariatric surgery.

OBJECTIVE: The objective of this study was to determine the role of body fat percentage (BFP) changes in diabetes remission (DR) and the association between baseline body composition and its changes after bariatric surgery. METHODS: We analyzed 203 patients with type 2 diabetes who underwent Roux-en-Y gastric bypass. Body composition was measured using a gold-standard-derived predictive equation and magnetic resonance imaging. Body composition changes were calculated as 100 × (baseline value - follow-up value)/baseline value. We verified the results in a laparoscopic sleeve gastrectomy cohort with 311 patients. RESULTS: Compared with non-remission patients in the Roux-en-Y gastric bypass cohort, those who achieved DR showed a higher baseline fat-free mass index (FFMI) and experienced the most significant changes in BFP (p < 0.001). In comparative analyses, BFP changes were significantly better than BMI changes in identifying short- and long-term DR. Linear regression analysis identified FFMI as the most significant baseline variable correlated with BFP changes (p < 0.001). Baseline BMI was positively correlated with changes in BFP but negatively correlated with changes in FFMI. These findings were replicated in the laparoscopic sleeve gastrectomy cohort. CONCLUSIONS: BFP changes determine DR after bariatric surgery, and baseline FFMI is crucial for BFP changes. A low initial BMI is associated with a smaller BFP reduction and greater FFMI loss after bariatric surgery.

Pleiotropic Effects of miR5504 Underlying Plant Height, Grain Yield and Quality in Rice.

MicroRNAs (miRNAs) are known to play critical roles in regulating rice agronomic traits through mRNA cleavage or translational repression. Our previous study indicated that miR5504 regulates plant height by affecting cell proliferation and expansion. Here, the two independent homozygous mir5504 mutants (CR1 and CR2) and overexpression lines (OE1 and OE2) were further used to investigate the functions of miR5504. The panicle length, 1000-grain weight and grain yield per plant of miR5504-OE lines were identical to those of Nipponbare (NIP), but the 1000-grain weight of mir5504 mutants was reduced by about 10% and 9%, respectively. Meanwhile, the grain width and thickness of mir5504 mutants decreased significantly by approximately 10% and 11%, respectively. Moreover, the cytological results revealed a significant decrease in cell number along grain width direction and cell width in spikelet in mir5504, compared with those in NIP. In addition, several major storage substances of the rice seeds were measured. Compared to NIP, the amylose content of the mir5504 seeds was noticeably decreased, leading to an increase of nearly 10 mm in gel consistency (GC) in mir5504 lines. Further investigation confirmed that LOC_Os08g16914 was the genuine target of miR5504: LOC_Os08g16914 over-expression plants phenocopied the mir5504 mutants. This study provides insights into the role of miR5504 in rice seed development.

SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction.

As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine learning methods have been developed for functional peptide prediction. However, the main research heavily relies on statistical features and few consider multifunctional peptide identification. So, we propose SME-MFP, a novel predictor in the imbalanced multi-label functional peptide datasets. First, we employ physicochemical and evolutionary information to represent the peptide sequence's initialization features from multiple perspectives. Second, the features are fused and then put into spatial feature extractors, where the residual connection and multiscale convolutional neural network extract more discriminative features of different lengths' peptide sequences. Besides, we also design AFT-based temporal feature extractors to fully capture the global interactions of the sequences. Finally, devising a new loss to replace the traditional cross entropy loss to settle the class imbalance problems. The results show that our framework not only enhances the model's ability to capture sequence features effectively, but also accuracy improves by 3.89% over existing methods on public peptide datasets.

Changes in physicochemical properties and formation process of colloidal nanoparticles (CNPs) during the lamb soup stewing.

Colloidal nanoparticles (CNPs), as carriers of nutrients, naturally exist in food or form during cooking. In this study, the colloidal properties, structures, rheological properties, and chemical composition location of CNPs were analyzed during 15 min to 5 h lamb soup stewing. With the increasing stewing time, the particle size and absolute value of the zeta potential of CNPs increased, indicating that CNPs became more stable. As the stewing time increased, the blue-shifted Fourier transform infrared spectroscopy absorption peaks and the red-shifted fluorescence spectroscopy absorption peaks certificated the structural changes in CNPs. And α-helix and ß-turn content decreased, while ß-sheet and random coil content increased in processing, potentially resulting in the opening CNPs structures. In addition, our findings revealed that proteins were encapsulated within the lipids in the inner part, while carbohydrates were dispersed in the outermost layers of the CNPs with a phospholipid bilayer.

