Good Model Research Articles

Comparison of the Recent ExPreS Score, WEANSNOW Score, and the Parsimonious HACOR Score as the Best Predictor of Weaning: An Externally Validated Prospective Observational Study.

Since weaning failure is multifactorial, comprehensive weaning scores encompassing not only the respiratory component but also nonrespiratory aspects are quintessential for successful weaning prediction. This was a single-center prospective observational study on 128 intensive care unit (ICU) patients undergoing spontaneous breathing trials (SBT). The extubation prediction score (ExPreS), heart rate, acidosis, consciousness, oxygenation, respiratory rate (HACOR), and weaning parameters, endotracheal tube size, arterial blood gas analysis, nutrition, secretions, neuromuscular affecting agents, obstructive airway problems and wakefulness (WEANSNOW) scores were compared for their diagnostic accuracy for successful weaning prediction. Out of 128 patients, 49 (38.3%) patients had weaning failure, and 79 (61.7%) had weaning success. The patients in the weaning failure group had significantly higher APACHE II scores, WEANSNOW scores, HACOR scores, MV days, and significantly lower ExPreS scores as compared to the successful weaning group. Multivariable regression analysis showed that ExPreS score p = 0.015, adjusted OR 0.960, 95% CI (0.929-0.992) and HACOR score p < 0.001, adjusted OR 1.357, 95% CI (1.176-1.567) were independent predictors of weaning failure. The HACOR score had an AUC of 0.830, cut-off ≥5, p < 0.001, sensitivity 76%, specificity 68%, diagnostic accuracy 70% to predict weaning failure. The ExPreS score had an AUC of 0.735, cut-off ≥69, p < 0.001, sensitivity of 70.9%, specificity of 69.4%, and diagnostic accuracy of 70.3% to predict weaning success. Both the HACOR and ExPreS scores were good models for predicting weaning outcomes (model quality 0.76 and 0.64 respectively). The parsimonious HACOR score is comparable to the ExPreS score for the prediction of weaning outcomes in critically ill patients. Nayak G, Chaudhuri S, Ravindranath S, Todur P. Comparison of the Recent ExPreS Score, WEANSNOW Score, and the Parsimonious HACOR Score as the Best Predictor of Weaning: An Externally Validated Prospective Observational Study. Indian J Crit Care Med 2024;28(3):273-279.

Bioaccumulation efficacy and physio-morphological adaptations in response to iron and aluminium contamination of Indian camphorweed (Pluchea indica L.) using different growth substrates.

The aim of this study was to assess the removal capability of Fe/Al contamination of Indian camphorweed (Pluchea indica; hereafter, P. indica) using different growth substrates (100% sand, gardening soil, vermiculite, and zeolite). In addition, the study aimed at observing the physio-morphological adaptation strategies of P. indica under excess Fe/Al levels in a controlled greenhouse environment. After a 4-week treatment, P. indica plants under excess Fe in the 100% sand substrate exhibited signs of decay and eventually death. In contrast, the growth performances of P. indica under gardening soil substrate remained sustained even when exposed to Fe/Al stress. Under zeolite substrate, Fe in the root tissues was 23.1 and 34.7mgg-1 DW after 1 and 4weeks of incubation, respectively. In addition, Al in the root tissues also increased to 1.54mgg-1 DW after 1week and 1.59mgg-1 DW after 4weeks, when subjected to 20mM Al treatment. Zeolite was observed to be a promising substrate to regulate the uptake of Fe (3.31mg plant-1) and Al (0.51mg plant-1) by the root tissues. The restriction of Fe and Al in the root and a low translocation to the leaf organ was indicated by a low translocation factor (< 1.0). High Fe concentrations in the root and leaf tissues negatively affected root elongation, and the net photosynthetic rate decreased by > 40% compared to positive control. Gas exchange parameters and leaf temperature were found the most sensitive to Fe/Al stress. Moreover, the limited transpiration rate under Fe/Al stress caused an increase of the leaf temperature and crop stress index. The findings suggest that P. indica grown using zeolite substrate may serve as a good model system for constructed wetlands, storing excess Al in the root tissues without any significant growth inhibition.

