Miracle-dz/UniFAR-FTUs huggingface dataset:
The rumen microbiome: a crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency

ABSTRACT Methane is generated in the foregut of all ruminant animals by the microorganisms present. Dietary manipulation is regarded as the most effective and most convenient way to reduce methane emissions (and in turn energy loss in the animal) and increase nitrogen utilization efficiency. This review examines the impact of diet on bovine rumen function and outlines what is known about the rumen microbiome. Our understanding of this area has increased significantly in recent years due to the application of omics technologies to determine microbial composition and functionality patterns in the rumen. This information can be combined with data on nutrition, rumen physiology, nitrogen excretion and/or methane emission to provide comprehensive insights into the relationship between rumen microbial activity, nitrogen utilisation efficiency and methane emission, with an ultimate view to the development of new and improved intervention strategies.
Effect of Gum Chewing Frequency on Oxygenation of the Prefrontal Cortex

Since increased cerebral oxygenation reflects cerebral activation, this study investigated the effect of mastication frequency on prefrontal cortex oxygenation. Eleven young volunteers (nine women, two men; M age = 20.9 years, SD = 0.9) carried out three trials in which they were asked to chew a tasteless gum for 3 min at varying (rates of mastication frequency: 30, 70, and 110). Breaks of 2 min each were interleaved between trials. The oxygenation of the left prefrontal cortex was monitored by near-infrared spectroscopy. We found a significant increase in cortical oxygenation during gum chewing in all three conditions (p < .05), compared with a resting level; we also found a significant difference between the Fast and Slow chewing conditions, and between the Fast and Normal (70 rpm) conditions, both findings seemingly related to activation of a motor command in frontal brain regions. To our knowledge, this is the first report on the effect of mastication frequency on cerebral oxygenation. Possible implications of this finding are discussed.
A Method of Indirect Configuration Propagation With Estimation of System State in Networked Multi-Agent Dynamic Systems

Networked multi-agent dynamic system (NMDS) exhibits emergent swarming behavior by using local control laws based on spatial information from the nearby environment and adjacent agents. One of the challenges in NMDS is the lack of appropriate methods for operators to interact with a large group of agents. Studies on human–agent interaction have mainly focused on the direct influence on the behavior of the multi-agents, but it is not feasible for a large-scale system in an error-prone environment. In this letter, a robust method for indirect configuration propagation by a human operator is proposed to influence the swarming behavior of NMDS in a decentralized manner. In addition, an estimation method is proposed for the operator to observe the system state, which helps the operator provide an optimal input to the NMDS at the appropriate time. Finally, we demonstrate the superiority of the proposed method through theoretical analysis and simulation results.
Survival and prognosis of young adults with gastric cancer

OBJECTIVES: Survival data for young adults (YA) with gastric cancer is conflicting and scarce in Brazil. The aim of this study was to compare the clinicopathological factors and survival rates of younger and older patients with gastric cancer. METHODS: Hospital registries for 294 gastric cancer patients from a reference cancer hospital in São Paulo, Brazil, were consulted for the retrieval of clinicopathological information and follow-up time. Patients were placed into the following groups: YA (≤40 years; N=71), older adult (OA: 41 to 65 years; N=129) and elderly (E: ≥66 years; N=94). Differences were assessed through Pearson's χ2 test, Kaplan-Meier analysis, Log rank test and Cox regression. RESULTS: More YA were diagnosed with advanced disease (clinical stage III/IV: 86.7% YA, 69.9% OA, and 67% E); however, fewer E patients underwent surgery (64.3% YA, 72.7% OA, and 52.4% E). The median overall survival among all patients was 16 months, and the overall survival rate was not significantly different among the age groups (p=0.129). There were no significant differences in the disease-free survival rate. Metastatic disease at diagnosis (HR=4.84; p<0.01) was associated with an increased hazard of death for YA. CONCLUSION: Overall survival was similar among age groups. Metastatic disease at diagnosis was the only factor associated with a poorer prognosis in YA. These results suggest that younger patients deserve special attention regarding the detection of early stage disease.
Breastfeeding self-efficacy and postpartum depression: a cohort study

