bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Some Heavy Metals, Their Fate and Transportation in Water, Sediment, and Some Biota within AL-Musayyib River, Babylon Governorate, Iraq
Authors: Salman Jasim Mohammed , Kaizal Ashwak  Falih , Kot Patryk 
Published: September 2022

This study estimated seven heavy metals (Fe, Cu, Zn, Pb, Ni, Cd, Cr) in water (dissolved and particulate phase), sediments and some aquatic organisms including two species from aquatic plants (Ceratophyllum demersum&Phragmites australis); one species of clam (Psedontopeses euphratics) and two species from fish (Oreochromis aureus& Leuciscus vorax)in four sites within Mashroo AL- Musayyib channel project/ branch of Euphrates river, Babylon , medial of Iraq . This aims to show the concentration of these elements, their fate and the mechanisms of their transmission through the food chain in this lotic aquatic system ; also in addition to examining some physicochemical properties of river water such as water temperature (WT),air temperature (AT),pH , electrical conductivity ( EC) , salinity ,total dissolved solid ( TDS), total solid suspended (TSS) , dissolved oxygen (DO) and biological oxygen demand ( BOD5) .The occurrence in heavy metals in water was found in the order in the Dissolved phase :- Zn> Fe > Cu> Ni>Pb> Cr > Cd, the zinc element recorded the highest rate in site two for spring season while the cadmium element recorded the lowest rate in site one for spring season. In the particulate phase:- Zn> Cu >Fe > Ni>Pb> Cr > Cd, the zinc element recorded the highest rate in site two for spring season while the cadmium element recorded the lowest rate in site four for spring season. The sediment order was Zn> Cu >Fe >Pb> Ni > Cd > Cr where the zinc element recorded the highest rate in site two for spring season where as the chromium element recorded the lowest rate in site two for spring. In aquatic plant the order was in C. demersum :Zn> Fe > Cu>Pb> Cr > Ni > Cd, the zinc element recorded the highest rate in site four for winter season while the cadmium element recorded the lowest rate in site one for autumn season. In P. australis: Zn> Fe > Cu>Pb> Cd > Ni > Cr , the zinc element recorded the highest rate in site two for spring season while the chromium element recorded the lowest rate in site two for spring season. In clam the order was: Zn> Fe > Cu> Ni >Pb> Cd > Cr where the zinc element recorded the highest rate in site two for spring season while the chromium recorded the lowest value in site 3 through winter 2021.In fish species (O. aureas) the order was Fe > Zn > Cu>Pb> Cr > Cd > Ni. The iron element recorded as the highest rate in site 2 in spring 2021 whilethe element that recorded the lowest rate was Ni in site 2for spring in L. vorax. The iron element recorded the highest rate in site one for summer season where as the Nickel was recorded a lower value in site four in spring2021.
An Immune-Related Clinical-Gene-Risk-Score (CGRS) prognosis Model for Bladder Cancer
Authors: Liu Sanhe , Duan Liqun , Wang Yiqi , Cui Diansheng , Deng Kangli , Wei Shaozhong , Dong Zhiqiang 
Published: September 2022

Abstract
Background Immunotherapy for bladder cancer is prevalent, while efficacy is uncertain. To predict the outcomes of immunotherapy in bladder cancer patients, an immune-related prognosis model was conducted in this study.
Methods Single sample Gene Set Enrichment Analysis was used to delineate the landscape of the tumor immune microenvironment based on two independent public databases. Hierarchical clustering analysis and t-distributed Stochastic Neighbor Embedding cluster map were used to classify the bladder cancers in the training cohort as well as the validating cohort into the immune high and immune low subtypes. Differentially expression analysis and The least absolute shrinkage and selection operator regression analysis identified 23 differentially expressed immune-related genes between the two subtype patients to build a prognostic gene risk score model. Further combining the model with other clinicopathologic signatures, a Clinical-Gene-Risk-Score (CGRS) prognosis model was developed.
Results Validations of the two models in independent cohorts revealed good accuracy.
Conclusions Our model could provide prognostic indicators and potential immunotherapeutic targets for bladder cancers.
Hyperaccumulation of lead using Agrostis tenuis
Authors: Anguilano Lorna , Onwukwe Uchechukwu , Dekhli Aghis , Venditti Susanna , Aryani Danny , Reynolds Alan 
Published: September 2022

