Children, adolescents, and young adults with at least one first-degree relative [familial high-risk (FHR)] with either schizophrenia (SZ) or bipolar disorder (BD) have a one-in-two risk of developing a psychiatric disorder. Here, we review functional magnetic resonance imaging (fMRI) studies which examined task-related brain activity in young individuals with FHR-SZ and FHR-BD. A systematic search identified all published task-related fMRI studies in children, adolescents, and young adults below an age of 27 years with a first-degree relative with SZ or BD, but without manifest psychotic or affective spectrum disorder themselves. The search identified 19 cross-sectional fMRI studies covering four main cognitive domains: 1) working memory (n = 3), 2) cognitive control (n = 4), 3) reward processing (n = 3), and 4) emotion processing (n = 9). Thirteen studies included FHR-BD, five studies included FHR-SZ, and one study included a pooled FHR group. In general, task performance did not differ between the respective FHR groups and healthy controls, but 18 out of the 19 fMRI studies revealed regional alterations in task-related activation. Brain regions showing group differences in peak activation were regions associated with the respective task domain and showed little overlap between FHR-SZ and FHR-BD. The low number of studies, together with the low number of subjects, and the substantial heterogeneity of employed methodological approaches within the domain of working memory, cognitive control, and reward processing impedes finite conclusions. Emotion processing was the most investigated task domain in FHR-BD. Four studies reported differences in activation of the amygdala, and two studies reported differences in activation of inferior frontal/middle gyrus. Together, these studies provide evidence for altered brain processing of emotions in children, adolescents, and young adults at FHR-BD. More studies of higher homogeneity, larger sample sizes and with a longitudinal study design are warranted to prove a shared or specific FHR-related endophenotypic brain activation in young first-degree relatives of individuals with SZ or BD, as well as to pinpoint specific alterations in brain activation during cognitive-, emotional-, and reward-related tasks. Schizophrenia (SZ) and bipolar disorder (BD) are severe and highly heritable  mental iSZ is characterized as a neurodevelopmental disorder and manifests in adolescence or early adulthood , 5, wherFirst-degree relatives of individuals with SZ or BD, referred to as individuals with familial high-risk (FHR), show impairments on a variety of neurocognitive and motor functions on a group level, even at young age –17. MoreThroughout post-natal development, the brain undergoes continuous maturation –29. ThisStudies on the brain development in young individuals at high risk of severe mental disorders may not only reveal underlying neurobiological mechanisms but also identify markers associated with risk and resilience. Multiple studies of FHR individuals have investigated cognitive, motor or social capabilities in vulnerable populations , 13, 17 Here, we systematically reviewed the existing literature investigating children, adolescents, and young adults at FHR for SZ (FHR-SZ) or FHR for BD (FHR-BD) with task-related functional magnetic resonance imaging (fMRI). We only considered studies of FHR individuals who had not yet manifested signs of serious mental illness. Half of all lifetime mental disorders start by the mid-teenage years and three quarters by the mid-20s . Therefohttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018086995). Identification, screening, eligibility, and inclusion procedures followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (Prisma) 2009 flow diagram  .Four studies were identified in young individuals at FHR-SZ or FHR-BD within the WM domain. An overview of the brain peak activity differences between individuals at FHR-SZ or FHR-BD relative to HC within the WM domain are shown in In summary, no behavioral differences were found between FHR-BD and HC, whereas one study in FHR-SZ reported differences between FHR-SZ and HC on response latency with FHR-SZ being faster . Brain aThe five included studies of FHR within the cognitive control domain have focused on attention, response inhibition, and cognitive flexibility Table 2.In summary, two studies found behavioral differences on selective attention, one between FHR-SZ and HC , the othThree studies investigated reward processing in children, adolescents, and young adults at FHR-BD and HC Table 2.In summary, differences in brain activity during reward processing in young individuals at FHR-BD relative to HC were mainly found in frontal cortical areas Figure 4We identified nine studies in young individuals at FHR-SZ or FHR-BD, dealing with brain responses related to the processing of faces showing emotional expressions or pictures with affective valence. This makes the emotion processing domain the most investigated domain in the present reviewed literature Table 2.In summary, young individuals at FHR-BD and FHR-SZ did not show behavioral differences compared with HC in the studies investigating emotional processing included in the present review. Across emotion processing paradigms, four studies reported altered activation  of amygdala in FHR-BD individuals compared with HC , 56–58, We have reviewed the existing literature to clarify whether children, adolescents, and young adults at familial high risk for either schizophrenia (FHR-SZ) or bipolar disorder (FHR-BD) show altered brain activation during task performance when compared to HC, and to determine shared or distinct patterns of brain activation in FHR-SZ versus FHR-BD. In this review, we have focused on task-based functional brain alterations with a specific emphasis on the susceptibility to SZ and BD, given the significant heritability for SZ or BD. The genetic variations that have shown to confer increased life-time risk to develop SZ or BD show a large overlap –10. ThisThe main results of this review revealed that young individuals at FHR-SZ or FHR-BD did not show behavioral impairments in the experimental setting of a neuroimaging environment. Normal task performance in fMRI studies contrasts with several neuropsychological studies that assessed the behavioral performance only –17. OnlyWithin the WM domain, young individuals with FHR-SZ or FHR-BD show altered brain activation patterns in frontal cortex, including the left VLPFC and left frontopolar regions compared to HC. These findings are in line with the existing neuroimaging literature in adult individuals at FHR-SZ , as wellIn the cognitive control domain, FHR individuals showed widespread altered brain activation in areas supporting cognitive control compared to HC. However, this finding was almost exclusively obtained in FHR-BD, and the number of studies and participants producing these widespread differences in activation patterns was low. Of note, cognitive control unifies several top-down cognitive processes supporting attention, problem-solving abilities and making appropriate decisions . Due to Studies in the reward processing domain only investigated young individuals at FHR-BD and showed differences in brain activity in frontal and medial areas during reward processing relative to HC. Depending on the investigated contrast of interest in the different studies reflecting brain responses to, e.g., anticipation, feedback, winning or losing, these activation differences in individuals at FHR-BD consisted of hypo- and hyper-activation relative to HC. Reward is a central component for facilitating motivation-based learning and the learning of appropriate responses to stimuli, as well as the formation of habits . The fouEmotion processing was the most investigated cognitive domain in children, adolescents, and young adults at FHR-BD. Altered amygdala function in individuals at FHR-BD was reported across different emotion processing studies , 56–58. A between-group comparison, including confirmed SZ or BD, FHR, and HC groups, may expand the current knowledge in three directions. First of all, brain-based measures found in FHR individuals and in individuals with confirmed SZ or BD, but not in HC, may reflect neurobiological markers of risk for severe mental illness, and thus may be classified as potential risk endophenotypes . Second,An alternative way to investigate this hypothesis is by examining whether behavioral and neurobiological impairments are already present in individuals with an At-Risk-Mental-State (ARMS), also known as individuals at UHR for psychosis. This UHR category was introduced with the goal of developing preventive strategies of psychotic disorders and requires individuals to present with either (a) positive symptoms that are typical of psychotic disorders but of subthreshold severity or duration or (b) genetic high risk accompanied by functional decline . IndividBehavioral differences between FHR individuals and HC were limited to the domains of WM and cognitive control. FHR-SZ showed lower response latency during an n-back task , and pooSample sizes in the included studies were in general small to moderate of size with the smallest being 10 and the largest being 56. The mean sample size of all included studies was 26 for HC and 20 for FHR. Large effect sizes (Cohen’s d > 0.8) have been found in individuals with manifest BD when tested on different cognitive domains, but in first-degree relatives effect sizes are generally small to medium (Cohen’s d < 0.5) yet still significantly different from HC . In addiTo fully establish a link between task-related activity and risk or resilience factors of psychiatric disorders, longitudinal fMRI data needs to be acquired. Indeed, longitudinal studies following symptom-free at risk or vulnerable individuals through maturation and possible development of severe mental illnesses would allow for identification of relevant pre-clinical and clinical markers of vulnerability, disease onset and/or resilience. The cross-sectional design of the studies included in the present review is thus a major limitation. While cross-sectional studies allow mapping of neurobiological differences between young individuals at FHR and typically developing controls, observed group differences may reflect vulnerability, brain alterations leading to disorder, or compensational mechanisms as well as variability in developmental stage. As an example of variation, grey matter volume in the frontal cortex peaks between age 7 to 11, but total cortical volume can vary up to 50% between typically developing individuals which enhances the relevance of inter-individual variability . LikewisIn line with the longitudinal perspective mentioned above, the age of the included participants, as well as the age-range in the groups are of importance when evaluating vulnerability factors. Investigating young individuals at FHR for severe mental illnesses is of importance given the brain’s susceptibility to maladaptive changes within this period of the lifespan . The stupost hoc analyses in which participants with existing diagnoses/non-medication naïve were excluded. The post hoc analyses ruled out effects of diagnoses on the main results . Even though this is an advantage, given that this age is before the usual onset of SZ or BD, it narrowed the number of included articles substantially. Lastly, the decision to include only publications in English further reduced the number of included articles in this systematic review.Mapping functional brain alterations in offspring of parents with confirmed SZ or BD may provide important insights into the underlying neurobiological processes that convey vulnerability to these disorders. Given the heterogeneity of the measures, methods and findings, supplementary fMRI studies are needed. These studies should preferentially aim at a longitudinal design and include large groups of individuals with a narrow age range to facilitate the interpretation of altered activity patterns during a specific cognitive task. Our literature search yielded no study that directly compared task-related brain activation between young individuals at FHR-SZ and young individuals at FHR-BD and HC. Given the empirical evidence by Lichtenstein et al.  and SchuThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.LJ, AV, BB, HS, and KP contributed to conception and design of the review. LJ and AV organized the database. LJ wrote the first draft of the manuscript. KL and KP wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.HS has received honoraria as speaker from Sanofi Genzyme, Denmark and Novartis, Denmark, as consultant from Sanofi Genzyme, Denmark and as senior editor (NeuroImage) and editor-in-chief  from Elsevier Publishers, Amsterdam, The Netherlands. HS has also received royalties as book editor from Springer Publishers, Stuttgart, Germany and Gyldendahl Publishers, Copenhagen, Denmark.