Miracle-dz/UniFAR-FTUs huggingface dataset:
AmpliconDesign – An interactive web server for the design of high-throughput targeted DNA methylation assays

Targeted analysis of DNA methylation patterns based on bisulfite-treated genomic DNA (BT-DNA) is considered as a gold-standard for epigenetic biomarker development. Existing software tools facilitate primer design, primer quality control or visualization of primer localization. However, high-throughput design of primers for BT-DNA amplification is hampered by limits in throughput and functionality of existing tools, requiring users to repeatedly perform specific tasks manually. Consequently, the design of PCR primers for BT-DNA remains a tedious and time-consuming process. To bridge this gap, we developed AmpliconDesign, a webserver providing a scalable and user-friendly platform for the design and analysis of targeted DNA methylation studies based on BT-DNA, e.g. deep amplicon bisulfite sequencing (ampBS-seq), EpiTYPER MassArray, or pyrosequencing. Core functionality of the web server includes high-throughput primer design and binding site validation based on in silico bisulfite-converted DNA sequences, prediction of fragmentation patterns for EpiTYPER MassArray, an interactive quality control as well as a streamlined analysis workflow for ampBS-seq. Availability and Implementation The AmpliconDesign webserver is freely available online at: https://amplicondesign.dkfz.de/. AmpliconDesign has been implemented using the R Shiny framework (Chang et al., 2018). The source code is publicly available under the GNU General Public License v3.0 (https://github.com/MaxSchoenung/AmpliconDesign). Contact Daniel B. Lipka (d.lipka@dkfz.de) & Maximilian Schönung (m.schoenung@dkfz.de)
AUDITOR: A System Designed for Automatic Discovery of Complex Integrity Constraints in Relational Databases

In this demonstration, we present a new definition of integrity constraint that is more powerful for anomalous data discovery. In our definition, a constraint is functioned on both categorical and numerical attributes in relational tables, as well as their derivative attributes, leading to a huge search space. Furthermore, we are the first to take into account attribute value distribution as part of a constraint. Based on the proposed integrity constraint, we build AUDITOR on top of relational tables from the industry of healthcare auditing and demonstrate its effectiveness and ease-of-use for domain experts to discover anomalous data.
Automating Exploratory Data Analysis via Machine Learning: An Overview

Exploratory Data Analysis (EDA) is an important initial step for any knowledge discovery process, in which data scientists interactively explore unfamiliar datasets by issuing a sequence of analysis operations (e.g. filter, aggregation, and visualization). Since EDA is long known as a difficult task, requiring profound analytical skills, experience, and domain knowledge, a plethora of systems have been devised over the last decade in order to facilitate EDA. In particular, advancements in machine learning research have created exciting opportunities, not only for better facilitating EDA, but to fully automate the process. In this tutorial, we review recent lines of work for automating EDA. Starting from recommender systems for suggesting a single exploratory action, going through kNN-based classifiers and active-learning methods for predicting users' interestingness preferences, and finally to fully automating EDA using state-of-the-art methods such as deep reinforcement learning and sequence-to-sequence models. We conclude the tutorial with a discussion on the main challenges and open questions to be dealt with in order to ultimately reduce the manual effort required for EDA.
An Improved Collaborative Filtering Algorithm Based on User Interest Diffusion and Time Correlation

This paper proposes an improved collaborative filtering recommendation algorithm based on user interest diffusion and the time correlation. Firstly, the algorithm improves user synthesis similarity calculation method based on user interest diffusion, calculates the direct similarity of user interest and the similarity of user interest diffusion, and obtains the synthesis similarity of user interest through parameter adjustment. Then, for the user interest change with time, the time correlation function is applied to the similarity calculation between users. Finally, the recommendation weight is divided into the time correlation data weight and the synthesis similarity data weight, so that a more accurate prediction score is obtained. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is sparse, and improves the precision of the recommendation algorithm.
Multi-metrics assessment of dynamic cerebral autoregulation in middle and posterior cerebral arteries in young fit women

