Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability.

Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of providing definitive confirmation of the condition, necessitating the assessment by dermatologists who specialize in vitiligo. However, the current scarcity of such specialized medical professionals presents a significant challenge. To mitigate this issue and enhance diagnostic accuracy, it is essential to build deep learning models that can support and expedite the detection process. This study endeavors to establish a deep learning framework to enhance the diagnostic accuracy of vitiligo. To this end, a comparative analysis of five models including ResNet (ResNet34, ResNet50, and ResNet101 models) and Swin Transformer series (Swin Transformer Base, and Swin Transformer Large models), were conducted under the uniform condition to identify the model with superior classification capabilities. Moreover, the study sought to augment the interpretability of these models by selecting one that not only provides accurate diagnostic outcomes but also offers visual cues highlighting the regions pertinent to vitiligo. The empirical findings reveal that the Swin Transformer Large model achieved the best performance in classification, whose AUC, accuracy, sensitivity, and specificity are 0.94, 93.82%, 94.02%, and 93.5%, respectively. In terms of interpretability, the highlighted regions in the class activation map correspond to the lesion regions of the vitiligo images, which shows that it effectively indicates the specific category regions associated with the decision-making of dermatological diagnosis. Additionally, the visualization of feature maps generated in the middle layer of the deep learning model provides insights into the internal mechanisms of the model, which is valuable for improving the interpretability of the model, tuning performance, and enhancing clinical applicability. The outcomes of this study underscore the significant potential of deep learning models to revolutionize medical diagnosis by improving diagnostic accuracy and operational efficiency. The research highlights the necessity for ongoing exploration in this domain to fully leverage the capabilities of deep learning technologies in medical diagnostics.

Impact of Three Thiazolidinone Compounds with Piperine Skeletons on Trehalase Activity and Development of Spodoptera frugiperda Larvae.

Trehalases (TREs) are pivotal enzymes involved in insect development and reproduction, making them prime targets for pest control. We investigated the inhibitory effect of three thiazolidinones with piperine skeletons (6a, 7b, and 7e) on TRE activity and assessed their impact on the growth and development of the fall armyworm (FAW), Spodoptera frugiperda. The compounds were injected into FAW larvae, while the control group was treated with 2% DMSO solvent. All three compounds effectively inhibited TRE activity, resulting in a significant extension of the pupal development stage. Moreover, the treated larvae exhibited significantly decreased survival rates and a higher incidence of abnormal phenotypes related to growth and development compared to the control group. These results suggest that these TRE inhibitors affect the molting of larvae by regulating the chitin metabolism pathway, ultimately reducing their survival rates. Consequently, these compounds hold potential as environmentally friendly insecticides.

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.

OBJECTIVE: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. METHODS: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. CONCLUSIONS: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

Interfacial Effect on the Transient Dielectric Function and Charge Transfer in a Monolayer WS2/Si Heterojunction.

Monolayer tungsten disulfide (WS2) is a highly promising material for silicon photonics. Thus, the WS2/Si interface plays a very important role due to the interfacial complex effects and abundant states. Among them, the effect of charge transfer on exciton dynamics and the optoelectronic property is determined by the dielectric function, which is very crucial for the performance of optoelectronic devices. However, research on the exciton dynamics or the transient dielectric function of WS2 in such WS2/Si junctions is still rare. In this work, both the transient dielectric function and charge transfer of WS2/Si heterojunctions are analyzed based on the transient reflectance spectra measured by the pump-probe spectrometer. The dynamic processes of the A exciton, affected by charge transfer within the WS2/Si heterojunction, are interpreted. Moreover, the transient dielectric function of WS2 is quantitatively analyzed. The dielectric function of WS2 exhibits a notable 19% change, persisting for more than 180 ps within the WS2/Si heterojunction. These findings can pave the way for the advancement of silicon photonic devices based on WS2.

