Miracle-dz/UniFAR-FTUs huggingface dataset:
A small number of human lineage mutations regulated RNA-protein binding of conserved genes and promoted human evolution

Whether human lineage mutations (HLM) have contributed to human evolution via post-transcriptional modification remained unknown. We applied deep learning models Seqweaver to predict how HLM impacts RNA-binding protein affinity (RBP). At the threshold of the top 1% of human common variation, only 0.27% of HLM had large impacts on RBP. These HLMs enriched in a set of conserved genes that are highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and involved in synapse organization and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia, and Schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, pointing to an essential role in cognition and bipedalism. We concluded that a very small number of human-specific mutations have contributed to human speciation via impacts on post-transcriptional modification of critical brain-related genes. Author summary Post-transcriptional modification has important functions in biological systems, but its role in evolution has long been a mystery due to the difficulty in predicting how a mutation could impact this process. Here we applied the newly developed deep learning model Seqweaver on human lineage mutations (HLM) to predict their effect on RNA-protein binding affinity profile (RBP). We found that only a very small number HLM are influential to RBP. Influential HLMs enriched in a set of conserved genes that are enriched in cells and functions critical for cognition and bipedal walking, and severe mutations on these genes cause the disruption of cognition (neurodevelopmental disorders) and bipedalism (ataxia). NTRK2 and ITPR1 had the most aggregated pieces of evidence of functional importance, pointing to an important role in cognition and bipedal walking. Taken together, our result demonstrated that there is a small number of human lineage mutations that modified the post-transcriptional modifications of conserved neuronal genes, which contributed to human speciation.
Development of Drosophila as a metazoan model to study inorganic polyphosphate biology

Inorganic polyphosphate (polyP), a linear polymer of orthophosphate residues, is found in all living organisms. While the biological and molecular functions of polyP have been studied in prokaryotes and unicellular eukaryotes, in metazoans it has often been referred to as a molecular fossil. Although certain functions of polyP, e.g. in blood clotting, have been uncovered in humans, a genetically tractable model system has not been developed to systematically study polyP function in metazoans. To explore Drosophila as a model to study polyP, we first developed methods to quantify polyP in flies and then analysed polyP levels in various tissues during different developmental stages. Interestingly, we found that the levels of polyP increase before pupal formation and significantly drop during metamorphosis. To check the in-situ localization of polyP, we used polyP binding domain of bacterial protein, namely PPBD to detect endogenous polyP in fly tissues. We report the presence of polyP in cytoplasm and enrichment in the nucleolus in most tissue types. Interestingly, during oogenesis, we found polyP is enriched in early stage egg chambers but get depleted in later stages suggesting that polyP may play a crucial role during ovariole development. Finally, we found polyP to be enriched in fly hemocytes and showed that polyP promotes hemolymph clotting suggesting that polyP function between flies and mammals is conserved. These observations strongly suggest that combining powerful Drosophila genetic and biochemical tools can accelerate the discovery of unknown biological functions of polyP in metazoans.
A conserved RWP-RK transcription factor VSR1 controls gametic differentiation in volvocine algae

