Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative and Interdisciplinary Challenges in Translational Bioinformatics

Translational bioinformatics (TBI) is an emerging interdisciplinary field, which aims to bridge the gap between molecular world and clinical world. Translational bioinformatics employs data mining and machine learning techniques to analyze increasingly massive biomedical data and generate knowledge for clinical applications. One of the major challenges in TBI is to integrate multi-dimensional heterogeneous biomedical information sources in order to elucidate new biomedical knowledge. The integrative methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they pose great interdisciplinary challenges. Bioinformatics, system biology and network science together with knowledge engineering and reverse engineering have great potential to push TBI forward. In this paper, we introduce the background of TBI and the great variety of biomedical data, discuss the computational tools for integrative analyses, and summarize several crucial interdisciplinary challenges and future directions in TBI.
The ASCAA principles for next-generation role-based access control

Role-based access control (RBAC) received serious academic attention in the early 1990’s, although traces of the underlying concepts had been in ad hoc commercial practise since the 1970’s. Through the 1990’s and 2000’s RBAC achieved remarkable success, and today is widely practised as the preferred form of access control. Adoption of the 2004 NIST/ANSI Standard RBAC Model [1] marks a maturity of concept and practice. The essential roots of this standard go back to the RBAC96 model [2]. While numerous enhancements and extensions of RBAC96 and related models have been proposed, the core ideas introduced in RBAC96 have proved to be notably stable and robust. RBAC96 was based on four principles, viz. abstract privileges, separation of administrative functions, least privilege and separation of duties. Advances in RBAC require reconsideration of its founding principles. In this paper we offer five founding principles for next-generation access control including next-generation RBAC, summarized as ASCAA for Abstraction, Separation, Containment, Automation and Accountability. Abstraction (i.e., abstract privileges) and separation (i.e., separation of administrative functions) are essentially retained from RBAC96. A generalized principle called containment is introduced, to subsume least privilege, separation of duties and other constraints, as well as modern techniques such as usage and rate limits [3], [4]. Next two new principles called automation and accountability are introduced. Automation covers automated acquisition of privileges as well as automated revocation. Traditional RBAC typically requires that user-role and permission-role assignment and revocation result from explicit actions of appropriately authorized administrators. Some aspects of automated user-role assignment [5], [6], [7] and user-role revocation [8] have been previously proposed. We elevate the notion of automation to a full-blown principle and specifically propose self-assignment of roles as a new element. Automating assignment and revocation enables agile lightweight systems by eliminating repeated human intervention. Crucially, of course, we want to do this without compromising security. Accountability has recently received considerable attention driven by emerging requirements of secure information sharing and continued recognition of the insider threat. We offer the paradigm of adjustment as a means to achieve accountability. Adjustment acknowledges that not all authorized actions are the same. Sensitive operations require an enhanced level of auditing, notification or authentication. For example, it is common place for websites to require additional authentication and notification for sensitive operations such as change of address. While we believe these five ASCAA principles (Abstraction, Separation, Containment, Automation and Accountability) are relevant to access control systems in general, the discussion in this paper is limited to their application to RBAC.
ERASMUS UNIVERSITY ROTTERDAM

The abnormal and cumulative abnormal returns to Western European target firms in cross-border and domestic acquisitions are studied to test for a target company cross-border effect in Western Europe. The analysis focused on a sample of 654 and 435 domestic and cross-border deals, respectively, from 1990 to 2018. In the month of the bid, an insignificant cross-border effect of, approximately, −6.8% is found, indicating that target firms in domestic deals outperform those in cross-border bids. The highest difference in cumulative abnormal returns between target firms in cross-border and domestic acquisitions is observed from day t0 to t-1 and t-1 to t+5, accounting to a highly significant positive cross-border effect of 5.7% and 3.3%, respectively. The Western European target company cross-border effect seems to be explained by the market access premium and differences in bid and firm specific characteristics, in particular, the payment method, percentage of stake acquired, and industry of the target firm. Once the payment method, percentage of shares acquired, and the target firms’ industries are controlled for, the target company crossborder effect is found to be insignificant.
RIT Scholar Works RIT Scholar Works

The growth of android applications is causing a threat and a serious issue towards Android’s security. The number of malware targeting the Android operating system is increasing daily. As a result, in recent days the traditional ways that are being used to detect malware are not able to defend alone against the rapid development of hackers attacking techniques and novel malware. This capstone project focuses on using predictive analytics toward detecting malware from the network traffic. In this capstone project, we aim to train and test our data to find the best machine learning model with the highest accuracy of detecting malware in the network traffic. Through a variety of machine learning algorithms and models, we focused on 5 models starting with the logistic regression that was successfully able to predict malware by 67%. Moving to the decision tree that was effectively able to predict malware by 69% which was exactly equal to the random forest prediction ability. The AdaBoost came about 84% exactness, and KNN came with the highest anticipation of 86% between all the models. This shows us the advantage of adopting predictive analytics in malware detection within the traditional approaches to build a strong and defendable Android operating system against malware.
Quantification of annual glacier surface mass balance for the Chhota Shigri Glacier, Western Himalayas, India using an Equilibrium-Line Altitude (ELA) based approach

