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Ambrosia trifida (giant ragweed) is an invasive species that causes habitat destruction and competitively excludes native plants in many parts of Europe and Asia. In this study, we evaluated the effects of selective cutting and uprooting on A. trifida and native plant diversity, as well as the effects of sowing the seeds of native annual, perennial and woody species after eradication. We hypothesised that: (i) selective uprooting will be more effective than cutting in controlling invasion by A. trifida because fewer propagules would be left behind, with no increase in the number of existing invasive propagules and (ii) sowing native seeds will increase invasion resistance and accelerate the recovery of native plant diversity. The eradication methods were applied in July 2022, seeds were sown in March 2023 and the response variables (i.e. importance values (%) of A. trifida and diversity index (H') of native species) were measured in September 2023. The importance values of A. trifida were lowest and diversity index of native species was highest in the uprooting treatment, supporting the first hypothesis. Sowing native seeds following invasion removal did not exert significant additional suppressive effects on invasion or increase native species diversity. These results reveal that selective uprooting is a promising tool to control A. trifida and to support the recovery of native diversity, while sowing native seeds does not improve the quality of restoration.

Urbanization is transforming landscapes globally, altering environmental conditions that affect ecosystem functioning, particularly in urban areas where trees are crucial for regulating microclimates, improving air quality, and sustaining biodiversity. This study investigates the environmental differences and tree leaf structure and morphology in urban and suburban sites in the Chicago Metropolitan Region. The leaf functional traits of Norway Maple and Little − leaved Linden were studied in three locations in the summer of 2023: an urban park (University of Illinois Chicago, Chicago, IL), a suburban park (Morton Arboretum, Lisle, IL), and a suburban residential site (Lombard, IL). The urban site had higher daytime and nighttime air, and land surface temperatures compared to the suburban sites with significant fluctuations observed across the sites. Cumulative growing degree days, a measure of potential photosynthetically active days, were also higher in the urban park than in the suburban sites between March and August. Norway Maple trees growing in the urban site displayed higher specific leaf area (SLA) and lower leaf dry matter content (LDMC) than in the suburban sites, resulting in thinner leaves. Similarly, Little−leaved Linden trees in the suburban residential site displayed higher SLA and lower LDMC than those in the suburban park. The values of gas exchange traits − namely photosynthetic assimilation, transpiration rates, and stomatal conductance − of Norway Maple were higher at the urban site compared to suburban sites as temperatures increased during the summer. Norway Maple gas exchange values decreased as the growing season progressed, as expected by ontogeny. In contrast, Little−leaved Linden maintained similar leaf gas exchange values throughout the growing season. Both species in the urban site exhibited lower instantaneous water use efficiency and reduced LDMC, suggesting greater water loss in response to elevated temperatures compared to suburban park and residential sites. Comparisons with existing global trait databases emphasize the need for localized data to accurately capture site−specific responses. Although some traits aligned with database values, others deviated significantly, underscoring the importance of comprehensive, site−specific datasets for robust ecosystem modeling and management strategies.

Abstract Urban heat stress is a critical issue, particularly in cities where dense infrastructure and limited green space exacerbate temperature extremes. This study investigates the impact of greenery (EVI2), canopy cover (CC), impervious cover (IC), and water bodies on heat index in Chicago using high‐resolution data from the Heat Watch campaign. We find that EVI2, CC and proximity to water body significantly reduce heat while IC increases heat, particularly in the afternoon when solar radiation is intense. Additionally, the effective radius that land cover impacts heat is smaller in the afternoon. The combined effect analysis indicates that enhancing total greenness, not just canopy cover, is the most effective strategy to reduce heat. This study underscores the importance of strategic vegetation management, highlighting the critical role of integrated approaches in reducing urban heat.

Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by immune dysregulation, skin barrier dysfunction, and a significant patient burden. Recent studies have highlighted the aryl hydrocarbon receptor (AhR) as a promising therapeutic target for AD management because of its pivotal role in modulating immune responses and maintaining skin barrier integrity. The dysfunction of the AhR pathway has been linked to AD pathogenesis, emphasizing the need for therapies that can restore its regulatory functions. Natural products have emerged as potential modulators of the AhR and are effective and safe alternatives to conventional treatments. Compounds such as curcumin, resveratrol, quercetin, and microbial metabolites have demonstrated the ability to activate AhR, reduce inflammation, and promote skin barrier function. These natural agents have fewer side effects and enhance patient compliance compared with conventional therapies, making them attractive candidates for long-term AD management. The integration of natural products targeting the AhR pathway provides a multifaceted approach that alleviates symptoms, addresses underlying disease mechanisms, and promotes sustainable improvements in skin health. This review highlights the therapeutic potential of natural AhR modulators and their potential roles in enhancing patient outcomes through novel integrative treatment strategies.

Glyoxal, methylglyoxal, and diacetyl are toxic α-dicarbonyl compounds found in heat-processed foods, including edible oils. Dispersive liquid–liquid microextraction was combined with gas chromatography mass spectrometry to determine the glyoxal, methylglyoxal, and diacetyl contents in sesame oil. Chloroform and methanol were selected as the optimal extraction and dispersive solvents, respectively. The maximum derivatization efficiency was obtained using 500 µg of the derivatization agent, o-phenylenediamine. The derivatization of glyoxal was completed in 1 h, whereas those of methylglyoxal and diacetyl were completed immediately. The optimized method was validated, and was found to exhibit a good linearity, recovery, intraday repeatability, and interday reproducibility. The α-dicarbonyl compound concentrations in the oils were dependent on the roasting temperature. The sesame oil concentrates contained 0–175.4, 0–990.5, and 0–220.9 ng g−1 of glyoxal, methylglyoxal, and diacetyl, respectively. For the perilla oils, the respective concentrations were 0–96.4, 0–410.8, and 0–197.5 ng g−1.

In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the conventional TsHARP technique, achieving a reduced RMSE of 1.90 °C, compared to 2.51 °C with TsHARP. Our approach utilizes the geostationary GOES satellite data alongside high-resolution ECOSTRESS data, enabling hourly LST downscaling to 70 m—a significant advancement over previous methodologies that typically measure LST only once daily. Applying these high-resolution LST data, we examined the hottest days in Chicago and their correlation with ethnic inequality. Our analysis indicated that Hispanic/Latino communities endure the highest LSTs, with a maximum LST that is 1.5 °C higher in blocks predominantly inhabited by Hispanic/Latino residents compared to those predominantly occupied by White residents. This study highlights the intersection of urban development, ethnic inequality, and environmental inequities, emphasizing the need for targeted urban planning to mitigate these disparities. The enhanced spatial and temporal resolution of our LST data provides deeper insights into diurnal temperature variations, crucial for understanding and addressing the urban heat distribution and its impact on vulnerable communities.

Abstract We compare high‐resolution land‐surface temperature (LST) estimates from the GOES‐16/17 (GOES) satellites to ERA‐5 Land (ERA‐5) reanalysis data across nine large US cities. We quantify the offset and find that ERA‐5 generally overestimates LST compared to GOES by 1.63°C. However, this overestimation is less pronounced in urban areas, underscoring the limitations of ERA‐5 in capturing the LST gradient between urban and non‐urban areas. We then examine three quantities: Surface Urban Heat Island Intensity (SUHII), extreme LST events, and LST exposure by population. We find that ERA‐5 does not accurately represent the diurnal variation and magnitude of SUHII in GOES. Furthermore, while ERA‐5 was on average too warm, ERA‐5 underestimates extreme heat by an average of 2.40°C. Our analysis reveals higher population exposure to high LST in the GOES data set across the cities studied. This discrepancy is especially pronounced when estimating the population fraction that are most exposed to heat.

