Miracle-dz/UniFAR-FTUs huggingface dataset:
Lactobacillus lactis and Pediococcus pentosaceus‐driven reprogramming of gut microbiome and metabolome ameliorates the progression of non‐alcoholic fatty liver disease

Abstract Background Although microbioa‐based therapies have shown putative effects on the treatment of non‐alcoholic fatty liver disease (NAFLD), it is not clear how microbiota‐derived metabolites contribute to the prevention of NAFLD. We explored the metabolomic signature of Lactobacillus lactis and Pediococcus pentosaceus in NAFLD mice and its association in NAFLD patients. Methods We used Western diet‐induced NAFLD mice, and L. lactis and P. pentosaceus were administered to animals in the drinking water at a concentration of 109 CFU/g for 8 weeks. NAFLD severity was determined based on liver/body weight, pathology and biochemistry markers. Caecal samples were collected for the metagenomics by 16S rRNA sequencing. Metabolite profiles were obtained from caecum, liver and serum. Human stool samples (healthy control [n = 22] and NAFLD patients [n = 23]) were collected to investigate clinical reproducibility for microbiota‐derived metabolites signature and metabolomics biomarker. Results L. lactis and P. pentosaceus supplementation effectively normalized weight ratio, NAFLD activity score, biochemical markers, cytokines and gut‐tight junction. While faecal microbiota varied according to the different treatments, key metabolic features including short chain fatty acids (SCFAs), bile acids (BAs) and tryptophan metabolites were analogously restored by both probiotic supplementations. The protective effects of indole compounds were validated with in vitro and in vivo models, including anti‐inflammatory effects. The metabolomic signatures were replicated in NAFLD patients, accompanied by the comparable levels of Firmicutes/Bacteroidetes ratio, which was significantly higher (4.3) compared with control (0.6). Besides, the consequent biomarker panel with six stool metabolites (indole, BAs, and SCFAs) showed 0.922 (area under the curve) in the diagnosis of NAFLD. Conclusions NAFLD progression was robustly associated with metabolic dys‐regulations in the SCFAs, bile acid and indole compounds, and NAFLD can be accurately diagnosed using the metabolites. L. lactis and P. pentosaceus ameliorate NAFLD progression by modulating gut metagenomic and metabolic environment, particularly tryptophan pathway, of the gut‐liver axis.
Evaluation of tracker performance

Automatic target tracking systems which are in a loop with imaging sensors (FUR, TV) are being more and more frequently integrated as components in militaiy weapon projects. At present, there is no agreed international standard for the assessment of such automatic tracking systems. Current procedures involve costly field trials or lengthy laboratoiy analysis . This research, which has been conducted by the tnnational (F, G, UK) Joint Trials Tracker Subgroup of the Armoured Vehicles Electronic Working Group, was aimed at developing and proving the procedures and instrumentation required to produce a laboratory based assessment procedure for automatic trackers or tracking systems.
Suitability of Formulated Entomopathogenic Fungi Against Hibiscus Mealybug, Nipaecoccus viridis (Hemiptera: Pseudococcidae), Deployed Within Mesh Covers Intended to Protect Citrus From Huanglongbing

