Enhancing Water Purification by Integrating Titanium Dioxide Nanotubes into Polyethersulfone Membranes for Improved Hydrophilicity and Anti-Fouling Performance.

Water pollution remains a critical concern, one necessitated by rapidly increasing industrialization and urbanization. Among the various strategies for water purification, membrane technology stands out, with polyethersulfone (PES) often being the material of choice due to its robust mechanical properties, thermal stability, and chemical resistance. However, PES-based membranes tend to exhibit low hydrophilicity, leading to reduced flux and poor anti-fouling performance. This study addresses these limitations by incorporating titanium dioxide nanotubes (TiO2NTs) into PES nanofiltration membranes to enhance their hydrophilic properties. The TiO2NTs, characterized through FTIR, XRD, BET, and SEM, were embedded in PES at varying concentrations using a non-solvent induced phase inversion (NIPS) method. The fabricated mixed matrix membranes (MMMs) were subjected to testing for water permeability and solute rejection capabilities. Remarkably, membranes with a 1 wt% TiO2NT loading displayed a significant increase in pure water flux, from 36 to 72 L m2 h-1 bar-1, a 300-fold increase in selectivity compared to the pristine sample, and a dye rejection of 99%. Furthermore, long-term stability tests showed only a slight reduction in permeate flux over a time of 36 h, while dye removal efficiency was maintained, thus confirming the membrane's stability. Anti-fouling tests revealed a 93% flux recovery ratio, indicating excellent resistance to fouling. These results suggest that the inclusion of TiO2 NTs offers a promising avenue for the development of efficient and stable anti-fouling PES-based membranes for water purification.

A novel SNP in NKX1-2 gene is associated with carcass traits in Dezhou donkey.

BACKGROUND: At present, donkey meat in the market shows an imbalance between supply and demand, and there is an urgent need to cultivate a meat-type Dezhou donkey breed. On the one hand, it can improve the imbalance in the market, and on the other hand, it can promote the rapid development of the donkey industry. This study aimed to reveal significant genetic variation in the NK1 homeobox 2 gene (NKX1-2) of Dezhou donkeys and investigate the association between genotype and body size in Dezhou donkeys. RESULTS: In this study, a SNP (g.54704925 A > G) was identified at the exon4 by high-depth resequencing of the Dezhou donkey NKX1-2 gene. The AA genotype is the dominant genotype. The g.54704925 A > G site was significantly associated with body length, thoracic girth, and hide weight (P < 0.05), while it was highly significantly associated with body height and carcass weight (P < 0.01) in Dezhou donkeys. CONCLUSION: Overall, the results of this study showed that the NKX1-2 gene could be a candidate gene for breeding meat-type Dezhou donkeys, and the g.54704925 A > G locus could be used as a marker locus for selection and breeding.

Innovative solid desiccant dehumidification using distributed microwaves.

Dehumidification is one of the key challenges facing the air conditioning (AC) industry in the treatment of moist air. Over many decades, the dual role of heat exchangers of AC chillers for the sensible and latent cooling of space has hindered the thermal-lift reduction in the refrigeration cycle due to the requirements of water vapor removal at dew-point and heat rejection to the ambient air. These practical constraints of AC chillers have resulted in the leveling of energy efficiency of mechanical vapor compressors (MVC) for many decades. One promising approach to energy efficiency improvement is the decoupling of dehumidification from sensible processes so that innovative but separate processes can be applied. In this paper, an advanced microwave dehumidification method is investigated in the laboratory, where the microwave (2.45 GHz) energy can be irradiated onto the dipole structure of water vapor molecules, desorbing rapidly from the pores of adsorbent. Results show a significant improvement in performance for microwave dehumidification, up to fourfold, as compared to data available in the literature.

Polymorphism detection of PRKG2 gene and its association with the number of thoracolumbar vertebrae and carcass traits in Dezhou donkey.

