Miracle-dz/UniFAR-FTUs huggingface dataset:
Stacknet based decision fusion classifier for network intrusion detection

: Network intrusion is a subject of great concern to a variety of stakeholders. Decision fusion (ensemble) models that combine several base learners have been widely used to enhance detection rate of unauthorised network intrusion. However, the design of such an optimal decision fusion classifier is a challenging and open problem. The Matthews Correlation Coefficient (MCC) is an effective measure for detecting associations between variables in many fields; however, very few studies have applied it in selecting weak learners to the best of the authors’ knowledge. In this paper, we propose a decision fusion model with correlation-based MCC weak learner selection technique to augment the classification performance of the decision fusion model under a StackNet strategy. Specifically, the proposed model sought to improve the association between the prediction accuracy and diversity of base classifiers. We compare our proposed model with five other ensemble models, a deep neural model and two stand-alone state-of-the-art classifiers commonly used in network intrusion detection based on accuracy, the Area Under Curve (AUC), recall, precision, F1-score and Kappa evaluation metrics. The experimental results using benchmark dataset KDDcup99 from Kaggle shows that the proposed model has a identified unauthorised network traffic at 99.8% accuracy, Extreme Gradient Boosting (Xgboost) (97.61%), Catboost (97.49%), Light Gradient Boosting Machine (LightGBM) (98.3%), Multilayer Perceptron (MLP) (97.7%), Random Forest (RF) (97.97%), Extra Trees Classifier (ET) (95.82%), Different decision ( DT) (96.95%) and , K-Nearest Neighbor (KNN) (95.56), indicating that it is a more efficient and better intrusion detection system.
Information-Theoretic Distributed Point Functions

A distributed point function (DPF) (Gilboa-Ishai, Eurocrypt 2014) is a cryptographic primitive that enables compressed additive secret-sharing of a secret weight-1 vector across two or more servers. DPFs support a wide range of cryptographic applications, including efficient private information retrieval, secure aggregation, and more. Up to now, the study of DPFs was restricted to the computational security setting, relying on one-way functions. This assumption is necessary in the case of a dishonest majority. We present the first statistically private 3-server DPF for domain size N with subpolynomial key size N o (1) . We also present a similar perfectly private 4-server DPF. Our constructions offer benefits over their computationally secure counterparts, beyond the superior security guarantee, including better computational complexity and better protocols for distributed key generation, all while having comparable communication complexity for moderate-sized parameters. 2012 ACM Subject Classification Theory of computation → Cryptographic primitives
Densely convolutional networks for breast cancer classification with multi-modal image fusion

: Breast cancer is the main health burden worldwide. Cancer is located in the breast, starts when the cell grows under control and begins as in-situ carcinoma and when spread into other parts known as invasive carcinoma. Breast cancer mass can early be found by image modality when discovering mass early can easily diagnose and treated. Multimodalities used for the classification of breast cancer Such as mammography, ultrasound, and Magnetic resonance imaging. Two types of fusion are used earlier fusion and later fusion. Early fusion it’s a simple relation between modalities while later fusion gives more interest to fusion strategy to learn the complex relationship between various modalities as a result, can get highly accurate results when using the later fusion. When combining two image modalities (mammography, ultrasound) and using an excel sheet containing the age, view, side, and status attribute associated with each mammographic image using DenseNet 201 with Layer level fusion strategy as later fusion by making connections between the various paths and same path by using Concatenated layer. Fusing at the feature level achieves the best performance in terms of several evaluation metrics (accuracy, recall, precision area under the curve, and F1 score) and performance.
Facilitating Science – Industry Effective Collaborative Research: Literature Review

Past research indicates that science-industry collaborative research might be a powerful source of innovation and a factor that contributes to high innovation performance and economic growth. Although a number of public policy initiatives promote collaborative research, its potential is still not adequately realised. This paper presents a review of existing literature on science-industry collaborative research. It elaborates and discusses motives and other collaborative factors, and identifies obstacles to joint science-industry collaborative research, from the companies’ and public research organisations’ perspective. Based on literature review, this paper provides recommendations for innovation policies.
Plasmid manipulation of bacterial behaviour through translational regulatory crosstalk

