Abstract Background This study was designed to investigate the clinical application, efficacy, and safety of immune checkpoint inhibitors (ICIs) in the treatment of lung cancer in the real world. Methods A retrospective, observational analysis was conducted on patients treated with ICIs in four tertiary hospitals in the region from January 2015 to March 2021, to evaluate the clinical efficacy of ICIs single-agent or combined chemotherapy and anti-vascular drugs in the first-line or second-line treatment of patients with lung cancer. Results Three hundred and fifteen patients were enrolled in this study. In patients with stage III-IV adenocarcinoma and Squamous cell carcinoma, the objective response rate (ORR) and disease control rate (DCR) were 35.5% (87/245) and 93.5% (229/245), respectively, the median progression-free survival (PFS) was 10.8 months, and the median overall survival (OS) was not reached. A total of 132 patients received ICIs as the first-line treatment, the median treatment cycle was 8 cycles (2–20 cycles), the short-term efficacy ORR was 38.6%, DCR was 93.9%, and the median PFS was 11.4 months. One hundred thirteen patients received ICIs treatment as second-line treatment, the median treatment cycle was five cycles (2–10 cycles), the short-term efficacy ORR was 31.9%, DCR was 92.9%, and the median PFS was 10.0 months. There were no statistically significant differences in ORR, DCR, or median PFS with ICIs as the first-line treatment compared with the second-line treatment(P > 0.05). The results of subgroup analysis showed that Eastern Cooperative Oncology Group performance status (ECOG PS), epidermal growth factor receptor (EGFR) mutation status, pathological type and number of treatment lines were not correlated with median PFS(P > 0.05). However, there were statistically significant differences in programmed death-ligand 1(PD-L1) expression, corticosteroid interference, and antibiotic (Abx) treatment among all groups (P < 0.05). In terms of safety, the overall incidence of adverse reactions in 315 patients was 62.5%, and the incidence of immune-related adverse events (irAEs) was 13.7%. Grade 1–2 and 3–4 incidence of adverse events were 34.9 and 27.65%, respectively. There were four patients who experienced fatal irAEs, two cases were liver damage leading to liver failure, one case was immune related pneumonia, and one case was immune related myocarditis. Conclusion In the real world, ICIs has a good effect on patients with lung cancer and significantly improves ORR and PFS. <<<

Cognitive control deficits are a hallmark of attention deficit hyperactivity disorder (ADHD) in children. Theoretical models posit that cognitive control involves reactive and proactive control processes but their distinct roles and inter-relations in ADHD are not known, and the contributions of proactive control remain vastly understudied. Here, we investigate the dynamic dual cognitive control mechanisms associated with both proactive and reactive control in 50 children with ADHD (16F/34M) and 30 typically developing (TD) children (14F/16M) aged 9-12 years across two different cognitive controls tasks using a within-subject design. We found that while TD children were capable of proactively adapting their response strategies, children with ADHD demonstrated significant deficits in implementing proactive control strategies associated with error monitoring and trial history. Children with ADHD also showed weaker reactive control than TD children, and this finding was replicated across tasks. Furthermore, while proactive and reactive control functions were correlated in TD children, such coordination between the cognitive control mechanisms was not present in children with ADHD. Finally, both reactive and proactive control functions were associated with behavioral problems in ADHD, and multi-dimensional features derived from the dynamic dual cognitive control framework predicted inattention and hyperactivity/impulsivity clinical symptoms. Our findings demonstrate that ADHD in children is characterized by deficits in both proactive and reactive control, and suggest that multi-componential cognitive control measures can serve as robust predictors of clinical symptoms. <<<