Abstract
In recent years the quest for a circular economy approach and the upcycling of secondary raw materials have been pushed in the global political agenda. Increased interest has been taken by the recovery of materials from sludges, brines, contaminated waters and other media, all included in the larger umbrella identified as “low-grade” mineralisation. Contaminated soils have an interesting role in this process, and various methodologies have been developed using chemical, bacteriological and pyrometallurgical cleaning procedures. However, these procedures all involve the movement of high volume of materials and the disruption of the industrial landscape; furthermore, they often require the use of hazardous solvents and high energy processes. This work proposes to identify less impactful methods aimed at the recovery of metals from mining areas while preserving the landscape and avoiding environmental impacts such as the increase of CO2 for transport and increase hazard through use of solvents, this takes particular importance in areas of industrial heritage status. In particular, this work focuses on the use of Agrostis tenuis, an autochthonous species in mining areas of the UK, as a “mining tool” for the removal of lead. The experiments and results presented in this paper indicate: 1) Agrostis Tenuis shows the ability to adjust to contamination and increase its accumulation capacity; 2) the metal collected by the plant is fully transposed in the aerial part of the plant in a stable compound form.
Multinomial classification to predict the most effective adjuvant combination therapies for breast cancer patients
Authors: Ertel Merouane , Amali Said , Faddouli Nour-eddine El 
Published: September 2022

Abstract
Accurately predicting effective treatment methods based on personalized tumor genetic profiles is a major goal of precision cancer medicine. Because people with breast cancer at comparable stages respond differently to treatment, it is essential to gain insight into the variables that influence treatment success. This study presents a supervised multinomial logistic regression model for predicting the best adjuvant therapy for breast cancer patients to lower the probability of metastatic recurrence. This model will assist health professionals (physicians) in making judgments about which medicinal regimens to suggest to patients. In addition, this article presents a comparison of several multinomial machine learning methods (Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (ANN)).The results reveal that the Random Forest classifier is more effective in terms of adjuvant therapy combination prediction accuracy.
Time Course of Neuroinflammation after Human Stroke – a pilot study using co-registered PET and MRI
Authors: D'Anna Lucio , Searle Graham , Harvey Kirsten , Matthews Paul M , Veltkamp Roland 
Published: September 2022

Abstract
Background. Microglial activation contributes to both inflammatory damage and repair in experimental ischemic stroke. However, because of the logistical challenges, there have been few clinical imaging studies directly describing inflammatory activation and its resolution after stroke. The purpose of our pilot study was to describe the spatio-temporal profile of brain inflammation after stroke using 18kD Translocator protein (TSPO) PET with magnetic resonance (MR) co-registration in the subacute and chronic stage after stroke.
Methods. Three patients underwent MRI and PET scans with TSPO ligand [11C]PBR28 15±3 and 90 ±7 days after an ischemic stroke. Regions of Interest (ROIs) were defined on MRI images and applied to the dynamic PET data to derive regional time-activity curves. Regional uptake was quantified as standardized uptake values (SUV) over 60 to 90 minutes post-injection. ROI analysis was applied to identify binding in the infarct, and in frontal, temporal, parietal, and occipital lobes and cerebellum excluding the infarcted area.
Results. The mean age of participants was 56 ± 20.4 years and mean infarct volume was 17.9 ± 18.1 ml. [11C]PBR28 showed increased tracer signal in the infarcted area compared to non-infarcted areas of the brain in the subacute phase of stroke (Patient 1 SUV 1.81; Patient 2 SUV 1.15; Patient 3 SUV 1.64). [11C]PBR28 uptake returned to the level of non-infarcted areas at 90 days Patient 1 SUV 0.99; Patient 3 SUV 0.80). No additional upregulation was detected elsewhere at either time point.
Conclusions. The neuroinflammatory reaction after ischemic stroke is limited in time and circumscribed in space suggesting that posischemic inflammation is tightly controlled but regulatory mechanisms.
Immunodominant protein P116 from M. pneumoniae transports cholesterol and essential lipids
Authors: Frangakis Achilleas , Sprankel Lasse , Vizarraga David , Martín Jesús , Manger Sina , Meier-Credo Jakob , Marcos Marina , Julve Josep , Rotllan Noemi , Scheffer Margot , Escolà-Gil Joan , Langer Julian , Piñol Jaume , Fita Ignacio 
Published: September 2022

