Using GPT-4 to Augment Unbalanced Data for Automatic Scoring

Machine learning-based automatic scoring can be challenging if students' responses are unbalanced across scoring categories, as it introduces uncertainty in the machine training process. To meet this challenge, we introduce a novel text data augmentation framework using GPT-4, a generative large language model, specifically tailored for unbalanced datasets in automatic scoring. Our experimental dataset comprised student-written responses to two science items. We crafted prompts for GPT-4 to generate responses resembling student-written answers, particularly for the minority scoring classes, to augment the data. We then finetuned DistillBERT for automatic scoring based on the augmented and original datasets. Model performance was assessed using accuracy, precision, recall, and F1 score. We incorporate varied amounts of augmented data to examine scoring performance, and our findings revealed remarkedly improved model performance. The average maximum increase observed across two items is: 3.5% for accuracy, 30.6% for precision, 21.1% for recall, and 24.2% for F1 score. Notably, using just 5% of the augmented data led to substantial improvements: 2.6%, 29.2%, 15.1%, and 19.6%. Interestingly, the extent of improvement varied depending on specific datasets. Moreover, we found that a varying amount of augmented data (5%-40%) was needed to obtain a stable improvement. We also compare models trained with GPT-4 augmented data and those trained with additional student-written responses. The findings indicate that former ones match or even exceed the performance of the latter. Specifically, there is an average difference of 1.7%, 1.9%, 11.0%, and 7.8% for four metrics separately. This research underscores the potential and effectiveness of data augmentation techniques utilizing GPT-4 in addressing unbalanced datasets within automated assessment

Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification

Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSIs are usually classified as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding process and a bag-level classification process, but they are prohibitively expensive to be trained end-to-end. Therefore, existing methods usually train them separately, or directly skip the training of the embedder. Such schemes hinder the patch embedder's access to slide-level labels, resulting in inconsistencies within the entire MIL pipeline. To overcome this issue, we propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges the loss back-propagation process from the bag-level classifier to the patch embedder. In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor. The refined embedder then generates better instance representations for achieving a more accurate bag-level classifier. By coupling the patch embedder and bag classifier at a low cost, our proposed framework enables information exchange between the two processes, benefiting the entire MIL classification model. We tested our framework on two datasets using three different backbones, and our experimental results demonstrate consistent performance improvements over state-of-the-art MIL methods. Code will be made available upon acceptance

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity—Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000–2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km² to 325.33 km² during the period 2000–2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth.the Strategic Priority Research Program of the Chinese Academy of Sciencesthe National Natural Science Foundation of ChinaPeer Reviewe

Anti-HIV-1 Activity of a New Scorpion Venom Peptide Derivative Kn2-7

For over 30 years, HIV/AIDS has wreaked havoc in the world. In the absence of an effective vaccine for HIV, development of new anti-HIV agents is urgently needed. We previously identified the antiviral activities of the scorpion-venom-peptide-derived mucroporin-M1 for three RNA viruses (measles viruses, SARS-CoV, and H5N1). In this investigation, a panel of scorpion venom peptides and their derivatives were designed and chosen for assessment of their anti-HIV activities. A new scorpion venom peptide derivative Kn2-7 was identified as the most potent anti-HIV-1 peptide by screening assays with an EC50 value of 2.76 µg/ml (1.65 µM) and showed low cytotoxicity to host cells with a selective index (SI) of 13.93. Kn2-7 could inhibit all members of a standard reference panel of HIV-1 subtype B pseudotyped virus (PV) with CCR5-tropic and CXCR4-tropic NL4-3 PV strain. Furthermore, it also inhibited a CXCR4-tropic replication-competent strain of HIV-1 subtype B virus. Binding assay of Kn2-7 to HIV-1 PV by Octet Red system suggested the anti-HIV-1 activity was correlated with a direct interaction between Kn2-7 and HIV-1 envelope. These results demonstrated that peptide Kn2-7 could inhibit HIV-1 by direct interaction with viral particle and may become a promising candidate compound for further development of microbicide against HIV-1