ABSTRACT In line with the increasing scientific interest on the Himalayan glaciers, this study focuses on estimating a long-term annual surface mass balance time series of the Chhota Shigri glacier, a ‘benchmark’ glacier in the western Himalayas. The approach used here is based on the fact that the annual glacier-wide surface mass balance can be deduced from the equilibrium-line altitude (ELA). Depending on the distribution and availability of multiple cloud free remotely sensed images during ablation period, a multi-temporal approach has been used to estimate ELA. When compared with field-based ELA, the results indicate that the multi-temporal approach resulted in better estimates of ELA than the conventional single image approach. Likewise, the annual surface mass balances quantified from this study closely match with field estimates over the common period (2003–2014) and even better than some estimates from earlier studies based on other proxies (meteorological data or glacier surface albedo). A sensitivity analysis shows that the annual surface mass balance quantified from the ELA-based approach is not very sensitive to changes in the mass balance gradient and average mass balance. Hence, the approach has been further applied to reconstruct the long-term annual surface mass balance series of the Chhota Shigri Glacier over the period 1989–2017. Our results show a good agreement between the reconstructed surface mass balance and estimates of other long-term studies. Therefore, this study indicates the great potential for this approach for quantifying the annual surface mass balance for glaciers with no ground data lying in same climatic zone.
The Innovation Premium: How Next Generation Companies Are Achieving Peak Performance and Profitability

Experts from Arthur D. Little, the world's leaders in innovation management, show companies of all sizes how to reap the financial and competitive rewards of successful innovation. In today's constantly shifting marketplace, "innovation" has become the catchword of companies large and small. In The Innovation Premium, Ron Jonash and Tom Sommerlatte draw on years of research and experience to demonstrate-for the first time-that those companies that consistently achieve innovation leadership enjoy measurable advantages, including an average 15 percent increase in shareholder returns. Bridging the gap between the technological and organizational aspects of innovation, the authors show managers at all levels how to move beyond continuous improvement of products and processes to create the "Next Generation Enterprise," an organization that thrives on innovation and knows how to harness it to create and capture value, spark and speed growth, and achieve the highest standards of performance.
Understanding transcriptional regulation by integrative analysis of transcription factor binding data

Repository Citation Cheng C, Alexander R, Min R, Leng J, Yip KY, Rozowsky J, Yan K, Dong X, Djebali S, Ruan Y, Davis CA, Carninci P, Lassman T, Gingeras TR, Guigo R, Birney E, Weng Z, Snyder M, Gerstein MB. (2012). Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Program in Bioinformatics and Integrative Biology Publications and Presentations. https://doi.org/ 10.1101/gr.136838. 111. Retrieved from https://escholarship.umassmed. edu/bioinformatics_pubs/10
Solving Planted-Motif Problem A New Approach

This paper presents a new method for solving planted-motif problem, which is a well-known motif-finding problem in the field of computational molecular biology. Motif-finding problem is considered to be a NP-complete problem and constant research is being carried out to solve the stated problem using some deterministic algorithm in polynomial time. This paper is an attempt to solve the plated-motif problem using a deterministic method in polynomial time. The method presented is not an exact algorithm but the probability of success is quite high. Significance of the technique is its simplicity and time efficiency. The presented technique is implemented as one of the modules in our general-purpose tool by the name "Sequence Comparison and Analysis Tool" for solving a number of sequence comparison problems encountered in the field of bioinformatics.
The Evolution of Occupational Segregation in the U.S., 1940-2010: Gains and Losses of Gender- Race/ethnicity Groups

The aim of this paper is twofold: a) to explore the evolution of occupational segregation of women and men of different racial/ethnic groups in the U. S. during the period 1940- 2010 and b) to assess the consequences of segregation for each of them. For that purpose, this paper proposes a simple index that measures the monetary loss or gain of a group derived from its overrepresentation in some occupations and underrepresentation in others. This index has a clear economic interpretation. It represents the per capita advantage (if the index is positive) or disadvantage (if it is negative) of the group, derived from its segregation, as a proportion of the average wage of the economy. Our index is a helpful tool not only for academics but also for institutions concerned with inequalities among demographic groups because it makes it possible to rank them according to their segregation nature.
Temporal Characteristics and Atmospheric Drivers of Onsets and Terminations of Soil Moisture Droughts in Europe