BACKGROUND: Previous studies have shown that the protein kinase cGMP-dependent 2 (PRKG2) gene is associated with dwarfism in humans, dogo Argentines, and Angus cattle, as well as with height and osteoblastogenesis in humans. Therefore, the PRKG2 gene was used as the target gene to explore whether this gene is associated with several thoracolumbar vertebrae and carcass traits in Dezhou donkeys. RESULTS: In this study, fifteen SNPs were identified by targeted sequencing, all of which were located in introns of the PRKG2 gene. Association analysis illustrated that the g.162153251 G > A, g.162156524 C > T, g.162158453 C > T and, g.162163775 T > G were significantly different from carcass weight. g.162166224 G > A, g.162166654 T > A, g.162167165 C > A, g.162167314 A > C and, g.162172653 G > C were significantly associated with the number of thoracic vertebrae. g.162140112 A > G was significantly associated with the number and the length of lumbar vertebrae, and g.162163775 T > G was significantly associated with the total number of thoracolumbar vertebrae. CONCLUSION: Overall, the results of this study suggest that PRKG2 gene polymorphism can be used as a molecular marker to breed high-quality Dezhou donkeys.

Task-specific polymeric membranes to achieve high gas-liquid mass transfer.

In the current study, Polyimide (P84)-based polymeric membranes were fabricated and used as spargers in the bubble column reactor (BCR) to get a high gas-liquid mass transfer (GL-MT) rate of oxygen in water. Different polymeric membranes were fabricated by incorporating polyvinyl pyrrolidone (PVP) as a porogen and a Zeolitic Imidazolate Framework (ZIF-8) to induce high porosity and hydrophobicity in the membranes. The GL-MT efficiency of membranes was evaluated by measuring the overall volumetric mass transfer coefficient (kLa) of oxygen in air. The kLa of O2 (in air) was measured by supplying the gas through a fixed membrane surface area of 11.94 cm2 at a fixed gas flow rate of 3L/min under atmospheric pressure. The results revealed that adding porogen and ZIF-8 increased the porosity of the membranes compared to the pure polymeric membranes. In comparison, the ZIF-8 (3 wt%) based membrane showed the highest porosity (80%), hydrophobicity (95° contact angle) and kLa of oxygen in air (241.2 h-1) with 78% saturation in only 60 s. ZIF-8 based membranes showed the potential to increase the amount of dissolved oxygen in BCR by reducing the bubble size, increasing the number of bubbles, and improving the hydrophobicity. The study showed that ZIF-8 based membrane diffusers are expected to produce high GL-MT in microbial syngas fermentation. To the best of our knowledge, this is the first study on the fabrication and application of polymeric membranes for GL-MT applications. Further research should be conducted under real fermentation conditions to assess the practicality of the system to support substrate utilization, microbial growth, and product formation.

Association of HOXC8 Genetic Polymorphisms with Multi-Vertebral Number and Carcass Weight in Dezhou Donkey.

An increase in the number of vertebrae can significantly affect the meat production performance of livestock, thus increasing carcass weight, which is of great importance for livestock production. The homeobox gene C8 (HOXC8) has been identified as an essential candidate gene for regulating vertebral development. However, it has not been researched on the Dezhou donkey. This study aimed to verify the Dezhou donkey HOXC8 gene's polymorphisms and assess their effects on multiple vertebral numbers and carcass weight. In this study, the entire HOXC8 gene of the Dezhou donkey was sequenced, SNPs at the whole gene level were identified, and typing was accomplished utilizing a targeted sequencing genotype detection technique (GBTS). Then, a general linear model was used to perform an association study of HOXC8 gene polymorphism loci, multiple vertebral numbers, and carcass weight for screening candidate markers that can be used for molecular breeding of Dezhou donkeys. These findings revealed that HOXC8 included 12 SNPs, all unique mutant loci. The HOXC8 g.15179224C>T was significantly negatively associated with carcass weight (CW) and lumbar vertebrae length (LL) (p < 0.05). The g.15179674G>A locus was shown to be significantly positively associated with the number of lumbar vertebrae (LN) (p < 0.05). The phylogenetic tree constructed for the Dezhou donkey HOXC8 gene and seven other species revealed that the HOXC8 gene was highly conserved during animal evolution but differed markedly among distantly related animals. The results suggest that HOXC8 is a vital gene affecting multiple vertebral numbers and carcass weight in Dezhou donkeys, and the two loci g.15179224C>T and g.15179674G>A may be potential genetic markers for screening and breeding of new strains of high-quality and high-yielding Dezhou donkeys.

