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Multidrug resistance is a substantial obstacle in treating non-small cell lung cancer (NSCLC) with therapies like cisplatin (DDP)-based adjuvant chemotherapy and EGFR-tyrosine kinase inhibitors (TKIs). Aaptamine-7 (AP-7), a benzonaphthyridine alkaloid extracted from Aaptos aaptos sponge, has been shown to exhibit a broad spectrum of anti-tumor activity. However, the anti-cancer activity of AP-7 in combination with DDP and its molecular mechanisms in multidrug-resistant NSCLC are not yet clear. Our research indicates that AP-7 bolsters the growth inhibition activity of DDP on multidrug-resistant NSCLC cells. AP-7 notably disrupts DDP-induced cell cycle arrest and amplifies DDP-induced DNA damage effects in these cells. Furthermore, the combination of AP-7 and DDP downregulates Chk1 activation, interrupts the DNA damage repair-dependent Chk1/CDK1 pathway, and helps to overcome drug resistance and boost apoptosis in multidrug-resistant NSCLC cells and a gefitinib-resistant xenograft mice model. In summary, AP-7 appears to enhance DDP-induced DNA damage by impeding the Chk1 signaling pathway in multidrug-resistant NSCLC, thereby augmenting growth inhibition, both in vitro and in vivo. These results indicate the potential use of AP-7 as a DDP sensitizer in the treatment of multidrug-resistant NSCLC.

Leaf chlorophyll content (LCC) is crucial in plant physiology and ecological research. Although several LCC products have recently been generated at a regional or global scale, understanding their accuracy is still a concern in the scientific community. We intercompared and analyzed five existing LCC products (MuSyQ LCC, MODIS LCC, MERIS LCC, GLCC, and GLOBMAP MERIS LCC) over China in terms of spatial continuity and spatiotemporal consistency over seven plant functional types. The products of 2011 and 2019 over China were used in this study. Research findings indicate (1) the 30 m-resolution MuSyQ LCC has the highest accuracy compared to field LCC of cropland and grassland types, with an RMSE of 19.5 μg/cm2, while MODIS LCC product demonstrates a more robust fit to the measured LCC, with an R2 of 0.341. (2) Interpolation of products with lower spatial resolution e.g. MODIS LCC, MERIS LCC, and GLOBMAP MERIS LCC, generally improves spatial continuity. The non-interpolated 30 m MuSyQ LCC exhibits good regional continuity due to its high spatial resolution. The lowest spatial continuity is found over shrubs for all products. (3) MODIS LCC and MuSyQ LCC of 2019 demonstrate high overall spatial consistency and exhibit the highest correlation over cropland sites. MODIS LCC and GLOBMAP MERIS LCC of 2011 demonstrate high temporal consistency over deciduous forests, evergreen forests, grasslands, and shrubs sites. The most robust overall temporal consistency is exhibited among all products in the deciduous needleleaved forest, followed by evergreen needleleaved forest and grassland. The findings of this research are essential for improving leaf chlorophyll content inversion algorithms and for understanding and better use of LCC products in land surface models.

Abstract: Philanthropy provides substantial support for science and research—does it do the same for technology and invention? We match patenting outcomes in the United States with philanthropic organizations as listed in the Candid database and find that at least 0.2% of U.S. patenting since 1976 received philanthropic support. After matching philanthropic patents to non-philanthropic patents based on technology, time, and the number of inventors, we find that philanthropic invention is more likely to be done by smaller teams and U.S. based inventors and less likely to rely on science research and non-U.S. prior art. Renewal rates are not significantly different from non-philanthropic patents and philanthropic patents are not more likely to be in underserved areas of technology. Philanthropic supported patents receive significantly fewer future prior art citations, thus implying that philanthropy supported patents are not exceptionally valuable.

