Dopamine has long been tightly associated with aspects of reinforcement learning and motivation in simple situations where there are a limited number of stimuli to guide behavior and constrained range of outcomes. In naturalistic situations, however, there are many potential cues and foraging strategies that could be adopted, and it is critical that animals determine what might be behaviorally relevant in such complex environments. This requires not only detecting discrepancies with what they have recently experienced, but also identifying similarities with past experiences stored in memory. Here, we review what role dopamine might play in determining how and when to learn about the world, and how to develop choice policies appropriate to the situation faced. We discuss evidence that dopamine is shaped by motivation and memory and in turn shapes reward-based memory formation. In particular, we suggest that hippocampal-striatal-dopamine networks may interact to determine how surprising the world is and to either inhibit or promote actions at time of behavioral uncertainty. It is often assumed, when faced to an unfamiliar environment, that our main task is to learn about this new world via a process of exploration, gathering information through trial and error. As experimenters trying to study these processes, we present our subjects with novel stimuli, different ways of responding and different types of reinforcer in order to determine how they learn about these elements of their world. Sometimes then we might change associations or add new cues to try to understand how new information is represented or existing associations modified. Indeed, when using animals as our subjects, often a lot of effort is expended to ensure that the task environment shares few, if any, features that the animals might have encountered at a previous point in their lives to ensure that new learning can proceed uncontaminated by past experience. Several decades of work has helped to map out how learning and adaptive behavior in these types of environment might be represented in brain circuits, with differing angles of focus within widespread frontal-temporal-striatal networks and particularly their interactions with the neurotransmitter dopamine.However, while these processes are inarguably critical for survival, this concentration of research into such constrained task set-ups has also indirectly diminished the amount of work on another critical factor in everyday learning and decision making. For what has sometimes been overlooked is the degree to which behavior in novel or changing environments relies not only on detecting discrepancies with what has recently been experienced, but also with identifying similarities with past situations stored in memory —in other words, a transient change in dopamine levels lasting between a few hundred milliseconds and several seconds—and how this can have an impact on both short- and longer-term behavior. However, we acknowledge that there are likely different functions of dopamine activity measured across minutes, hours and even days, which might very well correlate with different phases of behavior  and induce avoidance behavior or be positive  and motivate approach behavior. Unexpected delivery of a reward causes a brief increase in firing rate in a large population of putative midbrain dopamine containing cells as well as a phasic dopamine release in part of the ventral striatum such as the nucleus accumbens Schultz, . A promiand a decrease in firing that scales with anticipate future punishment  or by a difference in the eventual payoff for taking a particular option . In the “benefit” conditions, dopamine release elicited when either of the two options was presented consistently reflected the anticipated future reward associated with that option. This cue-elicited effect was unchanged over a range of testing sessions Figure . HoweverExactly why and when dopamine adapts in response to the task structure and what role dopamine plays in shaping learning of task structure is currently a matter of some debate  and long-term depression (LTD) are thought to be critical at the cellular level to underlie memory formation and long-lasting changes in synaptic plasticity Kandel, . Dopaminc-fos). The resulting proteins underlie the systemic consolidation necessary for long-term memory storage and recall  dopamine lesions affected location familiarity responses  the behavioral consequences of dopamine release in striatal regions, and (2) the function of the anatomical networks in which these striatal regions are embedded and how dopamine might facilitate selection of one system over another.release in terminal regions in these situations suggests a role beyond a passive process of learning.A critical issue when considering the role of dopamine concerns the question of what behavioral effect heterogeneous dopamine transmission has in different terminal regions. As has been discussed above, dopamine cell activity in many circumstances correlates highly with RPE signals. However, dopamine Across a range of studies, dopamine in the NAc, particularly in response to cues—and particularly in the core region—is required to activate animals to engage in a behavioral response. For instance, dopamine is only required to learn about cue-reward relationships in situations where cues acquire Pavlovian incentive values, which thus promote approach behavior, rather than simply being predictors of reward   how dopamine transmission affects the way in which potentially competing networks for valuation and behavior are selected; and (2) how dopamine release in terminal regions and afferent modulators of dopamine transmission interact to signal the current state of the world. In the remainder of this review, we will briefly outline some ideas about these processes. Specifically, we will focus on how dopamine transmission may interact with hippocampal circuits through the midbrain and NAc in a bi-directional manner to allow us to learn and behave appropriately in uncertain and changing environments.release remains to be determined.As briefly discussed in a previous section, there are strong inputs from the hippocampus into the NAc in both rats and primates , the two signals may in fact be regulated via dissociable processes , which in turn can influence synaptic plasticity and the efficiency with which particular inputs to the NAc can affect activity in this region  shape appropriate behavior toward the behaviorally- and motivationally-relevant elements of the environment, (2) code the degree of uncertainty between cue-outcome relationships, and (3) elicit molecular cascades strengthening or weakening the re-activated network. Consequently, any novel cue-outcome association can either be integrated in an existing memory or new memories can be laid down instead.In this review, we have tried to illustrate: first, how dopamine release is not only critical for learning but also to motivate animals to learn about the world at times of uncertainty; second, how past experience can shape dopamine-dependent learning; and, third, how dopamine might play a role not only in the initial learning of cue-reward associations but also in determining when to use stored experience and also when to consolidate associations between stimuli and outcome into memory. Furthermore, we have gone on to suggest ways in which the hippocampus might interact with NAc dopamine to facilitate these processes and to enable animals to react to what is behaviorally relevant in the given environment.As is common with most comparable reviews, we gladly acknowledge that there are still many details that remain to be fleshed out and many complexities that have been glossed over for the sake of coherence. For instance, throughout, we have concentrated mainly on phasic dopamine release at the expense of slower tonic changes . As was discussed in an earlier section, even at the “slow, phasic” timescale (~0.5–10 s post-event) dopamine activity can evolve over time to represent several different parameters. Similarly, quite how different regions of the hippocampus interact with dopamine transmission across different parts of the striatum during learning and behavior remains to be determined. We would contend that a general computation might be shared across structures , even if the specific information provided by, for instance, ventral hippocampus to the NAc shell region may be different to dorsal hippocampus and NAc core.Finally, while we have focused on hippocampus—VTA—NAc loops for simplicity, it is improbable that other temporal and frontal lobe regions are not also required to enable these processes to work optimally. For instance, orbitofrontal cortex, which receives hippocampal input and projects to the VTA, has been shown to provide input to allow dopamine cells to disambiguate similar states , particularly when there is a delay between a choice and its consequences (Takahashi et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background. Kaposi's sarcoma (KS) in transplant recipients is about 400 to 500 times rate in the general population. It is strongly associated to Human herpesvirus-8 (HHV-8) infection which has been found in 95% of KS lesions. The optimal approach to managing posttransplantation KS is to reduce or discontinue immunosuppressive therapy but this strategy carries a risk of the acute rejection of the graft. Recently, the use of an mTOR inhibitor has added new opportunities for KS treatment and prevention. Case Report. We report a case of 24 years-old Turkish woman with visceral HHV-8-associated Kaposi's sarcoma after orthotopic liver transplantation. Conclusion. Posttransplantation KS is considered an experimental model of virus induced tumor suggesting the usefulness of HHV-8 screening in transplant recipient and donor. Therapeutic approaches are complex and require a multidisciplinary team. Organ transplantation is associated with an increased risk of malignancies; Kaposi's sarcoma (KS) accounts for 5,7% of these malignancies ; it is mA 24 years-old Turkish woman underwent OLT in November 2008 for cirrhosis related to Von Gierke disease. Her immunosuppressive regimen consisted of tacrolimus and mycophenolate mofetil. Three years later, she was readmitted because of a cholestasis due to the stenosis of the biliobiliary anastomosis. Endoscopic Retrograde Cholangiopancreatography with placement of biliary stent was performed, but the procedure was complicated by pancreatitis and duodenal ulcer; therefore, a biliary repair surgery was decided. A computed tomography performed preoperatively allowed to fortuitous detection of several pulmonary nodules and multiple intraperitoneal lymph nodes . The patKaposi's sarcoma (KS) is about 400–500 times more common in transplant recipients than in general population , with anThe incidence of KS is greater in liver transplant recipients 1,24%) than in heart  or kidney 0,45%) transplantation , 4, 6 du% than in% transplAlthough the pathogenesis of KS in transplant patients is poorly understood, a strong correlation with immunosuppressive status and the reactivation of HHV-8 was demonstrated. In 1994, HHV-8 has been isolated in all four types of KS  . Over 95Although, infection with HHV-8 is essential but not sufficient for the development of KS in transplant recipient, several cofactors have been identified including extent and duration of immunosuppression, reduced levels of neutralizing antibodies, poor cytotoxic T-cell response to HHV-8, and concomitant viral infection  , 13; addNo consensus on the optimal treatment for post-transplantation KS is available; therapeutic choice is often made based upon the experience and medical discipline; many treatment modalities have been used: surgical excision, radiation therapy, intralesional injection of chemotherapeutic agents, reduction of immunosuppressive therapy and systemic chemotherapy. Because the disease is a consequence of prolonged immunosuppression, the reduction or cessation of immunosuppressive therapy may be the most effective and reasonable approach , 6, 12, Recently, some data suggest that switching to immunosuppressive therapy using a (Sirolimus or Everolimus) can induce the regression of iatrogenic KS through antiangiogenic activity related to impaired VEGF production and the limited proliferative response of endothelial cells to VEGF stimulation , 9; addiThe significant increase in the incidence of KS in transplant recipients shows the role of the immune system in the control of HHV-8 infection which is considered the principal, but not sufficient, etiologic agent for this neoplasia; there is evidence that screening transplant recipients and blood donors for HHV-8 infection may be beneficial. Further, studies are needed to identify immunologic, infectious and genetic cofactors possibly involved. The reduction of immunosuppression is a useful therapeutic approach of post-transplantation KS; the introduction of an mTOR inhibitor may permit the remission of the sarcoma; however, the total regression of the lesions should not be the goal because of the risk of graft rejection. In fact, therapeutic approaches are complex and require a skilled, experienced, multidisciplinary team.


Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches. Up to present, diverse WSD methods have been proposed. These methods are overviewed as machine learning (includes supervised and unsupervised) and external knowledge sources. Generally speaking, these methods have the potential bottleneck and limitation. However, almost all the methods, without exception, depend on the context in which the ambiguous word occurs. Moreover, the context size of the target word is too small to convey enough meaning for being disambiguated at a fine-grained level. More contexts which may not be necessarily helpful, on the contrary, will increase computational complexity. Consequently, in order to achieve disambiguation task, there are several challenges, as follows: (1) how to choose context, represent context, and determine context size for implementing all words disambiguation; (2) how to discover topic discriminative features from document for topic identification and implement disambiguation based on topical and semantic association. In this paper, we propose a novel approach aimed at disambiguating all words based on topical and semantic association. Our main contributions are the following: (1) combining topic chain and disambiguation context into topic semantic profile for identifying topic discriminative term and constructing topical graph based on the topic span intervals of topic discriminative term to implement the document's topic identification, (2) determining the unique sense of ambiguous term using topical-semantic association graph, paying more attention to exploiting syntactic features, semantic features, and topical features to implement verb and noun disambiguation. Finally, the evaluated experiments have been performed on the standard data set, and the results indicate our approach can achieve disambiguation task effectively.Word sense disambiguation is the ability to identify the words' sense in a computational manner . We can In this section, we introduce the description of word sense disambiguation in detail, which includes three core components, namely firstly, mapping a WordNet Sense to an ODP's Category Label for generating the term's topic semantic profile; secondly, extracting topic discriminative term through position feature, statistical feature, semantic feature, and topic span distribution feature, and leveraging topic discriminative terms for topic identification; finally, determining the unique sense of ambiguous term using topical-semantic association graph.We aim to construct a mapping relation from a WordNet sense to an ODP's category label. Our proposed approach effectively fuses the semantic knowledge with hierarchical topic category to generate topic semantic knowledge profile for expediently handling a series of research hot issues, such as information extraction, topic identification, and word sense disambiguation. For conveniences in describing follow-up contents, we give some basic terminologies.tm > ⋯>t2 > t1, where t1 is a top topic term and tm is a terminal topic term.A topic chain (TC) is a branch of topic hierarchy and represents a sequence of ordered topic category label terms in ODP. It represents a notation of A disambiguation context (DC) is a set of glosses, synonyms semantics, and hypernyms semantics for a term which may exist several senses in WordNet. DC represents the horizontal synonyms relation and the vertical hypernyms relation from lower-level concept to upper-level concept. Simultaneously, the glosses can also be available to calculate the semantic similarity. w, DC, TC〉, where DC denotes the term w's disambiguation context; TC denotes the term w's topic chain label name.A topic semantic profile (TSP), which characterizes term's semantic and its hierarchical topic category, is a sequence of 3-tuple and represents a notation of 〈 f (senseterm) : Disambiguation  ContextWordNet → Topic  ChainODP.Due to a variety of senses or the vague sense for a given term, the determination of its topic category label is the most difficult problem. In order to solve this problem, there are two significant aspects to be handled, one is to determinate a particular topic branch of term which is associated with multiple topics; the other is to assign the term's proper topic level, just in case too fine-grained hierarchical category to match the concept of user interests or information needs. Restricting semantic disambiguation context is a standard technique to mitigate the problem of term's cross topic. In addition, semantic similarity and cooccurrence information are also the ideal techniques for determining topic branch and topic level by pruning hierarchical category tree. During the process of our algorithm, we aim to determinate the mapping between WordNet and ODP. Formally, given the sense of a term in WordNet, we acquire a mapping to an ODP's topic chain as follows: Topic discriminative term (TDT) can be applied to characterize and highlight a term or phrase's related subject matters in the document. The term or phrase of high discriminative should be strongly associated with the semantic context, owns the number of sense as little as possible, and explicitly specifies the topical category. In addition, on account that technical terminology is monosemous in most cases, it can provide import clues for grasping the meaning and topic category. Sfirst, Slast], where the sentence identifiers of first and last occurrence are denoted as Sfirst and Slast, respectively.When a sentence is a basic processing unit, the reoccurrence topic span (TS) of TDT is defined as text spans for expressing the particular topical meaning, which starts form the first occurrence of TDT and ends to the last occurrence TDT. In this span interval, TDT may not appear in each sentence. Hence, the formulation of reoccurrence topic span interval (TSI) for a certain TDT is defined as .The hierarchical structure is the common characteristics in knowledge representation, such as the hypernym/hyponym relations in WordNet or the topic coverage in ODP. We utilize the hierarchical structure features for measuring the semantic similarity, that is, node depth and node distance. Intuitively, the deeper the depth of subsume, the greater their similarity. The node pair with the shorter distance between has the greater similarity than that of the pair with the longer distance between them. Assumei and TCj in ODP, the average similarity is measured their relatedness by using formula  The reoccurrences of topic discriminative terms in a given document indicate the presence of a certain topic. (2) A topic similarity set of topic discriminative terms which occur in the text fragment will share the identical topic and similar semantic context. Intuitively, the longer reoccurrences of the TDTs are preferred over shorter ones. The more the TDTs in the certain text fragment are, the more chance there is that they are related to a similar topic content.D is represented as a sequence of n sentences Si  (1 ⩽ i ⩽ n) which are the basic structure units. The K candidate topic discriminative terms distribute in these sentences and generate the K re-occurrence topic span intervals. A topical graph G =  is on undirected graph and may be consisted of m topical subgraphs. The vertices are represented for corresponding topic span intervals (TSI) of TDT; meanwhile, these TDTs associate with the corresponding topic semantic profiles which include disambiguation contexts and topic chains. The edges are connected according to the overlap relationship of topic span intervals of TDTs and the similarity relationship of TDTs' topic semantic profiles. In the process of generating topical graph, the subgraph is firstly constructed through immediate overlap of topic span intervals. Then, the multiple subgraphs are connected to the whole topical graph through the immediate adjoining relationship of topic span intervals of TDT, and these intervals are not overlapped.Formally, a document i, TCj) is calculated by formula xx.Next, we need to determine the unique sense of candidate TDT which includes more than one topic semantic profile. In the topical graph, we begin from the vertices whose TDTs are monosemous and the higher weight, iteratively calculate the similarity of corresponding topic chains between current vertex and its neighbor ones through formula , and choWij through formula begin, S(i)last] and , respectively. The overlap relationship of the conflict interval includes two cases, namely, complete inclusion and partial intersection. Consider the following:j) ∈ TSI(TDTi): compared with TDTi, if the similarity value between other TDTs and TDTj is greater than threshold λ, then the topic span interval of TDTi is splitted into  and ; Otherwise, the vertex of the TDTj is deleted.TSI(TDTj)∩TSI(TDTi) ≠ ∅: compared with TDTi, if the similarity value between other TDTs and TDTj is greater than threshold λ, then the topic span interval of TDTi is updated for  or ; Otherwise, the topic span interval of TDTj is updated for  or .TSI and Sim, respectively, denote the topic chain and disambiguation context similarity.The basic idea of disambiguation for 〈noun, noun〉 is mainly a process of topic and semantic context comparison between a target term and other adjoining ones. In order to reduce the computation complexity, given a disambiguation target, we firstly judge whether the concepts of its topic chain appear in the topical describing information. If the topic concept occurs, then the branch of the corresponding topic chain is determined for the unique sense. Otherwise, the sense is fixed through choosing the semantic branch of the maximum similarity. The formula  can be dThe treatment of verb sense disambiguation depends on three important clues, namely, the syntactic structure of sentence frame, the semantic information of synonyms, and the domain information of objects. The sentence frames state that different senses of verbs may occur with an infinitive, with a transitive, and with an intransitive syntactic frame. The syntactic structure information provided by WordNet is rather scarce and not enough to implement the task of verb disambiguation. Therefore, besides the above-mentioned sentence frame, WordNet also provides the form of 〈verb, domain〉 that characterizes the domain information of the verb associated object and a number of synonyms of a sense for target verb.synonyms  -  listsentence  frame  -  listobject]. The first four contents are immediately obtained and the last part that records the list of objects is incrementally discovered through updating the indexing in the future.In front of disambiguating verbs sense, we extract major sectors about the verbs senses' semantic information from WordNet and harness the Lucene to index them according to the form of [verb  -  〈verb, domain〉  -  listThe procedures of disambiguating the pattern of 〈verb, noun〉 are as follows. Firstly, compared with the syntactic structure of the target verb, the partial of senses may be filtered through sentence frames. Secondly, we calculate the similarity between the domain terms in the form of 〈verb, domain〉 and topic chains of the noun's object to choose the verb sense of maximum similarity. So, it is possible that the target verb has more than a sense. Finally, given a noun object, we can attain other synonyms verbs and retrieve the list of objects. If the result exists in the match content with object, then we choose the sense for the target verb.In this section, we exploit the SemCor corpus to evaluate our approach. The SemCor corpus was the largest freely available textual corpus of semantically annotated words and has been extensively used in evaluating WSD systems.The key foundation of topic identification is the extraction of topic discriminative terms. We evaluate our topical identification algorithm using the precision  on SemCor. We compare comprehensive features (All) with each feature, namely, statistical feature , positional and statistical feature (Pos + TF), semantic feature , and topic spanning distribution feature .We compare our WSD approach based on topical and semantic association (TSA) using WordNet + ODP with other state-of-the-art WSD approaches, namely, the ExtLesk algorithm and the SSI algorithm. In addition, we evaluate separately the performance on nouns only, verbs only, and all words.In this paper, we propose a novel approach for word sense disambiguation based on topical and semantic association. Our experiments show that the topic categories of Open Directory Project merged into WordNet are of high quality and, more importantly, it enables external knowledge-based WSD applications to perform better than the existing methods of only using WordNet. In addition, we also find that the applied topical and semantic association into determining the unique sense obviously influences WSD performance. We obtain a large improvement when adopting the WSD algorithm based on topical-semantic association graph.


