Prenatal diagnosis of autosomal recessive renal tubular dysgenesis caused by variants in the ACE gene: Two fetuses with anhydramnios.

INTRODUCTION: Autosomal recessive renal tubular dysgenesis (ARRTD) is a rare genetic disorder with a very high mortality rate. The typical symptoms of the disease during pregnancy are oligohydramnios, anhydramnios, and nearly all affected fetuses die after birth or have a stillbirth in late gestation, which can adversely increase maternal risks. METHODS: Oligohydramnios/anhydramnios can make both amniocentesis for diagnostic testing and morphological evaluation via ultrasound more difficult. In cases of oligohydramnios/anhydramnios suspicious for urinary tract anomalies, amnioinfusion is a meaningful technique that facilitates sampling of amniotic fluid for genetic diagnosis. RESULTS: We report two cases of fetuses with anhydramnios and invisible urinary bladder. Clinical exome sequencing from amniotic fluid revealed a biparentally inherited homozygous pathogenic nonsense ACE variant c.2503G 〉 T [p.Glu853Ter] in proband 1 and a biparentally inherited homozygous pathogenic nonsense ACE variant c.2992C 〉 T [p.Gln998Ter] in proband 2. The prognosis was poor and the patients elected to terminate the pregnancies. Additional post-mortem histopathological examination from the renal tissue of the second fetus showed renal tubular hypoplasia. CONCLUSION: To our knowledge for the first time, we describe the prenatal diagnosis of ARRTD in Vietnam, and highlight the benefit of detecting ACE variants associated with ARRTD in fetuses with oligohydramnios/anhydramnios through amnioinfusion and amniocentesis, which improves genotype-phenotype correlations and provides valuable information for reproductive counseling.

Advancement in nanomaterials for environmental pollutants remediation: a systematic review on bibliometrics analysis, material types, synthesis pathways, and related mechanisms.

Environmental pollution is a major issue that requires effective solutions. Nanomaterials (NMs) have emerged as promising candidates for pollution remediation due to their unique properties. This review paper provides a systematic analysis of the potential of NMs for environmental pollution remediation compared to conventional techniques. It elaborates on several aspects, including conventional and advanced techniques for removing pollutants, classification of NMs (organic, inorganic, and composite base). The efficiency of NMs in remediation of pollutants depends on their dispersion and retention, with each type of NM having different advantages and disadvantages. Various synthesis pathways for NMs, including traditional synthesis (chemical and physical) and biological synthesis pathways, mechanisms of reaction for pollutants removal using NMs, such as adsorption, filtration, disinfection, photocatalysis, and oxidation, also are evaluated. Additionally, this review presents suggestions for future investigation strategies to improve the efficacy of NMs in environmental remediation. The research so far provides strong evidence that NMs could effectively remove contaminants and may be valuable assets for various industrial purposes. However, further research and development are necessary to fully realize this potential, such as exploring new synthesis pathways and improving the dispersion and retention of NMs in the environment. Furthermore, there is a need to compare the efficacy of different types of NMs for remediating specific pollutants. Overall, this review highlights the immense potential of NMs for mitigating environmental pollutants and calls for more research in this direction.

Mechanism of chorismate dehydratase MqnA, the first enzyme of the futalosine pathway, proceeds via substrate-assisted catalysis.

MqnA, the only chorismate dehydratase known so far, catalyzes the initial step in the biosynthesis of menaquinone via the futalosine pathway. Details of the MqnA reaction mechanism remain unclear. Here, we present crystal structures of Streptomyces coelicolor MqnA and its active site mutants in complex with chorismate and the product 3-enolpyruvyl-benzoate, produced during heterologous expression in Escherichia coli. Together with activity studies, our data are in line with dehydration proceeding via substrate assisted catalysis, with the enol pyruvyl group of chorismate acting as catalytic base. Surprisingly, structures of the mutant Asn17Asp with copurified ligand suggest that the enzyme converts to a hydrolase by serendipitous positioning of the carboxyl group. All complex structures presented here exhibit a closed Venus flytrap fold, with the enzyme exploiting the characteristic ligand binding properties of the fold for specific substrate binding and catalysis. The conformational rearrangements that facilitate complete burial of substrate/product, with accompanying topological changes to the enzyme surface, could foster substrate channeling within the biosynthetic pathway.