Beyond their role in horizontal gene transfer, conjugative plasmids commonly encode homologues of bacterial regulators. Known plasmid regulator homologues have highly targeted effects upon the transcription of specific bacterial traits. Here, we characterise a plasmid translational regulator, RsmQ, capable of taking global regulatory control in Pseudomonas fluorescens and causing a behavioural switch from motile to sessile lifestyle. RsmQ acts as a global regulator controlling the host proteome through direct interaction with host mRNAs and interference with the host’s translational regulatory network. This mRNA interference leads to largescale proteomic changes in metabolic genes, key regulators and genes involved in chemotaxis, thus controlling bacterial metabolism and motility. Moreover, comparative analyses found RsmQ on a large number of divergent plasmids isolated from multiple bacterial host taxa, suggesting the widespread importance of RsmQ for manipulating bacterial behaviour across clinical, environmental, and agricultural niches. RsmQ is a widespread plasmid global translational regulator primarily evolved for host chromosomal control to manipulate bacterial behaviour and lifestyle. Significance Statement Plasmids are recognised for their important role in bacterial evolution as drivers of horizontal gene transfer. Less well understood are the effects of plasmids upon bacterial behaviours by manipulating the expression of key bacterial phenotypes. Until now, examples of plasmid manipulation of their bacterial hosts were limited to highly targeted transcriptional control of a few related traits. In contrast, here we describe the first plasmid global translational regulator evolved to control the bacterial behavioural switch from a motile to a sessile lifestyle and bacterial metabolism, mediated through manipulation of the bacterial proteome. Moreover, this global translational regulator is common across divergent plasmids in a wide range of bacterial host taxa, suggesting that plasmids may commonly control bacterial lifestyle in the clinic, agricultural fields, and beyond.
Between fishing and farming: palaeogenomic analyses reveal cross-cultural interactions triggered by the arrival of the Neolithic in the Danube Gorges

While early Neolithic populations in Europe were largely descended from early Aegean farmers, there is also evidence of episodic gene flow from local Mesolithic hunter-gatherers into early Neolithic communities. Exactly how and where this occurred is still unknown. Here we report direct evidence for admixture between the two groups at the Danube Gorges in Serbia. Analysis of palaeogenomes recovered from skeletons revealed that second-generation mixed individuals were buried amidst individuals whose ancestry was either exclusively Aegean Neolithic or exclusively local Mesolithic. The mixed ancestry is also reflected in a corresponding mosaic of grave goods. With its deep sequence of occupation and its unique dwellings that suggest at least semi-sedentary occupation since the late Mesolithic, the area of the Danube Gorges has been at the center of the debate about the contribution of Mesolithic societies to the Neolithisation of Europe. As suggested by our data, which were processed exclusively with uncertainty-aware bioinformatic tools, it may have been precisely in such contexts that close interactions between these societies were established, and Mesolithic ancestry and cultural elements were assimilated.
Patterns of interdivision time correlations reveal hidden cell cycle factors

The time taken for cells to complete a round of cell division is a stochastic process controlled, in part, by intracellular factors. These factors can be inherited across cellular generations which gives rise to, often non-intuitive, correlation patterns in cell cycle timing between cells of different family relationships on lineage trees. Here, we formulate a framework of hidden inherited factors affecting the cell cycle that unifies known cell cycle control models and reveals three distinct interdivision time correlation patterns: aperiodic, alternator and oscillator. We use Bayesian inference with single-cell datasets of cell division in bacteria, mammalian and cancer cells, to identify the inheritance motifs that underlie these datasets. From our inference, we find that interdivision time correlation patterns do not identify a single cell cycle model but generally admit a broad posterior distribution of possible mechanisms. Despite this unidentifiability, we observe that the inferred patterns reveal interpretable inheritance dynamics and hidden rhythmicity of cell cycle factors. This reveals that cell cycle factors are commonly driven by circadian rhythms, but their period may differ in cancer. Our quantitative analysis thus reveals that correlation patterns are an emergent phenomenon that impact cell proliferation and these patterns may be altered in disease.
Mapping the semi-nested community structure of 3D chromosome contact networks

