Nevin Manimala

J Community Psychol. 2021 Oct 18. doi: 10.1002/jcop.22734. Online ahead of print.

ABSTRACT

This article explores the incidence of psychosocial well-being within Lokaleng; a rural community located in the North West province of South Africa. While the community is unique, it shares similarities with many other rural communities in South Africa, such as being deprived of various resources and being impoverished, indicating a need to explore the incidence of psychosocial well-being in this community as there has been no research on this phenomenon in this community specifically. A quantitative, cross-sectional design was employed. A purposive sample of adult community members (n = 189) completed a battery of validated questionnaires and data were analyzed with the IBM SPSS Statistics 26 software package. The results are indicative that the sample experienced lower levels of psychosocial well-being, which supports similar findings of other South African studies that rural communities tend to report lower levels of psychosocial well-being.

PMID:34662437 | DOI:10.1002/jcop.22734

Mol Biol Evol. 2021 Oct 18:msab306. doi: 10.1093/molbev/msab306. Online ahead of print.

ABSTRACT

The soil bacterium Burkholderia pseudomallei is the causative agent of melioidosis and a significant cause of human morbidity and mortality in many tropical and sub-tropical countries. The species notoriously survives harsh environmental conditions but the genetic architecture for these adaptations remains unclear. Here we employed a powerful combination of genome-wide epistasis and co-selection studies (2,011 genomes), condition-wide transcriptome analyses (82 diverse conditions), and a gene knockout assay to uncover signals of “co-selection” – that is a combination of genetic markers that have been repeatedly selected together through B. pseudomallei evolution. These enabled us to identify 13,061 mutation pairs under co-selection in distinct genes and non-coding RNA. Genes under co-selection displayed marked expression correlation when B. pseudomallei was subjected to physical stress conditions, highlighting the conditions as one of the major evolutionary driving forces for this bacterium. We identified a putative adhesin (BPSL1661) as a hub of co-selection signals, experimentally confirmed a BPSL1661 role under nutrient deprivation, and explored the functional basis of co-selection gene network surrounding BPSL1661 in facilitating the bacterial survival under nutrient depletion. Our findings suggest that nutrient-limited conditions have been the common selection pressure acting on this species, and allelic variation of BPSL1661 may have promoted B. pseudomallei survival during harsh environmental conditions by facilitating bacterial adherence to different surfaces, cells, or living hosts.

PMID:34662416 | DOI:10.1093/molbev/msab306

J Infect Dis. 2021 Oct 18:jiab536. doi: 10.1093/infdis/jiab536. Online ahead of print.

ABSTRACT

A recurrent question is whether transient reactions to vaccines translate into better immune responses. Using clinical data from two large Phase 3 studies of the recombinant zoster vaccine, we observed a small but statistically significant association between the intensity of a frequent side-effect (pain) after vaccination and immune responses to vaccination. However, despite the statistical correlation, the impact on the immune response is so small, and the immune response in individuals without pain already sufficient, that pain cannot be a surrogate marker for an appropriate immune response. Reactogenicity cannot be used to predict immunity after vaccination.

PMID:34662415 | DOI:10.1093/infdis/jiab536

Plant Physiol. 2021 Oct 18:kiab476. doi: 10.1093/plphys/kiab476. Online ahead of print.

ABSTRACT

Bicontinuous membranes in cell organelles epitomize nature’s ability to create complex functional nanostructures. Like their synthetic counterparts, these membranes are characterized by continuous membrane sheets draped onto topologically complex saddle-shaped surfaces with a periodic network-like structure. Their structure sizes, (around 50-500 nm), and fluid nature make transmission electron microscopy (TEM) the analysis method of choice to decipher their nanostructural features. Here we present a tool, SPIRE (Surface Projection Image Recognition Environment), to identify bicontinuous structures from TEM sections through interactive identification by comparison to mathematical “nodal surface” models. The prolamellar body (PLB) of plant etioplasts is a bicontinuous membrane structure with a key physiological role in chloroplast biogenesis. However, the determination of its spatial structural features has been held back by the lack of tools enabling identification and quantitative analysis of symmetric membrane conformations. Using our SPIRE tool, we achieved a robust identification of the bicontinuous diamond surface as the dominant PLB geometry in angiosperm etioplasts in contrast to earlier long-standing assertions in the literature. Our data also provide insights into membrane storage capacities of PLBs with different volume proportions and hint at the limited role of a plastid ribosome localization directly inside the PLB grid for its proper functioning. This represents an important step in understanding their as yet elusive structure-function relationship.

