Articles de revues sur le sujet « Cluster weighted mod »

Abstract Objectives To examine the trends and influencing factors of suboptimal complementary feeding (CF) practices among children aged 6–23 months in Niger using national data from 2000 to 2018. Methods Using data from the 2000 Multiple Indicator Cluster Survey, 2012 Demographic and Health Surveys (DHS), and 2018 Standardized Monitoring and Assessment of Relief and Transitions survey in Niger, the trends and predictors of WHO-UNICEF CF indicators including timely introduction of complementary foods (INTRO), minimum meal frequency (MMF), minimum dietary diversity (MDD), and minimum acceptable diet (MAD) were estimated. Using the most completed data in CF and influencing factors (available from 2012 DHS), multilevel logistic regression models were applied to identify factors at the individual, household and community levels that were associated with meeting INTRO, MMF, MDD, and MAD. Results The weighted proportion of children aged 6–8 months meeting INTRO increased from 56.0% in 2000 to 84.4% in 2018. The weighted proportion of children meeting MMF (51.3% to 77.9%), MDD (9.8% to 14.2%), and MAD (5.6% to 10.9%) also increased between 2012 - 2018. At the individual level, children with mothers who were employed had higher odds of meeting all four indicators as compared to those whose mothers were unemployed. Older child age and maternal exposure to media were significantly associated with higher odds of meeting MMF, MDD, and MAD. Maternal education and child birth weight greater or smaller than average (reference) were positively associated with the odds of meeting MMF and MAD, and antenatal iron supplementation and currently breastfeeding were positively associated with the odds of achieving MDD and MAD. At the household level, the odds of meeting MMD and MAD were higher among children from wealthier households. At the community level, the odds of meeting INTRO and MMF were higher among children from communities with greater access to health services. Conclusions Despite the improvement in CF practices since 2000, only 10% of children from 6–23 months received minimum acceptable diets. Factors associated with CF were distributed across individual, household and community levels, suggesting the need for multi-level strategies to improve child nutrition in Niger. Funding Sources UNICEF West and Central Africa Regional Office (Grant # 43279190).

Abstract In a galaxy cluster, the relative spatial distributions of dark matter, member galaxies, gas, and intracluster light (ICL) may connote their mutual interactions over the cluster’s evolution. However, it is a challenging problem to provide a quantitative measure for matching the shapes between two multidimensional scalar distributions. We present a novel methodology, named the weighted overlap coefficient (WOC), to quantify the similarity of two-dimensional spatial distributions. We compare the WOC with a standard method known as the modified Hausdorff distance (MHD) method. We find that our method is robust, and performs well even with the existence of multiple substructures. We apply our methodology to search for a visible component whose spatial distribution resembles that of dark matter. If such a component could be found to trace the dark-matter distribution with high fidelity for more relaxed galaxy clusters, then the similarity of the distributions could also be used as a dynamical stage estimator of the cluster. We apply the method to six galaxy clusters at different dynamical stages, simulated within a GRT simulation, which is an N-body simulation using the galaxy replacement technique. Among the various components (stellar particles, galaxies, ICL), the ICL+brightest cluster galaxy (BCG) component most faithfully traced the dark-matter distribution. Among the sample galaxy clusters, the relaxed clusters show stronger similarity in the spatial distribution of the dark matter and ICL+BCG than the dynamically young clusters, while the results of the MHD method show a weaker trend with the dynamical stages.

Abstract Background Reductions in motivation figure prominently in the clinical presentation of schizophrenia (SZ) and major depressive disorder (MDD). One critical nexus in the motivation system that drives real-world behaviour is effort-based decision-making (EBDM), which refers to the cost-benefit calculations involved in computing the amount of effort one is willing to expend in order to obtain a desired reward. Important individual differences are associated with these processes, and impairments in motivation can arise if any relevant cost-benefit information is not properly computed, appraised, or integrated. Thus, in order to better understand the computations guiding choice behaviour, the present study sought to utilize a more person-centric approach to characterize individual differences in the effort-cost computations that underlie cost-benefit decision-making in individuals with SZ and MDD. Methods A sample of 51 individuals with SZ, 43 individuals with MDD, and 51 healthy control (HC) participants underwent a comprehensive clinical and cognitive characterization, and completed the Effort Expenditure for Rewards Task (EEfRT) as a measure of EBDM. Random effects modelling was conducted to estimate the subject-specific predictors of reward magnitude, probability, and perceived cost on choice behaviour. Cluster analysis was subsequently applied to these predictors in order to identify subtypes of impairments within the entire sample, irrespective of diagnostic status. Results Data-driven cluster analysis identified unique subgroups of individuals with distinct patterns of utilizing cost-benefit information to guide effort-based decision-making. Analyses of variance revealed significant differences between clusters with respect to their utilization of reward (F (3, 133) = 51.58, p < .001), probability (F (3, 133) = 48.71, p < .001), and cost (F (3, 133) = 45.24, p < .001). The first cluster was characterized by an indifference to all cost-benefit information, the second cluster was more influenced by perceived cost, the third cluster demonstrated a preference for reward-based information, and the fourth cluster mainly utilized probability to guide their decision-making. While the clusters did not differ in their severity of clinical amotivation (p = .11), there was a significant effect for cognition, specifically with impairments in clusters 1 and 2. All diagnostic groups were represented in each cluster, but the distribution of SZ, MDD, and HC participants was significantly different (X2 (6, N = 137) = 16.18, p = .013). Discussion The emergence of four distinct subgroups in our sample suggests that there are individual differences amongst SZ, MDD, and HC participants in their utilization of cost-benefit information to guide choice behaviour. Moreover, with elevated levels of clinical amotivation present in all four clusters, it is possible that these unique cost-benefit decision-making patterns represent different underlying motivational impairments, the nature of which depending on how reward magnitude, probability, and perceived cost are weighed. Thus, by characterizing the specific mechanisms underlying EBDM in SZ and MDD, the results of this work may be able to help guide the identification of more precise targets for the effective treatment of motivation deficits.

Introduction: From the perspective of social determinants, homelessness perpetuates poor health and creates barriers to effective chronic disease management, necessitating frequent use of emergency department (ED) services. In this study we developed a screening algorithm (checklist) from common comorbidities observed in the homeless population in the United States. The result was a theoretical screening tool (checklist) to aid healthcare workers in the ED, including residents, medical students, and other trainees, to provide more efficacious treatment and referrals for discharge. Methods: In this retrospective cohort study we used the Nationwide Emergency Department Sample (NEDS) to investigate comorbidities and ED utilization patterns relating to 23 injury-related, psychiatric, and frequent chronic medical conditions in the US adult (≥18 years of age) homeless population. Cases were identified from the NEDS database for 2014–2017 using International Classification of Diseases, 9th and 10 revisions, and Clinical Classification Software diagnosis codes. We performed a two-step cluster analysis including pathologies with ≥10% prevalence in the sample to identify shared comorbidities. We then compared the clusters by sociodemographic and ED-related characteristics, including age, gender, primary payer, and patient disposition from the ED. Chi-square analysis was used to evaluate categorical variables (ie, gender, primary payer, patient disposition from the ED), and analysis of variance for continuous variables (age). Results: The study included 1,715,777 weighted cases. The two-step cluster analysis identified nine groups denominated by most prevalent disease: 1) healthy; 2) mixed psychiatric; 3) major depressive disorder (MDD); 4) psychosis; 5) addiction; 6) essential hypertension; 7) chronic obstructive pulmonary disease (COPD); 8) infectious disease; and (9) injury. The MDD, COPD, infectious disease, and Injury clusters demonstrated the highest prevalence of co-occurring disease, with the MDD cluster displaying the highest proportion of comorbidities. Although the addiction cluster existed independently, substance use was pervasive in all except the healthy cluster (prevalence 36-100%). We used the extracted screening algorithm to establish a screening tool (checklist) for ED healthcare workers, with physicians as the first point of contact for the initial use of the screening tool. Conclusion: Healthcare workers in the ED, including residents, medical students, and other trainees, provide services for homeless ED users. Screening tools (checklists) can help coordinate care to improve treatment, referrals, and follow-up care to reduce hospital readmissions. The screening tool may expedite targeted interventions for homeless patients with commonly occurring patterns of disease.

