LAMP assay to detect Elsinoë necatrix; an important Eucalyptus shoot and leaf pathogen.

Eucalyptus scab and shoot malformation caused by Elsinoë necatrix is an emerging disease and a serious threat to the global commercial forestry industry. The disease was first discovered in North Sumatra, Indonesia and now requires a simple and effective method for early pathogen detection. In this study, a rapid and sensitive Loop-mediated isothermal amplification (LAMP) assay was developed for E. necatrix. A unique region in a secondary metabolite gene cluster was used as target for the assay. To test robustness of the assay, LAMP amplification was verified in 15 strains of E. necatrix. A specificity test against 23 closely related Elsinoë species and three fungal species commonly isolated on Eucalyptus showed that the LAMP assay exclusively identified E. necatrix isolates. The assay had a high level of sensitivity, able to detect 0.01 ng (approximately 400 target copies) of pure E. necatrix DNA. Furthermore, using a simple DNA extraction method, it was possible to use this assay to detect E. necatrix in infected Eucalyptus leaves.

RNA-guided genome engineering: paradigm shift towards transposons.

CRISPR-Cas systems revolutionized the genome engineering field but need to induce double-strand breaks (DSBs) and may be difficult to deliver due to their large protein size. Tn7-like transposons such as CRISPR-associated transposons (CASTs) can be repurposed for RNA-guided DSB-free integration, and obligate mobile element guided activity (OMEGA) proteins of the IS200/IS605 transposon family have been developed as hypercompact RNA-guided genome editing tools. CASTs and OMEGA are exciting, innovative genome engineering tools that can improve the precision and efficiency of editing. This review explores the recent developments and uses of CASTs and OMEGA in genome editing across prokaryotic and eukaryotic cells. The pros and cons of these transposon-based systems are deliberated in comparison to other CRISPR systems.

Exploring the nexus between fiscal decentralization and ecological sustainability: a fresh perspective from the moderating role of geopolitical risk and updated international evidence.

Fiscal decentralization has been long employed to enhance the utilization of financial resources for sustainable development. Nevertheless, its effectiveness in limiting ecological degradation is ambiguous, especially when a country faces geopolitical risks. Different from previous works which separately examine the impacts of either fiscal decentralization or geopolitical risks on ecological sustainability, this research examines the moderating role of geopolitical risks on the non-linear relationship between fiscal decentralization and ecological footprints across different levels of environmental condition. An advanced panel quantile regression is applied to a sample of 23 advanced and emerging market economies from 1990 to 2018. The empirical results indicate that the nexus between revenue decentralization and ecological footprint follows an inverted U-shaped pattern at the 20th to 60th quantiles of ecological footprint. Meanwhile, the linkage between expenditure and ecological footprint reflects a U-shaped pattern across all quantiles. Notably, geopolitical risk strongly moderates the connection between fiscal decentralization and ecological footprint with the role being stronger in the case of revenue decentralization. This research provides valuable implementations to tailor policies for transferring revenue and expenditure responsibilities to sub-governmental bodies towards sustainability targets based on their current ecological conditions and contexts of geopolitical instability.

Embedding viral hepatitis into primary healthcare: results of a strategic landscape analysis in Vietnam and the Philippines.

Chronic viral hepatitis is a significant public health concern in the Western Pacific, including in Vietnam and the Philippines. To accelerate progress toward meeting the 2030 elimination goals, the World Health Organization (WHO) encourages countries to adopt an integrated, people-centered health sector response to hepatitis, grounded in Primary Health Care (PHC). A review of the academic and grey literature, along with policy documents, was conducted to describe the national health system and PHC response to hepatitis B and C in Vietnam and the Philippines. Information was analyzed against the four strategic levers of the WHO Operational Framework for PHC to identify challenges and opportunities. The findings suggest that both countries have relatively robust policy frameworks, with some room for improvement. Vietnam may have stronger political commitment and funding than the Philippines, while the Philippines appears to be stronger in community engagement. Both countries share challenges and opportunities for learning to actualize viral hepatitis elimination utilizing a PHC approach.

Sigma Hole Potentials as Tools: Quantifying and Partitioning Substituent Effects.

