Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of SETD2 aggravates colorectal cancer progression caused by SMAD4 deletion through the RAS/ERK signalling pathway

Abstract Backgound Colorectal cancer (CRC) is a complex, multistep disease that arises from the interplay genetic mutations and epigenetic alterations. The histone H3K36 trimethyltransferase SET domain‐containing 2 (SETD2), as an epigenetic signalling molecule, has a 5% mutation rate in CRC. SETD2 expression is decreased in the development of human CRC and mice treated with Azoxymethane /Dextran sodium sulfate (AOM/DSS). Loss of SETD2 promoted CRC development. SMAD Family member 4 (SMAD4) has a 14% mutation rate in CRC, and SMAD4 ablation leads to CRC. The co‐mutation of SETD2 and SMAD4 predicted advanced CRC. However, little is known on the potential synergistic effect of SETD2 and SMAD4. Methods CRC tissues from mice and SW620 cells were used as research subjects. Clinical databases of CRC patients were analyzed to investigate the association between SETD2 and SMAD4. SETD2 and SMAD4 double‐knockout mice were established to further investigate the role of SETD2 in SMAD4‐deficient CRC. The intestinal epithelial cells (IECs) were isolated for RNA sequencing and chromatin immunoprecipitation sequencing (ChIP‐seq) to explore the mechanism and the key molecules resulting in CRC. Molecular and cellular experiments were conducted to analyze the role of SETD2 in SMAD4‐deficient CRC. Finally, rescue experiments were performed to confirm the molecular mechanism of SETD2 in the development of SMAD4‐dificient CRC. Results The deletion of SETD2 promotes the malignant progression of SMAD4‐deficient CRC. Smad4Vil‐KO; Setd2Vil‐KO mice developed a more severe CRC phenotype after AOM/DSS induction, with a larger tumour size and a more vigorous epithelial proliferation rate. Further mechanistic findings revealed that the loss of SETD2 resulted in the down‐regulation of DUSP7, which is involved in the inhibition of the RAS/ERK signalling pathway. Finally, the ERK1/2 inhibitor SCH772984 significantly attenuated the progression of CRC in Smad4Vil‐KO;Setd2Vil‐KO mice, and overexpression of DUSP7 significantly inhibited the proliferation rates of SETD2KO; SMAD4KO SW620 cells. Conclusions Our results demonstrated that SETD2 inhibits the RAS/ERK signaling pathway by facilitating the transcription of DUSP7 in SMAD4‐deficient CRC, which could provide a potential therapeutic target for the treatment of advanced CRC.
VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

Abstract VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed. tech/varcards2/.
Comparison of Evolutionary Relationships between Branchiostoma floridae, Ciona intestinalis, and Homo sapiens Globins Provide Evidence of Gene Co-Option and Convergent Evolution

Globins have been studied as model proteins to elucidate the principles of protein evolution. This was achieved by understanding the relationship between amino acid sequence, three-dimensional structure, physicochemical properties, and physiological function. Previous molecular phylogenies of chordate globin genes revealed the monophyletic evolution of urochordate globins and suggested convergent evolution. However, to provide evidence of convergent evolution, it is necessary to determine the physicochemical and functional similarities between vertebrates and urochordate globins. In this study, we determined the expression patterns of Ciona globin genes using real-time RT-PCR. Two genes (Gb-1 and Gb-2) were predominantly expressed in the branchial sac, heart, and hemocytes and were induced under hypoxia. Combined with the sequence analysis, our findings suggest that Gb-1/-2 correspond to vertebrate hemoglobin-α/-β. However, we did not find a robust similarity between Gb-3, Gb-4, and vertebrate globins. These results suggested that, even though Ciona globins obtained their unique functions differently from vertebrate globins, the two of them shared some physicochemical features and physiological functions. Our findings offer a good example for understanding the molecular mechanisms underlying gene co-option and convergence, which could lead to evolutionary innovations.
Synergy of the microRNA Ratio as a Promising Diagnosis Biomarker for Mucinous Borderline and Malignant Ovarian Tumors

