Large Amounts Of Missing Data Research Articles

Missing genotype imputation in non-model species using self-organizing maps.

Current methodologies of genome-wide single-nucleotide polymorphism (SNP) genotyping produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Genotype imputation is routinely used in well-studied species to buffer the impact in downstream analysis, and several algorithms are available to fill in missing genotypes. The lack of reference haplotype panels precludes the use of these methods in genomic studies on non-model organisms. As an alternative, machine learning algorithms are employed to explore the genotype data and to estimate the missing genotypes. Here, we propose an imputation method based on self-organizing maps (SOM), a widely used neural networks formed by spatially distributed neurons that cluster similar inputs into close neurons. The method explores genotype datasets to select SNP loci to build binary vectors from the genotypes, and initializes and trains neural networks for each query missing SNP genotype. The SOM-derived clustering is then used to impute the best genotype. To automate the imputation process, we have implemented gtImputation, an open-source application programmed in Python3 and with a user-friendly GUI to facilitate the whole process. The method performance was validated by comparing its accuracy, precision and sensitivity on several benchmark genotype datasets with other available imputation algorithms. Our approach produced highly accurate and precise genotype imputations even for SNPs with alleles at low frequency and outperformed other algorithms, especially for datasets from mixed populations with unrelated individuals.

The oral health of adults with learning disabilities: A secondary analysis of the Adult Dental Health Survey 2009.

Adults who have learning disabilities are a vulnerable group, little is known about their oral health and how this affects their quality of life. The aims of this secondary analysis of data from the 2009 Adult Dental Health Survey (ADHS) were to describe the oral health status of adults with learning disabilities, determine if severity of learning disability is associated with oral health and identify some of the methodological complexities of working with this population. The survey yields the most recent representative data on the oral health of adults with learning disabilities in England and importantly, contains information about oral health related quality of life (OHRQoL). Secondary analysis of data from a supplemental survey of adults with learning disabilities collected alongside the 2009 ADHS. 607 participants with a diagnosed learning disability aged 18 years and over. Adults with learning disabilities had similar levels of active dental caries, fewer natural teeth, and fewer fillings than comparable participants from the general population. Self-reported oral and general health were worse for adults with learning disabilities than the general population. Possible associations between the severity of learning disability and the numbers of decayed, missing or filled teeth were identified. However, large amounts of missing data limited the analysis. There are important questions relating to the accessibility of existing self-reported oral health questionnaires and the reliability of proxy-reported questions about OHRQoL that should be addressed to give a fuller picture of the oral health of adults with learning disabilities.

Predictive model for totally implanted venous access ports‑related long‑term complications in patients with lung cancer.

Totally implanted venous access ports (TIVAPs), which are typically used in oncological chemotherapy and parenteral nutritional support, are convenient and safe, and thus offer patients a higher quality of life. However, insertion or removal of the device requires a minor surgical operation. Long-term complications (>30 days post insertion), such as catheter migration, catheter-related thrombosis and infection, are major reasons for TIVAP removal and are associated with a number of factors such as body mass index and hemoglobin count. Since management of complications is typically time-consuming and costly, a predictive model of such events may be of great value. Therefore, in the present study, a predictive model for long-term complications following TIVAP implantation in patients with lung cancer was developed. After excluding patients with a large amount of missing data, 902 patients admitted to The First Affiliated Hospital with Nanjing Medical University (Nanjing, China) were ultimately included in the present study. Of the included patients, 28 had complications, indicating an incidence rate of 3.1%. Patients were randomly divided into training and test cohorts (7:3), and three machine learning-based anomaly detection algorithms, namely, the Isolation Forest, one-class Support Vector Machines (one-class SVM) and Local Outlier Factor, were used to construct a model. The performance of the model was initially evaluated by the Matthew's correlation coefficient (MCC), area under curve (AUC) and accuracy. The one-class SVM model demonstrated the highest performance in classifying the risk of complications associated with the use of the intracavitary electrocardiogram method for TIVAP implantation in patients with lung cancer (MCC, 0.078; AUC, 0.62; accuracy, 66.0%). In conclusion, the predictive model developed in the present study may be used to improve the early detection of TIVAP-related complications in patients with lung cancer, which could lead to the conservation of medical resources and the promotion of medical advances.