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The least absolute shrinkage and selection operator-penalized Cox proportional hazards regression analysis was used to identify a six-lncRNA signature. According to the median of the signature risk score, patients were divided into a high-risk group and a low-risk group with significant disease-free survival differences in the training cohort. A similar phenomenon was observed in validation cohorts . The six-lncRNA signature remained as independent prognostic factors after adjusting for clinical factors in these two cohorts. Furthermore, this signature significantly predicted the survival of grade III patients and estrogen receptor-positive patients. Furthermore, in another cohort , the low-risk patients that were treated with tamoxifen therapy had longer disease-free survival than those who underwent no therapy. Overall, the six-lncRNA signature can be a potential prognostic tool used to predict disease-free survival of patients and to predict the benefits of tamoxifen treatment in BRCA, which will be helpful in guiding individualized treatments for BRCA patients.Breast cancer (BRCA) is the most common cancer and a major cause of death in women. Long non-coding RNAs (lncRNAs) are emerging as key regulators and have been implicated in carcinogenesis and prognosis. In this study, we aimed to develop a lncRNA signature of BRCA patients to improve risk stratification. In the training cohort (GSE21653,  Breast cancer (BRCA) is the second leading cause of cancer death among women. More than 268,000 new patients are diagnosed with BRCA each year and 41,760 patients will die from BRCA . The curHOTAIR increased cancer invasiveness and metastasis through dependence on PRC2, and lncRNA HOXD-AS1 regulated the Rho GTPase activating protein 11A (ARHGAP11A), which resulted in induced metastasis  are a class of non-coding RNAs transcribed from mammalian genomes . Some lntastasis . In rece -positive BRCA patients. Moreover, the signature predicted survival benefits of tamoxifen therapy in BRCA patients.1. To analyze the correlation of lncRNA expression with disease-free survival (DFS) for BRCA, we selected those data sets that included patients with survival status information. In total, 232 samples from GSE21653 (n = 115)  , which c2 and uniquely mapped to the human genome (hg19). Specific probes of lncRNAs were obtained by matching the chromosomal position of probes to the chromosomal position of lncRNA genes based on the annotations from GENCODE (release 23) according to the previous studies (All the raw microarray data (CEL files) of BRCA patients were downloaded from the GEO database and background adjusted and normalized using the Robust Multichip Average (RMA) algorithm , b and “ studies . When mup-value of <0.01 were considered statistically significant. We then conducted the least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression analysis to select the prognostic markers of the above lncRNAs  to comprise a prognostic signature using a LASSO-penalized Cox proportional hazards regression analysis for the above 17 lncRNAs with optimal tuning parameters. All six lncRNAs have positive coefficients, which indicates that their high expressions are associated with shorter survival. Finally, we calculated the signature risk score based on a linear combination of the expression levels of six prognostic lncRNAs, weighted by the coefficients derived from the univariable Cox regression analysis as follows: Risk Score =  +  +  +  +  + .As summarized in the workflow , we firsn = 232). The patients were divided into a high-risk group (n = 116) and a low-risk group (n = 116) using the median risk score as the cutoff. Compared with the low-risk patients, the high-risk patients had shorter disease-free survival . The signature classified patients into two groups, including a high-risk group (n = 50) and a low-risk group (n = 51), based on the median risk score. The disease-free survival of the high-risk group was significantly shorter than that of the low-risk group , the high-risk group still had a poorer prognosis than the low-risk group , grade III  and grade II  were significantly correlated with DFS of patients (p = 0.007) and ER status  significantly independently predicted patients’ disease-free survival in the validation cohort GSE42568 (p = 0.043). These results indicate that the six-lncRNA signature is an independent prognostic factor for BRCA patients’ disease-free survival.Next, we assessed whether the prognostic value of the six-lncRNA signature was independent of other clinical factors. We performed univariate and multivariate Cox proportional hazards regression analysis for factors, including age, ER status, histological grade, and the signature. In the training cohort, the high-risk six-lncRNA signature  and the low-risk group (n = 61) in the training cohort. The signature showed a similar prognostic value for grade III patients in the validation cohort . In this cohort, there were 62 patients who received tamoxifen therapy and 47 who did not. We classified each patient into high- and low-risk groups based on the lncRNA signature risk score. Among the 58 low-risk patients, tamoxifen treatment could prolong the disease-free survival of these patients . A similar result was found between the signature and ER status (p = 0.997). Moreover, for the histological grade, we observed that the histologic grade combined with the six-lncRNA signature (AUC = 0.73) had a higher area under the ROC curve than the histological grade alone  and the six-lncRNA signature, we performed a time-dependent ROC analysis in the training cohort. We also constructed a prognostic model by combining our signature with histological grade or ER status. There were no significant differences between histological grade and the lncRNA signature  with different disease-free survival. We demonstrated that this signature could predict the survival of grade III BRCA patients. The ER-positive patients who were classified as the low-risk group achieved better survival benefits. Furthermore, by using this signature, we can find a subgroup of patients who are likely to benefit from tamoxifen therapy. In sum, the six-lncRNA signature for BRCA patients may be a prognostic tool that is helpful in guiding individualized treatment of patients.Histological classification of BRCA into grades I, II, and III, determines the treatment of BRCA patients . The tumGiven the heterogeneity of cancer, reliable prognostic biomarkers are needed to identify patients who can benefit from therapy . There iLINC00917, AL391840.1, TRIM52-AS1, AL355075.4, AC093802.2, and AC091544.4) signature in BRCA suggests that lncRNAs can be used as prognostic factors for the survival of patients. To avoid the influence of protein-coding genes, we annotated these probes with protein-coding genes, and found that only one lncRNA overlapped with protein-coding gene RPPH1. This gene had no predictive performance for survival, whether by itself or in combination with other lncRNAs  tissues compared to non-TNBC tissues by a RT-PCR  at accession numbers GSE21653, GSE42568, GSE20711, and GSE19615.All datasets used in this study were publicly available from the GEO database (JH conceived the idea and conceptualized the study. EZ, YL, FQ, and JX conducted the bioinformatics analysis and interpreted the results. EZ and JX collected and pre-processed data. EZ, YL, SA, and LW generated the figures and tables. EZ and YL wrote the manuscript. JH and JX supervised the whole study process and revised the manuscript. All authors have read and approved the final version of manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome-wide association studies (GWAS) are used to detect genetic variants that explain common human diseases in populations. The initial GWAS achieved notoriety by successfully identifying thousands of genes associated with a variety of genetic disorders. However, these identified genes have been most successful in establishing individual associations with Mendelian diseases and explaining only a small portion of the heritability. Complex diseases are likely better explained by multiple interacting genetic and environmental variants. Such non-linear, non-additive gene-gene interaction effects, i.e., epistasis, render traditional one-gene-at-a-time analysis methods ineffective for GWAS. Instead, powerful machine learning algorithms that can detect and characterize high-order interactions among multiple genetic variants are needed.The focus of this Special Topic Issue is to examine the novel design and application of machine learning algorithms in detecting interacting genetic variants for GWAS in six included articles.Liu et al. proposed a deep-learning framework using convolutional neural networks to predict the quantitative traits from single nucleotide polymorphisms (SNPs) and to investigate genotypic contributions to the trait using saliency maps. The authors evaluated the performance of the proposed approach using both simulation and experimental soybean datasets. The results showed that deep learning modeling can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than well-established statistical methods. The authors claim their approach effectively and efficiently identifies significant SNPs and SNP combinations associated with GWAS data.Zhang et al. presented circLGB, a machine learning-based framework to discriminate circRNA from other lncRNAs. This approach combined commonly used sequence-derived features and three new ones; adenosine to inosine (A-to-I) deamination, A-to-I density, and internal ribosome entry site. circLGB categorizes circRNAs by utilizing a LightGBM classifier with feature selection. In addition, the authors apply circMRT, another ensemble machine learning framework to systematically predict the regulatory information for circRNA, including their interactions with microRNA, RNA binding protein, and transcriptional regulation. Feature sets including sequence-based features, graph features, genome context, and regulatory information features were modeled in circMRT. Experiments on publicly available datasets and lab generated ones showed that the proposed algorithms outperform the available state-of-the-art methods.Nicholls et al., the authors discussed the landscape of ML applications in GWAS by following three components: selected models, input features, and output model performance. The authors focused particularly on the prioritization of complex disease-associated loci and explored the contributions ML has made toward reaching the GWAS end-game with consequent wide-ranging translational impact.In a review article by Leem et al. have proposed a permutation method for GWAS, i.e., ENhanced Permutation tests via multiple Pruning (ENPP). ENPP prunes the features in each permutation round if they were determined to be non-significant. Their simulation study showed that the ENPP method could remove about 50% of the features, at the first permutation round, and by the 100th permutation round, 98% of the features were removed. Only 7.4% of the compute time was required, compared to the original unpruned permutation approach. In addition, they applied this approach to a real data set of ~300 K SNPs, to find the association with a non-normal distributed phenotype.Arabnejad et al. designed a machine learning algorithm, i.e., Nearest-neighbor Projected-Distance Regression (NPDR), in order to detect complex multivariate effects for GWAS. NPDR used a regression formalism that allowed statistical significance testing and efficient control for multiple testing. In addition, the regression formalism provided a mechanism for NPDR to adjust for population structure, which was