Copper-Based Two-Dimensional Metal-Organic Frameworks for Fenton-like Photocatalytic Degradation of Methylene Blue under UV and Sunlight Irradiation.

To efficiently degrade organic pollutants, photocatalysts must be effective under both ultraviolet (UV) radiation and sunlight. We synthesized a series of new metal-organic frameworks by using mild hydrothermal conditions. These frameworks incorporate three distinct bipyridyl ligands: pyrazine (pyr), 4,4'-bipyridine (bpy), and 1,2-bis(4-pyridyl)ethane (bpe). The resulting compounds are denoted as [Cu(pyz)(H2O)2MF6], [Cu(bpy)2(H2O)2]·MF6, and [Cu(bpe)2(H2O)2]·MF6·H2O [M = Zr (1, 3, and 5) and Hf (2, 4, and 6)]. All six compounds exhibited a two-dimensional crystal structure comprising infinitely nonintersecting linear chains. Compound 3 achieved 100% degradation of methylene blue (MB) after 8 min under UV irradiation and 100 min under natural sunlight in the presence of H2O2 as the electron acceptor. For compound 5, 100% MB degradation was achieved after 120 min under sunlight and 10 min under UV light. Moreover, reactive radical tests revealed that the dominant species involved in photocatalytic degradation are hydroxyl (•OH), superoxide radicals (•O2-), and photogenerated holes (h+). The photodegradation process followed pseudo-first-order kinetics, with photodegradation rate constants of 0.362 min-1 (0.039 min-1) for 3 and 0.316 min-1 (0.033 min-1) for 5 under UV (sunlight) irradiation. The developed photocatalysts with excellent activity and good recyclability are promising green catalysts for degrading organic pollutants during environmental decontamination.

Thermodynamic, Spectroscopic, and Structural Study on a Sodium Uranyl Tri-2-Methoxybenzoate Dodecahydrate Coordination Compound with Considerably Low Solubility in Acidic Conditions.

A spontaneous crystallization of an uranium(VI)-organic coordination compound with sodium and 2-methoxybenzoate (2-mba) was observed in acidic solutions, and the solubility product, molecular vibrations, crystal structure, thermal stability, and emission properties of the atypically low-soluble U(VI) complex (Na[UO2(2-mba)3]·12H2O(s)) were fully investigated for the first time. A long-term solubility experiment and speciation modeling gave a solubility product of log Ks,0 = -12.18 ± 0.02 (T = 25 °C and I = 0.1 M NaClO4), and vibrational analyses confirmed the overall molecular structure of complex and the frequencies of characteristic stretching motions of uranyl moiety as well. The crystal quality of Na[UO2(2-mba)3]·12H2O(s) was improved by a digestion method, and X-ray diffraction analysis of the single crystalline specimen verified that the newly studied uranyl-organic compound contains one-dimensional channels with a diameter of 20 Å along the [001] direction; the sodium and water molecules are arranged in the channel structures. In the coordination environment around uranyl, three aromatic carboxylates are symmetrically bound in the equatorial plane of uranyl coplanarily, and the unit [UO2(2-mba)3]- complexes are further extended along the plane to form the layered-morphologies. The three-dimensional packing of [UO2(2-mba)3]- anions is driven by the parallel-displaced π-stacking of aromatic rings with a centroid-centroid distance of 3.7 Å. Additional thermogravimetric analysis confirmed that the Na[UO2(2-mba)3]·12H2O(s) is stable up to 250 °C, and dehydration and release of the organic ligand were subsequently observed beyond that temperature. Photoluminescence spectrum of the Na[UO2(2-mba)3]·12H2O(s) clearly displayed the characteristic U(VI) emission, and a band spacing between the ground electronic states of U(VI) uranyl was evaluated to be 831 ± 14 cm-1. Such detailed characterization of the unique Na[UO2(2-mba)3]·12H2O(s) is advancing upon a systematic understanding of the structural effects of the aromatic model ligands on U(VI) complexation, with relevance to the environmental chemistry of U(VI) and crystal engineering for development of diverse uranyl-organic frameworks.

