Immunogenicity analysis based on VP1 and VP2 proteins of bovine enterovirus.

Bovine enterovirus (BEV) infection manifests as a spectrum of clinical signs affecting the respiratory, gastrointestinal, and reproductive systems in cattle. Outbreaks of this disease results in large economic losses to the bovine industry worldwide. Currently there are no efficacious vaccines and medicines to prevent BEV infection. In the present study, reverse transcription-polymerase chain reaction was used to amplify the VP1 and VP2 genes of BEV, enabling the synthesis of a chimeric recombinant protein which contained partial sequences from both proteins. Subsequently, the emulsified purified proteins with Freund's adjuvant were used for triple-fold immunization of 4-week-old Institute of Cancer Research (ICR) mice. The mice were subsequently subjected to a challenge assay which elicited an immune response that was characterized by elevated titers of BEV-specific neutralizing antibodies. Notably, the results showed that the purification of pET32a-VP1 and pET32a-VP2 proteins markedly curtailed virus excretion and mitigated the histopathological damage usually associated with BEV infections. These were observed in the small intestines, kidneys and brain in infected animals. It also alleviated clinical symptoms such as hypothermia and weight loss. In summary, this study offers a theoretical and practical basis for BEV vaccine development.

Evaluating for Evidence of Sociodemographic Bias in Conversational AI for Mental Health Support.

The integration of large language models (LLMs) into healthcare highlights the need to ensure their efficacy while mitigating potential harms, such as the perpetuation of biases. Current evidence on the existence of bias within LLMs remains inconclusive. In this study, we present an approach to investigate the presence of bias within an LLM designed for mental health support. We simulated physician-patient conversations by using a communication loop between an LLM-based conversational agent and digital standardized patients (DSPs) that engaged the agent in dialogue while remaining agnostic to sociodemographic characteristics. In contrast, the conversational agent was made aware of each DSP's characteristics, including age, sex, race/ethnicity, and annual income. The agent's responses were analyzed to discern potential systematic biases using the Linguistic Inquiry and Word Count tool. Multivariate regression analysis, trend analysis, and group-based trajectory models were used to quantify potential biases. Among 449 conversations, there was no evidence of bias in both descriptive assessments and multivariable linear regression analyses. Moreover, when evaluating changes in mean tone scores throughout a dialogue, the conversational agent exhibited a capacity to show understanding of the DSPs' chief complaints and to elevate the tone scores of the DSPs throughout conversations. This finding did not vary by any sociodemographic characteristics of the DSP. Using an objective methodology, our study did not uncover significant evidence of bias within an LLM-enabled mental health conversational agent. These findings offer a complementary approach to examining bias in LLM-based conversational agents for mental health support.

Flavobacterium capsici sp. nov., isolated from the rhizospheric soils of bell pepper (Capsicum annuum L.).

Two separate bacterial strains, PMTSA4T and PMR2A8, were isolated from the rhizospheric soils of bell pepper plants grown in a plant nursery. These strains are Gram-negative, non-motile and rod-shaped and grow in aerobic conditions. They exhibit a positive reaction for catalase activity but negative results for oxidase activity. Phylogenetic analysis of the 16S rRNA gene sequences revealed that the strains PMTSA4T and PMR2A8 are closely related to Flavobacterium piscinae ICH-30T (95.6%, respectively), Flavobacterium ahnfeltiae 10Alg 130T (95.5%) and Flavobacterium maris KMM 9535T (95.3%), aligning them within the genus Flavobacterium. Digital DNA-DNA hybridization (dDDH) values and average nucleotide identities (ANIs) of the whole-genome sequences for the two strains and related Flavobacterium species were significantly below the established thresholds for prokaryotic species delineation (<70% for dDDH and <95% for ANI). The observed values were as follows: Flavobacterium aquatile LMG 4008T (dDDH: 19.8% and ANI: 75.5%), F. piscinae ICH-30T (dDDH: 18.6% and ANI: 73.3%) and F. stagni WWJ 16T (dDDH: 18.5% and ANI: 72.0%). The strains have genome sizes of 3 068 185 bp and 3 068 330 bp, with a G+C content of 32.5 mol%. In phenotypic characterization, the new strains grew at 10-35 °C and tolerated up to 4% NaCl at pH 5-9 (optimum pH 8). The predominant cellular fatty acids were observed to be iso-C15:0, iso-C17:0 3-OH and iso-C15:0 3-OH. Menaquinone-6 was the predominant quinone. Considering the results from phenotypic, chemotaxonomic, phylogenetic and genomic analyses, it is proposed that the strains PMTSA4T and PMR2A8 represent a novel species within the genus Flavobacterium.