Background: Activated factor XIII (FXIIIa), a transglutaminase, introduces fibrin–fibrin and fibrin–inhibitor cross-links, resulting in more mechanically stable clots. The impact of cross-linking on resistance to fibrinolysis has proved challenging to evaluate quantitatively. Methods:We used a whole blood model thrombus system to characterize the role of cross-linking in resistance to fibrinolytic degradation. Model thrombi, which mimic arterial thrombi formed in vivo, were prepared with incorporated fluorescently labeled fibrinogen, in order to allow quantification of fibrinolysis as released fluorescence units per minute. Results:A site-specific inhibitor of transglutaminases, added to blood from normal donors, yielded model thrombi that lysed more easily, either spontaneously or by plasminogen activators. This was observed both in the cell/platelet-rich head and fibrin-rich tail. Model thrombi from an FXIII-deficient patient lysed more quickly than normal thrombi; replacement therapy with FXIII concentrate normalized lysis. In vitro addition of purified FXIII to the patient's preprophylaxis blood, but not to normal control blood, resulted in more stable thrombi, indicating no further efficacy of supraphysiologic FXIII. However, addition of tissue transglutaminase, which is synthesized by endothelial cells, generated thrombi that were more resistant to fibrinolysis; this may stabilize mural thrombi in vivo. Conclusions:Model thrombi formed under flow, even those prepared as plasma ‘thrombi’, reveal the effect of FXIII on fibrinolysis. Although very low levels of FXIII are known to produce mechanical clot stability, and to achieve γ-dimerization, they appear to be suboptimal in conferring full resistance to fibrinolysis. Clots prepared from the patient plasma and analyzed by SDS-PAGE were close to normal in terms of γ–γ-dimer detection . Fully d−1; n = 4) than those from normal controls . The effect of TG inhibitor on patient thrombi was small, but still significant (P < 0.005). Thirty minutes after routine 4-weekly prophylaxis with Fibrogammin (10 U kg−1), plasma activity rose to 54.9% ± 7.9% of normal. The thrombi from post-FXIII administration blood had normal rates of lysis , and the greater effect of TG inhibition was restored (P < 0.005). Similar results were achieved when different concentrations of t-PA and u-PA (0.25–1 μg mL−1) were used for lysis or for spontaneous lysis in the absence of plasminogen activator (not shown). We then formed ‘thrombi’ from platelet-free plasma. Their lysis revealed a 5.1-fold increase in lysis rate (P < 0.005) and a significant contrast between normal and FXIII-deficient plasma . Bathing fluid harvested after the final time point (4 h) was analyzed by SDS-PAGE and western blot. A change in the pattern of fibrin degradation products was observed, especially in terms of accumulation of the D-monomer (90 kDa) band, which was present when the TG inhibitor was added to normal plasma. This band was also apparent, but at a lesser intensity, in bathing fluid from the patient plasma ‘model thrombi’ (Model thrombi prepared from the patient's blood were lysed more quickly by t-PA (4.4 ± 0.5 FU minthrombi’ .P = 0.15), but addition to FXIII-deficient blood consistently made the thrombi more resistant to lysis . For clarity, only the addition of 1 U mL−1 Fibrogammin to normal blood is shown, but similar results were obtained with 2.5 U mL−1. The data show that supraphysiologic levels of FXIII produce no more resistance to lysis than normal levels. Fibrin can also be cross-linked by TG2 with a pattern that is distinct from that with FXIII [−1, respectively. These differences were significant (P < 0.005) relative to no addition and for each concentration relative to the others. These data show that TG2 stabilization of thrombi against fibrinolytic degradation supplements that caused by FXIIIa.Addition of Fibrogammin to normal blood had no effect on the stability of model thrombi  [A causal relationship between FXIII-dependent cross-linking and decreased fibrinolysis is predicted and often assumed, but its quantitative demonstration in the laboratory has been controversial ,15–22. T−1) , reprodu−1) was chosen to reflect pharmacologic conditions and to avoid very high concentrations, which result in inefficient lysis [−1 t-PA and when no activator was added (not shown). Our data expand and quantify the observation made in 1966, in a similar flow system, with lysis assessed simply by inspection [The data presented here demonstrate an effect of inhibiting cross-linking on fibrinolysis, whether t-PA or u-PA was used to activate plasminogen, and also on the spontaneous lysis of model thrombi, owing primarily to u-PA activity from polymorphonuclear cells . These cnt lysis , but thespection ,14,27 (nThe patient used in this study patient 1 in ) is well in  is win vitro, but the impact of TGs on fibrinolytic resistance has been visualized in vivo with the use of an experimental model of pulmonary embolism [2AP is cross-linked lysed more slowly both in vitro [in vivo [2AP and TAFI all contribute to the stability of cross-linked clots and thrombi [2AP deficiency, as this inhibitor is markedly affected by cross-linking status [The role of FXIII in enhancing the clot strength and elasticity of fibrin has been described extensively in static clots –43. A siembolism . Lysis oembolism , whereasembolism  and the embolism . The proin vitro ,50, and [in vivo . PAI-1,  thrombi . The seng status ,50 and, g status .In vivo, it is likely that TG2 is present in mural thrombi, as it is an abundant protein in the vessel wall [in vivo and has been reported to be a feature of new thrombi [TG2 is known to catalyze cross-linking of fibrin, with some distinctions in the exact pattern as compared with FXIIIa ,10,35. Fsel wall  and can sel wall . It is n thrombi  but consIn conclusion, model thrombi prepared under flow are sensitive to the impact of cross-linking on fibrinolytic resistance. The system provides a convenient model in which to address many remaining questions, such as role of cellular FXIII, the influence of fibrin vs. inhibitor cross-linking, and the ability of different TGs to regulate these processes.