The objective of this study was to determine the effect of dietary supplementation of n-3 polyunsaturated fatty acids (PUFA) and n-6 PUFA on dry matter intake (DMI), energy balance, oxidative stress, and performance of transition cows. Forty-five multiparous Holstein dairy cows with similar parity, body weight (BW), body condition score (BCS), and milk yield were used in a completely randomized design during a 56-d experimental period including 28 d prepartum and 28 d postpartum. At 240 d of pregnancy, cows were randomly assigned to one of the 3 isoenergetic and isoprotein dietary treatments, including a control ration containing 1% hydrogenated fatty acid (CON), a ration with 8% extruded soybean (HN6, high n-6 PUFA source), and a ration with 3.5% extruded flaxseed (HN3; high n-3 PUFA source). The HN6 and HN3 diets had an n-6/n-3 ratio of 3.05:1 and 0.64:1 in prepartum cows and 8.16:1 and 1.59:1 in postpartum cows, respectively. During the prepartum period (3, 2, and 1 wk before calving), DMI, DMI per unit of BW, total net energy intake, and net energy balance were higher in the HN3 than in the CON and NH6 groups. During the postpartum period (2, 3, and 4 wk after calving), cows fed HN3 and HN6 diets both showed increasing DMI, DMI as a percentage of BW, and total net energy intake compared with those fed the CON diet. The BW of calves in the HN3 group was 12.91% higher than those in the CON group. Yield and nutrient composition of colostrum (first milking after calving) were not affected by HN6 or HN3 but milk yield from 1 to 4 wk of milking was significantly improved compared with CON. During the transition period, BW, BCS, and BCS changes were not affected. Cows fed the HN6 diet had a higher plasma NEFA concentration compared with the CON cows during the prepartum period. Feeding HN3 reduced the proportion of de novo fatty acids and increased the proportion of preformed long-chain fatty acids in regular milk. In addition, the n-3 PUFA-enriched diet reduced the n-6/n-3 PUFA ratio in milk. In conclusion, increasing the n-3 fatty acids concentration in the diet increased both DMI during the transition period and milk production after calving, and supplementing n-3 fatty acids was more effective in mitigating the net energy balance after calving. <<<

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL <<<

We develop a theory of complexity for numerical computations that takes into account the condition of the input data and allows for roundoff in the computations. We follow the lines of the theory developed by Blum, Shub and Smale for computations over $\mathbb{R}$ (which in turn followed those of the classical, discrete, complexity theory as laid down by Cook, Karp, and Levin, among others). In particular, we focus on complexity classes of decision problems and, paramount among them, on appropriate versions of the classes $\mathsf{P}$, $\mathsf{NP}$, and $\mathsf{EXP}$ of polynomial, nondeterministic polynomial, and exponential time, respectively. We prove some basic relationships between these complexity classes, and provide natural NP-complete problems. <<<

Miriam Ferrer, Nicholas Mourikis, Emma E Davidson, Sam O Kleeman, Marta Zaccaria, Jill Habel, Rachel Rubino, Qing Gao, Thomas R Flint, Lisa Young, Claire M Connell, Michael J Lukey, Marcus D Goncalves, Eileen P White, Ashok R Venkitaraman, Tobias Janowitz <<<

Glucose dependency of cancer cells can be targeted with a high-fat, low-carbohydrate ketogenic diet (KD). However, in IL-6-producing cancers, suppression of the hepatic ketogenic potential hinders the utilization of KD as energy for the organism. In IL-6-associated murine models of cancer cachexia, we describe delayed tumor growth but accelerated cachexia onset and shortened survival in mice fed KD. Mechanistically, this uncoupling is a consequence of the biochemical interaction of two NADPH-dependent pathways. Within the tumor, increased lipid peroxidation and, consequently, saturation of the glutathione (GSH) system lead to the ferroptotic death of cancer cells. Systemically, redox imbalance and NADPH depletion impair corticosterone biosynthesis. Administration of dexamethasone, a potent glucocorticoid, increases food intake, normalizes glucose levels and utilization of nutritional substrates, delays cachexia onset, and extends the survival of tumor-bearing mice fed KD while preserving reduced tumor growth. Our study emphasizes the need to investigate the effects of systemic interventions on both the tumor and the host to accurately assess therapeutic potential. These findings may be relevant to clinical research efforts that investigate nutritional interventions such as KD in patients with cancer. <<<

Marc J Lajoie, Scott E Boyken, Alexander I Salter, Jilliane Bruffey, Anusha Rajan, Robert A Langan, Audrey Olshefsky, Vishaka Muhunthan, Matthew J Bick, Mesfin Gewe, Alfredo Quijano-Rubio, JayLee Johnson, Garreck Lenz, Alisha Nguyen, Suzie Pun, Colin E Correnti, Stanley R Riddell, David Baker <<<