ABSTRACT Objective: to evaluate breastfeeding self-efficacy, the presence of postpartum depression symptons and the association between breastfeeding self-efficacy and postpartum depression with cessation of exclusive breastfeeding. Method: cohort study with 83 women. The instruments used were the Breastfeeding Self-Efficacy Scale and the Edinburgh Postnatal Depression Scale. Statistical analysis was conducted using the log-rank tests, analysis of variance and the Cox survival model. Results: breastfeeding self-efficacy (p = 0.315) and postpartum depression (p = 0.0879) did not show any statistical difference over time. The chances of cessation of exclusive breastfeeding decreased by 48% when self-efficacy changed from low to medium and by 80% when it changed from medium to high. Postpartum women who scored ≥10 on the Edinburgh Postnatal Depression Scale interrupt exclusive breastfeeding, on average, 10 days earlier than those with a score ≤9, whose median breastfeeding duration was 38 days postpartum. Conclusion: breastfeeding self-efficacy was proved to be a protective factor for exclusive breastfeeding, while postpartum depression is a risk factor.
Anterior Cervical Corpectomy and Fusion and Anterior Cervical Discectomy and Fusion Using Titanium Mesh Cages for Treatment of Degenerative Cervical Pathologies: A Literature Review

Anterior cervical corpectomy and fusion (ACCF) and anterior cervical discectomy and fusion (ACDF) are 2 effective and safe surgical treatments of degenerative cervical pathologies and are associated with a high percentage of excellent clinical outcomes when a graft or device must be used during the surgery, such as an allograft, autograft, nano-hydroxyapatite/polyamide cages, poly-ether-ether-ketone (PEEK) cages, and titanium mesh cages (TMCs). Although TMCs have been used in cervical surgeries for almost 2 decades, no specific reviews have been performed introducing the state of this material. Thus, in the present review, we discuss the status of using TMCs in anterior cervical surgeries. Studies that tested the usage of TMCs in treating degenerative cervical pathologies were included in this review. The development and progress of TMCs, the biomechanical analysis of TMCs, the radiological and clinical assessment of TMCs, the advantages and disadvantages of using TMCs, and their prospects for future applications as a device of ACCF and ACDF in treating degenerative cervical pathologies are discussed. Studies included in this review showed that TMCs can provide sufficient biomechanical stability. Furthermore, the TMCs used in anterior cervical fusion avoid the donor-site morbidity and achieve a solid bony fusion. However, there are some shortcomings. The structural characteristics and the design of TMCs cause the TMC subsidence rate to remain high, thus resulting in multiple related complications. We believe that due to the virtues of TMCs, they are worthy of application and promotion. However, the structure of TMCs should be further optimized to reduce the TMC subsidence rate and subsidence-related complications, ultimately achieving excellent clinical results.
Machine Learning-Based Quality-Aware Power and Thermal Management of Multistream HEVC Encoding on Multicore Servers

The emergence of video streaming applications, together with the users’ demand for high-resolution contents, has led to the development of new video coding standards, such as High Efficiency Video Coding (HEVC). HEVC provides high efficiency at the cost of increased complexity. This higher computational burden results in increased power consumption in current multicore servers. To tackle this challenge, algorithmic optimizations need to be accompanied by content-aware application-level strategies, able to reduce power while meeting compression and quality requirements. In this paper, we propose a machine learning-based power and thermal management approach that dynamically learns and selects the best encoding configuration and operating frequency for each of the videos running on multicore servers, by using information from frame compression, quality, encoding time, power, and temperature. In addition, we present a resolution-aware video assignment and migration strategy that reduces the peak and average temperature of the chip while maintaining the desirable encoding time. We implemented our approach in an enterprise multicore server and evaluated it under several common scenarios for video providers. On average, compared to a state-of-the-art technique, for the most realistic scenario, our approach improves BD-PSNR and BD-rate by 0.54 dB, and 8 percent, respectively, and reduces the encoding time, power consumption, and average temperature by 15.3, 13, and 10 percent, respectively. Moreover, our proposed approach enhances BD-PSNR and BD-rate compared to the HEVC Test Model (HM), by 1.19 dB and 24 percent, respectively, without any encoding time degradation, when power and temperature constraints are relaxed.
The use of WaveLight Contoura to create a uniform cornea: 6-month results with subjective patient surveys

