With rapid advances in sequencing technologies, tremendous progress has been made in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), thus revealing enormous genetic and clonal heterogeneity, and paving the way for precision medicine approaches. The successful development of precision medicine for patients with AML has been exemplified by the introduction of targeted FLT3, IDH1/IDH2 and BCL-2 inhibitors. When used as single agents, these inhibitors display moderate antileukaemic activity. However, augmented clinical activity has been demonstrated when they are administered in combination with drugs with broader mechanisms of action targeting epigenetic and/or other oncogenic signalling pathways or with conventional cytotoxic agents. The development of immunotherapies has been hampered by the expression of antigens that are expressed by both leukaemic and non-malignant haematopoietic progenitor cells; nonetheless, a diverse range of immunotherapies are now entering clinical development. This myriad of emerging agents also creates challenges, such as how to safely combine agents with different mechanisms of action, the need to circumvent primary and secondary resistance, and new challenges in future clinical trial design. In this Review, we discuss the current state of precision medicine for AML, including both the potential to improve patient outcomes and the related challenges. <<<

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction. <<<

Stefanie R Bailey, Sonika Vatsa, Rebecca C Larson, Amanda A Bouffard, Irene Scarfò, Michael C Kann, Trisha R Berger, Mark B Leick, Marc Wehrli, Andrea Schmidts, Harrison Silva, Kevin A Lindell, Ashley Demato, Kathleen M E Gallagher, Matthew J Frigault, Marcela V Maus <<<

Chimeric antigen receptor (CAR) T cells induce impressive responses in patients with hematologic malignancies but can also trigger cytokine release syndrome (CRS), a systemic toxicity caused by activated CAR T cells and innate immune cells. Although IFNγ production serves as a potency assay for CAR T cells, its biologic role in conferring responses in hematologic malignancies is not established. Here we show that pharmacologic blockade or genetic knockout of IFNγ reduced immune checkpoint protein expression with no detrimental effect on antitumor efficacy against hematologic malignancies in vitro or in vivo. Furthermore, IFNγ blockade reduced macrophage activation to a greater extent than currently used cytokine antagonists in immune cells from healthy donors and serum from patients with CAR T-cell-treated lymphoma who developed CRS. Collectively, these data show that IFNγ is not required for CAR T-cell efficacy against hematologic malignancies, and blocking IFNγ could simultaneously mitigate cytokine-related toxicities while preserving persistence and antitumor efficacy. <<<

Degang Wu, Jinzhuang Dou, Xiaoran Chai, Claire Bellis, Andreas Wilm, Chih Chuan Shih, Wendy Wei Jia Soon, Nicolas Bertin, Clarabelle Bitong Lin, Chiea Chuen Khor, Michael DeGiorgio, Shanshan Cheng, Li Bao, Neerja Karnani, William Ying Khee Hwang, Sonia Davila, Patrick Tan, Asim Shabbir, Angela Moh, Eng-King Tan, Jia Nee Foo, Liuh Ling Goh, Khai Pang Leong, Roger S Y Foo, Carolyn Su Ping Lam, Arthur Mark Richards, Ching-Yu Cheng, Tin Aung, Tien Yin Wong, Huck Hui Ng, SG10K Consortium, Jianjun Liu, Chaolong Wang <<<

Underrepresentation of Asian genomes has hindered population and medical genetics research on Asians, leading to population disparities in precision medicine. By whole-genome sequencing of 4,810 Singapore Chinese, Malays, and Indians, we found 98.3 million SNPs and small insertions or deletions, over half of which are novel. Population structure analysis demonstrated great representation of Asian genetic diversity by three ethnicities in Singapore and revealed a Malay-related novel ancestry component. Furthermore, demographic inference suggested that Malays split from Chinese ∼24,800 years ago and experienced significant admixture with East Asians ∼1,700 years ago, coinciding with the Austronesian expansion. Additionally, we identified 20 candidate loci for natural selection, 14 of which harbored robust associations with complex traits and diseases. Finally, we show that our data can substantially improve genotype imputation in diverse Asian and Oceanian populations. These results highlight the value of our data as a resource to empower human genetics discovery across broad geographic regions. <<<

David Tamborero, Rodrigo Dienstmann, Maan Haj Rachid, Jorrit Boekel, Adria Lopez-Fernandez, Markus Jonsson, Ali Razzak, Irene Braña, Luigi De Petris, Jeffrey Yachnin, Richard D Baird, Yohann Loriot, Christophe Massard, Patricia Martin-Romano, Frans Opdam, Richard F Schlenk, Claudio Vernieri, Michele Masucci, Xenia Villalobos, Elena Chavarria, Cancer Core Europe consortium, Judith Balmaña, Giovanni Apolone, Carlos Caldas, Jonas Bergh, Ingemar Ernberg, Stefan Fröhling, Elena Garralda, Claes Karlsson, Josep Tabernero, Emile Voest, Jordi Rodon, Janne Lehtiö <<<

There is a growing need for systems that efficiently support the work of medical teams at the precision-oncology point of care. Here, we present the implementation of the Molecular Tumor Board Portal (MTBP), an academic clinical decision support system developed under the umbrella of Cancer Core Europe that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data. Automating the interpretation and reporting of sequencing results decrease the need for time-consuming manual procedures that are prone to errors. The adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promotes consistent decision-making and structured data capture across the connected centers. The use of information-rich patient reports with interactive content facilitates collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers. <<<

