Naive B-cell trafficking is shaped by local chemokine availability and LFA-1-independent stromal interactions.

It is not known how naive B cells compute divergent chemoattractant signals of the T-cell area and B-cell follicles during in vivo migration. Here, we used two-photon microscopy of peripheral lymph nodes (PLNs) to analyze the prototype G-protein-coupled receptors (GPCRs) CXCR4, CXCR5, and CCR7 during B-cell migration, as well as the integrin LFA-1 for stromal guidance. CXCR4 and CCR7 did not influence parenchymal B-cell motility and distribution, despite their role during B-cell arrest in venules. In contrast, CXCR5 played a nonredundant role in B-cell motility in follicles and in the T-cell area. B-cell migration in the T-cell area followed a random guided walk model, arguing against directed migration in vivo. LFA-1, but not α4 integrins, contributed to B-cell motility in PLNs. However, stromal network guidance was LFA-1 independent, uncoupling integrin-dependent migration from stromal attachment. Finally, we observed that despite a 20-fold reduction of chemokine expression in virus-challenged PLNs, CXCR5 remained essential for B-cell screening of antigen-presenting cells. Our data provide an overview of the contribution of prototype GPCRs and integrins during naive B-cell migration and shed light on the local chemokine availability that these cells compute.

Virologic and immunologic responses in treatment-naive patients to ritonavir-boosted atazanavir or efavirenz with a common backbone

Background: Atazanavir boosted with ritonavir (ATV/r) and efavirenz (EFV) are both recommended as first-line therapies for HIV-infected patients. We compared the 2 therapies for virologic efficacy and immune recovery. Methods: We included all treatment-naïve patients in the Swiss HIV Cohort Study starting therapy after May 2003 with either ATV/r or EFV and a backbone of tenofovir and either emtricitabine or lamivudine. We used Cox models to assess time to virologic failure and repeated measures models to assess the change in CD4 cell counts over time. All models were fit as marginal structural models using both point of treatment and censoring weights. Intent-to-treat and various as-treated analyses were carried out: In the latter, patients were censored at their last recorded measurement if they changed therapy or if they were no longer adherent to therapy. Results: Patients starting EFV (n = 1,097) and ATV/r (n = 384) were followed for a median of 35 and 37 months, respectively. During follow-up, 51% patients on EFV and 33% patients on ATV/r remained adherent and made no change to their first-line therapy. Although intent-to-treat analyses suggest virologic failure was more likely with ATV/r, there was no evidence for this disadvantage in patients who adhered to first-line therapy. Patients starting ATV/r had a greater increase in CD4 cell count during the first year of therapy, but this advantage disappeared after one year. Conclusions: In this observational study, there was no good evidence of any intrinsic advantage for one therapy over the other, consistent with earlier clinical trials. Differences between therapies may arise in a clinical setting because of differences in adherence to therapy.

Limited clinical benefit of minority K103N and Y181C-variant detection in addition to routine genotypic resistance testing in antiretroviral therapy-naive patients

OBJECTIVE: The presence of minority nonnucleoside reverse transcriptase inhibitor (NNRTI)-resistant HIV-1 variants prior to antiretroviral therapy (ART) has been linked to virologic failure in treatment-naive patients. DESIGN: We performed a large retrospective study to determine the number of treatment failures that could have been prevented by implementing minority drug-resistant HIV-1 variant analyses in ART-naïve patients in whom no NNRTI resistance mutations were detected by routine resistance testing. METHODS: Of 1608 patients in the Swiss HIV Cohort Study, who have initiated first-line ART with two nucleoside reverse transcriptase inhibitors (NRTIs) and one NNRTI before July 2008, 519 patients were eligible by means of HIV-1 subtype, viral load and sample availability. Key NNRTI drug resistance mutations K103N and Y181C were measured by allele-specific PCR in 208 of 519 randomly chosen patients. RESULTS: Minority K103N and Y181C drug resistance mutations were detected in five out of 190 (2.6%) and 10 out of 201 (5%) patients, respectively. Focusing on 183 patients for whom virologic success or failure could be examined, virologic failure occurred in seven out of 183 (3.8%) patients; minority K103N and/or Y181C variants were present prior to ART initiation in only two of those patients. The NNRTI-containing, first-line ART was effective in 10 patients with preexisting minority NNRTI-resistant HIV-1 variant. CONCLUSION: As revealed in settings of case-control studies, minority NNRTI-resistant HIV-1 variants can have an impact on ART. However, the sole implementation of minority NNRTI-resistant HIV-1 variant analysis in addition to genotypic resistance testing (GRT) cannot be recommended in routine clinical settings. Additional associated risk factors need to be discovered.

Using Bayes' rule to define the value of evidence from syndromic surveillance

In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial "evidence" of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.

Twenty questions with noise: Bayes optimal policies for entropy loss

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We formulate this problem as a dynamic program and show that any policy optimizing the one-step expected reduction in entropy is also optimal over the full horizon. Two such Bayes optimal policies are presented: one generalizes the probabilistic bisection policy due to Horstein and the other asks a deterministic set of questions. We study the structural properties of the latter, and illustrate its use in a computer vision application.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Mice deficient in interleukin-4 (IL-4) or IL-4 receptor alpha have higher resistance to sporozoite infection with Plasmodium berghei (ANKA) than do naive wild-type mice

BALB/c interleukin-4 (IL-4(-/-)) or IL-4 receptor-alpha (IL-4ralpha(-/-)) knockout (KO) mice were used to assess the roles of the IL-4 and IL-13 pathways during infections with the blood or liver stages of plasmodium in murine malaria. Intraperitoneal infection with the blood-stage erythrocytes of Plasmodium berghei (ANKA) resulted in 100% mortality within 24 days in BALB/c mice, as well as in the mutant mouse strains. However, when infected intravenously with the sporozoite liver stage, 60 to 80% of IL-4(-/-) and IL-4ralpha(-/-) mice survived, whereas all BALB/c mice succumbed with high parasitemia. Compared to infected BALB/c controls, the surviving KO mice showed increased NK cell numbers and expression of inducible nitric oxide synthase (iNOS) in the liver and were able to eliminate parasites early during infection. In vivo blockade of NO resulted in 100% mortality of sporozoite-infected KO mice. In vivo depletion of NK cells also resulted in 80 to 100% mortality, with a significant reduction in gamma interferon (IFN-gamma) production in the liver. These results suggest that IFN-gamma-producing NK cells are critical in host resistance against the sporozoite liver stage by inducing NO production, an effective killing effector molecule against Plasmodium. The absence of IL-4-mediated functions increases the protective innate immune mechanism identified above, which results in immunity against P. berghei infection in these mice, with no major role for IL-13.