Fodder is an important source of feed of the ruminants in Nepal. In the mid hills of Nepal, farmers generally practice integrated farming system that combines crop cultivation with livestock husbandry and agroforestry. Tree fodders are good sources of protein during the forage and green grass scarcity periods especially in dry season. Local communities possess indigenous knowledge for the selection of grasses and tree fodders at different seasons in mid hills of western Nepal. A study was conducted on the perception of farmers with respect to selection of fodder species in eight clusters in Kaski and Lumjung districts that range 900-2000 meter above sea level and receive average precipitation of 2000- 4500mm per annum. During the fodder preference ranking, farmers prepared the inventory of fodders found around the villages and nearby forests and selected top ten most important fodders in terms of their availability, palatability, fodder yield, milk yield and milk fat yield. In total, 23 top ranking fodders species were selected from the eight clusters. These fodder species were also ranked using pairwise ranking and weighted scoring methods and ranking was done on the basis of merit numbers obtained from weighted scores. The analysis revealed Artocarpus lakoocha as best tree fodder followed by Ficus semicordata, Thysanolena maxima and Ficus calvata. Similarly, the calendar of fodders trees for lopping season and the best feeding time was prepared on the basis of farmers’ local knowledge. This study suggests strategies for promotion of locally preferred tree fodder species and supplementing tree fodder with feed in different seasons depending on their availability and local preferences.

Objective Although neuroimaging studies have shown volumetric reductions, such as the anterior cingulate, prefrontal cortices, and hippocampus in patients with major depressive disorder (MDD), few studies have investigated the volume of or shape alterations in the subcortical regions and the brainstem. We hypothesized that medication-naïve female adult patients with MDD might present with shape and volume alterations in the subcortical regions, including the brainstem, compared to healthy controls (HCs).Methods A total of 20 medication-naïve female patients with MDD and 21 age-matched female HCs, underwent 3D T1-weighted structural magnetic resonance scanning. We analyzed the volumes of each subcortical region and each brainstem region, including the midbrain, pons, and medulla oblongata. We also performed surface-based vertex analyses on the subcortical areas and brainstem.Results Female patients with MDD showed non-significant volumetric differences in the subcortical regions, whole brainstem, and each brainstem region compared to the HCs. However, in the surface-based vertex analyses, significant shape contractions were observed in both cerebellar peduncles located on the lateral wall of the posterior brainstem [threshold-free cluster enhancement, corrected for family-wise error (FWE) at p<0.05] in patients with MDD.Conclusion We revealed shape alterations in the posterior brainstem in female patients with MDD.

AbstractObjectivesTo demonstrate a spatial epidemiologic approach that could be used in the aftermath of disasters to (1) detect spatial clusters and (2) explore geographic heterogeneity in predictors for mental health and general wellness.MethodsWe used a cohort study of Hurricane Ike survivors (n=508) to assess the spatial distribution of postdisaster mental health wellness (most likely resilience trajectory for posttraumatic stress symptoms [PTSS] and depression) and general wellness (most likely resilience trajectory for PTSS, depression, functional impairment, and days of poor health) in Galveston, Texas. We applied the spatial scan statistic (SaTScan) and geographically weighted regression.ResultsWe found spatial clusters of high likelihood wellness in areas north of Texas City and spatial concentrations of low likelihood wellness in Galveston Island. Geographic variation was found in predictors of wellness, showing increasing associations with both forms of wellness the closer respondents were located to Galveston City in Galveston Island.ConclusionsPredictors for postdisaster wellness may manifest differently across geographic space with concentrations of lower likelihood wellness and increased associations with predictors in areas of higher exposure. Our approach could be used to inform geographically targeted interventions to promote mental health and general wellness in disaster-affected communities. (Disaster Med Public Health Preparedness. 2016;10:261–273)

AbstractObjectivesWe examined the association between housing type and household emergency preparedness among households in Oakland County, Michigan.MethodsWe used interview data on household emergency preparedness from a cluster design survey in Oakland County, Michigan, in 2012. We compared survey-weighted frequencies of household demographics, medical conditions, and preparedness measures in single-detached homes versus multi-unit dwellings, and determined the unadjusted odds ratios (OR) and the income-level adjusted OR for each preparedness measure.ResultsHouseholds had similar demographics and medical conditions between housing types. Unadjusted ORs were statistically significant for single detached homes having a generator (11.1), back-up heat source (10.9), way to cook without utilities (5.8), carbon monoxide (CO) detector (3.8), copies of important documents (3.4), evacuation routes (3.1), and 3-day supply of water (2.5). Income level adjusted ORs remained statistically significant except for owning a CO detector.ConclusionsHouseholds in multi-unit dwellings were less likely to have certain recommended emergency plans and supplies compared to those in single detached homes. Further research is required to explore the feasibility, barriers, and alternatives for households in multi-unit dwellings in terms of complying with these measures. (Disaster Med Public Preparedness. 2014;0:1–8)

The monitoring of underwater aquatic habitats and pipeline leakages and disaster prevention are assisted by the construction of an underwater wireless sensor network (UWSN). The deployment of underwater sensors consumes energy and causes delay when transferring the gathered sensed data via multiple hops. The consumption of energy and delays are minimized by means of an autonomous unmanned vehicle (AUV). This work addresses the idea of reducing energy and delay by incorporating an AUVs-assisted, three-dimensional UWSN (3D-UWSN) called DEDG 3D-UWSN. Energy in the sensor nodes is saved by clustering and scheduling; on the other hand, the delay is minimized by the movement of the AUV and inter-cluster routing. In clustering, multi-objective spotted hyena optimization (MO-SHO) is applied for the selection of the best sensor for the cluster head, which is responsible for assigning sleep schedules for members. According to the total number of members, an equal half of the members is provided with sleep slots based on the energy and hop counts. The redundancy in the gathered data is eliminated by measuring the Hassanat distance. Then, the moving AUV is able to predict its movement by the di-factor actor–critic path prediction method. The mid-point among the four heads is determined so that the AUV can collect data from four heads at a time. In cases where the waiting time of the CH is exceeded, three-step, inter-cluster routing is executed. The three steps are the discovery of possible routes, ignoring the longest paths and validating the filtered path with a fuzzy–LeNet method. In this 3D-UWSN, the sensed data are not always normal, and, hence, a weighted method is presented to transfer emergency events by selecting forwarders. This work is implemented on Network Simulator version 3.26 to test the results. It achieves better efficiency in terms of data collection delay, end-to-end delay, AUV tour length, network lifetime, number of alive nodes and energy consumption.