Adsorption of phosphate in aqueous solution by ash from the fruit peel of Caryocar coriaceum Wittm: adsorption characteristics and behavior.

High phosphate concentrations in natural waters are associated with eutrophication problems that negatively affect the fauna and flora of ecosystems. As an alternative solution to this problem, we evaluated the adsorptive capacity of the fruit peel ash (PPA) of Caryocar coriaceum Wittm and its efficiency in removing phosphate (PO43-) from aqueous solutions. PPA was produced under an oxidative atmosphere and calcinated at 500 °C. The XRF and EDS analyses of PPA after contact with an aqueous PO43- solution showed an increase in its PO43- content, thus confirming the adsorption of PO43-. The Elovich and Langmuir models are the ones fitting the kinetics and the equilibrium state of the process, respectively. The highest PO43- adsorption capacity was approximately 79.50 mg g-1 at 10 °C. PO43- adsorption by PPA is a spontaneous, favorable, and endothermic process involving structural changes. The highest removal efficiency was 97.08% using a 100 mg.L-1 PO43- solution. In sight of this, PPA has shown potential as an excellent natural bioadsorbent.

High-performance nanofiltration concentrate treatment by a five-chamber bioelectrochemical system.

A combination of bioelectrochemical systems and electrodialysis has been considered an effective strategy for removing salts from the nanofiltration (NF) concentrate of electroplating wastewater; however, the recovery efficiency of multivalent metals is low. Herein, a new process based on microbial electrolysis desalination and chemical-production cell with five chambers (MEDCC-FC) has been proposed for the simultaneous desalination and recovery of the multivalent metals from NF concentrate. The MEDCC-FC was found to be significantly superior to the MEDCC with the monovalent selective cation exchange membrane (MEDCC-MSCEM) and MEDCC with the cation exchange membrane (MEDCC-CEM), in terms of the elevated desalination efficiency, multivalent metal recovery efficiency, current density, and coulombic efficiency, and decreased energy consumption and membrane fouling. Within 12 h, the MEDCC-FC provided the desirable outcome, indicated by a maximum current density of 6.88 ± 0.06 A/m2, desalination efficiency of 88 ± 10%, metals recovery efficiency of >58%, and total energy consumption of 1.17 ± 0.11 kWh for the per kg total dissolved solids removal. Mechanistic studies revealed that the integration of CEM and MSCEM in the MEDCC-FC promoted the separation and recovery of multivalent metal. These findings revealed that the proposed MEDCC-FC was promising in treating NF concentrate of electroplating wastewater towards advantages of effectiveness, economic viability, and flexibility.

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding.

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

Novel compounds ZK-PI-5 and ZK-PI-9 regulate the reproduction of Spodoptera frugiperda (Lepidoptera: Noctuidae), with insecticide potential.

Trehalase inhibitors prevent trehalase from breaking down trehalose to provide energy. Chitinase inhibitors inhibit chitinase activity affecting insect growth and development. This is an important tool for the investigation of regulation of trehalose metabolism and chitin metabolism in insect reproduction. There are few studies on trehalase or chitinase inhibitors' regulation of insect reproduction. In this study, ZK-PI-5 and ZK-PI-9 were shown to have a significant inhibitory effect on the trehalase, and ZK-PI-9 significantly inhibited chitinase activity in female pupae. We investigated the reproduction regulation of Spodoptera frugiperda using these new inhibitors and evaluated their potential as new insecticides. Compounds ZK-PI-5 and ZK-PI-9 were injected into the female pupae, and the control group was injected with solvent (2% DMSO). The results showed that the emergence failure rate for pupae treated with inhibitors increased dramatically and aberrant phenotypes such as difficulty in wings spreading occurred. The oviposition period and longevity of female adults in the treated group were significantly shorter than those in the control group, and the ovaries developed more slowly and shrank earlier. The egg hatching rate was significantly reduced by treatment with the inhibitor. These results showed that the two new compounds had a significant impact on the physiological indicators related to reproduction of S. frugiperda, and have pest control potential. This study investigated the effect of trehalase and chitin inhibitors on insect reproduction and should promote the development of green and efficient insecticides.