Identification of plb1 mutation that extends longevity via activating Sty1 MAPK in Schizosaccharomyces pombe.

To understand the lifespan of higher organisms, including humans, it is important to understand lifespan at the cellular level as a prerequisite. So, fission yeast is a good model organism for the study of lifespan. To identify the novel factors involved in longevity, we are conducting a large-scale screening of long-lived mutant strains that extend chronological lifespan (cell survival in the stationary phase) using fission yeast. One of the newly acquired long-lived mutant strains (No.98 mutant) was selected for analysis and found that the long-lived phenotype was due to a missense mutation (92Phe → Ile) in the plb1+ gene. plb1+ gene in fission yeast is a nonessential gene encoding a homolog of phospholipase B, but its functions under normal growth conditions, as well as phospholipase B activity, remain unresolved. Our analysis of the No.98 mutant revealed that the plb1 mutation reduces the integrity of the cellular membrane and cell wall and activates Sty1 via phosphorylation.

Multispectral detection of dietary fiber content in Chinese cabbage leaves across different growth periods

Multispectral imaging, combined with stoichiometric values, was used to construct a prediction model to measure changes in dietary fiber (DF) content in Chinese cabbage leaves across different growth periods. Based on all the spectral bands (365–970 nm) and characteristic spectral bands (430, 880, 590, 490, 690 nm), eight quantitative prediction models were established using four machine learning algorithms, namely random forest (RF), backpropagation neural network, radial basis function, and multiple linear regression. Finally, a quantitative prediction model of RF learning algorithm is constructed based on all spectral bands, which has good prediction accuracy and model robustness, prediction performance with R2 of 0.9023, root mean square error (RMSE) of 2.7182 g/100 g, residual predictive deviation (RPD) of 3.1220 > 3.0. In summary, this model efficiently detects changes in DF content across different growth periods of Chinese cabbage, which offers technical support for vegetable sorting and grading in the field.

Mendeteksi Ancaman Kecanduan Gadget pada Anak Usia Dini

This article examines providing information for parents in detecting gadget addiction in young children from the start before young children become addicted to gadgets. This study was motivated by the fact that many aged children have gadget addiction disorders such as sleep disorders, aggressive nature, difficulty understanding learning, hunched body posture, reduced brain growth and development, strange behavior, irritability, and unwillingness to socialize. This study discusses 4 (four) discussions, namely (1) factors causing gadget addiction in early childhood, (2) the threat of gadget addiction in early childhood, (3) how to detect the threat of gadget addiction in early childhood, (4 ) The role of parents in preventing gadget addiction in early childhood. Based on the results of the analysis in this paper, it can be concluded that gadget addiction in early childhood is a serious problem that has various negative impacts. Several factors that cause gadget addiction include inappropriate parenting, neuroscientific factors, gadget technology designs that create dependency, and the influence of the surrounding environment. Parents play a key role in preventing gadget addiction by limiting the time they use gadgets, choosing appropriate applications, monitoring gadget use, and being good role models. Parents also need to detect signs of gadget addiction in their children, including changes in behavior, and physical, emotional, social, and academic performance. With the active role of parents, the negative impact of gadget addiction in young children can be minimized.

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging.

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour. To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data. Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared. For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI. The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Establishment of human induced pluripotent stem cell line SDQLCHi029-A from one Type 1 familial glucocorticoid deficiency patient carrying compound heterozygote mutations in MC2R gene

Type 1 familial glucocorticoid deficiency (FGD) is a rare autosomal recessive disorder due to variation of the melanocortin-2-receptor (MC2R) gene. Induced pluripotent stem cell (iPSC) line SDQLCHi029-A was successfully generated from peripheral blood mononuclear cells obtained from a 5-day-old girl with MC2R mutations (c.428C > T and c.409C > T). The iPSC line showed genetically stable and matched the donor’s PBMCs. displayed a normal karyotype, expressed high pluripotent markers, and exhibited differentiation potential of three germ layers in vitro. The iPSC line could be a good model to study the pathogenesis of FGD type 1 and screen new drugs for the disease.

