Enhanced Multimodal Representation Learning with Cross-modal KD

This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification.Comment: Accepted by CVPR202

Applying DTN Routing for Reservation-Driven EV Charging Management in Smart Cities

Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver’s experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs’ charging plans, aiming to minimize drivers’ trip duration through intermediate charging at CSs. The anticipated EVs’ charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs’ charging reservations,we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration

Redundancy-Adaptive Multimodal Learning for Imperfect Data

Multimodal models trained on complete modality data often exhibit a substantial decrease in performance when faced with imperfect data containing corruptions or missing modalities. To address this robustness challenge, prior methods have explored various approaches from aspects of augmentation, consistency or uncertainty, but these approaches come with associated drawbacks related to data complexity, representation, and learning, potentially diminishing their overall effectiveness. In response to these challenges, this study introduces a novel approach known as the Redundancy-Adaptive Multimodal Learning (RAML). RAML efficiently harnesses information redundancy across multiple modalities to combat the issues posed by imperfect data while remaining compatible with the complete modality. Specifically, RAML achieves redundancy-lossless information extraction through separate unimodal discriminative tasks and enforces a proper norm constraint on each unimodal feature representation. Furthermore, RAML explicitly enhances multimodal fusion by leveraging fine-grained redundancy among unimodal features to learn correspondences between corrupted and untainted information. Extensive experiments on various benchmark datasets under diverse conditions have consistently demonstrated that RAML outperforms state-of-the-art methods by a significant margin

Genetic Evaluation of Starch Synthesis-Related Genes and Starch Quality Traits in Special Rice Resources

The genetic diversity of 36 rice landraces and 43 breeding materials in the upper reaches of the Yangtze River in China was studied by intragenic molecular markers of 26 starch synthesis-related loci. And research on quality traits such as the amylose content (AC), gel consistency (GC) and alkali spreading value (ASV) to analyze genetic differences in quality traits. The results showed that the number of alleles, average gene diversity and polymorphism information content values of landraces were higher than those of breeding materials. The genetic similarity coefficient (GS) of 79 rice materials ranged from 0.392 to 1, with an average of 0.757.There were significant variations in the quality traits of rice landraces and breeding materials, and the high-quality compliance rates were low, only 6.3% of the varieties have an amylose content that reached grade 1. The results of cluster analysis and population structure analysis are generally consistent; that is, the two resource types are closely related and cannot be clustered independently. This study can provide a basis for genetic improvement of rice starch quality. Make full use of the quality genetic diversity of landraces in modern breeding work, further broaden the genetic base of rice and improve rice quality

Construction and validation of a predictive risk model for nosocomial infections with MDRO in NICUs: a multicenter observational study

ObjectivesThis study aimed to construct and validate a predictive risk model (PRM) for nosocomial infections with multi-drug resistant organism (MDRO) in neonatal intensive care units (NICUs), in order to provide a scientific and reliable prediction tool, and to provide reference for clinical prevention and control of MDRO infections in NICUs.MethodsThis multicenter observational study was conducted at NICUs of two tertiary children’s hospitals in Hangzhou, Zhejiang Province. Using cluster sampling, eligible neonates admitted to NICUs of research hospitals from January 2018 to December 2020 (modeling group) or from July 2021 to June 2022 (validation group) were included in this study. Univariate analysis and binary logistic regression analysis were used to construct the PRM. H-L tests, calibration curves, ROC curves and decision curve analysis were used to validate the PRM.ResultsFour hundred and thirty-five and one hundred fourteen neonates were enrolled in the modeling group and validation group, including 89 and 17 neonates infected with MDRO, respectively. Four independent risk factors were obtained and the PRM was constructed, namely: P = 1/ (1+ e−X), X = −4.126 + 1.089× (low birth weight) +1.435× (maternal age ≥ 35 years) +1.498× (use of antibiotics >7 days) + 0.790× (MDRO colonization). A nomogram was drawn to visualize the PRM. Through internal and external validation, the PRM had good fitting degree, calibration, discrimination and certain clinical validity. The prediction accuracy of the PRM was 77.19%.ConclusionPrevention and control strategies for each independent risk factor can be developed in NICUs. Moreover, clinical staff can use the PRM to early identification of neonates at high risk, and do targeted prevention to reduce MDRO infections in NICUs

Coupling and metabolic analysis of urbanization and environment between two resource-based cities in North China