Abstract
Mycoplasma pneumoniae, responsible for approximately 30% of community-acquired human pneumonia, needs to extract lipids from the host environment for survival and proliferation. Here, we report a comprehensive structural and functional analysis of the previously uncharacterized protein P116 (MPN_213). Single-particle cryo-electron microscopy of P116 reveals a homodimer presenting a previously unseen fold, forming a huge hydrophobic cavity, which is fully accessible to solvent. Lipidomics analysis shows that P116 specifically acquires essential lipids such as phosphatidylcholine, sphingomyelin and cholesterol. Structures of different conformational states reveal the mechanism by which lipids are transported. This finding immediately suggests a way to control Mycoplasma infection by interfering with lipid uptake.
Encryption Based Simple and Robust Subscriber – Client Authentication Protocol for Ultralightweight Sensor Networks
Authors: Chakraborty Rajdeep , Acharya Aheli , Paul Debasmita , Guha Debajyoti , Elngar Ahmed A. 
Published: September 2022

Abstract
Safety is the most important part of IoT networks. A maximum number of IoT devices are resource constrained, it is difficult to implement strong safety mechanisms. The maximum number of authentication techniques use certificates in which signing and verifying require additional computation and power. Thus, the goal of this paper is to propose a new lightweight encryption-based authentication protocol for resource-controlled devices. In this paper we proposed an authentication protocol that uses RRSC – v1 encryption and one subscriber and several clients. The main objective is to develop an ultralightweight protocol for a very low-resourced sensor network. In this protocol, we have two algorithms, first, one to one encryption-based authentication protocol where the client is the low resource sensor and the subscriber is the edge device in the IoT. Second, the RRSC – V1, encryption algorithm itself. It has many applications for temperature sensors, rain sensors, and smoke sensors as they have very low memories and computing capacity.
An easy-to-use prehospital indicator for recognition of severity in heat-related illness: An observational study in the Tokyo metropolitan area
Authors: Yamaguchi Junko , Kinoshita Kosaku , Takeyama Minami 
Published: September 2022

Abstract
Background: Quick transportation to the hospital is necessary to decrease mortality risk in patients with heat-related illnesses. This study aimed to establish a rapid, effective, easy-to-use indicator for healthcare providers and laypersons to identify the life-threatening severity of heat-related illness and facilitate quick transport of identified patients in a community setting.
Methods: This observational study was conducted using a database from 2016. Information concerning the clinical severity of heat-related illness in patients (n=2528) upon arrival at the hospital was extracted from prehospital transportation records.
Results: Patient-related risk factors included age, vital signs, location of the patient, and the severity of the illness. Respiratory rate (adjusted odds ratio [aOR], 3.34; 95% confidence interval [CI], 1.80–6.22), heart rate (aOR, 2.88, 95% CI, 1.57–5.29), axillary body temperature (aOR, 7.79, 95% CI, 4.02–15.1), and consciousness level (aOR, 38.3, 95% CI, 5.22–281.1) were independent factors associated with the severity of heat-related illness. Blood pressure was measured at the scene but was not found to be an independent factor relating to the severity of the patient's condition. A heart rate, respiratory rate, and body temperature of more than 120 beats/min, 24 breaths/min, and 38.6°C, respectively (highest areas under the ROC curves: 0.80, 95% CI 0.75–0.87; 0.73, 95% CI 0.67–0.81; and 0.83, 95% CI 0.77–0.91; respectively) were predictive of life-threatening conditions in patients with heat-related illnesses.
Conclusions: Attention should be paid to changes in the vital signs of patients with heat-related illness. Tachycardia and tachypnea are particularly sensitive and easy-to use indicators for laypersons to facilitate quick identification and transport of patients before they decline to a life-threatening situation.
Modified Oyster Shell Powder with iron (II) sulfate heptahydrate to improve arsenic uptake in solution and in contaminated soils
Authors: Torres-Quiroz Cecilia , Dissanayake Janith , Park Junboum 
Published: September 2022