Kaposi's sarcoma (KS) is a multicentric vascular neoplasm associated with the Kaposi's sarcoma-associated herpes virus (KSHV). KS can occur in immunocompromised patients as well as certain populations in Africa or in the Mediterranean. Less than 5% of KS cases can present with lymphangioma-like kaposi sarcoma (LLKS), which can occur in all KS variants. KS presents with characteristic skin lesions that appear as brown, red, blue, or purple plaques and nodules. The lesions are initially flat and if untreated will become raised. LLKS presents similarly to KS but is associated with severe lymphedema and soft tissue swelling as well as bulla-like vascular lesions. We present the case of an 85-year-old Lebanese, HIV negative, man who presented with a swollen and painful right lower extremity accompanied by necrotic lesions. Wound cultures were positive, and we began the work-up for secondarily infected gangrene. However, skin biopsy results revealed that he in fact had lymphangioma-like Kaposi sarcoma, which allowed us to shift our management. Advanced Kaposi's sarcoma can present similar to gangrene. It is important to recognize the typical skin lesions of KS and not to overlook Kaposi's sarcoma or LLKS within the differential. Kaposi's sarcoma is a vascular neoplasm that develops secondary to infection with the KSHV virus. The KSHV virus is also known as the human herpes virus 8 (HHV8). KS is often seen in people of African or Mediterranean descent who have a genetic susceptibility or immunocompromised patients such as those receiving chemotherapy or in those infected with the HIV virus. Lymphangioma-like Kaposi's sarcoma is a rare subtype that presents similar to KS but is also associated with severe lymphedema and soft tissue swelling and bulla-like vascular lesions. These skin lesions can be confused with other dermatologic findings including gangrene, especially if the lesion becomes superinfected and if the disease progresses without treatment. It is important to differentiate between LLKS and gangrene so that the patient receives the correct intervention. We report a case of LLKS that was initially diagnosed as gangrene.Enterococcus faecalis, were noted on wound culture and as such, the patient completed 2 weeks of ampicillin/sulbactam.We present the case of an 85-year-old Lebanese man with a history of coronary artery disease, diabetes, dyslipidemia, hypertension, and hypothyroidism who presented to our institution with a reported 2-week history of right foot edema, erythema, pain, and crusted lesions over the dorsum of the right foot. The patient was referred to our facility after receiving 2 days of IV antibiotics at a hospital in Jordan, where he had resided for the past several years. He presented to that institution after noticing red “bubbles,” which burst and crusted over with subsequent pain and swelling. He denied fevers and chills. On examination in the emergency room, the patient was noted to have weakly palpable pulses in the left lower extremity and nonpalpable pulses in the right lower extremity. His right lower extremity was edematous with blue/purple discolored lesions as well as areas of necrosis . The res Given the diagnosis of Kaposi's sarcoma, HIV testing was done, which was negative, and so no other tests for immunosuppression were performed. The patient also underwent computed tomography (CT) scans of his chest, abdomen, and pelvis to evaluate for metastatic disease. These studies were done without contrast as the patient developed acute kidney injury during his hospitalization, likely as a consequence of supratherapeutic vancomycin. Lastly, the patient received upper and lower endoscopies, both of which were negative for lesions suspicious for malignancy. As there was no evidence of metastases on this evaluation, the decision was made to provide local therapy only, under direction from dermatology. Unfortunately, the patient was lost to followup after discharge.Kaposi's Sarcoma is a multicentric vascular neoplasm-associated with the Kaposi's sarcoma associated herpes virus. The KSHV virus is also known as the human herpes virus 8. Studies have shown that KSHV-infected cells of vascular endothelial origin secrete cytokines, chemokines, and growth factors that allow for angiogenesis and further cancer pathogenesis . The KSHThere are four types of KS seen clinically. These include classic variant KS, endemic KS, transplant-associated KS, and AIDS-associated KS. Classic variant KS is slow growing, often limited to the skin, and often found in people of Mediterranean descent. Endemic KS is found in Africa in HIV negative patients. This form is aggressive and can affect children. Transplant-associated KS is seen in patients who are on immunosuppressive therapy. The last type is AIDS-associated KS, which can develop in HIV positive patients . It is uOur patient is of Mediterranean descent and is HIV negative, and the classic variant of KS in these patients tends to be more indolent in nature, these patients are often elderly as was our patients and occurs in men more often than women in a 15 to 1 ratio. The cancer often begins in the hands or feet and progresses towards the body, potentially taking several years to decades before the cancer metastasizes to the viscera . KS mostThe diagnosis of LLKS can be made based on clinical presentation and on histological examination of the lesions. LLKS is typified histologically by lymphangioma-like spaces. Often these patients also histologically show patterns associated with the other forms of KS. These patterns include spindle and endothelial cell proliferation, red blood cell extravasation, hemosiderin-laden macrophages, and other signs of an inflammatory reaction . In addiTreatment of KS includes simple excision of a single lesion and a combination of surgery, radiation, and chemotherapy for multiple and metastasized KS. Herein we describe a patient with an extremity lesion that was initially believed to be a necrotic gangrenous foot. Cultures from his left foot biopsy from one of his lesions grew Gram-positive cocci and Gram-negative rods. We began the patient on systemic broad-spectrum antibiotics to treat his superinfected gangrenous foot. It was only after a biopsy was attained and histological and immunohistochemical examination was performed that we discovered that the patient had LLKS and it was at that time when the oncology team was able to begin management with chemotherapy, radiation, and surgery. It is important to recognize the typical skin lesions of KS and not to overlook Kaposi's sarcoma within the differential of a patient who presented as ours did, especially in a patient who is of Mediterranean descent, elderly, and HIV negative.


In the past 15 years, new “omics” technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported “molecular signatures”. However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.The identification of molecular signatures from omics data has many promising applications including omics-based tests for disease-specific diagnostics and accurate phenotype classification. This is however, plagued by issues with data reproducibility – this review discusses the potential pitfalls in the discovery process and strategies for overcoming these issues in order to achieve personalized medicine. Given molecular measurements taken from a biological system, a natural goal is to develop a statistical model that uses these measurements to predict a clinical outcome of interest, such as disease status, survival time, or response to therapy. In this paper, we will discuss the process of using omics data to discover a molecular signature. Here, we define a molecular signature as a set of biomolecular features  together with a predefined computational procedure that applies those features to predict a phenotype of clinical interest on a previously unseen patient sample. A signature can be based on a single data type [In recent years, new high-throughput measurement technologies for biomolecules such as DNA, RNA, and proteins have enabled unprecedented views of biological systems at the molecular level. The fields of research associated with obtaining and understanding such measurements – for instance, genomics, transcriptomics, and proteomics – are sometimes referred to in aggregate as ata type  or on muata type . The oveMany possible clinical phenotypes might be predicted by a molecular signature; a few examples include prediction of disease risk and progression , responsDespite a few notable exceptions such as MammaPrint, the successful discovery of molecular signatures has largely been hampered by limited reproducibility and variable performance on independent test sets , as wellRoughly speaking, the process of molecular signature discovery on the basis of omics data consists of four major stages:(i) Defining the scientific and clinical context for the molecular signature;(ii) Procuring the data;(iii) Performing feature selection and model building; and(iv) Evaluating the molecular signature on independent datasets.In the sections that follow, we will discuss each of these stages in turn.We first consider the problem of selecting a suitable omics data type for a molecular signature. A signature intended to distinguish between cancer and normal tissue could be based upon a number of omics data types; for instance, one might base the signature upon gene expression measurements, if it is believed that this type of cancer shows altered expression of some genes relative to normal tissue, or upon DNA sequence data, if samples from this cancer are characterized by particular mutations or copy number changes. However, given a clinical phenotype of interest, certain types of omics data might not form the basis for a sensible molecular signature. For instance, it would not be reasonable to attempt to create a molecular signature to screen for adult onset (type II) diabetes on the basis of DNA sequence data alone because an individual's DNA sequence remains essentially static throughout his or her lifetime, but risk of developing the disease may change.We now consider the clinical context of the molecular signature. A gene expression-based signature that can distinguish between cancer and normal tissues would be of little practical use if a physician can easily make the same distinction using standard (and less expensive) clinical approaches. Similarly, a signature that can distinguish between two subtypes of cancer is useful only if those two subtypes differ in some clinically relevant way, such as in survival time or response to therapy, since otherwise the information about cancer subtype provided by the molecular signature may not serve a practical purpose. As an example, gastrointestinal stromal tumors (GISTs) and leiomyosarcomas (LMSs) are remarkably similar morphologically and were originally classified as being the same cancer. However, it was found that they respond very differently to distinct therapies, and thus a signature that can distinguish between these two diseases based on gene expression in tissue samples can be useful . An examThe development of a molecular signature requires the availability of adequate omics data for which the clinical phenotype of interest is available. In general, there are two ways in which such data can be procured: new data can be collected experimentally for the specific purpose of molecular signature discovery, or else existing data  can be used. There are pros and cons of either approach. Collecting new data has a major advantage, in that all aspects of the experiment can be carefully controlled. On the other hand, data collection is expensive, and given the large sample sizes necessary for successful molecular signature discovery, using existing datasets may be a more feasible approach. There are a number of public data repositories from which omics data and associated clinical phenotypes can be obtained. For instance, a useful source of gene expression data is NCBI Gene Expression Omnibus (GEO), a repository of over 26000 studies that continues to grow at a rapid pace. Other public data repositories include ArrayExpress  and Sequbatch effects, factors such as sample collection and processing procedures, laboratory personnel, study run-dates, reagent sources, measurement instruments, and data processing methods should be carefully controlled [In order for a dataset to be suitable for molecular signature discovery, the samples must be collected under appropriate experimental and analytical conditions. As an example, any biological factors  that may be associated with the clinical phenotype of interest or with the omics measurements should be taken into consideration in the process of data procurement. In addition, to reduce the prevalence of ntrolled . Deviatintrolled . IdeallyTo the extent that analytical and experimental factors do vary among the samples, these factors should be explicitly included in the model used to develop the classifier. Normalization