bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural Regression Modelling of Peptide based Drug Delivery Vectors for the Treatment of Triple-Negative Breast Cancer
Authors: Christian Yvonne , Redkar Amay Sanjay , Kumar Naveen , Jancy Shine Varghese , Chandrasekharan Aneesh , Santhoshkumar Thankayyan Retnabai , Ramakrishnan Vibin 
Published: May 2024

Abstract

Drug resistance in cancer poses a serious challenge in finding an effective remedy for cancer patients, because of the multitude of contributing factors influencing this complex phenomenon. One way to counter this problem is using a more targeted and dose-limiting approach for drug delivery, rather than relying on conventional therapies that exhibit multiple pernicious side-effects. Stability and specificity have traditionally been the core issues of peptide-based delivery vectors. In this study, we employed a structural regression modelling approach in the design, synthesis and characterization of a series of peptides that belong to approximately same topological cluster, yet with different electrostatic signatures encoded as a result of their differential positioning of amino acids in a given sequence. The peptides tagged with the fluorophore 5(6)-carboxyfluorescein,showed higher uptake in cancer cells with some of them colocalizing in the lysosomes. The peptides tagged with the anti-cancer drug methotrexate have displayed enhanced cytotoxicity and inducing apoptosis in triple-negative breast cancer cells. They also showed comparable uptake in side-population cells of lung cancer with stem-cell like properties. The most-optimized peptide showed accumulation in the tumor resulting in significant reduction of tumor size, compared to the untreated mice in in-vivostudies. Our results point to the following directives; (i) peptides can be design engineered for targeted delivery (ii) stereochemical engineering of peptide main chain can resist proteolytic enzymes and (iii) cellular penetration of peptides into cancer cells can be modulated by varying their electrostatic signatures.
Risk factors for Self-Reported Diagnosed Cataract among older adults in Poland. Findings from PolSenior2 Study.
Authors: Lange Natalia , Jagiełło Kacper , Bandosz Piotr 
Published: May 2024

Abstract

Purpose: The aim of our study was to investigate sociodemographic factors, comorbidities and health behaviors associated with self-reported diagnosed cataract in a large, nationally representative population of older adults in Poland, aged 60 and above.
Patient and Methods: An analysis was conducted on a survey among 5956 participants of the nationally representative PolSenior2 study conducted between 2018 and 2019. Multiple logistic regression analysis was employed to ascertain the association between self-reported diagnosed cataract and sociodemographic factors, health behaviors, and comorbidities.
Results: In the final multivariate model, the odds radio (OR) of self-reported diagnosed cataract were 1.71 times higher among women compared to men. Additionally, the odds increased significantly with age, with 70-79-year-olds having 3.38 times higher odds, 80-89-year-olds having 8.08 times higher odds, and those aged 90 years and older having 10.76 times higher odds compared to the reference group (60-69 years old). The prevalence of self-reported diagnosed cataract was found to be 1.47 times higher among individuals with diabetes, 1.20 times higher among those with hypertension, and 1.25 times higher among tobacco users compared to their respective counterparts. Additionally, rural dwellers exhibited a lower risk for self-reported cataract (OR = 0.63).
Conclusion: Our study revealed a positive correlation between several demographic and health factors—namely, older age, female gender, urban residence, hypertension, diabetes, and smoking—and an elevated risk of cataract.
A regional model for soil moisture concentration from remote sensing data and its application for agricultural drought hazard mapping
Authors: Eghtedarnezhad Mina , Malekinezhad Hossein , Rafiei-Sardooi Elham , Parteli Eric J. R. 
Published: May 2024