Precise cell targeting is challenging because most mammalian cell types lack a single surface marker that distinguishes them from other cells. A solution would be to target cells using specific combinations of proteins present on their surfaces. In this study, we design colocalization-dependent protein switches (Co-LOCKR) that perform AND, OR, and NOT Boolean logic operations. These switches activate through a conformational change only when all conditions are met, generating rapid, transcription-independent responses at single-cell resolution within complex cell populations. We implement AND gates to redirect T cell specificity against tumor cells expressing two surface antigens while avoiding off-target recognition of single-antigen cells, and three-input switches that add NOT or OR logic to avoid or include cells expressing a third antigen. Thus, de novo designed proteins can perform computations on the surface of cells, integrating multiple distinct binding interactions into a single output. <<<

David Capper, David T W Jones, Martin Sill, Volker Hovestadt, Daniel Schrimpf, Dominik Sturm, Christian Koelsche, Felix Sahm, Lukas Chavez, David E Reuss, Annekathrin Kratz, Annika K Wefers, Kristin Huang, Kristian W Pajtler, Leonille Schweizer, Damian Stichel, Adriana Olar, Nils W Engel, Kerstin Lindenberg, Patrick N Harter, Anne K Braczynski, Karl H Plate, Hildegard Dohmen, Boyan K Garvalov, Roland Coras, Annett Hölsken, Ekkehard Hewer, Melanie Bewerunge-Hudler, Matthias Schick, Roger Fischer, Rudi Beschorner, Jens Schittenhelm, Ori Staszewski, Khalida Wani, Pascale Varlet, Melanie Pages, Petra Temming, Dietmar Lohmann, Florian Selt, Hendrik Witt, Till Milde, Olaf Witt, Eleonora Aronica, Felice Giangaspero, Elisabeth Rushing, Wolfram Scheurlen, Christoph Geisenberger, Fausto J Rodriguez, Albert Becker, Matthias Preusser, Christine Haberler, Rolf Bjerkvig, Jane Cryan, Michael Farrell, Martina Deckert, Jürgen Hench, Stephan Frank, Jonathan Serrano, Kasthuri Kannan, Aristotelis Tsirigos, Wolfgang Brück, Silvia Hofer, Stefanie Brehmer, Marcel Seiz-Rosenhagen, Daniel Hänggi, Volkmar Hans, Stephanie Rozsnoki, Jordan R Hansford, Patricia Kohlhof, Bjarne W Kristensen, Matt Lechner, Beatriz Lopes, Christian Mawrin, Ralf Ketter, Andreas Kulozik, Ziad Khatib, Frank Heppner, Arend Koch, Anne Jouvet, Catherine Keohane, Helmut Mühleisen, Wolf Mueller, Ute Pohl, Marco Prinz, Axel Benner, Marc Zapatka, Nicholas G Gottardo, Pablo Hernáiz Driever, Christof M Kramm, Hermann L Müller, Stefan Rutkowski, Katja von Hoff, Michael C Frühwald, Astrid Gnekow, Gudrun Fleischhack, Stephan Tippelt, Gabriele Calaminus, Camelia-Maria Monoranu, Arie Perry, Chris Jones, Thomas S Jacques, Bernhard Radlwimmer, Marco Gessi, Torsten Pietsch, Johannes Schramm, Gabriele Schackert, Manfred Westphal, Guido Reifenberger, Pieter Wesseling, Michael Weller, Vincent Peter Collins, Ingmar Blümcke, Martin Bendszus, Jürgen Debus, Annie Huang, Nada Jabado, Paul A Northcott, Werner Paulus, Amar Gajjar, Giles W Robinson, Michael D Taylor, Zane Jaunmuktane, Marina Ryzhova, Michael Platten, Andreas Unterberg, Wolfgang Wick, Matthias A Karajannis, Michel Mittelbronn, Till Acker, Christian Hartmann, Kenneth Aldape, Ulrich Schüller, Rolf Buslei, Peter Lichter, Marcel Kool, Christel Herold-Mende, David W Ellison, Martin Hasselblatt, Matija Snuderl, Sebastian Brandner, Andrey Korshunov, Andreas von Deimling, Stefan M Pfister <<<

