To investigate the potential mechanism of splenic enlargement in Ang II/APOE model and the associations between the spleen volume and the indices of abdominal aortic aneurysm (AAA) in human.−/−) mice were treated with Ang II (1000 ng/kg/min) up to 28 days to generate AAA. We used Magnetic Resonance Imaging (MRI), liquid measurement, H&E and immunohistochemistry to analyze the morphological or pathological changes of spleen. To investigate the changes of spleen volume in human, a retrospective case-control study involving 30 male AAA patients and 25 male controls were performed. Spleen volume was measured on computed tomography images. Univariate analysis and multivariable sequential logistic regression analyses were used to analyze the association between spleen volume and maximal diameter (Dmax).To investigate the changes of spleen volume on AAA formation, apolipoprotein E knockout  compared with the small  for spleen volume . Regression analysis revealed a statistically significant positive linear correlation of spleen volume and Dmax of AAA .In Ang II/APOE model, we found splenic enlargement in mice with AAA compared with the sham group. Histopathological investigations revealed hypertrophies of splenic follicles and increased populations of CD3Mimicking the splenic pathology observed in murine AAA model, there is a strong positive correlation between spleen volume and the Dmax in male AAA patients. As Dmax is a valuable predictor of AAA rupture, the spleen enlargement may be another indicator. Abdominal aortic aneurysm (AAA) is a common vascular disease of the abdominal aorta, characterized by degeneration of the medial wall and various degrees of chronic aortic wall inflammation . Accordi+ T cells in AAA [−/−) mice [Inflammation is a key feature of AAA onset as demonstrated by extensive medial and adventitial inflammatory cell infiltration into the vessel wall . Previous in AAA , which cs in AAA . Early m/−) mice . The spl/−) mice , 10, ind/−) mice .Currently, it has been proved that Angiotensin II (Ang II)–induced inflammatory reactions will cause splenic enlargement in Ang II/APOE model , 13. Fur−/−mice (backcrossed 10 × into a C57BL/6 J background) were obtained from Department of Laboratory Animal Science . All mice were bred as littermate controls, and housed in a pathogen-free barrier facility. Mice were fed a high-fat and high-cholesterol diet (1.25% cholesterol and 45% ghee) from 8 weeks of age. Male mice (8–10 weeks of age) were implanted with Alzet osmotic minipumps , filled either with saline vehicle or Ang II solutions (1000 ng/kg/min) up to 4 weeks [n = 20); (2) Ang II (n = 20). For quantifying aneurysm incidence, an aneurysm was defined as a 50% or greater increase in the external width of the suprarenal aorta compared to aortas from saline-infused mice. This definition is consistent with a commonly used clinical standard to diagnose abdominal aortic aneurysm as an increase in aortic diameter of 50%, as described previously [Male C57BL/6 J and ApoE 4 weeks . The stueviously . All stuThe mice were killed after 4 weeks of treatment. The mice were anesthetized with 2% pentobarbital solution  by intraperitoneal injection, and then sacrificed by bleeding through the carotid artery. For morphological analysis, aortas were perfused with normal saline and fixed with 10% PBS and formalin for 5 min. Whole aortas were harvested and the aortic tissue was removed from the ascending aorta to the iliac bifurcation, as well as the spleen. The tissue was laid out on a blue background, and an image of the aorta was recorded. After fixed for 24 h and embedded in paraffin, and cross-sections (5 μm) were prepared. Paraffin sections were stained with H&E and immunostaining,as described previously . AntibodWe retrospectively analyzed 432 male patients’ imaging  from January 1, 2012 to December 31, 2014 in Shandong Medical Imaging Institution. All the patients underwent CTA. Exclusion criteria includes aneurysm surgery, patients’ age younger than 50 years or greater than 80 years, and other diseases leading to spleen size and/or shape changes such as tumors, cysts, infection, hematopoietic system diseases, the lymphatic system diseases, connective tissue disease, metabolic disease, autoimmune diseases and parasitic diseases, unexplained fever for a long time, etc. Besides, to rule out the influences of atherosclerosis on the control, 53 cases of male patients with abdominal aortic atherosclerosis were excluded. Eventually, 30 cases of male patients with AAAs  were included in the study. The median Dmax was 5.2 cm (ranged from 3 to 10.3 cm). A total number of 25 patients  with no anomalies in abdomen CT scan were selected as the control group. This was a retrospective study, and the institutional review board of Shandong Medical Imaging Research Institute, Shandong University, waived informed consent.2, the tube potential was 100 kV, while for BMI > 30 kg/cm2, the tube potential was 120 kV. The injection protocol was biphasic: 1.2 ml/kg of non-ionic low-osmolar contrast material (Iohexol 370 mg I/ml) was injected, followed by 50 ml of saline flush. A bolus tracking technique was used for each acquisition: the region of interest (ROI) was drawn on the descending aorta, and image acquisition was started automatically after reaching a threshold of 100 HU in the ROI. Data sets were filtered with a medium-soft convolution kernel (B26f), with 1.5 mm thickness and 1 mm layer interval. Image was transited to pretreatment workstation  to be reconstructed and diagnosed together by two experienced radiologists. AAA was defined as Dmax ≥3.0 cm or Dmax ≥1.5 × suprarenal aortic diameter [All CT examinations were performed with a DSCT . Imaging parameters were as follows: Detector collimation 2 × 32 × 0.6 mm; Slice collimation, 2 × 64 × 0.6 mm by means of a z flying focal spot, pitch 0.8; The tube current–time product was 380 mAs per rotation; For a patient of BMI < 30 kg/cmdiameter . For anyWe used Magnetic Resonance Imaging (MRI) to learn the morphological changes of the spleen in vivo after 4 weeks of treatment before killing the mice, as described previously . Images t test. Comparisons of parameters among more than two groups were made by one-way analysis of variance (ANOVA), and comparisons of different parameters between each group were made by a post hoc analysis using a Bonferroni test. A χ2 test was applied to the comparisons of AAA incidence and survival rate. We tested for a correlation between spleen volume and the age or Dmax in all the patients and the AAA patients respectively, using the nonparametric Spearman correlation test. Statistical significance was evaluated with SPSS17.0 . A value of P < 0.05 was considered to be statistically significant.Continuous variables are expressed as the means ± standard error (SEM) for the animal experiments and as means ± standard deviations (SD) for clinic research. Comparisons of parameters between two groups were made by + T lymphocytes within the lymphatic follicles of mice with AAA appeared higher . Nonparametric Spearman correlation test results revealed that with the increase of age, spleen volume showed a shrinking trend among the whole population. However, among 108 patients between the ages of 50–80, the trend was nonsignificant   Fig. . Backgro3 vs. 224.4 ± 78.8 cm3; P = 0.4546) (Table n = 15) and small AAA (Dmax < 5.5 cm) (n = 15). We found significantly higher values in large AAA compared with the small AAA for spleen volume   Table . As AAA 30) Fig. .r = 0.3611;P = 0.0423)  Fig. . In cont3 Fig. . + T cells infiltration in the vessel wall plays an important role. Spleen is an important secondary lymphoid organ, which is key to the function of T cells [Inflammation is a key feature of AAA onset, and CD4 T cells . Besides T cells . The fin+ T cells in both of the abdominal aorta and the splenic tissue of the animals [+ T lymphocytes in the splenic tissue of mice with AAAs. However, to the best of our knowledge, whether the spleens reveal similar changes of size in human has not been elucidated.Splenic enlargement may result from inflammatory/infectious diseases as well , and has animals . Consistr = −0.1150; P = 0.2337). Among those patients, we performed further analysis to find significantly higher values in large AAA (Dmax > 5.5 cm) compared with the small AAA (Dmax < 5.5 cm) for spleen volume    Fig. . Additio23) Fig. . We hypoIt has been well recognized that the main complication of untreated AAA is rupture, with more than 80% associated mortality . The mosHowever, there are some limitations in this study. First, our study is retrospective cross-sectional design. According to the report in the literature, normal spleen volume is affected by some factors including age, gender, height, weight, etc. . To reduIn conclusion, our study demonstrates that the changes of spleen size in patients with AAA revealed the similar pattern with the murine AAA model. Our data further demonstrated a positive correlation between spleen volume and the Dmax in male AAA patients. Based on these data, we hypothesize that the spleen expansion possibly contributing to or as a consequence of the progression of AAA, and may be a potential indicator of the risk of rupture. To our knowledge, this is the first time to report clinic data showing the association of changes in spleen volume and the Dmax of AAA. Because there were some limitations in our study, further studies are warranted to address these hypotheses and better delineate the potential significance of splenic enlargement to the prognosis of AAAs.