Individuals with low orthostatic tolerance show greater decrease in posterior cerebral artery mean blood velocity (PCAvmean). Since young fit women often experience presyncopal symptoms, their posterior cerebral circulation may be prone to greater decreases in PCAvmean, probably explained by an attenuated dynamic cerebral autoregulation (dCA). Regional differences in dCA have never been evaluated in young fit women. We compared dCA in the middle cerebral artery (MCA) and posterior cerebral artery (PCA) in 11 young fit women (25 ± 4y; ) in response to a sit-to-stand (5 min sitting followed by 5 min standing) and repeated squat-stand maneuvers performed at 0.05 Hz and 0.10 Hz. The cerebral pressure-flow relationship was characterized using four metrics: 1) percent reduction in blood velocity (BV) per percent reduction in MAP (% BV/% MAP) during initial orthostatic stress (0-15 s after sit-to-stand); 2) onset of the regulatory response (i.e. time delay before an increase in conductance (BV/MAP); 3) rate of regulation (RoR), following sit-to-stand and; 4) transfer function analysis (TFA) of forced MAP oscillations induced by repeated squat-stands. Upon standing, the relative decline in MCAvmean and PCAvmean was similar (−25 ± 9 vs. −30 ± 13%; p=0.29). The onset of the regulatory response (p=0.665), %ΔBV/%ΔMAP (p=0.129) and RoR (p=0.067) were not different between MCA and PCA. In regard to TFA, there was an ANOVA artery effect for gain (p<0.001) and a frequency effect for phase (p<0.001). These findings indicate the absence of regional differences in dCA in young fit women. New findings What is the central question of this study? Are there regional differences in the dynamic cerebral autoregulation in young fit women? What is the main finding and its importance? The key finding of this study is that there are no differences in dynamic cerebral autoregulation between both arteries. These results indicate that dynamic cerebral autoregulation does not seem to be responsible for making the posterior cerebral circulation more vulnerable to transient reduction in blood pressure in young fit women.
Etude crâniométrique du genre Pitymys

L'otude cräniomotrique de plus de 1 600 specimens de Pitymys europoens conduit ä un certain nombre de conclusions : la stabilite" du caractere «simple » ou «complexe » de la 3* molaire super! eure permet la separation du genre en deux groupes. Le groupe «simple », reprosente" par des especes isol£es dans les Balkans et en Italic, comprend en France et en peninsule ibe"rique trois especes qui peuvent <Hre sympatriques. On propose d'appeler lasitanicas toutes les populations placets sous les noms de lasüanicus, mariae, depressus, pelandonius, regalas et planiceps. On propose d'appeler gerbu toutes celles donommoes pyrenaicus. La craniomotrie permet une separation assez bonne de ces trois especes. Dans le groupe «complexe », ou il ne semble pas y avoir d'especes sympatriques, la cräniomelrie permet la separation en morphotypes dont la coincidence avec les especes chromosomiques ou celles de la nomenclature est tres incertaine. On suggere fortement l'etude du genre Pitymys par la gonetique biochimique.
Fine-Grained Complexity Analysis of Queries: From Decision to Counting and Enumeration

This paper is devoted to a complexity study of various tasks related to query answering such as deciding if a Boolean query is true or not, counting the size of the answer set or enumerating the results. It is a survey of some of the many tools from complexity measures trough algorithmic methods to conditional lower bounds that have been designed in the domain over the last years.
Biosystematics of Insects Living in Female Birch Catkins

The host plant relations of three species of Semudobia Kieffer, viz., S. betulae (Winnertz), S. tarda Roskam and S. skuhravae Roskam are studied. These gall midge species are sympatric and induce galls in fruit catkins of birches, viz., Betula pendula Roth, B. pubescens Ehrhart and intermediate forms (putative hybrids). No intermediate forms of the gall midge species are known. They may be found in the same birch specimen and often occur in the same fruit catkin. S. skuhravae induces galls in the fruit scales of birches; the other two species infect the fruits. Statistical analysis of gall midge provenance data showed S. betulae to predominate in B. pendula-like birches and S. tarda to be more common in B. pubescens-like specimens. Although both species occupy similar niches, there is a different occurrence on a 'hybrid index' scale of the host plant species.
Insecticidal Properties of Euphorbiaceae: Sebastiania corniculata-derived 8-Hydroxyquinoline and Its Derivatives against Three Planthopper Species (Hemiptera: Delphacidae)

This study examined the insecticidal effects of Sebastiania corniculata materials against Laodelphax striatellus, Nilaparvata lugens, and Sogatella furcifera. Based on the lethal dose50 (LD50) values, the chloroform fraction of S. corniculata showed the most potential activity against L. striatellus (1.09 μg/female), N. lugens (4.46 μg/female), and S. furcifera (2.32 μg/female). Therefore, we purified the active component of the chloroform fraction, using various chromatographies, and identified it as 8-hydroxyquinoline. To establish the structure-activity relationships, we tested 8-hydroxyquinoline and its derivatives against the 3 planthopper species. Based on 48 h LD50 values, isoquinoline and 6-methoxyquinoline were the most effective against the 3 species. 8-Hydroxyquinoline, 4-methylquinoline, 6-methylquinoline, and 8-hydroxy-2-methylquinoline showed high insecticidal effects. These results indicate that changing the nitrogen atom’s position in quinoline’s pyridine ring plays an important role in the insecticidal effects. We found a high correlation between the introduction of a functional group into the quinoline structure and toxicity against the 3 planthoppers. Our results indicate these insecticidal effects seem to require quinoline derivatives containing hydroxyl (R4 position), methyl (R2 and R3 position), and methoxy (R3 position) groups and a structural isomer of quinoline and suggest that these derivatives may be useful as new preventative agents against the damage caused by a wide range of pests in rice farming areas.
Alignment-free comparison of metagenomics sequences via approximate string matching