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology. <<<

Vladimir Ovchinnikov, Marcela Uliano-Silva, Mark Wilkinson, Jonathan Wood, Michelle Smith, Karen Oliver, Ying Sims, James Torrance, Alexander Suh, Shane A McCarthy, Richard Durbin, Mary J O'Connell <<<

We present genome sequences for the caecilians Geotrypetes seraphini (3.8 Gb) and Microcaecilia unicolor (4.7 Gb), representatives of a limbless, mostly soil-dwelling amphibian clade with reduced eyes, and unique putatively chemosensory tentacles. More than 69% of both genomes are composed of repeats, with retrotransposons being the most abundant. We identify 1,150 orthogroups that are unique to caecilians and enriched for functions in olfaction and detection of chemical signals. There are 379 orthogroups with signatures of positive selection on caecilian lineages with roles in organ development and morphogenesis, sensory perception, and immunity amongst others. We discover that caecilian genomes are missing the zone of polarizing activity regulatorysequence (ZRS) enhancer of Sonic Hedgehog which is also mutated in snakes. In vivo deletions have shown ZRS is required for limb development in mice, thus, revealing a shared molecular target implicated in the independent evolution of limblessness in snakes and caecilians. <<<

The system physiology approaches that emerge in western countries in recent years echo the holistic view of ancient Traditional Chinese Medicine (TCM) practices that deal with the root, rather than only the symptoms of diseases. Particularly, TCM practices, including acupuncture, emphasize the mobilization of self-healing mechanisms to bring back body homeostasis. Acupuncture has been practiced for over two thousand years to modulate body physiology stimulation at specific body regions (acupoints). With the development of various research on acupuncture therapy, its regulatory effect on the immune system has been gradually recognized, especially on immunological diseases, including infectious and allergic diseases. In this study, we reviewed the immunomodulatory mechanism of acupuncture and systematically integrates existing research to respectively elucidate the modulatory mechanisms of acupuncture on the innate immune system, adaptive immune system, and well-known neuroanatomical mechanisms, including intact somatosensory-autonomic reflex pathway. With the advances made in recent systems physiology studies, we now have a great opportunity to gain insight into how acupuncture modulates immunity, and subsequently improves its efficacy. <<<

Noninvasive prenatal testing (NIPT) consists of determining fetal aneuploidies by quantifying copy number alteration from the sequencing of cell-free DNA (cfDNA) from maternal blood. Due to the presence of cfDNA of fetal origin in maternal blood, in silico approaches have been developed to accurately predict fetal aneuploidies. Although NIPT is becoming a new standard in prenatal screening of chromosomal abnormalities, there are no integrated pipelines available to allow rapid, accurate and standardized data analysis in any clinical setting. Several tools have been developed, however often optimized only for research purposes or requiring enormous amount of retrospective data, making hard their implementation in a clinical context. Furthermore, no guidelines have been provided on how to accomplish each step of the data analysis to achieve reliable results. Finally, there is no integrated pipeline to perform all steps of NIPT analysis. To address these needs, we tested several tools for performing NIPT data analysis. We provide extensive benchmark of tools performances but also guidelines for running them. We selected the best performing tools that we benchmarked and gathered them in a computational pipeline. NiPTUNE is an open source python package that includes methods for fetal fraction estimation, a novel method for accurate gender prediction, a principal component analysis based strategy for quality control and fetal aneuploidies prediction. NiPTUNE is constituted by seven modules allowing the user to run the entire pipeline or each module independently. Using two cohorts composed by 1439 samples with 31 confirmed aneuploidies, we demonstrated that NiPTUNE is a valuable resource for NIPT analysis. <<<

Grid cells in the entorhinal cortex of freely moving rats provide a strikingly periodic representation of self-location which is indicative of very specific computational mechanisms. However, the existence of grid cells in humans and their distribution throughout the brain are unknown. Here we show that the preferred firing directions of directionally modulated grid cells in rat entorhinal cortex are aligned with the grids, and that the spatial organization of grid-cell firing is more strongly apparent at faster than slower running speeds. Because the grids are also aligned with each other, we predicted a macroscopic signal visible to functional magnetic resonance imaging (fMRI) in humans. We then looked for this signal as participants explored a virtual reality environment, mimicking the rats' foraging task: fMRI activation and adaptation showing a speed-modulated six-fold rotational symmetry in running direction. The signal was found in a network of entorhinal/subicular, posterior and medial parietal, lateral temporal and medial prefrontal areas. The effect was strongest in right entorhinal cortex, and the coherence of the directional signal across entorhinal cortex correlated with spatial memory performance. Our study illustrates the potential power of combining single-unit electrophysiology with fMRI in systems neuroscience. Our results provide evidence for grid-cell-like representations in humans, and implicate a specific type of neural representation in a network of regions which supports spatial cognition and also autobiographical memory. <<<