Abstract

Arsenic is a metalloid whose presence can be due to natural or anthropological causes. It is considered as a toxic chemical that puts human health at high risk. In this study, we evaluated a novel modified oyster shell (MOS) that was coated with iron (II) sulfate heptahydrate using two different proportions through batch sorption experiments in an arsenic solution and in arsenic-contaminated soils. The soils contaminated with Arsenic were extracted from a contaminated site in Cheonan, South Korea and the average arsenic concentration of the soil collected was 136.28 mg/kg. Different doses of oyster shell, and modified oyster was used to understand the effect of the addition of iron (II) sulfate heptahydrate via a sorption test with 50 g of contaminated soil, pot cultivation test with 150 g and column percolation test with 150 g. The authors observed a comparable improvement of Arsenic stabilization from 10–60% with the addition of iron (II) sulfate heptahydrate to oyster shell.
Novel Protein-repellent and Antibacterial Polymethyl Methacrylate Dental Resin in Water-aging for 6 Months
Authors: Cao Li , Xie Xianju , Yu Wenqi , Xu Hockin H. K. , Bai Yuxing , Zhang Ke , Zhang Ning 
Published: September 2022

Abstract
Background
The present study aimed to develop a novel protein-repellent and antibacterial polymethyl methacrylate (PMMA) dental resin with 2methacryloyloxyethyl phosphorylcholine (MPC) and quaternary ammonium dimethylaminohexadecyl methacrylate (DMAHDM), and to investigate the effects of water-aging for 6 months on the mechanical properties, protein adsorption, and antibacterial activity of the dental resin.
Methods
Four groups were tested: PMMA control; PMMA + 3% MPC; PMMA + 1.5% DMAHDM; and PMMA + 3% MPC + 1.5% DMADDM in acrylic resin powder. Specimens were water-aged for 1 d, 3 months, and 6 months at 37 ℃. Their mechanical properties were then measured using a three-point flexure test. Protein adsorption was measured using a micro bicinchoninic acid (BCA) method. A human saliva microcosm model was used to inoculate bacteria on water-aged specimens and to investigate the live/dead staining, metabolic activity of biofilms, and colony-forming units (CFUs).
Results
The flexural strength and elastic modulus showed a significant loss after 6 months of water-ageing for the PMMA control (mean ± SD; n = 10); in contrast, the new protein repellent and antibacterial PMMA resin showed no strength loss. The PMMA–MPC–DMAHDM-containing resin imparted a strong antibacterial effect by greatly reducing biofilm viability and metabolic activity. The biofilm CFU count was reduced by about two orders of magnitude (p < 0.05) compared with that of the PMMA resin control. The protein adsorption was 20% that of a commercial composite (p < 0.05). Furthermore, the PMMA–MPC–DMAHDM-containing resin exhibited a long-term antibacterial performance, with no significant difference between 1 d, 3 months and 6 months (p > 0.05).
Conclusions
The flexural strength and elastic modulus of the PMMA–MPC–DMAHDM-containing resin were superior to those of the PMMA control after 6 months of water-ageing. The novel PMMA resin incorporating MPC and DMAHDM exhibited potent and lasting protein-repellent and antibacterial properties.
Body Mass Index And Inflammation In Depression And Treatment-Resistant Depression: A Mendelian Randomization Study
Authors: Karageorgiou Vasilios , Casanova Francesco , O'Loughlin Jessica , Green Harry , McKinley Trevelyan J. , Bowden Jack , Tyrrell Jessica 
Published: September 2022

Abstract
Introduction: Major depressive disorder (MDD) has a significant impact on global burden of disease. Complications in clinical management can occur when response to pharmacological modalities is considered inadequate and symptoms persist (treatment-resistant depression (TRD)). We aim to investigate inflammation, proxied by C-reactive protein (CRP) levels, and body mass index (BMI) as putative causal risk factors for depression and subsequent treatment resistance, leveraging genetic
information to avoid confounding via Mendelian Randomization (MR).

