Efficient Approach Research Articles

Development and Evaluation of High-Resolution Melting Assays for Direct and Simultaneous Pathogen Identification in Bloodstream Infections in Pediatric Oncology Patients

Blood culture, the gold-standard method for identifying pathogens in bloodstream infections, is time-consuming and demonstrates low sensitivity. These drawbacks are related to high mortality, especially among pediatric oncology patients presenting febrile neutropenia episodes. Here we describe two novel High-Resolution Melting assays designed for pathogen detection in bloodstream infections. The assays were initially evaluated using five sepsis-associated pathogens. Both assays demonstrated 100% specificity, detected as low as 100 fg of bacterial DNA, and exhibited reproducibility. Clinical isolates from blood cultures were 100% identified by both assays. Moreover, blind and direct identification of blood samples from pediatric cancer patients demonstrated sensitivities of 61.5% and 69.2% for “Primer Set 1” and “Primer Set 2”, respectively. Our study highlights the potential of HRM-based assays as a rapid and efficient diagnostic approach for sepsis-related microorganisms. Further advancements could enhance their clinical utility for better management of febrile neutropenia episodes, especially in pediatric oncology patients.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Pickering emulsion via interfacial assembly of lignin particles and cationic surfactant: Formation of robust anchoring layer

Lignin, a byproduct of the paper and pulp industry, shows promise as a stabilizer for emulsions, offering potential value-added applications to direct disposal. However, utilizing particulate lignin directly as stabilizers is challenging due to limited emulsion stability. In this study, co-stabilization of Pickering emulsions is investigated using a combination of lignin particles and cationic surfactants (CTAB and DDAB), wherein lignin source and oil types are optimized with a focus on emulsion stability. A battery of characterization methods, including visual appearance, microstructure, droplet size, zeta potential, etc., is employed to evaluate emulsion stability. The findings reveal that kraft lignin nanospheres (LNS) are identified as the optimal stabilizing particles. The incorporation of cationic surfactants via electrostatic adsorption enhances the stabilizing effect of LNS, and the use of DDAB is found to significantly enhance emulsion stability compared to CTAB, attributing to higher charge shielding capability of DDAB and the formation of a more robust and compact nonpolar anchoring layer. The surfactant-assisted LNS can be used to stabilize a range of oil types, with tridecane as the optimal oil. This research presents a straightforward, efficient, and scalable approach for producing highly stable lignin-based Pickering emulsions, showing potential applications in recycling and value enhancement of waste lignin.

Hydrogen adsorption on titanium-decorated carbyne C12 ring: a DFT study

Abstract In the current landscape of increasing focus on green technology, hydrogen fuel emerges as a pivotal alternative energy source. While existing technology facilitates hydrogen use in fuel cells, the practicality of this fuel could be significantly enhanced with a more efficient and safer storage approach. Researchers are actively exploring one-dimensional systems as potential hydrogen storage solutions, yielding promising outcomes. A notable study delved into the hydrogen storage capacity and performance of a Ti-decorated carbyne ring using density functional theory calculations. The researchers observed a robust, non-deforming bond between the Ti adatom and the carbyne ring, displaying characteristics akin to ionic bonding. Detailed analyses of electronic properties, including density of states and band structure, highlighted a strong interaction through the alignment of p-orbitals with the Ti atom. Upon the adsorption of H2 onto the decorated carbyne ring, it was noted that the Ti-decorated systems could each adsorb up to six H2 molecules, exhibiting weak physisorption energies within the Van der Waals range. The charge density profile indicated a dipole-dipole interaction, affirming the potential of the material as a viable H2 storage medium. In conclusion, as green technology advances, hydrogen fuel, especially when stored innovatively with materials like the Ti-decorated carbyne ring, emerges as a crucial component in the pursuit of sustainable energy solutions.

