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Jie He and colleagues report exome sequencing of 113 tumor-normal pairs of esophageal squamous cell carcinoma. They highlight mutations in genes involved in cell cycle and apoptosis regulation, histone modifier genes and genes encoding members of the Hippo and Notch pathways. Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancers 1 . We performed exome sequencing on 113 tumor-normal pairs, yielding a mean of 82 non-silent mutations per tumor, and 8 cell lines. The mutational profile of ESCC closely resembles those of squamous cell carcinomas of other tissues but differs from that of esophageal adenocarcinoma. Genes involved in cell cycle and apoptosis regulation were mutated in 99% of cases by somatic alterations of TP53 (93%), CCND1 (33%), CDKN2A (20%), NFE2L2 (10%) and RB1 (9%). Histone modifier genes were frequently mutated, including KMT2D (also called MLL2 ; 19%), KMT2C ( MLL3 ; 6%), KDM6A (7%), EP300 (10%) and CREBBP (6%). EP300 mutations were associated with poor survival. The Hippo and Notch pathways were dysregulated by mutations in FAT1 , FAT2 , FAT3 or FAT4 (27%) or AJUBA ( JUB ; 7%) and NOTCH1 , NOTCH2 or NOTCH3 (22%) or FBXW7 (5%), respectively. These results define the mutational landscape of ESCC and highlight mutations in epigenetic modulators with prognostic and potentially therapeutic implications.

Cutaneous adverse drug reactions (ADRs) are common. However, no prospective study assessing cutaneous ADRs is available for Chinese populations. This study aimed to assess the incidence, manifestations, causative drugs, and other factors related to cutaneous ADRs. A total of 22,866 inpatients were surveyed prospectively from January to April 2012 at the Peking Union Medical College Hospital. Only cutaneous ADRs induced by systemic drugs were considered. Fifty cases were confirmed as cutaneous ADRs, for an estimated incidence of 2.2 per 1000 during this period (95 % confidence interval 1.6–2.8). Cases of cutaneous ADRs comprised 69 % females, while 63 % of all inpatients were female ( χ 2  = 0.641, P  = 0.427). The department of infectious diseases was the most frequently involved department. Morbilliform exanthema (40 %) was the most frequent cutaneous ADR, followed by urticaria (23.1 %). Anti-infection drugs (36.9 %) caused most cases of cutaneous ADRs, followed by iodinated contrast media (ICM, 18.5 %) and non-steroidal anti-inflammatory drugs (NSAIDs, 18.5 %). The most frequently associated disorders were cancer (24 %), infection (22 %), cardiovascular and cerebrovascular diseases (20 %), and autoimmune diseases (18 %). In this first prospective study assessing the incidence of cutaneous ADRs in China, anti-infection drugs were the most commonly involved drugs, followed by ICM and NSAIDs. No evidence of increased cutaneous ADR incidence in AIDS or SLE patients was observed. Our findings indicate that cancer and its treatments were often related to cutaneous ADRs in China.

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation was trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of 10 - 5 , showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

Ergotropy serves as a key indicator for assessing the performance of quantum batteries(QBs). Using the Redfield master equation, we investigate ergotropy dynamics in a non-Markovian QB composed of an N-spin chain embedded in a microcavity. Distinct from Markovian charging process, the thermal charging process exhibits a distinct oscillatory behavior in the extracted ergotropy. We show these oscillations are suppressible via synergistic control of coherent driving, cavity parameters, and spin-spin couplings. In addition, we analyze the influence of various system and environmental parameters on the time evolution of ergotropy, revealing rich dynamical features. Our results offer new insights into the control of energy extraction in QBs and may inform future designs of practical battery architectures.