A survey on adverse drug reaction studies: data, tasks and machine learning methods.

MOTIVATION: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. RESULTS: In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug-ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug-ADR prediction task. Finally, we discussed open problems for further ADR studies. AVAILABILITY: Data and code are available at https://github.com/anhnda/ADRPModels.

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.

MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interacting drugs and a side effect. The idea of these methods is that each side effect is caused by a unique combination of latent features of the corresponding interacting drugs. However, in reality, a side effect might have multiple, different mechanisms that cannot be represented by a single combination of latent features of drugs. Moreover, DDI data are sparse, suggesting that using a sparsity regularization would help to learn better latent representations to improve prediction performances. RESULTS: We propose SPARSE, which encodes the DDI hypergraph and drug features to latent spaces to learn multiple types of combinations of latent features of drugs and side effects, controlling the model sparsity by a sparse prior. Our extensive experiments using both synthetic and three real-world DDI datasets showed the clear predictive performance advantage of SPARSE over cutting-edge competing methods. Also, latent feature analysis over unknown top predictions by SPARSE demonstrated the interpretability advantage contributed by the model sparsity. AVAILABILITY AND IMPLEMENTATION: Code and data can be accessed at https://github.com/anhnda/SPARSE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Progress in Sensors for Monitoring Reinforcement Corrosion in Reinforced Concrete Structures-A Review.

Non-destructive monitoring methods and continuous monitoring systems based on them are crucial elements of modern systems for the management and maintenance of assets which include reinforced concrete structures. The purpose of our study was to summarise the data on the most common sensors and systems for the non-destructive monitoring of reinforced concrete structures developed over the past 20 years. We considered systems based on electrochemical (potentiometry, methods related to polarisation) and physical (electromagnetic and ultrasonic waves, piezoelectric effect, thermography) examination methods. Special focus is devoted to the existing sensors and the results obtained using these sensors, as well as the advantages and disadvantages of their setups or other equipment used. The review considers earlier approaches and available commercial products, as well as relatively new sensors which are currently being tested.

Application of deep learning models to detect coastlines and shorelines.

Identifying and monitoring coastlines and shorelines play an important role in coastal erosion assessment around the world. The application of deep learning models was used in this study to detect coastlines and shorelines in Vietnam using high-resolution satellite images and different object segmentation methods. The aims are to (1) propose indicators to identify coastlines and shorelines; (2) build deep learning (DL) models to automatically interpret coastlines and shorelines from high-resolution remote sensing images; and (3) apply DL-trained models to monitor coastal erosion in Vietnam. Eight DL models were trained based on four artificial-intelligent-network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. The high-resolution images collected from Google Earth Pro software were used as input data for training all models. As a result, the U-Net using an input-image size of 512 × 512 provides the highest performance of 98% with a loss function of 0.16. The interpretation results of this model were used effectively for the coastline and shoreline identification in assessing coastal erosion in Vietnam due to sea-level rise in storm events over 20 years. The outcomes proved that while the shoreline is ideal for observing seasonal tidal changes or the immediate motions of current waves, the coastline is suitable to assess coastal erosion caused by the influence of sea-level rise during storms. This paper has provided a broad scope of how the U-Net model can be used to predict the coastal changes over vietnam and the world.

Successful endoscopic endonasal surgery for very huge trigeminal schwannomas in nasopharynx.