Synthesis and structural evolution of coal-based mesophase pitch by hydrogenation-vacuum thermal polycondensation process

As high-performance carbon materials precursor, the controlled synthesis of mesophase pitches has received widespread attention. Coal tar pitch and Tetrahydronaphthalene (THN) were selected as raw material and hydrogen supply solvent to synthesize mesophase pitches through hydrogenation and vacuum polycondensation. The compositions and structure of resultant hydrogenated and mesophase pitches were characterized and analyzed by various methods. The results revealed that the introduction of THN could effectively increase the H content in hydrogenated pitches, and the transformation of HN to Har was observed with the extension of the reaction time. The softening point of the resultant mesophase pitches was 230-245oC, the Mn ranged from 1400 to 2000 Da, the anisotropic content was up to 96%, and the contents of the four subfractions were THFI > HI-TS > HS > TI-THFS in descending order. This work provided an efficient and convenient approach for the synthesis of mesophase pitches, with a view to providing a theoretical and practical foundation for spinning-grade mesophase pitch preparation.

In-phase and quadrature frequency-shift keying for low-power optical wireless communications

This article proposes using in-phase and quadrature frequency-shift keying (IQFSK) modulation for low-power optical wireless communications (OWC). IQFSK independently leverages both cosine and sine basis functions to enhance the system’s spectral efficiency (SE). It uses only the odd harmonic frequencies for these basis functions, allowing the clipping of negative amplitude excursions without losing information, making the waveform compatible with OWC The work presents optimal maximum likelihood and low-complexity sub-optimal detection mechanisms for IQFSK. The proposed scheme is analyzed analytically and with numerical simulations. The simulation and analytical results indicate that the proposed scheme is more energy-efficient, can attain a better energy and SE trade-off by exploiting the frame structure of the waveform, and has a lower minimum squared Euclidean distance relative to other state-of-the-art FSK-based counterparts, thus establishing it as one of the most efficient FSK approaches for low-power OWCs.

A sustainable approach to microwave assisted synthesis of 2-(het)/arylquinazolin-4(3H)-ones via ionic liquid supported Cu(II) catalyst

An efficient and sustainable approach for the microwave assisted synthesis of 2-(het)/arylquinazolin-4(3H)-ones from substituted 2-aminobenzamide and benzyl alcohols is introduced by using a reusable imidazolium based ionic liquid supported Cu(II) catalyst. This methodology exhibits high functional group tolerance and efficiently constructs desired products in good to excellent yields. Moreover, this method portrays operational simplicity, less reaction time, minimal cost and sustainable over the reported protocols. Furthermore, the catalyst can be recycled and reused up to 4 consecutive cycles with no significant loss of activity.

CARDIOPROTECTIVE PROPERTIES OF SGLT2-INHIBITORS

Due to its high mortality rate, heart failure (HF) presents a substantial worldwide health burden that calls for efficient treatment approaches. Inhibitors of the sodium-glucose co-transporter 2 (SGLT2) have become essential treatments for heart failure in all left ventricular ejection fraction (LVEF) levels. Their processes, which include enhanced metabolic efficiency and ventricular loading circumstances, present encouraging results. The effectiveness of SGLT2 inhibitors, dapagliflozin and empagliflozin, in lowering cardiovascular mortality and heart failure hospitalizations in heart failure patients with reduced, mildly reduced, and preserved ejection fraction has been shown in several clinical trials, including DAPA-HF, EMPEROR-Reduced, EMPEROR-Preserved, and DELIVER. These results represent a revolutionary development in the treatment of HF, emphasizing the use of SGLT2 inhibitors in comprehensive treatments, regardless of renal function or diabetes state.

Emerging Biohybrids of Aptamer-Based Nano-Biosensing Technologies for Effective Early Cancer Detection.