Community identification is an important technique for the investigation of complex networks because it makes it possible to examine mesoscopic features that are often connected to the organisational and functional properties of the underlying networks. In social media, there are billions of vertices and a variety of connections, thus community identification is a widely acknowledged approach of addressing the problem of grouping users. However, traditional methods are insufficient because of this. For the purpose of analysing social networks, overlapping community identification is crucial. On networks with complicated weight distributions, the current overlapping community recognition techniques seldom provide good results. Communities of any form may be easily and precisely found using density peaks clustering (DPC). Nevertheless, it also uses the truncation distance, and therefore is unable to automatically determine where the cluster centre is located. In this research work, an Adaptive density peak clustering (ADPC) with Modified Dragonfly Optimization (MDO) a suggested algorithm will decide the communities in a social network in an adaptable manner. Initially, the preprocessing methods such as Stemming, Stop-words removal, and Tokenization by bigrams, 1-to-3 grammars, and the unigram are three distinct types of data formats. Two feature extraction filters: Word Embedding Feature Extraction, and Term Frequency-Inverse Document Frequency (TF-IDF). In the Adaptive density peak clustering (ADPC) with Modified Dragonfly Optimization (MDO) algorithm, the clustering process is completed by determining the clustering centre by the MDO after determining the new local density based on the new neighborhood connection. ADPC-MDO adds a unique distance function based on common nodes to estimate the distance between nodes and takes weights into account to handle both weighted and unweighted social networks. A technique based on transitive consensus matrix building is used to generate a consensus matrix, which provides representative information of all dendrograms. Given a social networks dataset with a complicated weight distribution, the results show that the suggested ADPC-MDO performs better.

This report presents a study of crop water requirement and crop coefficient (Kc) for Tomato crop cultivated under irrigation in Pampaida Millennium Village Cluster, Ikara Local Government Area of Kaduna State, Nigeria, during the 2009/2010 dry season. A total of 7 tomato farmers were selected out of 45 farmers for the assessment exercise. Water applied per irrigation and soil moisture contents before and after irrigation was monitored throughout the seasons, while Tomato bulbs were harvested at the end of season and weighed. Average crop water use were estimated from the soil moisture content using the gypsum block, while daily reference Evapotranspiration (ETo) were computed from weather data using method Hargreaves equation. Crop coefficient values (Kc) were computed as the ratio of crop water use to ETo. The values of crop coefficients and seasonal crop water requirement per irrigation for different growth stages were determined, the computed *Kc values for different growth stage for the tomato crop grown in the study area was found to be between 0.77-1.15, the initial stage (*Kc =0.81; 20 mm/irrigation), crop development stage (*Kc = 1.09; 28 mm/irrigation), mid-season (*Kc = 1.15; 29 mm/ irrigation) and Late stage (*Kc = 0.77; 19 mm/irrigation), hence the mid-season gave the highest Kc value. However, the crop seasonal water requirement was found to be 386mm, which was within the recommended range. The crop coefficients and seasonal water requirement estimated in this study are reliable and could be used in irrigation design and scheduling for Tomato in the study area.

Abstract. Black carbon (BC) aerosol has important effects on the climate and hydrology of the Tibetan Plateau (TP). An intensive measurement campaign was conducted at Lulang (∼ 3300 m a.s.l. – above sea level), southeastern TP, from September to October 2015, to investigate the sources and physicochemical characteristics of refractory BC (rBC) aerosol. The average rBC mass concentration was 0.31 ± 0.55 µg m−3, which is higher than most prior results for BC on the TP. A clear diurnal cycle in rBC showed high values in the morning and low values in the afternoon. A bivariate polar plot showed that rBC loadings varied with wind speed and direction, which also reflected the dominant transport direction. The estimated net surface rBC transport intensity was +0.05 ± 0.29 µg s−1 m−2, indicating stronger transport from outside the TP compared with its interior. Cluster analysis and a concentration-weighted trajectory model connected emissions from north India to the high rBC loadings, but the effects of internal TP sources should not be overlooked. The average mass median diameter (MMD) of rBC was 160 ± 23 nm, with smaller MMDs on rainy days (145 nm) compared with non-rainy days (164 nm). The average number fraction of thickly coated rBC (FrBC) was 39 ± 8 %, and it increased with the O3 mixing ratios from 10:00 to 14:00 LT, indicating that photochemical oxidation played a role in forming rBC coatings. The average rBC absorption enhancement (Eabs) was estimated to be 1.9, suggesting that light absorption by coated rBC particles was greater than for uncoated ones. The Eabs was strongly positively correlated with the FrBC, indicating an amplification of light absorption for internally mixed rBC. For rBC cores < 170 nm, Eabs was negatively correlated with MMD, but it was nearly constant for rBC cores > 170 nm. Our study provides insight into the sources and evolution of rBC aerosol on the TP, and the results should be useful for improving models of the radiative effects of carbonaceous aerosols in this area.

AbstractBackgroundOn April 20, 2010, the Deepwater Horizon drilling unit exploded off the coast of Louisiana, resulting in 11 deaths and the largest marine petroleum release in history. Previous oil spill disasters have been associated with negative mental health outcomes in affected communities. In response to requests from Mississippi and Alabama, potential mental health issues resulting from this event were identified by implementing a novel use of a Community Assessment for Public Health Emergency Response (CASPER) in the months immediately following the Gulf Coast oil spill.PurposeThis assessment was repeated one year later to determine long-term mental health needs and changes.MethodsA two-stage sampling method was used to select households, and a questionnaire including the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System (BRFSS) questions was administered. Weighted cluster analysis was conducted, and BRFSS questions were compared to the most recent BRFSS reports and the 2010 results.ResultsIn 2011, 8.8%-15.1% of individuals reported depressive symptoms compared to 15.4%–24.5% of individuals in 2010, with 13.2%-20.3% reporting symptoms consistent with an anxiety disorder compared to 21.4%-31.5% of individuals in 2010. Respondents reporting decreased income following the oil spill were more likely to report mental health symptoms compared to respondents reporting no change in income.ConclusionsOverall, mental health symptoms were higher in the three assessment areas compared to BRFSS reports, but lower than 2010 surveys. These results suggest that mental health services are still needed, particularly in households experiencing decreased income since the oil spill.ButtkeD, VagiS, SchnallA, BayleyegnT, MorrisonM, AllenM, WolkinA. Community assessment for public health emergency response (CASPER) one year following the Gulf Coast oil spill: Alabama and Mississippi, 2011. Prehosp Disaster Med. 2012;27(6):1-7.

