Search

• First report of Paleoproterozoic incipient charnockite from the Yeongnam Massif. • Incipient charnockite is a peak-metamorphic product of biotite-dehydration melting. • Prolonged granitic magmatism, granulite-facies metamorphism, and fluid influx at ca. 1.88–1.84 Ga. • Paleoproterozoic hot orogenesis in the North China Craton linked to the Yeongnam Massif. The incipient charnockite or orthopyroxene-bearing granitic orthogneiss is a relatively common constituent in ancient granulite-facies terranes, and its formation has been often attributed to an influx of CO 2 -rich fluid or a removal of aqueous melt. Temporal relationship between the orthopyroxene formation and the fluid or melt migration, however, remains debatable. Here we report the first finding of incipient charnockite hosted by garnet-biotite-bearing granitic gneisses in the Sancheong–Hadong anorthosite complex, Yeongnam Massif. Both lithologies preserve a variety of field and microstructural evidence for partial melting and subsequent melt crystallization, as manifested by granitic leucosomes and biotite-rich residua. Based on the phase equilibria modeling and geothermobarometric calculation, peak metamorphic conditions of migmatities were estimated to be 5.4–6.0 kbar and 800–850 °C, which led to the biotite dehydration melting together with the orthopyroxene formation in incipient charnockite. Sensitive high-resolution ion microprobe (SHRIMP) U–Pb analyses of zircon from an incipient charnockite and a host gneiss yielded the weighted mean 207Pb/206Pb ages identical within errors; 1880 ± 5 Ma and 1872 ± 6 Ma for the cores, and 1862 ± 4 Ma and 1861 ± 4 Ma for the rims, respectively. The former is interpreted to represent the time for magmatic crystallization of granitic protoliths, whereas the latter for subsequent anatexis typified by peritectic growth of orthopyroxene. Such a timeline is consistent with two distinct thermal events previously reported from the Sancheong-Hadong anorthosite complex. By contrast, monazite grains from a biotite-bearing granitic gneiss in the charnockite zone yielded the weighted mean 207Pb/206Pb ages of 1866 ± 14 Ma and 1835 ± 11 Ma for the core and rim, respectively. The latter is consistent with 1842 ± 8 Ma, estimated from monazite neoblasts of incipient charnockite. The monazite growth during a post-peak metamorphic stage at ca. 1.84 Ga is attributed to a fluid influx event, apparently concentrated in the charnockite zone. The charnockite formation in the Yeongnam Massif further attests to the longevity (>ca. 40 m.y.) of hot orogenesis that has governed the Paleoproterozoic crustal evolution in eastern North China Craton. [ABSTRACT FROM AUTHOR]

In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners' cognitive load in real time using wireless portable EEG systems.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the US. Understanding the mechanisms of CRC progression is essential to improve treatment. Mitochondria is the powerhouse for healthy cells. However, in tumor cells, less energy is produced by the mitochondria and metabolic reprogramming is an early hallmark of cancer. The metabolic differences between normal and cancer cells are being interrogated to uncover new therapeutic approaches. Mitochondria targeting PTEN-induced kinase 1 (PINK1) is a key regulator of mitophagy, the selective elimination of damaged mitochondria by autophagy. Defective mitophagy is increasingly associated with various diseases including CRC. However, a significant gap exists in our understanding of how PINK1-dependent mitophagy participates in the metabolic regulation of CRC. By mining Oncomine, we found that PINK1 expression was downregulated in human CRC tissues compared to normal colons. Moreover, disruption of PINK1 increased colon tumorigenesis in two colitis-associated CRC mouse models, suggesting that PINK1 functions as a tumor suppressor in CRC. PINK1 overexpression in murine colon tumor cells promoted mitophagy, decreased glycolysis and increased mitochondrial respiration potentially via activation of p53 signaling pathways. In contrast, PINK1 deletion decreased apoptosis, increased glycolysis, and reduced mitochondrial respiration and p53 signaling. Interestingly, PINK1 overexpression in vivo increased apoptotic cell death and suppressed colon tumor xenograft growth. Metabolomic analysis revealed that acetyl-CoA was significantly reduced in tumors with PINK1 overexpression, which was partly due to activation of the HIF-1α-pyruvate dehydrogenase (PDH) kinase 1 (PDHK1)-PDHE1α axis. Strikingly, treating mice with acetate increased acetyl-CoA levels and rescued PINK1-suppressed tumor growth. Importantly, PINK1 disruption simultaneously increased xenografted tumor growth and acetyl-CoA production. In conclusion, mitophagy protein PINK1 suppresses colon tumor growth by metabolic reprogramming and reducing acetyl-CoA production., (© 2021. The Author(s), under exclusive licence to ADMC Associazione Differenziamento e Morte Cellulare.)