Abstract Background Postpartum depression (PPD) is a major public health problem worldwide. Previous studies have shown that postpartum negative life events and neuroticism are both important risk factors for PPD. However, few studies have considered the role of protective factors in the influence of postpartum negative life events and neuroticism on PPD. Based on the diathesis–stress model and Acceptance and Commitment Therapy (ACT), a moderated mediating model was established to examine the mediating role of neuroticism between postpartum negative life events and PPD, as well as the moderating role of psychological flexibility in this mediating effect. Methods A sample of 776 parturients from three different Grade A hospitals in China were assessed using the Edinburgh Postpartum Depression Scale, the Postpartum Negative Life Events Scale, the Neuroticism Subscale of the Big Five Personality Scale, and the Acceptance and Action Questionnaire– II. Results PPD, postpartum negative life events, neuroticism, and experiential avoidance were significantly positively correlated with one another. Neuroticism partially mediated the relationship between postpartum negative life events and PPD. In this mediation model, the direct path and the second half of the mediation path were moderated by psychological flexibility. Specifically, the links between postpartum negative life events and PPD, as well as between neuroticism and PPD, were stronger when psychological flexibility was low, but weaker when psychological flexibility was high. Conclusions The results show that psychological flexibility plays an important role in buffering the negative effects of postpartum negative life events and neuroticism on PPD. These findings provide implications for the prevention and intervention of PPD using an ACT approach.

This paper addresses the tracking control problem for a class of uncertain nonlinear systems with full state deferred constraints. The constraints considered occur after a period of system operation rather than from the beginning. An attempt has been made to use a shifting function to realize the purpose of deferred constraint. Moreover, the difficulty of asymmetric constraint on states comes from the design on barrier function. Due to this, an unified barrier function is proposed to prevent system states from violating the constraint range, indirectly. Meanwhile, it effectively removes the restriction condition used in existing results that the upper and lower bound signs of constraint functions are the same. Consequently, the goals of tracking a reference signal and state constraints are achieved through the control scheme designed. Theoretical analysis and simulations are presented to demonstrate the efficacy of the proposed control strategy.

Modulating Tankyrases (TNKS), interactions with USP25 to promote TNKS degradation, rather than inhibiting their enzymatic activities, is emerging as an alternative/specific approach to inhibit the Wnt/β-catenin pathway. Here, we identified UAT-B, a novel neoantimycin analog isolated from Streptomyces conglobatus, as a small-molecule inhibitor of TNKS–USP25 protein–protein interaction (PPI) to overcome multi-drug resistance in colorectal cancer (CRC). The disruption of TNKS–USP25 complex formation by UAT-B led to a significant decrease in TNKS levels, triggering cell apoptosis through modulation of the Wnt/β-catenin pathway. Importantly, UAT-B successfully inhibited the CRC cells growth that harbored high TNKS levels, as demonstrated in various in vitro and in vivo studies utilizing cell line-based and patient-derived xenografts, as well as APCmin/+ spontaneous CRC models. Collectively, these findings suggest that targeting the TNKS–USP25 PPI using a small-molecule inhibitor represents a compelling therapeutic strategy for CRC treatment, and UAT-B emerges as a promising candidate for further preclinical and clinical investigations.

Osteochondroma is one of the most common benign bone tumors, mainly involving the bone ends of long bones, and involving the spine is rare. It often involves the competing, followed by the thoracic and lumbar spine, and rarely involves the sacrum. We report the imaging findings of a solitary osteochondroma of the sacrum. The patient was a 37-year-old woman who presented clinically with progressive low back pain associated with left buttock pain and discomfort. CT and MRI showed that the lesion originated from the left lamina of S1 and grew anteriorly and superiorly, resulting in compressive resorption of the L5 vertebral bone, left foraminal stenosis and adjacent nerve root swelling. The patient underwent surgery and the mass was completely excised and recovered well postoperatively. Osteochondroma arising from the sacrum is rare and can lead to compressive resorption of adjacent bone, and imaging techniques are conducive to the localization and characterization of the lesion and provide useful information for clinical treatment.

