Adsorption and desorption behavior of arsenite and arsenate at river sediment-water interface.

The adsorption of inorganic arsenic (As) plays an important role in the mobility and transport of As in the river environment. In this work, the adsorption and desorption of arsenite [As(III)] and arsenate [As(V)] on river sediment were conducted under different pH, initial As concentrations, river water and sediment composition to assess As adsorption behavior and mechanism. Both adsorption kinetics and equilibrium results showed higher adsorption capacity of sediment for As(V) than As(III). Adsorption of As(III) and As(V) on river sediment was favored in acidic to neutral conditions and on finer sediment particles, while sediment organic matter marginally reduced adsorption capacity. In addition, higher adsorption affinity of As(III) and As(V) in river sediment was observed in deionised water than in river water. For the release process, the desorption of both As(III) and As(V) followed nonlinear kinetic models well, showing higher amount of As(III) release from sediment than As(V). Adsorption isotherm was well described by both Langmuir and Freundlich models, demonstrating higher maximum adsorption capacity of As(V) at 298.7 mg/kg than As(III) at 263.3 mg/kg in deionised water, and higher maximum adsorption capacity of As(III) of 234.3 mg/kg than As(V) of 206.2 mg/kg in river water. The XRD showed the changes in the peaks of mineral groups of sediment whilst FTIR results revealed the changes related to surface functional groups before and after adsorption, indicating that Fe-O/Fe-OH, Si(Al)-O, hydroxyl and carboxyl functional groups were predominantly involved in As(III) and As(V) adsorption on sediment surface. XPS analysis evidenced the transformation between these As species in river sediment after adsorption, whilst SEM-EDS revealed higher amount of As(V) in river sediment than As(III) due to the lower signal of Al.

Advances in As contamination and adsorption in soil for effective management.

Arsenic (As) is a heavy metal that causes widespread contamination and toxicity in the soil environment. This article reviewed the levels of As contamination in soils worldwide, and evaluated how soil properties (pH, clay mineral, organic matter, texture) and environmental conditions (ionic strength, anions, bacteria) affected the adsorption of As species on soils. The application of the adsorption isotherm models for estimating the adsorption capacities of As(III) and As(V) on soils was assessed. The results indicated that As concentrations in contaminated soil varying significantly from 1 mg/kg to 116,000 mg/kg, with the highest concentrations being reported in Mexico with mining being the dominating source. Regarding the controlling factors of As adsorption, soil pH, clay mineral and texture had demonstrated the most significant impacts. Both Langmuir and Freundlich isotherm models can be well fitted with As(III) and As(V) adsorption on soils. The Langmuir adsorption capacity varied in the range of 22-42400 mg/kg for As(V), which is greater than 45-8901 mg/kg for As(III). The research findings have enhanced our knowledge of As contamination in soil and its underlying controls, which are critical for the effective management and remediation of As-contaminated soil.

Chemical and microbiological risk assessment of urban river water quality in Vietnam.

The contamination and risk by nutrients (NH4+, NO2-, NO3- and PO43-), COD, BOD5, coliform and potentially toxic elements (PTEs) of As, Cd, Ni, Hg, Cu, Pb, Zn and Cr were investigated in urban river (Nhue River), Vietnam during 2010-2017. The extensive results demonstrated that concentrations of these contaminants showed significant spatial and temporal variations. The Nhue River was seriously polluted by NH4+ (0.025-11.28 mg/L), PO43- (0.17-1.72 mg/L), BOD5 (5.8-179.6 mg/L), COD (1.4-239.8 mg/L) and coliform (1540-326,470 CFU/100 mL); moderately polluted by As (0.2-131.15 µg/L) and Hg (0.11-4.1 µg/L); and slightly polluted by NO2- (0.003-0.33 mg/L) and Cd (2.1-18.2 µg/L). The concentrations of NH4+, PO43-, COD, BOD5 and coliform frequently exceeded both drinking water guidelines and irrigation water standards. Regarding PTEs, As, Cd and Hg concentrations were frequently higher than the regulatory limits. Human health risks of PTEs were evaluated by estimating hazard index (HI) and cancer risk through ingestion and dermal contacts for adults and children. The findings indicated that As was the most important pollutant causing both non-carcinogenic and carcinogenic concerns. The non-carcinogenic risks of As were higher than 1.0 at all sites for both adults (HI = 1.83-7.4) and children (HI = 2.6-10.5), while As posed significant carcinogenic risks for adults (1 × 10-4-4.96 × 10-4). A management strategy for controlling wastewater discharge and protecting human health is urgently needed.