Abstract Mesh exclusion bags are increasingly being adopted by Florida citrus growers to protect young citrus trees from Asian citrus psyllid, Diaphorina citri Kuwayama, and Huanglongbing disease. These mesh bags exclude larger insects such as D. citri but may allow entry of minute insects. Hibiscus mealybugs, Nipaecoccus viridis (Newstead), have been observed thriving in the micro-habitat created by these covers on trees. Entomopathogenic fungi (EPF) and insect growth regulators (IGRs) are effective against several mealybug species under various growing conditions, but their efficacy against N. viridis or within the microclimate within exclusion bags is unknown. Therefore, we evaluated various formulations of entomopathogenic fungi with and without IGR against N. viridis using laboratory bioassays. We then conducted semifield bioassays to determine effectiveness of EPF formulations alone and in combination with an IGR applied to citrus trees enveloped within mesh bags under field conditions. Survival probabilities of N. viridis nymphs exposed to all Beauveria bassiana-based products tested were comparable to malathion under laboratory conditions and reduced survival as compared to controls (water only). Under field conditions, mortality of N. viridis nymphs on leaves sprayed with each fungal formulation tested was significantly greater than on control treatments (Water, Suffoil X, 435 oil) up to four weeks post application. There were no differences in the colony forming units per leaf area amongst all fungal treatments. Formulated B. bassiana-based products applied alone or combined with an IGR should be effective tools for managing N. viridis populations on young citrus trees protected with mesh exclusion bags.
Determination of muscle fatigue using dynamically embedded signals

This paper explores a new method of analysing muscle fatigue within the muscles predominantly used during microsurgery. The captured electromyographic (EMG) data retrieved from these muscles are analysed for any defining patterns relating to muscle fatigue. The analysis consists of dynamically embedding the EMG signals from a single muscle channel into an embedded matrix. The muscle fatigue is determined by defining its entropy characterized by the singular values of the dynamically embedded (DE) matrix. The paper compares this new method with the traditional method of using mean frequency shifts in the EMG signal's power spectral density. Linear regressions are fitted to the results from both methods, and the coefficients of variation of both their slope and point of intercept are determined. It is shown that the complexity method is slightly more robust in that the coefficient of variation for the DE method has lower variability than the conventional method of mean frequency analysis.
Immunogenicity of heterologous BNT162b2 booster in fully vaccinated individuals with CoronaVac against SARS-CoV-2 variants Delta and Omicron: the Dominican Republic Experience

The recent emergence of the SARS-CoV-2 Omicron variant is raising concerns because of its increased transmissibility and by its numerous spike mutations with potential to evade neutralizing antibodies elicited by COVID-19 vaccines. The Dominican Republic was among the first countries in recommending the administration of a third dose COVID-19 vaccine to address potential waning immunity and reduced effectiveness against variants. Here, we evaluated the effects of a heterologous BNT162b2 mRNA vaccine booster on the humoral immunity of participants that had received a two-dose regimen of CoronaVac, an inactivated vaccine used globally. We found that heterologous CoronaVac prime followed by BNT162b2 booster regimen induces elevated virus-specific antibody levels and potent neutralization activity against the ancestral virus and Delta variant, resembling the titers obtained after two doses of mRNA vaccines. While neutralization of Omicron was undetectable in participants that had received a two-dose regimen of CoronaVac vaccine, BNT162b2 booster resulted in a 1.4-fold increase in neutralization activity against Omicron, compared to two-dose mRNA vaccine. Despite this increase, neutralizing antibody titers were reduced by 6.3-fold and 2.7-fold for Omicron compared to ancestral and Delta variant, respectively. Surprisingly, previous SARS-CoV-2 infection did not affect the neutralizing titers for Omicron in participants that received the heterologous regimen. Our findings have immediate implications for multiples countries that previously used a two-dose regimen of CoronaVac and reinforce the notion that the Omicron variant is associated with immune escape from vaccines or infection-induced immunity, highlighting the global need for vaccine boosters to combat the impact of emerging variants.
Adaptive fuzzy controller for controlling the dynamics of robot motion

Autonomous robots are of prime importance in everyday life because of their capability to handle the non-linear situations. This paper describes the autonomous robot motion control system based on fuzzy logic proportional integral (PI) controller that reaches to its target value faster than others. Fuzzy rules are embedded in the controller to tune the gain parameters of PI controller and to move the robot along the task trajectory with variation in heading angle (θ) towards the target to make it helpful in real time applications. It discusses the design aspects of the fuzzy PI controller for controlling the dynamics of autonomous robot that have higher distribution rejection properties (DRP), low settling time (ST) and high adaptability towards the variation in the angle towards the target. The performance of a robot (RM) has been verified using MATLAB and results obtained have been found to be robust. Overall, the performances criteria in terms of its response towards ST, target acquisition (TA) and DRP.
Prediction of SARS-CoV-2 Omicron Variant Immunogenicity, Immune Escape and Pathogenicity, through the Analysis of Spike Protein-Specific Core Unique Peptides