Cancer is a leading global cause of mortality, which underscores the imperative of early detection for improved patient outcomes. Biorecognition molecules, especially aptamers, have emerged as highly effective tools for early and accurate cancer cell identification. Aptamers, with superior versatility in synthesis and modification, offer enhanced binding specificity and stability compared with conventional antibodies. Hence, this article reviews diagnostic strategies employing aptamer-based biohybrid nano-biosensing technologies, focusing on their utility in detecting cancer biomarkers and abnormal cells. Recent developments include the synthesis of nano-aptamers using diverse nanomaterials, such as metallic nanoparticles, metal oxide nanoparticles, carbon-derived substances, and biohybrid nanostructures. The integration of these nanomaterials with aptamers significantly enhances sensitivity and specificity, promising innovative and efficient approaches for cancer diagnosis. This convergence of nanotechnology with aptamer research holds the potential to revolutionize cancer treatment through rapid, accurate, and non-invasive diagnostic methods.

The prediction of donor number and acceptor number of electrolyte solvent molecules based on machine learning

Electrolyte solvents have a critical impact on the design of high performance and safe batteries. Gutmann’s donor number (DN) and acceptor number (AN) values are two important parameters to screen and design superior electrolyte solvents. However, it is more time-consuming and expensive to obtain DN and AN values through experimental measurements. Therefore, it is essential to develop a method to predict DN and AN values. This paper presented the prediction models for DN and AN based on molecular structure descriptors of solvents, using four machine learning algorithms such as CatBoost (Categorical Boosting), GBRT (Gradient Boosting Regression Tree), RF (Random Forest) and RR (Ridge Regression). The results showed that the DN and AN prediction models based on CatBoost algorithm possesses satisfactory prediction ability, with R2 values of the testing set are 0.860 and 0.96. Moreover, the study analyzed the molecular structure parameters that impact DN and AN. The results indicated that TDB02m (3D Topological distance based descriptors - lag 2 weighted by mass) had a significant effect on DN, while HATS1s (leverage-weighted autocorrelation of lag 1 / weighted by I-state) plays an important role in AN. The work provided an efficient approach for accurately predicting DN and AN values, which is useful for screening and designing electrolyte solvents.

Subdermal dissection technique for pure skin SCIA and ALT perforator flaps in burns and trauma defects: Clinical experience.

The pure skin perforator (PSP) flap is gaining popularity for its remarkable thinness. The subdermal dissection technique was recently introduced, allowing for a quicker elevation of a PSP flap. In this report, we present our two-year experience utilizing subdermal dissection for harvesting PSP flaps. All patients who had undergone PSP flap reconstruction at our hospital from February 2021 to February 2023 were included. Demographic data, intraoperative variables, flap characteristics, and postoperative outcomes were collected. Surgical planning involved locating the perforator using ultrasound and harvesting the flap using the subdermal dissection technique. A total of 26 PSP flap reconstructions were conducted on 24 patients aged between 15 and 86 years. The flaps were based on perforators issuing from the superficial circumflex iliac artery in 24 cases, and from the descending branch of the lateral circumflex femoral artery in 2 cases. Flap sizes ranged from 3 × 1.5 cm to 19 × 6 cm, with a mean thickness of 3.48 mm. The average time for flap harvest was 131.92 min. Postoperatively, we observed four cases of partial necrosis, 1 total flap loss, and 2 instances of vascular thrombosis at the anastomosis site. The flaps exhibited good pliability without contracture, and no debulking procedures were required during the follow-up period (minimum 6 months, range 6-24; mean 9.4615). The subdermal dissection technique is a safe and efficient approach for elevating PSP flaps. Our initial experience with this technique has been encouraging, and it currently serves as our preferred reconstructive option for defects requiring thin reconstruction.

Construction of asymmetric ceramic membranes with identical source interlayer on fly ash support by dip-coating

As a highly efficient separation technology, ceramic membranes have been widely applied. The use of fly ash to prepare low-cost ceramic membranes has garnered significant attention in recent years. However, challenges remain in the preparation of asymmetric ceramic membranes with high performance. This study presents an efficient approach to fabricating asymmetric ceramic membranes with an identical source interlayer on fly ash supports using the dip-coating method. The alumina selective layer was co-sintered with the optimized interlayer at 1000°C. The average pore size of the asymmetric ceramic microfiltration membrane was approximately 120 nm, and the pure water permeance reached 870 L·m−2·h−1·bar−1. The ceramic microfiltration membrane demonstrated high efficiency in separating oil-in-water emulsions, achieving a stable permeance of 130 L·m−2·h−1·bar−1 and a TOC removal rate above 99 %. This work provides an efficient and effective construction method for producing fly ash-based ceramic membranes with high performance.