applied to GWAS data of Systemic Lupus Erythematosus (SLE). The authors also tested NPDR on benchmark simulated genetic variant data with epistatic effects, main effects, imbalanced data for case-control design, and continuous outcomes. NPDR identified potential epistatic and other effects that influence the complex SLE disorder.Ni et al., ~300 K stomach tissue-specific eSNPs with gastric cancer (GC) risk in three GWAS datasets were investigated. The authors conducted a gene-based analysis to calculate the cumulative effect of eSNPs through a sequence kernel association combined test and Sherlock integrative analysis. At the SNP-level, they identified two novel variants associated with GC risk. Gene-based analyses identified 2 novel susceptibility genes for GC which were significantly overexpressed in GC tissues than in their adjacent tissues and the high expression level of these two genes was associated with an unfavorable prognosis of GC patients. Co-expression genes with these two novel genes in normal stomach tissues were significantly enriched in several cancer-related pathways.Lastly, in the article by All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Developing countries, undernutrition remains significant public health attention, as it was a combined consequence of poor dietary consumption and recurrent infectious illness especially in countries same Ethiopia. Undernutrition is associated with morbidity and mortality among children. This study, therefore, was conducted to assess the prevalence and associated factors of undernutrition among under-five children from the model and non-model households at Eastern Gojjam administrative Zone, northwest Ethiopia.p-value less than 0.05 of was considered to declare a result as statistically significant.A community-based comparative cross-sectional study was conducted from 1st July 2015 to 30th August 2015 in East Gojjam Zone among 507 households (170 from model-household and 337 from non-model household) selected using a multistage sampling technique. Data were collected using questionnaire and nutritional anthropometric measurement. The Emergency Nutrition Assessment for Standardized Monitoring and Assessment of Relief and Transition was used to convert raw anthropometric data into Z-scores. The collected data were entered into EpiData, and analysis was conducted using Statistical Package for Social Sciences (SPSS) version 22. The Emergency Nutrition Assessment for Standardized Monitoring and Assessment of Relief and Transition was used to convert raw anthropometric data into Z scores. Descriptive statistics were used to report the prevalence of outcome variable, undernutrition . In addition, results were presented using narration, tables, and figures including frequency and percentage. Adjusted Odds Ratio (AOR) with its 95% Confidence Interval (CI) was computed. Univariate and multivariate logistic regression analyses were done. A This study found that the prevalence of undernutrition explained by stunting (height-for-age Z-score (HAZ) < − 2), underweight (weight-for-age Z-score (WAZ) < − 2) and wasting (weight-for-height Z-score (WHZ) < − 2) were 44.7% c, 15.3%  and 10% , and 52.5% , 24.3%  and 11.3%  in under-five children among model household and non-model respectively. Protected water  and less than three times daily intake of food  were predictors for undernutrition among under-five model household. Protected source of drinking water , households that ever-had education on complementary feeding  and starting complementary feeding on 6 month after birth  were significant predictors for under-five undernutrition among non-model households.The prevalence of undernutrition explained by stunting, underweight and wasting among under-five children in both model and non-model households were high. The prevalence of all the three parameters  was higher among the non-model households compared to the models, even if the differences were not statistically significant. Use of an unprotected source of drinking water and less than three times daily intake of food were found to be associated with undernutrition among under-five children in the model households. On the other hand, having educational exposure on complementary feeding, using drinking water from protected sources and initiation of complementary feedings at age of 6 months were found to be associated with undernutrition among children in the non-model households. Therefore, the concerned bodies must access safe and adequate water supply, works on information dissemination using mass media on timely initiation of complementary feeding, save water and on meal frequency should be strengthened.The online version of this article (10.1186/s40795-019-0290-y) contains supplementary material, which is available to authorized users. Childhood undernutrition is widespread in low and middle-income countries. In these countries, it is an important and indirect cause of child mortality. Worldwide, stunting and wasting along with intrauterine growth restriction are responsible for nearly 2.1 million deaths in under five children that comprise 21% of all deaths . The basPrevious studies have shown different factors associated with children undernutrition. These include: socio-demographic  , 11–15, Child undernutrition and mortality rates are sensible responses to both health programs and socioeconomic conditions such as income, unemployment, and illiteracy . In EthiA community-based comparative cross-sectional study was conducted in East Gojjam Zone, northwest Ethiopia from 1st July to 30th August 2015. East Gojjam Zone is a suburban area in Amhara region, and found 300 km away from Addis Ababa, the capital city of Ethiopia. All women who have under-five children were included in this study.The required sample size for the study was computed by Epinfo using two population proportion formula with the following assumptions: taking 80% power, 95% Confidence Interval (CI), and one to two ratio of model to non-model households. The proportion of undernutrition (46.9%) among model households was taken from a study conducted in rural parts of Tigray , prevaleFirst, a two-stage stratified sampling  used to select study participants. Then, from the existing 21 kebeles in the study settings, 6 of them were selected using lottery method. List of under-five children among from model and non-model household were extracted from community-based intervention for action (CBIA) data list in the selected kebeles. Second the calculated sample size was prevalence ally allocated to the model (n1 = 170) and non-model (n2 = 337) households. Third, the study participants were selected from the CBIA lists in each Kebele using simple random sampling method.Diarrheal disease: is the passage of unusually loose or watery stools, usually at least three times in a 24 h period and the mother or caretaker said diarrhea.Model household: A household graduated by the Ethiopian health extension program after fulfilling health extension packages and those who have graduation certificate.Wasting: Weight-for-height measurement three Standard Deviation (SD) or more below the mean by taking world Health Organization (WHO) standard reference values which is called “wasted”.Stunting: Children with height-for-age-z-score < − 2 SD as compared with the reference population were categorized as stunted.Underweight: Children with weight-for-age-z-score < − 2 SD as compared with the reference population were categorized as underweight.Improved water supply: It is a type of water source that, by nature of its construction or through active intervention, is likely to be protected from outside contamination, in particular from contamination with fecal matter ) among under five children in model households as compared with 64.4%  in non-model households , underweight (15.3%) and wasting (10%) among model households. For children of non-model households, the prevalence of stunting was 52.5%, underweight 24.3%) and wasting (11.3%). In addition, significant variation was observed in the percentage of undernutrition among children between model and non-model households. The prevalence of undernutrition was higher among children in the non-model compared to children in the model families. This might be due to the difference in economic and agricultural imputes. This finding is supported by previous studies conducted in Bule Hora, Ethiopia , Hidabu .3% and wThis study also showed a higher percentage of undernutrition compared with previous studies conducted in Gumbirt, northwest Ethiopia , in EgypThis study also focused on identifying factors that have the association with undernutrition status of under-five children both among model and non-model households. In the binary variable of model households, the number of children, protected source of drinking water, daily meal frequency, complimentary food start, and fever were associated with stunting. Whereas in non-model households’ sex, protected source of drinking water, complimentary food, education on complementary food, number of children and age of caregiver were found to be associated with stunting. Moreover, the binary variable of model households, food distribution; daily meal frequency, the source of drinking water and family income were associated with underweight. Whereas, in non-model households, the power to use the money, ANC service utilization, protected source of drinking water, food distribution, child waste disposal, and solid waste disposal were associated with underweight in non-model households. In addition, the binary variable of model households, ANC service, source of drinking water, complementary feeding at 6 months, priority to feed their children and complimentary food education were significantly associated with wasting in the model household. Whereas, in non-model households, complimentary food start, breastfeeding education, ANC service, solid waste disposal, and household incomes were significantly associated with wasting.Non-protected source of water identified as a predictor for the occurrence of under-five children under-nutrition/stunting/ among both model and non-model households. This might be that water from a contaminated source serves as a source for different parasitic and gastrointestinal infection. These infections expose children for undernutrition (stunting). According to the WHO, at least 50% of the combined undernutrition in children is related to unsafe water, inadequate sanitation or insufficient hygiene . This caMoreover, in this study daily meal frequency was also identified as a significant factor for the occurrence of undernutrition. Children who took meal frequency less than three times per day were more likely to be undernourished as compared to the counterparts. This can supported by study finding reported in Egypt .Similarly, a lack of education on complementary feeding practice contributes 61% for the occurrence of under-five children under-nutrition/stunting/ among non-model households compared with their counterparts. This might be due to a mother who knows the recommended complementary feeding practice might have a better commitment to start complementary feeding on the recommended time and continue .Children who start complementary feeding at 6 months were less likely to be wasted as compared with the counterpart. This finding is comparable with study finding reported in Bule Hora district south Ethiopia . This siFurthermore, the source of drinking water was also identified as significant predictors for an increased odd of undernutrition (stunted and underweight) among under-five children who were from both model and non-model household. Under-five children from model and non-model households, who use non-protected water, were 92 and 93% times more likely to be undernutrition as compared with their counterparts. This finding is comparable with previous study report done by Machakel district.The timing of complementary feeding was also recognized as predictors for the high prevalence of undernutrition among model households. Children who came from model households and those who did not start complementary feeding on 6 months had a 73%  higher odds of undernutrition/wasting/ as compared to their counterparts. Children who came from model households and those who served prior than elders had 80%  lower odds of undernutrition