Preoperative blood markers and intra-abdominal infection after colorectal cancer resection.

Colorectal cancer (CRC) has one of the highest morbidity and mortality rates among digestive tract tumors. Intra-abdominal infection (IAI) is a common postoperative complication that affects the clinical outcomes of patients with CRC and hinders their rehabilitation process. However, the factors influencing abdominal infection after CRC surgery remain unclear; further, prediction models are rarely used to analyze preoperative laboratory indicators and postoperative complications. To explore the predictive value of preoperative blood markers for IAI after radical resection of CRC. The data of 80 patients who underwent radical resection of CRC in the Anorectal Surgery Department of Suzhou Hospital affiliated with Anhui Medical University were analyzed. These patients were categorized into IAI (n = 15) and non-IAI groups (n = 65) based on whether IAI occurred. Influencing factors were compared; general data and laboratory indices of both groups were identified. The relationship between the indicators was assessed. Further, a nomogram prediction model was developed and evaluated; its utility and clinical applicability were assessed. The risk factors for IAI after radical resection of CRC were neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and carcinoembryonic antigen (CEA) levels. NLR was correlated with PLR and SII (r = 0.604, 0.925, and 0.305, respectively), while PLR was correlated with SII (r = 0.787). The nomogram prediction model demonstrated an area under the curve of 0.968 [95% confidence interval (CI): 0.948-0.988] in the training set (n = 60) and 0.926 (95%CI: 0.906-0.980) in the validation set (n = 20). The average absolute errors of the calibration curves for the training and validation sets were 0.032 and 0.048, respectively, indicating a good model fit. The decision curve analysis curves demonstrated high net income above the 5% threshold, indicating the clinical practicality of the model. The nomogram model constructed using NLR, PLR, SII, and CEA levels had good accuracy and reliability in predicting IAI after radical resection of CRC, potentially aiding clinical treatment decision-making.

The Chinese Adaptation and Validation of the Adolescents' Ageism Toward Older Adults Scale.

Objectives: The purpose of this study was to translate and validate the adolescents' ageism toward older adults scale (AGES) in the Chinese cultural context and examine its psychometric properties among Chinese adolescents. Methods: The study consists of two phases with two separate samples. In phase one (sample 1: n = 407), exploratory factor analysis (EFA) is conducted to determine the factor structure of the C-AGES. In phase two (sample 2: n = 379), confirmatory factor analysis (CFA) is performed to confirm the factor structure and assess the model fit of the C-AGES. Results: EFA reveals a two-factor structure consisting of 17 items for the C-AGES. CFA in sample 2 confirms the factor structure and demonstrates good model fit. The C-AGES also exhibits high criterion validity, internal consistency, and cross-gender invariance. Discussion: The results suggest that the C-AGES is a valid measurement tool for assessing agism among Chinese adolescents.

Disentangled deep generative models reveal coding principles of the human face processing network.

Despite decades of research, much is still unknown about the computations carried out in the human face processing network. Recently, deep networks have been proposed as a computational account of human visual processing, but while they provide a good match to neural data throughout visual cortex, they lack interpretability. We introduce a method for interpreting brain activity using a new class of deep generative models, disentangled representation learning models, which learn a low-dimensional latent space that "disentangles" different semantically meaningful dimensions of faces, such as rotation, lighting, or hairstyle, in an unsupervised manner by enforcing statistical independence between dimensions. We find that the majority of our model's learned latent dimensions are interpretable by human raters. Further, these latent dimensions serve as a good encoding model for human fMRI data. We next investigate the representation of different latent dimensions across face-selective voxels. We find that low- and high-level face features are represented in posterior and anterior face-selective regions, respectively, corroborating prior models of human face recognition. Interestingly, though, we find identity-relevant and irrelevant face features across the face processing network. Finally, we provide new insight into the few "entangled" (uninterpretable) dimensions in our model by showing that they match responses in the ventral stream and carry information about facial identity. Disentangled face encoding models provide an exciting alternative to standard "black box" deep learning approaches for modeling and interpreting human brain data.