Tackling Challenges in Data Pooling: Missing Data Handling in Latent Variable Models with Continuous and Categorical Indicators

Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle missing data in this context: (1) applying direct maximum likelihood by treating data as continuous (con-ML), and (2) applying categorical least squares using a polychoric correlation matrix computed from pairwise deletion (cat-LS). These approaches are available for free and relatively straightforward for empirical researchers to implement. Through simulation studies with confirmatory factor analysis and latent mediation analysis, we found cat-LS to be unsuitable for pooled data analysis, whereas con-ML yielded acceptable performance for the estimation of latent path coefficients barring severe nonnormality.

Long-term hydrological behavior of an Alpine glacier

Alpine glaciers are shrinking at a relentless pace, as an effect of global warming. The impact of these changes in the European Alps is relevant, given the importance of this territory from both ecological and economic viewpoints. While the ubiquitous reduction of glaciers’ mass through the Alps has been reported in several studies, its effect on streamflow is less studied. In the present work, we analyzed the long streamflow time series, available since 1976, of the Careser Stream, a headwater stream emerging from the Careser Glacier, in northeastern Italy. A large amount of missing data characterizes the streamflow time series, which we filled with a high level of confidence by using a Feed Forward Deep Neural Network algorithm. We explored the influence of climatic drivers on streamflow at monthly and annual time scales. Our analysis highlighted significant changes in the timing of streamflow due to the combined effect of early snow melting and a progressive reduction of the glacier’s area. The hydrological regime changed significantly with glacier water contributing to streamflow proportionally less after a tipping point identified in 1996. Projections performed by using eight bias-corrected climate models of the EURO-CORDEX collections confirmed this tendency with the complete transition of the catchment from the glacial to the nival regime by 2045 at the latest. The Deep Neural Network algorithm was very effective in filling the missing streamflow data and this offers an encouraging premise for applications in other glaciers of the Alps suffering from the same transformation.

RAD-seq data reveals robust phylogeny and morphological evolutionary history of Rhododendron

Rhododendron is famous for its high ornamental value. However, the genus is taxonomically difficult and the relationships within Rhododendron remain unresolved. In addition, the origin of key morphological characters with high horticulture value need to be explored. Both problems largely hinder utilization of germplasm resources. Most studies attempted to disentangle the phylogeny of Rhododendron, but only used a few genomic markers and lacked large-scale sampling, resulting in low clade support and contradictory phylogenetic signals. Here, we used restriction-site associated DNA sequencing (RAD-seq) data and morphological traits for 144 species of Rhododendron, representing all subgenera and most sections and subsections of this species-rich genus, to decipher its intricate evolutionary history and reconstruct ancestral state. Our results revealed high resolutions at subgenera and section levels of Rhododendron based on RAD-seq data. Both optimal phylogenetic tree and split tree recovered five lineages among Rhododendron. Subg. Therorhodion (clade I) formed the basal lineage. Subg. Tsutsusi and Azaleastrum formed clade II and had sister relationships. Clade III included all scaly rhododendron species. Subg. Pentanthera (clade IV) formed a sister group to Subg. Hymenanthes (clade V). The results of ancestral state reconstruction showed that Rhododendron ancestor was a deciduous woody plant with terminal inflorescence, ten stamens, leaf blade without scales and broadly funnelform corolla with pink or purple color. This study shows significant distinguishability to resolve the evolutionary history of Rhododendron based on high clade support of phylogenetic tree constructed by RAD-seq data. It also provides an example to resolve discordant signals in phylogenetic trees and demonstrates the application feasibility of RAD-seq with large amounts of missing data in deciphering intricate evolutionary relationships. Additionally, the reconstructed ancestral state of six important characters provides insights into the innovation of key characters in Rhododendron.