Regression with imbalanced data has been regarded as a more realistic scenario due to the difficulty of data acquisition and label annotations. However, it has not been extensively studied compared to the imbalanced classification. In imbalanced regression scenario, the classical regression approach may lead to regression bias toward high-frequency target regions. In this study, we present a novel framework for effectively handling imbalanced data in regression tasks. We introduce a density-based stochastic mask that perturbs the mini-batch distribution by assigning probabilities based on the data distribution statistics. The mask assigns a higher probability to more frequent samples on the Bernoulli distribution. Next, we employ consistency-based learning to encourage the encoder to produce similar representations for perturbed versions of the same input, drawing inspiration from modern consistency-based learning approaches. By jointly training with the two proposed learning objectives, we achieved state-of-the-art performance on AgeDB-DIR and IMDB-WIKI-DIR, which are representative imbalanced age estimation datasets. Furthermore, we evaluated the generalization performance using UTKFace. Through extensive experiments, we confirmed that our method demonstrates efficacy in dealing with imbalanced regression data. The forthcoming task involves extending the suggested approach to different uses, such as predicting the progress of diseases in medical diagnoses and estimating monocular depth in self-driving technology.

Abstract Mortality due to extreme temperatures is one of the most worrying impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature‐related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature‐related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre‐industrial levels, we find that climate change slightly reduces temperature‐related mortality in the U.S. because the reduction of cold‐related mortality exceeds the increase in heat‐related deaths. Above 3°C warming, whether the increase in heat‐related deaths exceeds the decrease in cold‐related deaths depends on the level of adaptation. Southern U.S. cities are already well adapted to hot temperatures and the reduction of cold‐related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat‐related mortality exceeds the reduction in cold‐related mortality. Thus, while the total number of climate‐related mortality may not change much, climate change will shift mortality in the U.S. to higher latitudes.

Acidogenic fermentation is an emerging biotechnology that allows for the utilization of food waste as a feedstock to produce high-value products such as short-chain fatty acids (SCFAs), effectively offering a tangible solution for food waste management as well as resource recovery. The objectives of the current study were to identify the ideal inoculum, waste-activated sludge (WAS) or anaerobic digester sludge (AD), for the acidogenic fermentation of food waste at room temperature, as well as to evaluate the impact of heat pretreatment of these inoculums on fermentation performance. The maximum hydrolysis yield of 399 g sCOD/kg VS added was obtained when untreated AD was used as the inoculum, whereas the pretreated AD inoculum provided the highest SCFA yield and conversion efficiency of 238 g sCODSCFA/kg VS added and 71%, respectively. Heat pretreatment had a detrimental impact on the WAS inoculum, leading to lower hydrolysis and SCFA yields, but exerted a positive influence on the AD inoculum. The microbial community showed that heat pretreatment negatively impacted the abundance of non-spore-forming hydrolytic and acidogenic microorganisms. Overall, this study demonstrates the critical role of inoculum type and heat pretreatment in optimizing the acidogenic fermentation process, laying the groundwork for future refinements in SCFA production from food waste through inoculum design.

Doxorubicin (DOX), an anthracycline-based chemotherapeutic agent, is widely used to treat various types of cancer; however, prolonged treatment induces cardiomyotoxicity. Although studies have been performed to overcome DOX-induced cardiotoxicity (DICT), no effective method is currently available. This study investigated the effects and potential mechanisms of Poncirus trifoliata aqueous extract (PTA) in DICT. Changes in cell survival were assessed in H9c2 rat cardiomyocytes and MDA-MB-231 human breast cancer cells. The C57BL/6 mice were treated with DOX to induce DICT in vivo, and alterations in electrophysiological characteristics, serum biomarkers, and histological features were examined. The PTA treatment inhibited DOX-induced decrease in H9c2 cell viability but did not affect the MDA-MB-231 cell viability. Additionally, the PTA restored the abnormal heart rate, R-R interval, QT interval, and ST segment and inhibited the decrease in serum cardiac and hepatic toxicity indicators in the DICT model. Moreover, the PTA administration protected against myocardial fibrosis and apoptosis in the heart tissue of mice with DICT. PTA treatment restored DOX-induced decrease in the expression of NAD(P)H dehydrogenase quinone acceptor oxidoreductase 1 in a PTA concentration-dependent manner. In conclusion, the PTA inhibitory effect on DICT is attributable to its antioxidant properties, suggesting the potential of PTA as a phytotherapeutic agent for DICT.