The 2019 coronavirus disease (COVID-19) pandemic impacts the learning and teaching experiences of faculty, students, and school staff at all levels of education around the world. While most schools face the challenges of closing down campuses, moving courses online, and relying on technology tools to deliver instructional activities, as one of the few countries with a regular semester schedule, universities in Taiwan have different experiences handling the current situation. This reflective essay aims to share the countermeasures that the Center of Teaching and Learning Development (CTLD) and Digital Learning Center (DLC) at National Taiwan University (NTU) took to keep everything going commonly during the uncommon time. The pandemic impact was discussed from the interrelationships of university policy, course, and instructor. We used Bloom's cognitive, psychomotor, and affective domains of knowledge to guide the implementation of related actions. Based on our learned lessons and reflections, we provide five suggestions regarding future precautions to other educational institutions.

Plant growth-promoting rhizobacteria (PGPR) are known to have the effect of promoting plant growth. In this paper, three PGPR strains were selected from the previous work, which had plant growth-promoting activities such as phosphate solubilization, nitrogen fixation, phosphorus mobilization, etc. These strains named FJS-3(Burkholderia pyromania), FJS-7(Pseudomonas rhodesiae), and FJS-16(Pseudomonas baetica), respectively, were prepared into solid biological agents. Three widely planted commercial crops (tea plant, tobacco, and chili pepper) were selected for PGPR growth promotion verification. The results showed that the new shoots of tea seedlings under PGPR treatment were much more than the control. We also used tobacco, another important crop in Guizhou, to test the growth-promoting effect of individual bacteria, and the results showed that each of them could promote the growth of tobacco plants, and FJS-3(Burkholderia pyrrocinia) had the best effect. In addition, we carried out experiments on tobacco and pepper using multi-strain PGPR, the tobacco plants' height, fresh, and root weight increased by 30.15 %, 37.36 %, and 54.5 %, respectively, and the pepper plants' increased by 30.10 %, 56.38 % and 43.18 %, respectively, which both showed significantly better effects than that of a single strain. To further test the field performance, field trials were carried out in a mature Longjing43 tea plantation in Guizhou. There were four treatments: no fertilization (T1), combined application of PGPR biological agent and compound fertilizer (T2), only application of PGPR (T3), and only application of compound fertilizer (T4). In terms of yield, grouped with or without PGPR, there was a 15.38 % (T2:T4) and 92.31 % (T3:T1) increase between them, respectively. The tea's yield and tea flavor substances such as tea polyphenols, caffeine, and theanine were detected, and the T2 showed the most significant positive effect on both sides. Especially, an important indicator of Matcha green tea is the color, chlorophyll content was then tested, and PGPR application increased it and improved the appearance. All these results demonstrated that the PGPR we screened could significantly promote plant growth and quality improvement, and had good application potential in crop planting, which could contribute to environmental protection and economic growth.

Due to the harsh marine environment of chloride ion invasion and corrosion, the issues of long-term chloride transport and durability life evaluation for marine infrastructures constructed/maintained by recycled aggregate concrete (RAC) after enhancement remain poorly understood. For our studies, an accelerated carbonation modification method for recycled coarse aggregate (RCA) was adopted to prepare carbonated recycled coarse aggregate (CRCA) samples, and the macroproperties, i.e., apparent density and water absorption, of CRCA were enhanced by approximately 1.40-3.97% and 16.3-21.8%, respectively, compared with those of RCA. An in-door experiment for chloride transport into concrete specimens subjected to a simulated marine environment of alternating drying-wetting cycles was conducted. The chloride profiles and transport characteristics of carbonated recycled coarse aggregate concrete (CRCAC), recycled coarse aggregate concrete (RCAC), and natural coarse aggregate concrete (NCAC) were analysed and compared. The results indicated that the chloride penetration depths and concentrations of CRCAC were approximately 52.6-96.2% of those of RCAC, which highlighted the better chloride resistance of CRCAC. A chloride transport model for marine concrete structures with various coarse aggregate types in a corrosive marine environment was established. Taking a certain harbour wharf as an example, the durability life of this case considering the application of the CRCAC was evaluated based on the chloride transport model, and the durability life of the CRCAC structure was improved by approximately 28.10% compared with that of the RCAC. The CRCAC developed in this paper has improved mechanical performance and durability than those of RCAC, and it has the potential to replace the NCAC and further support the construction and maintenance of marine infrastructures.