On May 25, 2016, a detainee at a U.S. Immigration and Customs Enforcement (ICE) detention center in Arizona who had been hospitalized with fever and a generalized maculopapular rash was confirmed to have measles by real-time polymerase chain reaction (rPCR). A second case of measles in a staff member was confirmed by rPCR the next day. The privately operated, city-contracted facility housed 1,425 detainees, and employed 510 staff members, including 95 federal ICE staff and 415 contract staff of four distinct employers. Outbreak control measures consisted of administration of measles-mumps-rubella (MMR) vaccine to 1,424 detainees housed at the facility during May 29–31 and isolation of the detainee patient and any additional detainee patients identified during their remaining infectious period (until 4 days after rash onset). Recommendations were made by federal, state, and local public health partners to exclude staff members with measles-compatible symptoms as well as exposed staff members without presumptive evidence of immunity to measles.*Epidemiologic investigations by local and state health departments and CDC identified 31 total cases of measles in 22 detainees and nine staff members, with rash onsets occurring May 6–June 26 . InitialReports of illness from personnel who had developed measles might have prompted other staff members to get vaccinated; by August 4, a total of 445 (87%) staff members were considered to have evidence of immunity, including 119 (23%) with documentation of receipt of 2 MMR doses before the start of the outbreak, 307 (60%) who had received 1 previous MMR dose and received a second dose during the outbreak, and 19 (4%) with serologic evidence of immunity. Although recommendations to exclude infectious staff members and nonimmune staff members suspected to have been exposed were made as soon as the outbreak was recognized, slow compliance with vaccination recommendations and incomplete implementation of exclusion recommendations, and restrictions on enforcing them, might have prolonged this outbreak.Outbreak response is expensive and resource-intensive (Recommendations for implementing measles control policies for detention and correctional facilities, similar to those recommended in health care facilities, could be considered. If permissible, contractual and interagency agreements could include similar provisions, such as requiring MMR vaccination for staff members who work in detention facilities and do not have documented evidence of immunity.


There is limited research on the use of evidence to inform policy-making in the Pacific. This study aims to identify and describe factors that facilitate or limit the use of evidence in food-related policy-making in the Health and Agriculture Ministries in Fiji.Semi-structured face-to-face interviews were conducted with selected policy-makers in two government ministries that were instrumental in the development of food-related policies in Fiji designed to prevent Non-Communicable Diseases (NCDs). Snowball sampling was used to recruit, as key informants, senior policy-makers in management positions such as national advisors and directors who were based at either the national headquarters or equivalent. Interviewees were asked about their experiences in developing food-related or other policies, barriers or facilitators encountered in the policy development and implementation process and the use of evidence. Each interview lasted approximately 45–60 minutes, and was conducted in English. Audio-recorded interviews were transcribed, thematically coded and analyzed using N-Vivo 8.0 software.n = 18) and the Ministry of Agriculture (MoA n = 13) in Fiji participated in the study. Whilst evidence is sometimes used in food-related policy-making in both the Health and Agriculture Ministries , it is not yet embedded as an essential part of the process. Participants indicated that a lack of resources, poor technical support in terms of training, the absence of clear strategies for improving competent use of evidence, procedures regarding engagement with other stakeholders across sectors, varying support from senior managers and limited consultation across sectors were barriers to evidence use. The willingness of organizations to create a culture of using evidence was reported as a facilitator.Thirty-one policy-makers from the Ministry of Health and Medical Services (MoHMS The use of evidence in policy-making will only become a reality in Fiji if it is a formalized part of the government’s policy-making systems. A systems approach to food-related policy-making and implementation may achieve this by helping Ministries manage the complex and dynamic nature of food-related policy-making in Fiji. Non-communicable Diseases (NCDs) are a growing problem and a major cause of disability and death in Fiji. The Global Burden of Disease study  found thFood-related policy is a promising strategy for population health –4 as dirEvidence-based policy-making is characterized as a systematic approach to accessing, appraising and using evidence to shape the decision-making processes . HoweverEffective use of evidence is determined by the policy-maker’s ability to access and analyse the best available evidence, and apply it to the formulation of policies. However, there is often limited capacity and resources for supporting the development of evidence-informed policies –22. ObstAlthough the Fijian government has recognized the importance of changing the obesogenic environment through multisectoral collaborations , barrierFood-related policy-making and implementation needs strengthening in Fiji. However, experience suggests that achievement of this aim is complex. Firstly, work is required to help other sectors recognize the importance of reducing NCDs; for example, the trade sector needs to be involved given that much of the unhealthy food available in Fiji is imported , 38. SecThis study aims to explore what is perceived to be ‘appropriate evidence’ by the different policy-makers, and how perceptions of this differ in the health and agriculture sectors in Fiji.This qualitative study entailed a collective case study design. The study builds on the Pacific Obesity Prevention In Communities (OPIC) project which took place from 2004–2009  and the Endorsement to work with the two selected government ministries was secured from their respective Permanent Secretary. Once secured, snowball sampling was used to recruit senior policy-makers from the two ministries who had relevant experience in policy development. Sampling continued until saturation of information from the interviews was reached. High-level policy-makers were defined as senior government officials with management positions such as national advisors and directors who are based either at the national headquarters in Suva or equivalent. Previous involvement in the OPIC and TROPIC projects made these participants ideal interviewees as a starting point for this study.Potential participants were initially contacted by email, followed by a phone call detailing how she/he had been nominated. A written invitation to take part in the project was provided along with a plain language statement outlining the study purpose; the requirements of participants; and the steps that would be taken to maintain confidentiality. All participants provided verbal and written consent.Semi-structured interviews of approximately 45–60 min’ duration were conducted in English in April and May 2015 by the lead investigator, generally in the interviewee’s office at a time convenient to them. An interview guide was developed using a combination of open-ended and probing questions. Interviewees were asked about their experiences in developing food-related policies or any other policies they had been involved in. This included questions about barriers encountered in the development and implementation process and the use of evidence in that process. Each interview was audio recorded using a digital recorder, with the permission of the interviewee.Alongside the interviews, key documents related to the development of food-related policy in Fiji were reviewed to ensure a better understanding of the nature and level of evidence used in the policy development process. These included publically available documents  and those produced for internal use  subject to availability. A formal process was followed to gain access to minutes of the high-level meetings as well as the Cabinet papers; this required completion of a Data Request Form lodged to the Data registry of the MoHMS and personal requests were made to officials of the MOA.Trained transcribers transcribed digital recordings of interviews. The transcribers validated all transcriptions by proofreading against the audio file and editing the transcript file accordingly. The interviewer audited all transcripts for accuracy. The recordings were de-identified to ensure confidentiality at the end of each interview and before transcribing. All transcripts were entered and managed using N-Vivo 8.0 software to identify emerging themes or patterns that related to study objectives. Key documents were also mapped against the evidence received from interviews.The study was managed by researchers based at the Pacific Research Centre for the Prevention of Obesity and Non-communicable Diseases (C-POND), School of Medicine, Fiji National University in Suva, in collaboration with researchers at Deakin University, Australia.n = 18) and the MOA (n = 13) in Fiji participated in the study. With approval to access key internal documents, the authors were able to access minutes of relevant meetings; analysis of these showed documentation that some policy proposals were recommended for resubmission due to insufficient evidence being used to justify policy issues. Other accessed documents such as internal reports and cabinet papers made effective use of evidence.Thirty-one policy-makers from the MoHMS Access to credible evidenceThe analysis identified three broad themes that influenced the use of evidence in the development and/or the implementation of food-related policy in Fiji: access to credible evidence, collaboration across sectors to get the right technical advice, and competence in collecting, analyzing and critically appraising evidence.most of the stations now have computers and have access to internet, we want to get computers to every station”. The need for accessing library services for easy access of evidence was raised by one participant: “….we used to have our own library; currently we don’t have a library ….”. Another astute observation was that credible evidence was particularly hard to access “…..the internet is always available but credible evidence is sometimes hard to access because you need to know  ……, we are in the process of purchasing motorcycles ……to improve (our) reach to the farmers and get those information that we lack especially with the data from the fields…. ”.(ii) Collaboration across sectors to get the right technical adviceSome strategies for improving access to credible evidence along with the problems that still exist were discussed by participants. One participant said: “…Another theme described by participants as hindering or facilitating the use of evidence in policy-making was the process of consultation and collaboration across sectors. Sectors include other government ministries, non-government organisations and civil societies with similar interests related to food policies.