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as machine-challenging tasks (MCTs) and aimed to investigate methods to enhance machine learning for MCTs. Specifically, we start with a natural question: Can a learning mechanism that mimics the human brain lead to the improvement of MCT performances? We hypothesized that a learning mechanism replicating the human brain is effective for tasks where machine intelligence is difficult. Multiple experiments corresponding to specific types of MCTs where machine intelligence has room to improve performance were performed using predictive coding, a more biologically plausible learning algorithm than backpropagation. This study regarded incremental learning, long-tailed, and few-shot recognition as representative MCTs. With extensive experiments, we examined the effectiveness of predictive coding that robustly outperformed backpropagation-trained networks for the MCTs. We demonstrated that predictive coding-based incremental learning alleviates the effect of catastrophic forgetting. Next, predictive coding-based learning mitigates the classification bias in long-tailed recognition. Finally, we verified that the network trained with predictive coding could correctly predict corresponding targets with few samples. We analyzed the experimental result by drawing analogies between the properties of predictive coding networks and those of the human brain and discussing the potential of predictive coding networks in general machine learning.

Patulin (PAT) is a natural mycotoxin found in decaying pome fruits. Although some toxicological studies have been conducted on PAT, recent research has highlighted its anticancer and antifungal effects. However, studies have yet to examine the effects and molecular mechanisms of PAT in other metabolic diseases. Obesity is a chronic disease caused by excessive food intake and abnormal lifestyle, leading to low-grade inflammation. Therefore, this study aimed to elucidate the effect of PAT on obesity at the cellular level. PAT treatment reduced lipid accumulation, suppressed glucose and LDL uptake, inhibited lipid deposition and triglyceride synthesis, upregulated fatty acid oxidation-related genes (Pgc1α), and downregulated adipogenic/lipogenic genes (Pparγ and C/ebpα) in hypertrophied 3T3-L1 adipocytes. Additionally, PAT treatment enhanced mitochondrial respiration and mass in differentiated adipocytes and alleviated inflammatory response in activated RAW 264.7 macrophages. Moreover, PAT treatment downregulated pro-inflammatory genes (il-6, Tnf-α, Cox-2, and inos), suppressed lipopolysaccharide (LPS)-induced increase in inflammatory mediators (IL-6, TNF-α, and NO), and restored mitochondrial oxidative function in LPS-stimulated macrophages by improving oxygen consumption and mitochondrial integrity and suppressing ROS generation. Overall, these findings suggest a potential for PAT in the prevention of lipid accumulation and inflammation-related disorders.

Summary: The heterotypic CIC structures formed of cancer and immune cells have been observed in tumor tissues. We aimed to assess the feasibility of using heterotypic CICs as a functional biomarker to predict NK susceptibility and drug resistance. The heterotypic CIC-forming cancer cells showed a lower response to NK cytotoxicity and higher proliferative ability than non-CIC cancer cells. After treatment with anticancer drugs, cancer cells that formed heterotypic CICs showed a higher resistance to anticancer drugs than non-CIC cancer cells. We also observed the formation of more CIC structures in cancer cells treated with anticancer drugs than in the non-treated group. Our results confirm the association between heterotypic CIC structures and anticancer drug resistance in CICs formed from NK and cancer cells. These results suggest a mechanism underlying immune evasion in heterotypic CIC cancer cells and provide insights into the anticancer drug resistance of cancer cells.