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training. <<<

Phase-synchronization is a manifestation of interaction between neuronal groups measurable from LFP, EEG or MEG signals, however, volume conduction can cause the coherence and the phase locking value to spuriously increase. It has been shown that the imaginary component of the coherency (ImC) cannot be spuriously increased by volume-conduction of independent sources. Recently, it was proposed that the phase lag index (PLI), which estimates to what extent the phase leads and lags between signals from two sensors are nonequiprobable, improves on the ImC. Compared to ImC, PLI has the advantage of being less influenced by phase delays. However, sensitivity to volume-conduction and noise, and capacity to detect changes in phase-synchronization, is hindered by the discontinuity of the PLI, as small perturbations turn phase lags into leads and vice versa. To solve this problem, we introduce a related index, namely the weighted phase lag index (WPLI). Differently from PLI, in WPLI the contribution of the observed phase leads and lags is weighted by the magnitude of the imaginary component of the cross-spectrum. We demonstrate two advantages of the WPLI over the PLI, in terms of reduced sensitivity to additional, uncorrelated noise sources and increased statistical power to detect changes in phase-synchronization. Another factor that can affect phase-synchronization indices is sample-size bias. We show that, when directly estimated, both PLI and the magnitude of the ImC have typically positively biased estimators. To solve this problem, we develop an unbiased estimator of the squared PLI, and a debiased estimator of the squared WPLI. <<<

连续性抽样调查由于能够描述目标总体随时间的动态变化过程,吸引了越来越多国内外学者的关注。国外连续性抽样的研究已经十分成熟,在已知的轮换模式下,建立合适的模型,使得模型能较好地描述数据的真实生成过程,从而得到精度更高的目标估计量。文章建立一般轮换模式rm1rm- 12下的时间序列模型,然后以6362模式为例,利用状态空间模型和卡尔曼滤波,给出已有信息下的最优估计,有效减少抽样误差,提高样本的估计精度。 <<<

BACKGROUND: This study investigated the association between suicidal ideation and family environment. The sample included 5183 Chinese university students. A number of studies on suicidal ideation have focused on individuals rather than families. This paper reviews the general principles of suicidal ideation and the consequences resulting from the family environment.METHODS: This study used six different colleges as the dataset, which included 2645 males and 2538 females. Students were questioned with respect to social demographics and suicidal ideation factors. The data were analyzed with factor and logistic analyses to determine the association between suicidal ideation and poor family environment.RESULTS: The prevalence of suicidal ideation was 9.2% (476/5183). Most participants with suicidal ideation had significant similarities: they had poor family structures and relationships, their parents had unstable work, and their parents used improper parenting styles. Female students were more likely to have suicidal thoughts than male students.CONCLUSIONS: This study shows that suicidal ideation is a public health issue among Chinese university students and demonstrates the importance of considering the family environment when examining university students' suicidal ideation. Understanding family-related suicidal ideation risk factors can help to predict and prevent suicides among university students. <<<

Alberto Santos, Ana R Colaço, Annelaura B Nielsen, Lili Niu, Maximilian Strauss, Philipp E Geyer, Fabian Coscia, Nicolai J Wewer Albrechtsen, Filip Mundt, Lars Juhl Jensen, Matthias Mann <<<

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making. <<<

Longlong Si, Huan Xu, Xueying Zhou, Ziwei Zhang, Zhenyu Tian, Yan Wang, Yiming Wu, Bo Zhang, Zhenlan Niu, Chuanling Zhang, Ge Fu, Sulong Xiao, Qing Xia, Lihe Zhang, Demin Zhou <<<

The conversion of life-threatening viruses into live but avirulent vaccines represents a revolution in vaccinology. In a proof-of-principle study, we expanded the genetic code of the genome of influenza A virus via a transgenic cell line containing orthogonal translation machinery. This generated premature termination codon (PTC)-harboring viruses that exerted full infectivity but were replication-incompetent in conventional cells. Genome-wide optimization of the sites for incorporation of multiple PTCs resulted in highly reproductive and genetically stable progeny viruses in transgenic cells. In mouse, ferret, and guinea pig models, vaccination with PTC viruses elicited robust humoral, mucosal, and T cell-mediated immunity against antigenically distinct influenza viruses and even neutralized existing infecting strains. The methods presented here may become a general approach for generating live virus vaccines that can be adapted to almost any virus. <<<

Anxiety is the most common form of psychopathology, and it is often characterized by chronic impairment across the lifespan. Researchers have identified core neural markers that confer risk for anxious outcomes. An increased error-related negativity (ERN) in anxious individuals has been shown to prospectively predict onset of anxiety disorders across development. Hence, it is critical to examine environmental factors that may shape the ERN. In the current study, we use a large sample of 170 female adolescents aged 10-17 to investigate whether the ERN mediates the relationship between parenting style and anxiety diagnostic status. This study replicates previous findings, and it extends previous work by suggesting that this relationship is more robust in young children as compared to adolescents. Interventions targeting the ERN via parenting may be most effective during childhood. <<<

Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability. Deep-learning accelerators aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics. Approaches so far have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment. Here we introduce a hybrid in situ-in silico algorithm, called physics-aware training, that applies backpropagation to train controllable physical systems. Just as deep learning realizes computations with deep neural networks made from layers of mathematical functions, our approach allows us to train deep physical neural networks made from layers of controllable physical systems, even when the physical layers lack any mathematical isomorphism to conventional artificial neural network layers. To demonstrate the universality of our approach, we train diverse physical neural networks based on optics, mechanics and electronics to experimentally perform audio and image classification tasks. Physics-aware training combines the scalability of backpropagation with the automatic mitigation of imperfections and noise achievable with in situ algorithms. Physical neural networks have the potential to perform machine learning faster and more energy-efficiently than conventional electronic processors and, more broadly, can endow physical systems with automatically designed physical functionalities, for example, for robotics, materials and smart sensors. <<<