Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of a Lactobacillus-Based Teat Spray on Udder Health in Lactating Dairy Cows

Teat disinfection is a common pre- and post-milking mastitis prevention practice that is part of a mastitis control program in dairy herds. Commercially available teat disinfectants are generally chemical-based products. The use of these products has occasionally raised concerns about the risk of chemical residues in milk. An alternative treatment or prevention strategy based on probiotics has the potential to circumvent this risk. Two treatments were compared in a cross-over clinical trial in a single herd: a lactobacillus-based, post-milking teat spray (LACT), and a commercial iodine-based post-milking teat disinfectant product as (positive control, PC). The effect of the two treatments on cow somatic cell counts was quantified using a multivariate mixed-effects linear regression model with cow fitted as a random effect. The odds of teat end scores increasing from a low to a high score tended to be lower (OR = 0.74, 95% CI 0.54–1.01, P = 0.06) for cows receiving LACT treatment. On average, there was also a tendency for a lower somatic cell counts in the LACT treated cows (antilog of coefficient = 0.91, 95% CI 0.80–1.03, P = 0.13) compared with the PC treated cows. The application of the lactobacillus-based product to teats could reduce the rate of teat end scores progression from low to higher scores, and potentially improve teat end sphincter functions and udder health. Further, larger scale validation work is required to support the findings of the current study.
Narrative immersion for information literacy: A pilot study

Aspects of storytelling may be leveraged to foster greater motivation in student research projects. Recognizing this possibility, the following mixed‐method pilot study explores the effects of reading creative nonfiction on student inquiry within an information literacy (IL) course to spark interest in their topic. Participants reported time‐on‐task and source exploration, responded to scales of narrative immersion, interest, and self‐regulation, then responded to reflective prompts. Results show a correlation between situational interest in the topics of IL and inquiry and mental and emotional involvement in a narrative. Further study, however, is necessary to establish the motivational affordances of storytelling and narrative in facilitating student inquiry. Such research may inform IL pedagogy and instructional design practices.
Inhibitory Effect of Morin Against Candida albicans Pathogenicity and Virulence Factor Production: An in vitro and in vivo Approaches

Candida albicans is considered an exclusive etiologic agent of candidiasis, a very common fungal infection in human. The expression of virulence factors contributes highly to the pathogenicity of C. albicans. These factors include biofilm formation, yeast-to-hyphal transition, adhesins, aspartyl proteases, and phospholipases secretion. Moreover, resistance development is a critical issue for the therapeutic failure of antifungal agents against systemic candidiasis. To circumvent resistance development, the present study investigated the virulence targeted therapeutic activity of the phyto-bioactive compound morin against C. albicans. Morin is a natural compound commonly found in medicinal plants and widely used in the pharmaceutical and cosmetic products/industries. The present study explicated the significant inhibitory potential of morin against biofilm formation and other virulence factors’ production, such as yeast-hyphal formation, phospholipase, and exopolymeric substances, in C. albicans. Further, qPCR analysis confirmed the downregulation of biofilm and virlence-related genes in C. albicans upon morin treatment, which is in correspondence with the in vitro bioassays. Further, the docking analysis revealed that morin shows strong affinity with Hwp-1 protein, which regulates the expression of biofilm and hyphal formation in C. albicans and, thereby, abolishes fungal pathogenicity. Moreover, the anti-infective potential of morin against C. albicans-associated systemic candidiasis is confirmed through an in vivo approach using biomedical model organism zebrafish (Danio rerio). The outcomes of the in vivo study demonstrate that the morin treatment effectively rescues animals from C. albicans infections and extends their survival rate by inhibiting the internal colonization of C. albicans. Histopathology analysis revealed extensive candidiasis-related pathognomonic changes in the gills, intestine, and kidney of animals infected with C. albicans, while no extensive abnormalities were observed in morin-treated animals. The results evidenced that morin has the ability to protect against the pathognomonic effect and histopathological lesions caused by C. albicans infection in zebrafish. Thus, the present study suggests that the utilization of morin could act as a potent therapeutic medication for C. albicans instigated candidiasis.
The Synergistic Effect of Mud Crab Antimicrobial Peptides Sphistin and Sph12−38 With Antibiotics Azithromycin and Rifampicin Enhances Bactericidal Activity Against Pseudomonas Aeruginosa

