Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Exploration of Genomic Conservation

Comparative analysis in genomics involves comparing two or more genomes to identify conserved genetic information. These duplicated regions can indicate shared ancestry and can shed light on an organism’s internal functions and evolutionary history. Due to rapid advances in sequencing technology, high-resolution genome data is now available for a wide range of species, and comparative analysis of this data can provide insights that can be applied in medicine, plant breeding, and many other areas. Comparative genomics is a strongly interactive task, and visualizing the location, size, and orientation of conserved regions can assist researchers by supporting critical activities of interpretation and judgement. However, visualization tools for the analysis of conserved regions have not Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. GI’20, May 21–22, 2020, Toronto, ON, Canada © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-6708-0/20/04. $15.00 DOI: https://doi.org/10.1145/3313831.XXXXXXX kept pace with the increasing availability of genomic information and the new ways in which this data is being used by biological researchers. To address this gap, we gathered requirements for interactive exploration from three groups of expert genomic scientists, and developed a web-based tool with novel interaction techniques and visual representations to meet those needs. Our tool supports multi-resolution analysis, provides interactive filtering as researchers move deeper into the genome, supports revisitation to specific interface configurations, and enables loosely-coupled collaboration over the genomic data. An evaluation of the system with five researchers from three expert groups provides evidence about the success of our system’s novel techniques for supporting interactive exploration of genomic conservation.
On Completeness of Inference Rules for Vague Functional and Vague Multivalued Dependencies in Two-Element Vague Relation

In this paper we pay attention to completeness of the inference rules for vague functional and vague multivalued dependencies in two-element, vague relation instances. Motivated by the fact that the set of the inference rules is a complete set, that is, these exists a vague relation instance on given relation scheme which satisfies all vague functional and vague multivalued dependencies in the closure of the union of some set of vague functional and some set of vague multivalued dependencies, and violates a vague functional, respectively, a vague multivalued dependency outside of the closure, we prove that the vague relation instance may be chosen to contain only two elements.
IEICE Proceeding Series

Reservoir computing is a decade old framework from the field of machine learning to use and train recurrent neural networks and it splits the network in a reservoir that does the computation and a simple readout function. This technique has been among the state-of-theart for a broad class of classification and recognition problems such as time series prediction, speech recognition and robot control. However, so far implementations have been mainly software based, while a hardware implementation offers the promise of being lowpower and fast. Despite essential differences between classical software implementation and a network of semiconductor optical amplifiers, we will show that photonic reservoirs can achieve an even better performance on a benchmark isolated digit recognition task, if the interconnection delay is optimized and the phase can be controlled. In this paper we will discuss the essential parameters needed to create an optimal photonic reservoir designed for a certain task. This design can lead to an efficient implementation of a photonic reservoir on a nanophotonic chip.
When the Choice Is Ours: Context and Agency Modulate the Neural Bases of Decision-Making

The option to choose between several courses of action is often associated with the feeling of being in control. Yet, in certain situations, one may prefer to decline such agency and instead leave the choice to others. In the present functional magnetic resonance imaging (fMRI) study, we provide evidence that the neural processes involved in decision-making are modulated not only by who controls our choice options (agency), but also by whether we have a say in who is in control (context). The fMRI results are noteworthy in that they reveal specific contributions of the anterior frontomedian cortex (viz. BA 10) and the rostral cingulate zone (RCZ) in decision-making processes. The RCZ is engaged when conditions clearly present us with the most choice options. BA 10 is engaged in particular when the choice is completely ours, as well as when it is completely up to others to choose for us which in turn gives rise to an attribution of control to oneself or someone else, respectively. After all, it does not only matter whether we have any options to choose from, but also who decides on that.
Tracking Moving Identities: After Attending the Right Location, the Identity Does Not Come for Free

Although tracking identical moving objects has been studied since the 1980's, only recently the study into tracking moving objects with distinct identities has started (referred to as Multiple Identity Tracking, MIT). So far, only behavioral studies into MIT have been undertaken. These studies have left a fundamental question regarding MIT unanswered, is MIT a one-stage or a two-stage process? According to the one-stage model, after a location has been attended, the identity is released without effort. However, according to the two-stage model, there are two effortful stages in MIT, attending to a location, and attending to the identity of the object at that location. In the current study we investigated this question by measuring brain activity in response to tracking familiar and unfamiliar targets. Familiarity is known to automate effortful processes, so if attention to identify the object is needed, this should become easier. However, if no such attention is needed, familiarity can only affect other processes (such as memory for the target set). Our results revealed that on unfamiliar trials neural activity was higher in both attentional networks, and visual identification networks. These results suggest that familiarity in MIT automates attentional identification processes, thus suggesting that attentional identification is needed in MIT. This then would imply that MIT is essentially a two-stage process, since after attending the location, the identity does not seem to come for free.
Predicting Stock Price Movement with Event-Driven Deep Learning Approach