Volvocine green algae are a model for understanding the evolution of mating types and sexes. They are facultatively sexual, with gametic differentiation occurring in response to nitrogen starvation (-N) in most genera, and to sex inducer hormone (SI) in Volvox. The conserved RWP RK family transcription factor (TF) MID is encoded by the minus mating type (MT) locus or male sex-determining region (SDR) of heterothallic volvocine species and dominantly determines minus or male gametic differentiation. However, the factor(s) responsible for establishing the default plus or female differentiation programs have remained elusive. We performed a phylo transcriptomic screen for autosomal RWP-RK TFs induced during gametogenesis in unicellular isogamous Chlamydomonas reinhardtii (Chlamydomonas) and in multicellular oogamous Volvox carteri (Volvox) and identified a single conserved ortho-group we named Volvocine Sex Regulator 1 (VSR1). Chlamydomonas vsr1 mutants of either mating type failed to mate and could not induce expression of key mating-type-specific genes. Similarly, Volvox vsr1 mutants in either sex could initiate sexual embryogenesis, but the presumptive eggs or androgonidia (sperm packet precursors) were infertile and unable to express key sex-specific genes. Yeast two-hybrid assays identified a conserved domain in VSR1 capable of self-interaction or interaction with the conserved N terminal domain of MID. In vivo co-immunoprecipitation experiments demonstrated association of VSR1 and MID in both Chlamydomonas and Volvox. These data support a new model for volvocine sexual differentiation where VSR1 homodimers activate expression of plus/female gamete-specific-genes, but when MID is present MID-VSR1 heterodimers are preferentially formed and activate minus/male gamete-specific-genes. Significance Statement Sex and recombination are conserved features of eukaryotic life cycles, but sex determination mechanisms are diverse, and are poorly understood in most major taxa. Our study identified a long-sought regulator of sexual differentiation in volvocine green algae—the RWP-RK family transcription factor (TF) VSR1— leading to the first complete paradigm for mating type or sex determination in this lineage. Our results support a model where gametically expressed VSR1 homodimerizes and activates plus/female specific genes. When the dominant sex-linked minus/male RWP-RK family TF MID is present MID-VSR1 heterodimers are preferentially formed and activate minus/male genes. The widespread association of RWP-RK TFs with gamete differentiation in the green lineage suggests that a similar paradigm may operate throughout the plant kingdom.
Fast Vocabulary Transfer for Language Model Compression

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.
Full-Stack Information Extraction System for Cybersecurity Intelligence

Due to rapidly growing cyber-attacks and security vulnerabilities, many reports on cyber-threat intelligence (CTI) are being published daily. While these reports can help security analysts to understand on-going cyber threats, the overwhelming amount of information makes it difficult to digest the information in a timely manner. This paper presents, SecIE , an industrial-strength full-stack information extraction (IE) system for the security domain. SecIE can extract a large number of security entities, relations and the temporal information of the relations, which is critical for cyberthreat investigations. Our evaluation with 133 labeled threat reports containing 108,021 tokens shows that SecIE achieves over 92% F1-score for entity extraction and about 70% F1-score for relation extraction. We also showcase how SecIE can be used for downstream security applications.
Kernel methods for unsupervised learning

Kernel Methods are algorithms that projects input data by a nonlinear mapping in a new space (Feature Space). In this thesis we have investigated Kernel Methods for Unsupervised learning, namely Kernel Methods that do not require targeted data. Two classical unsupervised learning problems using Kernel Methods have been tackled. The former is the Data Dimensionality Estimation, the latter is the Clustering. The dimensionality of a data set, called Intrinsic Dimension (ID), is the minimum number of free variables needed to represent the data without information loss. The Kernel PCA, has been investigated and compared, as ID estimator, with a classical dimensionality estimation method, namely the Principal Component Analysis (PCA). The study has been carried out both on a synthethic data set of known dimensionality and on real data benchmarks, i.e. MIT-CBCL Face database and Indoor-Outdoor Image database. The investigations have enlighted that Kernel PCA performs better than PCA, as dimensionality estimator, only when the kernel adopted is very close to the function that has generated the data set. Otherwise Kernel PCA can perform even worst than PCA. With regard to the Clustering problem, we have proposed a novel Kernel Method, the Kernel Grower [Cam04]. The Kernel Grower convergence is guaranteed, unlike several classical clustering algorithm, since it is an Expectation-Maximization algorithm. The main Kernel Grower quality consists, unlike all clustering algorithms published in the literature, in producing nonlinear separation surfaces among data. Kernel Grower compares better with popular clustering algorithms, namely K-MEANS, Neural Gas and Self Organizing Maps, on a synthethic dataset and two UCI real data benchmarks, i.e. IRIS data and Wisconsin breast cancer database. Kernel Grower is the main original result of the thesis.
Data quality and multichannel services