PMID:34662407 | DOI:10.1093/plphys/kiab476

Interact Cardiovasc Thorac Surg. 2021 Oct 18:ivab289. doi: 10.1093/icvts/ivab289. Online ahead of print.

ABSTRACT

OBJECTIVE: Spread through air spaces (STAS) is a pattern of invasion present in some adenocarcinomas (ADC). The goal of this study was to assess the impact of STAS in patients treated with different types of surgical resections and on the clinical outcome in patients with ADC of different diameters and with different degrees of nodal involvement.

METHODS: A total of 109 patients were reviewed. Complete surgical resection with systematic nodal dissection was achieved in all patients. The median follow-up was 65 months (3-90 months).

RESULTS: STAS was observed in 70 cases (64.2%); 13 patients (18.5%) had lymph node involvement (N1 and N2). Overall survival and progression-free survival were higher in patients without STAS (P = 0.042; P = 0.027). The presence of STAS in tumours ≤2 cm was a predictor of worse progression-free survival following sublobar resection compared to major resections (P = 0.011). Sublobar resection of N0 STAS-positive tumours was associated with worse long-term survival compared to a major resection (P = 0.04). Statistical analyses showed that age >70 years and recurrence were independent variables for survival; smoking pack-years >20, sublobar resection and nodal involvement were independent variables for recurrence; and smoking pack-years >20 were independent variables for a history of cancer and pleural invasion for local recurrence.

CONCLUSIONS: STAS seems to play a role in long-term survival, particularly for patients with N0 and tumours smaller than 2 cm. Further studies are necessary to validate this hypothesis.

PMID:34662397 | DOI:10.1093/icvts/ivab289

Eur Heart J. 2021 Oct 18:ehab702. doi: 10.1093/eurheartj/ehab702. Online ahead of print.

ABSTRACT

AIMS: Patients at high bleeding risk (HBR) represent a prevalent subgroup among those undergoing percutaneous coronary intervention (PCI). Early aspirin discontinuation after a short course of dual antiplatelet therapy (DAPT) has emerged as a bleeding avoidance strategy. The aim of this study was to assess the effects of ticagrelor monotherapy after 3-month DAPT in a contemporary HBR population.

METHODS AND RESULTS: This prespecified analysis of the TWILIGHT trial evaluated the treatment effects of early aspirin withdrawal followed by ticagrelor monotherapy in HBR patients undergoing PCI with drug-eluting stents. After 3 months of ticagrelor plus aspirin, event-free patients were randomized to 12 months of aspirin or placebo in addition to ticagrelor. A total of 1064 (17.2%) met the Academic Research Consortium definition for HBR. Ticagrelor monotherapy reduced the incidence of the primary endpoint of Bleeding Academic Research Consortium (BARC) 2, 3, or 5 bleeding compared with ticagrelor plus aspirin in HBR (6.3% vs. 11.4%; hazard ratio (HR) 0.53, 95% confidence interval (CI) 0.35-0.82) and non-HBR patients (3.5% vs. 5.9%; HR 0.59, 95% CI 0.46-0.77) with similar relative (Pinteraction = 0.67) but a trend towards greater absolute risk reduction in the former [-5.1% vs. -2.3%; difference in absolute risk differences (ARDs) -2.8%, 95% CI -6.4% to 0.8%, P = 0.130]. A similar pattern was observed for more severe BARC 3 or 5 bleeding with a larger absolute risk reduction in HBR patients (-3.5% vs. -0.5%; difference in ARDs -3.0%, 95% CI -5.2% to -0.8%, P = 0.008). There was no significant difference in the key secondary endpoint of death, myocardial infarction, or stroke between treatment arms, irrespective of HBR status.