Hydrological modeling in an agricultural basin in the Brazilian Cerrado using satellite precipitation data

Precipitation data are one of the main inputs of a hydrological model and, therefore, the low density of rain gauges and large amounts of missing data in certain regions are obstacles to the development of hydrological research. In this sense, the quality of satellite precipitation products (SPP) was evaluated by comparing them with conventional data obtained from rain gauges (OP); subsequently the Soil and Water Assessment Tool (SWAT) model was calibrated and validated for three distinct periods according to the change in land use. Periods 1 and 2 were calibrated considering only the observed precipitation data, while period 3 included the addition of precipitation data from the SPP - Tropical Rainfall Measuring Mission - satellite due to the many years with information missing. Finally, the generated flows were examined, discussing the feasibility of using alternative precipitation data for hydrological simulation in a basin with a scarcity of data. The SPP showed a strong correlation with the OP for the hydrographic basin under study according to the statistical metrics analyzed, despite overestimation of figures for wet periods. The OPs satisfactorily simulated the flow rates of the basin under study for the two periods of analysis, as well as period 3, when the SPP was added. However, periods 1 and 2, which only contained information from rain gauge observations, presented better statistics than period 3. The values of the statistical coefficients attested to the potential of using the satellite data, in addition to observed data, to enable the calibration and validation processes of the SWAT model in regions with low station density or missing data.

Documented nutritional therapy in relation to nutritional guidelines post burn injury – a retrospective observational study

Intensive nutritional therapy is an essential component of burn care. Regarding post-minor burn injuries, the literature is lacking. The aim of this study was to evaluate documented nutritional therapy in relation to international guidelines after both minor and major burn injuries. The secondary aim of this study was to evaluate the adequacy of energy and protein intake compared to individual nutritional goals post-burn injury. A retrospective observational single-centre study including patients admitted between 2017 and 2019at a burn centre in Sweden was performed. The patients included in the study were ≥18 years old and in need of hospital care for ≥72h post-burn injury. Information about patients' demographics, nutritional therapy, and clinical characteristics of burn injury was collected. The patients were divided according to total body surface area burnt (TBSA %) into minor burn injuries (TBSA <20%) and major burn injuries (TBSA ≥20%). Descriptive statistics were used to analyse data. Adherence to guidelines was established by comparing 24 nutritional therapy recommendations to documented treatment. If documented nutritional treatment were in accordance with guidelines, adherence was considered high (≥80%), moderate (60-79.9%) or low (<59.9%). One hundred thirty-four patients were included, 90 patients with minor burn injuries and 44 patients with major burn injuries. Documented adherence to the nutritional guideline was overall low. After minor burn injury, 8% (2/24) of nutritional therapy recommendations had a high adherence (fat intake <35% of total energy intake and enteral nutrition as prioritized feeding route), 17% (4/24) a moderate adherence, and 75% (18/24) a low adherence. In patients treated after a major burn injury, there were two recommendations with documented high adherence (Vitamin C and Zinc); 25% (6/24) had moderate adherence, and 67% (16/24) had low adherence. In addition, quite a large amount of missing data was found. Adequacy of documented nutritional intake, compared to the individual documented goal, was 78% (±23%) for energy and 66% (±22%) for protein after minor burn injury. After major burn injury, the adequacy was 89% (±21%) for energy and 78% (±19%) for protein, respectively. This study revealed low adherence to nutritional guidelines in patients treated for minor and major burn injuries. Compared to major burn injuries, lower documented adequacy for both energy and proteins was found in minor burn injuries. Given the disparity between guidelines and documented nutritional therapy, and the lack of specific guidelines for minor burn injuries, there could be a considerable risk of inadequate nutritional therapy post-burn injury.