This study evaluated the hydrolysis and acidogenesis of food waste at different operating pHs (uncontrolled, 5.5, 6.5, 7.5, 8.5) in a leachate bed reactor (LBR) at room temperature. LBR operation at pH 6.5–8.5 resulted in a hydrolysis yield of 718–729 g SCOD/kg VSadded, which was statistically (p ≤ 0.05) higher than that obtained at pH 5.5 (577 g SCOD/kg VSadded) and the uncontrolled pH (462 g SCOD/kg VSadded). The hydrolysis rate at pH 6.5 was the highest amongst all the pH values. Stabilization at pH at 6.5 also resulted in a high fatty acid (FA) yield of 643 g CODFA/kg VSadded. Butyrate was the main FA at the pH of 5.5–6.5, while acetate was the main FA at the pH of 7.5–8.5. At the uncontrolled pH, lactate production was the highest, indicating a shift in the microbial community from fatty-acid-producing bacteria to lactate-producing bacteria. The compositions of medium-chain fatty acids, such as caproate, were the highest at pH of 5.5.

One of the most important strategies for achieving the goal of a sustainable environment, according to the international climate action initiatives, is to increase the proportion of renewable energy in energy use [...]

Cancer is one the most malignant diseases that is a leading cause of death worldwide. Vegetables and fruits contain beneficial nutrients such as vitamins, minerals, folates, dietary fibers, and various natural bioactive compounds. These can prevent the pathological processes of many cancers and reduce cancer related mortality. Specifically, the anti-cancer effect of vegetables and fruits is largely attributable to the natural bioactive compounds present within them. A lot of bioactive compounds have very specific colors with pigments and the action of them in the human body varies by their color. Red-pigmented foods, such as apples, oranges, tomatoes, cherries, grapes, berries, and red wine, have been widely reported to elicit beneficial effects and have been investigated for their anti-tumor, anti-inflammatory, and antioxidative properties, as well as anti-cancer effect. Most of the anti-cancer effects of bioactive compounds in red-pigmented foods arise from the suppression of cancer cell invasion and metastasis, as well as the induction of apoptosis and cell cycle arrest. In this review, we assessed publications from the last 10 years and identified 10 bioactive compounds commonly studied in red-pigmented foods: lycopene, anthocyanin, β-carotene, pectin, betaine, rutin, ursolic acid, kaempferol, quercetin, and myricetin. We focused on the mechanisms and targets underlying the anti-cancer effect of the compounds and provided rationale for further investigation of the compounds to develop more potent anti-cancer treatment methods.

Makgeolli is a traditional Korean rice wine reported to have anticancer, anti-inflammatory, and antioxidant effects. We developed an approach involving solvent extraction coupled with gas chromatography-mass spectrometry to determine four bioactive compounds, farnesol (FOH), squalene (SQ), and newly identified 4-vinyl guaiacol (4-VG) and 2,4-di-tert-butylphenol (DTBP), in makgeolli. The method was validated with the linearity, limit of detection, limit of quantification, intra- and inter-day precision, and accuracy. The validated method was then applied to several makgeolli, beer, and wine samples. 4-VG and DTBP were identified in all beverages, but FOH and SQ were only identified in makgeolli.

Abstract Anaerobic oxidation of methane (AOM) is an important process for understanding the global flux of methane and its relation to the global carbon cycle. Although AOM is known to be coupled to reductions of sulfate, nitrite, and nitrate, evidence that AOM is coupled with extracellular electron transfer (EET) to conductive solids is relatively insufficient. Here, we demonstrate EET-dependent AOM in a biofilm anode dominated by Geobacter spp. and Methanobacterium spp. using carbon-fiber electrodes as the terminal electron sink. The steady-state current density was kept at 11.0 ± 1.3 mA/m2 in a microbial electrochemical cell, and isotopic experiments supported AOM-EET to the anode. Fluorescence in situ hybridization images and metagenome results suggest that Methanobacterium spp. may work synergistically with Geobacter spp. to allow AOM, likely by employing intermediate (formate or H2)-dependent inter-species electron transport. Since metal oxides are widely present in sedimentary and terrestrial environments, an AOM-EET niche would have implications for minimizing the net global emissions of methane.