Abstract

Soil moisture constitutes one essential variable in agriculture drought monitoring. However, because spatial and temporal soil moisture datasets from in situ observations are not accessible for all locations, remote sensing constitutes an indispensable approach in the assessment of surface soil moisture on a regional scale. In this study, a method to estimate regional-scale distribution of soil moisture (0–30 cm) from remote sensing observations is presented and applied to produce a drought hazard map, taking as case study area the arid region of Jiroft plain, Iran. For this study area, we dispose of remote sensing data available within the broad time span from 2007 to 2022, including satellite Vegetation Index and Land Surface Temperature, as well as observed soil moisture at a regional scale with a spatial resolution of 1 km2. Based on the spatial distribution of soil moisture appraise from these datasets, we calculate the relative exit of soil moisture associated with eight severe droughts in the Jiroft plain and the associated inventory map of agricultural drought. Machine learning models, including improved regression trees, multivariate discriminant analysis and support vector machine, are then applied to predict agricultural drought hazards. Using these different models, a model for agricultural drought hazard (ADH) is produced from ten independent variables characterizing environmental factors in the area. We find that plant available water capacity constitutes, together with soil moisture, the most important factor in ADH modeling. Furthermore, our results further indicate that, over the machine learning methods considered in our study, the support vector machine leads to the highest model accuracy in agricultural drought mapping (AUC = 0.95). We show how the ADH estimated with our model can be applied for predicting drought occurrence throughout Jiroft plain in future years. The results of our study provide quantitative information for drought risk assessment and management in Jiroft plain, and deliver insights that will help in the future development of agricultural drought hazard mapping in other arid regions of our planet – especially in areas with limited hydro-meteorological data.
Association between smoke exposure and headache among U.S adolescents: a cross-sectional study from the NHANES 1999-2004
Authors: Guo Yu , Wang Changli , Yan Shushu , Wang Jiafeng , Deng Xiaoming 
Published: May 2024

Abstract

Background
Headaches are increasingly common among adolescents, with smoking considered a contributing risk factor. Cotinine, a nicotine metabolite, serves as an objective measure of smoke exposure. However, there is limited clinical data on the relationship between serum cotinine levels and headaches in adolescents.
Methods
A cross-sectional study design was applied, and participants aged 12 to 19 years from the National Health and Nutrition Examination Survey (NHANES) 1999–2004 were included in the analysis. Headache data were obtained through interviews and self-reports. Smoke exposure levels were categorized as unexposed, low secondhand smoke (SHS) exposure, high SHS exposure, and active smoking groups based on serum cotinine levels. The main analyses employed weighted multivariable logistic regression models, accounting for the complex multi-stage sampling design of NHANES. Additionally, a restricted cubic spline model was used to investigate the non-linear relationship between serum cotinine levels and headache. Furthermore, a subgroup analysis was conducted.
Results
The analysis included 5590 adolescents aged 12 to 19 years, among whom 1442 (24.99%) experienced headaches. After adjusting for all covariates, a positive association was found between log2-transformed serum cotinine and headaches (odds ratio, R=1.04 (1.01, 1.07), P = 0.008). Adolescents with high SHS exposure (serum cotinine levels of 0.316 –10 ng/ml) and those in the active smoking group (serum cotinine≥10 ng/ml) had a higher risk of experiencing headaches compared to the unexposed group (serum cotinine < 0.05 ng/ml) [OR = 1.42 (1.07, 1.90), OR = 1.55 (1.06, 2.27), P for trend = 0.006]. A linear dose-response relationship was observed between log2-transformed serum cotinine and the risk of headaches (P non-linearity > 0.05). Subgroup analysis revealed that the positive associations remained regardless of gender and varied depending on age and central obesity.
Conclusion
In conclusion, higher smoke exposure, as measured by elevated cotinine levels, was significantly associated with an increased risk of headaches among adolescents. Future research should focus on identifying effective strategies to eliminate smoke exposure and discourage tobacco use in adolescents prevent in adolescents to prevent headaches.
FLT4 gene polymorphisms influence isolated ventricular septal defects predisposition in a Southwest China population
Authors: Zhang Yunhan , Dong Xiaoli , Zhang Jun , Zhao Miao , Wang Jiang , Chu Jiayou , Yang Zhaoqing , Ma Shaohui , Lin Keqin , Luo Zhiling , Sun Hao 
Published: May 2024