SIM-OFE: Structure Information Mining and Object-Aware Feature Enhancement for Fine-Grained Visual Categorization.

Fine-grained visual categorization (FGVC) aims to distinguish visual objects from multiple subcategories of the coarse-grained category. Subtle inter-class differences among various subcategories make the FGVC task more challenging. Existing methods primarily focus on learning salient visual patterns while ignoring how to capture the object's internal structure, causing difficulty in obtaining complete discriminative regions within the object to limit FGVC performance. To address the above issue, we propose a Structure Information Mining and Object-aware Feature Enhancement (SIM-OFE) method for fine-grained visual categorization, which explores the visual object's internal structure composition and appearance traits. Concretely, we first propose a simple yet effective hybrid perception attention module for locating visual objects based on global-scope and local-scope significance analyses. Then, a structure information mining module is proposed to model the distribution and context relation of critical regions within the object, highlighting the whole object and discriminative regions for distinguishing subtle differences. Finally, an object-aware feature enhancement module is proposed to combine global-scope and local-scope discriminative features in an attentive coupling way for powerful visual representations in fine-grained recognition. Extensive experiments on three FGVC benchmark datasets demonstrate that our proposed SIM-OFE method can achieve state-of-the-art performance.

Effect of water Resource utilization in Poyang lake area on carbon emissions based on decoupling theory.

The utilization of regional water resources has the potential to impact carbon emissions. Maintaining a decoupled relationship between water resources and carbon emissions facilitates harmonious regional development. Understanding the mechanism of their coordination is conducive to achieving the "Double Carbon" goal and control of regional carbon emissions and water resource consumption. This study examines the decoupling relationship between water resource utilization and carbon emissions in Poyang Lake area, China, employing the Tapio decoupling model and the LMDI(logarithmic mean divisia index) decomposition model. The results indicate that carbon emissions in Poyang Lake area exhibited a gradual increase, accompanied by an annual growth rate of 5.99 %. The water supply exhibited a slow expansion. They have exhibited state of affairs strong negative decoupling and expansive negative decoupling over the past 15 years. Moreover, this situation is most acute and worsening in the secondary industry. The water use structure effect and water economic benefit effect are the primary factors affecting carbon emission increases, contributing 57.93 % and 65.66 %, respectively. Carbon emissions intensity is the largest inhibiting factor, which accounts for a maximum contribution of 42.96 %. The order of potency of the driving factors is as follows: water economic benefit > carbon emission intensity > water use structure > water use efficiency. In summary, this research recognised the enhancement of the water economic efficiency index not only facilitates the decoupling phenomenon but also improves the water-carbon relationship, especially in the secondary industry. It serves as a compelling illustration of the significance of elucidating the interrelationship between regional water and carbon dynamics, and charting the course for the formulation of regional policies that would facilitate the advancement of environmentally conscious and carbon-neutral development, as well as water conservation.

An Exosuit System With Bidirectional Hand Support for Bilateral Assistance Based on Dynamic Gesture Recognition.

