Spatial variability in tree-ring carbon isotope discrimination in response to local drought across the entire loblolly pine natural range

Considering the temporal responses of carbon isotope discrimination (Δ13C) to local water availability in the spatial analysis of Δ13C is essential for evaluating the contribution of environmental and genetic facets of plant Δ13C. Using tree-ring Δ13C from years with contrasting water availability at 76 locations across the natural range of loblolly pine, we decomposed site-level Δ13C signals to maximum Δ13C in well-watered conditions (Δ13Cmax) and isotopic drought sensitivity (m) as a change in Δ13C per unit change of Palmer's Drought Severity Index (PDSI). Site water status, especially the tree lifetime average PDSI, was the primary factor affecting Δ13Cmax. The strong spatial correlation exhibited by m was related to both genetic and environmental factors. The long-term average water availability during the period relevant to trees as indicated by lifetime average PDSI correlated with Δ13Cmax, suggesting acclimation in tree gas-exchange traits, independent of incident water availability. The positive correlation between lifetime average PDSI and m indicated that loblolly pines were more sensitive to drought at mesic than xeric sites. The m was found to relate to a plant's stomatal control and may be employed as a genetic indicator of efficient water use strategies. Partitioning Δ13C to Δ13Cmax and m provided a new angle for understanding sources of variation in plant Δ13C, with several fundamental and applied implications.

A qualitative study of the acceptability of cognitive bias modification for paranoia (CBM-pa) in patients with psychosis

BACKGROUND

Cognitive Bias Modification (CBM) has been used successfully as a computer-based intervention in disorders such as anxiety. However, CBM to modify interpretations of ambiguous information relevant to paranoia has not yet been tested. We conducted a qualitative investigation of a novel intervention called CBM for paranoia (CBM-pa) to examine its acceptability in patients with psychosis.

METHODS

Eight participants with psychosis who completed CBM-pa were identified by purposive sampling and invited for a semi-structured interview to explore the facilitators and barriers to participation, optimum form of delivery, perceived usefulness of CBM-pa and their opinions on applying CBM-pa as a computerised intervention. The interviews were transcribed and analysed using thematic analysis by researchers working in collaboration with service users.

RESULTS

Themes emerged relating to participants' perception about delivery, engagement, programme understanding, factors influencing experience, perceived impact and application of CBM-pa. CBM-pa was regarded as easy, straightforward and enjoyable. It was well-accepted among those we interviewed, who understood the procedure as a psychological intervention. Patients reported that it increased their capacity for adopting alternative interpretations of emotionally ambiguous scenarios. Although participants all agreed on the test-like nature of the current CBM-pa format, they considered that taking part in sessions had improved their overall wellbeing. Most of them valued the computer-based interface of CBM-pa but favoured the idea of combining CBM-pa with some form of human interaction.

CONCLUSIONS

CBM-pa is an acceptable intervention that was well-received by our sample of patients with paranoia. The current findings reflect positively on the acceptability and experience of CBM-pa in the target population. Patient opinion supports further development and testing of CBM-pa as a possible adjunct treatment for paranoia.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN: 90749868 . Retrospectively registered on 12 May 2016.

Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal design, in volunteers weighing 44-150 kg, to identify the effect of obesity on dapsone pharmacokinetic parameters based on maximum-likelihood solution via the expectation-maximization algorithm. Artificial intelligence-based multivariate adaptive regression splines were used for covariate selection, and identified weight and/or age as predictors of absorption, systemic clearance, and volume of distribution. These relationships occurred only between certain patient weight and age ranges, delimited by multiple hinges and regions of discontinuity, not identified by standard pharmacometric approaches. Older and obese people have lower drug concentrations after standard dosing, but with complex patterns. Given that efficacy is concentration-dependent, optimal dapsone doses need to be personalized for obese patients.

Fractal Geometry-Based Decrease in Trimethoprim-Sulfamethoxazole Concentrations in Overweight and Obese People

Trimethoprim-sulfamethoxazole (TMP-SMX) is one of the most widely drugs on earth. The World Health Organization recommends it as an essential basic drug for all healthcare systems. Dosing is inconsistently based on weight, assuming linear relationships. Given that obesity is now a global "pandemic" it is vital that we evaluate the effect of obesity on trimethoprim-sulfamethoxazole concentrations. We conducted a prospective clinical experiment based on optimized design strategies and artificial intelligence algorithms and found that weight and body mass index (BMI) had a profound effect on drug clearance and volume of distribution, and followed nonlinear fractal geometry-based relationships. The findings were confirmed by demonstrating decreased TMP-SMX peak and area under the concentration-time curves in overweight patients based on standard regression statistics. The nonlinear relationships can now be used to identify new TMP-SMX doses in overweight and obese patients for each of the infections caused by the >60 pathogens for which the drug is indicated.

Efficacy of increasing dosages of clarithromycin for treatment of experimental Mycoplasma pneumoniae pneumonia

OBJECTIVES

Mycoplasma pneumoniae respiratory infection is a common cause of acute respiratory infection in children and adults. We evaluated the efficacy of increasing dosages of clarithromycin for the optimized therapy of M. pneumoniae respiratory infection in a mouse model.

METHODS

BALB/c mice were intranasally inoculated once with M. pneumoniae or SP4 broth (control). Groups of mice were treated with increasing dosages of clarithromycin (10, 25 or 75 mg/kg/day) or placebo subcutaneously daily. Groups of mice were evaluated after 1, 2, 3, 6 and 12 days of therapy. Outcome variables included quantitative M. pneumoniae culture, histopathological score of the lungs, bronchoalveolar lavage (BAL) cytokine/chemokine/growth factor concentrations and plethysmography after aerosolized methacholine to assess airway hyperresponsiveness.

