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.
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 (â¢OH), superoxide radicals (â¢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.
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.
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.
Glaucoma , Visual Fields , Humans , Reproducibility of Results , Eye , Algorithms , Glaucoma/diagnosis
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.
Intraocular Pressure , Visual Fields , Humans , Retrospective Studies , Reproducibility of Results , Visual Field Tests , Neural Networks, Computer , Disease Progression
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.
Mothers , Touch , Female , Humans , Infant , Cross-Sectional Studies , Language , Republic of Korea
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.
OBJECTIVE: We aimed to investigate the improvement in sleep quantity and quality when clonidine was used in children and adolescents with insomnia. We also examined how sociodemographic characteristics such as age, sex, underlying psychological problems, and levels of depression and anxiety affected the effect of clonidine. METHODS: We retrospectively reviewed outpatients aged 6 to 24 who took clonidine due to insomnia from September 2019 to September 2021 at the Department of Psychiatry at Eunpyeong St. Mary's Hospital of Catholic University. We used the Pittsburgh Sleep Quality Index (PSQI), Children's Depression Inventory (CDI), and State-Trait Anxiety Inventory (STAI) for our study. RESULTS: A total of 62 participants were included in our study (34 females, mean age 13.94±4.94 years). After using clonidine, there was a significant decrease in PSQI components 1, 2, and 5, especially PSQI component 2. There was a greater decrease in sleep latency when clonidine was used in females, those aged between 13 and 24, those with mood/anxiety disorder or attention-deficit/hyperactivity disorder, those whose sleep latency exceeded 60 minutes at baseline, and those who used clonidine for more than 14 days. Those with higher STAI-Trait scores and CDI scores at baseline showed less improvement in total PSQI scores. CONCLUSION: Considering that there are currently no Food and Drug Administration-approved sleep drugs for children and adolescents and no apparent difference in efficacy and safety among sleep drugs, we demonstrated that treatment with clonidine might be a good approach to improve sleep quality and quantity for children and adolescents.
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.
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.
Biosensing Techniques , Vascular Endothelial Growth Factor A , Biosensing Techniques/methods , Immunoassay/methods , Magnetic Phenomena , Magnetics/methods
Repeated cocaine use poses many serious health risks to users. One of the risks is hypoxia and ischemia (HI). To restore the biological system against HI, complex biological mechanisms operate at the gene level. Despite the complexity of biological mechanisms, there are common denominator genes that play pivotal roles in various defense systems. Among these genes, the cAMP response element-binding (Creb) protein contributes not only to various aspects of drug-seeking behavior and drug reward, but also to protective mechanisms. However, it is still unclear which Creb members are key players in the protection of cocaine-induced HI conditions. Herein, using one of the state-of-the-art deep learning methods, the generative adversarial network, we revealed that the OASIS family, one of the Creb family, is a key player in various defense mechanisms such as angiogenesis and unfolded protein response against the HI state by unveiling hidden mRNA expression profiles. Furthermore, we identified mysterious kinases in the OASIS family and are able to explain why the prefrontal cortex and hippocampus are vulnerable to HI at the genetic level.
Cocaine-Related Disorders , Cocaine , Cocaine/adverse effects , Cocaine-Related Disorders/complications , Cocaine-Related Disorders/genetics , Cyclic AMP Response Element-Binding Protein/metabolism , Humans , Hypoxia , Ischemia
This paper introduces a digital microfluidic (DMF) platform for portable, automated, and integrated Zika viral RNA extraction and amplification. The platform features reconfigurable DMF cartridges offering a closed, humidified environment for sample processing at elevated temperatures, as well as programmable control instrumentation with a novel thermal cycling unit regulated using a proportional integral derivative (PID) feedback loop. The system operates on 12 V DC power, which can be supplied by rechargeable battery packs for remote testing. The DMF system was optimized for an RNA processing pipeline consisting of the following steps: 1) magnetic-bead based RNA extraction from lysed plasma samples, 2) RNA clean-up, and 3) integrated, isothermal amplification of Zika RNA. The DMF pipeline was coupled to a paper-based, colorimetric cell-free protein expression assay for amplified Zika RNA mediated by toehold switch-based sensors. Blinded laboratory evaluation of Zika RNA spiked in human plasma yielded a sensitivity and specificity of 100% and 75% respectively. The platform was then transported to Recife, Brazil for evaluation with infectious Zika viruses, which were detected at the 100 PFU mL-1 level from a 5 µL sample (equivalent to an RT-qPCR cycle threshold value of 32.0), demonstrating its potential as a sample processing platform for miniaturized diagnostic testing.