A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation.

Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P < 0.05) and 1107 regions. Forty CpGs were located within known quantitative trait loci for gene expression traits in blood, and trait enrichment analysis suggested a strong association with immune-related, transcriptional control, and blood pressure regulation phenotypes. Decreasing fertility rates worldwide with the concomitant increased proportion of first-born children highlights a potential reflection of birth order-related epigenomic states on changing disease incidence trends.

Evaluation of the genetic basis of familial-associated early-onset hematologic cancers in an ancestral/ethnically diverse population.

Genetic predisposition to hematologic malignancies has historically been addressed utilizing patients recruited from clinical trials and pedigrees constructed at major treatment centers. Such efforts leave unexplored the genetic basis of variations in risk by race/ethnic group shown in population-based surveillance data where cancer registration, compulsory by law, delivers universal enrollment. To address this, we performed exome sequencing on DNA isolated from newborn bloodspots derived from sibling pairs with early-onset cancers across California in which at least one of the siblings developed a hematologic cancer, using unbiased recruitment from the full state population. We identified pathogenic/likely pathogenic (P/LP) variants among 1172 selected cancer genes that were private or present at low allele frequencies in reference populations. Within 64 subjects from 32 families, we found 9 LP variants shared between siblings, and an additional 7 such variants in singleton children (not shared with their sibling). In eight of the shared cases, the ancestral origin of the local haplotype that carries P/LP variants matched the dominant global ancestry of study participant families. This was the case for Latino sibling pairs on FLG and CBLB, non-Latino White sibling pairs in TP53 and NOD2, and a shared GATA2 variant for a non-Latino Black sibling pair. A new inherited mutation in HABP2 was identified in a sibling pair, one with diffuse large B-cell lymphoma and the other with neuroblastoma. Overall, the profile of P/LP germline variants across ancestral/ethnic groups suggests that rare alleles contributing to hematologic diseases originate within their race/ethnic origin parental populations, demonstrating the value of this discovery process in diverse, population-based registries.

Effects of phosphoglycerate kinase 1 and pyruvate kinase M2 on metabolism and physiochemical changes in postmortem muscle.

The objective of this work was to investigate the influence of phosphoglycerate kinase-1 (PGK1) and pyruvate kinase-M2 (PKM2) activity on glycolysis, myofibrillar proteins, calpain system, and apoptosis pathways of postmortem muscle. The activity of PGK1 and PKM2 was regulated by their inhibitors and activators to construct the postmortem glycolysis vitro model and then incubated at 4 °C for 24 h. The results showed that compared to PGK1 and PKM2 inhibitors groups, the addition of PGK1 and PKM2 activators could accelerate glycogen consumption, ATP and lactate production, while declining pH value. Moreover, the addition of PGK1 and PKM2 activators could increase desmin degradation, µ-calpain activity, and caspase-3 abundance. Interestingly, troponin-T degradation was significantly increased both in PKM2 inhibitor and activator groups. It was suggested that PGK1 and PKM2 might be used as robust indicators to regulate meat quality by affecting the glycolysis, myofibrillar proteins, µ-calpain and apoptosis pathways in postmortem muscle.

An antibacterial packaging film based on amylose starch with quaternary ammonium salt chitosan and its application for meat preservation.