Epithelial ovarian cancers (EOCs) are a heterogeneous collection of malignancies, each with their own developmental origin, clinical behavior and molecular profile. With less than 5% of EOC cases, mucinous ovarian carcinoma is a rare form with a poor prognosis and a 5-year survival of 11% for advanced stages (III/IV). At the early stages, these malignant forms are clinically difficult to distinguish from borderline (15%) and benign (80%) forms with a better prognosis due to the large size and heterogeneity of mucinous tumors. Improving their diagnosis is therefore a challenge with regard to the risk of under-treating a malignant form or of unnecessarily undertaking radical surgical excision. The involvement of microRNAs (miRNAs) in tumor progression and their potential as biomarkers of diagnosis are becoming increasingly recognized. In this study, the comparison of miRNA microarray expression profiles between malignant and borderline tumor FFPE samples identified 10 down-regulated and 5 up-regulated malignant miRNAs, which were validated by individual RT-qPCR. To overcome normalization issues and to improve the accuracy of the results, a ratio analysis combining paired up-regulated and down-regulated miRNAs was performed. Although 21/50 miRNA expression ratios were significantly different between malignant and borderline tumor samples, any ratio could perfectly discriminate the two groups. However, a combination of 14 pairs of miRNA ratios (double ratio) showed high discriminatory potential, with 100% of accuracy in distinguishing malignant and borderline ovarian tumors, which suggests that miRNAs may hold significant clinical potential as a diagnostic tool. In summary, these ratio miRNA-based signatures may help to improve the precision of histological diagnosis, likely to provide a preoperative diagnosis in order to adapt surgical procedures.
Spatial variability of soil total nitrogen,available phosphorus and available potassium in Huanghuadianzi watershed

The spatial autocorrelation,semi-variograms and Kriging interpolation were used to study the spatial variations of total nitrogen,available phosphorus and available potassium in Huanghuadianzi watershed in Aohan Chifeng,Inner Mongolia based on the field sampling. The results showed that total nitrogen,available phosphorus and available potassium were in island distribution on the whole. Total nitrogen and available phosphorus showed a scattered "fertile islands"distribution,while available potassium showed an obvious island distribution. The spatial autocorrelation of nutrients increased with the decrease of lag distance. There were positively and negatively correlated regions,corresponding to the spatial clustering and spatial isolation,respectively. The C0/( C + C0) value of available phosphorus was 17. 21%,belonging to a strong spatial correlation. Such values for total nitrogen and available potassium were 27. 03% and 44. 99%,respectively,belonging to a moderate spatial correlation. Both spatial autocorrelation distance and range were in order of total nitrogen available phosphorus available potassium. The nutrient contents under different land-use types were in order of cropland forestland grassland. Human activity was the main factor influencing the spatial variability of nutrients in the study area.
Effects of a High-Fat Diet and Docosahexaenoic Acid during Pregnancy on Fatty Acid Composition in the Fetal Livers of Mice

A high-fat diet (HFD) during pregnancy promotes fat accumulation and reduces docosahexaenoic acid (DHA) levels in the liver of the offspring at postnatal ages, which can depend on fetal sex. However, the prenatal mechanisms behind these associations are still unclear. Thus, we analyzed if an HFD alters DHA content and the expression of molecules related to fatty acid (FA) metabolism in the fetal liver. Female C57BL/6 mice were fed a control diet or HFD for 4–6 weeks before pregnancy until the gestational day (GD) 17.5. A subgroup of each diet received DHA (100 mg/Kg) orally from GD 6.5 until 16.5. On GD 17.5, maternal livers, placentas, and livers from male and female fetuses were collected for FA profiling with gas-chromatography and gene expression of molecules related to FA metabolism using qPCR. PPAR-α protein expression was evaluated using Western blot. The gene expression of placental FA transporters was also assessed. An HFD increased eicosapentaenoic acid (EPA) and decreased DHA levels and protein expression of PPAR-α in the fetal livers of both sexes. DHA increased the gene expression of Ppara, Cpt1, and Acsl1 in the livers of female fetuses. Therefore, an HFD reduces DHA levels and PPAR-α, a master regulator of gene expression, in the fetal liver. In turn, the livers of female fetuses seem to be more sensitive to DHA action.
Interactive comment on "Numerical simulations of the Cordilleran ice sheet through the last glacial cycle" by J. Seguinot et al.

Seguinot et al. present a well designed numerical model for the Cordilleran Ice Sheet (CIS) in North America for the last two glaciations occurring over the past 120 000 years. Although the constraints on such a model are not yet all fully understood, their proposed simulations attempt to take account all the complexity of the glacial system, and utilizes a variety of data sets. The modeling confirms what geologists have observed in the field; there was a fast decay of the CIS during MIS 2 and MIS 4 and non-glacial conditions existed during MIS (Olympia Nonglacial Interval; e.g., Plouffe and Jette, 1997). I applaud them for undertaking of such a difficult task, and notifying the reader where input data is sparse or inconclusive.
Understanding learning from EEG data: Combining machine learning and feature engineering based on hidden Markov models and mixed models

Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalography (EEG) datasets are very complex, making any changes in the neural signal related to behaviour difficult to interpret. However, multiple analytical methods are available to examine complex data structure, especially machine learning based techniques. These methods have shown high classification performance and the combination with feature engineering enhances the capability of these methods. This paper proposes using hidden Markov and linear mixed effects models to extract features from EEG data. Based on the engineered features obtained from frontal theta EEG data during a spatial navigation task in two key trials (first, last) and between two conditions (learner and non-learner), we analysed the performance of six machine learning methods (Polynomial Support Vector Machines, Non-linear Support Vector Machines, Random Forests, K-Nearest Neighbours, Ridge, and Deep Neural Networks) on classifying learner and non-learner participants. We also analysed how different standardisation methods used to pre-process the EEG data contribute to classification performance. We compared the classification performance of each trial with data gathered from the same subjects, including solely coordinate-based features, such as idle time and average speed. We found that more machine learning methods perform better classification using coordinate-based data. However, only deep neural networks achieved an area under the ROC curve higher than 80% using the theta EEG data alone. Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.
Laccolith: Hypervisor-Based Adversary Emulation with Anti-Detection

Advanced Persistent Threats (APTs) represent the most threatening form of attack nowadays since they can stay undetected for a long time. Adversary emulation is a proactive approach for preparing against these attacks. However, adversary emulation tools lack the anti-detection abilities of APTs. We introduce Laccolith, a hypervisor-based solution for adversary emulation with anti-detection to fill this gap. We also present an experimental study to compare Laccolith with MITRE CALDERA, a state-of-the-art solution for adversary emulation, against five popular anti-virus products. We found that CALDERA cannot evade detection, limiting the realism of emulated attacks, even when combined with a state-of-the-art anti-detection framework. Our experiments show that Laccolith can hide its activities from all the tested anti-virus products, thus making it suitable for realistic emulations.
Explainable History Distillation by Marked Temporal Point Process

Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this paper, we build a machine learning system to automatically generate explanations of happened events from history by \gls{ca} based on the \acrfull{tpp}. Specifically, we propose a new task called \acrfull{ehd}. This task requires a model to distill as few events as possible from observed history. The target is that the event distribution conditioned on left events predicts the observed future noticeably worse. We then regard distilled events as the explanation for the future. To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}. This work fills the gap between our task and existing works, which only spot the difference between factual and counterfactual worlds after applying a predefined modification to the environment. Experiment results on Retweet and StackOverflow datasets prove that \acrshort{model} significantly outperforms other \acrshort{ehd} baselines and can reveal the rationale underpinning real-world processes.
On the Quantum Chromatic Numbers of Small Graphs

We make two contributions pertaining to the study of the quantum chromatic numbers of small graphs. Firstly, in an elegant paper, Man\v{c}inska and Roberson [\textit{Baltic Journal on Modern Computing}, 4(4), 846-859, 2016] gave an example of a graph $G_{14}$ on 14 vertices with quantum chromatic number 4 and classical chromatic number 5, and conjectured that this is the smallest graph exhibiting a separation between the two parameters. We describe a computer-assisted proof of this conjecture, thereby resolving a longstanding open problem in quantum graph theory. Our second contribution pertains to the study of the rank-$r$ quantum chromatic numbers. While it can now be shown that for every $r$, $\chi_q$ and $\chi^{(r)}_q$ are distinct, few small examples of separations between these parameters are known. We give the smallest known example of such a separation in the form of a graph $G_{21}$ on 21 vertices with $\chi_q(G_{21}) = \chi^{(2)}_q(G_{21}) = 4$ and $ \xi(G_{21}) = \chi^{(1)}_q(G_{21}) = \chi(G_{21}) = 5$. The previous record was held by a graph $G_{msg}$ on 57 vertices that was first considered in the aforementioned paper of Man\v{c}inska and Roberson and which satisfies $\chi_q(G_{msg}) = 3$ and $\chi^{(1)}_q(G_{msg}) = 4$. In addition, $G_{21}$ provides the first provable separation between the parameters $\chi^{(1)}_q$ and $\chi^{(2)}_q$. We believe that our techniques for constructing $G_{21}$ and lower bounding its orthogonal rank could be of independent interest.
Coherence memory and amnesia in a mode-locked laser