Multi-Thoracolumbar Variations and NR6A1 Gene Polymorphisms Potentially Associated with Body Size and Carcass Traits of Dezhou Donkey.

The number of thoracolumbar vertebrae is a quantitative trait positively correlated with the economic traits of livestock. More thoracolumbar vertebrae individuals could genetically be used to improve the livestock population, as more thoracolumbar vertebrae means a longer carcass, which could bring more meat production. Nuclear receptor subfamily 6 group A member 1 (NR6A1) is considered a strong candidate gene for effecting the number of vertebrae in livestock. The purposes of this study are as follows: (a) Analyzing the effect of TLN variation on body size and carcass traits of Dezhou donkey; (b) Studying the distribution of seven single nucleotide variants (SNVs) in NR6A1 gene of Dezhou donkey; (c) Exploring the relationship between latent SNVs and TLN, the body size and carcass traits. We examined the thoracic and lumbar vertebrae number and seven SNVs in NR6A1 gene of 455 Dezhou donkeys, and analyzed the relationships between them. Five types of thoracolumbar combinations (T17L5 (individual with 17 thoracic and five lumbar vertebrae) 2.4%, T18L5 75.8%, T19L5 1.1%, T17L6 11.9%, and T18L6 8.8%) of Dezhou donkeys were detected in this study. For one thoracolumbar vertebra added, the body length of Dezhou donkey increases by 3 cm and the carcass weight increases by 6 kg. Seven SNVs (g.18093100G > T, g.18094587G > T, g.18106043G > T, g.18108764G > T, g.18110615T > G, g.18112000C > T and g.18114954T > G) of the NR6A1 gene were found to have a significant association with the TLN, body size and carcass traits of Dezhou donkey (p < 0.05), respectively. For instance, g.18114954C > T is significantly associated with lumber vertebrae number, the total number of thoracolumbar, and carcass weight, and individuals with TT genotype had significantly larger value than CC genotype (p < 0.05). Using these 7SNVs, 16 different haplotypes were estimated. Compared to Hap3Hap3, individuals homozygous for Hap2Hap2 showed significantly longer length in one thoracic spine (STL), the total thoracic vertebrae and one thoracolumbar spine. Our study will not only extend the understanding of genetic variation in the NR6A1 gene of Dezhou donkey, but also provide useful information for marker assisted selection in donkey breeding program.

Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep learning-based models have significantly improved breast lesions' detection, localization, risk assessment, and categorization. This study proposes a novel deep learning-based convolutional neural network (ConvNet) that significantly reduces human error in diagnosing breast malignancy tissues. Our methodology is most effective in eliciting task-specific features, as feature learning is coupled with classification tasks to achieve higher performance in automatically classifying the suspicious regions in mammograms as benign and malignant. To evaluate the model's validity, 322 raw mammogram images from Mammographic Image Analysis Society (MIAS) and 580 from Private datasets were obtained to extract in-depth features, the intensity of information, and the high likelihood of malignancy. Both datasets are magnificently improved through preprocessing, synthetic data augmentation, and transfer learning techniques to attain the distinctive combination of breast tumors. The experimental findings indicate that the proposed approach achieved remarkable training accuracy of 0.98, test accuracy of 0.97, high sensitivity of 0.99, and an AUC of 0.99 in classifying breast masses on mammograms. The developed model achieved promising performance that helps the clinician in the speedy computation of mammography, breast masses diagnosis, treatment planning, and follow-up of disease progression. Moreover, it has the immense potential over retrospective approaches in consistency feature extraction and precise lesions classification.