Industrial metal pollution in water and probabilistic assessment of human health risk.

Concentration of eight heavy metals in surface and groundwater around Dhaka Export Processing Zone (DEPZ) industrial area were investigated, and the health risk posed to local children and adult residents via ingestion and dermal contact was evaluated using deterministic and probabilistic approaches. Metal concentrations (except Cu, Mn, Ni, and Zn) in Bangshi River water were above the drinking water quality guidelines, while in groundwater were less than the recommended limits. Concentration of metals in surface water decreased as a function of distance. Estimations of non-carcinogenic health risk for surface water revealed that mean hazard index (HI) values of As, Cr, Cu, and Pb for combined pathways (i.e., ingestion and dermal contact) were >1.0 for both age groups. The estimated risk mainly came from the ingestion pathway. However, the HI values for all the examined metals in groundwater were <1.0, indicating no possible human health hazard. Deterministically estimated total cancer risk (TCR) via Bangshi River water exceeded the acceptable limit of 1 × 10-4 for adult and children. Although, probabilistically estimated 95th percentile values of TCR exceeded the benchmark, mean TCR values were less than 1 × 10-4. Simulated results showed that 20.13% and 5.43% values of TCR for surface water were >1 × 10-4 for adult and children, respectively. Deterministic and probabilistic estimations of cancer risk through exposure to groundwater were well below the safety limit. Overall, the population exposed to Bangshi River water remained at carcinogenic and non-carcinogenic health threat and the risk was higher for adults. Sensitivity analysis identified exposure duration (ED) and ingestion rate (IR) of water as the most relevant variables affecting the probabilistic risk estimation model outcome.

Photocatalytic Degradation of Pharmaceutical Residues from Water and Sewage Effluent Using Different TiO2 Nanomaterials.

Pharmaceuticals are widely used and often discharged without metabolism into the aquatic systems. The photocatalytic degradation of pharmaceutical compounds propranolol, mebeverine, and carbamazepine was studied using different titanium dioxide nanostructures suspended in water under UV and UV-visible irradiation. Among three different photocatalysts, the degradation was most effective by using Degussa P25 TiO2, followed by Hombikat UV100 and Aldrich TiO2. The photocatalytic performance was dependent on photocatalyst dosage, with an optimum concentration of 150 mg L-1. The natural aquatic colloids were shown to enhance the extent of photocatalysis, and the effect was correlated with their aromatic carbon content. In addition, the photocatalysis of pharmaceuticals was enhanced by the presence of nitrate, but inhibited by the presence of 2-propanol, indicating the importance of hydroxyl radicals. Under optimum conditions, the pharmaceuticals were rapidly degraded, with a half-life of 1.9 min, 2.1 min, and 3.2 min for propranolol, mebeverine, and carbamazepine, respectively. In treating sewage effluent samples, the photocatalytic rate constants for propranolol (0.28 min-1), mebeverine (0.21 min-1), and carbamazepine (0.15 min-1) were similar to those in water samples, demonstrating the potential of photocatalysis as a clean technology for the effective removal of pharmaceuticals from sewage effluent.

Adsorbents from rice husk and shrimp shell for effective removal of heavy metals and reactive dyes in water.

Widespread contamination by heavy metals (HMs) and dyes poses a major health risk to people and ecosystems requiring effective treatment. In this work, rice husk (RH) and shrimp shells were extracted to obtain amorphous silica and chitosan, respectively, which were utilized to produce nano-chitosan-coated silica (NCCS). To ensure the stability of the nanoparticles, silica was freeze-dried after being coated with nano-chitosan. Functional groups (-NH2, -OH, P]O) from chitosan nanoparticles (CNPs) were introduced to the surface of silica during this process. Dyes such as brilliant green (BG), methylene blue (MB) and reactive brown (RB) as well as HMs (Cr6+, Pb2+, Cd2+, Ni2+) were removed by adsorbents. CNPs showed the highest adsorption capacity for RB (59.52 mg/g) among dyes and Cr6+ (42.55 mg/g) among HMs. CNPs showed the highest adsorption capacity for HMs among different adsorbents. Although NCCS and CNPs showed similar adsorption capabilities for HMs and dyes, NCCS showed the best stability. The adsorption performance decreased as RB > Cr6+ > MB > BG > Pb2+ > Cd2+ > Ni2+. The adsorption reactions followed both pseudo-first-order and second-order kinetics, and was spontaneous from thermodynamic analysis. In summary, the waste-derived adsorbents demonstrated excellent potential for removing HMs and dyes from water, while supporting effective management solid waste.