The prediction of donor number and acceptor number of electrolyte solvent molecules based on machine learning

Electrolyte solvents have a critical impact on the design of high performance and safe batteries. Gutmann’s donor number (DN) and acceptor number (AN) values are two important parameters to screen and design superior electrolyte solvents. However, it is more time-consuming and expensive to obtain DN and AN values through experimental measurements. Therefore, it is essential to develop a method to predict DN and AN values. This paper presented the prediction models for DN and AN based on molecular structure descriptors of solvents, using four machine learning algorithms such as CatBoost (Categorical Boosting), GBRT (Gradient Boosting Regression Tree), RF (Random Forest) and RR (Ridge Regression). The results showed that the DN and AN prediction models based on CatBoost algorithm possesses satisfactory prediction ability, with R2 values of the testing set are 0.860 and 0.96. Moreover, the study analyzed the molecular structure parameters that impact DN and AN. The results indicated that TDB02m (3D Topological distance based descriptors - lag 2 weighted by mass) had a significant effect on DN, while HATS1s (leverage-weighted autocorrelation of lag 1 / weighted by I-state) plays an important role in AN. The work provided an efficient approach for accurately predicting DN and AN values, which is useful for screening and designing electrolyte solvents.

Zirconium effect on the lithiation mechanism of LiNi0.83Mn0.05Co0.12O2 positive electrode material

Ni-rich LiNi1-x-yMnxCoyO2 (x + y ≤ 0.2) materials are employed to enhance the energy density of lithium-ion batteries, while they are still facing the stability issue. Doping is considered as an efficient approach to improve the stability of the LiNi1-x-yMnxCoyO2 materials. A considerable number of studies have concluded that Zr-doping improves the stability when undoped and doped LiNi1-x-yMnxCoyO2 materials are lithiated under the same conditions. Unfortunately, a fundamental understanding of the Zr dopants role during lithiation conditions is still lacking. This work investigates the Zr effects on lithiation mechanism of LiNi0.83Mn0.05Co0.12O2 (NMC). The main findings are: First, the NMC and Zr-doped NMC have different lattice parameter growth speeds, and the (003) & (101) lattice planes form order structures at lower temperatures during lithiation process when the dopant is included. Second, Zr is prone to locate near the surface region of NMC secondary particles. Third, when lithiation time increase from 8 h to 12 h at 800 oC, the undoped NMC electrochemical performance deteriorates whereas the doped material improves. These findings highlight that understanding the mechanism behind lithiation conditions is essential for doped LiNi1-x-yMnxCoyO2. The insights revealed in this work can guide for design other dopants for Ni-rich positive electrode materials.

Effect of combined dialysis method on dewatering of coagulant pretreated sludge

The unique composition and condition of sludge pose significant challenges in water removal. Therefore, it is crucial to investigate a cost-effective and highly efficient approach for dehydrating sludge. In this paper, the domestic sludge was respectively pretreated with ferric chloride, polyaluminum chloride, and cationic polyacrylamide. Then the pretreated sludge was performed on gravity dehydration, centrifugal dehydration, dialysis dehydration and mixed method. The effect mechanism of coagulant and dewatering method on sludge dehydration was analyzed from micro perspective. Finally, the influence range of the dialytic solution was tested by model experiment. The results indicate that the dehydration efficiency of the pretreated sludge with PAC is slightly better than that pretreated with FeCl3 and CPAM, and the optimal dosage of PAC is 8%. The effect of dialysis dehydration is the best, which can significantly change the final moisture content of sludge (much less than 60%), effectively achieving the requirements of subsequent processing. The water content of the sludge treated by gravity dehydration, centrifugal dehydration and dialysis dehydration can reach 38%, which is much smaller than that of the sludge treated by a single dehydration method. The free water, occlusion, coating water, and bounding water are dehydrated by gravity, centrifugal force, and dialysis. The influence range of dialysis dehydration is less than 10cm. When the distance is more than 10cm, the difference of sludge moisture content at different relative heights is larger. The study has certain theoretical guidance significance for improving the performance of sludge concentration and dewatering.