as compared to their counterparts.Antenatal care service and solid waste disposal system were also identified as factors for the increased odds of undernutrition among children in non-model households. Non-model households, who had no ANC follow up had 80%  higher odds of undernutrition compared to their counterparts. Similarly, children from non-model households and those who had no solid waste disposal were 58%  times more likely to be undernutrition compared with the counterpart. This fact supported well by research findings reported by Nigeria . This miThis study recognized the following limitations: There may be a recall bias during data collection time that might be increased or decrease the prevalence of undernutrition. Moreover, this study did not address some factors as per UNICEF framework. As a result of disaggregate analyses about associated factors the wider confidence intervals were experienced.This study showed a higher prevalence s of undernutrition that is explained by stunting, wasting and underweight among under-five children who were from non-model households compared to children from model households. Moreover, the prevalence of undernutrition was higher compared with previous national pieces of evidence and studies finding reported in the country. Among model household, this high prevalence of undernutrition was associated with a drinking water source (not- protected) and daily food intake frequency (less than three times). Similarly, among non-model household, this high prevalence of undernutrition was associated with a drinking water source (not- protected) and education (those who had no education on complementary food and starting complementary food on 6 months). Therefore as a recommendation, the concerned bodies must access safe and adequate water supply, works on information dissemination using mass media about complementary feeding, save water and on meal frequency. The community should be encouraged to use drinking water from protected sources. Awareness creation on complementary feeding practice and timely initiation at 6 months should be strengthening in the community.Additional file 1:Bivariate and Multivariate analysis of factors associated with stunting for model and non-model households under-five children in Gozamen district, Ethiopia, 2015. (DOCX 18 kb)Additional file 2:Bivariate and Multivariate analysis of factors associated with underweight for model and non-model households under-five children in Gozamen district, Ethiopia, 2015. (DOCX 18 kb)Additional file 3:Bivariate and Multivariate analysis of factors associated with wasting for model and non-model households under-five children in Gozamen district, Ethiopia, 2015. 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Natural products hold a prominent position in the discovery and development of many drugs used nowadays, with diverse indications for human and animal health. Especially, plants have played a leading role as source of specialized metabolites with medical effects, while other organisms such as marine and terrestrial animals and microorganisms produce very important drug candidate molecules. Specialized metabolites from all these natural sources can be used directly as bioactive compounds, or as drug precursors. Due to their wide chemical diversity they can act as drug prototypes and/or be used as pharmacological tools for different targets.Molecules updated and new perspectives about natural products in drug discovery.Many scientists have contributed to this Special Issue-SI which includes 21 papers, among them original articles as well as survey articles, that give the readers of Tripterygium wilfordii, on behavioural dysfunctions observed in adult mice exposed to subanesthetic doses of the N-methyl-D-aspartate receptor antagonist ketamine at PNDs. This study suggested that the NOX inhibition by the early administration of celastrol can prevent ketamine-induced psychotic-like behavioural dysfunctions, as well as the increase of cerebellar oxidative stress and the reduction of anti-inflammatory cytokines. Results open up new pharmacological insights into the possible use of this phytochemical for neuroprotection during brain development.Trabace et al.  reportedArtemisia annua, is still mainly based on the use of cultivated plants. Various alternative strategies have been explored to improve its production in the plant. In their paper, Simonsen et al. [Physcomitrella patens, showing novel insights into the potential of this model organism for artemisinin production. The moss was shown to express endogenous enzymes with similar activity to that for artemisinin biosynthesis in A. annua, suggesting the possibility of engineering artemisinin biosynthesis and that of other related high-value terpenoids in P. patens.The current production of artemisinin, the antimalarial drug from n et al.  describeEruca sativa along with glucoerucin, its main glucosinolate, on streptozotocin induced diabetic neuropathic pain in mice. Both myrosinase bio-activated E. sativa meal and glucoerucin showed a dose-dependent pain relief effect in diabetic mice, with the meal being more active. Co-administration of the meal and glucoerucin with H2S scavengers abolished the induced pain relief. The authors also showed that repeated treatments did not induce tolerance to the anti-hypersensitive effect. The paper nicely indicates a potential of E. sativa seed meal to treat patients with diabetic neuropathy.Recent data highlight that glucosinolates, the sulfur compounds produced in the Brassicaceae plant family, have pain-relieving efficacy. Mannelli and coworkers  describeBacopa species  and screen for anti-lipid peroxidation activity. This approach allowed the identification of six inhibitors of lipid peroxidation from the three Bacopa species. Three of them were novel molecules. Data obtained by this method permitted to discover the potential bioactivity for each compound directly from the crude plant extracts prior to any physical separation process.Wolfender et al.  propose Phyllantus includes some Cuban endemic species traditionally used for the treatment of different diseases. Wessjohann et al. [Phyllantus, known to have in vitro antiviral activity and protective effect against UV-light induced DNA damage and genotoxicity. The chemical structure of a novel C-glycosylated flavonol, named fideloside, with a promising anti-inflammatory capacity in human explanted monocytes is described.The genus n et al.  report oCleistocalyx operculatus, was shown to be cytotoxic against PANC-1 and MIA PACA2 cells in a dose-dependent manner. In addition, treatment with DMC led to apoptosis of PANC-1 cells inducing proteolytic activation of caspase-3. A possible use of this natural product as chemotherapeutic agent to fight human pancreatic cancer is suggested.Tran et al.  describeThe paper by Avato and Argentieri  describeAstragalus have been extensively investigated, phytochemical and pharmacological information on A. boeticus is very limited. Scognamiglio et al. [O-acetyl-3-O-β-d-xylopyranosylcycloastragenol, with acylation at C-3 and C-6 and the C-25 free hydroxyl function, had the highest activity, thus confirming some structural requirements for the cytotoxicity of cycloartane derivatives.While several species of o et al.  report oTrypanosoma brucei and Leishmania mexicana. Besides considering the antibacterial effect of single hop components, the authors also describe the positive effect obtained by different combinations of xanthoumol with desmethylxanthohumol or with lupulone. They also investigated post-antibiotic effects and found that xanthoumol and desmethylxanthohumol cause a significant delay of bacterial re-growth. Among hops active principles, lupulone was shown as the most active against T. brucei and L. mexicana, while humulone was the less active.The study by Rivière et al.  reports Lee indica, an evergreen perennial shrub/small tree distributed in Southeast Asia, traditionally used as medicinal plant with various indications. A total of 31 compounds belonging to different chemical groups  have been identified. Three of them are novel dihydrochalcones: 4′,6′-dihydroxy-4-methoxydihydrochalcone 2′-O-rutinoside, 4′,6′-dihydroxy-4-methoxydihydro chalcone 2′-O-glucosylpentoside and 4′,6′-dihydroxy-4-methoxydihydrochalcone 2′-O--β-D-glucopyranoside.The paper by Koh et al.  represenTrifolium species originating from contrasting growing environments. Variations in the distribution of total phenolics were found between lowland and mountain germplasm rising some considerations on the different adaptive strategies. Accordingly, differences in the scavenging capacity of clove extracts from lowland and mountain germplasm and/or plant part were observed. Based on these results, the authors also discuss the possible link between environmental factors, chemical composition, and content of phenolics in Trifolium.Tava et al.  illustraMarine organisms are an important resource of peptides which due to their unique structure may have several physiological functions. Pang et al.  designedSennae folium on L6 rat skeletal muscle cells are described by Yang et al. [2+ levels. It is thus suggested that Sennae folium might have a role for the treatment of insulin resistance.The hypoglycemic effect and the mechanism of action of an ethanolic extract of g et al. . The druDasycladus vermicularis, widely distributed throughout tropical to temperate regions. The phytochemical analysis led to the isolation of two sulfated phenolic acids and four sulfated coumarins including two novel compounds, 5,8′-di-(6(6′),7(7′)-tetrahydroxy-3-sulfoxy-3′-sulfoxycoumarin), named dasycladin A and 7-hydroxycoumarin-3,6-disulfate, named dasycladin B. In addition, for the first time, a validated HPLC method for the separation and quantification of sulfated coumarins is presented.Hartmann et al.  describeBoesenbergia rotunda. Its antinociceptive activity was examined using chemical and thermal mice models of nociception. The authors show that cardamonin is able to produce significant analgesia in formalin-, capsaicin- and glutamate-induced paw licking tests. In addition, they demonstrated that the phytochemical induces a significant increase in the response latency time of animals subjected to hot-plate thermal stimuli. In conclusion, this study shows that cardamonin exerts significant peripheral and central antinociception in mice through the involvement of TRPV1, glutamate and opioid receptors.The work by Sulaiman et al.  aims to Imperata cylindrica and the growth inhibitory effects of each identified constituent on different cancer cell lines. They achieved the isolation of 2-methoxysterone, 11,16-dihydroxypregn-4-ene-3,20-dione, and tricin, which were found to inhibit the growth of some breast and colon cancer cell lines.Ho et al.  investigDryopteris fragrans, a valuable medicinal plant with anti-cancer activity. Isolation of six known compounds plus two new bioactive phenolics, namely dryofragone and dryofracoumarin B, was achieved by a cytotoxicity-guided tracking. The immunomodulatory capacity of these compounds has also been described and results showed that some of them may activate the LPS signaling pathway thus affecting the growth of tumor cells through immuno-regulation.The study by Chang et al.  aimed toLai et al.  review tMarine organisms are a potential sustainable source of peptides that act as ACE inhibitors and are considered as therapeutic agent to combat hypertension. The review by Pujiastuti et al.  summarizThe role of carotenoids to counteract oxidative stress and promote healthy aging is discussed by Tan and Norhalzan . As manyOrobanche. Both genuine metabolites produced by Orobanche species as well as natural products sequestered from their host plants are reviewed. In addition, an overview of the biological activity of extracts and pure compounds from different species of Orobanche is also given. Information was retrieved from SciFinder and ISI Web of Knowledge databases, taking into account reports until the end of 2017.The review by Scharenberg and Zidorn  illustraOverall, this special issue contributes to highlighting new biological activities for known plants and natural compounds. In addition, some of the studies have disclosed novel plant molecules with promising pharmacological applications.


Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems. Clustering is a fundamental unsupervised learning task commonly applied in exploratory data mining, image analysis, information retrieval, data compression, pattern recognition, text clustering and bioinformatics . The priClustering based on biological entities such as genes, diseases, proteins, pathways and small molecules depends on the amount, quality and type of input data  or samplHowever, since cluster analysis itself is not one specific algorithm, various techniques can be applied and differ in terms of understanding of what constitutes a cluster and how to efficiently find them. In practice, different problems require different similarity measures and separation techniques . FurtherIn contrast, CC algorithms  and speale data  are emplale data . The reaale data . Hence, On the other hand, only the fundamentals of deep learning (DL) are currently actively used  in bioinPrevious reviews , 23 on DAs summarized in Table However, pipeline methods are extensively discussed in the literature. Examples include deep embedding clustering (DEC) , deep clhttps://github.com/rezacsedu/Deep-learning-for-clustering-in-bioinformatics.More recent approaches include convolutional embedded networks (CENs) , deep coLet us consider the problem of clustering of Phase 1: parameter initialization and RL with a DNN architecture and training using non-clustering loss (e.g. standard RL1). Then clustering-friendly representations of the data called latent features (LFs) are extracted from one or more layers (depending on the type of network architecture).Phase 2: parameter optimization by iterating between computing an auxiliary target distribution and minimizing clustering loss  to modeIn bioimaging, different types of preprocessing required depending on modality types in order to mitigate bottlenecks like noise and artifact, e.g. rescaling, horizontal flipping and enhancement using histogram equalization and slight Perona–Malik filtering for radiographs, while MRIs require contrast enhancement, intensity regulation and noise elimination . Moreoveocument} .In the context of clustering, after the training, the decoder part of an AE is no longer used but only the encoder is left, which acts as the feature extractor. LF then can be extracted from one or more layers (depending on the type of network architecture). For example, if extracted from a single layer, features come typically from the last layer of the network. However, if extracted from a multilayer or deep network (from any hidden layer or the deepest layer), it is found that LF can lead to better feature representations that can enhance the separation of data points during the similarity computation . Table 3In some early approaches (e.g. NMMC and UMMC) DBN is employed as the feature extractor in which Restricted Boltzmann Machines (RBM)  formed tCNN and CAE: since a vanilla AE is not suitable for handling data with spatial invariance (e.g. HQ images), they are incapable of preserving spatial relationships between pixels in an object. However, CNN can be a better feature extractor as it can preserve local structure in which output from the deepest convolutional (conv) layer can be extracted as LF (e.g. JULE). On the other hand, instead of manually engineered conv filters, conv and pooling layers can be added to construct a CAE, where each layer consists of an encoder (that performs convolution and pooling operations), and a decoder (to perform unpooling and deconvolution operations), and a conv layer of the encoder calculates the  follows : (2)\docb-region . In the  follows : (3)\doce the CQ . BesidesVAE: generative variants of AE are also used in literature (e.g. VaDE [.g. VaDE ) in combTo generate a sample similar to a real data point  samples : (4)\docThe data generation process involving the encoding vector can be expressed in  53]. Ev. Ev53]. ased VAE ) are useased VAE , which iLSTM-AE: VAE or CAE are not the best options for handling sequence or time-series data, e.g. length of the input sequence in case of text clustering may vary while the network requires fixed-length inputs. Further, the temporal ordering of the observations makes the feature extraction difficult. Hence, regular AEs will fail to generate a sample sequence for a given input distribution in generative mode, whereas LSTM-AE can handle variable lengths as the encoder learns fixed-length vector representation of the input [he input , 66.Given TM units . The encTM units : (7)\dococument}  as shownentclass1pt{minimaAAE: in more recent approaches, adversarial AE is employed in which the adversarial training procedure is followed to match the aggregated posterior of the latent representation with the prior distribution. Thus, AAE can be used to generate artificial samples for bioinformatics use cases with a limited number of labeled or unlabeled (i.e. numeric or genomic) data, where the flexible nature of GAN can be utilized to extract discrete and continuous LF from large-scale data [ale data .In particular, information maximizing generative adversarial network (a.k.a. InfoGAN)  is used DAE: since a good representation is one that can be obtained robustly from a corrupted input, and that will be useful for recovering the corresponding clean input [an input , to impran input . As a reStacked AE: The input can be denoised and passed through by stacking autoencoders (SAE), e.g. where the input corruption is used only for the initial denoising. Once the mapping function  follows : (9)\docperation , \documerameters . Then grOnce the GLW training is finished, all the encoder and decoder layers are concatenated in reverse layer-wise training order, by forming a deep AE and fine-tuned to minimize the RL1. During the GLW pretraining, each layer is pretrained for a relatively higher number of iterations with a dropout. The result is a multilayer deep AE with a bottleneck-coding layer in the middle. Based on a similar principle, other types of AE can be stacked to form such a deep AE architecture.Training DL-based clustering algorithms may vary depending on the DNN architecture, different loss functions and training methods. However, since covering each of them in complete detail would be cumbersome in this comparative analysis, we discuss the detail of network updates and training for the pipeline methods (e.g. DEC) only that includes most of the possible steps explained in other DL-based approaches. In DL-based clustering, following two types of losses are optimized  is specific to the clustering method and the clustering-friendliness of the learned representations [ntations .In phase 1, RL1 of the AE is minimized. Once RL1 of the decoder module is optimized, the decoder module is no longer used, but only the LF are extracted from encoder with a bottleneck-coding layer in the middle. Then from an initial estimate of the non-linear mapping terature :Step 1: soft assignment of Step 2: updating the mapping t-distribution [These steps are repeated until a convergence criterion is met. Initializing clustering on LF generates the second type of loss called CAHL, which is specific to the clustering method and clustering-friendliness of the learned representations . Similarribution  is used ocument} . (13)\doterature , we set  follows : (14)\do follows : (15)\do results . To tackentclass1pt{minima results , 50 perfual loss : (16)\doemployed :Pre-training and fine-tuning phase:  Afterwards:  The combined loss  follows : (17)\doignments .In the literature , 30, thron (NMI) . In Elbo setting , which tion loss  as in  is calculated based on the permutation model (PM) as follows: e the CQ , has a ve the CQ . For exae the CQ . Therefoe the CQ , even thFurther, to evaluate the CQ, unsupervised clustering accuracy (ACC)  metric ilgorithm . Additiolgorithm . While tTo show the effectiveness of DL-based clustering approaches, we focus on clustering genomics data, biomedical text mining and bio-imaging using different methods. Since DEC is based on MLP, for each use case, CAE, VAE and LSTM-AE were trained with five different base clustering algorithms for the soft clustering assignment. For the brevity, however, we demonstrated CAE-, VAE- and LSTM-AE-based RL in detail for bioimaging, GE and text clustering, respectively. Finally, we evaluated the performance of all these approaches both quantitatively and qualitatively and provided a comparative analysis.https://github.com/rezacsedu/Deep-learning-for-clustering-in-bioinformatics.All programs were written in Python, and experiments were carried