A new generation of food packaging films is gradually replacing traditional plastic packaging films because of their biodegradability, safety, and some functional properties such as anti-bacterial and oxidant resistance. In the present work, an antibacterial packing film based on amylose starch and 2-hydroxypropyl-trimethylammonium chloride chitosan (HTCC) was prepared for meat preservation. The interfacial bonding mechanism between amylose, HTCC, and glutaraldehyde (GA) was determined experimentally and through molecular dynamics (MD) simulation. The macromolecular chains of amylose starch and HTCC became entangled via inter-molecular H-bonds and then cross-linked with GA via the Schiff base reaction. The interaction of amylose starch and HTCC improved the mechanical properties of the amylose films. Compared with the amylose films, the tensile strength and elongation at break of the optimal HTCC/amylose films reached to 16.13 MPa (an increase of 206.65 %) and 53.86 % (an increase of 109.49 %). The HTCC/amylose films were found to provide obvious bacteriostatic performance, a relatively low cytotoxicity, the lower transmittance in the UV region, and thus the ability to enhance the preservation of fresh meat. These excellent characteristics therefore suggest that HTCC/amylose films might be promising candidates for application in antibacterial food packaging films.

Spraying performance of umbrella wind-field-type atomization and its application to parameter optimization.

BACKGROUND: We designed an umbrella wind-field-type anti-drift spraying device to improve droplet deposit in the fruit tree canopy, reduce spray drift between fruit tree rows, and avoid uneven droplet deposit in the canopy. RESULTS: We used Computational Fluid Dynamics combined with wind field tests to optimize the parameters of the anti-drift spray device, and the results showed that airflow velocity at the outlet of the device after optimization was 24.5 m s-1 , which is 48% higher than that before optimization (16.5 m s-1 ) airflow velocity. We designed wind tunnel tests and field tests to analyze the anti-drift characteristics of the anti-drift spraying device. Wind tunnel test results showed that the side airflow velocity, outlet diameter, spray distance, and spray drift ratio were correlated. The mathematical models established by vertical and horizontal multifactor orthogonal tests were significant (P < 0.05, R2 0.947, 0.878, respectively). The results of the field tests showed that side airflow, velocity spray pressure and outlet diameter had significant effects on the droplet deposit characteristics (in descending order: the side airflow velocity, spray pressure, and outlet diameter). The maximum droplet deposit was 6.34 µL cm-2 when the side airflow velocity was 2 m s-1 , the spray pressure was 0.4 MPa, and the outlet diameter was 70 mm2 . When the side airflow velocity exceeded 2 m s-1 , the outlet diameter and spray pressure had to be reduced to ensure better droplet deposit. CONCLUSION: The results indicated that the umbrella wind field could reduce spray drift and ineffective deposit in off-target areas and provides a reference for the comprehensive analysis of the spray drift deposit law. © 2023 Society of Chemical Industry.

PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Association between frequency of self-monitoring of blood glucose and glycemic control in patients with type 2 diabetes.

AIM: To determine the association between the frequency of self-monitoring of blood glucose (SMBG) and glycemic control in patients with type 2 diabetes. METHODS: The retrospective study analyzed data from 3,630 patients with type 2 diabetes who were enrolled between August 2020 and July 2021. 2456 non-insulin-treated patients were divided into SMBG ≤ 6 times/week and > 6 times/week groups. 1174 insulin-treated patients were divided into SMBG ≤ 9 times/week and > 9 times/week groups. Propensity score matching was used to generate groups with well-balanced covariates. Primary outcomes were changes in fasting blood glucose (FBG) and postprandial blood glucose (PBG) from baseline to 6 months. RESULTS: In the non-insulin-treated group, a comparison in FBG and PBG reduction between the SMBG > 6 times/week and ≤ 6 times/week groups was statistically significant (-0.59 vs. -0.18 mmol/l, P < 0.001; -0.91 vs. -0.36 mmol/l, P < 0.001). In the insulin-treated group, there was no statistically significant reduction in FBG or PBG, patients with baseline FBG > 8 or 9 mmol/l and SMBG > 9 times/week had a significant FBG reduction than the ≤ 9 times/week group. CONCLUSION: Frequent SMBG was associated with better glycemic control in non-insulin-treated patients. Insulin-treated patients with poor glycemic control may benefit from frequent SMBG.

Teaching-Learning Optimization Algorithm Based on the Cadre-Mass Relationship with Tutor Mechanism for Solving Complex Optimization Problems.