Abstract

Background
Ventricular septal defect (VSD) is the most common congenital heart disease. Although a small number of genes associated with VSD have been found, the genetic factors of VSD remain unclear. In this study, we evaluated the association of 10 candidate single nucleotide polymorphisms (SNPs) with isolated VSD in a population from Southwest China.
Methods
Based on the results of 34 congenital heart disease whole-exome sequencing and 1000 gene databases, 10 candidate SNPs were selected. A total of 618 samples were collected from the population of Southwest China, including 285 VSD samples and 333 normal samples. Ten SNPs in the case group and the control group were identified by SNaPshot genotyping. The χ2 test was used to evaluate the relationship between VSD and each candidate SNP. The SNPs that had significant p values in the initial stage were further analysed using linkage disequilibrium, and haplotypes were assessed in 34 congenital heart disease whole-exome sequencing samples using Haploview software. The bins of SNPs that were in very strong linkage disequilibrium were further used to predict haplotypes by Arlequin software. ViennaRNA v2.5.1 predicted the haplotype mRNA secondary structure. We evaluated the correlation between mRNA secondary structure changes and ventricular septal defects.
Results
The χ2 results showed that the allele frequency of FLT4 rs383985 (P = 0.040) was different between the control group and the case group (P < 0.05). FLT4 rs3736061 (r2 = 1), rs3736062 (r2 = 0.84), rs3736063 (r2 = 0.84) and FLT4 rs383985 were in high linkage disequilibrium (r2 > 0.8). Among them, rs3736061 and rs3736062 SNPs in the FLT4 gene led to synonymous mutations of amino acids, but predicting the secondary structure of mRNA might change the secondary structure of mRNA and reduce the free energy.
Conclusions
These findings suggest a possible molecular pathogenesis associated with isolated VSD, which warrants investigation in future studies.
Field Robot Self-Exploration Based on Deep Reinforcement Learning and Obstacle Avoidance Strategy
Authors: Kang Jipeng , Zhang Zhibin , He Liqiang , Chen Xu 
Published: May 2024

Abstract

This paper addresses the challenges posed by deep reinforcement learning (DRL) algorithms when deployed for reactive robotic navigation in agricultural settings. These challenges include the complexity of network models, extensive parameterization, substantial computational demands, and susceptibility to local optima. The intricacies of farmland environments further complicate the application of conventional indoor navigation methods, which falter in tasks such as crop monitoring and interaction with humans. To overcome these obstacles, we introduce the Field Robot Autonomous Navigation System (FRANS), which employs DRL to facilitate the autonomous exploration of unknown farmlands by robots. FRANS is composed of two primary modules: Global Navigation (GN) and Local Navigation (LN). The GN module sets destination points and implements a robust control mechanism to navigate the robot, effectively mitigating issues associated with local optima. Meanwhile, the LN module leverages a streamlined DRL framework to formulate a motion strategy that directs the robot towards these waypoints. Crucially, FRANS operates without prior environmental knowledge and dynamically adapts its navigation strategy in response to real-time environmental changes. The system continuously updates the map as the robot traverses the terrain. Experimental evaluations show that FRANS surpasses competing methodologies in performance.
Rational Engineering of a Thermostable α-Oxoamine Synthase Biocatalyst Expands the Substrate Scope and Synthetic Applicability
Authors: Campopiano Dominic , Ashley Ben , Zhu Yaoyi , Mathew Sam , Sajjad Mariyah , Baslé Arnaud , Marles-Wright Jon 
Published: May 2024

Abstract

Carbon-carbon bond formation is one of the key pillars of organic synthesis. Green, selective and efficient biocatalytic methods for such are therefore highly desirable. The α-oxoamine synthases (AOSes) are a class of pyridoxal 5’-phosphate (PLP)-dependent, irreversible, carbon-carbon bond-forming enzymes, which have been limited previously by their narrow substrate specificity and requirement of acyl-CoA thioester substrates. We recently characterized a thermophilic enzyme from Thermus thermophilus (ThAOS) with a much broader substrate scope and described its use in a chemo-biocatalytic cascade process to generate pyrroles in good yields and timescales. Herein, we report the structure-guided engineering of ThAOS to arrive at variants able to use a greatly expanded range of amino acid and simplified N-acetylcysteamine (SNAc) acyl-thioester substrates. The crystal structure of the improved ThAOS V79A mutant with a bound PLP:penicillamine external aldimine ligand, provides insight into the properties of the engineered biocatalyst.
Opportunities for decentralised solar power to improve reliability, reduce emissions and avoid stranded assets
Authors: Nelson Jenny , Sandwell Philip , Beath Hamish , Winchester Benedict , Markides Christos N. , Mittal Shivika 
Published: May 2024