procedures have been proposed that are intended to reduce the effect of measured and unmeasured external factors on omics data ; howeverWhen existing data is used for omics-based molecular signature discovery, it is particularly important that sufficient information about the experiment is available to ensure that good experimental design was followed (this will be discussed further in Section 4). For instance, if the run date for each sample is not given, then one cannot be certain that the clinical phenotype of interest is not highly confounded with run date.Unfortunately, many omics studies have sample sizes substantially smaller than would be required for the successful identification of molecular signatures. A molecular signature that is developed on the basis of a small number of samples is more likely to be sensitive to technical and biological sources of noise and variation, and less likely to capture the aspects of the data that are truly associated with the phenotype of interest. This exacerbates the risk of over-fitting, wherein the signature performs well on the samples used for signature development but fails to correctly predict the clinical phenotype of interest in previously unseen samples. In contrast, global molecular characteristics of a particular phenotype may become more apparent as sample size increases. Therefore, having a large sample size, while by no means a cure-all, will greatly improve the odds that a given attempt at molecular signature discovery will prove fruitful. Integrating across multiple datasets of the same phenotypes from different labs can also help to amplify the primary biological signal of interest relative to noise. Of course, whether a given sample size is “large” or “small” depends the type of omics data being used for signature discovery, the clinical phenotype of interest, and many other factors.Once a scientific and clinical context has been established and one or more datasets have been identified, we can develop a molecular signature through (i) feature selection; and (ii) model building. These two tasks can be performed together or separately.feature selection is performed. Feature selection can be performed in a supervised manner , or in an unsupervised manner (e.g. the 20% of features with the highest variance are selected). Once a set of features has been selected, only those features are used in the model building process, which is described next.We first consider the task of feature selection. A typical omics experiment simultaneously measures thousands or even millions of biological features  on each patient sample. However, just because thousands of molecular measurements are obtained does not mean that thousands of molecular measurements should be used in the molecular signature. Since financial cost, technical practicality, and measurement robustness are important criteria to select signatures, then if all else is equal, a signature that could be ultimately measured via PCR or Western blot is favored over a signature that requires a technique involving many more protocol steps, such as in omics measurements. In order to reduce the number of features used in molecular signature development, model building – i.e. the process of developing a specific computational procedure that can be applied to the omics measurements from a future patient sample in order to predict the unknown clinical phenotype of interest for that sample. There are many possible approaches to building such a model, and in particular, the type of model used will depend on the clinical phenotype of interest. For instance, if we wish to develop a molecular signature to predict time to cancer recurrence, then a Cox proportional hazards model might be appropriate. On the other hand, to develop a molecular signature that can distinguish between cancer and normal tissue, one could use a classification approach, such as logistic regression, support vector machines, neural networks, or linear discriminant analysis. Some approaches for model-building involve first performing an unsupervised technique, such as clustering or principal components analysis, followed by a supervised procedure, such as logistic regression.We now consider the task of cross-validation.  Cross-validation involves repeatedly splitting the samples in the dataset into training and test sets, performing all aspects of feature selection and model building on the training set, and evaluating the model's performance on the test set. Cross-validation can also be used to select from among a small number of possible models: the model with the smallest cross-validation error rate should be chosen.Once we have developed a model, how can we determine whether it is any good? Despite certain drawbacks , 44, thet-statistics between cases and controls are largest. One then performs logistic regression, using only these features, to develop a classifier to distinguish between cases and controls. How should the cross-validation error rate be calculated? Consider the following two approaches:Cross-validation is a simple and intuitive approach to estimating the error rate associated with a model, but it must be performed with care. Most importantly, within each cross-validation fold, no information about the test set can be used in building the model on the training set. For instance, suppose that one performs feature selection by selecting the 10% of features whose Approach 1 (incorrect): identify the 10% of features that differ most between cases and controls, and use only those features henceforth. Perform cross-validation by repeatedly splitting the samples into training and test sets, fitting a logistic regression model on the training set (using just the 10% of features previously identified), and then evaluating the model's performance on the test set.Approach 2 (correct): perform cross-validation by repeatedly splitting the samples into a training set and a test set. Within each training set, identify the 10% of features that differ most between cases and controls, and use those features to fit a logistic regression model. Then, evaluate the performance of this model on the test set.The difference may seem subtle, but it is in fact crucial. Approach 1 will yield a woeful underestimate of the true error rate, because the 10% of features that differ most between cases and controls were identified using all of the samples, including those in the test set, rather than simply the training samples. In effect, if Approach 1 for cross-validation is taken, then perfect error rates can potentially be obtained even on datasets in which the “case” and “control” labels were assigned randomly! On the other hand, in Approach 2, feature selection is performed using the training set within each cross-validation fold, and so the resulting cross-validation error rate is valid. Unfortunately, the difference between Approaches 1 and 2 is often overlooked, and the literature is rife with papers in which extraordinarily low, but grossly inaccurate, cross-validation error rates are reported because some variant of Approach 1 has been performed. The key principle is that in computing cross-validation error rates, within each cross-validation fold only training observations can be used in any aspect of feature selection or model development. Deviations from this principle, even if seemingly innocuous, may result in dramatic underestimates of error.locked down – i.e. the precise computational procedure used to convert a new omics sample into a prediction of the clinical phenotype must be completely specified. Only then can the molecular signature be fairly evaluated on independent datasets, as described next.At the end of the feature selection and model building process, the molecular signature must be independent sample is one that is completely separate from the set of samples used for feature selection and model building. In particular, this means that the test set is not simply a random split from a large dataset (even if sequestered and not used in any training sets). If a molecular signature performs well on a truly independent set of samples, then this provides evidence that it will likely generalize to future patient samples. However, the amount of evidence for a molecular signature's performance based on independent data depends critically upon specific characteristics of the independent dataset.Once a promising molecular signature has been identified, its performance needs to be evaluated on completely independent patient samples. Unlike cross-validation, wherein the test set is drawn from the same population as that of the training set, an Lower level of evidence. Good performance on an independent dataset collected at the same institution using carefully controlled protocols. This provides evidence that the molecular signature works well in this particular setting, with these protocols, with the patient profile at this institution, etc. However, it may not hold up elsewhere. At the very least, its ability to work in other settings has not been demonstrated.Higher level of evidence. Good performance on multiple independent datasets collected at multiple institutions. Success in this setting is the best evidence that a molecular signature will perform well on future patient samples. This indicates that the signature is robust to the kinds of things that might change between locations: namely, aspects of the biology of the populations that tend to go to particular hospital, sample preparation and measurement techniques used, and so forth.Evaluation of a molecular signature on fully independent patient samples is the gold standard for assessing its performance. Unfortunately, it often is the case that molecular signatures that seem promising in the feature selection and model building stage  exhibit poor performance on independent data.A key principle of science is that other researchers must be able to reproduce the results. In order for a molecular signature to be reproduced, three essential pieces of information are required: (i) the experimental and analytical protocols; (ii) the raw data; and (iii) the source code used to develop the signature. We discuss each of these points in turn.In order for a molecular signature to be fully understood by other researchers, detailed information on the experimental protocol, including the patient selection criteria and experimental and analytic procedures, must be made available. Without this information, one cannot determine the scientific or clinical contexts in which the molecular signature is intended, appropriate, or useful.Second, in order for a molecular signature to be reproduced, the omics data used in its development, as well as the associated metadata and clinical data, must be made available. If the data are not released, then it simply is not possible for other research groups to determine whether the molecular signature is valid.Finally, even if the data are made available, other research groups will not be able re-derive the molecular signature based on the same data used for its discovery, and confirm that the signature does truly work well on independent data, unless all data processing techniques and all analytical and computational methods are made available. Unfortunately, in practice this information often is not provided in sufficient detail. For instance, there is a tendency for authors to publish a list of the features (e.g. genes) involved in the signature, without the detailed mathematical formulas required to understand precisely how the omics measurements are used in order to predict the clinical phenotype of interest. This is a major obstacle to progress in the field, as other research groups cannot reproduce or validate – much less build upon – research that is not sufficiently reported. In order to address this problem, the source code used to develop the molecular signature should be released. Ideally, this code should encompass all aspects of signature development, from processing and normalization of the raw omics data, to feature selection to model building to evaluation on an independent dataset.Thus far, we have described the development of a molecular signature on the basis of a single dataset, followed by evaluation of the signature on one or more independent datasets. However, in principle, multiple datasets can be used for molecular signature discovery. In fact, this can often lead to more accurate and more broadly applicable molecular signatures.When a molecular signature is developed on the basis of a single dataset and then tested on an independent dataset, its performance tends to degrade severely in the independent dataset relative to its cross-validation error rate in the dataset used for development. This drop in performance can stem from heterogeneity between studies due to underlying variance in the biology of the patients studied, as well as from technical variations in measurement, normalization, and analysis. That is, a signature developed using a single dataset may overfit certain aspects of the dataset that are not of primary scientific interest, leading to poor performance on independent data. This problem can be partially overcome by developing the signature on the basis of multiple datasets, collected at different institutions and at different time points .  