Abstract Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for more than 10,000 deaths in the US each year. Hence the pursuit for novel therapeutic agents remains critically important. Myeloma pathogenesis is associated in part with aberrant cell cycle progression. Inhibition of cyclin dependent kinases CDK4/6 results in cell cycle arrest and sensitization to Bortezomib and other active agents in MM (Huang, Blood 2012). Here, we show that ARK5, a novel member of the human AMPKfamily, is overexpressed in 70% of MM and helps promote proliferation and cell cycle progression via G1/S phase activation in an mTOR dependent manner. We examined the role of ARK5 using loss of function studies by ARK5 siRNA transfection in MM1.S, NCI-H929 cells as well as treatment with ON 123300, a dual CDK4/ARK5 kinase inhibitor. ARK5 siRNA knockdown decreased MM cell viability and cell proliferation via G1/S arrest compared to control siRNA. ARK5 siRNA treatment significantly (~70%) induced apoptosis in MM cells as detected by Annexin V/PI staining. We observed that phosphorylation of Rb, a critical cell cycle protein was significantly reduced in ARK5 depleted cells. Moreover, mTOR pathway inhibition was confirmed by reduction of pS6K in ARK5depleted cells as compared to control siRNA treated cells. ON 123300 decreased viability in MM cell lines and patient cells but was not lethal to normal PBMCs. A single treatment of 50nM drug stratified MM cell lines into 2 groups, 5 resistant (MM.1R, KMS11, U266, RPMI-8226 and ARP1) and 4 sensitive cell lines (>80% cell kill-MM.1S, EJM, JJN3, NCI-H929). ARK5 protein expression by western blot analysis was much higher in sensitive cell lines. ON 123300 triggered G0/G1 cell cycle arrest and induced apoptosis similar to the effect of ARK5 siRNA (80% vs 70%). ON 123300 treatment also reduced phosphorylation of pRb and pS6K downstream of mTOR pathway. These results confirm that cell inhibitory effects of ON 123300 in MM are mediated in a large part via inhibition of ARK5. Co-culture experiments with BMSCs showed that ON 123300 not only targets MM cells but also overcomes the cytoprotective effects of the MM-host BM microenvironment. 4/5 ARK5 positive primary samples with adverse cytogenetics including 1q amplification and CyclinD1 translocation were sensitive to ON 123300 (>80% cytotoxicity) at 50nM. Further, IP injection of ON 123300 (100mg/kg) in tumor xenograft models (MM1.S, NCI-H929) showed that ON 123300 is well tolerated and significantly inhibits tumor growth in vivo(p<0.001). To study the mechanism of action for ON 123300, we performed geneset enrichment analysis (GSEA) on drug induced gene expression signature of RNA-Seq data from pre-post treated cell lines. We interrogated a wide array of geneset libraries, including MSigDB (Subramanian, PNAS 2005), drug induced transcriptional modules (Iskar, Mol Sys Bio. 2013) and disease signatures (Sirota, Sci Transl Med 2011). GSEA showed significant representation of genes that are enriched in normal plasma cells and rapamycin sensitive geneset. Next, we developed a weighted co-expression network (WGCNA, Langfelder BMC Bioinformatics 2008) based classifier using 304 MM samples from MMRC collection and RNA-Seq from 28 MM patients from Mt. Sinai Hospital. WGCNA defines a network that continuously links all genes and then clusters the most highly co-expressed genes in defined modules. These network modules were associated with clinical traits and UAMS GEP classification (Zhan, Blood 2006) of each sample. There was significant overlap between highly proliferative “PR” and “Cyclin D1/2” patient subsets based on classification and sensitivity to CDK4/ARK5 inhibition. Our classifier accurately discriminated 4 sensitive primary samples from one resistant sample, all tested in vitro. All sensitive samples were either Cyclin D1/2 or PR as per UAMS classification. Our preclinical studies provide the basis for clinical evaluation of CDK4/ARK5 inhibitor ON 123300 due to its selective cytotoxicity on MM cells in vitro and in vivo. Using WGCNA we establish a systematic framework by constructing for the first time, MM-associated gene co-expression networks contributing to tumorigenesis and progression. Thus, WGCNA modeling is a novel approach for identification of MM patient subgroups that have a higher likelihood of response in clinical trials with CDK4/ARK5 inhibitors. Disclosures Chari: Array BioPharma: Scientific Advisory Board Other; Celgene Corporation: Scientific Advisory Board, Scientific Advisory Board Other; Millennium Pharmaceuticals, Inc: Scientific Advisory Board, Scientific Advisory Board Other; Onyx Pharmaceuticals, Inc.: Scientific Advisory Board, Scientific Advisory Board Other. Reddy:Onconova Therapeutics, Inc.: Consultancy, Patents & Royalties. Reddy:Onconova Therapeutics, Inc.: Consultancy, Patents & Royalties. Dudley:Ecoeos, Inc: Consultancy, Equity Ownership; GNS Healthcare: Consultancy; GlaxoSmithKline: Consultancy; Ayasdi, Inc: Equity Ownership; NuMedii, Inc: Equity Ownership; Ubalo, Inc.: Equity Ownership. Jagannath:Onyx Pharmaceuticals, Inc.: Consultancy; Celgene Corporation: Scientific Advisory Board, Scientific Advisory Board Other; Millennium Pharmaceuticals, Inc. : Scientific Advisory Board, Scientific Advisory Board Other.

AbstractIntroductionApproximately 1.2 million persons in Oakland County, Michigan (USA) reside less than 50 miles from the Fermi Nuclear Power Plant, Unit 2, but information is limited regarding how residents might react during a radiation emergency. Community Assessment for Public Health Emergency Response (CASPER) survey methodology has been used in disaster and nondisaster settings to collect reliable and accurate population-based public health information, but it has not been used to assess household-level emergency preparedness for a radiation emergency. To improve emergency preparedness plans in Oakland County, including how residents might respond during a radiation emergency, Oakland County Health Division (OCHD), with assistance from the Centers for Disease Control and Prevention (CDC) and the Michigan Department of Community Health (MDCH), conducted a CASPER survey.MethodsDuring September 2012, a 2-stage cluster sampling design was used to select 210 representative households in Oakland County. By using in-person surveys, the proportion of households with essential needs and supplies, how residents might respond to public health authorities’ instructions, and their main source for obtaining information during a radiation emergency were assessed. Data were weighted to account for the complex sampling design.ResultsOf the goal of 210 households, 192 (91.4%) surveys were completed: 64.7% and 85.4% of respondents indicated having 3-day supplies of water and of nonperishable food, respectively; 62.8% had a 7-day supply of prescription medication for each person who needed it. Additionally, 64.2% had a working carbon monoxide detector; 67.1% had a first-aid kit; and 52% had an alternative heat source. In response to instructions from public health officials during a radiation emergency, 93.3% of all respondents would report to a radiation screening center; 96% would evacuate; and 91.8% would shelter-in-place. During a radiation emergency, 55.8% of respondents indicated their main information source would be television, 18.4% radio, and 13.6% the Internet. The most trusted source for information would be the local public health department (36.5%), local news (23%), a physician (11.2%), and family members (11.1%). Including completed and incomplete interviews, refusals, and nonrespondents, 517 total households were contacted.ConclusionsCASPER data regarding how residents might react during a radiation emergency provided objective and quantifiable information that will be used to develop Oakland County's radiation emergency preparedness plans. Survey information demonstrates the feasibility and usefulness of CASPER methodology for radiation emergency preparedness planning.NyakuMK, WolkinAF, McFaddenJ, CollinsJ, MurtiM, SchnallA, BiesS, StanburyM, BeggsJ, BayleyegnTM. Assessing radiation emergency preparedness planning by using Community Assessment for Public Health Emergency Response (CASPER) methodology. Prehosp Disaster Med. 2014;29(3):1-9.