Identification of Circular RNAs of Testis and Caput Epididymis and Prediction of Their Potential Functional Roles in Donkeys.

Circular RNAs (circRNAs) are a class of noncoding RNAs with a covalently closed loop. Studies have demonstrated that circRNA can function as microRNA (miRNA) sponges or competing endogenous RNAs. Although circRNA has been explored in some species and tissues, the genetic basis of testis development and spermatogenesis in donkeys remain unknown. We performed RNA-seq to detect circRNA expression profiles of adult donkey testes. Length distribution and other characteristics were shown a total of 1971 circRNAs were differentially expressed and 12,648 and 6261 circRNAs were detected from the testis and caput epididymis, respectively. Among these circRNAs, 1472 circRNAs were downregulated and 499 circRNAs were upregulated in the testis. Moreover, KEGG pathway analyses and Gene Ontology were performed for host genes of circRNAs. A total of 39 upregulated circRNA host genes were annotated in spermatogenesis terms, including PIWIL2, CATSPERD, CATSPERB, SPATA6, and SYCP1. Other host genes were annotated in the focal adhesion, Rap1 signaling pathway. Downregulated expressed circRNA host genes participated in the TGF-ß signaling pathway, GnRH signaling pathway, estrogen signaling pathway, and calcium signaling pathway. Our discoveries provide a solid foundation for identifying and characterizing critical circRNAs involved in testis development or spermatogenesis.

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach.

Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer's high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model's diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

A novel zero-liquid discharge desalination system based on the humidification-dehumidification process: A preliminary study.

As a byproduct of desalination plants, brine is increasingly becoming a threat to the environment, and the design of zero-liquid discharge (ZLD) systems is gaining increasing attention. Existing ZLD systems are limited by a high energy intensity and high plant costs of their crystallizers. This study proposes a novel crystallization method based on the humidification-dehumidification (HDH) process, which exhibits the advantages of a low energy consumption, low component costs and a reduced scaling and fouling potential. A simple experimental setup is first designed to demonstrate the feasibility of the proposed system. Brine concentration and salt crystallization are successfully achieved with air heated to 40 °C as the heat source. Afterwards, a thermo-economic analysis is conducted for the whole system. The specific thermal energy and electricity consumption levels are found to range from 700-900 and 5-11 kJ, respectively, per kg of feed brine. The energy consumption is 56% lower than that of a conventional evaporative crystallizer, and the initial plant cost is reduced by 58%.

An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning.

BACKGROUND: Diagnosing breast cancer masses and calcification clusters have paramount significance in mammography, which aids in mitigating the disease's complexities and curing it at early stages. However, a wrong mammogram interpretation may lead to an unnecessary biopsy of the false-positive findings, which reduces the patient's survival chances. Consequently, approaches that learn to discern breast masses can reduce the number of misconceptions and incorrect diagnoses. Conventionally used classification models focus on feature extraction techniques specific to a particular problem based on domain information. Deep learning strategies are becoming promising alternatives to solve the many challenges of feature-based approaches. METHODS: This study introduces a convolutional neural network (ConvNet)-based deep learning method to extract features at varying densities and discern mammography's normal and suspected regions. Two different experiments were carried out to make an accurate diagnosis and classification. The first experiment consisted of five end-to-end pre-trained and fine-tuned deep convolution neural networks (DCNN). The in-depth features extracted from the ConvNet are also used to train the support vector machine algorithm to achieve excellent performance in the second experiment. Additionally, DCNN is the most frequently used image interpretation and classification method, including VGGNet, GoogLeNet, MobileNet, ResNet, and DenseNet. Moreover, this study pertains to data cleaning, preprocessing, and data augmentation, and improving mass recognition accuracy. The efficacy of all models is evaluated by training and testing three mammography datasets and has exhibited remarkable results. RESULTS: Our deep learning ConvNet+SVM model obtained a discriminative training accuracy of 97.7% and validating accuracy of 97.8%, contrary to this, VGGNet16 method yielded 90.2%, 93.5% for VGGNet19, 63.4% for GoogLeNet, 82.9% for MobileNetV2, 75.1% for ResNet50, and 72.9% for DenseNet121. CONCLUSIONS: The proposed model's improvement and validation are appropriated in conventional pathological practices that conceivably reduce the pathologist's strain in predicting clinical outcomes by analyzing patients' mammography images.