An immune reaction is a protector of our body but a target to be overcome for all non-self-derived medicine. Extracellular Vesicles (EVs), noted as a primary alternative to cell therapy products that exhibit immune rejection due to mismatching-major histocompatibility complex (MHC), were discovered to have excellent curative effects through the delivery of various biologically active substances. Although EVs are sure to incur immune reaction by immunogenicity due to alloantigens from their parental cells, their immune rejection is rarely known. Hence, to develop cell lines and EVs as medicines with no immune rejection, we noted the immune tolerance where the foetus, as semi-allograft, is perfectly protected from the maternal immune system. We designed the ex-vivo culture systems to simulate in-vivo environmental factors inducing extravillous trophoblast (EVT)-specific Human Leukocyte Antigen-G (HLA-G) expression and secretion of HLA-G-bearing EVs at the mother-foetus interface. Using them, we confirmed that immune-tolerized stem cells (itSCs) continuously expressing and secreting HLA-G like EVTs during pregnancy can be induced. Also, EVs secreted from itSCs are verified as immune-tolerized EVs (itSC-EVs) containing HLA-G and not causing immune rejection through various analytical methods. These findings can provide a new perspective on the local and extensive immune tolerance environment where HLA-G is expressed and secreted by pregnancy-related hormones and different biological conditions. Furthermore, they show the new way to develop itSCs-EVs-based therapeutics that are free from time, space, and donor limitation causing immune rejection. Abbreviations CFSE: carboxyfluorescein succinimidyl ester; DC: dendritic cells; ELISA: enzyme-linked immunosorbent assay; EV: extracellular vesicles; EVT: extravillous trophoblast; FSH: follicle stimulating hormone; HA: hyaluronic acid; hCG: human chorionic gonadotropin; HLA-G: human leukocyte antigen G; iPSC: induced pluripotent stem cells; itSC-EVs: immune-tolerized extracellular vesicles from itSCs; itTBC-EVs: immune-tolerized extracellular vesicles from itTBCs; itSCs: immune tolerized stem cells; itTBCs: immune-tolerized trophoblast cells; LH: luteinizing hormone; MHC: major histocompatibility complex; MSC: mesenchymal stem cells; NK: natural killer cells; NTA: nanoparticle tracking analysis; PBMC: peripheral blood mononuclear cells; PHA: phytohemagglutinin; SP-IRIS: single particle interferometric reflectance imaging sensing; STB: syncytiotrophoblast

Schisandra chinensis (Turcz.) Baill., which is known as omija in South Korea, is mainly cultivated in East Asia. The present study aimed to investigate the chemical composition of essential oil from the omija (OMEO) fruit obtained by supercritical fluid extraction using CO2 and to confirm the antioxidant and anti-inflammatory activity of OMEO using HaCaT human keratinocyte and RAW 264.7 murine macrophages. As a result of the chemical composition analysis of OMEO using gas chromatography-mass spectrometry, a total of 41 compounds were identified. The detailed analysis results are sesquiterpenoids (16), monoterpenoids (14), ketones (4), alcohols (3), aldehydes (2), acids (1), and aromatic hydrocarbons (1). OMEO significantly reduced the increased ROS levels in HaCaT keratinocytes induced by UV-B irradiation (p < 0.05). It was confirmed that 5 compounds (α-pinene, camphene, β-myrcene, 2-nonanone, and nerolidol) present in OMEO exhibited inhibitory activity on ROS production. Furthermore, OMEO showed excellent anti-inflammatory activity in RAW 264.7 macrophages induced by lipopolysaccharide. OMEO effectively inhibited NO production (p < 0.05) by suppressing the expression of the iNOS protein. Finally, OMEO was investigated for exhibition of anti-inflammatory activity by inhibiting the activation of NF-κB pathway. Taken together, OMEO could be used as a functional food ingredient with excellent antioxidant and anti-inflammatory activity.