Zika Virus Infection , Zika Virus , Humans , Molecular Diagnostic Techniques , Nucleic Acid Amplification Techniques , RNA , RNA, Viral/analysis , RNA, Viral/genetics , Real-Time Polymerase Chain Reaction , Sensitivity and Specificity , Specimen Handling , Zika Virus/genetics , Zika Virus Infection/diagnosis
The electronic nose is a reliable practical sensor device that mimics olfactory organs. Although numerous studies have demonstrated excellence in detecting various target substances with the help of ideal models, biomimetic approaches still suffer in practical realization because of the inability to mimic the signal processing performed by olfactory neural systems. Herein, we propose an electronic nose based on the programable surface chemistry of M13 bacteriophage, inspired by the neural mechanism of the mammalian olfactory system. The neural pattern separation (NPS) was devised to apply the pattern separation that operates in the memory and learning process of the brain to the electronic nose. We demonstrate an electronic nose in a portable device form, distinguishing polycyclic aromatic compounds (harmful in living environment) in an atomic-level resolution (97.5% selectivity rate) for the first time. Our results provide practical methodology and inspiration for the second-generation electronic nose development toward the performance of detection dogs (K9).
Biosensing Techniques , Electronic Nose , Animals , Bacteriophage M13 , Biomimetics , Dogs , Nose
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.
The hippocampus, which engages in the process of consolidating long-term memories and learning, shows active development during childhood and adolescence. The hippocampus also functionally influences attention. Based on the influence of hippocampal function on attention, it was expected that the volume of the hippocampus would be associated with the difference in attention during childhood and adolescence, in which the brain develops actively. Thus, this study examined the association between hippocampal volume and attention metrics measured by the continuous performance test (CPT) in 115 children and adolescents (mean age = 12.43 ± 3.0, 63 male and 52 female). In association studies with both auditory and visual attention, we found that the bilateral hippocampal volumes showed negative relationships with auditory omission errors. A smaller volume of the left hippocampus also led to a longer auditory response time. However, visual attention did not show any significant relationship with the hippocampal volume. These findings were consistent even after adjusting for the effects of the related covariates (e.g., age, insomnia, and depression). Taken together, this study suggested that the increase in hippocampal volume during childhood and adolescence was associated significantly with better auditory attention.
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.
Image Processing, Computer-Assisted , Supervised Machine Learning , Agriculture , Semantics
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.
Antiviral Agents/therapeutic use , Aptamers, Nucleotide/therapeutic use , Virus Diseases/diagnosis , Virus Diseases/drug therapy , Animals , DNA Viruses/drug effects , Humans , RNA Viruses/drug effects , Viral Proteins/drug effects , Virion/drug effects
BACKGROUND: Existing bacterial culture test results for infectious diseases are written in unrefined text, resulting in many problems, including typographical errors and stop words. Effective spelling correction processes are needed to ensure the accuracy and reliability of data for the study of infectious diseases, including medical terminology extraction. If a dictionary is established, spelling algorithms using edit distance are efficient. However, in the absence of a dictionary, traditional spelling correction algorithms that utilize only edit distances have limitations. OBJECTIVE: In this research, we proposed a similarity-based spelling correction algorithm using pretrained word embedding with the BioWordVec technique. This method uses a character-level N-grams-based distributed representation through unsupervised learning rather than the existing rule-based method. In other words, we propose a framework that detects and corrects typographical errors when a dictionary is not in place. METHODS: For detected typographical errors not mapped to Systematized Nomenclature of Medicine (SNOMED) clinical terms, a correction candidate group with high similarity considering the edit distance was generated using pretrained word embedding from the clinical database. From the embedding matrix in which the vocabulary is arranged in descending order according to frequency, a grid search was used to search for candidate groups of similar words. Thereafter, the correction candidate words were ranked in consideration of the frequency of the words, and the typographical errors were finally corrected according to the ranking. RESULTS: Bacterial identification words were extracted from 27,544 bacterial culture and antimicrobial susceptibility reports, and 16 types of spelling errors and 914 misspelled words were found. The similarity-based spelling correction algorithm using BioWordVec proposed in this research corrected 12 types of typographical errors and showed very high performance in correcting 97.48% (based on F1 score) of all spelling errors. CONCLUSIONS: This tool corrected spelling errors effectively in the absence of a dictionary based on bacterial identification words in bacterial culture and antimicrobial susceptibility reports. This method will help build a high-quality refined database of vast text data for electronic health records.
Atmospheric pressure plasma (APP) generates highly reactive species that are useful for surface activation. We demonstrate a fast regeneration of iron oxides, that are popular catalysts in various industrial processes, using microwave-driven argon APP under ambient conditions. The surface treatment of hematite powder by the APP with a small portion of hydrogen (0.5 vol%) lowers the oxide's reduction temperature. A near-infrared laser is used for localized heating to control the surface temperature. Controlled experiments without plasma confirm the catalytic effect of the plasma. Raman, XRD, SEM, and XPS analyses show that the plasma treatment changed the chemical state of the hematite to that of magnetite without sintering.