Data quality is an increasingly critical issue in the majority of informationintensive businesses. Indeed, nowadays, in the information era, data quality is a crucial requirement for modern enterprises to obtain a competitive advantage. In this thesis, a particular and widespread context characterization has been considered: multichannel cooperative information systems. It combines two adopted systems typical of the modern economy: multichannel information systems and cooperative information systems. The former classifies an organization considering its own architecture while the latter classifies the organization as part of a larger system considering its role in the interaction with other organizations. In such context, information quality plays a key role both inside organizations, as it reduces errors and increases process efficiency, and in inter-organizational relationships, as it represents a critical component of each company’s trustworthiness. Indeed, as regards multichannel information systems, each organization accesses its internal data and the primary goal of data quality assurance is the assessment and monitoring of data values and, possibly, their improvement. In cooperative information systems (CIS), data quality assurance is important in communication processes and data quality values should be exchanged along with data. In order to assure high data quality levels, companies must design and implement a complete data quality management program. In the literature, models for data quality management have been proposed. A big limit in the application of the proposed methodologies is that they only provide guidelines without providing tools or specific algorithms that support users in the assessment, monitoring, and improvement phases. Focusing on the critical issues related to the assessment phase, the literature does not provide an exhaustive set of metrics that organizations can apply. Only a few algorithms have been developed for a subset of dimensions, such as accuracy [53], completeness [44], consistency [42], and timeliness [5]. Quality assurance is instead faced by the need for objective measures of quality, since most users cannot judge the quality of data and simply have to trust data sources.
Senotherapeutics: An emerging approach to the treatment of viral infectious diseases in the elderly

In the context of the global COVID-19 pandemic, the phenomenon that the elderly have higher morbidity and mortality is of great concern. Existing evidence suggests that senescence and viral infection interact with each other. Viral infection can lead to the aggravation of senescence through multiple pathways, while virus-induced senescence combined with existing senescence in the elderly aggravates the severity of viral infections and promotes excessive age-related inflammation and multiple organ damage or dysfunction, ultimately resulting in higher mortality. The underlying mechanisms may involve mitochondrial dysfunction, abnormal activation of the cGAS-STING pathway and NLRP3 inflammasome, the role of pre-activated macrophages and over-recruited immune cells, and accumulation of immune cells with trained immunity. Thus, senescence-targeted drugs were shown to have positive effects on the treatment of viral infectious diseases in the elderly, which has received great attention and extensive research. Therefore, this review focused on the relationship between senescence and viral infection, as well as the significance of senotherapeutics for the treatment of viral infectious diseases.
Gastric microbiota dysbiosis and Helicobacter pylori infection

Helicobacter pylori (H. pylori) infection is one of the most common causes of gastric disease. The persistent increase in antibiotic resistance worldwide has made H. pylori eradication challenging for clinicians. The stomach is unsterile and characterized by a unique niche. Communication among microorganisms in the stomach results in diverse microbial fitness, population dynamics, and functional capacities, which may be positive, negative, or neutral. Here, we review gastric microecology, its imbalance, and gastric diseases. Moreover, we summarize the relationship between H. pylori and gastric microecology, including non-H. pylori bacteria, fungi, and viruses and the possibility of facilitating H. pylori eradication by gastric microecology modulation, including probiotics, prebiotics, postbiotics, synbiotics, and microbiota transplantation.
Acetobacteraceae as exopolysaccharide producers: Current state of knowledge and further perspectives

Exopolysaccharides formation against harmful biotic and abiotic environmental influences is common among bacteria. By using renewable resources as a substrate, exopolysaccharides represent a sustainable alternative to fossil-based polymers as rheological modifiers in food, cosmetics, and pharmaceutical applications. The family of Acetobacteraceae, traditionally associated with fermented food products, has demonstrated their ability to produce a wide range of structural and functional different polymers with interesting physicochemical properties. Several strains are well known for their production of homopolysaccharides of high industrial importance, such as levan and bacterial cellulose. Moreover, some Acetobacteraceae are able to form acetan-like heteropolysaccharides with a high structural resemblance to xanthan. This mini review summarizes the current knowledge and recent trends in both homo- and heteropolysaccharide production by Acetobacteraceae.
Smoking and attention in schizophrenia spectrum disorders: What are we neglecting?