“…..some areas of concern are the food safety and food quality. We still believe that the role should be (shared) between the two ministries , they should be working together with policies involving (food) production, processing and going into retailing processes, that chain should be well understood (by both parties)….. how food standard food quality policies can address the whole chain …”. The importance of receiving valuable technical advice from partners across sectors and how they affect policy outcomes was also raised; “….the Bureau of Stats is one of the important partners in policy planning as they provide us with the trend of trade data, we somehow liaise with our embassy in trying to see the demand from the overseas markets …”. Developing a cross-cutting policy is a complex process involving multiple factors and actors. One participant said: “… smoking, nutrition and alcohol are all trade issues …but the key players are trade and multi-national and we are struggling as how best to work with them”.Developing and working in partnerships with stakeholders was described by many participants as both a barrier and facilitator of evidence-use. One respondent said: “…the consultation effort which needs to be very much inclusive …in getting the right people so that at least they provide the right technical advice …to some extent, (the) Attorney General’s office needs to be incorporated in every consultation regarding trade. ….…”. Staff turn-over also affects the engagement process across government ministries: “…length of time senior staff spent in leadership level….lots of turnover in workforce….engagement and consultation process needs to be reviewed as right now is whom you know….”.The importance of engaging and communicating with the right people during collaboration was raised in the context of addressing the problems or policy challenges. One of the respondents said: our approach to policy is very much a medical approach and when you talk to government to create legislation, we have to move away from the medical approach. It has to be socio-economic approach and for that we need an economist or somebody who can think health economics and change our disease statistics to health economic argument ……they can understand the argument from the socio-economic perspective or environment in terms of legislation ……biggest challenge ……………….”. Another challenge of multisectoral collaboration is that different types of evidence are seen to be important in the different sectors. One participant shared “We are not reporting on health loss and not talking financial or economic language when dealing with trade and finance and we fail to convince them using…”.Many participants discussed the importance of achieving a balance between the right evidence and the skills required in engaging with other players outside of their own ministry. One participant said “…..we looked at where the problems are with advertising (of junk food to children), where the peak hours for all the junk foods, then with the assistance of WHO [World Health Organization] got the cabinet paper ready but the cabinet paper had been studied, endorsed and then sits with XX’s office…..for close to two years now. We are having difficulty with the other stakeholders ……..meaning the wholesalers, the producers, the commercial soft drinks, ………. they threatened that (the policy) will create unemployment, and trade doesn’t want that …..”.(iii) Competence in collecting, analyzing and critically appraising evidenceWeighing the benefits and seeking common ground was suggested as a good strategy for collaboration. One participant said: “……The third clear theme was staff competence in collecting, analyzing and critically appraising evidence.we have our statistics unit here but we need to know how to analyze and interpret data correctly…” Another participant commented “….we did not do our homework well, no supportive documents ….. it went from the lower house thrown back again twice, …….we were confronted with questions in our third approach but we have lots of evidence this time to justify the case, it’s just a matter of … understanding the needs, what type of evidence is needed and where to get it from …..”.Enhancing capacity to promote greater use of evidence in policy and decision-making is crucial in any government system. One respondent noted: “…“……..it goes back to proper documentation; making sure that the ministry is documenting each and every process…. and is kept in a very systematic way and is accessible to people needing that information; the people that would be getting that information are also knowledgeable in how to get them like electronically …”. Another participant said “…. we need (more) training to conduct survey, how to analyze it, which  package to use ……we even need more consultation and integration within the ministry and that is something we are working towards to”.Lack of capacity and skills to systematically document field research and analyze routine data is a constraint to the use of evidence. The comment of one policy-maker illustrates this; Some potential strategies highlighted by participants for improving competent use of evidence included the need to increase networks and research and training.my experience with the food safety regulation is that we have to go through the media… consult the people too for their comments…..other people they might have the expertise…”.Some participants felt that interaction and communication were the keys to improving competent use of evidence; one said: “………: “… our network with food industries has been very good… trying to get fisheries products … to the EU so we bring in all fish factories and so another reason why we have to update our legislation not only based on CODEX but also based on EU requirements”.The importance of networking was also discussedwe also) document farming system to see the profitability …. monetary crop like sugar cane is easy where you have some integrated system and sometimes farmers complain about this. We know that they are making money from some other things that can only be showing if you do some farming system analysis for the whole farmer’s budget, so those are the two areas that we champion and monitor with all the stakeholders within the ministry…”.The diversity of evidence gathered from providers was also highlighted as an essential component of understanding the views of different stakeholders. For example, some farmers are constantly making adjustments in their farms for smooth operations and profitability to enhance the financial return of the farm: “… level, we are expected to think policy but we are not trained to do policy, most of us came out from operational and most of our work here still is operational ……we need to attend training for human resource development, leadership and governance in policy-making. Secondly; ….. once you think policy the next level is evidence based, there is lack of (skills to access and use) evidence or the empowerment to be able to seek relevant evidence using technology that is available to apply this…..”. Strengthening organizational capacity requires significant investment in both financial and human resources. One participant said: “….well right now, our Statistics unit is conducting training with our staff…..recruiting staff to cater for the needs and these are the people that are going to the field…”.Whilst the importance of training policy-makers in adopting a culture towards the use of evidence was identified, no training and educational programs have been developed. “Firstly, This study is important and innovative because it captures policy-makers’ views and experiences on the use of evidence in food-related policy-making in both the Health and Agriculture Ministries in a middle-income country. The study findings indicate that whilst policy-makers value evidence in developing food-related policies, challenges of having a shared understanding of what evidence is, technical knowledge and tools to access and use sound and relevant evidence, and collaboration with other government sectors still exist. The low level of uptake of evidence may be attributed to these challenges and limited support for the use of evidence in decision-making within their organisation.Insufficient access to evidence identified from the perspectives of the interviewees illustrates how their work settings are driven by the constraints in organizational support towards use of evidence in policy-making. While it is important for all policy-makers across sectors to have access to and share evidence with other stakeholders, this is often not the case, particularly if an organization’s data or information management policy does not accommodate open-access. In addition, while organizational barriers limited the impact of using evidence, the challenge for these organizations was to introduce appropriate research tools for managing and analyzing research data such as N-Vivo 8.0 software , 21, 30.One of the reasons cited for inadequate use of evidence included the general lack of formal processes for engaging and collaborating with other stakeholders. The evidence-seeking patterns of policy-makers as shared in the interviews depend on their access to cross-governmental information systems or websites and their contacts with colleagues; this created an environment where they primarily work in isolation , 40. UltAs noted, the findings from this study demonstrate the absence of a clear formal direction that guides effective communication between potential partners. Efforts are needed for the MoHMS and the MOA to harmonize the policy approaches and methods, at least within areas with similar policy interests. Cross-sectoral collaboration that involves linking or sharing of evidence, policies, resources, activities and services by two or more organizations in the pursuit of a common goal  is one oThe other most essential element of using evidence as highlighted by participants was competence in collecting, analyzing and critically appraising evidence. Effective use of evidence is determined by the policy-maker’s ability to access and analyse the best available evidence, and apply it to the formulation of policies. However, strengthening capacity building and providing incentives are key to the successful adoption of and support for the development of evidence-informed policies –22.Many participants recognized the value and importance of using evidence in policy-making but did not have either the time or skills to search for the evidence themselves. Various strategies have been used to promote policy dialogues  like knoThe strengths of this study are that we were able to conduct face-to-face interviews with actual policy-makers; the involvement and cooperation of two government ministries; and the willingness of participants to engage and focus on authentic policy processes. A limitation was that, even though we assured interviewees that the data would be anonymized, some may have held back information through fear of repercussions. Also, not all key documents such as discussion papers and cabinet papers could be accessed; these may have been useful in developing a better understanding of the interview data and the wider context.This study has shown that evidence is sometimes used in the process of making food-related policies in Fiji but research tools, technical knowledge to access and use sound and relevant evidence, and collaboration with other government sectors stand in the way of more consistent and informed use of evidence. Each Ministry’s Executive Committee members required evidence to support policy briefs/proposal. Where insufficient or poor quality evidence was provided, this had the effect of slowing down the approval process because the Executive Committee would send the brief back for further input. In some cases evidence simply wasn’t available and this could stall policy implementation indefinitely.There is a need for an active network and functional information system in the Ministries comprising context-specific knowledge management and translation opportunities linking people and resources and building on existing expertise and professional networks. This provides a broad integrated view of dynamics associated with the use of evidence in the process of food-policies, thus accommodating the different perspectives of policy-makers. This study has shown that organizations need to look at all aspects of their systems to ensure that at every level and point in the process, evidence access and use is supported and encouraged.