Purpose The aim of this study was to report on the 6-month visual results of Contoura with Layer Yolked Reduction of Astigmatism (LYRA) protocol, as well as the subjective patient-reported outcomes (PRO) by standardized survey. Patients and methods A retrospective analysis was conducted in 50 consecutive eyes with 6-month results that had bilateral Contoura laser-assisted in situ keratomileusis (LASIK) correction of measured astigmatism and axis using the LYRA protocol. PRO were measured via the Refractive Status and Vision Profile (RSVP) standardized survey. Objective visual results, subjective patient results, and rates of regression were reported. Results Most (78%) of the eyes achieved an uncorrected visual acuity (UCVA) of 20/15 and 98% of the eyes achieved an UCVA of 20/20 vision after primary correction. After enhancements, the percentage of uncorrected eyes achieving 20/15 vision was 90 and 20/20 vision was 100. RSVP patient-related outcomes showed that all patients (100%) were either satisfied or very satisfied with their vision. Most patients (21/25, 84%) were very satisfied. On a numerical ordinal scale of 1–10 (where 10 is the best vision), as reported by patients post-operative (post-op), 15 (60%) patients reported a vision score of 10, nine (36%) patients reported a score of 9, and one (4%) patient reported a score of 8. Initial correction was accurate on all patients, but nine eyes had refractive error changes due to epithelial masking of higher order aberrations (HOAs) over the follow-up period, a number that was also likely artificially high due to study bias. Conclusion Contoura measured axis and astigmatism-eliminated HOA, resulting in a more uniform cornea with an accurate removal of astigmatism, excellent 20/15 and 20/20 visual outcomes, and favorable patient-reported subjective outcomes.
An Exploratory Search for Potential Molecular Targets Responsive to the Probiotic Lactobacillus salivarius PS2 in Women With Mastitis: Gene Expression Profiling vs. Interindividual Variability

Probiotics constitute an attractive alternative in the battle against microbial infections. Oral administration of certain strains of lactobacilli isolated from human milk has resulted in an effective reduction of the bacterial load as well as an improvement of the mastitis-associated symptoms. Nevertheless, little is yet known about the potential molecular mechanisms and specific targets implicated in these effects. Transcriptomic profiling has been used to search for disease-associated and therapy-responsive molecules in different disorders and experimental models. We have applied for the first time a gene expression-based molecular approach to explore for potential targets responsive to intervention with a probiotic in: (i) breast milk somatic cells (n = 17) and (ii) blood leukocytes (n = 19). Women with mastitis ingested a new strain of lactobacilli, Lactobacillus salivarius PS2 (3 × capsules per day, each capsule contained ~9.5 log10 CFU) for 21 days. We applied Affymetrix microarrays and Taqman one-step quantitative reverse transcription PCR (RT-qPCR) to analyze and compare gene expression changes between samples pre- and post-treatment. Our results substantiate the involvement of inflammatory and cell-growth related pathways and genes in the breast milk somatic cells following the intake of L. salivarius PS2. Individual analyses of selected genes: (1) supported the upregulation of STC1 and IL19 and the downregulation of PLAUR and IFNGR1 in the somatic cells of the patients as potential targets responsive to the probiotic, (2) detected a lack of a relationship between the gene expression responses in the two types of cells, and (3) evidenced a substantial interindividual variability in the gene expression changes in both types of cells. Our study provides an insight into the essentiality of incorporating the study of tissue-specific interindividual molecular responsivity into future clinical intervention trials to further understand the complexity of human gene expression responses to therapy and the potentiality of selecting appropriate responsive targets.
Prevalence of Plasmodium falciparum antimalarial drug resistance genes in Southeastern Gabon from 2011 to 2014