Abstract

Despite recent improvements to electricity access in lower-income countries, reliability remains low for many. Local renewable
energy infrastructure supplementing the national grid offers a promising route to improved reliability for rural communities.
However, improvements in the reliability of national grids create risks for investors including the possibility of “stranded”
renewable assets. We use energy-system modelling to explore ways in which solar PV-based mini-grids could be interconnected
with national grids. We explore the impact of reduced demand to quantify the investment risks of losing customers. Our results
indicate that national grid-connected mini-grids can reduce the unit electricity costs for communities whilst also increasing
reliability and reducing the carbon intensity of electricity in line with SDG 7 goals. Reductions in demand have a minimal impact
at lower levels but at moderate levels are likely to undermine economic viability. Finally, we discuss policy interventions to
facilitate and protect investing in national grid-connected mini-grids.
Pros and cons of counselling at online-only pharmacies – a mixed methods case study.
Authors: Pjengaard Daniel , Rasmussen Lærke Louise , Rossing Charlotte Verner , Norgaard Lotte Stig 
Published: May 2024

Abstract

Background:The proliferation of online-only pharmacies globally is notable, yet there remains a dearth of literature regarding the practices of counseling within these platforms.
Aim: This study aims to explore the breadth, content, and various platforms utilized for counseling within an online-only pharmacy in Denmark. It further seeks to delineate the advantages and disadvantages of online counseling as perceived by the pharmacy staff.
Method: Employing a mixed methods approach, the study involved quantitative data extraction utilizing a statistical tool (e-track), two days of observation of pharmacy staff, and six semi-structured interviews with pharmacy staff.
Results: Analysis revealed that the majority of inquiries received by the pharmacy via email pertained to prescription medicines (52%), while chat inquiries primarily focused on "other goods and skin care" (24%). Observations indicated that inquiries across telephone, chat, and email channels predominantly related to "other goods" and "technical questions". The pharmacy demonstrated particular efficacy in counseling on discreet topics, though there was a tendency towards inadequately patient-centered written communication. Furthermore, pharmacy staff occasionally overlooked non-verbal cues in counseling interactions.
Conclusion: The online-only pharmacy utilized three distinct counseling channels—telephone, chat, and email—each presenting unique advantages and challenges. Customer demographics varied, influencing the nature of counseling inquiries. Future research should explore patient experiences and preferences regarding counseling, as well as their specific informational channel preferences.
Assessing Relative Significance of Key Risk Factors for Child Undernutrition: Evidence from Pakistan
Authors: Burki Abid Aman 
Published: May 2024

Abstract

Background
Child undernutrition remains a critical issue worldwide, with Pakistan particularly struggling in South Asia. While recent studies have identified some risk factors, their relative importance is unclear. This study examines the extent of child undernutrition and the relative importance of risk factors in Punjab, Pakistan's largest province.
Methods
The study presents a trend analysis to examine undernutrition patterns in Punjab’s high-risk districts between 2004 and 2018 using data from five rounds of the Multiple Indicator Cluster Survey (MICS). Additionally, it uses binary logit regressions to assess the relative significance of 25 risk factors associated with stunting, underweight, and wasting among 38,495 preschool-age children from MICS 2018. The paper ranks a set of significant environmentally modifiable risk factors based on their strongest effect sizes. The sample of children aged 0 to 59 months was also divided into two subsets: children aged 0 to 5 months and those aged 6 to 35 months.
Results
The study observed significant reductions in stunting, underweight, and wasting, particularly in Central-North Punjab, though stunting rates have risen in 13 of 36 districts, and South Punjab experiences persistent regional disparities in underweight rates. Five key predictors identified for stunting and underweight include household poverty, maternal education, short birth intervals, underage childbirth, and low birth weight; and for wasting low birth weight, maternal education, incomplete vaccination, and recent fever. Lack of dietary diversity for children aged 6–35 months and non-exclusive breastfeeding and non-assisted deliveries for those aged 0–5 months are major challenges. A sensitivity analysis revealed consistent effect sizes, while an analysis of children with severe undernutrition revealed stable major correlates and some differences in their relative rankings.
Conclusions
The study suggests that the intervention strategies implemented in Punjab post-2007 have been partially effective in reducing childhood undernutrition, providing valuable insights. The analysis of risk factors highlights the need for a comprehensive, multifaceted approach that combines socioeconomic strategies with nutritional investment programs to effectively address undernutrition in the province. These policy implications are crucial for shaping future interventions aimed at reducing childhood undernutrition.
Design and Implementation of Collaborative Energy Efficient IoT-Based LoRa WAN
Authors: G Ravi Kumar M , Sarvagya Mrinal , Prasad S N , Kavya B S 
Published: May 2024

