Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic variability, correlation and path analysis in fodder yield and related traits in pearl millet {Pennisetum glaucum (L.) R. Br.}

An evaluation of 84 accessions of pearl millet {Pennisetum glaucum (L.) R. Br. } germplasm for green fodder yield over one season revealed highly significant differences among accessions for days to heading, plant height, tillers per plant, leaves per plant, leaf area, leaf to stem ratio, stem diameter, green fodder yield per plant, dry fodder yield per plant and green fodder yield per plot. The genotypic and phenotypic coefficient of variation were more or less similar for all the characters. Broad sense heritability was moderate 54.89% for tillers per plant to high {69.89% for green fodder yield per plot (kg)} confirming that genotypic variance has contributed substantially to the total variance. Based upon character association and path analysis, it may be concluded that tillers per plant was the major yield component, because it has high positive significant association and positive direct effect on green fodder yield. Based on mean performance of the accession IP-3613, IP-17423, IP-14450, IP-3585 and IP-17394 were observed superior due to high mean value of tillers per plant, leaves per plant and earliness in flowering. Therefore, these accession should be utilized in further breeding programme for developing superior varieties.
Perawatan Periodontal Pada Pasien Dengan Periodontitis Agresif (Laporan Kasus)

Periodontitis agresif merupakan penyakit periodontitis yang laju perkembangan dan kerusakannya terjadi sangat cepat. Penyakit ini biasanya menyerang dewasa muda yang berumur di bawah 30 tahun. Secara klinis ditandai dengan kerusakan jaringan periodontal berupa kehilangan perlekatan pada ligamen periodontal dan kerusakan tulang alveolar secara cepat. Pada tahap awal, tidak terjadi inflamasi yang parah, gingiva terlihat normal dan sehat, tetapi terjadi perdarahan saat probing ketika dilakukan pemeriksaan poket periodontal dan jumlah plak pada gigi yang terlibat biasanya sedikit. Pemeriksaan radiograf memperlihatkan kehilangan tulang alveolar secara vertikal. Laporan kasus ini bertujuan memperlihatkan bahwa kerusakan periodontal yang terjadi pada pasien dengan periodontitis agresif dapat dirawat dengan perawatan yang tepat sehingga penyebab penyakit dapat dihilangkan dan meningkatkan kualitas hidup pasien. Pasien seorang wanita berusia 24 tahun, mengalami kegoyangan gigi anterior pada rahang atas dan rahang bawah dengan kebersihan mulut baik. Gambaran radiograf memperlihatkan kerusakan tulang alveolar yang parah mencapai 1/3 apikal. Kasus ini dirawat dengan bedah flap menggunakan bone graft. Kerusakan yang dirawat dengan bone graft memperlihatkan peningkatan level tulang dan epitel penyatu dan pengurangan kedalaman poket. Kesimpulan perawatan periodontal dapat mengurangi kerusakan periodontal yang lebih lanjut, meskipun pada pasien yang telah mengalami kehilangan dukungan jaringan periodontal yang parah.
Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data

The paper presents and comparatively analyses several deep learning approaches to automatically detect tuberculosis related lesions in lung CTs, in the context of the ImageClef 2020 Tuberculosis task. Three classes of methods, different with respect to the way the volumetric data is given as input to neural network-based classifiers are discussed and evaluated. All these come with a rich experimental analysis comprising a variety of neural network architectures, various segmentation algorithms and data augmentation schemes. The reported work belongs to the SenticLab.UAIC team, which obtained the best results in the competition.
Improvement of a PCR Method with the Sph I-5 Primer Set for the Detection of Koi Herpesvirus (KHV)