A Universal Mathematical Methodology in Characterization of Materials for Tailored Design of Porous Surfaces.

Understanding adsorption phenomena is essential to optimize and customize the energy transformation in numerous industrial and environmental processes. The complex and heterogeneous structure of the adsorbent surface and the distinct interaction of adsorbent-adsorbate pairs are attributed to the diverse response of adsorption phenomena, measured by the state diagrams of adsorption uptake known as adsorption isotherms. To understand various forms of adsorption isotherms, the surface characteristics of the adsorbent surface with the heterogeneity of adsorption energy sites must be analyzed so that they can be modified for the tailored response of the material. Conventionally, such material synthesis is based on chemical recipes or post-treatment. However, if the adsorbent's surface characteristics and heterogeneity are known, then a directed change in the material structure can be planned for the desired results in the adsorption processes. In this paper, a theoretical and mathematical methodology is discussed to analyze the structure of various adsorbents in terms of the distribution of their adsorption energy sites. The change in their surface is then analyzed, which results in the tailored or customized response of the material.

Giant Humidity Effect on Hybrid Halide Perovskite Microstripes: Reversibility and Sensing Mechanism.

Despite the exceptional performance of hybrid perovskites in photovoltaics, their susceptibility to ambient factors, particularly humidity, gives rise to the well-recognized stability issue. In the present work, microstripes of CH3NH3PbI3 are fabricated on flexible substrates, and they exhibit much larger response to relative humidity (RH) levels than continuous films and single crystals. The resistance of microstripes decreases by four orders of magnitude on changing the RH level from 10 to 95%. Fast response and recovery time of 100 and 500 ms, respectively, are recorded. Because bulk diffusion and defect trapping are much slower processes, our result indicates a surface-dictated mechanism related to hydrate formation and electron donation. In addition, water uptake behavior of perovskites is studied for the first time, which correlates well with the resistance decrease of the CH3NH3PbI3 microstripes. Furthermore, we report that the photoresponse decreases with increasing humidity, and at the 85% RH level, the perovskite device is not photoresponsive anymore. Our work underscores patterned structures as a new platform to investigate the interaction of hybrid perovskites with ambient factors and reveals the importance of the humidity effect on optoelectronic performance.

Embedding 1D Conducting Channels into 3D Isoporous Polymer Films for High-Performance Humidity Sensing.

Isoporous block copolymer (BCP) films have received exponential interest as highly selective membranes, stemming from their unique morphological features, but their applications in functional devices remain to be realized. Now single-walled carbon nanotubes (CNTs) were efficiently incorporated into isoporous block copolymer films for chemiresistive sensing at room temperature. Leveraging the efficient charge extraction ability of CNTs together with nanochannel arrays aligned perpendicular to the surface of the films, an ultrafast response time of 0.3 s was achieved for humidity detection with a sensor response of about 800 on changing humidity from 10 % to 95 %. Furthermore, the sensor also responds to various organic vapors, underscoring its promising detection capability.

CO2-Philic Thin Film Composite Membranes: Synthesis and Characterization of PAN-r-PEGMA Copolymer.