Abstract Acute Myeloid Leukemia (AML) is a hematological malignancy with heterogeneous genetics and clinical course. Studies have shown that there is a strong correlation between xenograft in vivo engraftment potential and clinical outcome. Furthermore, such studies have conducted gene expression profiling to identify gene signatures associated with engraftment potential, leukemic stem cell (LSC) property, and prognosis. Most of these analyses were limited to protein coding genes. More recently, advances in next generation sequencing (NGS) have created a paradigm shift on our perception of transcription that, contrary to previous consensus, the human genome is largely transcribed and most of these transcripts do not code for proteins (ncRNA). In the current work, we conducted a comprehensive functional genomics study to identify long ncRNA (lncRNA) that significantly predict in vivo engraftment potential and LSC properties of AML and evaluate their functional and prognostic relevance. We first sorted normal granulocyte-macrophage progenitor (GMP), lymphoid-primed multi-potential progenitor (LMPP) and CD34-subpopulations from leukemic bulk of 15 AML patient samples. Sorted samples were then transplanted into the non-obese diabetic/severe combined immunodeficient Gamma (NSG) mouse model. Only the subpopulations showing robust leukemic engraftment (> 1%) were classified as LSC populations. In parallel, we performed total RNA based RNA-seq using Ovation® Single Cell RNA-Seq System (NuGEN Technologies Inc.) and evaluated both protein coding genes and lncRNAs. In line with previous studies, we observed that the engraftment potential primarily resides within the CD34+ subpopulations (GMPs and LMPPs). LMPPs/GMPs from 8 AML patients engrafted (defined as LSC populations), while subpopulations from the other 7 patients failed to engraft (non-LSC fraction). Using our RNA-seq data, we first conducted a global principal component analysis (PCA) based between group analysis (BGA) based on lncRNA expression levels. Interestingly, we noted that the LMPPs and GMPs primarily cluster based on their engraftment potential rather than cell type. In two subsequent independent analyses, we employed weighted correlation network analysis (WGCNA) and top scoring pairs (tsp) to identify lncRNAs that show significant correlation to engraftment percentages and could also categorically classify samples based on their engraftment status. Results of the WGCNA analysis showed that 830 lncRNAs are statistically significantly correlated to engraftment percentages of subpopulations (p < 0.05). In the tsp analysis, by taking all lncRNAs in the dataset in a pairwise fashion, we identified two lncRNAs that could statistically significantly classify samples into 'LSC' (engrafters) and 'non-LSCs' (non-engrafters) (mean classification error ~ 0.19 using bootstrap analysis; statistically significant with p-value < 0.0001 using Monte Carlo Simulation). Of interest here, the two lncRNAs were also in the list of significantly correlated lncRNAs based on the separately conducted WGCNA. Combining the two analyses and using a 3d plot, we looked at the expression levels of two lncRNAs and engraftment percentages of the subpopulations and were able to see that the GMP/LMPP LSCs formed a distinct cluster. The non-LSCs with GMP or LMPP phenotype were distantly clustered from their engrafting counterparts while all the CD34-negative non-LSCs clustered with no overlap to the other groups. Furthermore, a guilt-by-association analysis was carried out to identify potential functional link of the candidate lncRNAs by assessing their correlation to functionally annotated genes. 'JAK-STAT', 'Hematopoietic lineage', and 'Toll-like receptor signaling' were some of the significant pathways (p < 0.05; FDR < 0.25). We also tested some published AML LSC and engraftment associated signatures. The lncRNAs showed negative correlation to the Eppert et al. (2011; Nat. Med.) LSC signature and set of lineage affiliated genes (Goardon et al. 2011; Cancer Cell) including FLT3, NOTCH1, and RUNX1. Taken together, our approach shows that the two lncRNAs we identified sufficiently recapitulate the underlying engraftment potential of AMLs and predict LSC property with significant accuracy. We conclude that these findings highlight the necessity to focus on lncRNAs as key players in clinical and functional studies of AML. Disclosures Buske: Celltrion, Inc.: Consultancy, Honoraria. Mulaw:NuGEN: Honoraria.

Abstract Background More than a century of research on the neurobiological underpinnings of the Major Psychoses (Schizophrenia SZ, Bipolar Disorder BD, Major Depressive Disorder, Schizoaffective Disorder SZA) has been unable to identify diagnostic “markers”. An alternative approach is to study dimensional psychopathological syndromes that cut across categorical diagnoses. Brain imaging studies on the correlates of syndromes are thus far restricted to one diagnosis, however it is unclear, whether structural brain correlates of syndromes are the same across diagnoses. Previously, we have identified 7 syndromes in n=811 patients suffering from major psychoses, applying a confirmatory factor analysis, including depressed mood, negative symptoms, delusions, formal thought disorders, hallucinations, mania and increased appetite. The aim of the current study was to identify gray matter volume correlates of these syndromes across the major psychoses. Methods We tested the association of the above 7 psychopathological factors with whole brain GMV (voxel-based morphometry) in a sample of n=713 patients meeting DSM-IV criteria for MDD (n=550), BD (n=79), SZ (n=51) and SZA (n=33) (www.for2107.de). T1 weighted brain images were acquired at a 3-Tesla MRI. Images were pre-processed as implemented in the Cat12 (SPM12) toolbox. We performed multiple regression analyses for each factor separately and used the family wise error correction (FWE) to correct for multiple comparisons. Additionally, we tested if local VBM associations were driven by one diagnosis extracting the beta-volumes of the clusters and then comparing the subgroups using ANCOVA. Results The delusion factor was negatively correlated with gray matter volume in the left inferior temporal gyrus/fusiform gyrus (k=138 voxels, x/y/z=-48/-58/-15, t=5.23, p&lt;.05 FWE peak level) and the left amygdala/hippocampus (k=23 voxels, x/y/z=-15/-12/-12, t=4.84, p&lt;.05 FWE peak level). The hallucinatory syndrome was negatively correlated with volume in the right thalamus proper (k=54 voxels, x/y/z=8/-4/-2, t=4.9, p&lt;.05 FWE peak level). Extraction of the beta-volumes revealed no effect of diagnosis (delusions (F (3,708) p=.54; hallucinations (F (3,708) p=.542). Discussion Volume changes underlying psychopathological syndromes are independent of diagnosis. We could confirm previous results from much smaller studies which have restricted themselves to single diagnoses or case control designs. Our findings open a new avenue for neurobiological research of the major psychoses, using syndrome based, dimensional approaches rather than DSM or ICD diagnoses.