Many studies have focused on understanding the drivers of soil moisture droughts in Europe when the events have already intensified. Still, how atmospheric circulation changes throughout the entire life cycle of droughts, particularly during the transition periods to drought initiation and termination, has not been thoroughly investigated. Therefore, this study investigates temporal characteristics and atmospheric circulation associated with onsets (a transition period from a normal condition to 5 drought) and terminations (a transition period from drought to a normal condition) of soil moisture droughts in Europe during 1980–2020. The typical duration, preferred seasons of occurrence, and atmospheric circulation during onsets and terminations are examined. The regions of study are central (CEU) and Mediterranean (MED) Europe, and soil moisture from ERA5-Land, GLEAM version 3, SoMo.ml, Noah-LSM, and a simulation from the Community Land Model version 5 (CLM-TRENDY) are utilized. 10 Our findings indicate that the duration of onsets and terminations depends on the dataset: the five soil moisture datasets exhibit different mean duration of onsets and terminations, with ERA5-Land showing shorter and GLEAM longer duration across Europe. Nevertheless, within the same dataset, onsets and terminations exhibit similar durations, implying that onsets and terminations can occur at the same speed. Regarding the preferred seasons for onsets and terminations, onsets occur more during the wet seasons, specifically summer in CEU and autumn and winter in MED. Nevertheless, the frequencies 15 of occurrence during these seasons only slightly exceed that during other seasons. Terminations tend to occur more during the driest seasons. For atmospheric circulation, onsets come with large-scale anticyclonic atmospheric circulation patterns. Terminations
Effectiveness of Weed Solut-Ion as herbicide adjuvant to control weeds in oil palm plantations

The use of herbicides is an effective and efficient way to control weeds in oil palm plantations. However, overdoses application of herbicides is hazardous to the environment. Therefore, it is necessary to find a method to reduce hazardous effects caused by excessive doses of herbicides. Adjuvant herbicides are a well-known compound that could enhance herbicide performance. Weed Solut-ion (WS) contains 2-sodium amine allegedly reduces herbicide dosage at the same time enhances its performance. The objective of this research was to determine the response application of Weed Solution as herbicide adjuvant for paraquat and fluroxypyr by reducing the dosage so that the application can be more efficient and environmentally friendly. The experiment was conducted in the Socfin Group Plantation using a completely block design with two treatments (control; paraquat:WS = 50:50; fluroxypyr:WS = 50:50). The results showed that weeds control and regrowth rate from treatment (paraquat:WS = 50:50) were not significantly different from the control. The cost efficiency resulted from the treatment was 25.56%. The dose treatment of fluroxypyr combined with WS (doses 50:50) was not significantly different from control, and the cost efficiency was 36.43%.
Perspectives from Montiaceae (Portulacineae) Evolution. II. Ecological Evolution, Phylogenetic Comparative Analysis, and the Principle of Evolutionary Idiosyncraticity

The present paper reviews evidence for ecological evolution of Montiaceae. Montiaceae (Portulacineae) comprise a family of ca. 275 species and ca. 25 subspecific taxa of flowering plants distributed mainly in extreme western America, with additional endemism elsewhere, including other continents and islands. They have diversified repeatedly across steep ecological gradients. Based on narrative analysis, I argue that phylogenetic transitions from annual to perennial life history have been more frequent than suggested by computational phylogenetic reconstructions. I suggest that a reported phylogenetic correlation between the evolution of life history and temperature niche is coincidental and not causal. I demonstrate how statistical phylogenetic comparative analysis (PhCA) missed evidence for marked moisture niche diversification among Montiaceae. I discount PhCA evidence for the relation between Montiaceae genome duplication and ecological diversification. Based on the present analysis of Montiaceae evolution, I criticize the premise of the prevalent statistical approach to PhCA, which tests Darwinian deterministic hypotheses against stochastic evolutionary null models. I discuss theoretical/empirical evidence that evolution is neither stochastic, nor Darwinistically-determined, but idiosyncratic. Idiosyncraticity describes the outcome of a stochastically perturbed nonlinear chaos-like process. The Principle of Evolutionary Idiosyncraticity (PEI) is based on the evolutionary theory of Natural Drift, which maintains that determinism in evolution is a property of the organism and not, as maintained by the theory of Natural Selection, its traits or its milieu. This determinism is characteristic of chaotic functions, which are absolutely determinate, generate self-similarity, but remain absolutely unpredictable. PEI explains precisely observations that evolution proceeds not linearly, but chaotically, producing both quasi-linear fractal-like patterns and non-linear jumps. PEI has ramifications for all areas of macroevolutionary research. In particular, it demonstrates both the fallacy and futility of the statistical PhCA approach that interprets evolutionary causes in terms of evolutionary correlations. However, statistical methods of PhCA can be applied heuristically and fruitfully to reveal idiosyncraticity and discover evolutionary novelty. This, in turn, is demonstrated by the emergence of statistical anomalies in evolutionary analyses of Montiaceae.
An Empirical Study of the : A Studio-Based Activity for HCI Education

