Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.     

Towards an understanding of the role of intrinsic protein disorder on plant adaptation to environmental challenges

Intrinsic protein disorder is an interesting structural feature where fully functional proteins lack a three-dimensional structure in solution. In this work, we estimated the relative content of intrinsic protein disorder in 96 plant proteomes including monocots and eudicots. In this analysis, we found variation in the relative abundance of intrinsic protein disorder among these major clades; the relative level of disorder is higher in monocots than eudicots. In turn, there is an inverse relationship between the degree of intrinsic protein disorder and protein length, with smaller proteins being more disordered. The relative abundance of amino acids depends on intrinsic disorder and also varies among clades. Within the nucleus, intrinsically disordered proteins are more abundant than ordered proteins. Intrinsically disordered proteins are specialized in regulatory functions, nucleic acid binding, RNA processing, and in response to environmental stimuli. The implications of this on plants’ responses to their environment are discussed.     

小鼠阴道菌群紊乱模型的建立

目的建立小鼠阴道菌群紊乱模型,为调整阴道菌群紊乱的药物研发提供模型参考。方法采用雌激素化的C57BL/6小鼠经阴道以不同处理方式(链霉素50μg/只,加德纳菌50μL/只,链霉素50μg/只+抗链霉素加德纳菌50μL/只,每种处理方式各70只/组)诱导小鼠阴道菌群紊乱模型,采用细菌16S rDNA高通量测序考察造模后第1、第2、第3、第5、第7、第9、第11天内加德纳菌在阴道内的定植情况,以及阴道细菌结构和细菌生物多样性,并通过显微镜观察阴道组织的病理改变,比较3种造模方法的差异。另采用阳性药物(定君生,成分为德氏乳杆菌)对模型进行验证。结果采用链霉素联合抗链霉素加德纳菌能诱导形成典型的阴道菌群紊乱模型,造模后可见加德纳菌在阴道内的定植增多,优势菌以铜绿假单胞菌为主,且阴道细菌生物多样性增加,阴道组织可见明显的炎症浸润和上皮细胞坏死等病理改变。定君生能显著减少加德纳菌在阴道内的定植,减少阴道内的细菌生物多样性,并能显著改善阴道组织病理变化。结论采用链霉素联合抗链霉素加德纳菌诱导的小鼠阴道菌群紊乱模型方法更佳,模型的细菌学和组织病理学改变,以及对药物的反应性与临床有一定相似,具有临床应用价值。     

Propylisopropylacetic acid (PIA), a constitutional isomer of valproic acid,uncompetitively inhibits arachidonic acid acylation by rat acyl-CoA synthetase 4: A potential drug for bipolar disorder

Background

Mood stabilizers used for treating bipolar disorder (BD) selectively downregulate arachidonic acid (AA) turnover (deacylation–reacylation) in brain phospholipids, when given chronically to rats. In vitro studies suggest that one of these, valproic acid (VPA), which is teratogenic, reduces AA turnover by inhibiting the brain long-chain acyl-CoA synthetase (Acsl)4 mediated acylation of AA to AA-CoA. We tested whether non-teratogenic VPA analogues might also inhibit Acsl4 catalyzed acylation, and thus have a potential anti-BD action.

Methods

Rat Acsl4-flag protein was expressed in Escherichia coli, and the ability of three VPA analogues, propylisopropylacetic acid (PIA), propylisopropylacetamide (PID) and N-methyl-2,2,3,3-tetramethylcyclopropanecarboxamide (MTMCD), and of sodium butyrate, to inhibit conversion of AA to AA-CoA by Acsl4 was quantified using Michaelis–Menten kinetics.

Results

Acsl4-mediated conversion of AA to AA-CoA in vitro was inhibited uncompetitively by PIA, with a K_i of 11.4 mM compared to a published K_i of 25 mM for VPA, while PID, MTMCD and sodium butyrate had no inhibitory effect.

Conclusions

PIA's ability to inhibit conversion of AA to AA-CoA by Acsl4 in vitro suggests that, like VPA, PIA may reduce AA turnover in brain phospholipids in unanesthetized rats, and if so, may be effective as a non-teratogenic mood stabilizer in BD patients.     

Next-generation sequencing for identifying new genes in rare genetic diseases: many challenges and a pinch of luck

A report on the European Society of Human Genetics conference, held in Paris, France, June 8-11, 2013.     