Nature Communications8: Article number: 15263; DOI: 10.1038/ncomms15263 (2017); Published: 05092017; Updated: 08162017.The original manuscript presented evidence that the deposits studied were fluvial rather than marine to support the conclusion that evidence of life on land had been discovered in the 3.4 Ga Dresser Formation, Pilbara Craton, Australia.The authors acknowledge that the following reference, which was omitted, should have been cited to provide more detailed sedimentological and stratigraphic evidence that the deposits studied were indeed fluvial rather than marine.Assessing the Link Between Earth’s Earliest Convincing Evidence of Life and Hydrothermal Fluids: The c. 3.5 Ga Dresser Formation of the North Pole Dome, Pilbara Craton, Western Australia .Djokic, T.


In order to meet the higher accuracy and system reliability requirements, the information fusion for multi-sensor systems is an increasing concern. Dempster–Shafer evidence theory (D–S theory) has been investigated for many applications in multi-sensor information fusion due to its flexibility in uncertainty modeling. However, classical evidence theory assumes that the evidence is independent of each other, which is often unrealistic. Ignoring the relationship between the evidence may lead to unreasonable fusion results, and even lead to wrong decisions. This assumption severely prevents D–S evidence theory from practical application and further development. In this paper, an innovative evidence fusion model to deal with dependent evidence based on rank correlation coefficient is proposed. The model first uses rank correlation coefficient to measure the dependence degree between different evidence. Then, total discount coefficient is obtained based on the dependence degree, which also considers the impact of the reliability of evidence. Finally, the discount evidence fusion model is presented. An example is illustrated to show the use and effectiveness of the proposed method. With the development of science and technology, in order to meet the higher accuracy and system reliability requirements, the information fusion filters for multi-sensor systems have been widely applied . VariousThe fusion of different uncertain data is an important research topic of modern intelligent multi-sensor systems. Among these methods, Dempster–Shafer evidence theory (D–S theory) has been investigated for many applications in multi-sensor information fusion due to its flexibility in uncertainty modeling ,14,15. DIn D–S theory, Dempster’s rule plays a vital part in the process of information fusion. However, there is an issue that limits the application of evidence theory. The classical evidence theory is based on the assumption that the evidence is independent of each other . In pracFor the first category, the basic idea is to find a new evidence fusion method without considering dependence . Some scFor the second category, the basic idea is to reduce the repetitive computation of the dependent part of the information sources as far as possible . In thisThe second research idea is based on the discount evidence model. The main idea of this model is that dependent evidence shouldn’t be given the same weight as independent evidence in the process of information fusion since it provides less effective information . The depThis paper is organized as follows. In Some preliminaries are introduced in this section, including Dempster–Shafer evidence theory, the discounted evidence, Pignistic Probability Transformation and Spearman’s rank correlation coefficient.Definition 1.Θ as Frame of Discernment ,  and , respectively, represent the statuses of low water level, middle water level and high water level. By analysing the geometric relationship, That is, In addition, when In other words, the positive correlation or negative correlation does not substantially affect the fusion result for the information fusion system based on the D–S evidence theory.D:Supposing that there are M sensor sources, we can then establish dependency matrix Step 3: Calculate the total dependence degree of each sensor information source. The total dependence degree of Step 4: Considering that the evidence with strong relevance should be given a smaller discount factor, the correlation discount coefficient is defined asThinking of the need to experience the construction and fusion of BBA from the sensor information source and the final decision making, the analysis of the relevance of the evidence should begin with the initial information source, as the real application involves multi-sensor sources of information, and the relationship between the sensor information source is complex. In order to find a simple and effective representation, this paper adopts the following method:N groups of training data to establish BBA by M of them have correct classification results. The reliability of the information source In a multi-sensor information fusion system, the global system performance is closely related to each sensor’s reliability. The reliability of the information source refers to the correct rate obtained by direct decision by the information source. The higher reliability evidence helps to draw a more accurate result in the decision-making process. In addition, the higher reliability evidence should be given larger weight in evidence fusion. For the target recognition, it is assumed that there are On the basis of the above analyses, the total discounting coefficient of evidence from information source Suppose that BBA Equation ).In this paper, Iris Dataset from the machine learning database  is used The dependent evidence fusion method in this paper could effectively deal with uncertain information fusion issues existing in the real world. One of the applications of this method could be target recognition. For example, to recognize the enemy aircraft, which could be in the form of a bomber, the Air Early Warning plane or fighter plane, we could first acquire information such as airborne radar signal, infrared signal or the electronic support measure (ESM) information. Different types of enemy aircraft usually have different features in the information. Thus, the type of aircraft could be recognized based on the acquired information. The recognition rate can be improved by combining information from different sensors. The dependence among the sensors is also considered in the proposed method. Similar to the iris example, the airborne radar signal, infrared signal or the ESM information are associated with the iris attributes SL, SW, PL or PW, and the types of enemy aircraft are associated with the iris types Setosa, Versicolour or Virginica.Another possible application could be fault diagnosis. For example, in a power transformer fault diagnosis system, we want to recognize different fault types including transformer winding, transformer core, arc discharge or transformer insulation aging, and so on. In addition, the corresponding fault symptoms including transformer core earth current, insulation resistance or other symptoms could be acquired to establish different BBAs. Different fault symptoms may be dependent and the fusion method proposed in this article can handle this problem. In other words, the dependent information fusion model based on rank correlation coefficient could be investigated for applications in many fields.The main procedure of the proposed method to recognize iris class is shown in Step 1: Calculate the correlation discount coefficient Step 2: Build BBA. Four BBAs were established for SL, SW, PL and PW. The BBA was established according to the article .Step 3: Calculate reliability coefficient Step 4: Calculate the total discounting coefficient of four attributes according to Equation .Step 5: Model testing. Using the test data, we assume for four cases that the evidence is independent from each other, only considering the dependence between the evidence, only considering the reliability of the evidence, considering both dependence and reliability, and then calculating the recognition accuracy. Then, we increase the proportion of training set data, and repeat the above experiment. In the four cases, the fusion rule as follows:Group 1:Assume that the evidence is independent from each otherGroup 2:Only considering the dependence between the evidenceGroup 3:Only considering the reliability of the evidenceGroup 4:Considering both dependence and reliabilityIn the experiment, some samples were randomly selected as the training set, and the remaining samples were used as test set. The steps are as follows:The method of decision-making after fusion is based on the Pignistic Probability Transformation (PPT) proposed in .Step 1: The first step is to analyse the dependence among SL, SW, PL, and PW attributes. Rank correlation coefficients among attributes of iris data sets are shown in Then, we can establish dependency matrix D:Calculate the total dependence degree of each attribute as Equation . ResultsConsidering that the evidence with strong relevance should be given a smaller discount factor, the correlation discount coefficient is calculated as Equation . The resStep 2: Build BBA. Four BBAs were established for SL, SW, PL and PW. The BBA was established according to the article .Step 3: Calculate reliability coefficients of the four attributes as Equation . The resStep 4: The total discounting coefficient of the four attributes can be calculated as Equation . The resAfter normalization, the total discounting coefficient of the four attributes isStep 5: Model testing. Based on the above calculation, we begin to test our evidence fusion model. The detailed steps are shown in Step 6: Then, we