Rhus javanica has long been used in traditional medicines, and found to possess bioactive properties. In this study, we sought to investigate whether Rhus javanica extract (RJE) has preventive effects against UVB-induced inflammation and photoaging. RJE was identified as a promising candidate based on an MMP-1 promoter assay, and we confirmed suppressive effects on UVB-induced COX-2 and MMP-1 expression (67.6% and 80.9%, respectively) in immortalized human keratinocyte HaCaT cells. RJE suppressed both UVB-induced mitogen activated protein kinase (MAPK) and Akt signalling pathways as well as EGFR activity. RJE significantly suppressed repetitive UVB-induced wrinkle formation and COX-2 and MMP-13 expression in vivo. Among the compounds identified, syringic acid was found to exhibit the strongest inhibitory effect on UVB-induced MMP-1 promoter activity (45.2%). These results demonstrate that RJE has potent preventive activity for skin inflammation and photoaging which occurs via suppression of pathways related to EGFR.

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

Tumor necrosis factor-α (TNF-α) plays a significant role in inflammation and cancer-related apoptosis. We identified a TNF-α-mediated epigenetic mechanism of apoptotic cell death regulation in estrogen receptor-α (ERα)-positive human breast cancer cells. To assess the apoptotic effect of TNF-α, annexin V/ propidium iodide (PI) double staining, cell viability assays, and Western blotting were performed. To elucidate this mechanism, histone deacetylase (HDAC) activity assay and immunoprecipitation (IP) were conducted; the mechanism was subsequently confirmed through chromatin IP (ChIP) assays. Finally, we assessed HDAC3–ERα-mediated apoptotic cell death after TNF-α treatment in ERα-positive human breast cancer (MCF-7) cells via the transcriptional activation of p53 target genes using luciferase assay and quantitative reverse transcription PCR. The TNF-α-induced selective apoptosis in MCF-7 cells was negatively regulated by the HDAC3–ERα complex in a caspase-7-dependent manner. HDAC3 possessed a p53-binding element, thus suppressing the transcriptional activity of its target genes. In contrast, MCF-7 cell treatment with TNF-α led to dissociation of the HDAC3–ERα complex and substitution of the occupancy on the promoter by the p53–p300 complex, thus accelerating p53 target gene expression. In this process, p53 stabilization was accompanied by its acetylation. This study showed that p53-mediated apoptosis in ERα-positive human breast cancer cells was negatively regulated by HDAC3–ERα in a caspase-7-dependent manner. Therefore, these proteins have potential application in therapeutic strategies.

Irisin is an exercise-induced myokine that has various physiological functions, such as roles in energy expenditure, glucose/lipid metabolism, and muscle development. In muscle development, myoblast proliferation is known to be a first step, and recent studies have reported that an increased irisin level is involved in the promotion of cell proliferation in various cell types, including myoblasts. However, the exact mechanism of action by which irisin promotes myoblast proliferation has not been reported. In this study, we aimed to determine the pro-proliferative effect of irisin on C2C12 myoblasts and its mechanism of action. Irisin induced C2C12 cell proliferation and upregulated the mRNA levels of markers of proliferation Pcna, Mki67, and Mcm2. Irisin increased extracellular signal-regulated kinase (ERK) phosphorylation, and U0126, an ERK pathway inhibitor, suppressed irisin-induced C2C12 cell proliferation. Transcriptomic and qRT-PCR analysis showed that Ccl2, Ccl7, Ccl8, and C3 are potential downstream regulators of ERK signaling that promote C2C12 cell proliferation. Knockdown of Ccl7 revealed that irisin upregulates chemokine (C-C motif) ligand 7 (CCL7) and subsequently promotes C2C12 cell proliferation. These results suggest that irisin promotes C2C12 myoblast proliferation via ERK-dependent CCL7 upregulation and may aid in understanding how irisin contributes to muscle development.