MDTips: a multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data.

MOTIVATION: Screening new drug-target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. RESULTS: We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e. Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs' candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6766 drugs, which can be used for drug repurposing and discovery. AVAILABILITY AND IMPLEMENTATION: https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.

Potential inhibitory effects of compounds ZK-PI-5 and ZK-PI-9 on trehalose and chitin metabolism in Spodoptera frugiperda (J. E. Smith).

Introduction: Spodoptera frugiperda is an omnivorous agricultural pest which is great dangerous for grain output. Methods: In order to investigate the effects of potential trehalase inhibitors, ZK-PI-5 and ZK-PI-9, on the growth and development of S. frugiperda, and to identify new avenues for S. frugiperda control, we measured the content of the trehalose, glucose, glycogen and chitin, enzyme activity, and gene expression levels in trehalose and chitin metabolism of S. frugiperda. Besides, their growth and development were also observed. Results: The results showed that ZK-PI-9 significantly reduced trehalase activity and ZK-PI-5 significantly reduced membraned-bound trehalase activity. Moreover, ZK-PI-5 inhibited the expression of SfTRE2, SfCHS2, and SfCHT, thus affecting the chitin metabolism. In addition, the mortality of S. frugiperda in pupal stage and eclosion stage increased significantly after treatment with ZK-PI-5 and ZK-PI-9, which affected their development stage and caused death phenotype (abnormal pupation and difficulty in breaking pupa). Discussion: These results have provided a theoretical basis for the application of trehalase inhibitors in the control of agricultural pests to promote future global grain yield.

Polarization-sensitive narrowband infrared photodetection triggered by optical Tamm state engineering.

Polarization-sensitive narrowband photodetection at near-infrared (NIR) has attracted significant interest in optical communication, environmental monitoring, and intelligent recognition system. However, the current narrowband spectroscopy heavily relies on the extra filter or bulk spectrometer, which deviates from the miniaturization of on-chip integration. Recently, topological phenomena, such as the optical Tamm state (OTS), provided a new solution for developing functional photodetection, and we experimentally realized the device based on 2D material (graphene) for the first time to the best of our knowledge. Here, we demonstrate polarization-sensitive narrowband infrared photodetection in OTS coupled graphene devices, which are designed with the aid of the finite-difference time-domain (FDTD) method. The devices show narrowband response at NIR wavelengths empowered by the tunable Tamm state. The full width at half maximum (FWHM) of the response peak reaches ∼100 nm, and it can potentially be improved to ultra-narrow of about 10 nm by increasing the periods of dielectric distributed Bragg reflector (DBR). The responsivity and response time of the device reaches 187 mA/W and ∼290 µs at 1550 nm, respectively. Furthermore, the prominent anisotropic features and high dichroic ratios of ∼4.6 at 1300 nm and ∼2.5 at 1500 nm are achieved by integrating gold metasurfaces.

Real-World Experience of the Efficacy and Safety of Upadacitinib 15 mg in Patients with Atopic Dermatitis in Korea / 대한피부과학회지