Abstract

Wireless sensor network (WSN) architectures involve multiple sensor nodes that cover large areas. It is difficult to deploy such nodes efficiently. However, based on clustering and collaboration, WSNs can organize efficiently and prolong system lifetime. In this paper, we discuss the design and implementation of a collaborative LoRa WAN using ultra-low-energy hardware and smart utilization of battery power. We have chosen the robust LoRa modulation technique and clustering method to design the end nodes. The sensor data from each of the end nodes are transmitted using low-power, long distance, and noise-resilient LoRa radio channels to their corresponding Cluster Heads(CHs), where it is first stored and then processed before transmitting further to the Gateways. The storing and then transmitting technique reduces the bandwidth requirement significantly. Also, the stored sensor data can be analyzed for system optimization. Immediately after transmission, the microcontroller goes to hibernation mode, whereas, the sensor system and the LoRa module are powered off until the next cycle. In this way, the power consumption by the end node is kept at its minimum and the battery life is extended to 35 years. Several challenges related to hardware design and network parameters optimization to achieve energy-efficient IoT-based LoRa WAN are presented and discussed.
Wide spectrum multiple Fano resonances for high sensitivity detection of refractive index and temperature in MIM waveguides coupled with racetrack resonator nanorod-arrays
Authors: Bahri Hocine , Bensalah Hocine , Ingebrandt Sven , Hocini Abdsselam , Pachauri Vivek 
Published: May 2024

Abstract

In this paper, we designed a metal-insulator-metal (MIM) waveguides based plasmonic sensor coupled with a racetrack cavity adorned with zero Dimensional (0D) nanorod-arrays exhibiting high sensitivity detection of refractive index variations measured as spectral shifts in the resonance wavelengths. The finite-difference time-domain method (FDTD) was used to simulate and study the optical platform based on MIM waveguide and nanorod-array racetrack resonator as a detection refractive index (RI) sensor and for temperature and Diabetes sensing, with sensitivity as high as 4250 nm per RIU and 0.42 nm/°C and 3204 nm/RIU, respectively. The nanorod arrays on the racetrack resonator enhance the light-matter interaction. Which increased the sensitivity. We evaluated the impacts of radius and refractive index on the racetrack resonator's sensitivity using the spectra shifts as sensor performance. As a result, the proposed plasmonic structures exhibit strong potential for a variety of sensor applications for offering high-sensitivity detection of low concentrations of small molecules similar to glucose and temperature.
RCS reduction of wide-band, high-gain antenna array based on asymmetric transmission metasurfaces
Authors: WU TING , YAN LEI , ZHANG ZIKANG 
Published: May 2024

Abstract

In this paper, an ultra-wideband stealth antenna with high gain on the basis of asymmetric transmission metasurface (ATMS) is proposed. ATMS can convert an incident y-polarised sphere wave into an x-polarised plane wave at the front side and controls the scattering of the incident y-polarised wave to the back side. Excitation of ATMS via a horn antenna, a low radar cross-section (RCS) and wideband antenna system is designed. Furthermore, through design of the meta-atoms and optimization of the macrosequencing, broadband RCS reduction is achieved. The experimental data indicated the reduction of the RCS of the antenna system by up to 10 dB and more than 20 dB in the frequency range of 10.1 GHz to 18 GHz (relative bandwidth is 56.2%) and 13.9 GHz to 18 GHz (relative bandwidth is 25.7%), respectively. In addition, a 3 dB gain relative bandwidth of 57.4% is achieved between 10 GHz and 18 GHz, with a peak gain of 28.2 dB. It is noteworthy that the high gain and low scattering performance of the antenna are achieved in the same spectral range (10 GHz ~ 18 GHz), and there is no interference between the scattering performance and radiation performance of the antenna, which could be controlled separately.