Abstract Background The purpose of this study was to report our surgical experience in patients with lumbosacral degenerative diseases who underwent posterior decompression and interbody fusion fixed with cortical bone trajectory screw and sacral alar screw, which is known as low-profile posterior lumbosacral interbody fusion (LP-PLSIF). Methods Patients with lumbosacral degenerative disease who underwent LP-PLSIF and traditional PLSIF (control group) internally fixed with pedicle screws were included retrospectively. Patients’ demographic data, operative parameters, and perioperative complications were recorded and analyzed. Results A total of 18 patients were enrolled in this study, which included 9 patients (5 male and 4 female) who underwent LP-PLSIF, and 9 patients (4 male and 5 female) who underwent traditional PLSIF. There wasn’t a significant difference in the average age between the two groups, 56.78 ± 10.92 years in the LP-PLSIF group and 60.22 ± 8.21 years in the PLSIF group (p = 0.460). The bone mineral density (BMD) of the two groups of patients were -2.00 ± 0.26 T and -2.13 ± 0.19 T, respectively (P = 0.239). The mean postoperative follow-up time was 12.7 months (range, 12–14 months). The mean operation time was 142.78 ± 11.21 min and 156.11 ± 13.41 min in the LP-PLSIF group and PLSIF group respectively (P

Fault rupture propagation is more complex in the overlying soil with intercalation than in homogeneous soil, and it is challenging to simulate this phenomenon accurately using the finite element method. To address this issue, an improved nonlocal model that incorporates softening modulus modification is proposed. The methodology has the advantage that the solutions are independent of both mesh sizes and characteristic lengths, while maintaining objective softening rates of materials. Using the proposed methodology, a series of numerical simulations are conducted to investigate the effects of different mechanical parameters, such as elastic modulus, friction angle and dilation angle of the soil within the intercalation, as well as the impact of geometries, such as the depth and thickness of the intercalation, on the fault rupture progress. This study not only provides significant insights into the mechanisms of fault rupture propagation, specifically in relation to intercalations, but also shows a great value in promoting the current research on fault rupture.

Pipettes are essential tools for biomedical and analytical laboratories, analogous to workstations for computer scientists. Variation in pipetting is a known unknown, as it is generally accepted that variations exist, but thus far, there have been limited studies on the extent of these variations in practice. In this mini-review, we highlight how manual pipetting is a key technique in the laboratory, and, although simple, inaccuracy and imprecision exist. If variations are not adequately addressed, errors can be compounded and consequently compromise data quality. Determination of the accuracy and precision of manual pipetting is straightforward, and here we review two common approaches that use gravimetry and spectrophotometry as readouts. We also provide detailed protocols for determination of accuracy and precision using manual single and multi-channel pipettes. These simple-to-use methods can be used by any laboratory for competency training and regular checks. Having a common protocol for evaluation of variation will also enable cross-laboratory comparison and potentially facilitate establishment of a reference value of acceptable ranges for operator error. Such a value could be of relevance to the scientific community for benchmarking and assuring good laboratory practice.

The rapid development of electronic information technology and the use of high-power electrical equipment have led to the increasingly serious problem of electromagnetic interference (EMI). Therefore, it is of great significance to study electromagnetic wave absorbing (EWA) materials with good absorbing properties. Compared with other wave absorbing materials, carbon-based nanomaterials such as silicon carbide (SiC), carbon nanotube (CNT), carbon nanowire (CNW) and carbon nanofiber (CNF) have the advantages of low density, large specific surface area/aspect ratio and excellent dielectric properties, which make them stand out. However, impedance mismatch caused by high conductivity and a single microwave loss mechanism seriously affect the absorption performance. In order to overcome the above shortcomings, surface modification, component control, structural design and other methods have been widely used in EWA materials in recent years to improve their absorption performance. In this review, we introduce the mechanism of microwave absorption and summarizes the research progress of one-dimensional carbon based nanomaterials and their composites. The preparation methods and microwave absorption properties of one-dimensional carbon based nanocomposites with different components, morphologies, and microstructures were discussed in detail. Finally, the challenges and future development prospects of one-dimensional carbon based nanomaterials were summarized, providing reference for the development of high-performance microwave absorbing materials.