Background@#Upadacitinib is an oral Janus kinase1 (JAK1)-selective inhibitor, which showed a quick and significant effect on patients with atopic dermatitis in several phase 3 clinical studies. Although, an increasing number of studies have reported data on the real-world efficacy and safety of upadacitinib for the treatment of atopic dermatitis, no studies have yet been published in Korea. @*Objective@#We assessed the real-world efficacy and safety of upadacitinib for the treatment of atopic dermatitis in Korean patients. @*Methods@#A total of 17 patients with atopic dermatitis who received 15 mg of oral upadacitinib everyday for 16 weeks, were included in this retrospective single-center study. Based on electronic medical records, the clinical characteristics, Eczema Area and Severity Index (EASI) score, and adverse events were investigated. @*Results@#The mean EASI score was significantly reduced at 4 weeks of upadacitinib treatment (8.81±9.00) and gradually reduced at week 8 (5.70±7.38), week 12 (4.55±6.23), and week 16 (4.58±6.74) (p＜0.001). At week 16, 61.54%, 30.77%, and 15.38% of patients achieved EASI 75, EASI 90, and EASI 100 responses, respectively. There was no statistically significant difference between EASI 75 and EASI 90 by age or gender at week 16 (p＞0.05). A total of 13 people (76.5%) had adverse events, of which acne was the most common. In all patients, the symptoms were mild and self-limited, and no patient discontinued treatment. @*Conclusion@#Upadacitinib was effective and safe for Korean patients with atopic dermatitis in real-world clinical practice.

Improving Wheat Salt Tolerance for Saline Agriculture.

Salinity is a major abiotic stress that threatens crop yield and food supply in saline soil areas. Crops have evolved various strategies to facilitate survival and production of harvestable yield under salinity stress. Wheat (Triticum aestivum L.) is the main crop in arid and semiarid land areas, which are often affected by soil salinity. In this review, we summarize the conventional approaches to enhance wheat salt tolerance, including cross-breeding, exogenous application of chemical compounds, beneficial soil microorganisms, and transgenic engineering. We also propose several new breeding techniques for increasing salt tolerance in wheat, such as identifying new quantitative trait loci or genes related to salt tolerance, gene stacking and multiple genome editing, and wheat wild relatives and orphan crops domestication. The challenges and possible countermeasures in enhancing wheat salinity tolerance are also discussed.

Chitinase (CHI) of Spodoptera frugiperda affects molting development by regulating the metabolism of chitin and trehalose.

Chitin is the main component of insect exoskeleton and midgut peritrophic membrane. Insect molting is the result of the balance and coordination of chitin synthesis and degradation in chitin metabolism under the action of hormones. In this study, a 678 bp dsRNA fragment was designed and synthesized according to the known CHI (Chitinase) sequence of Spodoptera frugiperda. It was injected into the larvae to observe the molting and development of S. frugiperda. At the same time, the activities of trehalase and chitinase, the contents of trehalose, chitin and other substances were detected, and the expression of related genes in the chitin synthesis pathway was determined. The results showed that CHI gene was highly expressed at the end of each instar, prepupa and pupal stage before molting; At 12 and 24 h after dsRNA injection of CHI gene of S. frugiperda, the expression of CHI gene decreased significantly, and the chitinase activity decreased significantly from 12 to 48 h. The expression of chitin synthase (CHSB) gene decreased significantly, and the chitin content increased significantly. Some larvae could not molt normally and complete development, leading to certain mortality. Secondly, after RNAi of CHI gene, the content of glucose and glycogen increased first and then decreased, while the content of trehalose decreased significantly or showed a downward trend. The activities of the two types of trehalase and the expression levels of trehalase genes decreased first and then increased, especially the trehalase activities increased significantly at 48 h after dsCHI injection. And trehalose-6-phosphate synthase (TPS), glutamine: fructose-6-phosphate amidotransferase (GFAT), UDP-N-acetylglucosamine pyrophosphorylases (UAP), hexokinase (HK), glucose-6-phosphate isomerase (G6PI) and phosphoacetylglucosamine mutase (PAGM) all decreased significantly at 24 h, and then increased or significantly increased at 48 h. These results indicated that when the expression of chitinase gene of S. frugiperda was inhibited, it affected the degradation of chitin in the old epidermis and the formation of new epidermis, and the content of chitin increased, which led to the failure of larvae to molt normally. Moreover, the chitin synthesis pathway and trehalose metabolism were also regulated. The relevant results provide a theoretical basis for screening target genes and developing green insecticides to control pests by using the chitin metabolism pathway.