Overuse or abuse of antibiotics has undoubtedly accelerated the increasing prevalence of global antibiotic resistance crisis, and thus, people have been trying to explore approaches to decrease dosage of antibiotics or find new antibacterial agents for many years. Antimicrobial peptides (AMPs) are the ideal candidates that could kill pathogens and multidrug-resistant bacteria either alone or in combination with conventional antibiotics. In the study, the antimicrobial efficacy of mud crab Scylla paramamosain AMPs Sphistin and Sph12−38 in combination with eight selected antibiotics was evaluated using a clinical pathogen, Pseudomonas aeruginosa. It was interesting to note that the in vitro combination of rifampicin and azithromycin with Sphistin and Sph12−38 showed significant synergistic activity against P. aeruginosa. Moreover, an in vivo study was carried out using a mouse model challenged with P. aeruginosa, and the result showed that the combination of Sph12−38 with either rifampicin or azithromycin could significantly promote the healing of wounds and had the healing time shortened to 4–5 days compared with 7–8 days in control. The underlying mechanism might be due to the binding of Sphistin and Sph12−38 with P. aeruginosa lipopolysaccharides (LPS) and subsequent promotion of the intracellular uptake of rifampicin and azithromycin. Taken together, the significant synergistic antibacterial effect on P. aeruginosa in vitro and in vivo conferred by the combination of low dose of Sphistin and Sph12−38 with low dose of rifampicin and azithromycin would be beneficial for the control of antibiotic resistance and effective treatment of P. aeruginosa-infected diseases in the future.
The Prognostic Value of Reticulated Platelets in Patients With Coronary Artery Disease: A Systematic Review and Meta-Analysis

Background: Reticulated platelets (RPs) represent the young population in the circulating platelet pool, indicating platelet turnover. Preliminary studies suggested circulating levels of RPs were associated with cardiovascular events (CVEs) in patients with coronary artery disease (CAD). Methods: This study systematically searched PubMed, Scopus, Embase, and Web of Science for eligible studies which reported RPs as a prognostic factor and the incidence of CVEs in patients with CAD. The risk estimates and 95% confidence intervals (95% CI) were analyzed for adjusted and unadjusted associations separately using random-effects model. Meta-regression and subgroup analysis were used to identify the source of heterogeneity. Funnel plots, Egger's test, and trim and fill methods were used to assess the publication bias. Results: A total of six cohort studies were included in this meta-analysis. Four studies were rated as high quality with the remaining rated as moderate quality. The funnel plot, Egger's test, and trim and fill method suggested the presence of publication bias. The pooled results indicated elevated RPs were associated with a higher risk of composite CVEs [risk ratio (RR), 2.26; 95% CI, 1.72–2.98, with little heterogeneity] and cardiovascular death (RR, 2.33; 95% CI, 1.66–3.28, with little heterogeneity). Based on results of separate meta-analysis, we found RPs might be a good predictor for revascularization but not for myocardial infarction or cerebrovascular events. After adjustment of conventional prognostic factors, the pooled result still suggested the prognostic value of RPs for composite CVEs (RR, 2.00; 95% CI, 1.30–3.08; p < 0.00001, with substantial heterogeneity). Subgroup analysis and meta-regression of adjusted risk estimates revealed that the number of adjustment factors might be the source heterogeneity. Conclusion: Circulating level of RPs might be a useful prognostic marker for CVEs in patients with CAD, even after adjustment of other prognostic factors.
DNA Methylation Patterns of Chronic Explosive Breaching in U.S. Military Warfighters

Background: Injuries from exposure to explosions rose dramatically during the Iraq and Afghanistan wars, which motivated investigation of blast-related neurotrauma. We have undertaken human studies involving military “breachers” —exposed to controlled, low-level blast during a 3-days explosive breaching course. Methods: We screened epigenetic profiles in peripheral blood samples from 59 subjects (in two separate U.S. Military training sessions) using Infinium MethylationEPIC BeadChips. Participants had varying numbers of exposures to blast over their military careers (empirically defined as high ≥ 40, and conversely, low < 39 breaching exposures). Daily self-reported physiological symptoms were recorded. Tinnitus, memory problems, headaches, and sleep disturbances are most frequently reported. Results: We identified 14 significantly differentially methylated regions (DMRs) within genes associated with cumulative blast exposure in participants with high relative to low cumulative blast exposure. Notably, NTSR1 and SPON1 were significantly differentially methylated in high relative to low blast exposed groups, suggesting that sleep dysregulation may be altered in response to chronic cumulative blast exposure. In comparing lifetime blast exposure at baseline (prior to exposure in current training), and top associated symptoms, we identified significant DMRs associated with tinnitus, sleep difficulties, and headache. Notably, we identified KCNN3, SOD3, MUC4, GALR1, and WDR45B, which are implicated in auditory function, as differentially methylated associated with self-reported tinnitus. These findings suggest neurobiological mechanisms behind auditory injuries in our military warfighters and are particularly relevant given tinnitus is not only a primary disability among veterans, but has also been demonstrated in active duty medical records for populations exposed to blast in training. Additionally, we found that differentially methylated regions associated with the genes CCDC68 and COMT track with sleep difficulties, and those within FMOD and TNXB track with pain and headache. Conclusion: Sleep disturbances, as well as tinnitus and chronic pain, are widely reported in U.S. military service members and veterans. As we have previously demonstrated, DNA methylation encapsulates lifetime exposure to blast. The current data support previous findings and recapitulate transcriptional regulatory alterations in genes involved in sleep, auditory function, and pain. These data uncovered novel epigenetic and transcriptional regulatory mechanism underlying the etiological basis of these symptoms.
Beyond Lazy Training for Over-parameterized Tensor Decomposition