out on a machine having 32 cores, 256GB of RAM and Debian 9.9 OS, where the software stack consisted of Keras and scikit-learn with the TensorFlow backend. Network training is carried out on an NVIDIA Titan XP GPU with CUDA and cuDNN for faster network training. Interactive Python Notebooks and further technical details can be found at Results based on best hyperparameters produced through random search are reported empirically, where we verified whether the network converges to the optimal number of clusters by setting Breast cancer is one of the main causes of death worldwide. However, early diagnosis significantly increases treatment success for which histology images are essential . The diaThe schematic representation of the CA E-based clustering is depicted in Figure - Input layer: each - Convolutional layer: of size - BN layer: of size - Max-pooling layer: of size 2 - Convolutional layer: of size - BN layer: of size - Max-pooling layer: of size 2 - Convolutional layer: of size - BN layer: of size - Max-pooling layer: of size 2 - Convolutional layer: of size - BN layer: of size - Max-pooling layer: of size 2 - Upsampling layer: of size 2 - Deconvolutional layer: of size - BN layer: of size - Upsampling layer: of size 2 - Deconvolutional layer: of size - BN layer: of size - Upsampling layer: of size 2 - Deconvolutional layer: of size - BN layer: of size - Upsampling layer: of size 2 - Deconvolutional layer: of size Then clustering steps are similar to CAE as discussed before in which we initiate the clustering by setting Clustering results for this experiment are reported in Table Inspired by Rhee et al. , we furthttps://www.drugs.com/ on specific drugs, conditions and a 10-star rating reflecting overall user satisfaction. Reviews contain information about the effectiveness of the drugs and possible side-effects. We apply a DL-based unsupervised clustering to discover similarities among these reviews, and evaluate whether the clusters generated by the network correspond to the overall patient’s satisfaction with applied medications.With the exponential increase of online resources, e.g. scientific articles published, sentiments about drugs, diseases and treatment in the biomedical domain, there is a need to build automated systems to extract hidden knowledge from the unstructured texts . For exahttps://code.google.com/archive/p/word2vec/). Then to exploit the semantic similarities among the tokens, we fine-tuned it using gensim (https://radimrehurek.com/gensim/models/word2vec.html) based on the skip-gram method in which each token from the preprocessed input is embedded into a 300-dimensional real-valued vector. The LSTM-AE based on the literature [Inspired by the literature , we deriterature  is depic- Input shape: of size - Embedding layer: of size - LSTM layer: 128 hidden units- BN layer: 128 hidden units- Dropout layer: dropout rate of 20% as weight constraint- Repeat vector: - LSTM layer: 300 hidden units- BN layer: 300 hidden units- Dropout layer: dropout rate of 20% as weight constraint.During the RL phase, encoder learns fixed-length vector representation of the input texts, while the decoder uses this representation to reconstruct the original vector using the current hidden state and the value predicted at the previous time-step. The probabilistic encoder as an approximation function maps the input into a distribution. Then, the generative probabilistic decoder tries to generate the original sample by means of conditional probability. Next, clustering steps are similar to CAE we discussed before.The best clustering results are reported in Table Similar to bioimaging use case, we qualitatively assess whether the learned representations can express characteristics of different types of reviews, raw text vectors, encoder’s output (i.e. LSTM-AE latent vectors), clusters generated by the OPTICS clustering algorithm and clustering with the OPTICS clustering algorithm on LSTM-AE based LF vectors are plotted in Figure Further, since OPTICS is inherently better for sequences, it leads to a slightly better separability of the review texts. In particular, ‘negative’, ‘positive’ and ‘neutral’ reviews are well-separated, even though ‘negative’ and ‘neutral’ reviews are slightly mixed in the t-SNE plots. The reason could be the colloquial nature of review texts in which reviews about drugs were provided a neutral rating but still contain negative words. Consequently, embedding vectors also got ‘contaminated’ with both negative and neutral sentiments.The Pan-Cancer Analysis Project [A previous study  focused  Project , in whicThe random extraction contains only RNA-Seq data from 801 patients, each having 20 531 attributes covering breast carcinoma (BRCA), renal kidney carcinoma (KIRC), colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD) and prostate adenocarcinoma (PRAD). Samples are stored row-wise, and attributes of each sample are RNA-Seq GE levels measured by the Illumina HiSeq platform. A dummy name is given to each attribute, but all the attributes are ordered consistently with the original submission. Since GE data are very high dimensional and a significant number of genes have a small or no effect on the tumor, making them very weak features , we hypo- Input layer: of size - Dense layer: of size - BN layer: of size - Dropout layer: dropout rate of 20% as weight constraint.- Dense layer: of size - BN layer: of size - Dropout layer: dropout rate of 20% as weight constraint.- Dense layer: of size - Lambda layer: of size - Dense layer: of size - Dropout layer: dropout rate of 20% as weight constraint.- Dense layer: of size During the RL phase, the probabilistic encoder acts as an approximation function to map the input into a distribution. Then, the generative probabilistic decoder tries to generate the original sample by means of conditional probability. Then the clustering steps are similar to CAE we discussed before, whereas clustering results are reported in Table From Figure The clustering results summarized in Table For text clustering, we experience an ARI of 0.83, an NMI of 0.81 and an ACC of 0.82 in which each cluster contains members of a single class only in 70% of the cases and in 73% of the cases all members of a given class are assigned to the same cluster. Overall, AE performs the worst, even though K-means performs the best among the base clustering algorithms. In contrast, for clustering GE, approach based on CAE with the same base clustering algorithm performed moderately well giving an ARI of 0.86, an NMI of 0.85 and an ACC of 0.87 in which each cluster contains only members of a single class in 81% of the cases and in 83% of the cases all members of a given class are assigned to the same cluster. Here the MLP-based AE performs worst, even though the DBSCAN performs best among all the base clustering algorithms.In summary, for bioimaging, CAE + AC turns out to be the best option, while CAE + OPTICS also performs well. In contrast, AC and OPTICS performed the best on LF generated by LSTM-AE for both GE and text clustering. To summarize, results covering three different use cases show that clustering results depend on the data type, quality of RL and base clustering algorithms. It turns out that different problems require different techniques, i.e. there is no ‘one size fit all’.In this paper, we provide a comprehensive review of DL-based clustering approaches for bioinformatics research. Clustering results in three different use cases covering different types of data show that approaches based on DL outperform ML-based clustering algorithms. However, overall evaluations are hindered due to the limited amount of labeled data used for the bioimaging and clustering GE. However, neural networks typically require many samples to converge well toward generalization. This study suggests several future directions and an outlook for an improved clustering analysis for bioinformatics use cases:g. U-Net  can be a of DSAE  on LQ bientation , for exaensemble .ike BERT   alleviating more samples by combining genomics data from different sources and training a multimodal architecture, (ii) comparing studies on clustering based on feature extracted by DNN vs. PCA and (iii) improving the explanations about the predictions using both ante-hoc and post-hoc approaches. In particular, we plan to employ multimodality , since mWe declare that there is no conflict of interest regarding the publication of this article.MC probably best mapping abbreviation to expanded form. We can also use some abbreviation packages here.