Removal of Methylene Blue from a synthetic effluent by ionic flocculation.

Methylene Blue (MB) is a dye widely used in the industrial sector and in human and veterinary pharmacology. This dye, if improperly disposed of, can cause a significant environmental impact due to its low biodegradability, as it is a stable and complex substance. Additionally, it may affect human health and generate highly toxic byproducts. Hence, the purpose of this work is to assess the removal efficiency of MB from a synthetic effluent using a ionic flocculation process. Such a process consists of the dissolution of a biodegradable anionic surfactant (obtained from soybean oil used for frying food) in the synthetic effluent and the subsequent addition of calcium to the system. The addition of Ca leads to the formation of insoluble surfactant flocs with a high capacity to adsorb organic pollutants. The FTIR testing showed the presence of OH- and C=O groups in the surfactant flocs, which favor the removal of MB by an adsorption process. The maximum adsorption capacity of MB was 101.38 mg g-1. The process is in fact a chemisorption and has an exothermic nature. Desorption studies showed a desorption efficiency of up to 47.81% using an ethanol 1:2 solution. An MB removal efficiency of up to 93.71% was attained in just 0.5 min for an initial MB concentration of 100 mg L-1, showing that ionic flocculation is a very fast and effective process for the treatment of effluents.

AR-regulated ZIC5 contributes to the aggressiveness of prostate cancer.

The mechanisms by which prostate cancer (PCa) progresses to the aggressive castration-resistant stage remain uncertain. Zinc finger of the cerebellum 5 (ZIC5), a transcription factor belonging to the ZIC family, is involved in the pathology of various cancers. However, the potential effect of ZIC5 on PCa malignant progression has not been fully defined. Here, we show that ZIC5 is upregulated in PCa, particularly in metastatic lesions, in positive association with poor prognosis. Genetic inhibition of ZIC5 in PCa cells obviously attenuated invasion and metastasis and blunted the oncogenic properties of colony formation. Mechanistically, ZIC5 functioned as a transcription factor to promote TWIST1-mediated EMT progression or as a cofactor to strengthen the ß-catenin-TCF4 association and stimulate Wnt/ß-catenin signaling. Importantly, ZIC5 and the androgen receptor (AR) form a positive feed-forward loop to mutually stimulate each other's expression. AR, in cooperation with its steroid receptor coactivator 3 (SRC-3), increased ZIC5 expression through binding to the miR-27b-3p promoter and repressing miR-27b-3p transcription. In turn, ZIC5 potentiated AR, AR-V7, and AR targets' expression. Besides, ZIC5 inhibition reduced AR and AR-V7 protein expression and enhanced the sensitivity of PCa to enzalutamide (Enz) treatment, both in vitro and in vivo. These findings indicate that the reciprocal activation between AR and ZIC5 promotes metastasis and Enz resistance of PCa and suggest the therapeutic value of cotargeting ZIC5 and AR for the treatment of advanced PCa.

Polarization coincidence images from metasurfaces with HOM-type interference.

Metasurfaces provide a promising route for structuring light and generating holograms with designed amplitude, phase, and polarization profiles, leading to a versatile platform for integrating and constructing optical components beyond the conventional ones. At the same time, incorporating coincidence in imaging allows a high signal-to-noise ratio for imaging in very low light levels. As beneficial from the recent development in both metasurfaces and single-photon avalanche diode (SPAD) cameras, we combine the polarization-sensitive capability of metasurfaces with Hong-Ou-Mandel (HOM)-type interference in generating images with tailor-made two-photon interference and polarization coincidence signatures. By using orthogonal linear-polarized photons as incidence, correlated, anticorrelated, and uncorrelated polarization coincidence features can be observed within the same image from the pairwise second-order coherence statistics across different pixels of the image. Our work adds polarization to the demonstrated amplitude and phase sensitivity in the domain of "HOM microscopy" and can be useful for biological and security applications.