Empirical substituent constants, such as the Hammett parameters, have found important utility in organic and other areas of chemistry. They are useful both in predicting the impact of substitutions on chemical processes and in rationalizing after-the-fact observations on chemical bonding and reactivity. We assess the impact of substitutions on monoiodinated benzene rings and find that the modifications that substituents induce on the electrostatic potentials at the sigma hole on the terminal I center correlate strongly with established trends of common substituents. As an alternative to the experimental procedures involved in obtaining empirically based substituent constants, the computationally determined constants based on induced electrostatic potentials offer a model for quantifying the influence of mono- and polyatomic, neutral, and ionic substituents on their compounds. A partitioning scheme is proposed that allows us to discretely separate σ and π contributions to generate quantitative measures of substituent effects.

Enhanced Calvarial Bone Repair Using ASCs Engineered with RNA-Guided Split dCas12a System that Co-Activates Sox 5, Sox6, and Long Non-Coding RNA H19.

Healing of large calvarial bone defects remains challenging. An RNA-guided Split dCas12a system is previously harnessed to activate long non-coding RNA H19 (lncRNA H19, referred to as H19 thereafter) in bone marrow-derived mesenchymal stem cells (BMSCs). H19 activation in BMSCs induces chondrogenic differentiation, switches bone healing pathways, and improves calvarial bone repair. Since adipose-derived stem cells (ASCs) can be harvested more easily in large quantity, here it is aimed to use ASCs as an alternative cell source. However, H19 activation alone using the Split dCas12a system in ASCs failed to elicit evident chondrogenesis. Therefore, split dCas12a activators are designed more to co-activate other chondroinductive transcription factors (Sox5, Sox6, and Sox9) to synergistically potentiate differentiation. It is found that co-activation of H19/Sox5/Sox6 in ASCs elicited more potent chondrogenic differentiation than activation of Sox5/Sox6/Sox9 or H19 alone. Co-activating H19/Sox5/Sox6 in ASCs significantly augmented in vitro cartilage formation and in vivo calvarial bone healing. These data altogether implicated the potentials of the Split dCas12a system to trigger multiplexed gene activation in ASCs for differentiation pathway reprogramming and tissue regeneration.

Patient-GAT: Sarcopenia Prediction using Multi-modal Data Fusion and Weighted Graph Attention Networks.

Graph Attention Networks (GAT) have been extensively used to perform node-level classification on data that can be represented as a graph. However, few papers have investigated the effectiveness of using GAT on graph representations of patient similarity networks. This paper proposes Patient-GAT, a novel method to predict chronic health conditions by first integrating multi-modal data fusion to generate patient vector representations using imputed lab variables with other structured data. This data representation is then used to construct a patient network by measuring patient similarity, finally applying GAT to the patient network for disease prediction. We demonstrated our framework by predicting sarcopenia using real-world EHRs obtained from the Indiana Network for Patient Care. We evaluated the performance of our system by comparing it to other baseline models, showing that our model outperforms other methods. In addition, we studied the contribution of the temporal representation of the lab data and discussed the interpretability of this model by analyzing the attention coefficients of the trained Patient-GAT model. Our code can be found on Github.

Covalently Functionalized Graphene with Molecularly Imprinted Polymers for Selective Adsorption and Electrochemical Detection of Chloramphenicol.

In this report, we have presented a novel route to attach molecularly imprinted polymers (MIPs) on the surface of reduced graphene oxide (rGO) through covalent bonding. First, the surface of rGO was modified with maleic anhydride (MA) via a Diels-Alder reaction using a deep eutectic solvent (DES). Next, 3-propyl-1-vinylimidazolium molecular units were anchored and polymerized in the presence of ethylene glycol dimethacrylate (EGDMA) using chloramphenicol (CAP) as the template. Primarily, we investigated the effect of the molar ratio of individual precursors on the adsorption capacity of synthesized materials and accordingly fabricated the electrochemical sensor for CAP detection. Electrochemical results evidenced that the covalent bonding of MIP units enhanced the sensitivity of the respective sensor toward CAP in water as well as in real honey samples with high selectivity, stability, and reproducibility. This synthesis strategy involves the covalent binding of MIP on rGO materials via click chemisty under sonication power excluding harmful solvents and energy-intensive processes and thus could be a motivation for developing future electrochemical sensors through similar "green" routes.