Background The complex relationship between urbanization and environment in resource-based cities is of increasing concern. Methods As typical examples of rapid economic growth, obvious urbanization, and successful transformed production models, the cities of Dongying and Binzhou in Yellow River Delta High-tech Economic Zone were chosen for research. First, this study examines the coupling relationship between urbanization and the environment over the last seventeen years using the coupling degree model. Second, the emergy analysis method is used to further study the energy metabolism and environmental load in the two cities to reveal these couplings. Results Dongying and Binzhou were well-coupled and the coupling coordination degree was in the stage of mild coordination coupling showing an upward trend. The total metabolic energy of the two cities increased yearly from 2000 to 2016, and the emergy extroversion ratio data showed the cities’ dependence on external elements such as continuously increased imported resources. The total emergy used in the two cities showed an upward trend during 2000 and 2016, while the emergy per capita consumption increased significantly, suggesting that the society’s energy efficiency improved. During the same period, the environmental loading ratio increased gradually, and the elements causing the environmental load shifted from internal to external. Discussion The study shows that the factors of environmental load in developing cities are gradually shifting from internal to external, which is vital to understanding the impact of urban transformation and upgrading of resource-based cities on the environment

Umbilical Cord-Derived Mesenchymal Stem Cells Suppress Autophagy of T Cells in Patients with Systemic Lupus Erythematosus via Transfer of Mitochondria

Aberrant autophagy played an important role in the pathogenesis of autoimmune diseases, especially in systemic lupus erythematosus (SLE). In this study, we showed that T cells from SLE patients had higher autophagic activity than that from healthy controls. A correlation between autophagic activity and apoptotic rate was observed in activated T cells. Moreover, activation of autophagy with rapamycin increased T cell apoptosis, whereas inhibition of autophagy with 3-MA decreased T cell apoptosis. Umbilical cord-derived mesenchymal stem cells (UC-MSCs) could inhibit respiratory mitochondrial biogenesis in activated T cells to downregulate autophagy and consequently decrease T cell apoptosis through mitochondrial transfer and thus may play an important role in SLE treatment

Honokiol Crosses BBB and BCSFB, and Inhibits Brain Tumor Growth in Rat 9L Intracerebral Gliosarcoma Model and Human U251 Xenograft Glioma Model

BACKGROUND: Gliosarcoma is one of the most common malignant brain tumors, and anti-angiogenesis is a promising approach for the treatment of gliosarcoma. However, chemotherapy is obstructed by the physical obstacle formed by the blood-brain barrier (BBB) and blood-cerebrospinal fluid barrier (BCSFB). Honokiol has been known to possess potent activities in the central nervous system diseases, and anti-angiogenic and anti-tumor properties. Here, we hypothesized that honokiol could cross the BBB and BCSFB for the treatment of gliosarcoma. METHODOLOGIES: We first evaluated the abilities of honokiol to cross the BBB and BCSFB by measuring the penetration of honokiol into brain and blood-cerebrospinal fluid, and compared the honokiol amount taken up by brain with that by other tissues. Then we investigated the effect of honokiol on the growth inhibition of rat 9L gliosarcoma cells and human U251 glioma cells in vitro. Finally we established rat 9L intracerebral gliosarcoma model in Fisher 344 rats and human U251 xenograft glioma model in nude mice to investigate the anti-tumor activity. PRINCIPAL FINDINGS: We showed for the first time that honokiol could effectively cross BBB and BCSFB. The ratios of brain/plasma concentration were respectively 1.29, 2.54, 2.56 and 2.72 at 5, 30, 60 and 120 min. And about 10% of honokiol in plasma crossed BCSFB into cerebrospinal fluid (CSF). In vitro, honokiol produced dose-dependent inhibition of the growth of rat 9L gliosarcoma cells and human U251 glioma cells with IC(50) of 15.61 µg/mL and 16.38 µg/mL, respectively. In vivo, treatment with 20 mg/kg body weight of honokiol (honokiol was given twice per week for 3 weeks by intravenous injection) resulted in significant reduction of tumor volume (112.70±10.16 mm(3)) compared with vehicle group (238.63±19.69 mm(3), P = 0.000), with 52.77% inhibiting rate in rat 9L intracerebral gliosarcoma model, and (1450.83±348.36 mm(3)) compared with vehicle group (2914.17±780.52 mm(3), P = 0.002), with 50.21% inhibiting rate in human U251 xenograft glioma model. Honokiol also significantly improved the survival over vehicle group in the two models (P<0.05). CONCLUSIONS/SIGNIFICANCE: This study provided the first evidence that honokiol could effectively cross BBB and BCSFB and inhibit brain tumor growth in rat 9L intracerebral gliosarcoma model and human U251 xenograft glioma model. It suggested a significant strategy for offering a potential new therapy for the treatment of gliosarcoma