The Government needs to catch up with early years professionals

Last month, at the height of the pandemic, we were asked by the STA (Standards and Testing Agency) to review Test Items for its third attempt at Reception Baseline Assessment (RBA) in September 2023

Low-level laser therapy decreases local effects induced by myotoxins isolated from Bothrops jararacussu snake venom

The prominent myotoxic effects induced by Bothrops jararacussu crude venom are due, in part, to its polycationic myotoxins, BthTX-I and BthTX-II. Both myotoxins have a phospholipase A2 structure: BthTX-II is an active enzyme Asp-49 PLA2, while BthTX-I is a Lys-49 PLA2 devoid of enzymatic activity. In this study, the effect of low-level laser therapy (LLLT), 685 nm laser at a dose of 4.2 J/cm2 on edema formation, leukocyte influx and myonecrosis caused by BthTX-I and BthTX-II, isolated from Bothrops jararacussu snake venom, was analyzed. BthTX-I and BthTX-II caused a significant edema formation, a prominent leukocyte infiltrate composed predominantly by neutrophils and myonecrosis in envenomed gastrocnemius muscle. LLLT significantly reduced the edema formation, neutrophil accumulation and myonecrosis induced by both myotoxins 24 hours after the injection. LLLT reduced the myonecrosis caused by BthTX-I and BthTX-II, respectively, by 60 and 43%; the edema formation, by 41 and 60.7%; and the leukocyte influx, by 57.5 and 51.6%. In conclusion, LLLT significantly reduced the effect of these snake toxins on the inflammatory response and myonecrosis. These results suggest that LLLT should be considered a potential therapeutic approach for treatment of local effects of Bothrops species venom.Fundação Vale Paraibana de Ensin

'Too-much-of-a-good-thing'? The role of advanced eco-learning and contingency factors on the relationship between corporate environmental and financial performance

Inspired by the natural-resource-based view (NRBV) theory, we attempt to shed light on a controversy which has been persistent over the last decade, concerning the relationship between corporate environmental performance (CEP) and corporate financial performance (CFP). Using the 'too-much-of-a-good-thing' (TMGT) concept, which suggests that "too much can be worse than too little," we link mixed results and consider the roles of advanced eco-learning and contingency factors in influencing the CEP-CFP relationship. Based on a sample composed of ISO 14001 certified companies in Indonesia, and analyzing the data using consistent Partial Least Squares (PLSc), we found that: the CEP-CFP relationship follows an inverted U-shape; advanced eco-learning is a significant predictor of the CEP-CFP relationship, meaning that organizations able to develop higher eco-learning capability will be better able to identify the ideal boundaries of investment in environmental performance without reducing their financial performance; and that contingency factors such as environmental strategy and firm size have a significant role in influencing the CEP-CFP relationship. The study's limitations, implications for practitioners and a future research agenda are also detailed

Chimpanzee Autarky

Background: Economists believe that barter is the ultimate cause of social wealth—and even much of our human culture—yet little is known about the evolution and development of such behavior. It is useful to examine the circumstances under which other species will or will not barter to more fully understand the phenomenon. Chimpanzees (Pan troglodytes) are an interesting test case as they are an intelligent species, closely related to humans, and known to participate in reciprocal interactions and token economies with humans, yet they have not spontaneously developed costly barter. Methodology/Principle Findings: Although chimpanzees do engage in noncostly barter, in which otherwise value-less tokens are exchanged for food, this lack of risk is not typical of human barter. Thus, we systematically examined barter in chimpanzees to ascertain under what circumstances chimpanzees will engage in costly barter of commodities, that is, trading food items for other food items with a human experimenter. We found that chimpanzees do barter, relinquishing lower value items to obtain higher value items (and not the reverse). However, they do not trade in all beneficial situations, maintaining possession of less preferred items when the relative gains they stand to make are small. Conclusions/Significance: Two potential explanations for this puzzling behavior are that chimpanzees lack ownership norms, and thus have limited opportunity to benefit from the gains of trade, and that chimpanzees\u27 risk of defection is sufficiently high that large gains must be imminent to justify the risk. Understanding the conditions that support barter in chimpanzees may increase understanding of situations in which humans, too, do not maximize their gains

MOLA: a bootable, self-configuring system for virtual screening using AutoDock4/Vina on computer clusters