In this work, we report the successful fabrication of CO2-philic polymer composite membranes using a polyacrylonitrile-r-poly(ethylene glycol) methyl ether methacrylate (PAN-r-PEGMA) copolymer. The series of PAN-r-PEGMA copolymers with various amounts of PEG content was synthesized by free radical polymerization in presence of AIBN initiator and the obtained copolymers were used for the fabrication of composite membranes. The synthesized copolymers show high molecular weights in the range of 44⁻56 kDa. We were able to fabricate thin film composite (TFC) membranes by dip coating procedure using PAN-r-PEGMA copolymers and the porous PAN support membrane. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) were applied to analyze the surface morphology of the composite membranes. The microscopy analysis reveals the formation of the defect free skin selective layer of PAN-r-PEGMA copolymer over the porous PAN support membrane. Selective layer thickness of the composite membranes was in the range of 1.32⁻1.42 µm. The resulting composite membrane has CO2 a permeance of 1.37 × 10-1 m³/m²·h·bar and an ideal CO2/N2, selectivity of 65. The TFC membranes showed increasing ideal gas pair selectivities in the order CO2/N2 > CO2/CH4 > CO2/H2. In addition, the fabricated composite membranes were tested for long-term single gas permeation measurement and these membranes have remarkable stability, proving that they are good candidates for CO2 separation.

Rituximab as a rescue therapy in patients with glomerulonephritis.

To evaluate the use of rituximab in the treatment of severe glomerulonephritis (GN) in order to prevent progression of kidney disease toward the end stage, we designed a multicenter, retrospective study in Saudi Arabia about the efficacy and safety of the use of "off label" rituximab in a variety of severe refractory GN to conventional treatment and the progression of kidney disease for at least one year of follow-up. All the patients had kidney biopsies before treatment with rituximab, and proteinuria and glomerular filtration rate (GFR) were followed-up for the period of the study. The immediate side-effect at the time of administration of rituximab included itching in three patients, hypotension in one patient and anaphylaxis in one patient (dropped out from the study). After the administration of rituximab in 42 patients and during the first six months of therapy, 16 (38%) patients had complete remission (CR), 13 (31%) patients had partial remission (PR) and 13 (31%) patients had no remission. The mean follow-up period for the patients was 19.0 ± 6.97 months (median 18.0 months). The long-term follow-up during the study period disclosed a good hospitalization record for almost all of the patients. Membranous GN (MGN) was the largest group in the cohort (58% of the patients), and we observed CR and PR in 40% and 28% of them, respectively, which was comparable with the previous experience with rituximab in MGN patients with more CR than PR in our cohort. We conclude that our study suggests the safety and efficacy of the use of rituximab in patients with refractory GN and that larger and long-term prospective studies are required to define the role of rituximab in the different categories of these diseases.

Biodiesel from Citrullus colocynthis oil: sulfonic-ionic liquid-catalyzed esterification of a two-step process.

Biodiesel was prepared from Citrullus colocynthis oil (CCO) via a two-step process. The first esterification step was explored in two ionic liquids (ILs) with 1,3-disulfonic acid imidazolium hydrogen sulfate (DSIMHSO4) and 3-methyl-1-sulfonic acid imidazolium hydrogen sulfate (MSIMHSO4). Both ILs appeared to be good candidates to replace hazardous acidic catalyst due to their exceptional properties. However, the two sulfonic chains existing in DSIMHSO4 were found to increase the acidity to the IL than the single sulfonic chain in MSIMHSO4. Based on the results, 3.6 wt% of DSIMHSO4, methanol/CCO molar ratio of 12 : 1, and 150 °C offered a final FFA conversion of 95.4% within 105 min. A 98.2% was produced via second KOH-catalyzed step in 1.0%, 6 : 1 molar ratio, 600 rpm, and 60 °C for 50 min. This new two-step catalyzed process could solve the corrosion and environmental problems associated with the current acidic catalysts.