This project uses NLP methods to forecast stock movements with ﬁnancial news headlines, with the focus on input format. Whereas the majority of the work in this area resorts to simple text features, we use structured events representations and other mature NLP embedding methods to preprocess our input data. We also try a few CNN-and RNN-based model architectures. With Smooth Inverse Frequency embedding and LSTM network, we achieved test accuracy roughly comparable with Ding et al. [2015], the paper we set out to replicate and expand upon.
Plant traits and environment: floating leaf blade production and turnover of waterlilies

Floating leaf blades of waterlilies fulfill several functions in wetland ecosystems by production, decomposition and turnover as well as exchange processes. Production and turnover rates of floating leaf blades of three waterlily species, Nuphar lutea (L.) Sm. , Nymphaea alba L. and Nymphaea candida Presl, were studied in three freshwater bodies, differing in trophic status, pH and alkalinity. Length and percentages of leaf loss of marked leaf blades were measured weekly during the growing season. Area and biomass were calculated based on leaf length and were used to calculate the turnover rate of floating leaf blades. Seasonal changes in floating leaf production showed that values decreased in the order: Nymphaea alba, Nuphar lutea, Nymphaea candida. The highest production was reached for Nuphar lutea and Nymphaea alba in alkaline, eutrophic water bodies. The production per leaf was relatively high for both species in the acid water body. Nymphaea candida showed a very short vegetation period and low turnover rates. The ratio Total potential leaf biomass/Maximum potential leaf biomass (P/Bmax) of the three species ranged from 1.35–2.25. The ratio Vegetation period (Period with floating leaves)/Mean leaf life span ranged from 2.94–4.63, the ratio Growth period (Period with appearance of new floating leaves)/Vegetation period from 0.53–0.73. The clear differences between Nymphaea candida versus Nuphar lutea and Nymphaea alba, may be due to adaptations of Nymphaea candida to an Euro-Siberic climate with short-lasting summer conditions.
Antibody affinity and valency impact brain uptake of transferrin receptor-targeted gold nanoparticles

Rationale: The ability to treat invalidating neurological diseases is impeded by the presence of the blood-brain barrier (BBB), which inhibits the transport of most blood-borne substances into the brain parenchyma. Targeting the transferrin receptor (TfR) on the surface of brain capillaries has been a popular strategy to give a preferential accumulation of drugs or nanomedicines, but several aspects of this targeting strategy remain elusive. Here we report that TfR-targeted gold nanoparticles (AuNPs) can accumulate in brain capillaries and further transport across the BBB to enter the brain parenchyma. Methods: We characterized our targeting strategy both in vitro using primary models of the BBB and in vivo using quantitative measurements of gold accumulation by inductively-coupled plasma-mass spectrometry together with morphological assessments using light microscopy after silver enhancement and transmission electron microscopy with energy-dispersive X-ray spectroscopy. Results: We find that the uptake capacity is significantly modulated by the affinity and valency of the AuNP-conjugated antibodies. Specifically, antibodies with high and low affinities mediate a low and intermediate uptake of AuNPs into the brain, respectively, whereas a monovalent (bi-specific) antibody improves the uptake capacity remarkably. Conclusion: Our findings indicate that monovalent ligands may be beneficial for obtaining transcytosis of TfR-targeted nanomedicines across the BBB, which is relevant for future design of nanomedicines for brain
Metabolism of glucose and acids in stressed anaerobic digestors : an investigation by 13C-NMR spectroscopy

The metabolism of glucose and the acids produced from glucose were studied in non-stressed and stressed anaerobic digesters. The utilization of glucose and the acids, acetate and propionate, were observed by 13C-nmr spectroscopy. Rate constants were obtained for both stressed and non-stressed fermentations. The rate constants obtained for propionate utilization suggested that propionate oxidation was a limiting factor in the anaerobic fermentation of glucose. The observed carbon flow from 13C-l-glucose to the acids indicated that a major portion of the glucose was initially converted to lactate which was subsequently metabolized to either propionate or acetate. These studies confirmed an earlier suggestion that under high energy loads a major portion of the glucose was converted to reduced acids e.g, propionate. Under these conditions, these investigations indicated that approximately 70% of the glucose fed was converted to propionate. Thus, the methanogenic bacteria obtained a limited suppply of acetate rather than a saturating level. Possible metabolic intermediates of propionate catabolism indicated that both the acrylate and randomizing pathways had been utilized. STATEMENT OF PERMISSION OF OOPY In presenting this thesis in partial fulfillment of the requirements for an advance^ degree, at Montana State University, I agree that the Library shall make it freely available for inspection. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by my major professor, or, in his absence, by the Director of Libraries. It is understood that any copying or publication of this thesis for financial gain shall not be allowed without rry written permission. METABOLISM OF GL(XX)SE AND ACIDS IN STRESSED ANAERCBIC DIGESTORS: AN INVESTIGATION BY 13C-NMR SPECTROSCOPY
Risk factors for severe corona virus disease 2019 (COVID-19) patients : a systematic review and meta analysis