Over-parametrization is an important technique in training neural networks. In both theory and practice, training a larger network allows the optimization algorithm to avoid bad local optimal solutions. In this paper we study a closely related tensor decomposition problem: given an $l$-th order tensor in $(R^d)^{\otimes l}$ of rank $r$ (where $r\ll d$), can variants of gradient descent find a rank $m$ decomposition where $m > r$? We show that in a lazy training regime (similar to the NTK regime for neural networks) one needs at least $m = \Omega(d^{l-1})$, while a variant of gradient descent can find an approximate tensor when $m = O^*(r^{2.5l}\log d)$. Our results show that gradient descent on over-parametrized objective could go beyond the lazy training regime and utilize certain low-rank structure in the data.
Learning to Summarize Long Texts with Memory Compression and Transfer

We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recurrent neural network based encoder decoder architectures and we explore its use for abstractive document summarization. Mem2Mem transfers "memories" via readable/writable external memory modules that augment both the encoder and decoder. Our memory regularization compresses an encoded input article into a more compact set of sentence representations. Most importantly, the memory compression step performs implicit extraction without labels, sidestepping issues with suboptimal ground-truth data and exposure bias of hybrid extractive-abstractive summarization techniques. By allowing the decoder to read/write over the encoded input memory, the model learns to read salient information about the input article while keeping track of what has been generated. Our Mem2Mem approach yields results that are competitive with state of the art transformer based summarization methods, but with 16 times fewer parameters
Contextual Linear Types for Differential Privacy

Language support for differentially private programming is both crucial and delicate. While elaborate program logics can be very expressive, type-system-based approaches using linear types tend to be more lightweight and amenable to automatic checking and inference, and in particular in the presence of higher-order programming. Since the seminal design of Fuzz, which is restricted to ϵ-differential privacy in its original design, significant progress has been made to support more advanced variants of differential privacy, like (ϵ, δ)-differential privacy. However, supporting these advanced privacy variants while also supporting higher-order programming in full has proven to be challenging. We present Jazz, a language and type system that uses linear types and latent contextual effects to support both advanced variants of differential privacy and higher-order programming. Latent contextual effects allow delaying the payment of effects for connectives such as products, sums, and functions, yielding advantages in terms of precision of the analysis and annotation burden upon elimination, as well as modularity. We formalize the core of Jazz, prove it sound for privacy via a logical relation for metric preservation, and illustrate its expressive power through a number of case studies drawn from the recent differential privacy literature.
Learning Dual Semantic Relations With Graph Attention for Image-Text Matching

Image-Text Matching is one major task in cross-modal information processing. The main challenge is to learn the unified visual and textual representations. Previous methods that perform well on this task primarily focus on not only the alignment between region features in images and the corresponding words in sentences, but also the alignment between relations of regions and relational words. However, the lack of joint learning of regional features and global features will cause the regional features to lose contact with the global context, leading to the mismatch with those non-object words which have global meanings in some sentences. In this work, in order to alleviate this issue, it is necessary to enhance the relations between regions and the relations between regional and global concepts to obtain a more accurate visual representation so as to be better correlated to the corresponding text. Thus, a novel multi-level semantic relations enhancement approach named Dual Semantic Relations Attention Network(DSRAN) is proposed which mainly consists of two modules, separate semantic relations module and the joint semantic relations module. DSRAN performs graph attention in both modules respectively for region-level relations enhancement and regional-global relations enhancement at the same time. With these two modules, different hierarchies of semantic relations are learned simultaneously, thus promoting the image-text matching process by providing more information for the final visual representation. Quantitative experimental results have been performed on MS-COCO and Flickr30K and our method outperforms previous approaches by a large margin due to the effectiveness of the dual semantic relations learning scheme.
Deep Surrogate Q-Learning for Autonomous Driving