Abstract Clonal evolution of DCIS to invasion Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive breast cancer (IBC). If not treated, at least 3 out of 4 women with DCIS will not develop IBC1-3. This implies many women with non-progressive, low-risk DCIS are likely to carry the burden of overtreatment. To solve this DCIS dilemma, two fundamental questions need to be answered. The first question is, how the subsequent IBC is related to the initial DCIS lesion. The second question is how to distinguish high- from low-risk DCIS at the time of diagnosis. This is essential to take well-informed DCIS management decisions, i.e., surgery, followed by radiotherapy in case of breast conserving treatment with or without subsequent endocrine treatment, or test whether active surveillance for low-risk DCIS is safe. How is the subsequent IBC related to the initial DCIS? The high genomic concordance in DNA aberrations between DCIS and IBC suggest that most driver mutations and CNA events are acquired at the earliest stages of DCIS initiation. It has therefore been assumed that most solid tumours arise from a single cell and that the probability of two independent tumours arising from the same tissue is low4-6. However, lineage tracing and genomic studies strongly suggest both direct and independent clonal lineages during the initiation of DCIS and evolution to IBC. In these processes, mammary stem cells have been implicated in DCIS initiation. Role of mammary stem cells in DCIS initiation Lineage tracing mouse model experiments have shown the fate of individual cells and lineages that acquire mutations before a tumour is established7-9. This is also relevant for DCIS initiation10,11, as different pools of MaSCs drive the growth and development of the ductal network and are considered the cell of origin for breast cancers9,10. The ductal trees remain quiescent until puberty, during which extension, branching and termination of terminal end buds (TEBs) leads to its expansion throughout the fat pad7,12,13. Any oncogenic mutation that occurs in a fetal MaSC will spread throughout the ductal network to a large part of the ductal tree, leading to sick lobes9. By contrast, oncogenic mutations acquired by a single MaSC during puberty spread to a smaller number of offspring located in small clusters in a part of the ductal network8,14. Direct lineage models for DCIS progression Direct lineage models postulate that DCIS has a single cell of origin that acquires mutations and progresses to IBC15-18. This is also supported by the high genomic concordance of CNAs and mutations in synchronous DCIS–IBC regions6,15,17,19-21 and the results of a recent large longitudinal study that profiled pure DCIS and recurrent IBC using multiple sequencing techniques, which estimated direct clonal lineages in approximately ~80% of patients18. Two distinct direct lineage models have been proposed: the evolutionary bottleneck model and the multiclonal invasion model. In the evolutionary bottleneckmodel, a single clone (or a limited number of clones) with an invasive genotype is selected and breaks through the basement membrane to migrate into surrounding tissues15,16,22, while other clones are unable to escape the ducts21-28. The multiclonal invasion model posits that most or all subclones can escape the basement membrane, establishing invasive disease6,16,17,20. The multiclonal model has not been studied widely in pure DCIS and recurrent IBC samples. Independent lineage model for DCIS progression DCIS lesions and IBCs can arise from different initiating cells in the same breast independently5,20,29-32. An analysis of sequential DCIS–IBC pairs in a unique, large-scale, in-depth study of 95 matched pure DCIS and recurrent IBC showed that ~20% of the IBC recurrences were indeed clonally unrelated to the primary DCIS18, as is also supported by some mathematical model studies33. The potential role of a field effect IBC can develop in the same breast as an initial DCIS even after treatment, which could be explained by the presence of a field effect34-37. Alternatively, the sick lobe hypothesis proposes that a single lobe harbours first-hit mutations, acquired in utero or during early mammary development37-42. This could also explain the restriction of IBC to the ipsilateral side of the breast39,43,44. Germline mutations may also explain the emergence of independent lineages in DCIS and IBC patients, lowering the threshold for cancer development32,43-46. Convergent evolution model of DCIS progression A third model for the emergence of IBC from DCIS is convergent evolution, in which the same mutations and CNA are selected and expanded during tumour growth such that environmental factors fuel competition between distinct clones and push them towards a similar genotype. Ultimately, two independent clonal lineages from different ancestral cells then happen to share multiple genomic aberrations or driver mutations across regions47-49. Although independent lineages are considered uncommon (~20%) in ipsilateral recurrences, they occur at much higher frequencies in contralateral recurrences (&gt;80%), in which single-nucleotide polymorphism and comparative genomic hybridization microarrays show few (or no) genomic alterations shared in tumours from the contralateral breast cancer18,50,51. How to distinguish high- from low-risk DCIS at the time of diagnosis? The genomic and transcriptomic profile present at the time of DCIS diagnosis may contain crucial information on the risk of progression of DCIS to IBC. Thus far, it has been unclear whether prognostic gene expression markers can be used to separate indolent DCIS from potentially progressive DCIS. To this end, microarrays and RNA-seq have been applied for the comparison of bulk RNA from microdissected DCIS and IBC tissue. In synchronous DCIS–IBC, a limited number of transcriptional differences have been found and the few events discovered often varied extensively across different tumours52-56. Although these differences were strong, the added value of these studies is uncertain as they are often confounded by small sample size, lack of matched receptor status data, and low sample purity. Despite these limitations, these studies have implicated the epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) remodelling pathways as potentially relevant for the progression of DCIS to IBC55-62. We studied two large DCIS cohorts: the Sloane cohort, a prospective breast screening cohort from the UK (median follow-up of 12.5 years), and a Dutch population-based cohort (NKI, median follow-up of 13 years). FFPE tissue specimens from patients with pure primary DCIS after breast-conserving surgery (BCS) +/- RT that did develop a subsequent ipsilateral event (DCIS or invasive) were considered as cases, whereas patients that did not develop any form of recurrence up to the last follow-up or death were considered as controls. We performed copy number analysis (CNA) and RNAseq analysis on 229 cases (149 IBC recurrences and 80 DCIS recurrences) and 344 controls. We classified DCIS into the PAM50 subtypes using RNAseq data which revealed an enrichment of luminal A phenotype in DCIS that did not recur (P = 0.01, Fisher Exact test). No single copy number aberration was more common in cases compared to controls. RNAseq data did not reveal any genes significantly over/under expressed in cases versus controls after false discovery rate (FDR) correction. However, by limiting the analysis to samples that had not had RT and excluding pure DCIS recurrences we developed a penalized Cox model from RNAseq data. The model was trained on weighted samples (to correct for the biased sampling of the case control dataset) from the NKI series with double loop cross validation. Using this predicted hazard ratio, the samples were split into high, medium and low risk quantiles, with a recurrence risk of 20%, 9% and 2.5%, respectively at 5 years (p&lt;0.001, Wald test). The NKI-trained predictor was independently validated in the Sloane No RT cohort (p = 0.02, Wald test). GSEA analysis revealed proliferation hallmarks enriched in the recurrence predictor (FDR = 0.058). The NKI-RNAseq predictor was more predictive of invasive recurrence than PAM50, clinical features (Grade, Her2 and ER) and the 12-gene Oncotype DCIS score (p &lt; 0.001, permutation test using the Wald statistic) in both the NKI and Sloane series. In the methylation analysis, 50 controls were compared with 35 cases. We could identify Variably Methylation Regions (VMRs) and Differentially Methylated Regions (DMRs) between cases and controls. Interestingly, VMRs were enriched in cell adhesion pathways Conclusion The recently acquired knowledge described above on how often the subsequent IBC is directly related to the initial DCIS and on molecular markers predicting the risk of DCIS progression is essential for accurate DCIS risk assessment. 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Abstract Background The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms. Methods The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA). Results Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster. Conclusions Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

Abstract Aiming at the defects of weighted comprehensive assessment (WCA) method, this paper proposes a method based on k-means to realize the classification of drought risk. On the basis of calculating the drought risk value of cluster points, the inverse distance weight interpolation (IDWI) method and multidimensional normal diffusion (MND) method were used to quantify the drought risk, and the discrimination between the risk value and grade was improved by interval mapping adjustment. In this paper, the drought risk of Anhui Province from 2000 to 2020 was calculated to verify the above method. The results show that: (1) The drought risk quantification method based on information redistribution of k-means cluster point can not only realize automatic risk classification but also requantify the risk of the assessment object in the same risk grade, which makes up for the defects that the WCA cannot carry out grade division and the conventional clustering classification method cannot assign the risk value of the assessment object. (2) The information redistribution based on MND is closer to the actual drought situation and more reasonable than IDWI. (3) The mapping adjustment of drought risk interval can improve the risk refinement ability of the information redistribution based on k-means cluster.

Enhancing leadership competencies is crucial for non-academic employees to get promoted to an extant position. This study determined the leadership competencies of managers in a university as a basis for a development plan. More so, it used descriptive and correlational research methods and utilized weighted mean, independent t-test, one-way ANOVA, Pearson product-moment correlation, and Friedman test for data analysis. The embedded design in the mixed method technique was used for data collection of the five-leadership competency cluster on People, Team, Execution, Education (foundation), and Technical among the 123 respondents from three universities. Descriptive and correlational research method was utilized. Weighted mean, Independent t-test, one-way ANOVA, Pearson Product Moment Correlation, and Friedman test were used for data analysis. Key findings showed that females subjugated the non-academic discipline were mostly mid-’50s, married, finished a higher degree, and enjoyed a career for about 16-20 years. The most developed leadership competency was the technical cluster. SUCs managers were rated as more competent than private managers. On the assessment made by managers regarding the level of leadership competency based on demographic profile, the result showed that sex, age, civil status, educational attainment, and years in service had no significant effect. The correlation between performance rating and level of competencies has no significance. Using the assessed leadership competencies, a development plan for employees in the non-teaching position was crafted. Keywords: leadership competency, non-academic employees, development plan, performance rating

Cancer classification is very important in the field of bioinformatics for diagnosis of cancer cells. Accurate prediction of cancer is very important for providing better treatment and to avoid the additional cost associated with wrong therapy. In recent years for classifying the cancer numbers of methods have been exist. The main objective is to find the smallest set of genes by using machine learning algorithms. The proposed method initially uses Naive Bayes classifier with great flexibility. However, the Naive Bayes is not suitable for the classification of large datasets because of significant computational problems. The Naive Bayes combined with the mid k-means clustering (Km-Naive Bayes) is a fast algorithm developed to accelerate both the training and the prediction of Naive Bayes classifiers by using the cluster centers obtained from the k-means clustering. The new techniques namely weighted Mid K-means-Naive Bayes is implemented to improve accuracy and to reduce misclassification and noise arising from irrelevant genes. The proposed algorithm was evaluated with different classifier algorithms which were applied on the same database. The experimental results achieved the proposed Mid K-means - Naive Bayes classification accuracy has 88.8%.