Hand motor impairment has seriously affected the daily life of the elderly. We developed an electromyography (EMG) exosuit system with bidirectional hand support for bilateral coordination assistance based on a dynamic gesture recognition model using graph convolutional network (GCN) and long short-term memory network (LSTM). The system included a hardware subsystem and a software subsystem. The hardware subsystem included an exosuit jacket, a backpack module, an EMG recognition module, and a bidirectional support glove. The software subsystem based on the dynamic gesture recognition model was designed to identify dynamic and static gestures by extracting the spatio-temporal features of the patient's EMG signals and to control glove movement. The offline training experiment built the gesture recognition models for each subject and evaluated the feasibility of the recognition model; the online control experiments verified the effectiveness of the exosuit system. The experimental results showed that the proposed model achieve a gesture recognition rate of 96.42% ± 3.26 %, which is higher than the other three traditional recognition models. All subjects successfully completed two daily tasks within a short time and the success rate of bilateral coordination assistance are 88.75% and 86.88%. The exosuit system can effectively help patients by bidirectional hand support strategy for bilateral coordination assistance in daily tasks, and the proposed method can be applied to various limb assistance scenarios.

Image Super-Resolution via Efficient Transformer Embedding Frequency Decomposition With Restart.

Recently, transformer-based backbones show superior performance over the convolutional counterparts in computer vision. Due to quadratic complexity with respect to the token number in global attention, local attention is always adopted in low-level image processing with linear complexity. However, the limited receptive field is harmful to the performance. In this paper, motivated by Octave convolution, we propose a transformer-based single image super-resolution (SISR) model, which explicitly embeds dynamic frequency decomposition into the standard local transformer. All the frequency components are continuously updated and re-assigned via intra-scale attention and inter-scale interaction, respectively. Specifically, the attention in low resolution is enough for low-frequency features, which not only increases the receptive field, but also decreases the complexity. Compared with the standard local transformer, the proposed FDRTran layer simultaneously decreases FLOPs and parameters. By contrast, Octave convolution only decreases FLOPs of the standard convolution, but keeps the parameter number unchanged. In addition, the restart mechanism is proposed for every a few frequency updates, which first fuses the low and high frequency, then decomposes the features again. In this way, the features can be decomposed in multiple viewpoints by learnable parameters, which avoids the risk of early saturation for frequency representation. Furthermore, based on the FDRTran layer with restart mechanism, the proposed FDRNet is the first transformer backbone for SISR which discusses the Octave design. Sufficient experiments show our model reaches state-of-the-art performance on 6 synthetic and real datasets. The code and the models are available at https://github.com/catnip1029/FDRNet.

Demequina capsici sp. nov., a novel plant growth-promoting actinomycete isolated from the rhizosphere of bell pepper (Capsicum annuum).

Demequina, commonly found in coastal and marine environments, represents a genus of Actinomycetes. In this study, strains Demequina PMTSA13T and OYTSA14 were isolated from the rhizosphere of Capsicum annuum, leading to the discovery of a novel species, Demequina capsici. Bacteria play a significant role in plant growth, yet there have been no reports of the genus Demequina acting as plant growth-promoting bacteria (PGPB). Comparative genomics analysis revealed ANI similarity values of 74.05-80.63% for PMTSA13T and 74.02-80.54% for OYTSA14, in comparison to various Demequina species. The digital DNA-DNA hybridization (dDDH) values for PMTSA13T ranged from 19 to 39%, and 19.1-38.6% for OYTSA14. Genome annotation revealed the presence of genes associated with carbohydrate metabolism and transport, suggesting a potential role in nutrient cycling and availability for plants. These strains were notably rich in genes related to 'carbohydrate metabolism and transport (G)', according to their Cluster of Orthologous Groups (COG) classification. Additionally, both strains were capable of producing auxin (IAA) and exhibited enzymatic activities for cellulose degradation and catalase. Furthermore, PMTSA13T and OYTSA14 significantly induced the growth of Arabidopsis thaliana seedlings primarily attributed to their capacity to produce IAA, which plays a crucial role in stimulating plant growth and development. These findings shed light on the potential roles of Demequina strains in plant-microbe interactions and agricultural applications. The type strain is Demequina capsici PMTSA13T (= KCTC 59028T = GDMCC 1.4451T), meanwhile OYTSA14 is identified as different strains of Demequina capsici.