calculate the confidence interval of this experiment. For each piece of evidence, we can acquire a BBA, and suppose its recognition result is A. Then, the Belief function (Bel(A)), defined as a sum of the mass probabilities of all the proper subsets of A, is calculated as follows:In this section, the correlation of iris data sets is performed. The main procedures are as follows:In addition, the Plausibility function ( Pl(A) ), defined as maximum belief of A, is calculated as follows:Then, the Belief Interval is defined as In four cases, the average classification recognition accuracy test results are shown in The confidence interval of this experiment as shown in Comparing Group 1 with Group 3 (or Group 2 with Group 4), it is obvious that the recognition accuracy is significantly improved when considering reliability. However, comparing Group 1 with Group 2 (or Group 3 with Group 4), the recognition accuracy is decreased. In addition, further research shows the reliability coefficients of the four attributes and the correlation coefficients in From A case study is used to show the effectiveness of the proposed method. Assume that the framework is g result .Case1: Combining these four independent pieces of evidence according to the traditional Dempster’s rule:The recognition result is A, which is correct by combining four independent pieces of evidence.Case2: Let us consider another condition: we have the fifth piece of evidence R5, which is the same as R4, that is, The recognition result is B; therefore, using traditional Dempster’s rule without considering dependent can draw a wrong result. The reason is that one of the pieces of evidence is totally dependent on another piece of evidence, which means that one of the pieces of evidence has been counted twice.Case3: By considering the dependent evidence based on the method proposed in Then, the fusion rule isThe final result isThe recognition result is A. Thus, the proposed dependent evidence fusion model could improve the decision-making result especially in the fusion of low recognition evidence.With the rapid development of artificial intelligence, the acquisition of information has increasing importance. In industrial applications, the multi-sensor information fusion system based on Dempster–Shafer evidence theory plays a more and more important role in information collection and decision-making. However, the classical evidence theory assumes that the evidence is independent from each other, which is often difficult to establish in practice. To address this issue, this paper analyzes the present researche about dependent evidence fusion. Comparing with all kinds of correlation measurement methods, we select the Spearman’s rank correlation coefficient as the metric to measure dependence existing in evidence. Rank correlation coefficient is a parameter-free measure for correlations, which may be used to measure the level of agreement between two stochastic variables without making assumptions regarding the parametric structure of the probability distribution of the variables. Then, a dependent evidence fusion model based on rank correlation coefficient is established. Finally, an experiment is developed to verify the effectiveness of this model based on iris data sets. Experiment results suggest that considering reliability will improve accuracy of decision-making and considering dependent evidence helps draw more reasonable and robust conclusions. In other words, it is necessary to consider the influence of dependent evidence in information fusion to gain a more credible result.


Limbic tracts are affected in various neurological and psychiatric diseases/disorders   and major depressive disorder (MDD); the left-side dominancy in the effect of TBI was interpreted in relation to the leftward asymmetry observed in normal development (Ewing-Cobbs et al., Several limitations should be noted. The limited number of participants might introduce a selection bias. The limbic pathways investigated were limited; the uncinate fasciculus, stria terminalis, and the mammillothalamic fasciculus were not included. The parameters investigated from diffusion MRI were limited to those derived from the tensor model; parameters acquired through advanced models, such as diffusion spectrum imaging or high-angular resolution diffusion imaging, remain to be investigated. For the evaluation of intra-individual variation, a longitudinal design would be needed Baltes, . To overKO contributed to all aspects of the work, including conception and writing, and is accountable for all aspects of this commentary.inHealth Pilot Project grant from the Johns Hopkins Individualized Health Initiative. The content of this commentary is solely the responsibility of the author and does not necessarily represent the official view of NIH/NICHD or the Johns Hopkins Individualized Health Initiative.The author is supported by NIH grant R01HD065955 and an The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


While modeling approaches seek to draw on the best available evidence to project health impact of improved coverage of specific interventions, uncertainty around the outputs often remains. When the modeling estimates are used for advocacy, these uncertainties should be communicated to policy makers clearly and openly to ensure they understand the model's limits and to maintain their confidence in the process. While modeling approaches seek to draw on the best available evidence to project health impact of improved coverage of specific interventions, uncertainty around the outputs often remains. When the modeling estimates are used for advocacy, these uncertainties should be communicated to policy makers clearly and openly to ensure they understand the model's limits and to maintain their confidence in the process. Askew et al., Rodriguez et al., and Jones-Hepler et al.See related articles by World Development Report 1993: Investing in Health,In efforts to further the use of evidence for policy and planning decision making, there has been considerable use in global health—across a variety of technical areas—of quantitative modeling approaches that attempt to project health impact of improved coverage of specific “evidence-based” interventions. This approach has roots in analyses done for the World Bank's This issue of GHSP includes 3 papers on the use of such models, one in the family planning field  and as an input to program planning by ministries of health, global technical agencies, and donors.In the maternal and child health field, LiST (Lives Saved Tool) has a well-established presence. The pioneering 2003 BMC Public Health.The website provides detailed documentation on how the model works, the evidence base for the intervention effect sizes, and the data inputs used. Further documentation has recently been published in a supplement in As described in the article by Jones-Hepler et al.,In its current form, MANDATE has built-in datasets for India and sub-Saharan Africa and generates region-wide (rather than country-specific) estimates. In contrast to LiST, it has largely been used for exploring different scenarios to determine what strategies for improving care may be most effective in driving down mortality attributable to specific causes of maternal and newborn deaths. Also in contrast to LiST, it has not been used extensively for advocacy.In short, LiST and MANDATE have been developed for somewhat different purposes and they are constructed somewhat differently. However, as with modeling for other areas of global health, the 2 approaches share similarities with regard to both utility and potential pitfalls. Importantly, both permit exploration of different possible scenarios, making use of best available epidemiologic data and intervention efficacy estimates. This is important and helpful. But there are also problems.Bringing evidence to bear on strategy, prioritization, and policymaking is an essential, but challenging, process on multiple levels. Inevitably, policymaking is driven not solely by evidence. Power, stakeholder or special interests, and emotional appeals can weigh heavily. It is no accident that LiST uses the emotionally engaging metric of “lives saved” rather than the drier notion of “mortality rate reductions.” But contributing to advocacy efforts raises a dilemma for those engaged in evidence generation. In principle, science has some tolerance of uncertainty and ambiguity. Indeed, in peer-reviewed scientific papers, modesty concerning causal claims is valued, and transparent discussion of assumptions, methods, and study limitations, including uncertainties associated with estimated quantities, is expected. But for advocacy, simplicity, certainty, and a good relatable story are prized. These sets of values stand in tension with each other.Threat of residual confounding when observational studies are used to estimate intervention effect sizeImportant factors present in the original setting where these studies were conducted that could be effect modifiers, amplifying or attenuating effect size that could otherwise have been evident under other conditionsCausal simplifications that do not model important potential interactions, for example between nutrition status and infection, or between different infectious diseasesMeasurement issues such as using verbal autopsy approaches for cause-of-death determinations or ascertaining coverage through population-based surveysCause-of-death distributions in sub-populations  that may not necessarily be generalizable to national scale or to neighboring jurisdictionsDetermining effect sizes from trials originally designed only to provide all-cause mortality