The Covid-19 outbreak has hit all countries across the globe, including Indonesia, in which the impact is detrimental and costly. We investigated 14 determinants that could spatially influence Covid-19 cases in Central Kalimantan and South Kalimantan provinces in mid-2020 by using the Geographically Weighted Negative Binomial Regression (GWNBR) and Mixed Geographically Weighted Negative Binomial Regression (MGWNBR). This study conducted iterative Limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundaries (L-BFGS-B) to utilize the numerical parameter estimation of MGWNBR. MGWNBR identified that the adjacent regions tend to group in 8 clusters containing the same significant determinants. Through MGWNBR, the comorbid prevalences (acute respiratory infection, pneumonia, and diabetes) were positively associated with the Covid-19 increasing cases in most regions. The unemployment rate and the number of health care facilities were negatively related to the increase of Covid-19 cases in some regions. MGWNBR was better than GWNBR in terms of AIC, deviance, and pseudo R-sq. The residual map also suggested that MGWNBR produced a more accurate projection than GWNBR. HIGHLIGHTS The statistical models for two Kalimantan provinces of Indonesia during early stage of Covid-19 pandemic Those models consisting of global, local, and mixed models estimated the effect of various social, economical, and health determinants on Covid-19 cases Mixed model parameters are estimated iteratively using L-BFGS-B weighted by adaptive bisquare kernel weight Comparison of models’ performance are applied using deviance, AIC, and pseudo R-sq GRAPHICAL ABSTRACT

Sound levels from occupied classrooms have been gathered from 220 classrooms across four grade levels (3, 5, 8 and 11) over six school days each and processed with k-means clustering into speech and non-speech clusters. Three metrics describing the classroom acoustics, including the average daily A-weighted equivalent level for non-speech, the average daily difference between the A-weighted equivalent levels for speech and non-speech (a signal to noise ratio), and the mid-frequency averaged reverberation time, were analyzed against classroom-aggregated standardized reading and math achievement test scores, while controlling for classroom demographics including socioeconomic status. Interactions between the metrics and demographics were also tested. A statistically significant relationship was found between the average daily non-speech levels in classrooms and math test scores; higher daily non-speech levels were correlated with lower math test scores (p &lt; 0.05). No statistically significant main effects of acoustic metrics were found on reading achievement. There were some significant differences and an interaction found between grades, but these may be due to uneven sample distributions as there were fewer grade 8 and 11 classrooms measured. Children learn in occupied classrooms, and the findings from this investigation based on data from occupied conditions suggest that designing for lower unoccupied sound levels can lead to occupied environments that are conducive to better student learning outcomes.

In the present study, we perform accurate calculations via multireference configuration interaction and coupled cluster methodologies on the dimolybdenum molecule in conjunction with complete series of correlation and weighted core correlation consistent basis sets up to quintuple size. The bonding, dissociation energies, and spectroscopic parameters of the seven states that correlate to the ground state products are calculated. The ground state has a sextuple chemical bond and each of the calculated excited state has one less bond than the previous one. The calculated values for the ground(X1Σg+ ) state of Mo2 have been extrapolated to the complete basis set limits. Our final values, re=1.9324 Å and De(D0)=4.502{plus minus}0.007(4.471{plus minus}0.009) eV, are in excellent agreement with the experimental values of re=1.929, 1.938(9) Å and D0=4.476(10) eV. The Mo2 in 13Σg+ state is a weakly bound dimer, forming 5s...5pz bonds, with De=0.120 eV at re=3.53 Å. All calculated excited states (except 13Σg+) have a highly multireference character (C0=0.25-0.55). The ordering of the molecular bonding orbitals changes as the spin is increased from quintet to septet state. The quite low bond dissociation energy of the ground state is due to the splitting of the molecular bonding orbitals in two groups differing in energy by ~3 eV. Finally, the bond breaking of Mo2, as the multiplicity of spin is increased, is analyzed in parallel with the Mo-Mo bond breaking in a series of Mo2Clx complexes when x is increased. Physical insight into the nature of the sextuple bond and its low dissociation energy is provided.

Late-season bunch rots cause major losses in grape production every year in the Mid-Atlantic United States, but the causal agents are not well characterized. In this study, 265 fungal isolates were collected from rotten grapes from 2014 to 2020 and identified to the genus level according to ITS sequences. The most prevalent of the 15 genera were Botrytis, Colletotrichum, Aspergillus, Alternaria, Pestalotiopsis, and Neopestalotiopsis. Of these, isolates within three prevalent, yet understudied genera were identified to be Aspergillus uvarum, Alternaria alternata, and Neopestalotiopsis rosae. The pathogenicity of these three fungal species was evaluated in two field trials by artificially inoculating wounded and non-wounded grapes of four cultivars at the phenological stages of bloom, veraison, and pre-harvest. Upon ripening, fruit were weighed and assessed for severity of multiple diseases. On non-wounded fruit, A. uvarum caused significantly higher disease severity than the control in both seasons. On wounded fruit, each inocula caused significantly higher disease than the respective controls in the first season, but only A. uvarum and B. cinerea caused this in the second season. Also, wounding was found to have a detrimental effect on cluster weight, which was significantly influenced by inoculation timing and cultivar. Lastly, A. uvarum and N. rosae were tested for sensitivity to azoxystrobin, boscalid, and difenoconazole. The A. uvarum isolates were found to be more sensitive to boscalid and difenoconazole in general, with varying sensitivity to azoxystrobin. N. rosae isolates were resistant to boscalid and azoxystrobin but displayed much higher sensitivity to difenoconazole. Evidence from the isolate collection and field trials demonstrates that A. uvarum could be a significant pathogen of wine grapes in the Mid-Atlantic. Results from this study will be useful for the identification and management of the understudied Alternaria, Aspergillus, and Neopestalotiopsis fruit rots of wine grapes.