The teaching-learning-based optimization (TLBO) algorithm, which has gained popularity among scholars for addressing practical issues, suffers from several drawbacks including slow convergence speed, susceptibility to local optima, and suboptimal performance. To overcome these limitations, this paper presents a novel algorithm called the teaching-learning optimization algorithm, based on the cadre-mass relationship with the tutor mechanism (TLOCTO). Building upon the original teaching foundation, this algorithm incorporates the characteristics of class cadre settings and extracurricular learning institutions. It proposes a new learner strategy, cadre-mass relationship strategy, and tutor mechanism. The experimental results on 23 test functions and CEC-2020 benchmark functions demonstrate that the enhanced algorithm exhibits strong competitiveness in terms of convergence speed, solution accuracy, and robustness. Additionally, the superiority of the proposed algorithm over other popular optimizers is confirmed through the Wilcoxon signed rank-sum test. Furthermore, the algorithm's practical applicability is demonstrated by successfully applying it to three complex engineering design problems.

Rice miR5504 regulates plant height by affecting cell proliferation and expansion.

miRNAs play critical roles in the regulation of plant growth and development by cleaving mRNA or repressing transcription. In our previous study, miR5504 with unknown functions was captured by small RNA sequencing. Here, the function and characters of miR5504 were extensively analyzed using CRISPR/Cas9, overexpression strategy, Northern blot, cytological analysis, and transcriptomics analysis. We found that the dwarf phenotype of mir5504 mutants (mir5504-1 and mir5504-2) appeared on 35-day seedlings and became more apparent at the mature stage. The cytological results showed a substantial decrease in the vascular bundle number, cell number and cell length in the mir5504 mutant compared with NIP. In addition, we found that miR5504 regulated plant height by targeting LOC_Os08g16914. The results of RNA-seq revealed that numerous biological processes were mainly enriched, including DNA-binding transcription factor activity, transferase activity, regulation of transcription, metabolic process, and protein binding. Meanwhile, KEEG analysis showed that numerous proteins were associated with cellular processes and metabolism pathways. Taken together, miR5504 may be involved in the regulation of plant height by affecting cell expansion and division of internode in rice.

Epigenomic signature of major congenital heart defects in newborns with Down syndrome.

BACKGROUND: Congenital heart defects (CHDs) affect approximately half of individuals with Down syndrome (DS), but the molecular reasons for incomplete penetrance are unknown. Previous studies have largely focused on identifying genetic risk factors associated with CHDs in individuals with DS, but comprehensive studies of the contribution of epigenetic marks are lacking. We aimed to identify and characterize DNA methylation differences from newborn dried blood spots (NDBS) of DS individuals with major CHDs compared to DS individuals without CHDs. METHODS: We used the Illumina EPIC array and whole-genome bisulfite sequencing (WGBS) to quantitate DNA methylation for 86 NDBS samples from the California Biobank Program: (1) 45 DS-CHD (27 female, 18 male) and (2) 41 DS non-CHD (27 female, 14 male). We analyzed global CpG methylation and identified differentially methylated regions (DMRs) in DS-CHD versus DS non-CHD comparisons (both sex-combined and sex-stratified) corrected for sex, age of blood collection, and cell-type proportions. CHD DMRs were analyzed for enrichment in CpG and genic contexts, chromatin states, and histone modifications by genomic coordinates and for gene ontology enrichment by gene mapping. DMRs were also tested in a replication dataset and compared to methylation levels in DS versus typical development (TD) WGBS NDBS samples. RESULTS: We found global CpG hypomethylation in DS-CHD males compared to DS non-CHD males, which was attributable to elevated levels of nucleated red blood cells and not seen in females. At a regional level, we identified 58, 341, and 3938 CHD-associated DMRs in the Sex Combined, Females Only, and Males Only groups, respectively, and used machine learning algorithms to select 19 Males Only loci that could distinguish CHD from non-CHD. DMRs in all comparisons were enriched for gene exons, CpG islands, and bivalent chromatin and mapped to genes enriched for terms related to cardiac and immune functions. Lastly, a greater percentage of CHD-associated DMRs than background regions were differentially methylated in DS versus TD samples. CONCLUSIONS: A sex-specific signature of DNA methylation was detected in NDBS of DS-CHD compared to DS non-CHD individuals. This supports the hypothesis that epigenetics can reflect the variability of phenotypes in DS, particularly CHDs.