High Biocompatibility, MRI Enhancement, and Dual Chemo- and Thermal-Therapy of Curcumin-Encapsulated Alginate/Fe3O4 Nanoparticles.

(1) Background: Magnetite (Fe3O4) nanoparticles have great potential for biomedical applications, including hyperthermia and magnetic resonance imaging. In this study, we aimed to identify the biological activity of nanoconjugates composed of superparamagnetic Fe3O4 nanoparticles coated with alginate and curcumin (Fe3O4/Cur@ALG) in cancer cells. (2) Methods: The nanoparticles were evaluated for the biocompatibility and toxicity on mice. The MRI enhancement and hyperthermia capacities of Fe3O4/Cur@ALG were determined in both in vitro and in vivo sarcoma models. (3) Results: The results show that the magnetite nanoparticles exhibit high biocompatibility and low toxicity in mice at Fe3O4 concentrations up to 120 mg/kg when administered via intravenous injection. The Fe3O4/Cur@ALG nanoparticles enhance the magnetic resonance imaging contrast in cell cultures and tumor-bearing Swiss mice. The autofluorescence of curcumin also allowed us to observe the penetration of the nanoparticles into sarcoma 180 cells. In particular, the nanoconjugates synergistically inhibit the growth of sarcoma 180 tumors via magnetic heating and the anticancer effects of curcumin, both in vitro and in vivo. (4) Conclusions: Our study reveals that Fe3O4/Cur@ALG has a high potential for medicinal applications and should be further developed for cancer diagnosis and treatment.

Anxiety and associated factors among Vietnamese students during COVID-19 pandemic: A cross-sectional study.

Mental health care for students in general, particularly anxiety, is a significant problem that needs more attention, especially during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to describe the prevalence of anxiety and examine the associated factors among students during the COVID-19 pandemic in Vietnam. A cross-sectional study was conducted from August to September 2021 among 5730 students. An online survey was used to collect sociodemographic information, and the generalized anxiety disorder questionnaire (GAD-7) was used to assess anxiety symptoms among Vietnamese students. Results showed that the prevalence of anxiety among study participants was 16.2% (95% confidence interval [CI]: 15.3%-17.2%). Factors related to anxiety among students were gender, type of housemate, COVID-19 exposure/infection status, vaccination status, health status, academic performance, and social relationships during the COVID-19 pandemic. A significant number of students experienced anxiety during COVID-19, and this rate was related to several factors. Psychological interventions are required to support students during and after the COVID-19 pandemic and other health crises. Further studies are required to confirm our findings.

Split dCas12a activator for lncRNA H19 activation to enhance BMSC differentiation and promote calvarial bone healing.

Healing of large calvarial bone defects in adults is challenging. We previously showed that inducing chondrogenic differentiation of mesenchymal stem cells from bone marrow (BMSC) or adipose tissue (ASC) before implantation can switch the repair pathway and improve calvarial bone healing. Split dCas12a activator is a new CRISPR activation system comprising the amino (N) and carboxyl (C) fragments of dCas12a protein, each being fused with synthetic transcription activators at both termini. The split dCas12a activator was shown to induce programmable gene expression in cell lines. Here we exploited the split dCas12a activator to activate the expression of chondroinductive long non-coding RNA H19. We showed that co-expression of the split N- and C-fragments resulted in spontaneous dimerization, which elicited stronger activation of H19 than full-length dCas12a activator in rat BMSC and ASC. We further packaged the entire split dCas12a activator system (13.2 kb) into a hybrid baculovirus vector, which enhanced and prolonged H19 activation for at least 14 days in BMSC and ASC. The extended H19 activation elicited potent chondrogenic differentiation and inhibited adipogenesis. Consequently, the engineered BMSC promoted in vitro cartilage formation and augmented calvarial bone healing in rats. These data implicated the potentials of the split dCas12a activator for stem cell engineering and regenerative medicine.

Rational genome and metabolic engineering of Candida viswanathii by split CRISPR to produce hundred grams of dodecanedioic acid.