can predict disease phenotypes and clinical outcomes. Integrating multiple types of omics data may allow for the development of increasingly accurate and robust molecular signatures. For example, gene expression data can be combined with copy number variation data or DNA sequence data. Successful multi-scale integration of different types of biological information is one of the current challenges in systems biology , 49. In A number of methods to combine diverse types of omics data across different measurement platforms and laboratories have been proposed , 49, 56,The use of network-based approaches is a promising avenue for molecular signature discovery. These networks represent a complex web of interactions among diverse components in a cell, and can be used to develop more reproducible and accurate molecular signatures by exploiting the underlying biology of the system. Network-based approaches extend beyond simple integration of different omics data types, and can involve evaluating complex interactions that can vary due to disease or other perturbations.Most statistical methods for feature selection and model building do not take a network-based approach: they implicitly assume that the features are independent, or that they are only weakly dependent, though this has begun to change in recent years . Howeverp-values for gene set enrichment, DIRAC builds a network-based molecular signature that identifies robust differences in pathway activity between two disease states.In order to identify features that are associated with a clinical phenotype of interest, features can be mapped onto a priori defined and manually curated modules or “pathways”. Gene Set Enrichment Analysis (GSEA)  is a verHowever, one major caveat to such pathway-based approaches is that a priori defined pathways do not fully represent the complexity of the underlying biology, and may not be accurate within the particular physiological context. To overcome this limitation, molecular features can be mapped into more comprehensive interaction networks, such as protein-protein or protein-DNA interaction networks, which can be much more comprehensive and unbiased, as well as disease and context specific. Specifically, biological networks can be used as a structured framework to integrate omics data for the purpose of molecular signature development. For example, Chuang et al.  integratIn the particular case of prion disease, a set of neurodegenerative disorders caused by the misfolding of prion proteins in the brain, Hwang et al.  analyzedIn the work summarized above, thousands of feature measurements for static biological states were used to characterize molecular networks. However, a more complete understanding of molecular networks requires perturbing the biological system under study in order to understand how the network components, as well as the clinical phenotype of interest, are affected by those perturbations. For example, stimulating one or more signaling pathways using in vitro cytokine assays can lead to different immunologic and metabolic responses in different diagnostic phenotypes , such asGiven that two molecular signatures seem to perform well on independent datasets, how can we decide which is better? If all else is equal, we should prefer the molecular signature for which there is a plausible biological mechanism, as such a signature is much more likely to hold up in future patient samples as opposed to having overfit the data used in its development. Ideally, if sufficient numbers of samples were available, then a molecular signature's performance on one or many independent datasets would be the preferred way of assessing its suitability, regardless of whether or not a mechanism for its performance is known. But in reality, sample sizes are limited, and thus a molecular signature for which there is a plausible biological mechanism tends to be more convincing than one for which no such mechanism is known. Such biologically motivated signatures can also hold great promise to be developed as companion diagnostics for therapies, which may be motivated by the underlying mechanism. Thus, while lack of a known biological mechanism underlying a molecular signature certainly does not preclude its use provided that it works well in practice on independent samples, mechanistic information can increase our confidence that the signature will hold up to further scrutiny.Another major challenge in omics-based molecular signature discovery is the prevalence of overly optimistic accuracies in reported results. This problem is not unique to omics research but is problematic in many data-driven research settings . Such biIn this paper, we have discussed some of the key considerations and challenges facing the discovery of omics-based molecular signatures of clinical phenotypes, such as good experimental design, careful data procurement, avoidance of over-fitting, validation on independent datasets, and integration of multiple datasets and data types. For guidance to the reader, Box 1 summarizes the key steps in molecular signature discovery that were discussed throughout this paper. We hope that this methodological checklist will aid investigators interested in identifying omics-based molecular signatures.Since the emergence of the field of omics-based molecular signature discovery, researchers have developed an improved understanding of how to discover (and how not to discover!) such signatures. The field is still young, and as time passes, best practices in this area will continue to evolve. Currently, the number of validated and useful molecular signatures is disappointingly (but not surprisingly) small relative to the number of signatures that have been reported in the literature. However, we remain optimistic that as experimental and analytical practices improve, as sample sizes increase, and as techniques for data type integration continue to develop, omics-based molecular signatures will indeed transform the practice of medicine.Clearly define clinical phenotypes of interestEnsure that, if discovered, a molecular signature has the potential to be useful in the clinicOnly use types of omics data that are suitable for addressing the task of interestDetermine acceptable sensitivity and specificitysufficient sample size can be obtainedall aspects of the experimental and analytical procedures are carefully controlled to avoid batch effectsno confounding occurs between datasets of different phenotypes from factors unrelated to phenotype of interest>When collecting new experimental data, ensure that:sufficient sample size can be obtainedsufficient patient information is available for omics samplesproper normalization is implemented to make samples comparable across different datasetsWhen using existing data, ensure that:approach with cautioncan lead to molecular signatures that are more accurate and robustConsider integrating multiple datasets and data types:Perform feature selection in either a supervised or an unsupervised mannerChoose models that are well-suited for the context of the study and nature of phenotypes of interestConsider mapping features onto biological pathways or more comprehensive interaction networksConsider choosing models that show clear insight into plausible biological mechanismsEnsure that all cross-validation steps are performed correctlyApproach favorable cross-validation results with cautionTest promising molecular signatures on independent datasetsIndependent test sets are not created equal. The strength of evidence from an independent test is based on the characteristics of the independent dataset used Encourage the evaluation of the molecular signature by independent research groupsDisclose: information on the clinical context in which molecular signature is intended, patient selection criteria, clinical data (i.e. patient information), raw data, meta-data (if applicable), data processing and normalization methods, feature selection and model building methods, experimental protocols, records on study run-dates, lab technicians, reagent sources, etc., analytical methods, and source codeEncourage the objective assessment of molecular signatures by reporting both positive and negative outcomes Make data publicly available after publication


Activation of dopamine receptors in forebrain regions, for minutes or longer, is known to be sufficient for positive reinforcement of stimuli and actions. However, the firing rate of dopamine neurons is increased for only about 200 milliseconds following natural reward events that are better than expected, a response which has been described as a “reward prediction error” (RPE). Although RPE drives reinforcement learning (RL) in computational models, it has not been possible to directly test whether the transient dopamine signal actually drives RL. Here we have performed optical stimulation of genetically targeted ventral tegmental area (VTA) dopamine neurons expressing Channelrhodopsin-2 (ChR2) in mice. We mimicked the transient activation of dopamine neurons that occurs in response to natural reward by applying a light pulse of 200 ms in VTA. When a single light pulse followed each self-initiated nose poke, it was sufficient in itself to cause operant reinforcement. Furthermore, when optical stimulation was delivered in separate sessions according to a predetermined pattern, it increased locomotion and contralateral rotations, behaviors that are known to result from activation of dopamine neurons. All three of the optically induced operant and locomotor behaviors were tightly correlated with the number of VTA dopamine neurons that expressed ChR2, providing additional evidence that the behavioral responses were caused by activation of dopamine neurons. These results provide strong evidence that the transient activation of dopamine neurons provides a functional reward signal that drives learning, in support of RL theories of dopamine function. There is extensive evidence, particularly from pharmacological studies, that dopamine can cause positive reinforcement of stimuli and actions RPE drives reinforcement in computational models of RL First, although the transient response of dopamine neurons has the appropriate properties to drive RL from a computational perspective, there is little direct evidence that it actually does. Virtually all the direct evidence that dopamine causes reinforcement has come from manipulations that modulate dopamine for periods of at least minutes or longer It has not previously been possible to directly test for a causal role of the transient dopamine signal in reinforcement. Pharmacological and other manipulations act on a much slower timescale, whereas electrical stimulation is not able to selectively activate dopamine neurons. However, it should now be possible to test the RL theory of dopamine function in a direct and definitive manner by using optogenetic techniques, which allow temporal control of neurons with a precision in the range of milliseconds, together with the ability to target genetically defined neuronal populations Tsai and colleagues first demonstrated the feasibility of optogenetic stimulation of dopamine neurons in mice It is critical to note that the study of Tsai and colleagues Channelrhodopsin-2 (ChR2) was introduced into the right VTA of dopamine transporter (DAT) IRES-Cre mutant mice Behavioral experiments commenced 14 days or more after injection of vector. A nose poke to the “active” port caused delivery (after a 100 ms delay) of a single, 200 ms square-wave pulse of blue light to the right VTA via an optic fiber . The precise temporal pattern of depolarization and action potentials that was induced in dopamine neurons is not known and is not critical to our hypothesis. The only critical point is that this excitation will increase firing rate within a temporal window of a few hundred milliseconds Each mouse was conditioned in an operant chamber over 9–18 days. After several days of conditioning, each of the four ChR2-H mice showed significant responding for optical stimulation  Movie S. On daysIn order to better quantify the reward value of the optical stimulation, we used a progressive ratio schedule (PRS) of reinforcement in which animals had to perform a larger number of responses for each additional pulse of light. The PRS was not administered in separate sessions, but rather in the same sessions described above. Each day (excluding extinction training on days 9–18), each of the first 20 nose pokes was followed by a pulse of light (a FR-1 schedule). Additional responses resulted in light pulses according to the PRS. Whereas In contrast to