Abstract Background Besides, the presence of national law, the country has to set up its own mid-term and long term goals to bring about a significant reduction in child marriages in Ethiopia. As my search concerned, there is no study conducted on the spatial distribution of early marriage in Ethiopia. Determining the spatial distribution of early marriage and factors associated is important for government, other concerned bodies, program implementers, and policy developers to end up early childhood marriage. Thus, this study aimed to assess the spatial distribution and associated factors of Early marriage among reproductive-age women in Ethiopia. Methods This study analyzed retrospectively a cross-sectional data on a weighted sample of 11,646 reproductive age women after requesting from Ethiopian Demographic and Health Survey 2016. ArcGIS and SaTScan software were for spatial analysis. Multiple logistic regression analysis was used to identify factors associated with early marriage. Finally, variables with a p-value of less than or equal 0.05 were considered as statistically significant. Results In this analysis, about 62.8% (95% CI 61.9, 63.74%) of the study participants were married before they reached 18 years. The overall median age at first marriage was 17.1 with IQR 5 years. The high clustering of early marriage was located in Amhara, Afar, and Gambella Regions. In spatial Scan statistics, 87 clusters (RR = 1.28) significant primary clusters were identified. The associated factors of early marriage were lesser among women’s attending primary (AOR = 0.60; 95% CI 0.51, 0.71), secondary (AOR = 0.19; 95% CI 0.13, 0.26) and tertiary education (AOR = 0.11; 95% CI 0.07, 0.18). Similarly, women found in Addis Ababa were at a lesser risk of early marriage compared to other regions of the country. Conclusion Marriage below age 18 is high in Ethiopia. High-risk area of early marriage was located in Amhara, Afar, and Gambella. Governmental and non-governmental organizations should design an effective intervention in these regions to reduce Early marriage. Therefore, providing educational opportunities to young girls was important in addition to inhibiting the marriage of girls under 18 years.

The unique geographical and climatic settings of the eastern Pamirs make this region sensitive to the Westerlies and global climate change. Holocene fluctuations in water-level of Lake Kalakuli, a proglacial lake located to the northwest of the Muztag Ata glacier, were reconstructed based on diatoms from a ∼15 m long sediment core spanning the last ∼9,900 years. To establish how diatom species distribute in relation to water depth in Lake Kalakuli, a dataset of 45 surface sediment samples was retrieved from different water depth. Statistical analyses such as cluster analysis (TWINSPAN) and redundancy analysis (RDA) were used to demonstrate that the water depth gradient is the main environmental gradient driving the distribution of these diatom assemblages. A diatom-water depth transfer function, was then developed using a weighted averaging partial least squares component 2 model (R2 = 0.89, RMSEP = 1.85 m) and applied to the Holocene diatom sequence from Lake Kalakuli. Due to the large residual errors in the model only the general trends in water level are proposed. Effective moisture increased rapidly during the early Holocene, as the water depth reached a high level from the lowest level within about two thousand years. Only small amplitude fluctuations were recorded during the mid- and late Holocene until the last few hundred years when a marked increase occurred. Changes in summer insolation over the northern hemisphere drove the advances and retreats of the Muztag Ata glacier, which in turn controlled the fluctuations of water level in this lake. The diatom-derived paleoclimatic trend from Lake Kalakuli is consistent with the Holocene climate evolution in the Westerlies-dominated area of Central Asia.

Despite extreme physical and chemical characteristics, deep-sea hydrothermal vents provide a place for fauna survival and reproduction. The symbiotic relationship of chemotrophic microorganisms has been investigated in the gill of Rimicaris exoculata, which are endemic to the hydrothermal vents of the Mid-Atlantic Ridge. However, only a few studies have examined intestinal symbiosis. Here, we studied the intestinal fauna in juvenile and adult Rimicaris kairei, another species in the Rimicaris genus that was originally discovered at the Kairei and Edmond hydrothermal vent fields in the Central Indian Ridge. The results showed that there were significant differences between juvenile and adult gut microbiota in terms of species richness, diversity, and evenness. The values of Chao1, observed species, and ASV rarefaction curves indicated almost four times the number of species in adults compared to juveniles. In juveniles, the most abundant phylum was Deferribacterota, at 80%, while in adults, Campilobacterota was the most abundant, at 49%. Beta diversity showed that the intestinal communities of juveniles and adults were clearly classified into two clusters based on the evaluations of Bray–Curtis and weighted UniFrac distance matrices. Deferribacteraceae and Sulfurovum were the main featured bacteria contributing to the difference. Moreover, functional prediction for all of the intestinal microbiota showed that the pathways related to ansamycin synthesis, branched-chain amino acid biosynthesis, lipid metabolism, and cell motility appeared highly abundant in juveniles. However, for adults, the most abundant pathways were those of sulfur transfer, carbohydrate, and biotin metabolism. Taken together, these results indicated large differences in intestinal microbial composition and potential functions between juvenile and adult vent shrimp (R. kairei), which may be related to their physiological needs at different stages of development.

ABSTRACT The microbiota of the built environment is an amalgamation of both human and environmental sources. While human sources have been examined within single-family households or in public environments, it is unclear what effect a large number of cohabitating people have on the microbial communities of their shared environment. We sampled the public and private spaces of a college dormitory, disentangling individual microbial signatures and their impact on the microbiota of common spaces. We compared multiple methods for marker gene sequence clustering and found that minimum entropy decomposition (MED) was best able to distinguish between the microbial signatures of different individuals and was able to uncover more discriminative taxa across all taxonomic groups. Further, weighted UniFrac- and random forest-based graph analyses uncovered two distinct spheres of hand- or shoe-associated samples. Using graph-based clustering, we identified spheres of interaction and found that connection between these clusters was enriched for hands, implicating them as a primary means of transmission. In contrast, shoe-associated samples were found to be freely interacting, with individual shoes more connected to each other than to the floors they interact with. Individual interactions were highly dynamic, with groups of samples originating from individuals clustering freely with samples from other individuals, while all floor and shoe samples consistently clustered together. IMPORTANCE Humans leave behind a microbial trail, regardless of intention. This may allow for the identification of individuals based on the “microbial signatures” they shed in built environments. In a shared living environment, these trails intersect, and through interaction with common surfaces may become homogenized, potentially confounding our ability to link individuals to their associated microbiota. We sought to understand the factors that influence the mixing of individual signatures and how best to process sequencing data to best tease apart these signatures.

Background: The aim of this study is to determine the prevalence of SARS-CoV-2 seropositivity and to examine the risk factors for seropositivity among the people of Ardabil, in the northwestern part of Iran. Methods: A community-based survey was carried out involving 1013 participants (690 from urban and 323 from rural areas), who were selected based on the cluster sampling method. Iran’s FDA-approved Pishtaz Teb SARS-CoV-2 ELISA kits were used to assess the presence of SARS-CoV-2-specific immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies in serum samples. Weighted seroprevalence, the number of infections, infection to case ratio (ICR), and infection fatality ratio (IFR) were estimated after adjusting for survey design and serial test performance. The factors associated with IgG/IgM positive were determined using logistic regression. Results: Between May 20 and June 7, out of 1013 survived people, 123 (12.11%) were IgG positive, 49 (4.8%) were IgM positive and 122 (12.04%) were having both IgG and IgM antibodies. The highest frequency of positive test for IgG and IgM antibodies was found in people with diabetes, followed by people with obesity and heart disease, respectively. Multivariate logistic regression showed old age (2.04, 95% CI: 1.02 to 11.74), male sex (1.52, 95% CI: 1.15 to 2.13), urbanization (1.40, 95% CI: 1.02 to 3.22), higher family number (9.44, 95% CI: 1.69 to 52.13), obesity (2.14, 95% CI: 1.11 to 5.86), NCDs (1.22, 95% CI: 1.01 to 2.16), having symptoms (3.02, 95% CI: 1.64 to 8.61), traveling (2.70, 95% CI: 1.76 to 10.8), history of contact with infected patients (2.38, 95% CI: 1.08 to 7.03), as factors associated with IgG/IgM positive test. Conclusion: Around the mid of May 2020, SARS-CoV-2 seroprevalence was low among Ardabil's adult population. Several factors have been found to be associated with SARS-CoV-2 seroprevalence, which should be considered by policymakers to set policies against the SARS-CoV-2 pandemic.