Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

In this policy forum the authors argue that data cleaning is an essential part of the research process, and should be incorporated into study design

Spontaneous Uterine Rupture in the First Trimester: A Case Report

Uterine rupture is one of the most feared obstetric complications affecting the pregnant woman and fetus. Most of the cases have various risk factors and mainly occur during the second or third trimester. However, spontaneous uterine rupture during the first trimester is extremely rare. We experienced a case of spontaneous uterine rupture in a 36-yr-old multiparous woman without definite risk factors. The initial impression was a hemoperitoneum of an unknown origin with normal early pregnancy. Intensive surgical method would be needed for accurate diagnosis and immediate management in bad situation by hemoperitoneum even though a patient was early pregnancy

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Antifungal Testing and High-Throughput Screening of Compound Library against Geomyces destructans, the Etiologic Agent of Geomycosis (WNS) in Bats

Bats in the northeastern U.S. are affected by geomycosis caused by the fungus Geomyces destructans (Gd). This infection is commonly referred to as White Nose Syndrome (WNS). Over a million hibernating bats have died since the fungus was first discovered in 2006 in a cave near Albany, New York. A population viability analysis conducted on little brown bats (Myotis lucifugus), one of six bat species infected with Gd, suggests regional extinction of this species within 20 years. The fungus Gd is a psychrophile (“cold loving”), but nothing is known about how it thrives at low temperatures and what pathogenic attributes allow it to infect bats. This study aimed to determine if currently available antifungal drugs and biocides are effective against Gd. We tested five Gd strains for their susceptibility to antifungal drugs and high-throughput screened (HTS) one representative strain with SpectrumPlus compound library containing 1,920 compounds. The results indicated that Gd is susceptible to a number of antifungal drugs at concentrations similar to the susceptibility range of human pathogenic fungi. Strains of Gd were susceptible to amphotericin B, fluconazole, itraconazole, ketoconazole and voriconazole. In contrast, very high MICs (minimum inhibitory concentrations) of flucytosine and echinocandins were needed for growth inhibition, which were suggestive of fungal resistance to these drugs. Of the1,920 compounds in the library, a few caused 50% - to greater than 90% inhibition of Gd growth. A number of azole antifungals, a fungicide, and some biocides caused prominent growth inhibition. Our results could provide a theoretical basis for future strategies aimed at the rehabilitation of most affected bat species and for decontamination of Gd in the cave environment

Overcoming Barriers to Skills Training in Borderline Personality Disorder: A Qualitative Interview Study

Despite evidence suggesting that skills training is an important mechanism of change in dialectical behaviour therapy, little research exploring facilitators and barriers to this process has been conducted. The study aimed to explore clients’ experiences of barriers to dialectical behaviour therapy skills training and how they felt they overcame these barriers, and to compare experiences between treatment completers and dropouts. In-depth qualitative interviews were conducted with 40 clients with borderline personality disorder who had attended a dialectical behaviour therapy programme. A thematic analysis of participants’ reported experiences found that key barriers to learning the skills were anxiety during the skills groups and difficulty understanding the material. Key barriers to using the skills were overwhelming emotions which left participants feeling unable or unwilling to use them. Key ways in which participants reported overcoming barriers to skills training were by sustaining their commitment to attending therapy and practising the skills, personalising the way they used them, and practising them so often that they became an integral part of their behavioural repertoire. Participants also highlighted a number of key ways in which they were supported with their skills training by other skills group members, the group therapists, their individual therapist, friends and family. Treatment dropouts were more likely than completers to describe anxiety during the skills groups as a barrier to learning, and were less likely to report overcoming barriers to skills training via the key processes outlined above. The findings of this qualitative study require replication, but could be used to generate hypotheses for testing in further research on barriers to skills training, how these relate to dropout, and how they can be overcome. The paper outlines several such suggestions for further research