A self-training algorithm based on the two-stage data editing method with mass-based dissimilarity.

A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number of labeled samples and a large number of unlabeled samples to train a classifier. However, the existing self-training algorithms consider only the geometric distance between data while ignoring the data distribution when calculating the similarity between samples. In addition, misclassified samples can severely affect the performance of a self-training algorithm. To address the above two problems, this paper proposes a self-training algorithm based on data editing with mass-based dissimilarity (STDEMB). First, the mass matrix with the mass-based dissimilarity is obtained, and then the mass-based local density of each sample is determined based on its k nearest neighbors. Inspired by density peak clustering (DPC), this study designs a prototype tree based on the prototype concept. In addition, an efficient two-stage data editing algorithm is developed to edit misclassified samples and efficiently select high-confidence samples during the self-training process. The proposed STDEMB algorithm is verified by experiments using accuracy and F-score as evaluation metrics. The experimental results on 18 benchmark datasets demonstrate the effectiveness of the proposed STDEMB algorithm.

Gene-Environment Analyses Reveal Novel Genetic Candidates with Prenatal Tobacco Exposure in Relation to Risk for Childhood Acute Lymphoblastic Leukemia.

BACKGROUND: Associations between maternal tobacco exposure during pregnancy and childhood acute lymphoblastic leukemia (ALL) have yielded mixed results. This may be due to biases in self-reported smoking or other differences in individual-level risk factors. We utilized a biological marker of maternal tobacco exposure to evaluate the association between maternal tobacco exposure during pregnancy, genetics, and subsequent childhood ALL risk in two large population-based studies of childhood ALL in California. METHODS: Maternal exposure to tobacco smoke was assessed with a validated methylation marker (cg05575921) of the aryl hydrocarbon receptor repressor (AHRR) gene in newborn dried blood spots. We adjusted for sex, birthweight, gestational age, mode of delivery, year of birth, AHRR quantitative trait locus (mQTL) rs77111113, and a polygenetic risk score for childhood ALL. We additionally adjusted for principal components in a gene-environment interaction testing method that incorporates gene-only and environment-only effects along with interactions. RESULTS: AHRR hypomethylation overall was not associated with childhood ALL. In gene-environment interaction testing, several genetic variants displayed significant interaction with AHRR hypomethylation and childhood ALL. CONCLUSIONS: Our results suggest that novel candidates in PTPRK and DPP6 may play a role in tobacco-related leukemogenesis. Further research is necessary to better understand the effects of tobacco and these variants on childhood ALL risk. IMPACT: Despite the lack of an overall "main effect," tobacco exposure during pregnancy affects childhood ALL risk depending on specific genetic variants.

An Enhanced Neural Network Algorithm with Quasi-Oppositional-Based and Chaotic Sine-Cosine Learning Strategies.

Global optimization problems have been a research topic of great interest in various engineering applications among which neural network algorithm (NNA) is one of the most widely used methods. However, it is inevitable for neural network algorithms to plunge into poor local optima and convergence when tackling complex optimization problems. To overcome these problems, an improved neural network algorithm with quasi-oppositional-based and chaotic sine-cosine learning strategies is proposed, that speeds up convergence and avoids trapping in a local optimum. Firstly, quasi-oppositional-based learning facilitated the exploration and exploitation of the search space by the improved algorithm. Meanwhile, a new logistic chaotic sine-cosine learning strategy by integrating the logistic chaotic mapping and sine-cosine strategy enhances the ability that jumps out of the local optimum. Moreover, a dynamic tuning factor of piecewise linear chaotic mapping is utilized for the adjustment of the exploration space to improve the convergence performance. Finally, the validity and applicability of the proposed improved algorithm are evaluated by the challenging CEC 2017 function and three engineering optimization problems. The experimental comparative results of average, standard deviation, and Wilcoxon rank-sum tests reveal that the presented algorithm has excellent global optimality and convergence speed for most functions and engineering problems.