Abstract Luminescent materials that display quick spectral responses to thermal stimuli have attracted pervasive attention in sensing technologies. Herein, a programmable luminescence color switching in lanthanide‐doped LiYO2 under thermal stimuli, based on deliberate control of the monoclinic (β) to tetragonal (α) phase transition in the crystal lattice, is reported. Specifically, a lanthanide‐doping (Ln3+) approach to fine‐tune the phase‐transition temperature in a wide range from 294 to 359 K is developed. Accordingly, an array of Ln3+‐doped LiYO2 crystals that exhibit progressive phase transition, and thus sequential color switching at gradually increasing temperatures, is constructed. The tunable optical response to thermal stimuli is harnessed for colorimetric temperature indication and quantitative detection, demonstrating superior sensitivity and temperature resolution (Sr = 26.1% K−1, δT = 0.008 K). The advances in controlling the phase‐transition behavior of luminescent materials also offer exciting opportunities for high‐performance personalized health monitoring.

BackgroundPneumoconiosis is the most important occupational disease all over the world, with high prevalence and mortality. At present, the monitoring of workers exposed to dust and the diagnosis of pneumoconiosis rely on manual interpretation of chest radiographs, which is subjective and low efficiency. With the development of artificial intelligence technology, a more objective and efficient computer aided system for pneumoconiosis diagnosis can be realized. Therefore, the present study reported a novel deep learning (DL) artificial intelligence (AI) system for detecting pneumoconiosis in digital frontal chest radiographs, based on which we aimed to provide references for radiologists.MethodsWe annotated 49,872 chest radiographs from patients with pneumoconiosis and workers exposed to dust using a self-developed tool. Next, we used the labeled images to train a convolutional neural network (CNN) algorithm developed for pneumoconiosis screening. Finally, the performance of the trained pneumoconiosis screening model was validated using a validation set containing 495 chest radiographs.ResultsApproximately, 51% (25,435/49,872) of the chest radiographs were labeled as normal. Pneumoconiosis was detected in 49% (24,437/49,872) of the labeled radiographs, among which category-1, category-2, and category-3 pneumoconiosis accounted for 53.1% (12,967/24,437), 20.4% (4,987/24,437), and 26.5% (6,483/24,437) of the patients, respectively. The CNN DL algorithm was trained using these data. The validation set of 495 digital radiography chest radiographs included 261 cases of pneumoconiosis and 234 cases of non-pneumoconiosis. As a result, the accuracy of the AI system for pneumoconiosis identification was 95%, the area under the curve was 94.7%, and the sensitivity was 100%.ConclusionDL algorithm based on CNN helped screen pneumoconiosis in the chest radiographs with high performance; thus, it could be suitable for diagnosing pneumoconiosis automatically and improve the efficiency of radiologists.

AbstractThioredoxin interacting protein (TXNIP) is a potential drug target for type 2 diabetes mellitus (T2DM) treatment. A series of quinazoline derivatives were designed, synthesised, and evaluated to inhibit TXNIP expression and protect from palmitate (PA)-induced β cell injury. In vitro cell viability assay showed that compounds D-2 and C-1 could effectively protect β cell from PA-induced apoptosis, and subsequent results showed that these two compounds decreased TXNIP expression by accelerating its protein degradation. Mechanistically, compounds D-2 and C-1 reduced intracellular reactive oxygen species (ROS) production and modulated TXNIP-NLRP3 inflammasome signalling, and thus alleviating oxidative stress injury and inflammatory response under PA insult. Besides, these two compounds were predicted to possess better drug-likeness properties using SwissADME. The present study showed that compounds D-2 and C-1, especially compound D-2, were potent pancreatic β cell protective agents to inhibit TXNIP expression and might serve as promising lead candidates for the treatment of T2DM.