RESULTS

Elevated dosages of clarithromycin resulted in greater antimicrobial efficacy with significantly reduced M. pneumoniae quantitative cultures (P < 0.05), as well as greater improvement in markers of disease severity with significantly reduced lung histopathology scores, BAL cytokine concentrations and airway hyperresponsiveness (P < 0.05).

CONCLUSIONS

Escalated dosing of clarithromycin resulted in significantly greater therapeutic efficacy in the treatment of experimental M. pneumoniae respiratory infection.

A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints

We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping clusters, and overcomes the difficulties imposed by deterministic constraints. The algorithm's convergence is proven and its applicability demonstrated for genetic linkage analysis.

A systematic review of exercise trials post stroke

OBJECTIVE

To perform a systematic review of exercise trials post stroke.

DESIGN

A systematic review of controlled clinical trials.

SEARCH STRATEGY

MEDLINE, EMBASE, CINAHL, Amed, Sports Discus, Cochrane controlled trials register and PEDro were searched for relevant trials.

INCLUSION CRITERIA

Studies--randomized or quasi-randomized controlled clinical trials. Participants--Adults of any age with a clinical diagnosis of stroke. Interventions--Any cardiovascular exercise intervention aimed at improving cardiovascular fitness and/or function.

OUTCOMES

Impairment: gait speed, strength, endurance, balance, flexibility, tonus and exercise capacity. Disability: global dependency, functional independence. Extended activities of daily living. Quality of life. Death.

DATA COLLECTION AND ANALYSIS

Two independent reviewers categorized selected trials, documented the methodological quality and extracted the relevant data. Comparisons of cardiovascular exercise interventions versus no cardiovascular intervention were made. Statistical comparisons were carried out using a random effects model to calculate standardized mean differences.

RESULTS

We identified three eligible trials. Small numbers and heterogeneous outcomes limited the analyses and comparisons. Based on the limited data available, we found that cardiovascular exercise post stroke was no better than no exercise with respect to disability, impairment, extended activities of daily living, quality of life and death.

CONCLUSION

Insufficient evidence was identified to establish if cardiovascular exercise has a positive effect on disability, impairment, extended activities of daily living, quality of life and case fatality post stroke.

Assessing the efficacy of a ram-genotyping programme to reduce susceptibility to scrapie in Great Britain

Susceptibility to clinical scrapie is associated with polymorphisms in the PrP gene. The 'ARR' allele of this gene reduces susceptibility to clinical disease caused by all known strains of the transmissible spongiform encephalopathy (TSE) agent. The British government proposes to use a ram-genotyping scheme to breed genetic resistance to clinical scrapie into the national sheep population. We considered how best to target limited genotyping resources to achieve the maximum rate of genotype evolution. We created a metapopulation model of the British sheep industry, which includes the major pure-breeds of sheep and the cross-breeds produced by crossing these pure-bred animals. The main criterion for assessing the efficacy of different strategies was the time taken to increase the prevalence of the ARR allele in the slaughter-lamb population. Our model predicted that the most-effective strategy would be to target genotyping to those rams used for pure-breeding (i.e. mated with the same breed of ewe). This strategy was compared to two further strategies, in which the proportion of rams genotyped in each breed depended on the prevalence of the ARR/ARR genotype in that breed. A policy in which the proportion of animals genotyped is reduced as the ARR prevalence in that breed increases is efficient. The most-effective policy was targeting the hill sector in the early years and gradually switching to genotyping more terminal-sire and longwool rams as the resistance of the hill sector increases.

Finding a Path is Harder than Finding a Tree

I consider the problem of learning an optimal path graphical model from data and show the problem to be NP-hard for the maximum likelihood and minimum description length approaches and a Bayesian approach. This hardness result holds despite the fact that the problem is a restriction of the polynomially solvable problem of finding the optimal tree graphical model.

Reply to Comments

We will respond to our commentators individually, but the order of our responses follows naturally from the issues they bring up. Judea Pearl describes SEM's unfortunate retreat from the clear causal semantics articulated by Sewall Wright (1921) and later by Haavelmo (1943) to the algebraic interpretation preferred more recently by econometricians. We agree with Pearl about the history and also the problem, namely that the algebraic interpretation is suitable for estimation but expressively too weak to even distinguish among competing causal claims. Here we try to elaborate on the distinction between the semantics of a causal SEM and the epistemological connections between statistical data, background knowledge, and causal structure. We argue that many modern critics of SEM make their hay by conflating this distinction. Having tried to make it clear, we then turn to the assumptions that give the epistemological issues their structure, namely the Causal Independence and Faithfulness assumptions. Jim Woodward questions these assumptions at length, especially the Causal Independence assumption, and we spend the second part of our response defending it. Phil Wood seems to accept the fundamental assumptions upon which TETRAD rests, and even the utility of tools like it, but he brings out a wide array of subtle difficulties that we have not had time to discuss, some of which we now cover. Kwok-fai Ting questions the utility of any specification search done by computer, and we attempt to address his concerns last.

The TETRAD Project: Constraint Based Aids to Causal Model Specification

The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model's parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model's specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural equation model entails familiar constraints on the covariance matrix for all admissible values of its parameters; we survey results on the equivalence of structural equation models, and we discuss search strategies for model specification. We end by presenting several algorithms that are implemented in the TETRAD I1 program.

An evaluation of machine-learning methods for predicting pneumonia mortality

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model's potential to assist a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each model's predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30% of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.