Candida viswanathii is a promising cell factory for producing dodecanedioic acid (DDA) and other long chain dicarboxylic acids. However, metabolic engineering of C. viswanathii is difficult partly due to the lack of synthetic biology toolkits. Here we developed CRISPR-based approaches for rational genome and metabolic engineering of C. viswanathii. We first optimized the CRISPR system and protocol to promote the homozygous gene integration efficiency to >60%. We also designed a split CRISPR system for one-step integration of multiple genes into C. viswanathii. We uncovered that co-expression of CYP52A19, CPRb and FAO2 that catalyze different steps in the biotransformation enhances DDA production and abolishes accumulation of intermediates. We also unveiled that co-expression of additional enzyme POS5 further promotes DDA production and augments cell growth. We harnessed the split CRISPR system to co-integrate these 4 genes (13.6 kb) into C. viswanathii and generated a stable strain that doubles the DDA titer (224 g/L), molar conversion (83%) and productivity (1.87 g/L/h) when compared with the parent strain. This study altogether identifies appropriate enzymes/promoters to augment dodecane conversion to DDA and implicates the potential of split CRISPR system for metabolic engineering of C. viswanathii.

Support of deep learning to classify vocal fold images in flexible laryngoscopy.

PURPOSE: To collect a dataset with adequate laryngoscopy images and identify the appearance of vocal folds and their lesions in flexible laryngoscopy images by objective deep learning models. METHODS: We adopted a number of novel deep learning models to train and classify 4549 flexible laryngoscopy images as no vocal fold, normal vocal folds, and abnormal vocal folds. This could help these models recognize vocal folds and their lesions within these images. Ultimately, we made a comparison between the results of the state-of-the-art deep learning models, and another comparison of the results between the computer-aided classification system and ENT doctors. RESULTS: This study exhibited the performance of the deep learning models by evaluating laryngoscopy images collected from 876 patients. The efficiency of the Xception model was higher and steadier than almost the rest of the models. The accuracy of no vocal fold, normal vocal folds, and vocal fold abnormalities on this model were 98.90 %, 97.36 %, and 96.26 %, respectively. Compared to our ENT doctors, the Xception model produced better results than a junior doctor and was near an expert. CONCLUSION: Our results show that current deep learning models can classify vocal fold images well and effectively assist physicians in vocal fold identification and classification of normal or abnormal vocal folds.

Room-temperature spin injection from a ferromagnetic semiconductor.

Spin injection using ferromagnetic semiconductors at room temperature is a building block for the realization of spin-functional semiconductor devices. Nevertheless, this has been very challenging due to the lack of reliable room-temperature ferromagnetism in well-known group IV and III-V based semiconductors. Here, we demonstrate room-temperature spin injection by using spin pumping in a BiSb/(Ga,Fe)Sb heterostructure, where (Ga,Fe)Sb is a ferromagnetic semiconductor (FMS) with high Curie temperature (TC) and BiSb is a topological insulator (TI). Despite the very small magnetization of (Ga,Fe)Sb at room temperature (45 emu/cc), we detected spin injection from (Ga,Fe)Sb by utilizing the large inverse spin Hall effect (ISHE) in BiSb. Our study provides the first demonstration of spin injection at room temperature from a FMS.

High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism.

Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments of core samples, which makes a fast and reliable prediction method highly demanded. In this article, we propose a deep learning model that combines the 1-D convo- lutional layer and the bidirectional long short-term memory network to predict reservoir permeability and porosity. The mapping relationship between logging data and reservoir parameters is established by training a network with a combination of nonlinear and linear modules. Optimization algorithms, such as layer normalization, recurrent dropout, and early stopping, can help obtain a more accurate training model. Besides, the self-attention mechanism enables the network to better allocate weights to improve the prediction accuracy. The testing results of the well-trained network in blind wells of three different regions show that our proposed method is accurate and robust in the reservoir parameters prediction task.

Ferromagnetism modulation by ultralow current in a two-dimensional polycrystalline molybdenum disulphide atomic layered structure.