The recently discovered Omicron variant of the SARS-CoV-2 corona virus has raised a new, global, awareness, since it is considered as a new variant of concern from all major health organizations, including WHO and ECDC. Omicron variant is characterized by 30 amino acid changes, three small deletions and one small insertion in the Spike protein. In this study, we have identified the Core Unique Peptides (CrUPs) that reside exclusively in the Omicron variant of Spike protein and are absent from the human proteome, thus creating a new dataset of peptides named as C/H-CrUPs. Furthermore, we have analyzed their protein locations and compared them with the respective ones of Alpha and Delta SARS-CoV-2 variants. In Omicron, 115 C/H-CrUPs were generated and 119 C/H-CrUPs were lost, almost four times as many compared to the other two variants. From position 440 to position 508, at the Receptor Binding Motif (RBM), 8 mutations were detected, resulting in the construction of 28 novel C/H-CrUPs. Most importantly, in Omicron variant, new C/H-CrUPs carrying two or three mutant amino acids were produced, as a consequence of the accumulation of multiple mutations in the RBM. Remarkably, these Omicron-derived C/H-CrUPs that bear several mutated amino acids could not be recognized in any other viral Spike variant. We suggest that virus binding to the ACE2 receptor is facilitated by the herein identified C/H-CrUPs in contact point mutations and Spike-cleavage sites, while the immunoregulatory NF9 peptide is not detectably affected. Taken together, our findings indicate that Omicron variant contains intrinsic abilities to escape immune-system attack, while its mutations can mediate strong viral binding to the ACE2 receptor, leading to highly efficient fusion of the virus to the target cell. However, the intact NF9 peptide suggests that Omicron exhibits reduced pathogenicity compared to Delta variant.
Mechanism of bisphosphonate-related osteonecrosis of the jaw (BRONJ) revealed by targeted removal of legacy bisphosphonate from jawbone using competing inert hydroxymethylene diphosphonate

Bisphosphonate-related osteonecrosis of the jaw (BRONJ) presents as a morbid jawbone lesion in patients exposed to a nitrogen-containing bisphosphonate (N-BP). Although it is rare, BRONJ has caused apprehension among patients and healthcare providers and decreased acceptance of this anti-resorptive drug class to treat osteoporosis and metastatic osteolysis. We report here a novel method to elucidate the pathological mechanism of BRONJ by the selective removal of legacy N-BP from the jawbone using an intra-oral application of hydroxymethylene diphosphonate (HMDP) formulated in deformable nanoscale vesicles (DNV). After maxillary tooth extraction, zoledronate-treated mice developed delayed gingival wound closure, delayed tooth extraction socket healing and increased jawbone osteonecrosis consistent with human BRONJ lesion. Single cell RNA sequencing of mouse gingival cells revealed oral barrier immune dysregulation and unresolved pro-inflammatory reaction. HMDP-DNV topical applications to nascent mouse BRONJ lesions resulted in accelerated gingival wound closure and bone socket healing as well as attenuation of osteonecrosis development. The gingival single cell RNA sequencing demonstrated resolution of chronic inflammation by increased anti-inflammatory signature gene expression of lymphocytes and myeloid-derived suppressor cells. This study suggests that BRONJ pathology was predominantly induced by the oral N-BP and demonstrates the potential of HMDP-DNV as an effective BRONJ therapy.
Predicting the course of Covid-19 and other epidemic and endemic disease