Machine learning simulations of open quantum dynamics often rely on recursive predictors that accumulate error. We develop a non-recursive convolutional neural networks (CNNs) that maps system parameters and a redundant time encoding directly to excitation-energy-transfer populations in the Fenna-Matthews-Olson complex. The encoding-modified logistic plus \\(\\) functions-normalizes time and resolves fast, transitional, and quasi-steady regimes, while physics-informed labels enforce population conservation and inter-site consistency. Trained only on \\(0 7 ps\\) reference trajectories generated with a Lindblad model in QuTiP, the network accurately predicts \\(0100 ps\\) dynamics across a range of reorganization energies, bath rates, and temperatures. Beyond \\(20 ps\\), the absolute relative error remains below 0.05, demonstrating stable long-time extrapolation. By avoiding step-by-step recursion, the method suppresses error accumulation and generalizes across timescales. These results show that redundant time encoding enables data-efficient inference of long-time quantum dissipative dynamics in realistic pigment-protein complexes, and may aid the data-driven design of light-harvesting materials.

Predicting future physical behavior from limited theoretical simulation data is an emerging research paradigm driven by the integration of artificial intelligence and quantum physics. In this work, charge transport (CT) behavior was predicted over extended time scales using a deep learning model-the long short-term memory (LSTM) network with an error-threshold training method-in the photosystem II reaction center (PSII-RC). Theoretical simulation data within 8 fs were used to train the modified LSTM network, yielding distinct predictions with differences on the order of \\(10^-4\\) over prolonged periods compared to the training set collection time. The results highlight the potential of LSTM to uncover the underlying physics governing CT beyond conventional quantum physical methods. These findings warrant further investigation to fully explore the scope and efficacy of LSTM in advancing our understanding of photosynthesis at the molecular scale.

Predicting future physical behavior through the limited theoretical simulation data available is an emerging research paradigm resulted by the integration of artificial intelligence technology and quantum physics. In this work, the charge-transport(CT) behavior was forecasted over a long time by a deep learning model, the long short-term memory (LSTM) network with error threshold training method in the photosynthesis II reaction center (PSII-RC). The theoretical simulation data within 8 fs was fed to the modified LSTM network for training, which brings out a distinct prediction with difference of \\(10^-4\\) orders of magnitude over a long time period compared to the collection time for training sets. The results indicate the potential of employing LSTM to reveal the physics governing CT in addition to quantum physical methods. The implications of this work warrant further investigation to fully elucidate the scope and efficacy of LSTM for advancing our understanding of photosynthesis at the molecular scale.

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26\\% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of \\(10^-5\\), showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26\\% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of \\(10^-5\\), showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

Background Over the last two or three decades, the pace of development of treatments for osteosarcoma tends has been slow. Novel effective therapies for osteosarcoma are still lacking. Previously, we reported that tumor-suppressing STF cDNA 3 ( TSSC3 ) functions as an imprinted tumor suppressor gene in osteosarcoma; however, the underlying mechanism by which TSSC3 suppresses the tumorigenesis and metastasis remain unclear. Methods We investigated the dynamic expression patterns of TSSC3 and autophagy-related proteins (autophagy related 5 (ATG5) and P62) in 33 human benign bone tumors and 58 osteosarcoma tissues using immunohistochemistry. We further investigated the correlations between TSSC3 and autophagy in osteosarcoma using western blotting and transmission electronic microscopy. CCK-8, Edu, and clone formation assays; wound healing and Transwell assays; PCR; immunohistochemistry; immunofluorescence; and western blotting were used to investigated the responses in TSSC3-overexpressing osteosarcoma cell lines, and in xenografts and metastasis in vivo models, with or without autophagy deficiency caused by chloroquine or ATG5 silencing. Results We found that ATG5 expression correlated positively with TSSC3 expression in human osteosarcoma tissues. We demonstrated that TSSC3 was an independent prognostic marker for overall survival in osteosarcoma, and positive ATG5 expression associated with positive TSSC3 expression suggested a favorable prognosis for patients. Then, we showed that TSSC3 overexpression enhanced autophagy via inactivating the Src-mediated PI3K/Akt/mTOR pathway in osteosarcoma. Further results suggested autophagy contributed to TSSC3-induced suppression of tumorigenesis and metastasis in osteosarcoma in vitro and in vivo models. Conclusions Our findings highlighted, for the first time, the importance of autophagy as an underlying mechanism in TSSC3-induced antitumor effects in osteosarcoma. We also revealed that TSSC3-associated positive ATG5 expression might be a potential predictor of favorable prognosis in patients with osteosarcoma.