Genotype of pioneer plant Miscanthus is not a key factor in the structure of rhizosphere bacterial community in heavy metal polluted sites.

Miscanthus is a common pioneer plant with abundant genetic variation in abandoned mines in southern China. However, the extent to which genetic differentiation among species modulates rhizosphere bacterial communities remains unclear. Miscanthus samples were collected from 26 typical abandoned heavy-metal mines with different soil types in southern China, tested using 14 pairs of simple sequence repeats (SSR) primers, and classified into two genotypes based on Nei's genetic distance. The structure and diversity of rhizosphere bacterial communities were examined using 16 S rRNA sequencing. The results showed that among the factors affecting the rhizosphere bacterial community structure of Miscanthus samples, the role of genotype was not significant, and geographical conditions were the most important factors, followed by pH and total organic carbon (TOC). The process of rhizospheric community assembly varied among different genotypes; however, the recruited species and their abundances were similar. Collectively, we provided an approach based on genetic differentiation to quantify the relative contribution of genotypes to the rhizosphere bacterial community, demonstrating that genotypes contribute less than soil conditions. Our findings provide new insights into the role of host genetics in the ecological processes of plant rhizosphere bacterial communities in abandoned mines and provide theoretical support for microbe-assisted phytoremediation.

Highly Flexible and Superelastic Graphene Nanofibrous Aerogels for Intelligent Sign Language.

Highly flexible and superelastic aerogels at large deformation have become urgent mechanical demands in practical uses, but both properties are usually exclusive. Here a trans-scale porosity design is proposed in graphene nanofibrous aerogels (GNFAs) to break the trade-off between high flexibility and superelasticity. The resulting GNFAs can completely recover after 1000 fatigue cycles at 60% folding strain, and notably maintain excellent structural integrity after 10000 cycles at 90% compressive strain, outperforming most of the reported aerogels. The mechanical robustness is demonstrated to be derived from the trans-scale porous structure, which is composed of hyperbolic micropores and porous nanofibers to enable the large elastic deformation capability. It is further revealed that flexible and superelastic GNFAs exhibit high sensitivity and ultrastability as an electrical sensors to detect tension and flexion deformation. As proof, The GNFA sensor is implemented onto a human finger and achieves the intelligent recognition of sign language with high accuracy by multi-layer artificial neural network. This study proposes a highly flexible and elastic graphene aerogel for wearable human-machine interfaces in sensor technology.

MECOM: A Meta-Completion Network for Fine-Grained Recognition With Incomplete Multi-Modalities.

Our work focuses on tackling the problem of fine-grained recognition with incomplete multi-modal data, which is overlooked by previous work in the literature. It is desirable to not only capture fine-grained patterns of objects but also alleviate the challenges of missing modalities for such a practical problem. In this paper, we propose to leverage a meta-learning strategy to learn model abilities of both fast modal adaptation and more importantly missing modality completion across a variety of incomplete multi-modality learning tasks. Based on that, we develop a meta-completion method, termed as MECOM, to perform multimodal fusion and explicit missing modality completion by our proposals of cross-modal attention and decoupling reconstruction. To further improve fine-grained recognition accuracy, an additional partial stream (as a counterpart of the main stream of MECOM, i.e., holistic) and the part-level features (corresponding to fine-grained objects' parts) selection are designed, which are tailored for fine-grained nature to capture discriminative but subtle part-level patterns. Comprehensive experiments from quantitative and qualitative aspects, as well as various ablation studies, on two fine-grained multimodal datasets and one generic multimodal dataset show our superiority over competing methods. Our code is open-source and available at https://github.com/SEU-VIPGroup/MECOM.

SlCRCa is a key D-class gene controlling ovule fate determination in tomato.

Cell fate determination and primordium initiation on the placental surface are two key events for ovule formation in seed plants, which directly affect ovule density and seed yield. Despite ovules form in the marginal meristematic tissues of the carpels, angiosperm carpels evolved after the ovules. It is not clear how the development of the ovules and carpels is coordinated in angiosperms. In this study, we identify the S. lycopersicum CRABS CLAW (CRC) homologue SlCRCa as an essential determinant of ovule fate. We find that SlCRCa is not only expressed in the placental surface and ovule primordia but also functions as a D-class gene to block carpel fate and promote ovule fate in the placental surface. Loss of function of SlCRCa causes homeotic transformation of the ovules to carpels. In addition, we find low levels of the S. lycopersicum AINTEGUMENTA (ANT) homologue (SlANT2) favour the ovule initiation, whereas high levels of SlANT2 promote placental carpelization. SlCRCa forms heterodimer with tomato INNER NO OUTER (INO) and AGAMOUS (AG) orthologues, SlINO and TOMATO AGAMOUS1 (TAG1), to repress SlANT2 expression during the ovule initiation. Our study confirms that angiosperm basal ovule cells indeed retain certain carpel properties and provides mechanistic insights into the ovule initiation.

A Multidisciplinary Assessment of ChatGPT's Knowledge of Amyloidosis: Observational Study.

BACKGROUND: Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. OBJECTIVE: We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. METHODS: In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). RESULTS: ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to "general questions" (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4's responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). CONCLUSIONS: Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT's responses exceeded the American Medical Association's recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology's limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.

Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model.

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., "vandalism", is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks and design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method. Captions are publicly released at https://github.com/Roc-Ng/VAR.

Covalent Tethering of Cobalt Porphyrins on Phenolic Resins for Electrocatalytic Oxygen Reduction and Evolution Reactions.

The front cover artwork is provided by Rui Cao's group at Shaanxi Normal University. The image shows the design of Co-porphyrin-engineered phenolic resins with intramolecular phenolic hydroxyl groups to facilitate proton and electron transfers for efficient oxygen electrocatalysis, which is bioinspired by cytochrome c oxidases, and shows the excellent performance of Zn-air batteries assembled with the hybrid material. Read the full text of the Research Article at 10.1002/cphc.202400017.

Covalent Tethering of Cobalt Porphyrins on Phenolic Resins for Electrocatalytic Oxygen Reduction and Evolution Reactions.

Using functionalized supporting materials for the immobilization of molecular catalysts is an appealing strategy to improve the efficiency of molecular electrocatalysis. Herein, we report the covalent tethering of cobalt porphyrins on phenolic resins (PR) for improved electrocatalytic oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). A cobalt porphyrin bearing an alkyl bromide substituent was covalently tethered on phenolic resins, through the substitution reaction of alkyl bromides with phenolic hydroxyl groups, to afford molecule-engineered phenolic resins (Co-PR). The resulted Co-PR was efficient for electrocatalytic ORR and OER by displaying an ORR half-wave potential of E1/2=0.78 V versus RHE and an OER overpotential of 420 mV to get 10 mA/cm2 current density. We propose that the many residual phenolic hydroxyl groups on PR will surround the tethered Co porphyrin and play critical roles in facilitating proton and electron transfers. Importantly, Co-PR outperformed unmodified PR and PR loaded with Co porphyrins through simple physical adsorption (termed Co@PR). The zinc-air battery assembled using Co-PR displayed a performance comparable to that using Pt/C+Ir/C. This work is significant to present phenolic resins as a functionalized material to support molecular electrocatalysts and demonstrate the strategy to improve molecular electrocatalysis with the use of phenolic resin residues.

Pseudoxanthomonas sp. JBR18, a halotolerant endophytic bacterium, improves the salt tolerance of Arabidopsis seedlings.