The Gompertz Function is an accurate model for epidemics from Cholera in 1853 to Spanish Flu in 1918 and Ebola in 2014. It also describes the acute phase of annual outbreaks of endemic influenza and in all of these instances it has significant predictive power. For Covid-19, we show that the Gompertz Function provides accurate forecasts not just for cases and deaths but, independently, for hospitalisations, intensive care admissions and other medical requirements. In particular Gompertz Function projections of healthcare requirements have been reliable enough to allow planning for: hospital admissions,intensive care admissions,ventilator usage, peak loads and duration. Analysis of data from the Spanish Flu pandemic and the endemic influenza cycle reveals alternating periods of Gompertz Function growth and linear growth in cumulative cases or deaths. Linear growth means the Reproduction Number is equal to 1 which in turn indicates endemicity. The same pattern has been observed with Covid-19. All the initial outbreaks ended in linear growth. Each new outbreak has been preceded by a period of linear growth and has ended with a transition from Gompertz Function growth to linear growth. This suggests that each of these outbreak cycles ended with a transition to endemicity for the current dominant strain and that the normal seasonal respiratory virus periods will continue to see new outbreaks. It remains to be seen if widespread vaccination will disrupt this cyclicality. Because both Gompertz Function Growth and linear growth are accurately predictable, the forecasting problem is reduced to identifying the transition between these modes and to improving the performance in the early Gompertz Function growth phase where its predictive power is lowest. The dynamics of the Gompertz Function are determined by the Gumbel probability distribution. This is an exceptional distribution with respect to the geometry determined by the affine group on the line which is the key to the Gumbel distribution's role as an Extreme Value Theory attractor. We show that this, together with the empirically observed asymmetry in epidemic data, makes the Gompertz Function growth essentially inevitable in epidemic models which agree with observations.
What About Your Next Job? Predicting Professional Career Trajectory Using Neural Networks

Accurate and effective analysis of professional career trajectories can help job seekers make the right job switch quickly. However, it is a non-trivial task to develop an effective model to predict the next job of users. Previous works either focus on feature engineering that resulted in heavy computations or not supporting complete solutions from the perspective of individuals. To this end, we propose an end-to-end model to predict the next job of users comprehensively. The problem of predicting the next job is broken into three tasks, i.e., position name prediction, salary level prediction, company size prediction. These three tasks share the same framework of the model with different output dimensions. Specifically, we reorder the raw features and regard each resume as a textual sentence consists of several key phrases. Word2Vec is utilized to train the hidden vectors of all phrases in the sentences. Followed by a feature extracting component comprised of a convolutional neural network (CNN) or a long short-term memory (LSTM) network. Experiments on a real-world dataset validate that our proposed model significantly outperforms baselines and reveal interesting insights into job transitions from two perspectives.
WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block. The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.
A Review on Vessel Segmentation of X-Ray Coronary Angiography Images Based on Deep Learning

Coronary artery disease (CAD) is a common heart disease, vascular segmentation is conducive to clinical diagnosis and follow-up treatment. In recent years, deep learning has become the most widely used method for coronary artery segmentation. At present, there are many papers on the segmentation of Coronary artery, but most of them are magnetic resonance imaging(MRI)and computed tomography(CT)[1]. X-ray coronary angiography(CAG) is the main imaging technology in the diagnosis of coronary artery disease ,and is regarded as the "gold standard" in clinical treatment. In this paper, we provide a review of main stream vessel segmentation of X-ray coronary angiography images using deep learning. Finally, we discuss the challenges and limitations of current deep learning based methods, and propose potential directions for future research.
C3MNet: Customized Multi-Channel Muscle Motion Network for Yawn-Based Fatigue Emotion Detection