CONCLUSIONS: Among HBR patients undergoing PCI who completed 3-month DAPT without experiencing major adverse events, aspirin discontinuation followed by ticagrelor monotherapy significantly reduced bleeding without increasing ischaemic events, compared with ticagrelor plus aspirin. The absolute risk reduction in major bleeding was larger in HBR than non-HBR patients.

PMID:34662382 | DOI:10.1093/eurheartj/ehab702

Int J Epidemiol. 2021 Oct 18:dyab204. doi: 10.1093/ije/dyab204. Online ahead of print.

ABSTRACT

BACKGROUND: We aimed to study whether pre-eclampsia is associated with childhood asthma, allergic and non-allergic asthma, accounting for family factors and intermediate variables.

METHODS: The study population comprised 779 711 children born in 2005-2012, identified from Swedish national health registers (n = 14 823/7410 exposed to mild/moderate and severe pre-eclampsia, respectively). We used Cox regression to estimate the associations of mild/moderate and severe pre-eclampsia with incident asthma, before and after age 2 years. Cox regressions were controlled for familial factors using sibling comparisons, then stratified on high and low risk for intermediate variables: caesarean section, prematurity and small for gestational age. We used logistic regression for allergic and non-allergic prevalent asthma at 6 years as a measure of more established asthma.

RESULTS: The incidence of asthma in children was 7.7% (n = 60 239). The associations varied from adjusted hazard ratio (adjHR) 1.11, 95% confidence interval (CI): 1.00, 1.24 for mild/moderate pre-eclampsia and asthma at >2 years age, to adjHR 1.78, 95% CI: 1.64, 1.95 for severe pre-eclampsia and asthma at <2 years age. Sibling comparisons attenuated most estimates except for the association between severe pre-eclampsia and asthma at <2 years age (adjHR 1.45, 95% CI: 1.10, 1.90), which also remained when stratifying for the risk of intermediates. Mild/moderate and severe pre-eclampsia were associated with prevalent non-allergic (but not allergic) asthma at 6 years, with adjusted odds ratio (adjOR) 1.17, 95% CI: 1.00, 1.36 and adjOR 1.51, 95% CI: 1.23, 1.84, respectively.

CONCLUSIONS: We found evidence that severe, but not mild/moderate, pre-eclampsia is associated with asthma regardless of familial factors and confounders.

PMID:34662374 | DOI:10.1093/ije/dyab204

J Anim Sci. 2021 Oct 18:skab294. doi: 10.1093/jas/skab294. Online ahead of print.

ABSTRACT

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making it impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1=0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains such as housing, nutrition or climate, that including more and diverse data sources increases prediction performance and that the re-use of existing data can create actionable information for preventive interventions. Our findings pave the way towards data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

PMID:34662372 | DOI:10.1093/jas/skab294

PLoS One. 2021 Oct 18;16(10):e0258677. doi: 10.1371/journal.pone.0258677. eCollection 2021.

ABSTRACT

This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean meteorological data within a certain period between seeding and harvest as predictor variables and one-year-leave-out cross-validation procedure, performance of machine learning models based on neural network (NN), random forest (RF), support vector machine regression (SVR), partial least squares regression (PLS), and cubist regression (CB) were compared to a multiple linear regression model (MLR) and a null model that returns an average yield of the municipality. The root mean square errors of PLS, SVR, and RF were 872, 982, and 1,024 kg ha-1 and were smaller than those of MLR (1,068 kg ha-1) and null model (1,035 kg ha-1). These models outperformed the controls in other metrics including Pearson’s correlation coefficient and Nash-Sutcliffe efficiency. Variable importance analysis on PLS indicated that minimum air temperature and precipitation during the grain-filling period had major roles in the prediction and excluding predictors in this period (i.e. yield forecast with a longer lead-time) decreased forecast performance of the models. These results were consistent with our understanding of meteorological impacts on wheat yield, suggesting usefulness of explainable machine learning in meteorological crop yield prediction under wet climate.

PMID:34662365 | DOI:10.1371/journal.pone.0258677

PLoS One. 2021 Oct 18;16(10):e0258439. doi: 10.1371/journal.pone.0258439. eCollection 2021.

ABSTRACT

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.

PMID:34662344 | DOI:10.1371/journal.pone.0258439