the ChR2-H mice, none of the ChR2-L or AAV- mice showed clear evidence of an increase in responding on the active port over 9 days of conditioning, nor did they show any difference in responding between active and inactive ports  2-way A.In addition to its reinforcing effects, dopamine from VTA is also known to increase locomotion The responding observed in ChR2-H mice  & Fig. 32 = 0.99, p = 0.01) and ChR2-L  mice (without correction for multiple comparisons) . There was also evidence of correlations across mice between the three behavioral responses in both ChR2-H and ChR2-L mice, as can be seen by comparing the responses of individual mice between the panels of 2 = 0.98, p = 0.01), and the same trend was found in ChR2-L mice .The level of ChR2 expression across mice correlated strongly with each of the 3 behavioral responses (p<0.01 in each case) . This isarisons) . Operantarisons)  and contarisons)  showed tThese correlations suggests that despite the very small numbers of ChR2+ neurons in ChR2-L mice, optical stimulation may have elicited a small but behaviorally relevant activation of dopamine neurons. The more general inference that we make based on these correlations is that the number of ChR2 positive neurons is tightly linked to the magnitude of the optically-driven dopamine response, and that the magnitude of the dopamine response is tightly linked to behavior.The three types of behaviors measured here  are all known to result from increases in dopamine transmission that are sustained over periods of minutes or longer e.g. . Given tThe optically driven reinforcement of operant responding observed here is comparable to the electrically driven “brain stimulation reward” (BSR) described first by Olds and Milner Adamantidis and colleagues have recently tested the effect of optical activation of dopamine neurons on operant responding in mice, using an optogenetic technique virtually identical to that used here Our results are complemented by those of Zweifel and colleagues Although the present results demonstrate that the transient activation of dopamine neurons is sufficient for positive reinforcement, it does not appear to be necessary for reinforcement It has long been thought that that some drugs are addictive because they mimic the natural reward signal of dopamine neurons Male dopamine transporter (DAT) IRES-Cre mice http://www.addgene.org/pgvec1?f=c&cmd=findpl&identifier=18917 fromthe University of North Carolina Gene Therapy Vector Core). AAV-Flex-ChR2-tdTomato virus  was injected through a cannula into the right VTA  over a 10 minute period. Behavioral experiments began 14 or more days following injection of virus.We used an adeno-associated virus (AAV-FLEX-ChR2-tdTomato) to drive Cre-dependent expression of Channelrhodopsin-2 (ChR2) A 100 mW, 473 nm blue laser  was coupled to a 200 µm-diameter optical fiber  through a rotary joint (Doric Lenses). Light irradiance, as measured with an 818-SL photodetector (Newport Co.), was 477 mW/mm2 at the fiber tip, within the typical range of light intensities used for brief ChR2 activation. The optic fiber was inserted into the brain through the same cannula that was used for injection of virus. We found no evidence of any behavioral effects of light in the absence of ChR2 .The duration of 200 ms for the pulse of light was chosen to mimic the duration of the increase in firing rate of dopamine neurons that is observed following the sudden onset of a natural reward event e.g. . The parThe short delay of 100 ms between detection of a nose poke and onset of optical reward was chosen to maximize the speed and efficacy of reinforcement. Longer delays would be expected to result in slower acquisition of operant responding, and after acquisition, a lower asymptotic reward value due to temporal discounting. It is noteworthy that 100 ms is also the approximate delay between onset of a reward-predicting sensory stimulus and onset of the increase in firing rate of dopamine neurons e.g. .Operant responding was measured in separate sessions in a chamber that was dark except for light emanating from each of two nose poke ports . Inside If a mouse completed 20 nose pokes within one session, for the remainder of the session a progressive ratio schedule (PRS) was applied in which a larger number of nose pokes were required for each additional pulse of optical stimulation. The schedule was 1, 2, 4, 6, 9, 12, 15, 20, 25, 32, 40 and 50 nose pokes, with 50 being the maximum break point that we observed across all sessions in all mice. Our use of the PRS may have quantitively altered responding on the FR-1 schedule (on the subsequent day following the PRS), but any such alteration is not a primary concern given that our interest is simply in determining whether or not the dopamine RPE is sufficient to drive reinforcement.Locomotor responses to optical stimulation were measured in separate behavioral sessions. Both head speed and rotations were tracked with a Noldus EthoVision automated tracking system. Mice were habituated to the behavior chamber for 20 minutes. In the following 20 minutes, there were alternating periods of 2 minutes of optical stimulation followed by 2 minutes without stimulation, for a total of 5 periods and 10 minutes of each condition . During The counts of TH-positive and ChR2-positive neurons  were perWe did not find evidence of any ChR2-tdTomato expression in SN, but we do not exclude the possibility that some SN dopamine neurons may have expressed ChR2 and may have contributed to behavioral responses. Although we targeted dopamine neurons of the VTA, it was not the purpose of this study to distinguish VTA from SN.2) was calculated to measure correlations.Statistical significance was defined as p<0.05 in all tests. Operant responses in individual mice  were anaMovie S1A mouse working for optical stimulation on a FR1 schedule. Light can be seen emanating from both the active and inactive ports.(MOV)Click here for additional data file.Information S1We reviewed the literature to investigate the possibility that our AAV vector might confer light sensitivity in the absence of ChR2 expression. We cited 29 studies that have used AAV vector in combination with optical stimulation of neural tissue, and we categorized these using a Venn diagram. The text reads “In the present work, we controlled for nonspecific effects of light, both in our AAV- group and by demonstrating a strong correlation of behavioral responses with ChR2 expression. We did not control for the possibility that AAV might confer light sensitivity in the absence of ChR2 expression. While this is certainly possible, we consider it extremely unlikely given basic knowledge of photochemistry and membrane excitability. In addition, the technique of optical stimulation in combination with AAV vector is no longer so new. To investigate the available data on this issue, we have reviewed a total of 29 papers that have used AAV for optogenetic experiments, including 6 papers on dopamine neurons. 13 of these 29 papers included AAV control experiments (3 of 6 papers on dopamine neurons). None of the 13 papers found any effect of light in AAV controls. 16 of the 29 papers did not include any AAV controls. We note that most of the papers without AAV controls were published in well respected journals, suggesting that many reviewers were not particularly concerned about the lack of AAV controls.”(PDF)Click here for additional data file.


The granuloma is an elaborated aggregate of immune cells found in non-infectious as well as infectious diseases. It is a hallmark of tuberculosis (TB). Predominantly thought as a host-driven strategy to constrain the bacilli and prevent dissemination, recent discoveries indicate the granuloma can also be modulated into an efficient tool to promote microbial pathogenesis. The aim of future studies will certainly focus on better characterization of the mechanisms driving the modulation of the granuloma functions. Here, we provide unique perspectives from both the innate and adaptive immune system in the formation and the role of the TB granuloma. As macrophages (Mϕs) comprise the bulk of granulomas, we highlight the emerging concept of Mϕ polarization and its potential impact in the microbicide response, and other activities, that may ultimately shape the fate of granulomas. Alternatively, we shed light on the ability of B-cells to influence inflammatory status within the granuloma. The terHere, we will focus exclusively in the role of Mϕ polarization in the formation and function of TB granulomas. Likewise, we will provide a unique perspective on the significance of B-cells, whose immune-modulatory function has long been ignored in TB.ido1, ptgs2, il12b/il23a, socs3, marco, cd86, irf3/irf5, and stat1/stat5, among others , a process that is dependent on the region of difference-1 (RD1) virulence locus of M. marinum and independent of T-cells  challenge  in bronchoalveolar lavage (BAL) during the first week  dominated in BAL and granulomas, coinciding with a peak of IFN-γ in infected lungs  in Mϕs surrounding the core, accompanied by elevated type-2 inflammatory signals , siRNA-based technology, or gene-knockout strategy , may be used at different stages of granuloma formation in animal models. The strategies above could be used in combination with global array-based transcriptomics and proteomics approaches in order to assess the granuloma and local lung environment in the presence or absence of Mϕ subsets. Collectively, we expect there would be more future efforts to bridge results obtained in animals into the human context as discussed in the conclusion section.p.i., when a peak of IFN-γ/TNF coincides with CD4+ T-cell involvement, an event that impacts the organization of nascent granuloma structures. Yet, mice in which CD4+ T-cells are unable to produce IFN-γ/TNF are still resistant to TB, suggesting a complex scenario for protection  impairs the control of natural and vaccine-induced immunity to Salmonella  through either IL-2  through IL-6 and IFN-γ production during Salmonella infection, mice with a targeted deletion of MyD88 in B-cells exhibited an accumulation of neutrophils in the spleen, an effect that likely depends on Bregs-mediated IL-10 production (Neves et al., Another cell influencing TB granuloma formation is the neutrophil, whose migration can be controlled by B-cells. During Based on the above observation, it is tempting to propose that B-cell could act at different levels during TB such as during granuloma progression and by influencing the effector function of third-party cells like Mϕs. To directly examine this, studying B-cell contribution through comparison of B-cell-competent vs. B-cell-deficient animals should now be further complemented by studies examining the direct response of B-cells to Mtb infection, and through analyses in animal models lacking specific pathways in B-cells and biological consequences.Among trends emerging in TB etiology, the notion that the local lung environment shifts from a host-protective nature toward one favorable to microbial resilience is discussed here at the granuloma level and in the context of Mϕ polarization and B-cell function (see also an illustration in Figure The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The global threat of antimicrobial resistance (AMR) needs to be addressed urgently. The global surveillance of AMR pathogens is patchy and limited by financial and technical constraints. Without an early-warning system, the emergence and spread of AMR often goes unnoticed until a given strain has become endemic.Enterobacteriaceae (CRE), we analyzed the potential role of the International Health Regulations (IHR), a legally binding agreement between 194 States Parties, whose aim is “to prevent, protect against, control and provide a public health response to the international spread of disease” with respect to AMR and assess whether selected CRE events fulfil the four criteria of Annex 2 of the IHR.Using the example of carbapenem-resistant Certain events marking the emergence and international spread of KPC and NDM-1-producing CRE fulfil the criteria for notifiability to WHO. This can be extrapolated to other types of AMR. At the same time, ambiguities in Annex 2 and limited specific WHO guidance may make notification decisions a matter of debate. Obstacles for the application of the IHR to AMR include a lack of capacities within WHO.The global threat posed by the spread of AMR requires a coordinated international response. Recognizing the applicability of the IHR to AMR could serve as a “wake-up call” and obligate WHO and States Parties to strengthen surveillance and response, which could in turn contribute to containing the spread of AMR and preserve the efficacy of antimicrobials. Although States Parties and WHO share a collective responsibility in the process, WHO must clearly delineate its position regarding AMR and the intended role of the IHR in this context.None declared.