Improvement of a PCR method using the Sph I-5 primer set, which has been used for detection of KHV in Japan, was carried out to reduce negative factors, such as the appearance of non-specific reactions and the comparatively long reaction time. Moreover, there was a wrong sequence in the original reverse primer. The modified PCR protocol with the corrected primer set is as follows : initial denaturation at 94°C for 30s, 40 cycles of amplification (denaturation at 94°C for 30s, annealing at 63°C for 30s and elongation at 72°C for 30s) and final elongation at 72°C for 7min. The improved PCR reduced non-specific reactions and the total reaction time to almost half reguired by the original one.
Handicapping the Race for the Last Mile?: A Critique of Open Access Rules for Broadband Platforms

“The technology to supply almost limitless bandwidth is now at hand. Broadband networks already occupy the top tiers of the telephone network, operated by regional and national telephone companies, and the top tiers of the broadcast networks, operated by video carriers. Only the last mile remains to be conquered. ” †† This Article evaluates the battle to conquer the last mile, by surveying the leading platforms and technologies for providing broadband telecommunications to individual customers. The Article also describes current regulation of those platforms, focusing on the extent to which the owners must provide open access to unaffiliated companies wishing to use the platforms. Addressing current arguments made by companies seeking mandatory open access rules for broadband platforms, this Article concludes that the nature of consumer demand for a broadband access platform, which will be strongly responsive to the variety of content services made available over the platform, makes open access rules unnecessary and potentially counterproductive.
A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands

Infectious diseases on farms pose both public and animal health risks, so understanding how they spread between farms is crucial for developing disease control strategies to prevent future outbreaks. We develop novel Bayesian nonparametric methodology to fit spatial stochastic transmission models in which the infection rate between any two farms is a function that depends on the distance between them, but without assuming a specified parametric form. Making nonparametric inference in this context is challenging since the likelihood function of the observed data is intractable because the underlying transmission process is unobserved. We adopt a fully Bayesian approach by assigning a transformed Gaussian process prior distribution to the infection rate function, and then develop an efficient data augmentation Markov Chain Monte Carlo algorithm to perform Bayesian inference. We use the posterior predictive distribution to simulate the effect of different disease control methods and their economic impact. We analyse a large outbreak of avian influenza in the Netherlands and infer the between‐farm infection rate, as well as the unknown infection status of farms which were pre‐emptively culled. We use our results to analyse ring‐culling strategies, and conclude that although effective, ring‐culling has limited impact in high‐density areas.
Learning Anatomical Segmentations for Tractography from Diffusion MRI

Deep learning approaches for diffusion MRI have so far focused primarily on voxel-based segmentation of lesions or white-matter fiber tracts. A drawback of representing tracts as volumetric labels, rather than sets of streamlines, is that it precludes point-wise analyses of microstructural or geometric features along a tract. Traditional tractography pipelines, which do allow such analyses, can benefit from detailed whole-brain segmentations to guide tract reconstruction. Here, we introduce fast, deep learning-based segmentation of 170 anatomical regions directly on diffusion-weighted MR images, removing the dependency of conventional segmentation methods on T 1-weighted images and slow pre-processing pipelines. Working natively in diffusion space avoids non-linear distortions and registration errors across modalities, as well as interpolation artifacts. We demonstrate consistent segmentation results between 0 . 70 and 0 . 87 Dice depending on the tissue type. We investigate various combinations of diffusion-derived inputs and show generalization across different numbers of gradient directions. Finally, integrating our approach to provide anatomical priors for tractography pipelines, such as TRACULA, removes hours of pre-processing time and permits processing even in the absence of high-quality T 1-weighted scans, without degrading the quality of the resulting tract estimates.
DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

We present a novel approach (DyNODE) that captures the underlying dynamics of a system by incorporating control in a neural ordinary differential equation framework. We conduct a systematic evaluation and comparison of our method and standard neural network architectures for dynamics modeling. Our results indicate that a simple DyNODE architecture when combined with an actor-critic reinforcement learning (RL) algorithm that uses model predictions to improve the critic's target values, outperforms canonical neural networks, both in sample efficiency and predictive performance across a diverse range of continuous tasks that are frequently used to benchmark RL algorithms. This approach provides a new avenue for the development of models that are more suited to learn the evolution of dynamical systems, particularly useful in the context of model-based reinforcement learning. To assist related work, we have made code available at this https URL .
Multi-gigayear White Dwarf Cooling Delays from Clustering-enhanced Gravitational Sedimentation