Quantifying pairwise sequence similarities is a key step in metagenomics studies. Alignment-free methods provide a computationally efficient alternative to alignment-based methods for large-scale sequence analysis. Several neural network-based methods have recently been developed for this purpose. However, existing methods do not perform well on sequences of varying lengths and are sensitive to the presence of insertions and deletions. In this paper, we describe the development of a new method, referred to as AsMac, that addresses the aforementioned issues. We proposed a novel neural network structure for approximate string matching for the extraction of pertinent information from biological sequences and developed an efficient gradient computation algorithm for training the constructed neural network. We performed a large-scale benchmark study using real-world data that demonstrated the effectiveness and potential utility of the proposed method. The open-source software for the proposed method and trained neural-network models for some commonly used metagenomics marker genes were developed and are freely available at www.acsu. buffalo.edu/~yijunsun/lab/AsMac.html.
A Two-Part Strategy using Genomic Selection in Hybrid Crop Breeding Programs

Hybrid crop breeding programs using a two-part strategy produced the most genetic gain, but a maximum avoidance of inbreeding crossing scheme was required to increase long-term genetic gain. The two-part strategy uses outbred parents to complete multiple generations per year to reduce the generation interval of hybrid crop breeding programs. The maximum avoidance of inbreeding crossing scheme manages genetic variance by maintaining uniform contributions and inbreeding coefficients across all crosses. This study performed stochastic simulations to quantify the potential of a two-part strategy in combination with two crossing schemes to increase the rate of genetic gain in hybrid crop breeding programs. The two crossing schemes were: (i) a circular crossing scheme, and (ii) a maximum avoidance of inbreeding crossing scheme. The results from this study show that the implementation of genomic selection increased the rate of genetic gain, and that the two-part hybrid crop breeding program generated the highest genetic gain. This study also shows that the maximum avoidance of inbreeding crossing scheme increased long-term genetic gain in two-part hybrid crop breeding programs completing multiple selection cycles per year, as a result of maintaining higher levels of genetic variance over time. The flexibility of the two-part strategy offers further opportunities to integrate new technologies to further increase genetic gain in hybrid crop breeding programs, such as the use of outbred training populations. However, the practical implementation of the two-part strategy will require the development of bespoke transition strategies to fundamentally change the data, logistics, and infrastructure that underpin hybrid crop breeding programs. Key message Hybrid crop breeding programs using a two-part strategy produced the most genetic gain by using outbred parents to complete multiple generations per year. However, a maximum avoidance of inbreeding crossing scheme was required to manage genetic variance and increase long-term genetic gain.
A global data-driven census of Salmonella small proteins and their potential functions in bacterial virulence

Small proteins are an emerging class of gene products with diverse roles in bacterial physiology. However, a full understanding of their importance has been hampered by insufficient genome annotations and a lack of comprehensive characterization in microbes other than Escherichia coli. We have taken an integrative approach to accelerate the discovery of small proteins and their putative virulence-associated functions in Salmonella Typhimurium. We merged the annotated small proteome of Salmonella with new small proteins predicted with in silico and experimental approaches. We then exploited existing and newly generated global datasets that provide information on small open reading frame expression during infection of epithelial cells (dual RNA-seq), contribution to bacterial fitness inside macrophages (TraDIS), and potential engagement in molecular interactions (Grad-seq). This integrative approach suggested a new role for the small protein MgrB beyond its known function in regulating PhoQ. We demonstrate a virulence and motility defect of a Salmonella ΔmgrB mutant and reveal an effect of MgrB in regulating the Salmonella transcriptome and proteome under infection-relevant conditions. Our study highlights the power of interpreting available “omics” datasets with a focus on small proteins, and may serve as a blueprint for a data integration-based survey of small proteins in diverse bacteria.
Robustification of RosettaAntibody and Rosetta SnugDock

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.
Transcranial Magnetic Stimulation-Induced Motor Cortex Activity Influences Visual Awareness Judgments