Recent Advances in Centrifugal Spinning and Their Applications in Tissue Engineering.

Over the last decade, researchers have investigated the potential of nano and microfiber scaffolds to promote wound healing, tissue regeneration, and skin protection. The centrifugal spinning technique is favored over others due to its relatively straightforward mechanism for producing large quantities of fiber. Many polymeric materials have yet to be investigated in search of those with multifunctional properties that would make them attractive in tissue applications. This literature presents the fundamental process of fiber generation, and the effects of fabrication parameters (machine, solution) on the morphologies such as fiber diameter, distribution, alignment, porous features, and mechanical properties. Additionally, a brief discussion is presented on the underlying physics of beaded morphology and continuous fiber formation. Consequently, the study provides an overview of the current advancements in centrifugally spun polymeric fiber-based materials and their morphological features, performance, and characteristics for tissue engineering applications.

Super-Adsorptive Biodegradable Hydrogel from Simply Treated Sugarcane Bagasse.

There is a great demand for biodegradable hydrogel, and cellulose enriched wastes materials are widely used to serve this purpose for various advance applications (e.g., biomedical and environmental). Sugarcane bagasse is cellulose-enriched agro-waste, abundantly grown in Bangladesh. This study aimed to treat sugarcane bagasse-based agro-waste using a sustainable and ecofriendly approach to produce hydrogel with super-swelling capacity for adsorption of copper, chromium, iron ions, methylene blue and drimaren red dyes. To increase the swelling property of hydrogels, copolymerization of hydrophilic monomers is an effective technique. Therefore, this study aimed to prepare hydrogel via free radical graft-copolymerization reaction among acrylamide, methyl methacrylate and treated bagasse in the presence of N,N-methylene-bis-acrylamide as a crosslinker and potassium persulphate as an initiator. To obtain maximum yield, reaction conditions were optimized. It was found that hydrogel obtained from chemically treated sugarcane bagasse showed maximum water absorption capacity of 228.0 g/g, whereas untreated bagassebased hydrogel could absorb ~50 g/g of water. Maximum adsorption capacity of 247.0 mg/g was found for copper ion. In addition, organic pollutant removal from industrial effluent also showed good performance, removing >90% of methylene blue and 62% of drimaren red dye, with shorter kinetics. The biodegradability study showed that after 90 days of exposure, the hydrogels degraded to about 43% of their own mass. Therefore, the produced hydrogel could be an alternative adsorbent to remove pollutants and also for other potential applications.

Metals extraction processes from electronic waste: constraints and opportunities.

The skyrocketing demand and progressive technology have increased our dependency on electrical and electronic devices. However, the life span of these devices has been shortened because of rapid scientific expansions. Hence, massive volumes of electronic waste (e-waste) is generating day by day. Nevertheless, the ongoing management of e-waste has emerged as a major threat to sustainable economic development worldwide. In general, e-waste contains several toxic substances such as metals, plastics, and refractory oxides. Metals, particularly lead, mercury, nickel, cadmium, and copper along with some valuable metals such as rare earth metals, platinum group elements, alkaline and radioactive metal are very common; which can be extracted before disposing of the e-waste for reuse. In addition, many of these metals are hazardous. Therefore, e-waste management is an essential issue. In this study, we critically have reviewed the existing extraction processes and compared among different processes such as physical, biological, supercritical fluid technologies, pyro and hydrometallurgical, and hybrid methods used for metals extraction from e-waste. The review indicates that although each method has particular merits but hybrid methods are eco-friendlier with extraction efficiency > 90%. This study also provides insight into the technical challenges to the practical realization of metals extraction from e-waste sources.

Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer.

Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes.

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16-5p, 155-5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Recent Developments of Carboxymethyl Cellulose.

Carboxymethyl cellulose (CMC) is one of the most promising cellulose derivatives. Due to its characteristic surface properties, mechanical strength, tunable hydrophilicity, viscous properties, availability and abundance of raw materials, low-cost synthesis process, and likewise many contrasting aspects, it is now widely used in various advanced application fields, for example, food, paper, textile, and pharmaceutical industries, biomedical engineering, wastewater treatment, energy production, and storage energy production, and storage and so on. Many research articles have been reported on CMC, depending on their sources and application fields. Thus, a comprehensive and well-organized review is in great demand that can provide an up-to-date and in-depth review on CMC. Herein, this review aims to provide compact information of the synthesis to the advanced applications of this material in various fields. Finally, this article covers the insights of future CMC research that could guide researchers working in this prominent field.

Microplastic particles in the aquatic environment: A systematic review.

Microplastics (MPs) pollution has become one of the most severe environmental concerns today. MPs persist in the environment and cause adverse effects in organisms. This review aims to present a state-of-the-art overview of MPs in the aquatic environment. Personal care products, synthetic clothing, air-blasting facilities and drilling fluids from gas-oil industries, raw plastic powders from plastic manufacturing industries, waste plastic products and wastewater treatment plants act as the major sources of MPs. For MPs analysis, pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), Py-MS methods, Raman spectroscopy, and FT-IR spectroscopy are regarded as the most promising methods for MPs identification and quantification. Due to the large surface area to volume ratio, crystallinity, hydrophobicity and functional groups, MPs can interact with various contaminants such as heavy metals, antibiotics and persistent organic contaminants. Among different physical and biological treatment technologies, the MPs removal performance decreases as membrane bioreactor (> 99%) > activated sludge process (~98%) > rapid sand filtration (~97.1%) > dissolved air floatation (~95%) > electrocoagulation (> 90%) > constructed wetlands (88%). Chemical treatment methods such as coagulation, magnetic separations, Fenton, photo-Fenton and photocatalytic degradation also show moderate to high efficiency of MP removal. Hybrid treatment technologies show the highest removal efficacies of MPs. Finally, future research directions for MPs are elaborated.

Visible and UV photocatalysis of aqueous perfluorooctanoic acid by TiO2 and peroxymonosulfate: Process kinetics and mechanistic insights.

The global occurrence and adverse environmental impacts of perfluorooctanoic acid (PFOA) have attracted wide attention. This study focused on the PFOA photodegradation by using photocatalyst TiO2 with peroxymonosulfate (PMS) activation. Aqueous PFOA (50 mg L-1) at the pH 3 was treated by TiO2/PMS under 300 W visible light (400-770 nm) or 32 W UV light (254 nm and 185 nm). The addition of PMS induced a significant degradation of PFOA under powerful visible light compared with sole TiO2. Under visible light, 0.25 g L-1 TiO2 and 0.75 g L-1 PMS in the solution with the initial pH 3 provided optimum condition which achieved 100% PFOA removal within 8 h. Under UV light irradiation at 254 nm and 185 nm wavelength, TiO2/PMS presented excellent performance of almost 100% removal of PFOA within 1.5 h, attributed to the high UV absorbance by the photocatalyst. The intermediates analysis showed that PFOA was degraded from a long carbon chain PFOA to shorter chain intermediates in a stepwise manner. Furthermore, scavenger experiments indicated that SO4•-radicals from PMS and photogenerated holes from TiO2 played an essential role in degrading PFOA. The presence of organic compounds in real wastewater reduced the degradation efficacy of PFOA by 18-35% in visible/TiO2/PMS system. In general, TiO2/PMS could be an ideal and effective photocatalysis system for the degradation of PFOA from wastewater using either visible or UV light source.

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression.

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

Genetic effects of welding fumes on the progression of neurodegenerative diseases.