Abstract Background Single-agent chemotherapy using methotrexate or actinomycin D is the first-line treatment for patients with low-risk gestational trophoblastic neoplasia. Various methotrexate-based and actinomycin D-based single-agent regimens can be used. However, there is insufficient evidence to determine the superior regimen. To guide doctors in selecting a single-agent chemotherapy regimen for patients with low-risk gestational trophoblastic neoplasia, we will compare two regimens. Methods We will conduct a multicentre, randomized, prospective clinical trial. Selected low-risk gestational trophoblastic neoplasia patients (FIGO score 0–4) will be randomized 1:1 to a biweekly single-dose actinomycin D group or a multiday methotrexate therapy group. The actinomycin D group will receive IV pulse actinomycin D (1.25 mg/m2) every 14 days, and the methotrexate group will receive methotrexate (50 mg) intramuscularly on days 1, 3, 5, and 7 (4 doses per cycle) and leucovorin (15 mg) intramuscularly on days 2, 4, 6, and 8. This process will be repeated every 14 days. The primary endpoints will include the complete remission rate by single-agent therapy and the overall complete remission rate. The secondary endpoints will include the duration needed to achieve complete remission after single-agent chemotherapy, number of courses needed to achieve complete remission after single-agent chemotherapy, incidence and severity of adverse effects, effects on menstrual conditions and ovarian function based on the anti-Mullerian hormone level, and patient-reported quality of life. Discussion Previous clinical trials comparing biweekly single-dose actinomycin D with multiday methotrexate therapy for treating low-risk gestational trophoblastic neoplasia patients failed to meet the expected case number. Through this multicentre study, the complete remission ratio and efficacy difference between biweekly single-dose actinomycin D and multiday methotrexate therapy will be obtained. This study will also provide the basis for formulating a preferred regimen for treating patients with low-risk gestational trophoblastic neoplasia. Trial registration ClinicalTrials.gov: NCT04562558, Registered on 13 September 2020 (Protocol version 2020–9-24, version 1.0).

Automated evaluation of educational quality can be an effective measure of the efficiency of teaching methods, enabling the determination of the best teaching approach based on the educational environment conditions. Machine learning techniques have been used in various research studies as tools for automated evaluation of teaching quality. However, the low accuracy of classical learning models in quantitative quality estimation remains a challenge. To address this issue, one can utilize optimization strategies for learning models or combine multiple learning models as an ensemble system. These aspects are studied in the current paper. In the proposed method, automated evaluation of English teaching quality is performed through two phases: ''identification of prominent quality evaluation indicators'' and ''quality estimation based on ensemble learning.'' In the first phase, the Black Hole Optimization (BHO) algorithm is used to determine the minimum number of indicators required for accurate quality estimation. In the second phase, the proposed method combines multiple artificial neural networks (ANNs) to predict education quality. In this ensemble model, the configuration and weight vector of each ANN are adjusted by the BHO algorithm to minimize training error. Then, an averaging strategy is employed to determine the final output of the ensemble system. The performance of the proposed method is evaluated using real English teaching data. Based on the results, the proposed method can achieve an average accuracy of 98.53 % in estimating English teaching quality, which represents a 2.19 % improvement compared to previous methods.