Self-organization of temporal modes in mode-locked lasers usually starts from quantum noise. In this process, incoherent spontaneous emission is steered into coherent ultrashort pulses by dissipation and nonlinearity. In this work, we investigated self-organization dynamics in a mode-locked Mamyshev oscillator starting from coherent pulse seeds as opposed to quantum noise. We observed that the coherence of the seed can be remembered or forgotten depending on the initial inverse population. The excessive nonlinearity in the coherence amnesia regime can devastate the seed coherence, causing the oscillator to undergo a chaotic transition lasting hundreds of round trips before regaining coherence. Conversely, the oscillator converges in only a few round trips for the coherence memory regime. A heterodyne technique was developed to record the fast varying optical phase and characterize these two regimes. Dissipative soliton molecules were synthesized from external pulse pair seeds via the coherence memory pathway. In this case, a plateau of the generated pulse spacing independent from seed pulse spacing, i.e., amnesia of the seed spacing, was observed for close spaced seed pulse pairs. Moreover, we show that pulse seeds can be used for laser reconfiguration and pulse pattern control. Our work paves a way to control transient pulse dynamics and steady pulse forms on demand in mode-locked lasers.
Convolutional Neural Networks Exploiting Attributes of Biological Neurons

In this era of artificial intelligence, deep neural networks like Convolutional Neural Networks (CNNs) have emerged as front-runners, often surpassing human capabilities. These deep networks are often perceived as the panacea for all challenges. Unfortunately, a common downside of these networks is their ''black-box'' character, which does not necessarily mirror the operation of biological neural systems. Some even have millions/billions of learnable (tunable) parameters, and their training demands extensive data and time. Here, we integrate the principles of biological neurons in certain layer(s) of CNNs. Specifically, we explore the use of neuro-science-inspired computational models of the Lateral Geniculate Nucleus (LGN) and simple cells of the primary visual cortex. By leveraging such models, we aim to extract image features to use as input to CNNs, hoping to enhance training efficiency and achieve better accuracy. We aspire to enable shallow networks with a Push-Pull Combination of Receptive Fields (PP-CORF) model of simple cells as the foundation layer of CNNs to enhance their learning process and performance. To achieve this, we propose a two-tower CNN, one shallow tower and the other as ResNet 18. Rather than extracting the features blindly, it seeks to mimic how the brain perceives and extracts features. The proposed system exhibits a noticeable improvement in the performance (on an average of $5\%-10\%$) on CIFAR-10, CIFAR-100, and ImageNet-100 datasets compared to ResNet-18. We also check the efficiency of only the Push-Pull tower of the network.
GT4Py: High Performance Stencils for Weather and Climate Applications using Python

All major weather and climate applications are currently developed using languages such as Fortran or C++. This is typical in the domain of high performance computing (HPC), where efficient execution is an important concern. Unfortunately, this approach leads to implementations that intermix optimizations for specific hardware architectures with the high-level numerical methods that are typical for the domain. This leads to code that is verbose, difficult to extend and maintain, and difficult to port to different hardware architectures. Here, we propose a different strategy based on GT4Py (GridTools for Python). GT4Py is a Python framework to write weather and climate applications that includes a high-level embedded domain specific language (DSL) to write stencil computations. The toolchain integrated in GT4Py enables automatic code-generation,to obtain the performance of state-of-the-art C++ and CUDA implementations. The separation of concerns between the mathematical definitions and the actual implementations allows for performance portability of the computations on a wide range of computing architectures, while being embedded in Python allows easy access to the tools of the Python ecosystem to enhance the productivity of the scientists and facilitate integration in complex workflows. Here, the initial release of GT4Py is described, providing an overview of the current state of the framework and performance results showing how GT4Py can outperform pure Python implementations by orders of magnitude.
On The Evaluation of Collision Probability along a Path

Characterizing the risk of operations is a fundamental requirement in robotics, and a crucial ingredient of safe planning. The problem is multifaceted, with multiple definitions arising in the vast recent literature fitting different application scenarios and leading to different computational approaches. A basic element shared by most frameworks is the definition and evaluation of the probability of collision for a mobile object in an environment with obstacles. We observe that, even in basic cases, different interpretations are possible. This paper proposes an index we call Risk Density, which offers a theoretical link between conceptually distant assumptions about the interplay of single collision events along a continuous path. We show how this index can be used to approximate the collision probability in the case where the robot evolves along a nominal continuous curve from random initial conditions. Indeed under this hypothesis the proposed approximation outperforms some well-established methods either in accuracy or computational cost.
Towards Transaction as a Service

This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a transaction as a service (TaaS) layer, the transaction processing can be independently scaled for high resource utilization and can be independently upgraded for development agility. Accordingly, we architect an execution-transaction-storage three-layer cloud-native database. By connecting to TaaS, 1) the AP engines can be empowered with ACID TP capability, 2) multiple standalone TP engine instances can be incorporated to support multi-master distributed TP for horizontal scalability, 3) multiple execution engines with different data models can be integrated to support multi-model transactions, and 4) high performance TP is achieved through extensive TaaS optimizations and consistent evolution. Cloud-native databases deserve better architecture: we believe that TaaS provides a path forward to better cloud-native databases.