Layered materials, such as graphene and transition metal dichalcogenides, are able to obtain new properties and functions through the modification of their crystal arrangements. In particular, ferromagnetism in polycrystalline MoS2 is of great interest because the corresponding nonmagnetic single crystals exhibit spontaneous spin splitting only through the formation of grain boundaries. However, no one has reported direct evidence of this unique phenomenon thus far. Herein, we demonstrate ferromagnetism modulation by an ultralow current density < 103 A/cm2 in 7.5-nm-thick polycrystalline MoS2, in which magnetoresistance shows three patterns according to the current intensity: wide dip, nondip and narrow dip structures. Since magnetoresistance occurs because of the interaction between the current of 4d electrons in the bulk and localized 4d spins in grain boundaries, this result provides evidence of the current modulation of ferromagnetism induced by grain boundaries. Our findings pave the way for the investigation of a novel method of magnetization switching with low power consumption for magnetic random access memories.

Combinatorial CRISPR Interference Library for Enhancing 2,3-BDO Production and Elucidating Key Genes in Cyanobacteria.

Cyanobacteria can convert CO2 to chemicals such as 2,3-butanediol (2,3-BDO), rendering them promising for renewable production and carbon neutralization, but their applications are limited by low titers. To enhance cyanobacterial 2,3-BDO production, we developed a combinatorial CRISPR interference (CRISPRi) library strategy. We integrated the 2,3-BDO pathway genes and a CRISPRi library into the cyanobacterium PCC7942 using the orthogonal CRISPR system to overexpress pathway genes and attenuate genes that inhibit 2,3-BDO formation. The combinatorial CRISPRi library strategy allowed us to inhibit fbp, pdh, ppc, and sps (which catalyzes the synthesis of fructose-6-phosphate, acetyl-coenzyme A, oxaloacetate, and sucrose, respectively) at different levels, thereby allowing for rapid screening of a strain that enhances 2,3-BDO production by almost 2-fold to 1583.8 mg/L. Coupled with a statistical model, we elucidated that differentially inhibiting all the four genes enhances 2,3-BDO synthesis to varying degrees. fbp and pdh suppression exerted more profound effects on 2,3-BDO production than ppc and sps suppression, and these four genes can be repressed simultaneously without mutual interference. The CRISPRi library approach paves a new avenue to combinatorial metabolic engineering of cyanobacteria.

Efficient spin current source using a half-Heusler alloy topological semimetal with back end of line compatibility.

Topological materials, such as topological insulators (TIs), have great potential for ultralow power spintronic devices, thanks to their giant spin Hall effect. However, the giant spin Hall angle (θSH > 1) is limited to a few chalcogenide TIs with toxic elements and low melting points, making them challenging for device integration during the silicon Back-End-of-Line (BEOL) process. Here, we show that by using a half-Heusler alloy topological semi-metal (HHA-TSM), YPtBi, it is possible to achieve both a giant θSH up to 4.1 and a high thermal budget up to 600 °C. We demonstrate magnetization switching of a CoPt thin film using the giant spin Hall effect of YPtBi by current densities lower than those of heavy metals by one order of magnitude. Since HHA-TSM includes a group of three-element topological materials with great flexibility, our work opens the door to the third-generation spin Hall materials with both high θSH and high compatibility with the BEOL process that would be easily adopted by the industry.

Ultrahigh efficient spin orbit torque magnetization switching in fully sputtered topological insulator and ferromagnet multilayers.

Spin orbit torque (SOT) magnetization switching of ferromagnets with large perpendicular magnetic anisotropy has a great potential for the next generation non-volatile magnetoresistive random-access memory (MRAM). It requires a high performance pure spin current source with a large spin Hall angle and high electrical conductivity, which can be fabricated by a mass production technique. In this work, we demonstrate ultrahigh efficient and robust SOT magnetization switching in fully sputtered BiSb topological insulator and perpendicularly magnetized Co/Pt multilayers. Despite fabricated by the magnetron sputtering instead of the laboratory molecular beam epitaxy, the topological insulator layer, BiSb, shows a large spin Hall angle of θSH = 10.7 and high electrical conductivity of σ = 1.5 × 105 Ω-1 m-1. Our results demonstrate the feasibility of BiSb topological insulator for implementation of ultralow power SOT-MRAM and other SOT-based spintronic devices.