BACKGROUND: Welding involves exposure to fumes, gases and radiant energy that can be hazardous to human health. Welding fumes (WFs) comprise a complex mixture of metallic oxides, silicates and fluorides that may result in different health effects. Inhalation of WFs in large quantities over a long periods may pose a risk of developing neurodegenerative diseases (NDGDs), but the nature of this risk is poorly understood. To address this we performed transcriptomic analysis to identify links between WF exposure and NDGDs. METHODS: We developed quantitative frameworks to identify the gene expression relationships of WF exposure and NDGDs. We analyzed gene expression microarray data from fume-exposed tissues and NDGDs including Parkinson's disease (PD), Alzheimer's disease (AD), Lou Gehrig's disease (LGD), Epilepsy disease (ED) and multiple sclerosis disease (MSD) datasets. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways and protein-protein interaction sub-network using multilayer network topology and neighborhood-based benchmarking. RESULTS: We observed that WF associated genes share 18, 16, 13, 19 and 19 differentially expressed genes with PD, AD, LGD, ED and MSD respectively. Gene expression dysregulation along with relationship networks, pathways and ontologic analysis indicate that WFs may be linked to the progression of these NDGDs. CONCLUSIONS: Our developed network-based approach to analysis and investigate the genetic effects of welding fumes on PD, AD, LGD, ED and MSD neurodegenerative diseases could be helpful to understand the causal influences of WF exposure for the progression of the NDGDs.

Removing arsenic from water by coprecipitation with iron: Effect of arsenic and iron concentrations and adsorbent incorporation.

Arsenic (As) contamination of drinking water is a major cause of As toxicity in many parts of the world. A study was conducted to evaluate As removal from water containing 100-700 µg/L of As and As to Fe concentration ratios of 1:5-1:1000 using the coprecipitation process with and without As/Fe adsorption onto granular activated carbon (GAC). Fe concentration required to reduce As concentrations in order to achieve the WHO standard level of 10 µg/L increased exponentially with the increase in initial As concentration. When small amounts of GAC were added to the As/Fe solutions the Fe required to remove these As concentrations reduced drastically. This decline was due to the GAC adsorption of Fe and As, enhancing the removal of these metals through coprecipitation. Predictive regression equations were developed relating the GAC dose requirement to the initial As and Fe concentrations. Zeta potential data revealed that As was adsorbed on the GAC by outer-sphere complexation whereas Fe was adsorbed by inner-sphere complexation reversing the negative charge on GAC to positive values. X-ray diffraction of the GAC samples in the presence of Fe had an additional peak characteristic of ferrihydrite (Fe oxide) compared to that of the GAC sample without Fe. The study showed that incorporating an adsorbent into the coprecipitation process has the advantage of removing As from waters at all concentrations of Fe and As compared to coprecipitation alone which does not remove As to the required levels if Fe concentration is low.

A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue.

Identification of genes whose regulation of expression is functionally similar in both brain tissue and blood cells could in principle enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of gene expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinson's Disease (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology approaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain tissue and blood cells by examining cell pathway commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may, with clinical validation, enable prediction of neurological dysfunction using blood cell transcript profiling.

Genetic effects of welding fumes on the development of respiratory system diseases.

BACKGROUND: The welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. METHODS: We developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. RESULTS: We observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. CONCLUSIONS: Our network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

Photocatalysis of estrone in water and wastewater: Comparison between Au-TiO2 nanocomposite and TiO2, and degradation by-products.

Gold-modified TiO2 (Au-TiO2) photocatalysts were utilised for the degradation of estrone (E1), a major endocrine disrupting chemical in water and wastewater. Au-TiO2 catalysts were synthesised by a deposition-precipitation method with gold loadings of 0-8% (wt%). The Au-TiO2 nanocomposite exhibited superior activity compared to P25 TiO2 under UVA (λ=365nm), cool white (λ>420nm) and green (λ=523nm) light emitting diodes (LEDs), for treating 1mgl-1 of E1. The 4wt% Au loading was found to produce the best photocatalytic activity with a rate constant of 2.44±0.36h-1, compared to 0.06±0.01h-1 for P25 TiO2, under visible light. In total 4 by-products were identified, one from negative ionization mode (m/z=269) and three from positive ionization mode (m/z=287) during photocatalysis, which were also degraded with time by Au-TiO2. For different water matrices, the photodegradation rate of E1 decreased in the order: ultrapure water>synthetic wastewater≈wastewater effluent from membrane bio-reactor. Overall, 4wt% Au-TiO2 demonstrated superior performance compared to P25 TiO2 in water and wastewater.