Virtual Reality exposure therapy (VRET) is an evidence-based treatment of phobias and recent research suggests that this applies also to self-contained, automated interventions requiring no therapist guidance. With the advent and growing adoption of consumer VR technology, automated VR intervention have the potential to close the considerable treatment gap for specific phobias through dissemination as consumer applications, self-help at clinics, or as blended treatment. There is however a lack of translational effectiveness studies on VRET treatment effects under real-world conditions.We conducted a single-arm (n = 25), single-subject study of automated, gamified VRET for fear of spiders, under simulated real-world conditions. After setup and reading instructions, participants completed the automated, single-session treatment by themselves. Self-rated fear of spiders and quality of life served as outcome measures, measured twice before, and one and two weeks after treatment, and at a six-month follow-up. Session characteristics and user experience measures were collected at the end of the session.Mixed-effects modeling revealed a significant and large (d = 1.26) effect of treatment-onset on phobia symptoms (p < .001), and a small (d = 0.49) effect on quality of life (p = .025). Results were maintained at a six-month follow-up (p > .053). The intervention was tolerable and practical. There were no significant correlations between any user experience measure and decrease in phobia symptoms (p > .209).An automated VRET intervention for fear of spiders showed equivalent effects on phobia symptoms under effectiveness conditions as previously reported under efficacy conditions. These results suggest that automated VRET applications are promising self-help treatments also when provided under real-world conditions.https://doi.org/10.17605/OSF.IO/78GUB.Open Science Foundation,  VRET is structured in a manner similar as traditional exposure therapy : via grag occurs . Since tg occurs , and eveg occurs . Importag occurs , there ag occurs  and effig occurs . Clinicig occurs –10, and g occurs .Despite these promising results, as of yet, there have been few attempts to distribute VRET at a larger scale, or implement VRET in regular clinical settings . Arguablin-vivo exposure therapy using a randomized non-inferiority design, with behavioral avoidance of a real spider serving as the primary outcome measure. Results revealed that the VRET group showed large improvements on behavioral avoidance immediately after treatment (d = 1.49), albeit not as large as the in-vivo group (d = 2.39). However, the VRET group continued to improve during the follow-up period, and behavioral approach scores were non-inferior at both three- and twelve-month follow-ups   .This randomized non-inferiority trial featured some design aspects that strengthened the validity of the treatment comparison, yet may at the same time have limited the generalizability of the VRET within-group effect to naturalistic conditions. First, only participants meeting DSM-5 diagnostic criteria for specific phobia were included, as assessed using a structured clinical interview during screening. While specific phobias are some of the most prevalent mental disorders, with a lifetime prevalence estimate of 7.4% , sub-synin-vivo BAT prior to treatment may constitute a minor in-vivo exposure task in-itself, which would confound the estimate of the effect of VRET. Since the previous trial did not include a control group with the same BAT procedure but without treatment, or even additional administration of self-report measures after the BAT but before the treatment session, the degree of confounding is unknown. Clinical experience from running the trial did however suggest that some participants experienced the BAT as therapeutic, making it an important translational research question to examine treatment effectiveness without this possible confounder , having already shown efficacy using a gold-standard BAT in the previous trial.Second, the primary outcome measure in the trial was a behavioral approach test (BAT) , which iin-vivo exposure when presented with both , the VRET intervention was delivered with a “technician”  in the same room at all times, who was instructed to provide emotional support if deemed necessary. This occurred in an estimated 20% of the sessions . The autith both , and metith both  . All included participants provided informed written consent at the start of the treatment session. Participants were not reimbursed, yet of n = 25 included participants, n = 12 participants were psychology students who received course credit for their participation.This study was approved by the Regional Ethical Review Board in Stockholm (2018/1640-32) and pre-registered at the Open Science Foundation  and what to do if overwhelmed or experiencing cybersickness. Participants were also provided with a telephone number to reach the session leader in case of problems or early treatment completion. Before leaving the room, the session leader assisted the participant in putting on the headset and making adjustments. When 20 min of the session remained (or earlier in case of early treatment completion), the session leader returned and provided the participant with a link to an online questionnaire covering their experience and any issues that arose (see below). The therapist completed a similar questionnaire on how the session transpired (see below). Unlike in the previous trial , VRET paAlthough the large effect size found in the previous trial on the same outcome measure (d = 1.33) meant that a sample size of n = 7 would be enough to detect (with 80% power and two-tailed p =.05) the same within-group effect with a paired t-test, we hypothesized a lower effect size in the current study for the reasons stated above. While n = 15 would be required to detect d = 0.80 with 80% power, we chose n = 25 to protect against sampling risk and to make correlational analyses possible (r > 0.5 with 80% power). This final sample size would give 99.99% power to detect d = 1.33 and 97% power to detect d = 0.80. Power calculations were performed using the GPower 3.1 software and based on a simplified yet near-equivalent analytic technique to the mixed effects modeling used in actual analysis .Itsy at the Samsung and Oculus digital marketplaces, was used in treatment. Itsy was designed to be a complete, standalone intervention and includes both psychoeducation, a virtual therapist and spider expert, and a gamified level design with eight sequential main levels with increasingly realistic and frightening spiders  and user experience were also collected through digital questionnaires completed at the end of the session by the session leader and participant (respectively). See jmv, lme4 , with random slopes and intercepts, were used to model outcomes as a function of time (two plus two measures), coded as either zero (prior to treatment phase) or one (after treatment phase). To assess the stability of scores within each phase before running the mixed models, paired t-tests were conducted; none were significant , suggesting stabile phases and appropriateness of a phase-based time variable in the mixed models. Clinically significant change was defined as a change exceeding two standard deviations . To testmv, lme4 , 34 and   and a trend towards further symptom decrease on the FSQ . See Virtual Reality exposure therapy has well-established efficacy, and several recent studies show that this applies also to automated interventions , 16, 18.Importantly, the current study shows that consumer-targeted VRET applications can have comparable effects also under simulated real-world conditions. Specific phobias show one of the largest treatment gaps, with few sufferers seeking treatment. With continued growth in consumer VR adoption, consumer-target VRET applications, disseminated through ordinary digital marketplaces, have the potential to make a substantial public health impact. Pending mass-adoption, automated VR applications could be provided at clinics or pharmacies, without the need for a qualified therapist. Automated VRET interventions could also be used as homework exercises between traditional therapy sessions, akin to so called blended internet interventions . While tOverall, session characteristics and user experience measures suggest that the VRET interventions is tolerable and practical, although a lack of comparison group or validated norm data makes it difficult to draw firm conclusions. Interestingly, although designed to be played through in around two and half hours , average usage time in the current study was approximately one and a half . It should be noted that even in three-hour, in-vivo exposure protocols , only bea priori power calculation revealed that a correlation coefficient r > .50 could be detected with 80% power and a sample size of n = 25. For example, previous meta-analytic research suggests an association between sense of presence and SUDs of r = .28  further decrease in phobia symptoms at the six-month follow-up. In the non-inferiority trial, the VRET group continued to improve as measured by both the behavioral approach test and the Spider Phobia Questionnaire , althougatistics  should bin-vivo exposure exercise in-itself, and would not be available to at-home users. A waiting-list control group would have given unbiased estimates of treatment effects even with an included BAT, but was not included in the current study. Including more measures per phase (e.g. to model a pre-treatment trajectory), and a multiple baseline design, would have allowed for stronger inferences about causality.The current study complements the earlier randomized non-inferiority trial  in termsAutomated, gamified VRET for spider phobia, in the form of a consumer application, is tolerable and practical also when delivered under simulated real-world conditions, with effects (fear reduction) remarkably similar to those previously observed in a randomized trial under efficacy conditions. Our findings show that automated VRET is a promising treatment option for self-help use, delivered either at clinics  or at-home as consumer-oriented applications.The datasets generated for this study are available on request to the corresponding author.This study was reviewed and approved by Stockholm Regional Ethical Review Board. The patients/participants provided their written informed consent to participate in this study.Designed the study: PL, AM, PC. Technical development of the intervention: WH. Carried out data collection: CB. Made important contributions to study design and interpretation of findings: CB, WH, GA. Drafted the manuscript: PL. Made significant contributions to manuscript: AM, CB, WH, GA, and PC.Author WH is the founder and chief technology officer of Mimerse, a company specializing in developing VR interventions for mental health. Author PL has consulted for Mimerse but holds no financial stake in the company.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Coronavirus disease (COVID-19) presents in children usually with less severe manifestations than in adults. Although fever and cough were reported as the most common symptoms, children can have non-specific symptoms. We describe an infant with aplastic anemia as the main manifestation.We describe a case of SARS-CoV-2 infection in an infant without any respiratory symptoms or signs while manifesting principally with pallor and purpura. Pancytopenia with reticulocytopenia was the predominant feature in the initial laboratory investigations, pointing to aplastic anemia. Chest computed tomography surprisingly showed typical findings suggestive of SARS-CoV-2 infection. Infection was later confirmed by positive real-time reverse transcription polymerase chain reaction assay (RT-PCR) for SARS-CoV-2.Infants with COVID-19 can have non-specific manifestations and a high index of suspicion should be kept in mind especially in regions with a high incidence of the disease. Chest computed tomography (CT) and testing for SARS-CoV-2 infection by RT-PCR may be considered even in the absence of respiratory manifestations. The new coronavirus was first reported in Wuhan, China, in December 2019, as the cause of severe pneumonia . AlthougNone of the cases described by the CONFIDENCE study  nor by LThe patient is a 71-day-old, full-term, previously healthy male infant. Before admission, he developed a low-grade fever and two vomiting episodes for 2 days. He received paracetamol as outpatient then fever and vomiting subsided. No other drugs were taken. Three days later, he developed acute onset of pallor and purpura which were the principal presentations to the emergency department on 28 April 2020. No respiratory symptoms or any other symptoms were reported. His general condition was good. He had sinus tachycardia with a heart rate of 176/min but with normal respiratory rate, capillary refill time, blood pressure, and oxygen saturation. He had no dysmorphic features or any skeletal anomalies. His chest examination was normal with no respiratory distress. However, hepatosplenomegaly was detected. No focal signs of infection were found. His initial laboratory testing . Drug intake, viral infection, HLH, malignancy, bone marrow infiltration by a storage disorder, and inherited bone marrow failure syndromes (IBMFS) were the main suspected causes of AA in this case. Serology markers of Epstein-Barr virus (EBV) and cytomegalovirus (CMV) were negative. There was no history of drug intake prior to the presentation apart from paracetamol. Similarly, there were no features to suggest IBMFS . Features suggestive of HLH in this patient were markedly elevated ferritin, high triglycerides, and pancytopenia in addition to clinically detected hepatosplenomegaly and fever. So, a diagnosis of AA secondary to HLH was considered.Neither the Italian CONFIDENCE study  nor Lu eAs regards the CT chest findings, this case report suggests that infants with SARS-CoV-2 infection may not exhibit respiratory symptoms and signs even in the presence of radiological findings of pneumonia. Similarly, Lu et al.  reportedThis case highlights that aplastic anemia due to sHLH can be the initial and the principal presenting feature of COVID-19. Reliance on respiratory symptoms and signs only for testing will easily miss cases in whom these symptoms and signs are absent while fever alone or symptoms of other organ system affection are the first presenting features. As the predominant clinical manifestations of COVID-19 can vary from one patient to another, one should keep a high index of suspicion, especially in the setting of increasing incidence of COVID-19 cases.SARS-CoV-2 infection in infants should be kept in mind in those presenting with non-specific clinical picture such as fever and vomiting, especially in epidemic areas. Chest CT, in addition to nucleic acid testing for SARS-CoV-2, may be considered for diagnosing cases regardless of the presence or absence of respiratory symptoms or signs, particularly in regions with increasing incidence of COVID-19 cases. We are still in need of further reporting of the clinical presentation pattern of COVID-19 cases in children.