<p>Abstract</p> <p>Background</p> <p>Virtual screening of small molecules using molecular docking has become an important tool in drug discovery. However, large scale virtual screening is time demanding and usually requires dedicated computer clusters. There are a number of software tools that perform virtual screening using AutoDock4 but they require access to dedicated Linux computer clusters. Also no software is available for performing virtual screening with Vina using computer clusters. In this paper we present MOLA, an easy-to-use graphical user interface tool that automates parallel virtual screening using AutoDock4 and/or Vina in bootable non-dedicated computer clusters.</p> <p>Implementation</p> <p>MOLA automates several tasks including: ligand preparation, parallel AutoDock4/Vina jobs distribution and result analysis. When the virtual screening project finishes, an open-office spreadsheet file opens with the ligands ranked by binding energy and distance to the active site. All results files can automatically be recorded on an USB-flash drive or on the hard-disk drive using VirtualBox. MOLA works inside a customized Live CD GNU/Linux operating system, developed by us, that bypass the original operating system installed on the computers used in the cluster. This operating system boots from a CD on the master node and then clusters other computers as slave nodes via ethernet connections.</p> <p>Conclusion</p> <p>MOLA is an ideal virtual screening tool for non-experienced users, with a limited number of multi-platform heterogeneous computers available and no access to dedicated Linux computer clusters. When a virtual screening project finishes, the computers can just be restarted to their original operating system. The originality of MOLA lies on the fact that, any platform-independent computer available can he added to the cluster, without ever using the computer hard-disk drive and without interfering with the installed operating system. With a cluster of 10 processors, and a potential maximum speed-up of 10x, the parallel algorithm of MOLA performed with a speed-up of 8,64× using AutoDock4 and 8,60× using Vina.</p

Endurance of methanogenic archaea in anaerobic bioreactors treating oleate-based wastewater

Methanogenic archaea are reported as very sensitive to lipids and long chain fatty acids (LCFA). Therefore, in conventional anaerobic processes, methane recovery during LCFA-rich wastewater treatment is usually low. By applying a start-up strategy, based on a sequence of step feeding and reaction cycles, an oleate-rich wastewater was efficiently treated at an organic loading rate of 21 kg COD m(-3) day(-1) (50 % as oleate), showing a methane recovery of 72 %. In the present work, the archaeal community developed in that reactor is investigated using a 16S rRNA gene approach. This is the first time that methanogens present in a bioreactor converting efficiently high loads of LCFA to methane are monitored. Denaturing gradient gel electrophoresis profiling showed that major changes on the archaeal community took place during the bioreactor start-up, where phases of continuous feeding were alternated with batch phases. After the start-up, a stable archaeal community (similarity higher than 84 %) was observed and maintained throughout the continuous operation. This community exhibited high LCFA tolerance and high acetoclastic and hydrogenotrophic activity. Cloning and sequencing results showed that Methanobacterium- and Methanosaeta-like microorganisms prevailed in the system and were able to tolerate and endure during prolonged exposure to high LCFA loads, despite the previously reported LCFA sensitivity of methanogens.This study has been financially supported by FEDER funds through the Operational Competitiveness Programme (COMPETE) and by national funds through the Portuguese Foundation for Science and Technology (FCT) in the frame of the projects FCOMP-01-0124-FEDER-007087 and FCOMP-01-0124-FEDER-014784. Financial support from FCT and the European Social Fund (ESF) through PhD grants SFRH/BD/48960/2008 and SFRH/BD/24256/2005 attributed to Andreia Salvador and Ana Julia Cavaleiro is also acknowledged

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

Performance indicators for clinical practice management in primary care in Portugal : consensus from a Delphi study

Early OnlineBackground: Performance indicators assessing the quality of medical care and linked to pay for performance may cause disagreement. Portuguese indicators included in recent health care reform are controversial. Objectives: To obtain consensus from opinion leaders in family medicine regarding the performance indicators for practice management used in the evaluation of Family Health Units in Portugal. Methods: Eighty-nine specialists in primary care were invited to answer the following question in an online Delphi study: 'Which performance indicators should be assessed regarding the organization and management of clinical practice in primary care in Portugal?' A Likert scale was used to evaluate validity, reliability, feasibility and sensitivity to change. Twenty-seven experts participated in the second round and achieved a high degree of consensus. Eight categories were created for analysis. Results: The experts suggested the use of existing indicators as well as new indicators. Thirty-nine indicators suggested by the experts are currently in use in Portugal. The assessment of the number of clinical acts performed, the number of administrative acts, and evaluation of the clinical demographic profile achieved a high degree of consensus. The expert panel suggested fifty new indicators. Five categories of these new indicators had a high degree of consensus, and three categories had a low degree of consensus. Conclusion: The expert panel recommended that performance indicators of practice management should first assess the quantity of clinical and administrative activities undertaken. These indicators must take into account the human and financial resources available to the clinic and its demographic context

To Each According to His Need? Variability in the Responses to Inequity in Nonhuman Primates

While it is well established that humans respond to inequity, it remains unclear the extent to which this behavior occurs in our nonhuman primate relatives. By comparing a variety of species, spanning from New World and Old World monkeys to great apes, scientists can begin to answer questions about how the response to inequity evolved, what the function of this response is, and why and how different contexts shape it. In particular, research across nonhuman primate species suggests that the response is quite variable across species, contexts and individuals. In this paper, we aim to review these differences in an attempt to identify and better understand the patterns that emerge from the existing data with the goal of developing directions for future research. To begin, we address the importance of considering socio-ecological factors in nonhuman primates in order to better understand and predict expected patterns of cooperation and aversion to inequity in different species, following which we provide a detailed analysis of the patterns uncovered by these comparisons. Ultimately, we use this synthesis to propose new ideas for research to better understand this response and, hence, the evolution of our own responses to inequity

Building Science Gateways for Analysing Molecular Docking Results Using a Generic Framework and Methodology

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties