PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Gold-based optical biosensor for single-mismatched DNA detection using salt-induced hybridization

In this study, a gold nanoparticle (Au-NP)-based detection method for sensitive and specific DNA-based diagnostic applications is described. A sandwich format consisting of Au-NPs/DNA/PMP (Streptavidin-coated MagnetSphere Para-Magnetic Particles) was fabricated. PMPs captured and separated target DNA while Au-NPs modified with oligonucleotide detection sequences played a role in recognition and signal production. Due to the much lower stability of mismatched DNA strands caused by unstable duplex structures in solutions of relatively low salt concentration, hybridization efficiency in the presence of different buffers was well investigated, and thus, the optimized salt concentration allowed for discrimination of single-mismatched DNA (MMT) from perfectly matched DNA (PMT). Therefore, quantitative information concerning the target analyte was translated into a colorimetric signal, which could easily and quantitatively measured by low-cost UV–vis spectrophotometric analysis. The results indicated this to be a very simple and economic strategy for detection of single-mismatched DNA strands.

A Probe-Goal Approach to Agreement and Incorporation Restrictions in Southern Tiwa

Southern Tiwa (Tanoan) exhibits agreement with up to three arguments (ergative, absolutive, dative). This agreement is subject to certain restrictions resembling the Person-Case Constraint paradigm (Bonet 1991). Moreover, there is a correlation between agreement restrictions and conditions on (the obviation of) noun-incorporation in Southern Tiwa, as explicitly and elegantly captured by Rosen (1990) in terms of a heterogeneous feature hierarchy and rules of association. We attempt to recast Rosen’s central insights in terms of Anagnostopoulou’s probe-sharing model of Person-Case Constraint effects (Anagnostopoulou 2003, 2006), to show that the full range of Southern Tiwa agreement and (non-)incorporation restrictions can be given a single, unified analysis within the probe-goal-Agree framework of Chomsky (2001). In particular, we argue that Southern Tiwa’s triple-agreement system is characterized by (a) an independent class probe located on the heads T and v, and (b) a rule that allows this class probe to be deleted in the context of local-person T-agreement. The various restrictions on agreement and non-incorporation then reduce to a single source: failure of class-valuation with DP (as opposed to NP) arguments.

Near-Field Plasmonics of an Individual Dielectric Nanoparticle above a Metallic Substrate

We simulate and discuss the local electric-field enhancement in a system of a dielectric nanoparticle placed very near to a metallic substrate. We use finite-element numerical simulations in order to understand the field-enhancement mechanism in this dielectric NP-on-mirror system. Under appropriate excitation conditions, the gap between the particle and the substrate becomes a "hot spot", i.e., a region of intense electromagnetic field. We also show how the optical properties of the dielectric NP placed on a metallic substrate affect the plasmonic field enhancement in the nanogap and characterize the confinement in the gap. Our study helps to understand and design systems with dielectric NPs on metallic substrates which can be equally as effective for SERS, fluorescence, and nonlinear phenomena as conventional all plasmonic structures.

Brief announcement: Maintaining large dense subgraphs on dynamic networks

In distributed networks, some groups of nodes may have more inter-connections, perhaps due to their larger bandwidth availability or communication requirements. In many scenarios, it may be useful for the nodes to know if they form part of a dense subgraph, e.g., such a dense subgraph could form a high bandwidth backbone for the network. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs (subgraphs that the member nodes are aware of). The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model in the sense that only messages of O(log n) size are permitted, where n is the number of nodes in the network. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that approximate both the densest subgraph, i.e., the subgraph of the highest density in the network, and the at-least-k-densest subgraph (for a given parameter k), i.e., the densest subgraph of size at least k. We give a (2 + e)-approximation algorithm for the densest subgraph problem. The at-least-k-densest subgraph is known to be NP-hard for the general case in the centralized setting and the best known algorithm gives a 2-approximation. We present an algorithm that maintains a (3+e)-approximation in our distributed, dynamic setting. Our algorithms run in O(Dlog n) time. © 2012 Authors.

Perceptions of the Nurse Practitioner Role in Facilitating the Implementation of an Onsite Pain Management Team in Long Term Care

Background: One strategy to improve pain management in long term care (LTC) is to optimize the emerging role of the nurse practitioner (NP) in LTC. The purpose of this sub study was to learn about the NP role in implementing an onsite, interdisciplinary Pain Team in the LTC home setting.

Methods: We used a case study design that included two NPs who worked at separate LTC homes. Each of the NPs completed a weekly questionnaire of pain-related activities that they engaged in over a one-year implementation period; and a diary, using critical reflection, about their experiences and strategies used to implement the Pain Team. Descriptive statistics and thematic content analysis were used to analyze the case study data.

Findings: NPs tended to be most engaged in pain assessment and collaborated more with licensed nurses and personal support workers; less with pharmacists. NPs were more involved in organizational level activities, such as participating in committee work or assisting with the development of policies and procedures about pain. NPs created palliative care and pain service protocols; engaged in policy development, in-servicing, quality assurance and advocacy; and encouraged best practices. NPs were challenged with time constraints for pain management and balancing other role priorities and felt that increased scope of practice for them was needed.

Conclusions: The results of this study highlight how NPs implemented a Pain Team in LTC which may be helpful to others interested in implementing a similar strategy to reduce residents’ pain.

Dendrons with spermine surface groups as potential building blocks for nonviral vectors in gene therapy

This paper investigates a series of dendrons based on the Newkome dendritic scaffold that displays a naturally occurring polyamine (spermine) on their surface. These dendrons have previously been shown to interact with DNA in a generation dependent manner with the more highly branched dendrons exhibiting a strong multivalency effect for the spermine surface groups. In this paper, we investigate the ability of these dendrons to transfect DNA into cells (human breast carcinoma cells, MDA-MB-231, and murine myoblast cells, C2C12) as determined by the luciferase assay. Although the dendrons are unable to transfect DNA in their own right, they are capable of delivering DNA in vitro when administered with chloroquine, which assists with escape from endocytic vesicles. The cytotoxicity of the dendrons was determined using the XTT assay, and it was shown that the dendrons were nontoxic either alone or in the presence of DNA. However, when administered with DNA and chloroquine, the most highly branched dendron did exhibit some cytotoxicity. This paper elucidates the relationship between in vitro transfection efficiency and toxicity. While transfection efficiencies are modest, the low toxicity of the dendrons, both in their own right, and in the presence of DNA, provides encouragement that this type of building block, which has a relatively high affinity for DNA, will provide a useful starting point for the further synthetic development of more effective gene transfection agents.

Nanoencapsulation of ABT-737 and camptothecin enhances their clinical potential through synergistic antitumor effects and reduction of systemic toxicity

The simultaneous delivery of multiple cancer drugs in combination therapies to achieve optimal therapeutic effects in patients can be challenging. This study investigated whether co-encapsulation of the BH3-mimetic ABT-737 and the topoisomerase I inhibitor camptothecin (CPT) in PEGylated polymeric nanoparticles (NPs) was a viable strategy for overcoming their clinical limitations and to deliver both compounds at optimal ratios. We found that thrombocytopenia induced by exposure to ABT-737 was diminished through its encapsulation in NPs. Similarly, CPT-associated leukopenia and gastrointestinal toxicity were reduced compared with the administration of free CPT. In addition to the reduction of dose-limiting side effects, the co-encapsulation of both anticancer compounds in a single NP produced synergistic induction of apoptosis in both in vitro and in vivo colorectal cancer models. This strategy may widen the therapeutic window of these and other drugs and may enhance the clinical efficacy of synergistic drug combinations.

Positioning Clinical Nurse Specialists and Nurse Practitioners as Change Champions to Implement a Pain Protocol in Long-Term Care

Pain management for older adults in long-term care (LTC) has been recognized as a problem internationally. The purpose of this study was to explore the role of a clinical nurse specialist (CNS) and nurse practitioner (NP) as change champions during the implementation of an evidence-based pain protocol in LTC. In this exploratory, multiple-case design study, we collected data from two LTC homes in Ontario, Canada. Three data sources were used: participant observation of an NP and a CNS for 18 hours each over a 3-week period; CNS and NP diaries recording strategies, barriers, and facilitators to the implementation process; and interviews with members of the interdisciplinary team to explore perceptions about the NP and CNS role in implementing the pain protocol. Data were analyzed using thematic content analysis. The NP and CNS used a variety of effective strategies to promote pain management changes in practice including educational outreach with team members, reminders to nursing staff to highlight the pain protocol and educate about practice changes, chart audits and feedback to the nursing staff, interdisciplinary working group meetings, ad hoc meetings with nursing staff, and resident assessment using advanced skills. The CNS and NP are ideal champions to implement pain management protocols and likely other quality improvement initiatives.

Establishing a nurse practitioner collaborative: evolution, development, and outcomes

The first Australian palliative care nurse practitioner (NP) was endorsed in 2003. In 2009 the Victoria Department of Health funded the development of the Victorian Palliative Care Nurse Practitioner Collaborative (VPCNPC). Its aim was to promote the NP role, develop resources, and provide education and mentorship to NPs, nurse practitioner candidates (NPCs), and health service managers. Four key objectives were developed: identify the demographic profile of palliative care NPCs in Victoria; develop an education curriculum and practical resources to support the training and education of palliative care NPCs and NPs; provide mentorship to NPs, NPCs, and service managers; and ensure effective communication with all key stakeholders. An NPC survey was also conducted to explore NPC demographics, models of care, the hours of study required for the role, the mentoring process, and education needs. This paper reports on the establishment of the VPCNPC, the steps taken to achieve its objectives, and the results of the survey. The NP role in palliative care in Australia continues to evolve, and the VPCNPC provides a structure and resources to clearly articulate the benefits of the role to nursing and clinical services. The advanced clinical practice role of the nurse practitioner (NP) has been well established in North America for several decades and across a range of specialties (Ryan-Woolley et al, 2007; Poghosyan et al, 2012). The NP role in Australia and the UK is a relatively new initiative that commenced in the early 2000s (Gardner et al, 2009). There are over 1000 NPs across all states and territories of Australia, of whom approximately 130 work in the state of Victoria (Victorian Government Health Information, 2012). Australian NPs work across a range of specialties, including palliative, emergency, older person, renal, cardiac, respiratory, and mental health care. There has been increasing focus nationally and internationally on developing academic programmes specifically for nurses working toward NP status (Gardner et al, 2006). There has been less emphasis on identifying the comprehensive clinical support requirements for NPs and NP candidates (NPCs) to ensure they meet all registration requirements to achieve and/or maintain endorsement, or on articulating the ongoing requirements for NPs once endorsed. Historically in Australia there has been a lack of clarity and limited published evidence on the benefits of the NP role for patients, carers, and health services (Quaglietti et al, 2004; Gardner and Gardner, 2005; Bookbinder et al, 2011; Dyar et al, 2012). An NP is considered to be at the apex of clinical nursing practice. The NP role typically entails comprehensively assessing and managing patients, prescribing medicines, making direct referrals to other specialists and services, and ordering diagnostic investigations (Australian Nursing and Midwifery Council, 2009). All NPs in Australia are required to meet the following generic criteria: be a registered nurse, have completed a Nursing and Midwifery Board of Australia approved postgraduate university Master's (nurse practitioner) degree programme, and be able to demonstrate a minimum of 3 years' experience in an advanced practice role (Nursing and Midwifery Board of Australia, 2011). An NPC in Victoria is a registered nurse employed by a service or organisation to work toward meeting the academic and clinical requirements for national endorsement as an NP. During the period of candidacy, which is of variable duration, NPCs consolidate their competence to work at the advanced practice level of an NP. The candidacy period is a process of learning the new role while engaging with mentors (medical and nursing) and accessing other learning opportunities both within and outside one's organisation to meet the educational requirements. Integral to the NP role is the development of a model of care that is responsive to identified service delivery gaps that can be addressed by the skills, knowledge, and expertise of an NP. These are unique to each individual service. The practice of an Australian NP is guided by national standards (Nursing and Midwifery Board of Australia 2014). It is defined by four overarching standards: clinical, education, research, and leadership. Following the initial endorsement of four Victorian palliative care NPs in 2005, there was a lull in recruitment. The Victoria Department of Health (DH) recognised the potential benefits of NPs for health services, and in 2008 it provided funding for Victorian public health services to scope palliative care NP models of care that could enhance service delivery and patient outcomes. The scoping strategy was effective and led to the appointment of 16 palliative care nurses to NPC positions over the ensuing 3 years. The NPCs work across a broad range of care settings, including inpatient, community, and outpatient in metropolitan, regional, and rural areas of Victoria. At the same time, the DH also funded the Centre for Palliative Care to establish the Victorian Palliative Care Nurse Practitioner Collaborative (VPCNPC) to support the NPs and NPCs. The Centre is a state-wide service that is part of St Vincent's Hospital Melbourne and a collaborative Centre of the University of Melbourne. Its primary function is to provide training and conduct research in palliative care. The purpose of the VPCNPC was to provide support and mentorship and develop resources targeted at palliative care NPs, NPCs, and health service managers. Membership of the VPCNPC is open to all NPs, NPCs, health service managers, and nurses interested in the NP role. The aim of this paper is to describe the development of the VPCNPC, its actions, and the outcomes of these actions.

Probabilistic Inference in Credal Networks: New Complexity Results

Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence. We show that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one. We prove that under epistemic irrelevance the polynomial-time complexity of inferences in credal trees is not likely to extend to more general models (e.g., singly connected topologies). These results clearly distinguish networks that admit efficient inferences and those where inferences are most likely hard, and settle several open questions regarding their computational complexity. We show that these results remain valid even if we disallow the use of zero probabilities. We also show that the computation of bounds on the probability of the future state in a hidden Markov model is the same whether we assume epistemic irrelevance or strong independence, and we prove an analogous result for inference in Naive Bayes structures. These inferential equivalences are important for practitioners, as hidden Markov models and Naive Bayes networks are used in real applications of imprecise probability.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.

On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.

On the Complexity of Strong and Epistemic Credal Networks

Credal networks are graph-based statistical models whose parameters take values on a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The result of inferences with such models depends on the irrelevance/independence concept adopted. In this paper, we study the computational complexity of inferences under the concepts of epistemic irrelevance and strong independence. We strengthen complexity results by showing that inferences with strong independence are NP-hard even in credal trees with ternary variables, which indicates that tractable algorithms, including the existing one for epistemic trees, cannot be used for strong independence. We prove that the polynomial time of inferences in credal trees under epistemic irrelevance is not likely to extend to more general models, because the problem becomes NP-hard even in simple polytrees. These results draw a definite line between networks with efficient inferences and those where inferences are hard, and close several open questions regarding the computational complexity of such models.

Generalized loopy 2U: A new algorithm for approximate inference in credal networks

Credal networks generalize Bayesian networks by relaxing the requirement of precision of probabilities. Credal networks are considerably more expressive than Bayesian networks, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal networks. The algorithm is based on an important representation result we prove for general credal networks: that any credal network can be equivalently reformulated as a credal network with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal network is then updated by L2U, a loopy approximate algorithm for binary credal networks. Overall, we generalize L2U to non-binary credal networks, obtaining a scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences with respect to other state-of-the-art algorithms is evaluated by extensive numerical tests.