Mammalian DNA folds into 3D structures that facilitate and regulate genetic processes such as transcription, DNA repair, and epigenetics. Several insights derive from chromosome capture methods, such as Hi-C, which allow researchers to construct contact maps depicting 3D interactions among all DNA segment pairs. These maps show a complex cross-scale organization spanning megabase-pair compartments to short-ranged DNA loops. To better understand the organizing principles, several groups analyzed Hi-C data assuming a Russian-doll-like nested hierarchy where DNA regions of similar sizes merge into larger and larger structures. Apart from being a simple and appealing description, this model explains, e.g., the omnipresent chequerboard pattern seen in Hi-C maps, known as A/B compartments, and foreshadows the co-localization of some functionally similar DNA regions. However, while successful, this model is incompatible with the two competing mechanisms that seem to shape a significant part of the chromosomes’ 3D organization: loop extrusion and phase separation. This paper aims to map out the chromosome’s actual folding hierarchy from empirical data. To this end, we take advantage of Hi-C experiments and treat the measured DNA-DNA interactions as a weighted network. From such a network, we extract 3D communities using the generalized Louvain algorithm. This algorithm has a resolution parameter that allows us to scan seamlessly through the community size spectrum, from A/B compartments to topologically associated domains (TADs). By constructing a hierarchical tree connecting these communities, we find that chromosomes are more complex than a perfect hierarchy. Analyzing how communities nest relative to a simple folding model, we found that chromosomes exhibit a significant portion of nested and non-nested community pairs alongside considerable randomness. In addition, by examining nesting and chromatin types, we discovered that nested parts are often associated with active chromatin. These results highlight that crossscale relationships will be essential components in models aiming to reach a deep understanding of the causal mechanisms of chromosome folding.
Accurate and efficient protein sequence design through learning concise local environment of residues

Protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired. Here we present ProDESIGN-LE, an accurate and efficient design approach, which adopts a concise but informative representation of residue’s local environment and trains a transformer to select an appropriate residue at a position from its local environment. ProDESIGN-LE iteratively applies the transformer on the positions in the target structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. ProDESIGN-LE designed sequences for 68 naturally occurring and 129 hallucinated proteins within 20 seconds per protein on average, and the predicted structures from the designed sequences perfectly resemble the target structures with state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in E. coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein.
Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls

Despite the accumulation of data and studies, deciphering animal vocal communication remains highly challenging. While progress has been made with some species for which we now understand the information exchanged through vocal signals, researchers are still left struggling with sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets offer a valuable but distorted vision of communication systems. Adopting the best practices in their analysis is therefore essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces along with three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. We highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Finally, using a Uniform Manifold Approximation and Projection (UMAP), we show that classifiers generate parsimonious data descriptions which help to understand the clustering of the bonobo acoustic space. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) adopting Support Vector Machines as the baseline classification approach; iii) explicitly evaluating data leakage and possibly implementing a mitigation strategy; iv) visualizing the dataset with UMAPs applied to classifier predictions rather than to raw acoustic features.
Underlying beneficial effects of Rhubarb on constipation-induced inflammation, disorder of gut microbiome and metabolism

Background Although constipation is a common syndrome and a worldwide health problem. Constipation patients are becoming younger with a 29.6% overall prevalence in the children, which has captured great attention because of its epigenetic rejuvenation and recurrent episodes. Despite the usage of rhubarb to relieve constipation, novel targets and genes involved in target-relevant pathways with remarkable functionalities should still be sought after. Materials and methods We established a reliable constipation model in C57B/6N male mice using intragastric administration diphenoxylate and the eligible subjects received 600mg/25g rhubarb extraction to ameliorate constipation. Resultant constipation was morphological and genetically compared with the specimen from different groups. Results The constipation mice exhibited thicker muscle layers, improved content of cytokines, including IL-17 and IL-23, and lower content of IL-22. The bacterial abundance and diversity varied tremendously. Notably, the alterations were reversed after rhubarb treatment. Additionally, SCFA and MLCFA were significantly influenced by constipation accompanied by enhanced expressions of SCFA receptors, GPR41 and GPR43. Conclusion This thesis has provided an insight that rhubarb promoted the flexibility of collagen fiber, reduced pro-inflammatory cytokines and enhanced anti-inflammatory cytokines, and maintained intestinal microflora balance with potential effects on affecting the metabolism of fatty acids and polyamines.
The Staphylococcus aureus iron-regulated surface determinant A (IsdA) increases SARS CoV-2 replication by modulating JAK-STAT signaling