DNA repair mechanisms in dividing and non-dividing cells

DNA damage created by endogenous or exogenous genotoxic agents can exist in multiple forms, and if allowed to persist, can promote genome instability and directly lead to various human diseases, particularly cancer, neurological abnormalities, immunodeficiency and premature aging. To avoid such deleterious outcomes, cells have evolved an array of DNA repair pathways, which carry out what is typically a multiple-step process to resolve specific DNA lesions and maintain genome integrity. To fully appreciate the biological contributions of the different DNA repair systems, one must keep in mind the cellular context within which they operate. For example, the human body is composed of non-dividing and dividing cell types, including, in the brain, neurons and glial cells. We describe herein the molecular mechanisms of the different DNA repair pathways, and review their roles in non-dividing and dividing cells, with an eye toward how these pathways may regulate the development of neurological disease.     

DISC1-related signaling pathways in adult neurogenesis of the hippocampus

Disrupted-in-schizophrenia 1 (DISC1) is a multifunctional scaffold protein which plays an important role in neurogenesis and neural development in the adult brain, especially in the dentate gyrus (DG) of the hippocampus. Accumulated research has unveiled the role of DISC1 in several aspects of neural development and neurogenesis, such as neuronal maturation, proliferation, migration, positioning, differentiation, dendritic growth, axonal outgrowth, and synaptic plasticity. Studies on the function of this protein have explored multiple facets, including variants and missense mutants in genetics, proteins interactivity and signaling pathways in molecular biology, and pathogenesis and treatment targets of major mental illness, and more. In this review, we present several signaling pathways discussed in recent research, such as the AKT signaling pathway, GABA signaling pathway, GSK3β signaling pathway, Wnt signaling pathway, and NMDA-R signaling pathway. DISC1 interacts, directly or indirectly, with these signaling pathways and they co-regulate the process of adult neurogenesis in the hippocampus.     

Dementia from the ABCD1 mutation c.1415-1416delAG in a female carrier

Objectives

Progressive dementia is a rare phenotypic feature of female X-ALD carriers. Even rarer is the additional presence of further risk factors for dementia, such as diabetes, hypothyroidism, and hepatopathy. We report a unique female X-ALD carrier presenting with severe, progressive dementia, paraspasticity, sphincteric dysfunction, and multisystem disease.

Case report

A 79 years-old female with a history of strumectomy, diabetes, hepatopathy, hypothyroidism, arterial hypertension, hiatal hernia, left retinal ablation, ovariectomy, hysterectomy, osteoporosis, bilateral hip endoprosthesis, and neurogenic bladder dysfunction developed slowly progressive cognitive decline since age of 77 years. She had been identified as a female carrier of X-ALD in 12/2010 upon a family screening. At age of 79 years she presented with severe dementia, anxiety, unsteadiness, helplessness, hypertelorism, exaggerated patella tendon reflexes, reduced Achilles tendon reflexes, club feet, contractures of the ankles, the knees, and the hips, and the inability to stay or walk. Cerebral CT showed diffuse atrophy, demyelination periventricularly, small lacunas in the basal ganglia, and small calcifications of the basal ganglia and the temporal lobe on the right side. Differential diagnoses of dementia were considered but were all excluded upon the clinical presentation, blood chemical investigations, imaging studies, and the pattern of neuropsychological deficits.

Conclusions

With progression of the disease manifesting X-ALD carriers may develop progressive severe dementia, severe paraspasticity, and sphincteric dysfunction. Female carriership of X-ALD can be a differential diagnosis of dementia.     

The relationship between the presence of ADHD and certain candidate gene polymorphisms in a Turkish sample

Due to the high heritability of attention-deficit hyperactivity disorder (ADHD), parents of children with ADHD appear to represent a good sample group for investigating the genetics of the disorder. The aim of this study was to investigate the association between ADHD and six polymorphisms in five candidate genes [5-HT2A (rs6311), NET1 (rs2242447), COMT (rs4818), NTF3 (rs6332), SNAP-25 (rs3746544) and (rs1051312)]. We included 228 parents of children diagnosed with ADHD and 109 healthy parents as the control group. The polymorphisms were genotyped using polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) assays and analyzed using the chi-square test and the multinomial logit model. SNAP-25 (rs3746544) polymorphism was associated with loading for ADHD, while 5-HT2A (rs6311) and NET1 (rs2242447) polymorphisms were associated with ADHD. On the other hand, there was no significant association between the SNAP-25 (rs1051312), NTF3 (rs6332), or COMT (rs4818) gene polymorphisms and ADHD.     

Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions

More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%–~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.