Open challenges for deep reinforcement learning systems are their adaptivity to changing environments and their efficiency w.r. t. computational resources and data. In the application of learning lane-change behavior for autonomous driving, the number of required transitions imposes a bottleneck, since test drivers cannot perform an arbitrary amount of lane changes in the real world. In the off-policy setting, additional information on solving the task can be gained by observing actions from others. While in the classical RL setup this knowledge remains unused, we use other drivers as surrogates to learn the agent's value function more efficiently. We propose Surrogate Q-learning that deals with the aforementioned problems and reduces the required driving time drastically. We further propose an efficient implementation based on a permutation equivariant deep neural network architecture of the Q-function to estimate action-values for a variable number of vehicles in sensor range. We evaluate our method in the open traffic simulator SUMO and learn well performing driving policies on the real highD dataset.
Fast and incremental loop closure detection with deep features and proximity graphs

In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications. This article proposes an appearance‐based loop closure detection pipeline named “Fast and Incremental Loop closure Detection (FILD++). First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted. Subsequently, a hierarchical navigable small‐world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features. Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route. An image‐to‐image pairing follows, which exploits local features to evaluate the spatial information. Thus, in the proposed article, we propose a single network for global and local feature extraction in contrast to our previous work (FILD), while an exhaustive search for the verification process is adopted over the generated deep local features avoiding the utilization of hash codes. Exhaustive experiments on eleven publicly available data sets exhibit the system's high performance (achieving the highest recall score on eight of them) and low execution times (22.05 ms on average in New College, which is the largest one containing 52,480 images) compared to other state‐of‐the‐art approaches.
Migration and breeding biology of Arctic terns in Greenland

This thesis presents novel fi ndings for the Arctic tern in Greenland. Included is a study on Arctic tern migration – the longest annual migration ever recorded in any animal. The study documented how Greenland and Iceland breeding terns conduct the roundtrip migration to the Weddell Sea in Antarctica and back. Although the sheer distance (71,000 km on average) travelled by the birds is interesting, the study furthermore showed how the birds depend on high-productive at-sea areas and global wind systems during their massive migration. Furthermore included is the fi rst quantifi ed estimate of the capacity to produce a replacement clutch in Arctic terns. We found that approximately half of the affected birds would produce a replacement clutch when the eggs were removed late in the incubation period. At a level of more national interest, the study produced the fi rst estimates of the key prey species of the Arctic tern in Greenland. Although zooplankton and various fi sh species were present in the chick diet of terns breeding in Disko Bay, Capelin was the single most important prey species found in all age groups of chicks. The thesis also includes a study on the fl uctuating breeding found in Arctic terns. Breeding birds showed a considerable variation in colony size between years in the small and mid sized colonies of Disko Bay. These variations were likely to be linked to local phenomena, such as disturbance from predators, rather than to large-scale occurring phenomena. Included is also a study that documents the breeding association between Arctic terns and other breeding waterbirds. At Kitsissunnguit a close behavioural response to tern alarms could be identifi ed. These fi ndings imply that the altered distributions of waterbirds observed at Kitsissunnguit were governed by the distribution of breeding Arctic terns. Included in the thesis are furthermore results with an appeal to the Greenland management agencies. Along with estimates of the Arctic tern population size at the two most important Arctic tern colonies in West Greenland and East Greenland, the study produced recommendations on how a potential future sustainable egg harvest could be carried out, and on how to monitor Arctic tern colonies in Greenland.
Approximation-Robust Inference in Dynamic Discrete Choice

Estimation and inference in dynamic discrete choice models often relies on approximation to lower the computational burden of dynamic programming. Unfortunately, the use of approximation can impart substantial bias in estimation and results in invalid confidence sets. We present a method for set estimation and inference that explicitly accounts for the use of approximation and is thus valid regardless of the approximation error. We show how one can account for the error from approximation at low computational cost. Our methodology allows researchers to assess the estimation error due to the use of approximation and thus more effectively manage the trade-off between bias and computational expedience. We provide simulation evidence to demonstrate the practicality of our approach.
On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Applications

Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purposes, we evaluate the suitability of such setup for verifying the users through iris recognition. In this work, we first evaluate a set of iris recognition algorithms suitable for HMD devices by investigating three well-established handcrafted feature extraction approaches, and to complement it, we also present the analysis using four deep learning models. While taking into consideration the minimalistic hardware requirements of stand-alone HMD, we employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Further, to account for non-ideal and non-collaborative capture of iris, we define a new iris quality metric that we termed as Iris Mask Ratio (IMR) to quantify the iris recognition performance. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD. Through the experiments on a publicly available OpenEDS dataset, we show that performance with EER = 5% can be achieved using deep learning methods in a general setting, along with high accuracy for continuous user authentication.