[Analysis of A Pedigree with Hereditary Coagulation Factor â « Deficiency Caused by Compound Heterozygous Mutations].

OBJECTIVE: To analysis clinical phenotype and potential genetic cause of a family affected with hereditary coagulation factor Ⅻ deficiency. METHODS: The prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), D-Dimer (D-D), coagulation factor Ⅻ activity (FⅫ:C) and coagulation factor Ⅻ antigen (FⅫ:Ag) were determined for phenotype diagnosis of the proband and his family members(3 generations and 5 people). Targeted capture and whole exome sequencing were performed in peripheral blood sample of the proband. Possible disease-causing mutations of F12 gene were obtained and further confirmed by Sanger sequencing. The corresponding mutation sites of the family members were analyzed afterwards. The online bioinformatics software AutoPVS1 and Mutation Taster was used to predict the effects of mutation sites on protein function. RESULTS: The APTT of the proband was significantly prolonged, reaching 180.9s. FⅫ:C and FⅫ:Ag of the proband was significantly reduced to 0.8% and 4.17%, respectively. The results of whole exome sequencing displayed that there were compound heterozygous mutations in F12 gene of the proband, including the c.1261G＞T heterozygous nonsense mutation in exon 11 (causing p.Glu421*) and the c.251dupG heterozygous frameshift mutation in exon 4 (causing p.Trp85Metfs*53). Both mutations are loss of function mutations with very strong pathogenicity, leading to premature termination of the protein. AutoPVS1 and Mutation Taster software predicted both mutations as pathogenic mutations. The results of Sanger sequencing revealed that c.1261G＞T heterozygous mutation of the proband was inherited from his mother, for which his brother and his daughter were c.1261G＞T heterozygous carriers. Genotype-phenotype cosegregation was observed in this family. CONCLUSION: The c.1261G＞T heterozygous nonsense mutation in exon 11 and the c.251dupG heterozygous frameshift mutation in exon 4 of the F12 gene probably account for coagulation factor Ⅻ deficiency in this family. This study reports two novel pathogenic F12 mutations for the first time worldwide.

Large-area long-wave infrared broadband all-dielectric metasurface absorber based on markless laser direct writing lithography.

Scalable and low-cost manufacturing of broadband absorbers for use in the long-wave infrared region are of enormous importance in various applications, such as infrared thermal imaging, radiative cooling, thermal photovoltaics and infrared sensor. In recent years, a plethora of broadband absorption metasurfaces made of metal nano-resonators with plasmon resonance have been synthesized. Still, their disadvantages in terms of complex structure, production equipment, and fabrication throughput, limit their future commercial applications. Here, we propose and experimentally demonstrate a broadband large-area all-dielectric metasurface absorber comprised of silicon (Si) arrys of square resonators and a silicon nitride (Si3N4) film in the long-wave infrared region. The multiple Mie resonance modes generated in a single-size Si resonator are utilized to enhance the absorption of the Si3N4 film to achieve broadband absorption. At the same time, the transversal optical (TO) phonon resonance of Si3N4 and the Si resonator's magnetic dipole resonance are coupled to achieve a resonator size-insensitive absorption peak. The metasurface absorber prepared by using maskless laser direct writing technology displays an average absorption of 90.36% and a peak absorption of 97.55% in the infrared region of 8 to 14 µm, and still maintains an average absorption of 88.27% at a inciedent angle of 40°. The experimentally prepared 2 cm × 3 cm patterned metasurface absorber by markless laser direct writing lithography (MLDWL) exhibits spatially selective absorption and the thermal imaging of the sample shows that the maximum temperature difference of 17.3 °C can exist at the boundary.

Multimodal reasoning based on knowledge graph embedding for specific diseases.

MOTIVATION: Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. RESULTS: This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. AVAILABILITY AND IMPLEMENTATION: The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.