Cooling white dwarfs (WDs) can yield accurate ages when theoretical cooling models fully account for the physics of the dense plasma of WD interiors. We use MESA to investigate cooling models for a set of massive and ultramassive WDs (0.9–1.3 ) for which previous models have failed to match kinematic age indicators based on Gaia DR2. We find that the WDs in this population can be explained as C/O cores experiencing unexpectedly rapid 22Ne sedimentation in the strongly liquid interior just prior to crystallization. We propose that this rapid sedimentation is due to the formation of solid clusters of 22Ne in the liquid C/O background plasma. We show that these heavier solid clusters sink faster than individual 22Ne ions and enhance the sedimentation heating rate enough to dramatically slow WD cooling. MESA models including our prescription for cluster formation and sedimentation experience cooling delays of ≈4 Gyr on the WD Q branch, alleviating tension between cooling ages and kinematic ages. This same model then predicts cooling delays coinciding with crystallization of 6 Gyr or more in lower-mass WDs (0.6–0.8 ). Such delays are compatible with, and perhaps required by, observations of WD populations in the local 100 pc WD sample and the open cluster NGC 6791. These results motivate new investigations of the physics of strongly coupled C/O/Ne plasma mixtures in the strongly liquid state near crystallization and tests through comparisons with observed WD cooling.
Heilpflanzenporträt: Pestwurz (Petasites hybridus L.)

Familie: Asteracae (Korbblütler) Namensgebung: Die gemeine Pestwurz (Petasites hybridus [L.] Gaertn., Meyer et Scherb., syn. Petasites officinalis Moench) hat viele volkstümliche Namen: Rote Pestwurz, Falscher Huflattich, Großer Huflattich, Wasserklette, Hutpflanze, Kraftwurz, Schneewurz und seit dem 16. Jh. auch Pestilenzwurz. Der Name petasus bedeutet im lat. „Reisehut“ und bezieht sich auf die regenschirmgroßen Blätter. Herkunft: Die Pflanze wächst in Europa und NW-Asien an Flussufern, am Rande überschwemmter Wiesen.
Evaluación de la validez y confiabilidad de la Escala Cognoscitiva para el Delirium

El objetivo de este estudio fue el de traducir al castellano y evaluar la validez y la confiabilidad de la Escala Cognoscitiva para Delirium (ECD). Metodo: Los pacientes provenian de cinco centros hospitalarios del Instituto Mexicano del Seguro Social de la ciudad de Mexico, con diagnostico clinico de delirium, demencia, esquizofrenia y depresion, de acuerdo con los criterios del DSM III-R. En la fase piloto, dos evaluadores le aplicaron la ECD a 10 pacientes. A todos se les splico la Escala Cognoscitiva para el delirium, y el Examen Cognoscitivo Breve, y se recabaron los datos clinicos y demograficos. Resultados: Se incluyeron 22 pacientes con delirium, 22 con demencia, 25 con esquizofrenia y 30 con depresion mayor. La ECD mostro una confiabilidad interevaluador de 0.72 y una consistencia interna de 0.77. Los pacientes con delirium calificaron significativamente mas bajo (8.2+-2.5) que los de demencia (18.15+-5.2), esquizofrenia (22.2+-3.1) y depresion (24.6+-2.4) (P < 0.0001). Conclusion: La version en castellano de la ECD mostro un comportamiento psicometrico similar al de la version inglesa.
On cosmography in the cosmic dark ages: are we still in the dark?