effect estimates, requiring modelers to convene  experts to guess what proportion of averted death can be attributed to a specific causeIn the absence of any trial evidence on mortality effects, relying on systematically collected expert opinion through Delphi-type processesModeling approaches such as LiST and those highlighted in this GHSP issue seek to draw upon the best available evidence. The reality, however, is that in many instances the best available evidence isn't very complete or robust, and much remains uncertain. Potential sources of uncertainty or bias remain in these models, for example:Science has some tolerance of uncertainty and ambiguity, but advocacy prizes simplicity, certainty, and a good story.must be some validity of the findings, when in fact findings may be highly misleading.Those directly involved in reviewing and collating such diverse data sources—and who are thus well aware of the simplifying assumptions one must make with models like these—recognize the very considerable uncertainties associated with the output of these models. They can see both the strengths and the limitations of the model assumptions and data inputs, and are thus well positioned to take the model outputs with a grain of salt, or two. Unfortunately, in many instances, when the outputs of these models are pressed into the service of advocacy there is little, if any, acknowledgment of these uncertainties. As a result, rather than evidence-based policymaking, the process may become an appeal to the mystique of science, asking that the model output be heeded like the authoritative pronouncement of an oracle. Many policy makers may simply be overawed by the complexity of the mathematical modeling and assume that there When modeling is used for advocacy purposes, there is little, if any, acknowledgment of the uncertainties associated with the model outputs.Models such as these are valuable tools—especially for exploring different scenarios, in our efforts to identify promising strategies for population health gains. But let's seek to play a more open, neutral role with our colleagues on the policymaking and resource-mobilization side, sharing insights from our evidence base as clearly as we can but not misrepresenting the remaining uncertainty.Models are valuable tools, but we should seek to play a more open, neutral role with our policymaking colleagues to represent any remaining uncertainty in the models.


Cooperation and competition were compared in the present study. Brain correlates  and hemodynamic measure of functional near-infrared spectroscopy  were acquired during a joined cooperative (Experiment 1) or competitive (Experiment 2) task. Subjects were required to match each other’s cognitive performance (cooperation) or to make better than others (competition) in terms of accuracy  and response time (RT). In addition, a personality trait measure  was used to distinguish subjects based on their rewarding attitude. Self-perception of social ranking and real performance were considered in response to subjects’ performance .An increased left prefrontal cortical (PFC) responsiveness was found for subjects who had higher BAS rating in case of both cooperation and competition conditions. Moreover, subjects with higher BAS ratings showed greater frontal left activity during the cooperative task. These subjects also concomitantly perceived an increasing in social ranking and improved their performance.Present results demonstrated that some trait components (BAS) and cooperative condition induce a positive self-representation in term of ranking and a best way to perform the task, as underlined by self-perception and cognitive outcomes. Indeed the higher BAS trait proved to be related with the representation of higher social ranking and with the perception of improved cognitive outcomes, with also a significant increased left PFC activity in cooperative contexts. Cooperation and competition are part of our daily life. When a cooperative or competitive interpersonal task is performed, it may induce different effects that are influenced by the “social” meaning of cooperation or competition and by self-perception in that interpersonal context. When considering social hierarchy, the occurrence of a cognitive task performed together can be accompanied by a modification in our self-representations according to the outcomes. In detail, the perception we built about ourselves is the result of a social analysis on our ranking within a specific situation in which we receive a feedback for our performance. Indeed firstly comparing my own and others’ performance on a specific interpersonal task may (or may not) enhance my rank perception in term of efficacy, taking into account the pre-existing condition. In second instance, the performance related to our cognitive efforts is able to influence self-perception and has an important role in creating a pertinent awareness of our own skills. This process is fundamental for the development of self-improvement in the future . FinallyBut how does this process take place in different interpersonal situations such as cooperation and competition?Cooperative or competitive performance gained in an interpersonal task substantially implies a process of social comparison together with the explicit assessment of individual performances. Previous work already investigated the relation between self-perception, perceived efficacy, and social hierarchy within a competitive scenario. Findings demonstrated that competition is able to improve individual performances and, contemporarily, to contribute to higher perceptions of the social ranking position based on the behavioral performance –4. HowevTherefore it is relevant and urgent to distinguish the self-perception of our social efficacy and our position within a social ranking in different interpersonal conditions—namely in competitive or cooperative situations, which produce qualitatively distinct social and psychological dynamics. For example, in our previous study social ranking, as perceived by the individual, was already investigated within a competitive paradigm taking into account also other personality variables . The anaCooperation and competition also call on different social and cognitive processes. Specifically, it was suggested that empathic and mentalizing attitudes differ somehow between cooperation and competition. Gallagher and Frith  suggesteThus, the strong increase in the prefrontal cortex activity—mainly the medial prefrontal cortex—observed during competition may in part mirror higher executive processing demands . SpecifiMoreover, recent research on the structure and function of neural circuits associated with social perception, social efficacy and social ranking offers preliminary evidence for an anterior neural circuit for those processes related to social cognition. Indeed, it was observed that neural circuits linking limbic, PFC, and striatal structures may be involved in such circuits and related to social responses in their affective, cognitive and behavioral components . Both doFinally, a main important role is related to motivational aspects and “rewarding” conditions activated by cooperation or competition. In fact, specific brain areas are involved according to task type and rewarding condition. Previously it was found that cooperation furnishes a social motivation and is related to right orbitofrontal activation. Competition, instead, is less socially rewarding, but requires supplementary mentalizing resources. It is associated with higher activity in medial prefrontal areas. Moreover recent research found that motivations and emotions can influence the perception of social position by creating a (more) positive versus negative predisposition in social relationships. Therefore, we supposed that the way people evaluate their position in the social hierarchy partially relies upon some motivational and emotional components, such as the degree to which their actions are well balanced between “approach” attitudes in relation to rewards and absence of punishment, as well as “withdraw” from punishments and absence of reward.Specifically, it was previously shown that high-BAS individuals  show moOn the contrary, high-BIS individuals  are often associated with submissive attitudes, with negative consequences on social representations . GeneralIn addition BAS is generally connected with feelings of dominance and high-BAS people are more sensitive to approach-related emotional contexts, with a favorable and dominant behavior toward the context –33. In pHowever, as for cortical correlates of cooperation/competition, it remains to be explored if and how the neural activity is differently modulated by competition- or cooperation-induced social evaluations when the cognitive outcome is experimentally manipulated. Indeed no previous studies have manipulated the performance and the ranking position to explore that comparison. Available evidences indicate that during social exchange many brain areas are involved, but it is still to be explored which is the specific contribution of each of them to the agents’ different mind-sets when they compete or cooperate to achieve a shared goal.To explore the cortical impact of cooperation and competition and the main role of motivational components such as BAS trait, in the present research we monitored electroencephalographic (EEG) and hemodynamic  activity in two different experiments (Experiments 1 and 2).Indeed, firstly, EEG activity may be considered as a good measure of brain responsiveness, and it has often been used to describe distinct responsiveness by the two hemispheres to different emotional and