Importance: With the increasing number of infections for COVID-19, the global health resources are deficient. At present, we don't have specific medicines or vaccines against novel coronavirus pneumonia (NCP) and our assessment of risk factors for patients with severe pneumonia was limited. In order to maximize the use of limited medical resources, we should distinguish between mild and severe patients as early as possible. Objective: To systematically review the evidence of risk factors for severe corona virus disease 2019 (COVID-19) patients. Evidence Review: We conducted a comprehensive search for primary literature in both Chinese and English electronic bibliographic data bases including China National Knowledge Infrastructure (CNKI), Wanfang, Weipu, Chinese Biomedicine Literature Database (CBM-SinoMed), MEDLINE (via PubMed), EMBASE, Cochrane Central Register, and Web of science. The American agency for health research and quality (AHRQ) tool were used for assessing risk of bias. Mata-analysis was undertaken using STATA version 15.0. Results: 20 articles (N=4062 participants) were eligible for this systematic review and meta-analysis. First in this review and meta-analysis, we found that elderly male patients with a high body mass index, high breathing rate and a combination of underlying diseases (such as hypertension, diabetes, cardiovascular disease, and chronic obstructive pulmonary disease) were more likely to develop into critically ill patients. second, compared with ordinary patients, severe patients had more significant symptom such as fever and dyspnea. Besides, the laboratory test results of severe patients had more abnormal than non-severe patients, such as the elevated levels of white-cell counts, liver enzymes, lactate dehydrogenase, creatine kinase, c-reactive protein and procalcitonin, etc, while the decreased levels of lymphocytes and albumin, etc. Interpretation: This is the first systematic review investigating the risk factors for severe corona virus disease 2019 (COVID-19) patients. The findings are presented and discussed by different clinical characteristics. Therefore, our review may provide guidance for clinical decision-making and optimizes resource allocation.
COVID-19: Recovering estimates of the infected fatality rate during an ongoing pandemic through partial data

In an ongoing epidemic, the case fatality rate is not a reliable estimate of a disease's severity. This is particularly so when a large share of asymptomatic or pauci-symptomatic patients escape testing, or when overwhelmed healthcare systems are forced to limit testing further to severe cases only. By leveraging data on COVID-19, we propose a novel way to estimate a disease's infected fatality rate, the true lethality of the disease, in the presence of sparse and partial information. We show that this is feasible when the disease has turned into a pandemic and data comes from a large number of countries, or regions within countries, as long as testing strategies vary sufficiently. For Italy, our method estimates an IFR of 1.1% (95% CI: 0.2% - 2.1%), which is strongly in line with other methods. At the global level, our method estimates an IFR of 1.6% (95% CI: 1.1% - 2.1%). This method also allows us to show that the IFR varies according to each country's age structure and healthcare capacity.
Efficacy of remdesivir versus placebo for the treatment of COVID-19: A protocol for systematic review and meta-analysis of randomized controlled trials

Background: In spite of the global containment on prevention efforts, the spread of coronavirus disease 2019 (COVID-19) is continuing to rise, with 1.1 million confirmed cases and 60,124 deaths recorded worldwide since 04 April 2020. The outbreak has a significant threat to international health and economy. At present, there is no approved vaccine or treatment for the disease, while efforts are underway. Remdesivir, a nucleotide-analogue antiviral drug developed for Ebola, is determined to prevent and stop infections with COVID-19, while results are yet controversial. Here, we aim to conduct a systematic review and meta-analysis of randomized controlled trials to compare the effectiveness of remdesivir and placebo in patients with COVID-19. Method and analysis: We will search MEDLINE-PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and Google scholar databases without restriction in year of publication. We will include randomized controlled trials that assessed the effectiveness of remdesivir versus placebo for patients confirmed with COVID-19. We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA 2015) guidelines for the design and reporting of the results. The primary endpoint will be time to clinical recovery. The secondary endpoints will be all cause mortality, discharged date, frequency of respiratory progression, and treatment-emergent adverse events. Two independent authors will perform study selection, data extraction, and methodology quality assessment. RevMan 5.3 software will be used for statistical analysis. Random/fixed effect model will be carried out to calculate mean differences for continuous outcomes and risk ratio for dichotomous outcomes between remdesivir and placebo. Ethics and dissemination: This study does not require ethical approval, because no participants data will be involved in this systematic review and meta-analysis. The findings of this study will be published in reputable and peer-reviewed journal. Registration: This review protocol is submitted in PROSPERO database for registration and we will include the registration number in the revised version of the manuscript. Keywords: 2019 novel coronavirus, 2019-nCoV, Coronavirus diseases 2019, COVID-19, SARS-cov-2, Remdesivir, Randomized Controlled Trials. Systematic review, Meta-analysis, protocol
An "Infodemic": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak

Background: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment has evolved in the early stages of any outbreak, including the COVID-19 epidemic, has not been described. Objective: To quantify and understand early changes in Twitter activity, content, and sentiment about the COVID-19 epidemic. Design: Observational study. Setting: Twitter platform. Participants: All Twitter users who created or sent a message from January 14th to 28th, 2020. Measurements: We extracted tweets matching hashtags related to COVID-19 and measured frequency of keywords related to infection prevention practices, vaccination, and racial prejudice. We performed a sentiment analysis to identify emotional valence and predominant emotions. We conducted topic modeling to identify and explore discussion topics over time. Results: We evaluated 126,049 tweets from 53,196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Nearly half (49.5%) of all tweets expressed fear and nearly 30% expressed surprise. The frequency of racially charged tweets closely paralleled the number of newly diagnosed cases of COVID-19. The economic and political impact of the COVID-19 was the most commonly discussed topic, while public health risk and prevention were among the least discussed. Conclusion: Tweets with negative sentiment and emotion parallel the incidence of cases for the COVID-19 outbreak. Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and target public health messages based on user interest and emotion.
Quantifying treatment effects of hydroxychloroquine and azithromycin for COVID-19: a secondary analysis of an open label non-randomized clinical trial (Gautret et al, 2020)

Human infections with a novel coronavirus (SARS-CoV-2) were first identified via syndromic surveillance in December of 2019 in Wuhan China. Since identification, infections (coronavirus disease-2019; COVID-19) caused by this novel pathogen have spread globally, with more than 250,000 confirmed cases as of March 21, 2020. An open-label clinical trial has just concluded, suggesting improved resolution of viremia with use of two existing therapies: hydroxychloroquine (HCQ) as monotherapy, and in combination with azithromycin (HCQ-AZ). The results of this important trial have major implications for global policy in the rapid scale-up and response to this pandemic. The authors present results with p-values for differences in proportions between the study arms, but their analysis is not able to provide effect size estimates. To address this gap, more modern analytical methods including survival models, have been applied to these data, and show modest to no impact of HCQ treatment, with more significant effects from the HCQ-AZ combination, potentially suggesting a role for co-infections in COVID-19 pathogenesis. The trial of Gautret and colleagues, with consideration of the effect sizes, and p-values from multiple models, does not provide sufficient evidence to support wide-scale rollout of HCQ monotherapy for the treatment of COVID-19; larger randomized studies should be considered. The trial of Gautret and colleagues, with consideration of the effect sizes, and p-values from multiple models, does not provide sufficient evidence to support wide-scale rollout of HCQ monotherapy for the treatment of COVID-19; larger randomized studies should be considered. These data also suggest further randomized-controlled studies of HCQ-AZ combination therapy should be undertaken.
Estimating the real-time case fatality rate of COVID-19 using Poisson mixtures model

Importance: The case fatality rate (CFR) estimation during the early stage of a disease outbreak is important, but the current estimation methods are heavily biased. Objective: We proposed using a Poisson mixtures model that utilized requires data of deaths, recoveries, and total confirmed cases recorded in each day since the outbreak of a population. Design: We collected data of deaths, recoveries, and total confirmed cases of COVID-19 in Hubei Province, China, and other parts of China up to 10th April 2020. Main Outcome(s) and Measure(s): The CFR of COVID-19 was estimated by minimizing the chi-square goodness-of-fit statistic. Results: Our proposed CRF estimates for Hubei Province became stable on 5th February in the range of 4.5% to 7.0% at which the simple CFR estimators overestimated the actual CFR by more than five folds, and that for the other parts of China (1.5% to 1.6%) were all very close to the actual CFR on the first day of CFR estimation (25th January) at which the simple CFRs were in the range between 15% and 35%. Conclusions and Relevance: Our CFR estimates for Hubei Province and other parts of China were superior to the simple CFR estimators.