Purpose The introduction of artemisinin-based combination therapies (ACTs) in treating uncomplicated malaria and sulfadoxine–pyrimethamine (SP) as intermittent preventive treatment during pregnancy drastically decreased the burden of malarial disease around the world. However, ACTs are known to select for drug resistance markers. In Gabon, artemether–lumefantrine induced an increase in the prevalence of N86-Pfmdr1, which is associated with treatment failure. However, little data are available regarding resistance markers in Southeastern Gabon. This study aimed to evaluate the evolution of resistance haplotypes in the Pfcrt, Pfdhps, Pfdhfr, and PfK13 genes from 2011 to 2014 in Southeastern Gabon. Methods A total of 233 Plasmodium falciparum DNA samples were collected from febrile pediatric patients in South Gabon: Franceville, an urban area; Koulamoutou, a semi-urban area; and Lastourville, a rural area. Pfcrt, Pfdhps, Pfdhfr, and the propeller domain of PfK13 were sequenced for all isolates. Results The overall prevalence (3.7%–11.5%) of the wild-type haplotype Pfcrt 72-76 CVMNK was not significantly different between 2011 and 2014 in Southeast Gabon. For Pfdhfr (codons 51, 59, 108, 164), the IRNI triple-mutant haplotype was the most prevalent (>89.0%). The ICNI and NCNI mutant haplotypes and the NCSI wild-type haplotype showed a minor prevalence. There were no differences in the distributions of these haplotypes across the 4 years and the three study sites. For Pfdhps, the AAKAA and SGKAA mutant haplotypes and the SAKAA wild-type haplotype were similarly present in the three areas during the study period. The AGKAA double mutant was first observed in 2013 in Franceville and in 2014 in Koulamoutou and Lastourville. Interestingly, only the A578S mutation (0.4%) and two new A494V (0.4%) and V504A (0.9%) mutations were found in PfK13. Conclusion Despite the withdrawal of chloroquine, the frequency of the resistant allele 76T remained high in the south of Gabon. Moreover, a high level of resistant haplotypes against IPTp-SP was found.
Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification

Online multi-object tracking is a fundamental problem in time-critical video analysis applications. A major challenge in the popular tracking-by-detection framework is how to associate unreliable detection results with existing tracks. In this paper, we propose to handle unreliable detection by collecting candidates from outputs of both detection and tracking. The intuition behind generating redundant candidates is that detection and tracks can complement each other in different scenarios. Detection results of high confidence prevent tracking drifts in the long term, and predictions of tracks can handle noisy detection caused by occlusion. In order to apply optimal selection from a considerable amount of candidates in real-time, we present a novel scoring function based on a fully convolutional neural network, that shares most computations on the entire image. Moreover, we adopt a deeply learned appearance representation, which is trained on large-scale person re-identification datasets, to improve the identification ability of our tracker. Extensive experiments show that our tracker achieves real-time and state-of-the-art performance on a widely used people tracking benchmark.
Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques

This paper presents a machine learning based model for predicting the sugarcane yield grade of an individual plot. The dataset used in this work is obtained from a set of sugarcane plots around a sugar mill in Thailand. The features used in the prediction consist of the plot characteristics (soil type, plot area, groove width, plot yield/ yield grade of the last year), sugarcane characteristics (cane class and type), plot cultivation scheme (water resource type, irrigation method, epidemic control method, fertilizer type/formula) and rain volume. We use two predictive algorithms: (i) random forest classification, and (ii) gradient boosting tree classification. The accuracies of our machine learning based predictive methods are 71.83% and 71.64%, respectively. Meanwhile, the accuracies of two non-machine-learning baselines are 51.52% (using the actual yield of the last year as the prediction) and 65.50% (the target yield of each plot is manually predicted by human expert), respectively. This shows that our work is accurate enough to be applied for decision making of sugar mill operation planning.
Microbial metabolism of methanol and methylamine in the Gulf of Mexico: insight into marine carbon and nitrogen cycling