Degradation modelling and lifetime assessment for boiler waterwall with incomplete inspection data

Thermal fatigue cracking is a common problem for boiler waterwall tubes in power plants, resulting the risk of unscheduled plant shutdown. Developing an effective degradation model to predict the future condition of waterwall tubes is vital to assess boiler remaining life. Even though many numerical studies are available in the literature for understanding the physical mechanism (thermal fatigue cracking), the development of predictive models for future risks that can inform decision making are limited. One of the key impediments for developing predictive models is the lack of data. In the case of boiler critical subsystems, very few failures (if any) are recorded in its history. In addition, the number of inspections is limited due to access difficulties. Thus, in many practical cases, the condition data available for degradation modelling is sparse with both infrequent and (spatially) incomplete inspections. This paper presents the development of a stochastic degradation model prediction of thermal fatigue cracking severity in a boiler waterwall when the data is limited. The time evolution of the condition indicator for each waterwall tube is modelled using Markov Models and Bayesian identification algorithms are used to tractably address the large amount of missing data. The methodology presented in this paper employs a Markov Chain Monte Carlo (MCMC) scheme to account for the parameter uncertainty in the presence of missing data. Moreover, this paper also incorporates a novel grouping strategy in which the neighbouring components are grouped to tackle the problem of (spatial) data sparsity. The degradation modelling and prediction tools developed in this paper will support renewal decision making by using available onsite asset data and knowledge. A real-world case study of a boiler waterwall operating in an Australian power industry is also presented. It is found that the waterwall degradation appears to be slowing, but an additional inspection is recommended to confirm the trend.

Abstract WMP66: Early Rising Pain Scores Predict Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage

Introduction: Persistent pain following subarachnoid hemorrhage (SAH) is common and follows distinct trajectories. Cerebral vasospasm (CV) and delayed cerebral ischemia are late complications of SAH and may represent modifiable causes of secondary brain injury. Headache and CV share common risk factors including age, blood burden and cigarette smoking, however the relationship between pain and CV is unknown. We hypothesize that patients with increasing pain will be at increased risk for developing CV. Methods: We conducted a retrospective review of patients with non-traumatic SAH at two large academic medical centers from 2015-2022. Pain scores were recorded for 14 days. We excluded patients with onset of CV before post-bleed day (PBD) 4. We employed group-based trajectory modeling to identify distinct trajectories of 24 q2h pain scores during PBDs 2 and 3. Pain scores prior to PBD 2 were omitted due to the large amount of missing data. The incidence of CV was compared between trajectory groups using chi-squared analysis. Results: We identified 442 patients (median [IQR] age 56 [47-64] years, 294 (66%) women, 33 (7.4%) peri-mesencephalic, 330 (75%) Hunt-Hess 1-3, 393 (88%) modified Fisher 3-4) for analysis. We considered 3 through 8-group models and compared model fit by comparing AIC, BIC, and visual inspection of trajectories between models. Ultimately, a 4-group model fit the data best with four distinct trajectories of pain: persistently low (41% of patients), persistently high (24%), falling (18%) and rising (16%). Baseline clinical variables were comparable between trajectory groups. The median [IQR] day of CV onset was PBD 7 [5-10]. Patients with rising pain had significantly higher rates of vasospasm than those with persistently high or falling pain (54% vs 34% and 36%, respectively, Chi2 P = 0.02). Results were similar in a sensitivity analysis limited to patients with verbally reported pain scores. Conclusion: Distinct pain trajectories exist in the early hospital course following SAH. Our multicenter, retrospective cohort found that an initial rising pain trajectory is associated with development of CV. Future studies should validate this finding in separate cohorts and explore the pathophysiological mechanisms of this association.