Molecular Cloning, Expression and Macrophage Activation of an Immunoregulatory Protein from <i>Cordyceps militaris</i>

Protein components of C. militaris have been reported to possess various biological activities. In our previous research, a Cordyceps militaris-derived immunoregulatory protein (CMIP) was naturally isolated and showed the activity of inhibiting the metastasis of breast cancer cells. This study aimed to obtain recombinant CMIP (rCMIP) using recombinant expression and elucidate its ability to activate macrophages. Recombinant CMIP showed one band at approximately 15 kDa or 30 kDa, or two bands at 15 kDa and 30 kDa, under different denaturation conditions of electrophoresis. The cell binding assay showed that rCMIP selectively binds to the surface of macrophages. After adhesion, it did not induce the apoptosis of RAW 264.7 cells, but promoted their proliferation. Moreover, rCMIP significantly induced the expression of M1 macrophage polarization-related molecules. The mean fluorescence intensity (MFI) of CD 86 was enhanced by 2.1-fold and 3.2-fold under 0.64 μM and 1.6 μM of rCMIP treatment, respectively. Cytokines typically expressed in M1 macrophages, such as TNF-α, iNOS, IL-6, CCL 4, CCL 5 and CXCL 10, were also considerably induced by rCMIP, while the expression of cytokines in typical M2 macrophages, like Arg-1, CCL17 and CCL22, were not changed or slightly decreased. Under rCMIP treatment, the release of NO was also appreciably induced. In the present study, we reported cloning, expression and functional characterization of rCMIP, which was naturally isolated from the fruiting body of C. militaris in our previous study. The data imply that rCMIP possesses immunomodulatory activity in macrophages

Inactivation of <i>Bacillus subtilis</i> by Curcumin-Mediated Photodynamic Technology through Inducing Oxidative Stress Response

Photodynamic sterilization technology (PDT) is widely used in disease therapy, but its application in the food industry is still at the research stage because of the limitations of food-grade photosensitizers. Curcumin exhibits photosensitivity and is widely used as a food additive for its natural color. This study aimed to determine the effect of curcumin-mediated photodynamic technology (Cur-PDT) on Bacillus subtilis and to elucidate the anti-bacterial mechanism involved. First, the effects of curcumin concentration, duration of light irradiation, light intensity, and incubation time on the inactivation of B. subtilis were analyzed. It was found that Cur-PDT inactivated 100% planktonic cells with 50 μmol/L curcumin in 15 min (120 W). Then, the cell morphology, oxidation state and the expression of membrane structure- and DNA damage-related genes of B. subtilis vegetative cells were investigated under different treatment conditions. The membrane permeability of cells was enhanced and the cell membrane structure was damaged upon treatment with Cur-PDT, which were exacerbated with increases of treatment time and curcumin concentration. Meanwhile, the production of reactive oxygen species increased and the activities of the antioxidant enzymes SOD, GPX, and CAT decreased inside the cells. Furthermore, the Cur-PDT treatment significantly downregulated the mRNA of the membrane protein TasA and upregulated the DNA damage recognition protein UvrA and repair protein RecA of B. subtilis. These results suggested that curcumin-mediated PDT could effectively inactivate B. subtilis by inducing cell redox state imbalance, damaging DNA, and disrupting membrane structures

Boron and nitrogen Co-doped holey graphene aerogels with rich B–N motifs for flexible supercapacitors