The influence of non-visual information on visual awareness judgments has recently gained substantial interest. Using single-pulse transcranial magnetic stimulation (TMS), we investigate the potential contribution of evidence from the motor system to judgment of visual awareness. We hypothesized that TMS-induced activity in the primary motor cortex (M1) would increase reported visual awareness as compared to the control condition. Additionally, we investigated whether TMS-induced motor-evoked potential (MEP) could measure accumulated evidence for stimulus perception. Following stimulus presentation and TMS, participants first rated their visual awareness verbally using the Perceptual Awareness Scale (PAS), after which they responded manually to a Gabor orientation identification task. Delivering TMS to M1 resulted in higher average awareness ratings as compared to the control condition, in both correct and incorrect identification task response trials, when the hand with which participants responded was contralateral to the stimulated hemisphere (TMS-response-congruent trials). This effect was accompanied by longer PAS response times (RTs), irrespective of the congruence between TMS and identification response. Moreover, longer identification RTs were observed in TMS-response-congruent trials in the M1 condition as compared to the control condition. Additionally, the amplitudes of MEPs were related to the awareness ratings when response congruence was taken into account. We argue that MEP can serve as an indirect measure of evidence accumulated for stimulus perception and that longer PAS RTs and higher amplitudes of MEPs in the M1 condition reflect integration of additional evidence with visual awareness judgment. In conclusion, we advocate that motor activity influences perceptual awareness judgments.
Targeted CRISPR screening identifies PRMT5 as synthetic lethality combinatorial target with gemcitabine in pancreatic cancer cells

Significance Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers to treat. Existing drugs rarely work as a single agent. Therefore, identifying novel drug combinations that will synergistically and selectively kill PDAC cells is a significant unmet need. Here, we used CRISPR gene knockout screening to identify combinatorial targets of gemcitabine (Gem) in PDAC. We discovered that PRMT5 depletion makes PDAC cells significantly sensitive to Gem. Our mechanistic findings show that PRMT5 inhibition impairs DNA repair mechanism. Hence the combination results in synergistic accumulation of Gem-induced DNA damage. We show that the combination leads to synergistic cell death in PDAC cells in vitro and in vivo. Pancreatic ductal adenocarcinoma (PDAC) remains one of the most challenging cancers to treat. Due to the asymptomatic nature of the disease and lack of curative treatment modalities, the 5-y survival rate of PDAC patients is one of the lowest of any cancer type. The recurrent genetic alterations in PDAC are yet to be targeted. Therefore, identification of effective drug combinations is desperately needed. Here, we performed an in vivo CRISPR screen in an orthotopic patient-derived xenograft (PDX) model to identify gene targets whose inhibition creates synergistic tumor growth inhibition with gemcitabine (Gem), a first- or second-line chemotherapeutic agent for PDAC treatment. The approach revealed protein arginine methyltransferase gene 5 (PRMT5) as an effective druggable candidate whose inhibition creates synergistic vulnerability of PDAC cells to Gem. Genetic depletion and pharmacological inhibition indicate that loss of PRMT5 activity synergistically enhances Gem cytotoxicity due to the accumulation of excessive DNA damage. At the molecular level, we show that inhibition of PRMT5 results in RPA depletion and impaired homology-directed DNA repair (HDR) activity. The combination (Gem + PRMT5 inhibition) creates conditional lethality and synergistic reduction of PDAC tumors in vivo. The findings demonstrate that unbiased genetic screenings combined with a clinically relevant model system is a practical approach in identifying synthetic lethal drug combinations for cancer treatment.
An Improved CRISPR/dCas9 Interference Tool for Neuronal Gene Suppression

The expression of genetic material governs brain development, differentiation, and function, and targeted manipulation of gene expression is required to understand contributions of gene function to health and disease states. Although recent improvements in CRISPR/dCas9 interference (CRISPRi) technology have enabled targeted transcriptional repression at selected genomic sites, integrating these techniques for use in non-dividing neuronal systems remains challenging. Previously, we optimized a dual lentivirus expression system to express CRISPR-based activation machinery in post-mitotic neurons. Here we used a similar strategy to adapt an improved dCas9-KRAB-MeCP2 repression system for robust transcriptional inhibition in neurons. We find that lentiviral delivery of a dCas9-KRAB-MeCP2 construct driven by the neuron-selective promoter human synapsin 1 enabled transgene expression in primary rat neurons. Next, we demonstrate transcriptional repression using CRISPR sgRNAs targeting diverse gene promoters, and show superiority of this system in neurons compared to existing RNA interference methods for robust transcript specific manipulation at the complex Brain-derived neurotrophic factor (Bdnf) gene. Our findings advance this improved CRISPRi technology for use in neuronal systems for the first time, potentially enabling improved ability to manipulate gene expression states in the nervous system.