We present a 60-year-old female diagnosed with a giant trigeminal tumor (5.2*6.4*8.2 cm) situated in the middle cranial fossa and nasopharyngeal area. The patient was operated on by endoscopic endonasal transmaxillary, transpterygoid and infratemporal approaches. Postoperatively she was stable, with no neurologic complication and no cerebrospinal fluid leakage. We review the literature on extremely large trigeminal schwannomas.

A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies.

One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.

Modulation of Junction Modes in SnSe2/MoTe2 Broken-Gap van der Waals Heterostructure for Multifunctional Devices.

We study the electronic and optoelectronic properties of a broken-gap heterojunction composed of SnSe2 and MoTe2 with gate-controlled junction modes. Owing to the interband tunneling current, our device can act as an Esaki diode and a backward diode with a peak-to-valley current ratio approaching 5.7 at room temperature. Furthermore, under an 811 nm laser irradiation the heterostructure exhibits a photodetectivity of up to 7.5 × 1012 Jones. In addition, to harness the electrostatic gate bias, Voc can be tuned from negative to positive by switching from the accumulation mode to the depletion mode of the heterojunction. Additionally, a photovoltaic effect with a fill factor exceeding 41% was observed, which highlights the significant potential for optoelectronic applications. This study not only demonstrates high-performance multifunctional optoelectronics based on the SnSe2/MoTe2 heterostructure but also provides a comprehensive understanding of broken-band alignment and its applications.

Magnetic Ni-Doped TiO2 Photocatalysts for Disinfection of Escherichia coli Bacteria.

Ni-doped TiO2 nanoparticles have been synthesized by a modified sol-gel method. The crystal phase composition, particle size, and magnetic and optical properties of the samples were comprehensively examined using x-ray diffraction analysis, transmission electron microscopy, Brunauer-Emmett-Teller surface area analysis, Raman spectroscopy, magnetization measurements, and ultraviolet-visible (UV-Vis) absorption techniques. The results showed that the prepared Ni-doped TiO2 samples sintered at 400°C crystallized completely in anatase phase with average particle size in the range from 8 nm to 10 nm and presented broad visible absorption. The bactericidal efficiency of TiO2 was effectively enhanced by Ni doping, with an optimum Ni doping concentration of 6% (x = 0.06), at which 95% of Escherichia coli were killed after just 90 min of irradiation. Density functional theory (DFT) calculations revealed good agreement with the experimental data. Moreover, the Ni dopant induced magnetic properties in TiO2, facilitating its retrieval using a magnetic field after use, which is an important feature for photocatalytic applications.

Multi-Sensor Data Fusion in A Real-Time Support System for On-Duty Firefighters.

While working on fire ground, firefighters risk their well-being in a state where any incident might cause not only injuries, but also fatality. They may be incapacitated by unpredicted falls due to floor cracks, holes, structure failure, gas explosion, exposure to toxic gases, or being stuck in narrow path, etc. Having acknowledged this need, in this study, we focus on developing an efficient portable system to detect firefighter's falls, loss of physical performance, and alert high CO level by using a microcontroller carried by a firefighter with data fusion from a 3-DOF (degrees of freedom) accelerometer, 3-DOF gyroscope, 3-DOF magnetometer, barometer, and a MQ7 sensor using our proposed fall detection, loss of physical performance detection, and CO monitoring algorithms. By the combination of five sensors and highly efficient data fusion algorithms to observe the fall event, loss of physical performance, and detect high CO level, we can distinguish among falling, loss of physical performance, and the other on-duty activities (ODAs) such as standing, walking, running, jogging, crawling, climbing up/down stairs, and moving up/down in elevators. Signals from these sensors are sent to the microcontroller to detect fall, loss of physical performance, and alert high CO level. The proposed algorithms can achieve 100% of accuracy, specificity, and sensitivity in our experimental datasets and 97.96%, 100%, and 95.89% in public datasets in distinguishing between falls and ODAs activities, respectively. Furthermore, the proposed algorithm perfectly distinguishes between loss of physical performance and up/down movement in the elevator based on barometric data fusion. If a firefighter is unconscious following the fall or loss of physical performance, an alert message will be sent to their incident commander (IC) via the nRF224L01 module.