(2012). An empirical study of the " prototype walkthrough " : a studio-based activity for HCI education. Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page.
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We trained two Random Forest (RF) machine learning models for cloud mask and cloud thermodynamic-phase detection using spectral observations from Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad-viewing zenith angle range, which is a great beneﬁt to overall model performance. The all-day model uses three VIIRS infrared (IR) bands (8.6, 11, and 12 µm), and the daytime model uses ﬁve Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 µm) together with the three IR bands to detect clear, liquid water, and ice cloud pixels. Up to seven surface types, i
Innovations in Payment Technologies and the Emergence of Digital Currencies

Modern electronic payment systems rely on trusted, central third parties to process payments securely. Recent developments have seen the creation of digital currencies like Bitcoin, which combine new currencies with decentralised payment systems. Although the monetary aspects of digital currencies have attracted considerable attention, the distributed ledger underlying their payment systems is a significant innovation. As with money held as bank deposits, most financial assets today exist as purely digital records. This opens up the possibility for distributed ledgers to transform the financial system more generally.
Unit Capacity Maxﬂow in Almost O ( m 4 / 3 ) Time

—We present an algorithm, which given any m -edge n -vertex directed graph with positive integer capacities at most U computes a maximum s - t ﬂow for any vertices s and t in O ( m 4 / 3+ o (1) U 1 / 3 ) time. This improves upon the previous best running times of O ( m 11 / 8+ o (1) U 1 / 4 ) [1], (cid:2) O ( m √ n log U ) [2] and O ( mn ) [3] when the graph is not too dense and doesn’t have large capacities. We build upon advances for sparse maxﬂow based on interior point methods [1], [4], [5]. Whereas these methods increase the energy of local (cid:2) 2 -norm minimizing electrical ﬂows, we instead increase the Bregman divergence value of ﬂows which minimize the Bregman divergence with respect to a weighted log barrier. This allows us to trace the central path with progress depending only on (cid:2) ∞ norm bounds on the congestion vector as opposed to the (cid:2) 4 norm, which arises in these prior works. Further, we show that smoothed (cid:2) 2 - (cid:2) p ﬂows [6], [7] which were used to maximize energy [1] can also be used to efﬁciently maximize divergence, thereby yielding our desired runtimes. We believe our approach towards Bregman divergences of barriers may be of further interest.
Value of Health Apps?

ABSTRACT Within only few years Health Apps have reached a wide range of healthy and chronically ill people worldwide. Based on the huge number of health and medical apps available in the big app stores and the lack of general regulation and quality control in Germany and the US there is a high need for orientation. As information disclosed by most app developers is in general incomplete, transparency is bad. That is why Health Care Professionals (HCP) as well as patients are looking for criteria to assess quality and trust-worthiness of health apps in order to understand the drivers for risk and avoid impairment due to safety and data rights infringements or wrong or biased health information. Quality of health apps derives primarily from value and safety for its users and correlates with aspects of reach and user orientation as well. Only a minority of health and medical apps offered in German language are so far able to reach relevant download numbers, retention rates are in general low which questions the value of apps in reaching long-term health goals. Risks associated with health apps vary remarkably dependent on the supporting functions an app delivers and the health goals users are following with a
Molecular Biomarker studies on Ecotoxicological impact of Pollutants on the Marine Gastropods along the Goa Coast

Antioxidant defense plays a crucial role in the response of an organism to pollutants. Several processes stimulate the production of free radicals or deplete the antioxidant defense, which if not regulated properly, may cause oxidative stress in the organisms, leading to damage in DNA, proteins or lipids. Free radicals are also beneficial as it plays an important role in defense against infectious agents, and signal transduction. Hence a delicate balance between antioxidants and free radicals is required. Oxidative stress biomarkers are very useful in disease etiology and environmental toxicological studies. The increase in anthropogenic activities and environmental awareness has resulted in an explosive increase of research in the field of oxidative stress. Snails are excellent organisms for environmental biomonitoring and contribute a major proportion of the invertebrate biomass. In our article, we have summarized the research carried out using glutathione S-transferase (GST), catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), and lipid peroxidation (LPO) in snails exposed to various toxicants and their implication in the environmental monitoring programs. In the end, we have discussed different factors affecting the variations in oxidative biomarkers response for a better understanding of the phenomenon.