One carbon (C1) metabolism plays an important role in marine carbon cycling but the dynamics and modes of C1 transformations are not fully understood. We made contemporaneous measurements of methylamine and methanol metabolism to elucidate the role of C1 compounds as sources of carbon, energy and nitrogen. Methanol and methylamine were predominantly used as an energy source in offshore waters (oxidation rate constant: kmethanol : 0.02-0.10 day-1 ; kmethylamine : 0.01-0.18 day-1 ), but were also important sources of biomass carbon in coastal waters (assimilation rate constant: kmethanol : 0.04-0.10 day-1 ; kmethylamine : 0.01-0.05 day-1 ). The relative extent of assimilation versus oxidation for these substrates correlated positively with chlorophyll, nutrients and heterotrophic bacterial production. Methanol oxidation and assimilation were stimulated significantly by nutrient addition. In contrast, methylamine metabolism was inhibited by ammonium or nitrate, suggesting that methylamine served as a nitrogen source. A preliminary metagenomic survey revealed a diverse population of putative C1-utilizing microorganisms. These results show that the remineralization of methylamine could provide both C and N sources for microbes. Both methanol and methylamine contribute to microbial energetic and carbon substrate demands with a distinctly different signature in nearshore versus offshore environments.
Cognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory

One of the important topics in the study of robotic cognition is to enable robot to perceive, plan, and react to situations in a real-world environment. We present a novel angle on this subject, by integrating active navigation with sequence learning. We propose a neuro-inspired cognitive navigation model which integrates the cognitive mapping ability of entorhinal cortex (EC) and episodic memory ability of hippocampus to enable the robot to perform more versatile cognitive tasks. The EC layer is modeled by a 3-D continuous attractor network structure to build the map of the environment. The hippocampus is modeled by a recurrent spiking neural network to store and retrieve task-related information. The information between cognitive map and memory network are exchanged through respective encoding and decoding schemes. The cognitive system is applied on a mobile robot platform and the robot exploration, localization, and navigation are investigated. The robotic experiments demonstrate the effectiveness of the proposed system.
Performance and Behavior Characterization of Amazon EC2 Spot Instances

Amazon EC2's spot instances (SIs) represent a competitive Cloud resource in terms of price compared to reliable and fixed price options. The drawback, however, is that SIs may not always be available and they can be revoked at any given time. In this paper, we describe a comprehensive experimental evaluation for EC2 SIs to characterize their performance and behavior in three different regions each of which in a different continent. We describe the life cycle of SIs with the most important phases of an SI, introduce the most relevant events that can prevent a user from obtaining SIs, and draw important conclusions that can be exploited by the research community to effectively use the spot market. Our results reveal the fulfillment rate of requests for SIs, waiting time until requested SIs become fulfilled, details about the frequency of SI interruption, and how long SIs run before being interrupted. Our study also indicates that the SI frequency of interruption influences the fulfillment rate, SIs are highly reliable in the first 20 to 30 minutes after deployment, and SIs can be reclaimed by EC2 regardless of an SI's bid price and current workload when EC2 lacks resources for On-Demand and Reserved instances.
Changes in the genetic requirements for microbial interactions with increasing community complexity

Microbial community structure and function rely on complex interactions whose underlying molecular mechanisms are poorly understood. To investigate these interactions in a simple microbiome, we introduced E. coli into an experimental community based on a cheese rind and identified the differences in E. coli’s genetic requirements for growth in interactive and non-interactive contexts using Random Barcode Transposon Sequencing (RB-TnSeq) and RNASeq. E. coli’s genetic requirements varied among pairwise growth conditions and between pairwise and community conditions. Our analysis points to mechanisms by which growth conditions change as a result of increasing community complexity and suggests that growth within a community relies on a combination of pairwise and higher order interactions. Our work provides a framework for using the model organism E. coli as a probe to investigate microbial interactions regardless of the genetic tractability of members of the studied ecosystem.