Introduction Individuals with schizophrenia spectrum disorders (SSDs) record elevated rates of smoking, which is often attributed to their effort to self-medicate cognitive and attentional symptoms of their illness. Empirical evidence for this hypothesis is conflicting, however. In this study, we aimed to test predictions derived from the cognitive self-medication hypothesis. We predicted that cigarette smoking status and extent would predict the attentional performance of participants with SSDs. Simultaneously, we wished to address methodological gaps in previous research. We measured distinct attentional components and made adjustments for the effects of other, attention-modulation variables. Methods Sixty-one smokers (82.0% males, 26.73 ± 6.05 years) and 61 non-smokers (50.8% males, 27.10 ± 7.90 years) with recent-onset SSDs completed an X-type Continuous Performance Test, which was used to derive impulsivity and inattention component scores. Relationships between the two component scores and cigarette smoking status and extent were assessed using hierarchical regression. Effects of estimated premorbid intellectual functioning and antipsychotic medication dosage were held constant. Results Smokers had significantly higher inattention component scores than non-smokers when covariates were controlled (p = 0.026). Impulsivity remained unaffected by smoking status (p = 0.971). Cigarette smoking extent, i.e., the number of cigarettes smoked per day, was not associated with either inattention (p = 0.414) or impulsivity (p = 0.079). Conclusion Models of smoking-related attentional changes can benefit from the inclusion of sample-specific component scores and attention-modulating covariates. Under these conditions, smokers with SSDs can show a partial attentional benefit. However, the limited scope of this benefit suggests that the cognitive self-medication hypothesis requires further testing or reconsidering.
Organic field effect transistor mobility from transient response analysis

An electronic method for analyzing the transient response of a pentacene organic field effect transistor at time scales below 100ns is presented with analysis that allows extraction of estimated field-dependent device mobility from the measured carrier velocity. A second technique we propose is the use of T-SPICE simulations of transient response data of the device behavior between ∼100ns and ∼3μs. These results are compared with lower field-effect mobilities extracted from the transient data at 250μs and the dc drain current (Id) versus source-drain voltage (Vds) characteristics in the saturation regime. This trend of decreasing mobility with increasing time is perhaps due to the absence of the bias stress effect at small time scales.
Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.
Artificial microRNA-mediated resistance against Oman strain of tomato yellow leaf curl virus

Tomato yellow leaf curl virus (TYLCV) is a global spreading begomovirus that is exerting a major restraint on global tomato production. In this transgenic approach, an RNA interference (RNAi)-based construct consisting of sequences of an artificial microRNA (amiRNA), a group of small RNA molecules necessary for plant cell development, signal transduction, and stimulus to biotic and abiotic disease was engineered targeting the AC1/Rep gene of the Oman strain of TYLCV-OM. The Rep-amiRNA constructs presented an effective approach in regulating the expression of the Rep gene against TYLCV as a silencing target to create transgenic Solanum lycopersicum L. plant tolerance against TYLCV infection. Molecular diagnosis by PCR followed by a Southern hybridization analysis were performed to confirm the effectiveness of agrobacterium-mediated transformation in T0/T1-transformed plants. A substantial decrease in virus replication was observed when T1 transgenic tomato plants were challenged with the TYLCV-OM infectious construct. Although natural resistance options against TYLCV infection are not accessible, the current study proposes that genetically transformed tomato plants expressing amiRNA could be a potential approach for engineering tolerance in plants against TYLCV infection and conceivably for the inhibition of viral diseases against different strains of whitefly-transmitted begomoviruses in Oman.