Molecular switches can toggle between two (or more) stable states encoding different physical features. They allow complex systems to respond to changes in their environment defined by light intensity, pH, temperature or voltage. As such, molecular switches play a key role in biology and information technology and have become important components of advanced materials.Chemists have developed a large number of synthetic switches, complementing the many types found in nature. Given their prominence in biology, it is not surprisingly that photoswitches, which are actuated by ultraviolet, visible or infrared light, have been an especially productive field of study. Despite their long history, new types of synthetic photoswitches continue to emerge and “old friends”, such as azobenzenes are constantly improved and optimized. This is not only true with respect to their photophysical properties but also with regard to their synthetic accessibility. Indeed, as molecular switches are applied in ever-increasing quantities, the efficiency of their syntheses becomes a primary concern, requiring the development of new synthetic methods and strategies. The corresponding results, however, are often buried in the Supporting Information of papers that focus on functional aspects, which does not reflect the relative effort that goes into the design and synthesis of these molecules.Caged compounds are a subclass of (photo)switches that have special functional features. As opposed to “true” switches, they can only be turned ON and the abatement of their activity depends on secondary processes . In terms of their biological application, especially in neurobiology, they are much further developed than reversibly switchable molecules. For instance, two-photon activation, which allows for very precise localization, is fairly well established with caged compounds but in its infancy in the case of reversible photoswitches.cis-azobenzenes with rapid thermal isomerization kinetics (Velasco), and highlight the azobenzene moiety as one of the smallest light-driven molecular motors conceivable (Merino). Issues of bistability are also addressed in an account on shape-persistent, optically active macrocycles (Pasini). Molecular switches that respond to changes in pH are covered as well (Haberhauer and Aprahamian), reflecting their importance in biology and in materials science. Finally, switching is highlighted as a synthetic strategy for building advanced materials that are useful in photovoltaics (Matile).The present Thematic Series of the Beilstein Journal of Organic Chemistry addresses the crucial role that chemistry, and particularly organic chemistry, can play in tuning the functional features of molecular switches and cages. These features include their absorption spectra, conductivity, geometry and bistability, as well as their polarity, solubility, efficacy, or catalytic activity. Photoswitches are covered extensively, ranging from diarylethenes  to dihydroazulenes/vinylheptafulvenes (Nielsen) and azobenzenes . Two reviews discuss Chemists, by nature, like to control things and probably actuate macroscopic switches more often than the average person. We hope that this collection of papers will motivate some of our colleagues to also consider nanoscopic versions of switches (and cages). For those of us who deal with these molecules on a daily basis, this Thematic Series will hopefully provide further inspiration and an impetus to continue work in this fascinating field of research.Dirk TraunerMunich, June 2012


Sarcoidosis is a granulomatous disorder of unknown cause, affecting multiple organs, but mainly the lungs. The exact order of immunological events remains obscure. Reviewing current literature, combined with careful clinical observations, we propose a model for granuloma formation in pulmonary sarcoidosis. A tight collaboration between macrophages, dendritic cells, and lymphocyte subsets, initiates the first steps toward granuloma formation, orchestrated by cytokines and chemokines. In a substantial part of pulmonary sarcoidosis patients, granuloma formation becomes an on-going process, leading to debilitating disease, and sometimes death. The immunological response, determining granuloma sustainment is not well understood. An impaired immunosuppressive function of regulatory T cells has been suggested to contribute to the exaggerated response. Interestingly, therapeutical agents commonly used in sarcoidosis, such as glucocorticosteroids and anti-TNF agents, interfere with granuloma integrity and restore the immune homeostasis in autoimmune disorders. Increasing insight into their mechanisms of action may contribute to the search for new therapeutical targets in pulmonary sarcoidosis. Addectively .Specific HLA class II antigens are associated with certain sarcoidosis disease phenotypes. For example, the HLA-DRB1*03 and DQB1*0201 alleles have been associated with an acute disease onset, Löfgren syndrome and resolving disease, whereas in contrast HLA-DRB1*15 and DQB1*0601 are associated with chronic sarcoidosis –27. It iIn this article we review the current knowledge on the role of the immune activation in pulmonary sarcoidosis and propose a hypothesis on the origin of granuloma formation. Secondly, we aim to discuss granuloma integrity, highlighting areas for research into new therapeutical targets.+ T helper (Th) cells, but also rare CD8+ T cells and B cells (A well-developed sarcoid granuloma consists of a tightly formed conglomerate of epithelioid- and multinucleated-giant cells (MGCs) encircled by lymphocytes, especially CD4 B cells . Both gr B cells . The chr+ T cell alveolitis. An interstitial pneumonitis, found on open lung biopsy, is classically thought to represent a very early stage of granuloma formation  sequentially, arguably pulmonary sarcoidosis starts in the draining LN. As stage I (bihilar lymphadenopathy) is most often asymptomatic, it is conceivable that it precedes pulmonary involvement, seen in stage II and III. Additionally, progression of stage I to II disease is well known, while development of stage I after stage III is uncommon. Finally, a recent trial found an increased diagnostic sensitivity of LN-derived fine needle aspirates, compared with transbronchial lung biopsies . These dConsequently, LN-specific immune reactions are important in early sarcoid granuloma formation, such as antigen presentation by dendritic cells (DCs). DCs are the only cells capable to pick up antigens and migrate to the LN where they present antigens to naïve T cells. Hereby they initiate highly specific clonal T cell differentiation and proliferation . Alterna+ T cell alveolitis  induce persistent stimulation of the immune response, mediated by HLA-related proteins, leading to continuous recruitment and local expansion of lymphocytes and eventually granuloma formation. The central localization of macrophages within the final epithelioid aggregate supports an important role in antigen presentation at the site of granuloma formation. Alternatively, DCs may play a critical role in antigen presentation within the granuloma. Their capacity for antigen sampling within the lymph fluid makes them likely candidates to contribute to the induction of the perilymphatic localized granulomas , 34.In the following paragraphs we describe the current knowledge on the role of macrophages, DCs, and lymphocytes in sarcoid granuloma formation in more detail, also summarized in Figure Upon activation, macrophages release nuclear factor (NF)-κB-dependent pro-inflammatory cytokines, such as interleukin (IL)-1 and tumor necrosis factor-α (TNF-α) . In sarcin vitro correlate with the presence of aggregates of AMs in the tissue  produce increased amounts of IL-1 and TNF-α –43 and ae tissue , 47. Onle tissue –51. These tissue –54.+ and CXCR6+ CD4+ Th cell recruitment  is increased in the BALF compared with controls  . During controls . Furthercontrols .Once recruited, TNF-α is needed for leukocyte adhesion, since an abrogation of tightly formed granulomas in TNF-α-deficient mice is observed following mycobacterial infection . In sarcin vitro, compared with healthy controls and other granulomatous diseases  can fuse to MGCs when stimulated by local cytokines, such as TNF-α, GM-CSF, IL-17A, CCL20, and INF-γ , 69. Patdiseases .Importantly, sarcoid-derived AMs have an increased accessory function on autologous blood- and lung-derived T lymphocytes, when compared with controls –73. MacrIn summary, macrophages are important for the initial accumulation, aggregation, and fusion of the cellular building blocks needed for granuloma formation. This process is mediated by the strong immune modulatory capacities of TNF-α and assisted by NK cells, which produce INF-γ.+HLA-DR+DC-LAMP+) DCs is found surrounding LN granulomas, adjacent to CD3+ lymphocytes, suggesting DC-T cell interaction at this site (+CD86+) DCs are found surrounding granulomas in sarcoid-derived mucosal biopsies  has been suggested to contribute to granuloma formation, as clearance of the putative antigen may be ineffective and the immune system turns to granuloma formation as a default immunological response  Tregs fail to inhibit granuloma growth in an in vitro granuloma culture model (Regulatory T cells (Tregs) play an important role in diminishing Th cell specific responses and are pivotal for maintenance of self-tolerance and immune homeostasis . An impare model , 113. Sure model –116. The+ T cell subset dysfunction , the main stay of sarcoidosis therapy, partially exert their beneficial effect by repression of NF-κB-related cytokine gene transcription and induction of lymphocyte apoptosis , 122. Us, is not . This ph, is not , which i, is not .Whether GCs and anti-TNF agents interfere with the delicate Th/Treg balance in pulmonary sarcoidosis, remains to be elucidated. Research into this field will shed more light on the role of Tregs in sarcoid pathology and whether Treg induction holds a promising new therapeutical strategy. Finally, an interplay between anergic Th cells, IL-10, alternatively activated macrophages (M2), CCL18 and lung fibroblasts has recently been suggested to contribute to fibrotic remodelling of the lung in chronic sarcoidosis . These iSarcoidosis is an intriguingly complex granulomatous disorder, characterized by an exaggerated Th1/17 immune response, initiated by APCs, and maintained due to malfunctioning of Tregs. Refining insight into immunological events that determine granuloma fate may help identify new therapeutical targets and patients who will benefit such therapy in the future.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.