Abstract Carbon dots (CDs) were paid more and more attention for detecting ferric ions (Fe3+) containing in the organism or environment. Thus, nitrogen and phosphorus co‐doping green CDs were synthesized using p‐Phenylenediamine as the carbon source and phosphoric acid as phosphorus source by the solvo‐thermal method. As‐prepared CDs were characterized by morphology, optical properties, and surface functional groups. The CDs can emit better green light peak at 511 nm excited by 380 nm UV light. Excitation independence property of N, P‐CDs was determined by measuring the photoluminescence (PL) spectra with different excitation wavelengths. The ions detection ability was investigated by analyzing the emission intensity of CDs in eight different kinds of ions solutions and different concentrations of Fe3+ ions solutions. The N, P‐CDs show high selectivity towards Fe3+ ions with a limit of detection (LOD) of 0.11 μM in the concentration region of 300 to 450 μM. The results illuminated that this kind of CDs can be a candidate material for Fe3+ ions detection.

The physical and mechanical properties of recycled coarse aggregate (RCA) are worse than those of natural coarse aggregate (NCA), and the overall performance of recycled concrete prepared from RCA is worse than that of natural aggregate concrete. Treatment of RCA by CO2-accelerated carbonation effectively improves the macroscopic properties of RCA. The degree of influence of raw material factors, i.e., the original concrete strength (OCS) and initial moisture content (IMC) of RCA, on the carbonation of RCAs is very complex. Herein, an accelerated carbonation experiment for RCA with different material factors as variables was carried out to explore the influence of the abovementioned factors on the physical properties of carbonated recycled coarse aggregate (CRCA). By analyzing the microstructure of the RCA with the best modification effect before and after carbonation, the carbonation modification mechanism of the RCA was revealed. The physical performance indexes, including the apparent density, water absorption and carbonation rate, of the dried RCA with an OCS of C40 and C50 were significantly improved. The research results can provide basic data and theoretical support for promoting the popularization and application of RCA and recycled concrete in practical engineering.

Cloud detection is an essential preprocessing step when using satellite-borne infrared hyperspectral sounders for data assimilation and atmospheric retrieval. In this study, we propose a cloud detection algorithm based solely on the sensitivity and detection characteristics of the FY-4A Geostationary Interferometric Infrared Sounder (GIIRS), rather than relying on other instruments. The algorithm consists of four steps: (1) combining observed radiation and clear radiance data simulated by the Community Radiative Transfer Model (CRTM) to identify clear fields of view (FOVs); (2) determining the number of clouds within adjacent 2 × 2 FOVs via a principal component analysis of observed radiation; (3) identifying whether there are large observed radiance differences between adjacent 2 × 2 FOVs to determine the mixture of clear skies and clouds; and (4) assigning adjacent 2 × 2 FOVs as a cloud cluster following the three steps above to select an appropriate classification threshold. The classification results within each cloud detection cluster were divided into the following categories: clear, partly cloudy, or overcast. The proposed cloud detection algorithm was tested using one month of GIIRS observations from May 2022 in this study. The cloud detection and classification results were compared with the FY-4A Advanced Geostationary Radiation Imager (AGRI)’s operational cloud mask products to evaluate their performance. The results showed that the algorithm’s performance is significantly influenced by the surface type. Among all-day observations, the highest recognition performance was achieved over the ocean, followed by land surfaces, with the lowest performance observed over deep inland water. The proposed algorithm demonstrated better clear sky recognition during the nighttime for ocean and land surfaces, while its performance was higher for partly cloudy and overcast conditions during the day. However, for inland water surfaces, the algorithm consistently exhibited a lower cloud recognition performance during both the day and night. Moreover, in contrast to the GIIRS’s Level 2 cloud mask (CLM) product, the proposed algorithm was able to identify partly cloudy conditions. The algorithm’s classification results departed slightly from those of the AGRI’s cloud mask product in areas with clear sky/cloud boundaries and minimal convective cloud coverage; this was attributed to the misclassification of clear sky as partly cloudy under a low-resolution situation. AGRI’s CLM products, temporally and spatially collocated to the GIIRS FOV, served as the reference value. The proportion of FOVs consistently classified as partly cloudy to the total number of partly cloudy FOVs was 40.6%. In comparison with the GIIRS’s L2 product, the proposed algorithm improved the identification performance by around 10%.