Quasars and gamma-ray bursts show considerable promise as standard candles in a high-redshift window beyond Type Ia supernovae. Recently, Risaliti, Lusso & collaborators [1, 2, 3] have succeeded in producing a high redshift Hubble diagram ($ z \lesssim 7$) that supports "a trend whereby the Hubble diagram of quasars is well reproduced by the standard flat $\Lambda$CDM model up to $z \sim 1.5 -2$, but strong deviations emerge at higher redshifts". This conclusion hinges upon a log polynomial expansion for the luminosity distance. In this note, we demonstrate that this expansion typically can only be trusted up to $z \sim 1.5-2$. As a result, a breakdown in the validity of the expansion may be misinterpreted as a (phantom) deviation from flat $\Lambda$CDM. We further illustrate the problem through mock data examples.
Probabilistic dipole inversion for adaptive quantitative susceptibility mapping

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation. In PDI, a deep convolutional neural network (CNN) is used to represent the multivariate Gaussian distribution as the approximate posterior distribution of susceptibility given the input measured field. Such CNN is first trained on healthy subjects via posterior density estimation, where the training dataset contains samples from the true posterior distribution. Domain adaptations are then deployed on patient datasets with new pathologies not included in pre-training, where PDI updates the pre-trained CNN’s weights in an unsupervised fashion by minimizing the Kullback-Leibler divergence between the approximate posterior distribution represented by CNN and the true posterior distribution from the likelihood distribution of a known physical model and pre-defined prior distribution. Based on our experiments, PDI provides additional uncertainty estimation compared to the conventional MAP approach, while addressing the potential issue of the pre-trained CNN when test data deviates from training. Our code is available at https://github.com/Jinwei1209/Bayesian_QSM.
COVID - TRANSFORMER : DETECTING TRENDING TOPICS ON TWITTER USING UNIVERSAL SENTENCE ENCODER

The novel corona-virus disease (also known as COVID-19) has led to a pandemic, impacting more than 200 countries across the globe. With its global impact, COVID-19 has become a major concern of people almost everywhere, and therefore there are a large number of tweets coming out from every corner of the world, about COVID-19 related topics. In this work, we try to analyze the tweets and detect the trending topics and major concerns of people on Twitter, which can enable us to better understand the situation, and devise better planning. More speciﬁcally we propose a model based on the universal sentence encoder to detect the main topics of Tweets in recent months. We used universal sentence encoder in order to derive the semantic representation and the similarity of tweets. We then used the sentence similarity and their embeddings, and feed them to K-means clustering algorithm to group similar tweets (in semantic sense). After that, the cluster summary is obtained using a text summarization algorithm based on deep learning, which can uncover the underlying topics of each cluster. Through experimental results, we show that our model can detect very informative topics, by processing a large number of tweets on sentence level (which can preserve the overall meaning of the tweets). Since this framework has no restriction on speciﬁc data distribution, it can be used to detect trending topics from any other social media and any other context rather than COVID-19. Experimental results show superiority of our proposed approach to other baselines, including TF-IDF, and latent Dirichlet allocation (LDA).
The SPICE study: 48-week activity of combinations of saquinavir soft gelatin and nelfinavir with and without nucleoside analogues. Study of Protease Inhibitor Combinations in Europe.