social conditions , 37, 38.Secondly, even if previous work provided functional imaging data associated with social ranking, the temporal features of such processes still need to be addressed. The classical imaging  measures do not seem to completely describe the real nature of the social inter-personal processes. Thanks to the sudden development of affective and interactive contexts, they require to be studied by imaging techniques that can also provide a sufficient resolution in both temporal and spatial domains and then allow recording event-related hemodynamic responses, such as NIRS . In addiTherefore based on our hypotheses, competition versus cooperation may ingenerate different cortical response in PFC based on the underlying more or less rewarding social outcomes. In addition personality components (BAS) was hypothesized to influence perceived hierarchical position—in terms of higher self-perceived abilities for higher BAS—and cognitive performance—namely, improved cognitive outcomes. In other words, the perceived effectiveness of behavior in term of performance during a competitive or cooperative task may be positively modulated by reward mechanisms and consequently these mechanisms may impact on the real cognitive outcomes (improved performance for higher BAS). That is, the improving performance effect should be prominent for high-BAS subjects as a consequence of perceived dominant position and rewarding context, which are clearly positively judged by high-BAS people. Such processes should result in a left lateralized prefrontal activation, as previously discussed about the neural circuits underlying cognitive and social representation. Thus, the need to better explore the role of PFC and its different hemispheric contribution to self-perceived social ranking in combination with personality components , 21, 55 To summarize, the three compartments of social ranking perception, personality components and cognitive performance should have a common trend, since we expected a correlated increased self-perception of social ranking and a better performance in relation to higher-BAS, with a subsequent higher activation over left frontal areas.Twenty-two undergraduate students  took part in the experiment. All participants were right-handed with normal or corrected-to-normal visual acuity. Exclusion criteria consisted in the presence of a psychopathological history for the subjects and immediate family. In addition, State-Trait-Anxiety-Inventory  and BecParticipants were accommodated in a moderately lit room in front of a monitor screen positioned at around 60 cm from their eyes. They were required to complete a simple task on sustained selective attention . SubjecThe instruction was to choose target stimuli between non-targets. Stimuli consisted in geometric shapes and were arranged according to four shape/color combinations: triangles or circles, blue or green. The target remained on the video to be memorized and then all the experimental stimuli were displayed one after another. The target stimulus changed every 25 trials. Subjects were required to make a two-alternative forced-choice by pressing a left/right button. Stimuli were displayed for 500 ms, and separated by a 300 ms inter-stimulus interval (ISI). After each trial, constituted by three stimuli, a feedback was presented on the screen as two up-arrows (high cooperation score), a dash (mean performance), or two down-arrows (low cooperation score). The feedback was presented for 5000 ms, and it was preceded and followed by 5000 ms blanks. The task was composed by 8 blocks  Fig. . HalfwayBAS scores were calculated for each subject . An ElectroCap with Ag/AgCl electrodes was applied to record EEGs from active sites on the scalp referred to the earlobes . Data wThe digital EEG data were bandpass filtered in the frequency bands: delta (.5–4), theta (4–8), alpha 8–12 Hz), beta (14–20) . To obtain a signal proportion to the power of the EEG frequency band, the filtered signal samples (epoch 1000 ms) were squared . An aver Hz, betafNIRS measurements were performed with the NIRScout System  using an 8-channel arrangement of optodes (4 light sources/emitters and 4 detectors) positioned over the prefrontal area. Optodes were placed on frontal and fronto-central sites   and deoxygenated hemoglobin (HHb). A starting baseline (120 s) was also recorded. Signals obtained from the eight channels were acquired with a sampling rate of 6.25 Hz, and analyzed and transformed according to their wavelength and location. The result of this procedure consisted in values for the changes in the concentration of oxygenated and deoxygenated hemoglobin for each channel. Hemoglobin quantity is scaled in mmol ∗ mm, implying that all concentration changes depend on the path length of the NIR light in the brain.1 − m2)/s. Accordingly, m1 and m2 are the mean concentration values during the baseline and trial, and s means the SD of the baseline. The mean concentration value of 5 s immediately before the trial was used as event-related baseline. Then, the effect sizes obtained from the eight channels were averaged in a way to increase the signal-to-noise ratio. Although NIRS raw data were originally relative values and could not be directly compared across subjects or channels, this procedure that normalized data allowed averaging regardless of the unit  = 8.23, p ≤ .001, η2 = .37). Indeed high-BAS rating showed a decreased ER compared to low-BAS rating. In order to display the high-BAS versus low-BAS rating differences, Fig. Results indicated a significant effect for BAS , with a general decreased RTs for high-BAS compared to low-BAS rating . Indeed high-BAS rating showed higher ranking perception than low-BAS rating  and Lat × BAS . The significant post hoc effects, that we reported, showed increased left theta activity for high-BAS compared to low-BAS rating , whereas no significant differences were found within the right hemisphere based on high-low-BAS . A higher left than right responsiveness was also revealed for high-BAS rating . About alpha ANCOVA indicated significant main effects for Lat × BAS , with decreased left alpha activity (increased brain response) for high-BAS rating compared to low-BAS rating ; whereas no significant differences were found within the right hemisphere . In addition high-BAS rating showed decreased alpha in the left than in the right side   and the right  channels.F  = 7.98, p ≤ .001, η2 = .35) and Lat × BAS effects were significant . About the main effect, it was observed a general increased left activity and a specific left increased response for high-BAS scoring . Moreover, about the simple effects, it was observed an increased response for high-BAS scoring within the left more than the right hemisphere   and performance (RTs) . In addition BAS was inversely correlated with alpha within the left hemisphere  (increased left brain activity in concomitance with higher BAS), and directly correlated with theta  and D modulation . In addition self-ranking was inversely correlated with RTs  and directly correlated with theta  (increased left activity in concomitance with higher self-ranking) and O2Hb . Finally O2Hb and theta band proved to be correlated .A series of correlation analysis was applied to cognitive performance , self-perception, D, and EEG modulation. Pearson correlation coefficients were calculated between them. BAS revealed significant positive correlation with self-ranking  took part in the experiment. The same selection criteria adopted for Experiment 1 were used in the Experiment 2.The same procedure of Experiment 1 was adopted, with a specific variation in term of the nature of the task. Indeed in Experiment 2 the competitive task was stressed: participants were told that the scoring was based on the capacity to beat the partner, in term of accuracy and speed. Also in this case a general (fake) evaluation was presented halfway. Subjects were told that their performance was “well above” or “well below” than their rival’s one and were required to maintain their outcomes (for winners) or to improve it (for losers) during the second half of the experiment . During the task, the trial feedbacks constantly reinforced participants about their good performance (in the case of winners) by presenting the up-arrows in 70% of cases, while the dash or the down-arrows only in 30% of cases (mainly at the beginning of the task) to make the task more credible and plausible. The opposite arrangement was proposed in the case of losers.As shown by post-session questionnaire, participants were strongly engaged in the hierarchical situation (92%), with high trust in the feedback (96%), with a good perception of relevance of the task for social status (94%), and with a perceived improved ranking position during the task (93%).The total scores for each scale were respectively: BAS: M = 47.90 (SD = 3.91); Reward: M = 23.76 (SD = 2.03); Drive: M = 12.98 (SD = 1.15); Fun Seeking: M = 13.09 (SD = 1.23). Cronbach’s alpha was calculated for BAS (.98) and for each BAS subscale .F  = 6.78, p ≤ .001, η2 = .32). High-BAS showed a decreased ER than low-BAS , with decreased RTs for high-BAS than low-BAS scoring . Indeed high-BAS scoring showed higher ranking perception than low-BAS scoring , with increase left than right activity; and significant interaction effect for Lat × BAS , with increased left theta activity for high-BAS scoring compared to low-BAS scoring . A more left than right responsiveness was also revealed for high-BAS scoring . As for alpha activity, the analysis indicated significant interaction effect Lat × BAS , with decreased left alpha activity (increased brain response) for