Neural Scene Representation for Locomotion on Structured Terrain

We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot’s trajectory, the algorithm estimates the topography in the robot’s vicinity. The raw measurements from these cameras are noisy and only provide partial and occluded observations that in many cases do not show the terrain the robot stands on. Therefore, we propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement. The model consists of a 4D fully convolutional network on point clouds that learns the geometric priors to complete the scene from the context and an auto-regressive feedback to leverage spatio-temporal consistency and use evidence from the past. The network can be solely trained with synthetic data, and due to extensive augmentation, it is robust in the real world, as shown in the validation on a quadrupedal robot, ANYmal, traversing challenging settings. We run the pipeline on the robot’s onboard low-power computer using an efficient sparse tensor implementation and show that the proposed method outperforms classical map representations.

Maximizing Molecular Data From Low-Quality Fluid-Preserved Specimens in Natural History Collections

Over the past decade, museum genomics studies have focused on obtaining DNA of sufficient quality and quantity for sequencing from fluid-preserved natural history specimens, primarily to be used in systematic studies. While these studies have opened windows to evolutionary and biodiversity knowledge of many species worldwide, published works often focus on the success of these DNA sequencing efforts, which is undoubtedly less common than obtaining minimal or sometimes no DNA or unusable sequence data from specimens in natural history collections. Here, we attempt to obtain and sequence DNA extracts from 115 fresh and 41 degraded samples of homalopsid snakes, as well as from two degraded samples of a poorly known snake, Hydrablabes periops. Hydrablabes has been suggested to belong to at least two different families (Natricidae and Homalopsidae) and with no fresh tissues known to be available, intractable museum specimens currently provide the only opportunity to determine this snake’s taxonomic affinity. Although our aim was to generate a target-capture dataset for these samples, to be included in a broader phylogenetic study, results were less than ideal due to large amounts of missing data, especially using the same downstream methods as with standard, high-quality samples. However, rather than discount results entirely, we used mapping methods with references and pseudoreferences, along with phylogenetic analyses, to maximize any usable molecular data from our sequencing efforts, identify the taxonomic affinity of H. periops, and compare sequencing success between fresh and degraded tissue samples. This resulted in largely complete mitochondrial genomes for five specimens and hundreds to thousands of nuclear loci (ultra-conserved loci, anchored-hybrid enrichment loci, and a variety of loci frequently used in squamate phylogenetic studies) from fluid-preserved snakes, including a specimen of H. periops from the Field Museum of Natural History collection. We combined our H. periops data with previously published genomic and Sanger-sequenced datasets to confirm the familial designation of this taxon, reject previous taxonomic hypotheses, and make biogeographic inferences for Hydrablabes. A second H. periops specimen, despite being seemingly similar for initial raw sequencing results and after being put through the same protocols, resulted in little usable molecular data. We discuss the successes and failures of using different pipelines and methods to maximize the products from these data and provide expectations for others who are looking to use DNA sequencing efforts on specimens that likely have degraded DNA.Life Science Identifier (Hydrablabes periops)urn:lsid:zoobank.org:pub:F2AA44 E2-D2EF-4747-972A-652C34C2C09D.

TOURISM 4.0 TOOLS FOR FACILITATION OF SUSTAINABLE TOURISM DATA COMPARISON (USING A CASE STUDY FROM VYLKOVE AND SFÂNTU GHEORGHE IN THE DANUBE DELTA)