In this paper, we propose an effective facial expression recognition (FER) method for yawn-based fatigue detection. Yawning is often considered an early symptom of fatigue. As facial expression however yawning and speaking have similar features. Our objective is to clearly distinguish yawning, speaking, and neutral (no emotion) facial expressions. To enable accurate classification, we construct a model based on the movement of the facial muscles corresponding to these states. It is shown that in yawning the most active regions in the face are the mouth, nose, nasolabial folds, and eyes. We then establish a multi-channel CNN+LSTM prediction network model for yawning. The proposed method achieves 97.62% ACC on the YawDD [1] dataset, which is significantly higher than the state-of-the-art, e.g., VGG [2].
Enhancing Hybrid OSS Development Through Agile Methods and High Media Synchronicity

Open source software (OSS) is increasingly being developed by hybrid teams that consist of a mix of company employees and volunteer developers. While hybrid OSS teams are becoming more prevalent, they also face unique challenges due to the involvement of different constituents. To address those challenges, this paper develops and validates a new organizing model. Specifically, the paper draws on media synchronicity theory (MST) to theorize that hybrid OSS teams would benefit from adopting an organizing model that involves practicing agile methods and using communication tools with multiple symbols sets and high transmission velocity. The paper also extends MST by conceptualizing the theory's key concept of communication convergence as consisting of two distinct dimensions: affective and cognitive convergence. Using primary survey data from hybrid OSS teams, the paper presents empirical evidence that such an organizing model can enhance those teams' affective convergence and cognitive convergence and, in turn, their development productivity and the quality of the software. In addition, the results show that affective convergence has a stronger impact on hybrid OSS teams' performance than cognitive convergence.
The Philosopher's Corner

A minimalist view of theory will render it more accessible for a wider range of research questions. More accessible theory will facilitate constant improvement between representations predicting particular relationships among entities of interest in IS and observations of those relationships in practice. This is needed in IS to direct research energy toward knowledge that can lead to more effective and helpful interventions for computing technology stakeholders. The paper also addresses a range of anomalies created by prevailing traditional views of theory, particularly regarding the distinction between theory existence and theory level of support. Discussion includes identifying barriers to conversion to this view of theory as well as criteria for evaluation of proposed new theory.
Interspecific parental care by a European Starling (Sturnus vulgaris) at the nest of a Hairy Woodpecker (Dryobates villosus)

ABSTRACT The European Starling (Sturnus vulgaris) is a cavity-nesting species that aggressively competes with other cavity nesters, such as woodpeckers, and usurps their nest cavities. Interspecific parental care among birds is uncommon and scarcely reported for European Starlings and woodpeckers. Here, we report an observation of a clutch of 3 Hairy Woodpecker (Dryobates villosus) nestlings being fed by both an adult female Hairy Woodpecker and at least 1 adult European Starling in southern Ontario, Canada. There are differences in parental behavior observed between the starling and female Hairy Woodpecker, namely only the starling entered the cavity (7.0 times/h) and removed fecal sacs (4.2 times/h) while the female Hairy Woodpecker never showed these behaviors during our observation period, but she fed nestlings 7.4 times/h compared to 4.9 times/h by the starling. Although proximate causes of this behavior are unclear, nest site competition, overlap of the timing of breeding and preferred habitat, or the loss of the male woodpecker may have triggered this behavior.
Vocal coordination of provisioning in Black Phoebes (Sayornis nigricans)

ABSTRACT Parent birds often vocalize near the nest to communicate with each other and their young. We observed a call made by Black Phoebes (Sayornis nigricans) as they approach the nest to feed their young that has not previously been studied in this context. The Wee call is more complex than the simple call that males and females commonly make near the nest. We hypothesized that one parent makes the call when arriving at the nest with food to signal to a brooding parent to depart from the nest. Over 3 years, we collected observational data at phoebe nests by observing them once during the incubation stage and then early and late in the nestling stage. As we predicted, phoebes did not make the Wee call during the incubation stage. When individuals approached the nest with food and made the Wee call during the nestling stage, the brooding parent normally responded by leaving the nest. Additionally, the brooding bird was likely to return to the nest if the other parent made the Wee call upon departing. While vocalizations near the nest can attract predators, in phoebes these calls appear to coordinate parental care efforts.