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System.

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user's states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).

Improvement in energy performance from the construction of inlet guide vane and diffuser vane geometries in an axial-flow pump.

Advanced inlet guide vane (IGV) and diffuser vane (DV) geometries were constructed in an effort to increase the energy performance of an axial-flow pump at the best efficiency point (BEP). DV setting angles were also investigated to increase energy performance at the off-design points. By integrating the advantages of an adjustable IGV, combinations of adjustable IGV and DV geometries were constructed and thoroughly analyzed by way of energy loss. The asymmetrical geometry of the IGV, upgraded through the use of a hydrofoil profile, resulted in higher hydraulic performance compared to that of the reference model. The efficiency and total head at the BEP increased significantly with the implementation of the new DV, by 1.456% and 5.756% over those of the reference model, respectively. Using the new DV reduced the unsteady turbulent flow behind the trailing edge of the DV under all flow rate conditions, resulting in a reduction in vibration and noise. The positive setting angles of the DV increased the energy performance in the high-flow-rate region, whereas the negative DV setting angles produced a good performance in the low-flow-rate region. Combining an adjustable IGV with an adjustable DV model resulted in a significant increase in the total head, with more optimal energy performance provided by the positive IGV setting angles. At the BEP and under high-flow-rate conditions, the low-velocity zone is closely related to high entropy generation. Furthermore, these high-entropy generation regions follow the trajectory of the low-velocity zones. However, the low-velocity zone is not strongly associated with the high-entropy generation region when operating under low-flow-rate conditions.

Ultrahigh-porosity Ranunculus-like MgO adsorbent coupled with predictive deep belief networks: A transformative method for phosphorus treatment.

Phosphorus is a nonrenewable material with a finite supply on Earth; however, due to the rapid growth of the manufacturing industry, phosphorus contamination has become a global concern. Therefore, this study highlights the remarkable potential of ranunculus-like MgO (MO4-MO6) as superior adsorbents for phosphate removal and recovery. Furthermore, MO6 stands out with an impressive adsorption capacity of 596.88 mg/g and a high efficacy across a wide pH range (2-10) under varying coexisting ion concentrations. MO6 outperforms the top current adsorbents for phosphate removal. The process follows Pseudo-second-order and Langmuir models, indicating chemical interactions between the phosphate species and homogeneous MO6 monolayer. MO6 maintains 80 % removal and 96 % recovery after five cycles and adheres to the WHO and EUWFD regulations for residual elements in water. FT-IR and XPS analyses further reveal the underlying mechanisms, including ion exchange, electrostatic, and acid-base interactions. Ten machine learning (ML) models were applied to simultaneously predict multi-criteria (sorption capacity, removal efficiency, final pH, and Mg leakage) affected by 15 diverse environmental conditions. Traditional ML models and deep neural networks have poor accuracy, particularly for removal efficiency. However, a breakthrough was achieved by the developed deep belief network (DBN) with unparalleled performance (MAE = 1.3289, RMSE = 5.2552, R2 = 0.9926) across all output features, surpassing all current studies using thousands of data points for only one output factor. These captivating MO6 and DBN models also have immense potential for effectively applying in the real water test with error < 5 %, opening immense horizons for transformative methods, particularly in phosphate removal and recovery.

Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers.

Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph-neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 h images could predict the cell types obtained 3 weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.

2D van der Waals Heterostructure with Tellurene Floating-Gate for Wide Range and Multi-Bit Optoelectronic Memory.