Oxana Dmitrieva-Posocco, Andrea C Wong, Patrick Lundgren, Aleksandra M Golos, Hélène C Descamps, Lenka Dohnalová, Zvi Cramer, Yuhua Tian, Brian Yueh, Onur Eskiocak, Gabor Egervari, Yemin Lan, Jinping Liu, Jiaxin Fan, Jihee Kim, Bhoomi Madhu, Kai Markus Schneider, Svetlana Khoziainova, Natalia Andreeva, Qiaohong Wang, Ning Li, Emma E Furth, Will Bailis, Judith R Kelsen, Kathryn E Hamilton, Klaus H Kaestner, Shelley L Berger, Jonathan A Epstein, Rajan Jain, Mingyao Li, Semir Beyaz, Christopher J Lengner, Bryson W Katona, Sergei I Grivennikov, Christoph A Thaiss, Maayan Levy <<<

Colorectal cancer (CRC) is among the most frequent forms of cancer, and new strategies for its prevention and therapy are urgently needed. Here we identify a metabolite signalling pathway that provides actionable insights towards this goal. We perform a dietary screen in autochthonous animal models of CRC and find that ketogenic diets exhibit a strong tumour-inhibitory effect. These properties of ketogenic diets are recapitulated by the ketone body β-hydroxybutyrate (BHB), which reduces the proliferation of colonic crypt cells and potently suppresses intestinal tumour growth. We find that BHB acts through the surface receptor Hcar2 and induces the transcriptional regulator Hopx, thereby altering gene expression and inhibiting cell proliferation. Cancer organoid assays and single-cell RNA sequencing of biopsies from patients with CRC provide evidence that elevated BHB levels and active HOPX are associated with reduced intestinal epithelial proliferation in humans. This study thus identifies a BHB-triggered pathway regulating intestinal tumorigenesis and indicates that oral or systemic interventions with a single metabolite may complement current prevention and treatment strategies for CRC. <<<

Robert Bentham, Kevin Litchfield, Thomas B K Watkins, Emilia L Lim, Rachel Rosenthal, Carlos Martínez-Ruiz, Crispin T Hiley, Maise Al Bakir, Roberto Salgado, David A Moore, Mariam Jamal-Hanjani, TRACERx Consortium, Charles Swanton, Nicholas McGranahan <<<

The immune microenvironment influences tumour evolution and can be both prognostic and predict response to immunotherapy. However, measurements of tumour infiltrating lymphocytes (TILs) are limited by a shortage of appropriate data. Whole-exome sequencing (WES) of DNA is frequently performed to calculate tumour mutational burden and identify actionable mutations. Here we develop T cell exome TREC tool (T cell ExTRECT), a method for estimation of T cell fraction from WES samples using a signal from T cell receptor excision circle (TREC) loss during V(D)J recombination of the T cell receptor-α gene (TCRA (also known as TRA)). TCRA T cell fraction correlates with orthogonal TIL estimates and is agnostic to sample type. Blood TCRA T cell fraction is higher in females than in males and correlates with both tumour immune infiltrate and presence of bacterial sequencing reads. Tumour TCRA T cell fraction is prognostic in lung adenocarcinoma. Using a meta-analysis of tumours treated with immunotherapy, we show that tumour TCRA T cell fraction predicts immunotherapy response, providing value beyond measuring tumour mutational burden. Applying T cell ExTRECT to a multi-sample pan-cancer cohort reveals a high diversity of the degree of immune infiltration within tumours. Subclonal loss of 12q24.31-32, encompassing SPPL3, is associated with reduced TCRA T cell fraction. T cell ExTRECT provides a cost-effective technique to characterize immune infiltrate alongside somatic changes. <<<