Data Preprocessing and Augmentation Improved Visual Field Prediction of Recurrent Neural Network with Multi-Central Datasets.

INTRODUCTION: The purpose of this study was to determine whether data preprocessing and augmentation could improve visual field (VF) prediction of recurrent neural network (RNN) with multi-central datasets. METHODS: This retrospective study collected data from five glaucoma services between June 2004 and January 2021. From an initial dataset of 331,691 VFs, we considered reliable VF tests with fixed intervals. Since the VF monitoring interval is very variable, we applied data augmentation using multiple sets of data for patients with more than eight VFs. We obtained 5,430 VFs from 463 patients and 13,747 VFs from 1,076 patients by setting the fixed test interval to 365 ± 60 days (D = 365) and 180 ± 60 days (D = 180), respectively. Five consecutive VFs were provided to the constructed RNN as input and the 6th VF was compared with the output of the RNN. The performance of the periodic RNN (D = 365) was compared to that of an aperiodic RNN. The performance of the RNN with 6 long- and short-term memory (LSTM) cells (D = 180) was compared with that of the RNN with 5-LSTM cells. To compare the prediction performance, the root mean square error (RMSE) and mean absolute error (MAE) of the total deviation value (TDV) were calculated as accuracy metrics. RESULTS: The performance of the periodic model (D = 365) improved significantly over aperiodic model. Overall prediction error (MAE) was 2.56 ± 0.46 dB versus 3.26 ± 0.41 dB (periodic vs. aperiodic) (p < 0.001). A higher perimetric frequency was better for predicting future VF. The overall prediction error (RMSE) was 3.15 ± 2.29 dB versus 3.42 ± 2.25 dB (D = 180 vs. D = 365). Increasing the number of input VFs improved the performance of VF prediction in D = 180 periodic model (3.15 ± 2.29 dB vs. 3.18 ± 2.34 dB, p < 0.001). The 6-LSTM in the D = 180 periodic model was more robust to worsening of VF reliability and disease severity. The prediction accuracy worsened as the false-negative rate increased and the mean deviation decreased. CONCLUSION: Data preprocessing with augmentation improved the VF prediction of the RNN model using multi-center datasets. The periodic RNN model predicted the future VF significantly better than the aperiodic RNN model.

Mothers' use of touch across infants' development and its implications for word learning: Evidence from Korean dyadic interactions.

Caregivers' touches that occur alongside words and utterances could aid in the detection of word/utterance boundaries and the mapping of word forms to word meanings. We examined changes in caregivers' use of touches with their speech directed to infants using a multimodal cross-sectional corpus of 35 Korean mother-child dyads across three age groups of infants (8, 14, and 27 months). We tested the hypothesis that caregivers' frequency and use of touches with speech change with infants' development. Results revealed that the frequency of word/utterance-touch alignment as well as word + touch co-occurrence is highest in speech addressed to the youngest group of infants. Thus, this study provides support for the hypothesis that caregivers' use of touch during dyadic interactions is sensitive to infants' age in a way similar to caregivers' use of speech alone and could provide cues useful to infants' language learning at critical points in early development.

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments.

Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor's coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach.

Thermodynamic Studies on the Hydrolysis of Trivalent Plutonium and Solubility of Pu(OH)3(am).