© 2019 Elsevier Ltd Boron and Nitrogen co-doped holey graphene aerogels (BN-HGA) are fabricated using ammonia borane as triple-functional precursor. The as-prepared BN-HGA possesses high specific surface area (249 m2 g−1) and rich B–N motifs with high surface polarity, which contributes to the rich and stable redox sites for the enhanced pseudo-capacitance. Moreover, the high hydrogen content in the ammonia borane cultures a reducing environment to preserve the integrity of carbon matrix, which gives rise to the high electronic conductivity. In addition, the well-developed hierarchically porous structure facilitates the ion diffusion in the electrode. Thanks to these structural merits, the BN-HGA electrode furnishes good specific capacitance of 456 F g−1 at 1 A g−1 in three-electrode systems using sulfuric acid as electrolyte. Meanwhile, the all-solid-state flexible supercapacitors based on the symmetric BN-HGA electrodes demonstrate high specific capacitance (345 mF cm −2 at 1 mA cm −2) and outstanding rate performance (80% retention at 20 mA cm −2). Furthermore, the flexible supercapacitor exhibits pleasant flexibility with marginable capacity loss as bent to arbitrary angles, which endows it a promising device for wearable energy storage

Heteroleptic chiral bis(phthalocyaninato) terbium double-decker single-ion magnets

For the purpose of engineering magnetic anisotropy, chiral binaphthyl substituents with slight electron-withdrawing ability and dibutylamino substituents with intense electron-donating nature are incoporated onto the periphery of each phthalocyanine ligand in the bis(phthalocyaninato) terbium double-decker compound, resulting in the chiral heteroleptic bis(phthalocyaninato) terbium complex (R)/(S)-[Pc(OBNP)(4)]Tb{Pc[N(C4H9)(2)](8)} (R/S-1) {Pc(OBNP)(4) = {tetrakis(dinaphtho[1,2-e:1,2-g]-1,4-dioxocine)[2,3-b;2,3-k;2,3-t;2,3-c]phthalocyanine}; Pc[N(C4H9)(2)](8) = [2,3,9,10,16,17,23,24-octakis(dibutylamino)phthalocyanine]}. Magnetic studies reveal the typical single-ion magnet (SIM) nature of this chiral bis(phthalocyaninato) terbium double-decker with spin reversal energy barrier of 638 K. A butterfly-shaped magnetic hysteresis loop was observed at even 25 K for R-1 under the sweep rate of 500 Oe s(-1). The performance of this SIM is well rationalized on the basis of theoretical analysis of the electrostatic potential according to calculations using the density functional theory method

Characterizing Spatiotemporal Variations in the Urban Thermal Environment Related to Land Cover Changes in Karachi, Pakistan, from 2000 to 2020

Understanding the spatiotemporal patterns of urban heat islands and the factors that influence this phenomenon can help to alleviate the heat stress exacerbated by urban warming and strengthen heat-related urban resilience, thereby contributing to the achievement of the United Nations Sustainable Development Goals. The association between surface urban heat island (SUHI) effects and land use/land cover features has been studied extensively, but the situation in tropical cities is not well-understood due to the lack of consistent data. This study aimed to explore land use/land cover (LULC) changes and their impact on the urban thermal environment in a tropical megacity&mdash;Karachi, Pakistan. Land cover maps were produced, and the land surface temperature (LST) was estimated using Landsat images from five different years over the period 2000&ndash;2020. The surface urban heat island intensity (SUHII) was then quantified based on the LST data. Statistical analyses, including geographically weighted regression (GWR) and correlation analyses, were performed in order to analyze the relationship between the land cover composition and LST. The results indicated that the built-up area of Karachi increased from 97.6 km&sup2; to 325.33 km&sup2; during the period 2000&ndash;2020. Among the different land cover types, the areas classified as built-up or bare land exhibited the highest LST, and a change from vegetation to bare land led to an increase in LST. The correlation analysis indicated that the correlation coefficients between the normalized difference built-up index (NDBI) and LST ranged from 0.14 to 0.18 between 2000 and 2020 and that NDBI plays a dominant role in influencing the LST. The GWR analysis revealed the spatial variation in the association between the land cover composition and the SUHII. Parks with large areas of medium- and high-density vegetation play a significant role in regulating the thermal environment, whereas the scattered vegetation patches in the urban core do not have a significant relationship with the LST. These findings can be used to inform adaptive land use planning that aims to mitigate the effects of the UHI and aid efforts to achieve sustainable urban growth