Nonalcoholic fatty liver disease (NAFLD) may develop into liver cirrhosis and hepatocellular carcinoma (HCC). The aim of this study was to compare the clinical patterns and survival outcomes of NAFLD-related HCC patients and those of alcoholic liver disease (ALD)-related or hepatitis B virus (HBV)-related HCC patients. n = 56), ALD (n = 173), or HBV infection (n = 393) were enrolled. The clinical characteristics and survival were analyzed according to the underlying liver diseases.  A total of 622 HCC patients with associated NAFLD (P=0.047), a larger tumor (P=0.001), more macrovascular invasion (P=0.022), and exceeded the Milan criteria (P=0.001), but was less frequently diagnosed during tumor surveillance (P=0.025). Survival analysis did not show any difference among NAFLD-related, ALD-related, and HBV-related HCC patients. Furthermore, propensity score matching analysis did not reveal a significant difference in the median survival between the different groups , 2.0–26.0] vs. 13.0 months ; P=0.667, NAFLD vs. HBV, 14.0 months  vs. 12.0 months ; P=0.573).  NAFLD-related HCC patients were more commonly older women and had more metabolic risk factors but were less likely to have cirrhosis and ascites, compared to ALD-related or HBV-related HCC patients. NAFLD-related HCC more often had an infiltrative pattern ( NAFLD-related HCCs were more often detected at an advanced stage with infiltrative patterns, although they showed no significant difference in survival compared to ALD-related or HBV-related HCCs. A future prospective research should be focused on identifying NAFLD patients who require strict surveillance in order to early detect and timely treat HCC. Liver cancer is the sixth most common solid tumor with over half a million new cases and the second leading cause of cancer death worldwide . HepatocThis was a retrospective, comparative, observational study of HCC patients who were treated in Dankook University Hospital between January 2000 and January 2016. After reviewing the medical records of 1,036 HCC patients, we excluded patients who had undergone initial treatment in other hospitals (306 patients), patients with HCV-related HCC (90 patients), and patients with unknown origin of HCC (18 patients). A total of 622 HCC patients with liver issues associated with NAFLD (56 patients), ALD (173 patients), or chronic HBV infection (393 patients) who underwent initial treatment at OO University Hospital were enrolled in the present study .HCC was diagnosed histologically or clinically according to the 2018 Practice Guidance by the American Association for the Study of Liver Diseases (AASLD) . HCC surAt the time of the first HCC diagnosis, the following data were recorded: demographic variables , metabolic risk factors , liver function test results, detection pattern of the tumor, tumor characteristics, and type of treatment. The treatment performed at the time of entry into the study was the first treatment. Survival was analyzed according to the etiology of the underlying liver disease. Treatment was selected according to the current guidelines after considering the clinical, biochemical, and oncologic characteristics of patients. When no oncologic treatment was administered, treatment was categorized as “best supportive care.” Patients who were lost to follow-up were censored at the last time they were examined.t-test. Categorical variables were expressed as the number of cases and proportions and were compared using the Fisher's exact test. Across-group comparisons of quantitative variables were performed with analysis of variance (ANOVA). Survival was measured as the interval between the time of HCC diagnosis at Dankook University Hospital and the time of the last follow-up or death; it was analyzed and compared using the Kaplan–Meier method with the log-rank test. To reduce bias due to confounding variables, we performed propensity score matching analysis while considering the main variables that have a clinically known impact on survival and that show significant differences between NAFLD-related and ALD-related HCC patients or NAFLD-related and HBV-related HCC patients. This propensity model was used to perform one-to-one matching using the nearest-neighbor matching method, in which the matching variables, such as age, gender, Child-Pugh classification, tumor characteristics, and type of treatment were entered. A P value < 0.05 was considered statistically significant.Statistical analysis was performed by using SPSS version 20.0 for Windows . Continuous variables were expressed as the mean and standard deviation or median and interquartile range after adjusting for normal distribution and were compared using the Student's P < 0.001), less commonly men , and more frequently had metabolic risk factors that included diabetes mellitus  and hypertension  compared to ALD-related or HBV-related HCC patients. Liver cirrhosis  and ascites  were also less common, and Child-Pugh score  and Model for End-Stage Liver Disease (MELD) score  were slightly lower in NAFLD-related HCC patients than in the other two groups.The baseline demographic and clinical characteristics of patients are shown in P=0.025) compared to ALD-related or HBV-related HCCs. In addition, NAFLD-related HCCs had a larger tumor , more often had an infiltrative pattern  and macrovascular invasion , and exceeded the Milan criteria  compared to the other two groups. Barcelona Clinic Liver Cancer (BCLC) stage 0  or A  was significantly less common while BCLC stage C , which is more advanced disease, was more frequent in NAFLD-related HCC patients than in other two groups. The median levels of alpha-fetoprotein  and protein induced by vitamin K absence II (PIVKAII)  was less commonly used in NAFLD-related HCC patients. More patients with NAFLD-related HCC compared to the other two groups were eligible for liver resection  and underwent sorafenib treatment .The tumor characteristics of patients are shown in e groups . TranscaP=0.135).Survival curves of patients with HCC according to the background liver disease are shown in P=0.677; P=0.573; The comparison of baseline characteristics of NAFLD-related and ALD-related HCC patients after propensity score matching analysis is shown in NAFLD is the most common chronic liver disease in developed societies and its prevalence is increasing rapidly , 23, 24.In this retrospective study, NAFLD-related HCC patients were more commonly women and had metabolic risk factors more often, including diabetes mellitus and hypertension, compared to ALD-related or HBV-related HCC patients. This result is similar to that observed in previous studies about NAFLD-related and HCV-related HCC patients , 15, 23.In the present study, we found that tumor characteristics showed significant differences among the three groups. NAFLD-related HCCs were more often detected at an advanced tumor stage with an infiltrative pattern compared to ALD-related or HBV-related HCCs, consistent with previous results , 23. NAFAlthough NAFLD-related HCC patients were more often detected at an advanced tumor stage due to delayed diagnosis, more patients with NAFLD-related HCC were eligible for liver resection compared to those in the other two groups. This might be because their liver function measurements, such as Child-Pugh score and MELD score, were preserved to a higher extent compared to the scores in the other two groups.The present study demonstrated that the survival of NAFLD-related HCC patients was similar to that of ALD-related or HBV-related HCC patients. Several confounders with known impact on survival were significantly different among three groups. When confounders were eliminated using propensity score matching analysis, the survival also showed no significant difference between NAFLD-related and ALD-related or HBV-related HCC patients. NAFLD-related HCC showed two conflicting aspects that affect the survival of patients. The favorable aspects were the absence of potential risk factors, less common accompanying liver cirrhosis with ascites, and relatively better liver function considering the Child-Pugh score and MELD score. In contrast, the unfavorable aspects were older age, irregular tumor surveillance, more advanced tumor and clinical-stage with tumor size, vascular invasion, infiltrative pattern, Milan criteria, and BCLC staging. These aspects are likely to make an explanation for the similar overall survival observed among the patients with NAFLD-related, ALD-related, or HBV-related HCC.The current study has several limitations. First, the retrospective nature of the study design may make it difficult to generalize our findings owing to selection or information bias. Especially, as Korea is known as the HBV endemic area, a portion of patients with isolated anti-HBc may have occult HBV infection affecting the progression of liver diseases to liver cirrhosis and HCC. Because serostatus of anti-HBc was not available in some cases of HCC in this retrospective study, occult HBV infection would be likely ignored in NAFLD-related and ALD-related HCC patients. Second, owing to the different clinical settings in NAFLD, ALD, and HBV patients, several baseline variables could not be sufficiently taken into account through a propensity score model. However, we tried to maximally reduce and adjust a clinical bias for all the baseline variables using propensity score analysis with the nearest-neighbor manner-based one-by-one matching. Third, this is a single-center study that may not reflect the diversity and availability of treatment modality by other medical institutions, being also the main factor to affect the survival of patients. Fourth, the lead-time bias caused by the time between early diagnosis with screening and the time at which diagnosis would have been made without screening  could haWe conclude that NAFLD-related HCCs were more often detected at an advanced tumor stage with infiltrative patterns, although they showed no significant difference in survival compared to ALD-related or HBV-related HCCs. Nevertheless, a strict surveillance program with better screening modalities is required for the early detection and timely treatment of HCC in patients with NAFLD. A future prospective research should be focused on identifying NAFLD patients who require strict surveillance.