The temperature-dependent reaction properties of actinide elements are of particular interest in the safety assessment of high-level radioactive waste (HLRW) disposal systems. In this study, the hydrolysis of Pu(III) and the solubility of Pu(OH)3(am) were investigated at various temperatures (10-40 °C) in 0.1 M NaClO4. A strong reducing condition for maintaining the oxidation state of Pu(III) while slowly increasing the pH of the solution was realized by electrolysis. The formation constants of the first hydrolysis species, log *ß1', and the solubility products of Pu(OH)3(am), log *Ks,0', at 10, 17, and 40 °C were experimentally determined using spectrophotometry, laser-induced breakdown detection, and radiometry. The enthalpy and entropy changes for these reactions were estimated using the van't Hoff equation. The first hydrolysis of Pu(III) is endothermic (ΔrHm° = 34.10 ± 4.48 kJ mol-1), and the dissolution of Pu(OH)3(am) is exothermic (ΔrHm° = -294.29 ± 23.05 kJ mol-1) with negative entropy changes. These thermodynamic data will contribute to improving the reliability of the safety assessment of HLRW disposal facilities and understanding the geochemical behavior of Pu under reducing or anoxic aqueous conditions at elevated temperatures.

Magnetic supercluster particles for highly sensitive magnetic biosensing of proteins.

A strategy is reported to improve the detection limits of current giant magnetoresistance (GMR) biosensors by augmenting the effective magnetic moment that the magnetic tags on the biosensors can exert. Magnetic supercluster particles (MSPs), each of which consists of ~ 1000 superparamagnetic cores, are prepared by a wet-chemical technique and are utilized to improve the limit of detection of GMR biosensors down to 17.6 zmol for biotin as a target molecule. This value is more than four orders of magnitude lower than that of the conventional colorimetric assay performed using the same set of reagents except for the signal transducer. The applicability of MSPs in immunoassay is further demonstrated by simultaneously detecting vascular endothelial growth factor (VEGF) and C-reactive protein (CRP) in a duplex assay format. MSPs outperform commercially available magnetic nanoparticles in terms of signal intensity and detection limit.

KIn0.33IIITe0.67VITe2IVO7: Zirconolite-like Mixed-Valent Metal Oxide with a 3D Framework.

We provide the material synthesis method, crystal structure information, and characterization of a novel mixed-valent metal oxide KIn0.33IIITe0.67VITe2IVO7, closely related to zirconolite (CaZrTi2O7), a radioactive waste immobilized material, having a 3D framework. The reported metal oxide containing an alkali-metal cation (K+), main-group cation (In3+), tellurate, and tellurite has been synthesized as both single crystals and a pure polycrystalline phase through a hydrothermal synthesis method. Single-crystal X-ray diffraction indicates that KIn0.33Te2.67O7 crystallizing in the orthorhombic space group Cmcm (No. 63) reveals a 3D framework structure with a 1D channel consisting of Te/InO6 octahedra and TeO4 polyhedra. An interesting transition reaction from KIn0.33Te2.67O7 to KIn(TeO3)2 under hydrothermal conditions at 230 °C is discussed.

Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester.

Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.

Aptamers for Anti-Viral Therapeutics and Diagnostics.

Viral infections cause a host of fatal diseases and seriously affect every form of life from bacteria to humans. Although most viral infections can receive appropriate treatment thereby limiting damage to life and livelihood with modern medicine and early diagnosis, new types of viral infections are continuously emerging that need to be properly and timely treated. As time is the most important factor in the progress of many deadly viral diseases, early detection becomes of paramount importance for effective treatment. Aptamers are small oligonucleotide molecules made by the systematic evolution of ligands by exponential enrichment (SELEX). Aptamers are characterized by being able to specifically bind to a target, much like antibodies. However, unlike antibodies, aptamers are easily synthesized, modified, and are able to target a wider range of substances, including proteins and carbohydrates. With these advantages in mind, many studies on aptamer-based viral diagnosis and treatments are currently in progress. The use of aptamers for viral diagnosis requires a system that recognizes the binding of viral molecules to aptamers in samples of blood, serum, plasma, or in virus-infected cells. From a therapeutic perspective, aptamers target viral particles or host cell receptors to prevent the interaction between the virus and host cells or target intracellular viral proteins to interrupt the life cycle of the virus within infected cells. In this paper, we review recent attempts to use aptamers for the diagnosis and treatment of various viral infections.