Enhancing the scaling resistance of corrugated polyvinylidene fluoride hydrophobic membrane in membrane distillation: Impact of CF4 plasma modification, pulse flow, and negative pressure

Membrane distillation (MD) is an emerging technology for brine concentration, but plagued by scaling/fouling/wetting in realistic applications. There has been tremendous progress in the mitigation of scaling via chemical modification to reduce the surface energy and hydraulic operation to change flow state in feed channel. But there still lacks an undisputed cognition yet which approach outperforms the others. To address this issue, this study comprehensively investigates the impact of state-of-the-art technologies on scaling mitigation based on a surface corrugated hydrophobic PVDF membrane with high flux and potential for mass production. The technology selected include CF4 plasma modification, pulse flow, and negative feed pressure from both thermodynamic and hydraulic perspectives. CF4 plasma modification resulted in a low surface energy superhydrophobic surface, but the scaling resistance of the membranes was not significantly improved. Pulse flow effectively mitigated scaling, yet the intrusion of calcium sulfate into the membrane matrix still occurred. Negative pressure at the feed channel created a stable air layer, leading to excellent stable vapor flux and distillation conductivity. The results indicated that the hydraulic operation using negative pressure at feed was an efficient approach for scaling mitigation. This study further elucidated the mechanism underlying the scaling resistance of superhydrophobic membranes and underscored the importance of hydraulic factors in enhancing the scaling resistance in MD.

A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve

Abstract The planning and development of windfarms require accurate prediction of the thrust coefficient (cT ) of wind turbines, which significantly affects the downstream wake. Traditional methods, such as blade element momentum theory (BEMT), often necessitate detailed geometric information of wind turbines for cT computation, information that is not frequently available, especially in the early stages of windfarm planning. This paper aims to address this challenge by presenting a novel and efficient approach to predict cT for horizontal-axis wind turbines (HAWTs). The proposed method integrates classical momentum theory with power curve data to estimate the average axial induction factor (a), thereby enabling the calculation of cT without requiring detailed geometric information of HAWTs. The method was validated against thirty-five existing pitch-controlled HAWTs, with R2 values ranging from 0.9604 to 0.9989. This validation confirms the accuracy of the method, making it a viable alternative to traditional techniques that demand comprehensive wind turbine geometric details. The method has demonstrated both rapidity and precision in cT computation for turbine wake analysis, ensuring high levels of prediction accuracy and potentially lowering the barrier to entry for windfarm development. Unlike existing models predominantly focused on wind turbine power curves, cT modelling has largely been overlooked. This study makes a unique contribution to the field by proposing a novel method for cT prediction, thereby filling a critical gap in windfarm planning and development. However, while the study shows promising results, further research is warranted to explore its applicability in diverse windfarm scenarios and turbine configurations.

Small-group originating model: Optimized individual-level GWAS simulation featured by SLiM and using open-access data