high-BAS scoring compared to low-BAS scoring ; whereas no significant differences were found within the right hemisphere . In addition high-BAS scoring showed decreased alpha more in the left side than in the right side   and Lat × BAS were significant , with a general increased left more than right brain activity. Moreover, reporting the significant simple effect, it was observed an increased response for high-BAS scoring within the left more than the right hemisphere . In addition left PFC was more responsive for high-BAS than low-BAS scoring   and inverse correlation with performance (RTs) . In addition BAS was inversely correlated with alpha within the left hemisphere  (increased left brain activity in concomitance with higher BAS), and directly correlated with theta  and O2Hb modulation . In addition self-ranking was inversely correlated with RTs , alpha modulation , and directly correlated with theta and O2Hb . Finally O2Hb and theta band were significantly correlated .Pearson correlation coefficients were calculated between ERs, RTs, self-perception, O2Hb and EEG. BAS revealed significant positive correlation with self-ranking  was added to previous statistical design . We reported only the significant effects where Exp factor was significant, to synthesize the main results.F  = 7.85, p ≤ .001, η2 = .36) and BAS × Exp . Firstly, Exp 1 showed decreased ERs than Exp 2 . Secondly in Exp 1 high-BAS scoring revealed reduced ERs than in Exp 2 .ANCOVA indicated significant effect for Exp  and BAS × Exp . Indeed in Exp 1 self-perceived ranking was evaluated higher than in Exp 2 . Finally high-BAS scoring showed increased self-ranking perception in Exp 1 than in Exp 2 .About the evaluation of their ranking position, ANCOVA indicated significant effect for Exp : a more left responsiveness was revealed for high-BAS scoring in Exp 1 than in Exp 2 . In addition high-BAS compared to low-BAS scoring showed increased theta in Exp 1 .While no main or interaction effects proved to be significant for alpha, delta and beta data, ANCOVA revealed significant effects for theta bands. About theta Lat × BAS × Exp was significant  interaction effects were significant. It was observed an increased response for high-BAS scoring within the left more in Exp 1 than Exp 2 .As shown by ANCOVA, Lat × BAS × Exp  in social ranking perception and cognitive performance during a task which included a cooperative (Experiment 1) or a competitive joint-action (Experiment 2). Specifically EEG (brain oscillations) and fNIRS hemodynamic brain activity (O2Hb) were considered in subject showing high- and low-BAS profile, to elucidate how this motivational trait component may affect subjects in formulating self-representation (ranking position) and self-improvement (cognitive performance) in an interpersonal cooperative and competitive context.A first main effect elucidated by the present research was related to the implication of PFC during the cooperative and competitive task. Indeed in both Experiments 1 and 2 the electrodes positioned over the PFC showed to be significantly modulated in response to cooperation and competition, with a significant increased activity mainly within the left side. In addition, both EEG and fNIRS measures showed a significant and increased lateralization effect in conjunction with BAS score. Such finding replicated previous results which suggested a similar role for some prefrontal areas (such as the VMPFC) in response to status perception . Recent As for the specific contribution of some frequency bands  that we found to be relevant to explain the cortical activation, we may suggest that from one hand alpha may function as an index of brain lateralized activation. Specifically, it has been proven that a decrease in alpha power could indicate increased brain activity and a differential responsiveness by the two hemispheres in relation to specific cognitive or affective tasks , 72. FroIndeed, previous work highlighted that event-related theta activity signal sustained visual stimulation with affective content , 53, 73 A second interesting finding consists in the recruitment of the PFC that was modulated as a function of both BAS trait and the cooperative/competitive nature of the task. This effect emerged in the modulation of EEG activity recorded over prefrontal sites and was confirmed by optical imaging analyses (fNIRS).Indeed, in addition to this general enhanced left PFC activity, a specific lateralization pattern was found, with increased left activation with respect to the right one, mainly for higher-BAS and mainly in the case of cooperative task. Firstly, the “left hemisphere effect” in integration with the “approach attitude” was found to be leading to explain our results. This is in line with previous studies, which reported that high-BAS profiles were more likely to relate to the dominant and “proactive” attitudes in situations that were shown to induce a positive and rewarding effect . SpecifiSecondly, high-BAS scoring showed significantly greater PFC activation in case of cooperation than of competition and this brain response was related to improved performance and increased self-perceived ranking position, as was highlighted by both EEG  and fNIRS . As underlined by previous data, we may explain these results taking into account evidences from evolutionary and developmental psychology, which underline that cooperating is more gratifying than competing from a social point of view since cooperation may be represented as a source of positive social feedback on the joint performance towards the common objective, in addition to the intra-subjective positive feedback as in competition . PreviouAs for the cognitive performance it was also found a relevant direct relationship between the brain activity within the left PFC, the cognitive behavior and the social representation for both cooperation and competition, as shown by the correlation analyses. The effect related to the cortical “unbalance” over the left hemisphere in response to cooperation and competition was also associated with an increased sense of social efficacy and a concomitant better performance (decreased ERs and RTs) could support a possible connection between left-sided prefrontal activation, social hierarchy representation, and behavioral change.The cortical lateralization could support the presence of a significant over-activation of left anterior regions and, in parallel, the role of such areas in managing the subjective perception to be upper in ranking. To verify this hypothesis, previous work highlighted that the subjective perception of being powerful within the social context  is related to increased left-sided activity , 21, 88.However, we found also an improved performance in terms of ERs in the case of cooperation compared to competition. That is, we may state that cooperating induces a better perception of self, as well as an increased cognitive performance by the subjects. In other words, it seem probable that the self-perception of well-performing in a cooperative joined-action produces a more consistent and significant cognitive outcome. Based on these results we may suggest that to cooperate is better than to compete also from a cognitive point of view. Previous research showed some contrasting results, since in some cases subjects showed better performances in cognitive tasks during competition than during cooperation . HoweverIt should be specified that this increased cognitive and self-perception effect induced by cooperation was mainly remarked by BAS trait. High-BAS participants were better performers and they perceived themselves as higher in ranking. Such effects are consistent with previous findings that proved a left-cortical asymmetry in the case of approach-related motivations, with increased high-frequency electrocortical oscillations over the left with respect to the right PFC  and imprTo summarize, as reveled by the present results, the contribution of PFC and specifically of left structures is crucial to support the cooperative and competitive joined-action. However, this effect was mainly related with the BAS construct, since the increased left PFC activation for both EEG and fNIRS measures was directly related to the high-BAS trait. Moreover, BAS trait appears to endorse the predisposition to influence self-perceived ranking, as well as actual behavioral performance, since high-BAS participants presented a self-attribution of higher ranking position and general improved cognitive outcomes. Therefore, cooperation proved to be the best condition to perform the task, as underlined by self-perception and cognitive results. In addition, the representation of higher-level in hierarchy related to improved cognitive performance is linked to a clear activity in the DLPFC more for high-BAS people when they operate in a cooperative context.However, an intrinsic limitation of the present study is related to the low ecological value of the task if compared with the real common situations where cooperation or competition are displayed. Secondly, variations in type of tasks which could not include only a cognitive performance should be provided in future research, to eventually compare the present results with those of more “social” tasks. Thirdly future research should also provide a complete analysis of the inter-personal strategies used by cooperators/competitors by using a more specific hyperscanning methodology in order to consider the joined brain activities of the subjects. Finally the specific cortical side effect and the left/right hemispheric lateralization should be better analyzed also independently from the BAS trait measure.