Keratin 23 Is a Peroxisome Proliferator-Activated Receptor Alpha-Dependent, MYC-Amplified Oncogene That Promotes Hepatocyte Proliferation.

Chronic activation of the nuclear receptor peroxisome proliferator-activated receptor alpha (PPARA) promotes MYC-linked hepatocellular carcinoma (HCC) in mice. Recent studies have shown that MYC can function as an amplifier of transcription where MYC does not act as an "on-off" switch for gene expression but rather accelerates transcription rates at active promoters by stimulating transcript elongation. Considering the possibility that MYC may amplify the expression of PPARA target genes to potentiate cell proliferation and liver cancer, gene expression was analyzed from livers of wild-type and liver-specific Myc knockout (MycΔHep ) mice treated with the PPARA agonist pirinixic acid. A subset of PPARA target genes was amplified in the presence of MYC, including keratin 23 (Krt23). The induction of Krt23 was significantly attenuated in MycΔHep mice and completely abolished in Ppara-null mice. Reporter gene assays and chromatin immunoprecipitation confirmed direct binding of both PPARA and MYC to sites within the Krt23 promoter. Forced expression of KRT23 in primary hepatocytes induced cell cycle-related genes. These data indicate that PPARA activation elevates MYC expression, which in turn potentiates the expression of select PPARA target genes involved in cell proliferation. Finally, KRT23 protein is highly elevated in human HCCs. Conclusion: These results revealed that MYC-mediated transcriptional potentiation of select PPARA target genes, such as Krt23, may remove rate-limiting constraints on hepatocyte growth and proliferation leading to liver cancer.

Flexible metal-oxide devices made by room-temperature photochemical activation of sol-gel films.

Amorphous metal-oxide semiconductors have emerged as potential replacements for organic and silicon materials in thin-film electronics. The high carrier mobility in the amorphous state, and excellent large-area uniformity, have extended their applications to active-matrix electronics, including displays, sensor arrays and X-ray detectors. Moreover, their solution processability and optical transparency have opened new horizons for low-cost printable and transparent electronics on plastic substrates. But metal-oxide formation by the sol-gel route requires an annealing step at relatively high temperature, which has prevented the incorporation of these materials with the polymer substrates used in high-performance flexible electronics. Here we report a general method for forming high-performance and operationally stable metal-oxide semiconductors at room temperature, by deep-ultraviolet photochemical activation of sol-gel films. Deep-ultraviolet irradiation induces efficient condensation and densification of oxide semiconducting films by photochemical activation at low temperature. This photochemical activation is applicable to numerous metal-oxide semiconductors, and the performance (in terms of transistor mobility and operational stability) of thin-film transistors fabricated by this route compares favourably with that of thin-film transistors based on thermally annealed materials. The field-effect mobilities of the photo-activated metal-oxide semiconductors are as high as 14 and 7 cm(2) V(-1) s(-1) (with an Al(2)O(3) gate insulator) on glass and polymer substrates, respectively; and seven-stage ring oscillators fabricated on polymer substrates operate with an oscillation frequency of more than 340 kHz, corresponding to a propagation delay of less than 210 nanoseconds per stage.

FAST: Size-Selective, Clog-Free Isolation of Rare Cancer Cells from Whole Blood at a Liquid-Liquid Interface.

Circulating tumor cells (CTCs) have great potential to provide minimally invasive ways for the early detection of cancer metastasis and for the response monitoring of various cancer treatments. Despite the clinical importance and progress of CTC-based cancer diagnostics, most of the current methods of enriching CTCs are difficult to implement in general hospital settings due to complex and time-consuming protocols. Among existing technologies, size-based isolation methods provide antibody-independent, relatively simple, and high throughput protocols. However, the clogging issues and lower than desired recovery rates and purity are the key challenges. In this work, inspired by antifouling membranes with liquid-filled pores in nature, clog-free, highly sensitive (95.9 ± 3.1% recovery rate), selective (>2.5 log depletion of white blood cells), rapid (>3 mL/min), and label-free isolation of viable CTCs from whole blood without prior sample treatment is achieved using a stand-alone lab-on-a-disc system equipped with fluid-assisted separation technology (FAST). Numerical simulation and experiments show that this method provides uniform, clog-free, ultrafast cell enrichment with pressure drops much less than in conventional size-based filtration, at 1 kPa. We demonstrate the clinical utility of the point-of-care detection of CTCs with samples taken from 142 patients suffering from breast, stomach, or lung cancer.