The development of analytical methods for Genome-wide Association Studies (GWAS) has outpaced the evolution of simulation techniques and pipelines. This disparity underscores the importance of innovative simulation methods that can keep pace with the rapidly increasing scale of GWAS. The median sample size of GWAS over the past ten years has exceeded 50,000 individuals, a trend that emphasizes the need for simulation tools capable of generating data on a similar or larger scale. This paper introduces a novel method, the small-group originating (SGO) model, utilizing the SLiM software for simulating individual-level GWAS data. Our standardized protocol facilitates the generation of tens of thousands of pseudo-individuals with millions of variants from small (30−90) open-access datasets.SGO stands out, especially when compared to the widely-used resampling method in HapGen, showcasing superior simulation efficiency for large sample sizes (> 13,000) of unrelated individuals. This capability is particularly relevant given the current trajectory towards larger GWAS, necessitating tools that can simulate datasets reflective of this growth. Additionally, SGO provides customization options and can model dynamic life cycles and mating across generations, positioning it as a highly promising alternative for GWAS simulations.In a case study, sensitivity analyses of chromosome-level principal component analysis and kinship coefficient estimation were conducted. The results highlighted the poor robustness of chromosome-level quality control (QC) indexes and the uneven distribution of population structure across chromosomes and ancestries, advocating for the caution against relying solely on chromosome-level QC statistics.With its flexible and efficient approach to generating pseudo GWAS data, our standardized SGO protocol emerges as a crucial asset for method development, power analysis, and benchmarking in GWAS research. It is especially vital in the context of accommodating the demands for large-scale simulations, aligning with the current and future scale of GWAS.

Carboxylated albumin as a dual-purpose template for fabricating gadolinium oxide nanoprobe with high metal loading and exceptional relaxivity

Biomineralization is a highly efficient approach to synthesizing magnetic resonance imaging (MRI) probes but encounters two limitations: low metal loading and insufficient sensitivity. Herein, based on the Solomon-Bloembergen-Morgan theory and hard and soft acids and bases principle, we proposed the use of fully carboxylated albumin as a dual-purpose template for fabricating gadolinium oxide (BSA-COOH-Gd2O3) nanoprobe with high metal loading and exceptional relaxivity for the first time. The proposed nanoprobe was synthesized using a straightforward one-pot method under mild conditions, exhibiting excellent biocompatibility, good water solubility, high Gd content (6 %), and outstanding relaxivity (41.17 mM-1s−1). In vivo magnetic resonance angiography (MRA) demonstrated that the BSA-COOH-Gd2O3 nanoprobe (0.05 mmol Gd/kg, equivalent to half of the clinical dose) exhibited a sustained and significantly enhanced vascular enhancement effect for at least 2 h with exceptional resolution, enabling clear visualization of vessels as small as 0.3 mm at 3.0 T and 0.13 mm at 9.4 T. Both the severity and location of vascular narrowing can be accurately identified in a carotid artery stenosis model and the tumor can be sensitively detected in a tumor model via the nanoprobe-based MRI. Our study provides an upgraded biomineralization technique for synthesizing high-performance MRI nanoprobes for various applications.

Single-Atom 3d Transition Metals on SnO2 as Model Cell for Conversion Mechanism: Revealing Thermodynamic Catalytic Effects on Enhanced Na Storage of Heterostructures.

Since the discovery in 2000, conversion-type materials have emerged as a promising negative-electrode candidate for next-generation batteries with high capacity and tunable voltage, limited by low reversibility and severe voltage hysteresis. Heterogeneous construction stands out as a cost-effective and efficient approach to reducing reaction barriers and enhancing energy density. However, the second term introduced by conventional heterostructure inevitably complicates the electrochemical analysis and poses great challenges to harvesting systematic insights and theoretical guidance. A model cell is designed and established herein for the conversion reactions between Na and TMSA-SnO2, where TMSA-SnO2 represents single atom modification of eight different 3d transition elements (V, Cr, Mn, Fe, Co, Ni, Cu or Zn). Such a model unit fundamentally eliminates the interference from the second phase and thus enables independent exploration of activation manifestations of the heterogeneous architecture. For the first time, a thermodynamically dependent catalytic effect is proposed and verified through statistical data analysis. The mechanism behind the unveiled catalytic effect is further elucidated by which the active d orbitals of transition metals weaken the surface covalent bonds and lower the reaction barriers. This research provides both theoretical insights and practical demonstrations of the advanced heterogeneous electrodes.