Topicality. Tourism is recognized as one of the most important commercial activities today. In 2019 it generated 10% of the total employment and represented a share of 10.4% of global GDP. Following the lead of cities, a growing number of towns and villages in rural areas are seeking to benefit from tourism, based on their local cultural and natural assets. This trend can be observed in the transboundary Danube Delta Biosphere Reserve of Romania and Ukraine where many formerly remote settlements have started to host increasing numbers of visitors. However, tourism can also impose a number of negative economic, social and environmental impacts on the destination and its region that could undermine the very attractiveness of the destinations concerned. Aims and tasks. A particular problem for authorities charged with developing regional rural tourism strategies and regulations is to have a standard method for assessing and comparing the visitor capacity and sustainability of different destinations. Emerging Tourism 4.0 technologies such as High-Performance Data Analytics (HPDA) can improve the efficiency and effectiveness of strategic planning and environmental sustainability. The general objective of the research reported here was to compare the current situation of two similar settlements in the Danube Delta Biosphere Reserve: Vylkove in Ukraine and Sfântu Gheorghe in Romania. In collaboration with the municipal councils, local businesses and civic organisations, we applied the Tourism Impact Model (TIM) developed by the Slovene company Arctur to data collected using the same methodology between August 2020 and May 2021. Overall, 295 questions were addressed. The data could be quantitative (amount of electricity or water used per day), or more subjective expert opinion (whether and when the settlement suffered from traffic congestion or satisfaction of residents with levels of incoming tourists). The data were also quality controlled and labelled according to their accuracy, type (digital or analogue) and frequency of collection. Research results. With respect to Vylkove and Sfântu Gheorghe, in both cases the tourism data collected were not sufficient to produce a full analysis for an overall DCC assessment. However, it was possible to examine the data for each of the main TIM pillars individually (Environment, Economy, Social / Cultural and Collaboration) and make a comparison between the destinations. From these data, it is clear that while both destinations are generally managing tourism poorly (as Sleepers), Sfântu Gheorghe has a better overall condition than Vylkove across the four pillars since it scores higher in the Champion character. Vylkove suffers from having a large amount of missing data, probably because the town council derives little direct benefit from tourism (it is a receiver of impacts but most of the revenue goes to external parties) so it does not collect information. On the other hand, compared with Sfântu Gheorghe, it does not misuse or exploit its environment. Both destinations have a good level of collaboration between the tourism sector and the local community. Conclusion. The results from the first application of the Tourism Impact Model in Vylkove and Sfântu Gheorghe show that both destinations have considerable potential for improving their performance and the sustainability of their tourism offers. At present, the lead is mostly taken by private initiative and investment, with little involvement of the local authorities. In particular, environmental, social and economic data are largely unavailable for planning sustainable tourism development, gaining benefits or mitigating impacts. These aspects merit further action and research as a matter of urgency.

Placing Ancient DNA Sequences into Reference Phylogenies.

Joint phylogenetic analysis of ancient DNA (aDNA) with modern phylogenies is hampered by low sequence coverage and post-mortem deamination, often resulting in overconservative or incorrect assignment. We provide a new efficient likelihood-based workflow, pathPhynder, that takes advantage of all the polymorphic sites in the target sequence. This effectively evaluates the number of ancestral and derived alleles present on each branch and reports the most likely placement of an ancient sample in the phylogeny and a haplogroup assignment, together with alternatives and supporting evidence. To illustrate the application of pathPhynder, we show improved Y chromosome assignments for published aDNA sequences, using a newly compiled Y variation data set (120,908 markers from 2,014 samples) that significantly enhances Y haplogroup assignment for low coverage samples. We apply the method to all published male aDNA samples from Africa, giving new insights into ancient migrations and the relationships between ancient and modern populations. The same software can be used to place samples with large amounts of missing data into other large non-recombining phylogenies such as the mitochondrial tree.

Naturalistic Study on the Effects of Electroconvulsive Therapy (ECT) on Depressive Symptoms.