Prodrugs are one of the most common strategies for the design of targeted anticancer agents. However, their application is often hampered by the modifiable groups available on parent drugs. Herein, a carbon-carbon (C−C) bond cleavage-based prodrug activation strategy is reported, which was successfully used to design prodrugs of β-lapachone (β-lap), an ortho-quinone natural product without traditional modifiable groups for the construction of C−N/C−O bond cleavage-based prodrugs. The designed β-lap prodrug with a reactive oxygen species-specific trigger was quickly activated, releasing β-lap. It exerted anticancer efficacy via NAD(P)H:quinone oxidoreductase 1 (NQO1)-mediated futile redox cycling, resulting in potent cytotoxicity that was highly selective for NQO1-rich cancer cells over normal cells both in vitro and in vivo. This significantly amplified the therapeutic window of β-lap. This study provides a practical strategy for the design of prodrugs for parent drugs that do not contain traditional modifiable groups. <<<

Due to the extremely low intensity contrast between the white matter (WM) and the gray matter (GM) at around 6 months of age (the isointense phase), it is difficult for manual annotation, hence the number of training labels is highly limited. Consequently, it is still challenging to automatically segment isointense infant brain MRI. Meanwhile, the contrast of intensity images in the early adult phase, such as 24 months of age, is a relatively better, which can be easily segmented by the well-developed tools, e.g., FreeSurfer. Therefore, the question is how could we employ these high-contrast images (such as 24-month-old images) to guide the segmentation of 6-month-old images. Motivated by the above purpose, we propose a method to explore the 24-month-old images for a reliable tissue segmentation of 6-month-old images. Specifically, we design a 3D-cycleGAN-Seg architecture to generate synthetic images of the isointense phase by transferring appearances between the two time-points. To guarantee the tissue segmentation consistency between 6-month-old and 24-month-old images, we employ features from generated segmentations to guide the training of the generator network. To further improve the quality of synthetic images, we propose a feature matching loss that computes the cosine distance between unpaired segmentation features of the real and fake images. Then, the transferred of 24-month-old images is used to jointly train the segmentation model on the 6-month-old images. Experimental results demonstrate a superior performance of the proposed method compared with the existing deep learning-based methods. <<<

Long Li, Zhitao Tian, Jie Chen, Zengdong Tan, Yuting Zhang, Hu Zhao, Xiaowei Wu, Xuan Yao, Weiwei Wen, Wei Chen, Liang Guo <<<

BACKGROUND: Seed oil content is an important agronomic trait of Brassica napus (B. napus), and metabolites are considered as the bridge between genotype and phenotype for physical traits.RESULTS: Using a widely targeted metabolomics analysis in a natural population of 388 B. napus inbred lines, we quantify 2172 metabolites in mature seeds by liquid chromatography mass spectrometry, in which 131 marker metabolites are identified to be correlated with seed oil content. These metabolites are then selected for further metabolite genome-wide association study and metabolite transcriptome-wide association study. Combined with weighted correlation network analysis, we construct a triple relationship network, which includes 21,000 edges and 4384 nodes among metabolites, metabolite quantitative trait loci, genes, and co-expression modules. We validate the function of BnaA03.TT4, BnaC02.TT4, and BnaC05.UK, three candidate genes predicted by multi-omics analysis, which show significant impacts on seed oil content through regulating flavonoid metabolism in B. napus.CONCLUSIONS: This study demonstrates the advantage of utilizing marker metabolites integrated with multi-omics analysis to dissect the genetic basis of agronomic traits in crops. <<<

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used. <<<

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. <<<

Given the challenges and complexities introduced while dealing with Dialect Arabic (DA) variations, Transformer based models, e.g., BERT, outperformed other models in dealing with the DA identification task. However, to fine-tune these models, a large corpus is required. Getting a large number high quality labeled examples for some Dialect Arabic classes is challenging and time-consuming. In this paper, we address the Dialect Arabic Identification task. We extend the transformer-based models, ARBERT and MARBERT, with unlabeled data in a generative adversarial setting using Semi-Supervised Generative Adversarial Networks (SS-GAN). Our model enabled producing high-quality embeddings for the Dialect Arabic examples and aided the model to better generalize for the downstream classification task given few labeled examples. Experimental results showed that our model reached better performance and faster convergence when only a few labeled examples are available. <<<