Recombinant adenovirus infection suppresses hTERT expression through virus-associated RNA-mediated induction of type 1 interferon.

Adenovirus vector is one of the most widely used vectors in gene therapy applications for the treatment of diverse human diseases including cancer. In this study, we showed that infection with E1E3-deleted recombinant human adenovirus serotype 5 reduced human telomerase reverse transcriptase (hTERT) mRNA levels in hepatoma cell lines. We defined the mechanisms by which the recombinant adenovirus vector reduces hTERT mRNA levels as follows: Using the virus-associated RNA I/II (VAI/II) expression construct, we demonstrated that the expression of VAI and VAII RNAs led to an increase in IFN-α2 level, and IFN-α2 induction was responsible for the decrease in hTERT mRNA levels. We showed that the effects of VA RNAs were specific for the replication-incompetent E1E3-deleted adenovirus vector, because wild-type adenovirus affected neither IFN-α2 nor hTERT mRNA levels. Moreover, we demonstrated that adenovirus vector-mediated delivery of the hTERT-targeting anti-cancer reagent could additively reduce the levels of hTERT mRNA that were specifically overexpressed in most of the cancer cells. This study showed that E1E3-deleted adenovirus vector system reduced hTERT mRNA levels through VA RNA-mediated induction of type 1 interferon; hence the recombinant adenovirus itself could have anti-cancer activity. These results indicate that recombinant adenovirus vector could be an effective means to deliver anti-cancer reagents for combating cancerous cells more effectively.

Fully integrated lab-on-a-disc for nucleic acid analysis of food-borne pathogens.

This paper describes a micro total analysis system for molecular analysis of Salmonella, a major food-borne pathogen. We developed a centrifugal microfluidic device, which integrated the three main steps of pathogen detection, DNA extraction, isothermal recombinase polymerase amplification (RPA), and detection, onto a single disc. A single laser diode was utilized for wireless control of valve actuation, cell lysis, and noncontact heating in the isothermal amplification step, thereby yielding a compact and miniaturized system. To achieve high detection sensitivity, rare cells in large volumes of phosphate-buffered saline (PBS) and milk samples were enriched before loading onto the disc by using antibody-coated magnetic beads. The entire procedure, from DNA extraction through to detection, was completed within 30 min in a fully automated fashion. The final detection was carried out using lateral flow strips by direct visual observation; detection limit was 10 cfu/mL and 10(2) cfu/mL in PBS and milk, respectively. Our device allows rapid molecular diagnostic analysis and does not require specially trained personnel or expensive equipment. Thus, we expect that it would have an array of potential applications, including in the detection of food-borne pathogens, environmental monitoring, and molecular diagnostics in resource-limited settings.

Mesoporous silica-supported platinum nanocatalysts for colorimetric detection of glucose, cholesterol, and C-reactive protein.

Noble metal nanoparticles decorated on a catalyst support with a large specific surface area can exhibit enhanced catalytic activity. To this end, a synthetic method to heterogeneously and evenly nucleate platinum nanoparticles (Pt NPs) onto mesoporous silica nanoparticles (MSNs) is developed. The obtained Pt NP-modified MSNs (Pt-MSNs) are characterized as a thin layer of 3 nm-sized Pt NPs densely assembled on the MSN surface, by which the throughput of the peroxidase-like activity of Pt-MSNs is greatly improved. The utility of Pt-MSNs in colorimetric detection of analytes is validated for two different assay schemes. Firstly, colloidally dispersed Pt-MSNs are employed as a peroxidase-mimic in a two-step cascade reaction to quantitate glucose/cholesterol based on the amount of H2O2 produced by glucose/cholesterol oxidase. Secondly, detection of C-reactive protein (CRP) is conducted on a solid substrate by adopting a sandwich immunoassay format. Detection limits are estimated to be 20 µM, 55 µM, and 3.9 pM for glucose, cholesterol, and CRP, respectively.