OBJECTIVES: To compare the efficacy and safety of saquinavir soft gelatin capsules (SQV-SGC) and nelfinavir (NFV), with or without two concomitant nucleoside reverse transcriptase inhibitors (NRTIs), in an exploratory objective to identify populations most likely to benefit from quadruple therapy. DESIGN: Phase II/III, open-label, randomized, parallel-arm, multicenter trial. PARTICIPANTS: Enrollment included 157 protease inhibitor-naive adults (> or = 13 years) with HIV-1 RNA > or = 10,000 copies/ml; 132 participants completed 48 weeks of therapy. INTERVENTIONS: SQV-SGC 1200 mg, NFV 750 mg, SQV-SGC 800 mg plus NFV 750 mg, all with two NRTIs, and SQV-SGC 800 mg plus NFV 750 mg alone, all three times daily for 48 weeks. MAIN OUTCOME MEASURES: Proportion of participants with HIV-1 RNA <50 copies/ ml (16 and 48 weeks); time to virologic relapse (48 weeks). RESULTS: Proportions of patients with HIV RNA <50 copies/ml were not statistically significantly different between arms at 16 or 48 weeks, although trends favored the quadruple-therapy arm. In patients experiencing virologic relapse, time to relapse was statistically significantly longer in the quadruple-therapy arm than in the other three arms (p = .007). Quadruple therapy provided benefit in NRTI-experienced patients and those with viral loads above the median value at baseline. Adverse events were mainly mild gastrointestinal disorders in all treatment arms. CONCLUSIONS: Quadruple therapy, including SQV-SGC and NFV, gave a more durable response than triple therapy with either single protease inhibitor. Quadruple therapy might particularly benefit NRTI-experienced patients and those with high baseline viral loads.
Metis: Multi-Agent Based Crisis Simulation System

With the advent of the computational technologies (Graphics Processing Units - GPUs) and Machine Learning, the research domain of crowd simulation for crisis management has flourished. Along with the new techniques and methodologies that have been proposed all those years, aiming to increase the realism of crowd simulation, several crisis simulation systems/tools have been developed, but most of them focus on special cases without providing users the ability to adapt them based on their needs. Towards these directions, in this paper, we introduce a novel multi-agent-based crisis simulation system for indoor cases. The main advantage of the system is its ease of use feature, focusing on non-expert users (users with little to no programming skills) that can exploit its capabilities a, adapt the entire environment based on their needs (Case studies) and set up building evacuation planning experiments with some of the most popular Reinforcement Learning algorithms. Simply put, the system's features focus on dynamic environment design and crisis management, interconnection with popular Reinforcement Learning libraries, agents with different characteristics (behaviors), fire propagation parameterization, realistic physics based on popular game engine, GPU-accelerated agents training and simulation end conditions. A case study exploiting a popular reinforcement learning algorithm, for training of the agents, presents the dynamics and the capabilities of the proposed systems and the paper is concluded with the highlights of the system and some future directions.
Understanding the Dynamics of Drivers' Locations for Passengers Pickup Performance: A Case Study

With the emergence of e-hailing taxi services, a growing number of scholars have attempted to analyze the taxi trips data to gain insights from drivers' and passengers' flow patterns and understand different dynamics of urban public transportation. Existing studies are limited to passengers' location analysis e.g., pick-up and drop-off points, in the context of maximizing the profits or better managing the resources for service providers. Moreover, taxi drivers' locations at the time of pick-up requests and their pickup performance in the spatial-temporal domain have not been explored. In this paper, we analyze drivers' and passengers' locations at the time of booking request in the context of drivers' pick-up performances. To facilitate our analysis, we implement a modified and extended version of a co-clustering technique, called sco-iVAT, to obtain useful clusters and co-clusters from big relational data, derived from booking records of Grab ride-hailing service in Singapore. We also explored the possibility of predicting timely pickup for a given booking request, without using entire trajectories data. Finally, we devised two scoring mechanisms to compute pickup performance score for all driver candidates for a booking request. These scores could be integrated into a booking assignment model to prioritize top-performing drivers for passenger pickups.
Three Mammals New to the Pleistocene Fauna of Florida, and a Reconsideration of Five Earlier Records

Mormoops megalophylla (Peters), Conepatus leuconotus (Lichtenstein) and Felis pardalis Linn. are reported from Pleistocene deposits in Florida. Molossides floridanus G. M. Allen is transferred to the genus Eumops . The material upon which the reports of Erethizon dorsatum (Linn.), Mammuthus primigenius (Blumenbach) and Cervus canadensis Erxleben are founded is not adequate to justify inclusion of these species in the Pleistocene fauna of Florida. The record of Ovibovinae in the Pleistocene fauna of Florida is spurious.