Intensive research on optoelectronic memory (OEM) devices based on two-dimensional (2D) van der Waals heterostructures (vdWhs) is being conducted due to their distinctive advantages for electrical-optical writing and multilevel storage. These features make OEM a promising candidate for the logic of reconfigurable operations. However, the realization of nonvolatile OEM with broadband absorption (from visible to infrared) and a high switching ratio remains challenging. Herein, we report a nonvolatile OEM based on a heterostructure consisting of rhenium disulfide (ReS2), hexagonal boron nitride (hBN) and tellurene (2D Te). The 2D Te-based floating-gate (FG) device exhibits excellent performance metrics, including a high switching on/off ratio (∼106), significant endurance (>1000 cycles) and impressive retention (>104 s). In addition, the narrow band gap of 2D Te endows the device with broadband optical programmability from the visible to near-infrared regions at room temperature. Moreover, by applying different gate voltages, light wavelengths, and laser powers, multiple bits can be successfully generated. Additionally, the device is specifically designed to enable reconfigurable inverter logic circuits (including AND and OR gates) through controlled electrical and optical inputs. These significant findings demonstrate that the 2D vdWhs with a 2D Te FG are a valuable approach in the development of high-performance OEM devices.

Comparison between U-shaped structural deep learning models to detect landslide traces.

Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.

Synthesis, crystal structure and photophysical properties of a dinuclear MnII complex with 6-(di-ethyl-amino)-4-phenyl-2-(pyridin-2-yl)quinoline.

A new quinoline derivative, namely, 6-(di-ethyl-amino)-4-phenyl-2-(pyridin-2-yl)quinoline, C24H23N3 (QP), and its MnII complex aqua-1κO-di-µ-chlorido-1:2κ4 Cl:Cl-di-chlorido-1κCl,2κCl-bis-[6-(di-ethyl-amino)-4-phenyl-2-(pyridin-2-yl)quinoline]-1κ2 N 1,N 2;2κ2 N 1,N 2-dimanganese(II), [Mn2Cl4(C24H23N3)2(H2O)] (MnQP), were synthesized. Their compositions have been determined with ESI-MS, IR, and 1H NMR spectroscopy. The crystal-structure determination of MnQP revealed a dinuclear complex with a central four-membered Mn2Cl2 ring. Both MnII atoms bind to an additional Cl atom and to two N atoms of the QP ligand. One MnII atom expands its coordination sphere with an extra water mol-ecule, resulting in a distorted octa-hedral shape. The second MnII atom shows a distorted trigonal-bipyramidal shape. The UV-vis absorption and emission spectra of the examined compounds were studied. Furthermore, when investigating the aggregation-induced emission (AIE) properties, it was found that the fluorescent color changes from blue to green and eventually becomes yellow as the fraction of water in the THF/water mixture increases from 0% to 99%. In particular, these color and intensity changes are most pronounced at a water fraction of 60%. The crystal structure contains disordered solvent mol-ecules, which could not be modeled. The SQUEEZE procedure [Spek (2015 ▸). Acta Cryst. C71, 9-18] was used to obtain information on the type and qu-antity of solvent mol-ecules, which resulted in 44 electrons in a void volume of 274 Å3, corresponding to approximately 1.7 mol-ecules of ethanol in the unit cell. These ethanol mol-ecules are not considered in the given chemical formula and other crystal data.

Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions.

Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e., side effects) for each pair of nodes in a DDI graph, of which nodes are drugs and edges are interacting drugs with known labels. State-of-the-art methods for this problem are graph neural networks (GNNs), which leverage neighborhood information in the graph to learn node representations. For DDI, however, there are many labels with complicated relationships due to the nature of side effects. Usual GNNs often fix labels as one-hot vectors that do not reflect label relationships and potentially do not obtain the highest performance in the difficult cases of infrequent labels. In this brief, we formulate DDI as a hypergraph where each hyperedge is a triple: two nodes for drugs and one node for a label. We then present CentSmoothie , a hypergraph neural network (HGNN) that learns representations of nodes and labels altogether with a novel "central-smoothing" formulation. We empirically demonstrate the performance advantages of CentSmoothie in simulations as well as real datasets.