An sn-2 regioselective lipase with cis-fatty acid preference from Cordyceps militaris: Biochemical characterization and insights into its regioselective mechanism.

Lipase with unique regioselectivity is an attractive biocatalyst for elaborate lipid modification. However, the excavation of novel sn-2 regioselective lipases is difficult due to their scarcity in nature, with Candida antarctica lipase A (CALA) being the pronouncedly reported one. Here, we identified a novel CALA-like lipase from Cordyceps militaris (CACML7) via in silico mining. Through chiral-phase high-performance liquid chromatography, we determined that CACML7 displays sn-2 regioselectivity (>68 %) as does CALA, but exhibits distinctive chain length selectivity and bias against unsaturated fats. Notably, the curvature of the acyl-binding tunnel was expected to contribute to the 2.2-fold higher preference for cis-fatty acid (C18:1, cis-Δ9) over trans-fatty acid (C18:1, trans-Δ9) unlike trans-active CALA. Random pose docking of trioleoylglycerol (TOG) into the active site of a lid-truncated mutant of CACML7 revealed that TOG accepts a tuning fork conformation, of which the precise positioning of the reactive ester group towards the catalytic center was only favorable via sn-2 binding mode. The unique active site morphology, which we refer to as an "acyl-binding tunnel with a narrow entrance," may contribute to the sn-2 regioselectivity of CACML7. Our data provide an attractive model to better understand the mechanism underlying sn-2 regioselectivity.

Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory.

Introduction: Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. Methods: A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. Results and discussion: The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

Visual field prediction using a deep bidirectional gated recurrent unit network model.

Although deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression. Here, we proposed a bidirectional gated recurrent unit (Bi-GRU) algorithm to predict visual field loss. In total, 5413 eyes from 3321 patients were included in the training set, whereas 1272 eyes from 1272 patients were included in the test set. Data from five consecutive visual field examinations were used as input; the sixth visual field examinations were compared with predictions by the Bi-GRU. The performance of Bi-GRU was compared with the performances of conventional linear regression (LR) and long short-term memory (LSTM) algorithms. Overall prediction error was significantly lower for Bi-GRU than for LR and LSTM algorithms. In pointwise prediction, Bi-GRU showed the lowest prediction error among the three models in most test locations. Furthermore, Bi-GRU was the least affected model in terms of worsening reliability indices and glaucoma severity. Accurate prediction of visual field loss using the Bi-GRU algorithm may facilitate decision-making regarding the treatment of patients with glaucoma.

Lab-on-a-disc for fully integrated multiplex immunoassays.

This paper presents a cost-effective, rapid, and fully automated lab-on-a-disc for simultaneous detection of multiple protein biomarkers in raw samples such as whole blood or whole saliva. For the diagnosis of cardiovascular disease, here, a novel centrifugal microfluidic layout was designed to conduct the simultaneous detection of high sensitivity C-reactive protein, cardiac troponin I, and N-terminal pro-B type natriuretic peptide based on a bead-based sandwich type enzyme-linked immunosorbent assay (ELISA). Three reaction chambers are initially interconnected for the common processes such as sample injection, incubation, and washing and then isolated on-demand for the independent processes such as substrate incubation and final detection. The assay performances such as the limit of detection and the dynamic range were comparable with those of the conventional ELISA despite the significant reduction of the minimum sample volume (200 µL), the amount of washing buffer (700 µL), and the total process time (20 min).