Salinization of land is globally increasing due to climate change, and salinity stress is an important abiotic stressor that adversely affects agricultural productivity. In this study, we assessed a halotolerant endophytic bacterium, Pseudoxanthomonas sp. JBR18, for its potential as a plant growth-promoting agent with multiple beneficial properties. The strain exhibited tolerance to sodium chloride concentration of up to 7.5 % in the R2A medium. In vitro evaluation revealed that strain JBR18 possessed proteolytic, protease (EC 3.4), and cellulase (EC 3.2.1.4) activities, as well as the ability to produce indole-acetic acid, proline, and exopolysaccharides. Compared with the controls, co-cultivation of Arabidopsis seedlings with the strain JBR18 improved plant growth, rosette size, shoot and root fresh weight, and chlorophyll content under salinity stress. Moreover, JBR18-inoculated seedlings showed lower levels of malondialdehyde, reactive oxygen species, and Na+ uptake into plant cells under salt stress but higher levels of K+. Additionally, seedlings inoculated with JBR18 exhibited a delayed response time and quantity of salt-responsive genes RD29A, RD29B, RD20, RD22, and KIN1 under salt stress. These multiple effects suggest that Pseudoxanthomonas sp. JBR18 is a promising candidate for mitigating the negative impacts of salinity stress on plant growth. Our findings may assist in future efforts to develop eco-friendly strategies for managing abiotic stress and enhancing plant tolerance to salt stress.

Recent Trends in Self-Powered Photoelectrochemical Sensors: From the Perspective of Signal Output.

Revolutionary developments in analytical chemistry have led to the rapid development of self-powered photoelectrochemical (PEC) sensors. Different from conventional PEC sensors, self-powered PEC sensors do not require an external power source or complex devices for the sensitive detection of targets. As a result, these sensors have enormous application potential for the development of novel portable sensors. An increasing body of work is making excellent progress toward the implementation of self-powered PEC sensors for detection, but there have been no reviews to date. The present review first introduces the state of the art in the development of self-powered PEC sensors. Then, different types of self-powered PEC sensors are summarized and discussed in detail, including their current, power, and potential. Additionally, single- and dual-photoelectrode systems are classified and systematically compared. Finally, the current developments and major challenges that need to be addressed are also summarized. This review provides valuable insights into the current state of self-powered PEC sensors to promote further progress in this field.

I2C: Invertible Continuous Codec for High-Fidelity Variable-Rate Image Compression.

Lossy image compression is a fundamental technology in media transmission and storage. Variable-rate approaches have recently gained much attention to avoid the usage of a set of different models for compressing images at different rates. During the media sharing, multiple re-encodings with different rates would be inevitably executed. However, existing Variational Autoencoder (VAE)-based approaches would be readily corrupted in such circumstances, resulting in the occurrence of strong artifacts and the destruction of image fidelity. Based on the theoretical findings of preserving image fidelity via invertible transformation, we aim to tackle the issue of high-fidelity fine variable-rate image compression and thus propose the Invertible Continuous Codec (I2C). We implement the I2C in a mathematical invertible manner with the core Invertible Activation Transformation (IAT) module. I2C is constructed upon a single-rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors. Extensive experiments demonstrate that the proposed I2C method outperforms state-of-the-art variable-rate image compression methods by a large margin, especially after multiple continuous re-encodings with different rates, while having the ability to obtain a very fine variable-rate control without any performance compromise.

Study on the Frying Performance Evaluation of Refined Soybean Oil after PLC Enzymatic Degumming.

It is known that phospholipase C (PLC) enzymatic degumming can hydrolyze phospholipids into diacylglycerol (DAG), which improves the efficiency of oil processing. However, it is unclear whether the presence of DAG and the use of enzymes affect the performance of the oil. This paper evaluated the frying performance of PLC-degummed refined soybean oil. Following the chicken wings and potato chips frying trials, results revealed that after 30 cycles of frying, free fatty acid (FFA) levels were 0.22% and 0.21%, with total polar compounds (TPC) at 23.75% and 24.00%, and peroxide value (PV) levels were 5.90 meq/kg and 6.45 meq/kg, respectively. Overall, PLC-degummed refined soybean oil showed almost the same frying properties as traditional water-degummed refined oil in terms of FFA, PV, TPC, polymer content, viscosity, color, foaming of frying oils, and appearance of foods. Moreover, FFA, TPC, polymer content, foaming, and color showed significant positive correlations with each other (p < 0.05) in soybean oil intermittent frying processing.