The effectiveness of ECT under naturalistic conditions has not been well-studied. The current study aimed to 1) characterize a naturalistic sample of ECT patients; and 2) examine the long-term outcomes of ECT on depressive symptoms (Beck Depression Inventory-II; BDI-II) and functional disability symptoms (WHO Disability Assessment Schedule 2.0) in this sample. Participants were adults who received ECT for a major depressive episode at an ambulatory ECT clinic between September 2010 and November 2020. Clinical and cognitive assessments were completed at baseline (n = 100), mid-ECT (n = 94), 2-4 weeks post-ECT (n = 64), 6-months post-ECT (n = 34), and 12-months post-ECT (n = 19). At baseline, participants had severe levels of depressive symptoms (BDI-II: M = 41.0, SD = 9.4), and 62.9% screened positive for multiple psychiatric diagnoses on the MINI International Neuropsychiatric Interview. Depressive symptoms (F(4,49.1) = 49.92, P < 0.001) and disability symptoms (F(3,40.72) = 12.30, P < 0.001) improved significantly following ECT, and this was maintained at 12-months follow-up. Improvement in depressive symptoms trended towards significantly predicting reduction in disability symptoms from baseline to post-ECT, (F(1,56) = 3.67, P = 0.061). Although our clinical remission rate of 27% (BDI-II score 13 and 50% improvement) and overall response rate of 41.3% (50% improvement in BDI-II score) were lower than the rates reported in the extant RCT and community ECT literature, 36% of those treated with ECT were lost to follow-up and did not complete post-ECT rating scales. At baseline, remitters had significantly fewer psychiatric comorbidities, lower BDI-II scores, and lower disability symptoms than non-responders (P < 0.05). Participants were severely symptomatic and clinically complex. ECT was effective at reducing depressive symptoms and functional disability in this heterogeneous sample. Although a large amount of missing data may have distorted our calculated response/remission rates, it is also likely that clinical heterogeneity and severity contribute to lower-than-expected remission and response rates to ECT.

A double-blind, placebo-controlled, randomized trial of PXT3003 for the treatment of Charcot–Marie–Tooth type 1A

BackgroundCharcot–Marie–Tooth disease type 1A (CMT1A) is a rare, orphan, hereditary neuromuscular disorder with no cure and for which only symptomatic treatment is currently available. A previous phase 2 trial has shown preliminary evidence of efficacy for PXT3003 in treating CMT1A. This phase 3, international, randomized, double-blind, placebo-controlled study further investigated the efficacy and safety of high- or low-dose PXT3003 (baclofen/naltrexone/D-sorbitol [mg]: 6/0.70/210 or 3/0.35/105) in treating subjects with mild to moderate CMT1A.MethodsIn this study, 323 subjects with mild-to-moderate CMT1A were randomly assigned in a 1:1:1 ratio to receive 5 mL of high- or low-dose PXT3003, or placebo, orally twice daily for up to 15 months. Efficacy was assessed using the change in Overall Neuropathy Limitations Scale total score from baseline to months 12 and 15 (primary endpoint). Secondary endpoints included the 10-m walk test and other assessments. The high-dose group was discontinued early due to unexpected crystal formation in the high-dose formulation, which resulted in an unanticipated high discontinuation rate, overall and especially in the high-dose group. The statistical analysis plan was adapted to account for the large amount of missing data before database lock, and a modified full analysis set was used in the main analyses. Two sensitivity analyses were performed to check the interpretation based on the use of the modified full analysis set.ResultsHigh-dose PXT3003 demonstrated significant improvement in the Overall Neuropathy Limitations Scale total score vs placebo (mean difference: − 0.37 points; 97.5% CI [− 0.68 to − 0.06]; p = 0.008), and consistent treatment effects were shown in the sensitivity analyses. Both PXT3003 doses were safe and well-tolerated.ConclusionThe high-dose group demonstrated a statistically significant improvement in the primary endpoint and a good safety profile. Overall, high-dose PXT3003 is a promising treatment option for patients with Charcot–Marie–Tooth disease type 1A.