Methods: We used the European UK Biobank subcohort (n = 451, 025), the mental health questionnaire (MHQ) and clinical records. For treatment resistance, a previously curated phenotype based on GP records and prescription data was employed. We applied univariable and multivariable MR models to genetically predict the exposures and assess their causal contribution to a range of depression outcomes . We used weak and pleiotropy, robust estimation techniques within univariable, multivariable and mediation MR models in order to address
our research question with maximum rigour. In addition, we developed a novel statistical procedure to apply pleiotropy robust multivariable MR to one sample data and employed an unfamiliar bootstrap procedure to accurately quantify estimate uncertainty in mediation analysis which outperforms
standard approaches.

Results: In univariable MR models, genetically predicted BMI was positively associated with depression outcomes, including MDD and TRD, with a larger magnitude in women and with age acting
as a moderator of the effect of BMI on PHQ9 (p = 0.011). Multivariable MR analyses suggested an independent causal effect of BMI on TRD not through CRP. Our mediation analyses suggested
that the effect of CRP on PHQ9 was partly mediated by BMI. Individuals with TRD (n = 2, 199) observationally had higher CRP and BMI compared with individuals with MDD alone and healthy
controls. A positive causal association was noted for both the exposures, but in multivariable analyses only BMI retained statistical significance at the 5% level.

Discussion: Our work supportst the assertion BMI exerts a causal effect on a range of clinical and questionnaire-based depression phenotypes, with the effect being stronger in women and in younger individuals. We show that this effect is independent of inflammation proxied by CRP levels as the effects of CRP do not persist when jointly estimated with BMI. This is consistent with previous evidence suggesting that overweight contributed to depression even in the absence of any metabolic consequences. It appears that BMI exerts an effect on TRD that persists when we account for BMI influencing MDD.
Evaluating the effects of vegetation and land management on runoff control using field plots and machine learning models
Authors: Gholami Vahid , Sahour Hossein , Khaleghi Mohammad Reza , Yousefi Atena 
Published: September 2022

Abstract

Excess surface water after heavy rainfalls leads to soil erosion and flash floods, resulting in human and financial losses. Reducing runoff is an essential management tool to protect water and soil resources. This study aimed to evaluate the effects of vegetation and land management methods on runoff control and to provide a model to predict runoff values. Filed plot data and three machine learning methods, including artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIS), and extreme gradient boosting (EGB), were used in a test site in the north of Iran. In this regard, plots with various vegetation and land management treatments including bare soil treatment, rangeland cover treatment, forest litter treatment, rangeland litter treatment, tillage treatment in the direction of slope, tillage treatment perpendicular to the slope, and repetition of treatments under forest canopy were constructed on a hillslope. After each rainfall event, the amount of rainfall and corresponding runoff generated in each plot was recorded. Three machine learning models (ANN, CANFIS, and EGB) were used to establish relationships between amounts of recorded runoff and its controlling factors (rainfall, antecedent soil moisture (A.M.C), shrub canopy percentage and height, tree canopy percentage and height, soil texture (clay, silt, and sand percent), slope degree, leaf litter percentage of soil, and tillage interval). These data were normalized, randomized, and divided into training and testing subsets. Results showed that the ANN performed better than the other two models in predicting runoff in training (R-sqr = 0.98; MSE = 0.004) and the test stages (R-sqr = 0.90; MSE = 0.95). Statistical analysis and sensitivity analysis of inputs factors showed that rainfall, rangeland cover, and A.M.C are the three most important factors controlling runoff generation. The adopted method can be used to predict the effect of different vegetation and land management scenarios on runoff generation in the study area and the areas with similar settings elsewhere.