The emergence and spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) and the associated Coronavirus disease (COVID-19) pandemic have affected millions globally. Like other respiratory viruses, a significant complication of COVID-19 infection is secondary bacterial co-infection, which is seen in approximately 25% of severe cases. The most common organism isolated from co-infection is the Gram-positive bacterium Staphylococcus aureus. Here, we developed an in vitro co-infection model where both CoV-2 and S. aureus replication kinetics can be examined. We demonstrate CoV-2 infection does not alter how S. aureus attaches to or grows in host epithelial cells. In contrast, the presence of replicating S. aureus enhances the replication of CoV-2 by 10-15-fold. We identify this pro-viral activity is due to the S. aureus iron-regulated surface determinant A (IsdA) and this effect is mimicked across different SARS CoV-2 permissive cell lines infected with multiple viral variants. Analysis of co-infected cells demonstrated an IsdA dependent modification of host transcription. Using chemical inhibition, we determined S. aureus IsdA modifies host Janus Kinase – Signal Transducer and Activator of Transcription (JAK-STAT) signalling, ultimately leading to increased viral replication. These findings provide key insight into the molecular interactions that occur between host cells, CoV-2 and S. aureus during co-infection. Importance Bacterial co-infection is a common and significant complication of respiratory viral infection, including in patients with COVID-19, and leads to increased morbidity and mortality. The relationship between virus, bacteria and host is largely unknown, which makes it difficult to design effective treatment strategies. In the present study we created a model of co-infection between SARS CoV-2 and Staphylococcus aureus, the most common species identified in COVID-19 patients with co-infection. We demonstrate that the S. aureus protein IsdA enhances the replication of SARS CoV-2 in vitro by modulating host cell signal transduction pathways. The significance of this finding is in identifying a bacterial component that enhances CoV-2 pathogenesis, which could be a target for the development of co-infection specific therapy in the future. In addition, this protein can be used as a tool to decipher the mechanisms by which CoV-2 manipulates the host cell, providing a better understanding of COVID-19 virulence.
The ribosome inhibitor chloramphenicol induces motility deficits in human spermatozoa: A proteomic approach identifies potentially involved proteins

Mature spermatozoa are almost completely devoid of cytoplasm; as such it has long been believed that they do not contain ribosomes and are therefore not capable of synthesising proteins. However, since the 1950s, various studies have shown translational activity within spermatozoa, particularly during their in vitro capacitation. Most of them demonstrated that mitochondrial (and not cytoplasmic) ribosomes would be involved in the translation of mitochondrial and nuclear-encoded cytoplasmic mRNAs. However, some evidence suggests that cytoplasmic ribosomes could also be active. Here, we investigate the presence and activity of the two types of ribosomes in mature human spermatozoa. By targeting ribosomal RNAs and proteins, we show that both types of ribosomes are localized in the midpiece as well as in the neck and the base of the head of the spermatozoa. We assessed the impact of cycloheximide (CHX) and chloramphenicol (CP), inhibitors of cytoplasmic and mitochondrial ribosomes, respectively, on different sperm parameters. Neither CHX, nor CP impacted sperm vitality, mitochondrial activity (measured through the ATP content), or capacitation (measured through the content in phosphotyrosines). However, increasing CP concentrations induced a decrease in total and progressive motilities as well as on some kinematic parameters while no effect was observed with CHX. A quantitative proteomic analysis was performed by mass spectrometry in SWATH mode to compare the proteomes of spermatozoa capacitated in the absence or presence of the two ribosome inhibitors. Among the ∼ 700 proteins identified in the different tested conditions, 3, 3 and 25 proteins presented a modified abundance in the presence of 1 and 2 mg/ml of CHX, and 1 mg/ml of CP, respectively. The observed abundance variations of some CP-down regulated proteins were validated using Multiple-Reaction Monitoring (MRM). Taken together, our results show that the sperm motility deficits induced in the presence of CP could be linked to the observed decrease of the abundance of several proteins, at least FUNDC2 and QRICH2.