Long Term Outcomes of Blended CBT Compared to Face-to-Face CBT and Treatment as Usual for Adolescents with Depressive Disorders: Analyses at 12 Months Post-Treatment

Depression is a major problem in youth mental health and identified as the leading cause of disability worldwide. There is ample research on the acute effects of treatment, with estimated small-to-moderate effect sizes. However, there is a lack of research on long-term outcomes. A total of 129 adolescents with clinical depression (82.2% female), aged 13–22 (M = 16.60, SD = 2.03), received blended CBT, face-to-face CBT or treatment as usual. Data were collected at 12 months after the intervention and compared between treatment conditions. Clinical diagnosis, depressive symptoms, suicide risk, internalizing symptoms and externalizing symptoms decreased significantly over time, from baseline to the 12-month follow-up, and also from post-treatment to the 12-month follow-up in all three conditions. Changes were not significantly different between conditions. At the long-term, improvements following the treatment continued. Due to the large amount of missing data and use of history control condition, our findings need to be interpreted with caution. However, we consider these findings as a clinical imperative. More evidence might contribute to convincing adolescents to start with therapy, knowing it has lasting effects. Further, especially for adolescents for whom it is not possible to receive face-to-face treatment, blended treatment might be a valuable alternative. Our findings might contribute to the implementation of blended CBT.

A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a

ABSTRACT The Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 chlorophyll-a (Chl-a) product is one of the widely used ocean colour products that is often used for water quality monitoring of marine ecosystems. However, this product includes a large amount of missing data due to high surface reflectance and cloudy conditions that inevitably affect its suitability for spatiotemporal analysis of water quality. The objective of this study was to compare four Machine Learning (ML) techniques including K-nearest neighbour (KNN), Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Network (ANN) with well-known Data Interpolation Empirical Orthogonal Function (DINEOF) method for spatiotemporal missing imputation of MODIS Chl-a. The Southern Caspian Sea, which has a high Chl-a concentration, was selected as the case study. A cross-validation approach ranging missing data ratio from 0.1 to 0.8 was implemented to investigate the optimal parameters of the models and compare their performance for missing imputation. The results indicated that all ML models, except KNN, outperformed the DINEOF method for missing imputation of MODIS Chl-a. The SVR with the highest accuracy and the lowest variability of errors had the best performance among the five competing models, while the KNN showed the worst performance. The main reason for the better accuracy of the SVR than the other models is its structural risk minimization procedure that leads to the better generalization of the SVR model. The current results showed that the ML techniques used in the current study, the SVR in particular, are able to produce reliable imputations of the MODIS Chl-a missing data and can be a useful tool in water quality monitoring of marine ecosystems.

Missing Data, Complex Processes and Differing Contexts: A Case File Analysis of School Staff's Involvement in Child Protection Cases for Concerns of Neglect

This paper reports the findings from the analysis of 119 children's social work case files from three local authorities in Wales. The study offers new understanding about the extent of school staff's involvement in the provision of statutory support when there are concerns that a child is living with neglect. Findings demonstrate evidence of a broad range of early and preventative school‐based support provided in 42 per cent of the sample, some gaps in schools' contributions to statutory assessments and a decline in the level of involvement of members of school staff following the initial child protection conference. The discussion also acknowledges a number of challenges encountered during the study: the complexity of social work processes, the variance in understandings of neglect within differing regional contexts and large amounts of missing data on children's files and the restrictions this created for statistical measurement. Findings have important implications for interprofessional relationships between schools and social work teams, at individual and agency levels. The author makes a call for social workers to actively engage members of school staff in child protection processes with the purpose of increasing information sharing across agencies, while enhancing interprofessional safeguarding practice.‘Reports the findings from the analysis of 119 children's social work case files from three local authorities in Wales’Key Practitioner Messages Schools are key safeguarding partners and provide an extensive range of support to children and their families, both prior to and during statutory intervention. Members of school staff hold expert knowledge on children living with neglect which should be utilised more consistently within statutory assessments. Social workers should ensure members of school staff are invited to attend child protection conferences and develop interprofessional relationships to improve information sharing across agencies.

New Horizons for the Journal: Familiar Landscapes in Child Protection

New Horizons for the Journal: Familiar Landscapes in Child Protection