Parenting and other potential protective factors associated with polysubstance use among public school students in Lagos, Nigeria.

Substance use is a growing problem in Nigeria. The present study extended recent work documenting the importance of parenting as protective against substance use in Nigerian youth by testing a model linking parenting, additional protective factors and polysubstance use. Public school students (N = 1607; 56% female; M age = 14.88; SD = .44 years) living in the greater Lagos region participated in school-based data collection. Lifetime polysubstance use, defined as use of two or more substances including alcohol or illicit drugs, or misuse of over-the-counter medications, was reported by 5.2% of the sample. Structural equation modelling that accounted for adolescent age and sex on all constructs revealed good model fit. Positive parenting (support and solicitation) was significantly associated with higher perceived harmfulness of substance use, religiosity and positive relationships at school. Positive school relationships were associated with a decreased likelihood of polysubstance use. Multiple group analysis revealed no overall sex differences in the model paths. Strengthening parent-adolescent relationships may have a cascading effect on protective factors and subsequent substance use, and should be included in youth substance use prevention programmes.

Detection and enzymatic characterization of human saliva amylase.

As a rule, an experiment carried out at school or in undergraduate study courses is rather simple and not very informative. However, when the experiments are to be performed using modern methods, they are often abstract and difficult to understand. Here, we describe a quick and simple experiment, namely the enzymatic characterization of ptyalin (human salivary amylase) using a starch degradation assay. With the experimental setup presented here, enzyme parameters, such as pH optimum, temperature optimum, chloride dependence, and sensitivity to certain chemicals can be easily determined. This experiment can serve as a good model for enzyme characterization in general, as modern methods usually follow the same principle: determination of the activity of the enzyme under different conditions. As different alleles occur in humans, a random selection of test subjects will be quite different with regard to ptyalin activities. Therefore, when the students measure their own ptyalin activity, significant differences will emerge, and this will give them an idea of the genetic diversity in human populations. The evaluation has shown that the pupils have gained a solid understanding of the topic through this experiment.

Concurrent factors associated with adherence to adjuvant endocrine therapy among women with non-metastatic breast cancer.

Adjuvant endocrine therapy (AET) reduces breast cancer morbidity and mortality, yet women often report suboptimal adherence. Though correlates of AET adherence are well-documented, few studies examine the relative importance of multi-level factors associated with adherence. The aim of this study was to identify factors most strongly associated with AET adherence in women with breast cancer. Between 10/2019 and 6/2021, women (N = 100) with non-metastatic, hormone receptor-positive breast cancer, taking AET who reported AET-related distress enrolled into a clinical trial. Participants completed baseline measures, including the Medication Adherence Rating Scale-5, sociodemographics, and validated measures of anxiety, depression, medication-taking self-efficacy, social support, and treatment satisfaction. We created a latent factor and tested associations between sociodemographic, medical, and psychosocial characteristics and adherence. Associated predictors (p < .10) were entered into a structural model, which was corroborated via multivariate regression modeling. A four-indicator latent adherence factor demonstrated good model fit. Participants (Mage = 56.1years, 91% White) who were unemployed (B = 0.27, SE = 0.13, p = .046) and reported greater treatment convenience (B = 0.01, SE = 0.01, p = .046) reported greater adherence. Scores of participants who reported greater medication-taking self-efficacy (p = .097) and social support (p = .062) approached better adherence. Greater medication-taking self-efficacy (B = 0.08, SE = 0.02, p < .001) and being unemployed (B = 0.28, SE = .14, p = .042) were most strongly associated with greater adherence, independent of other predictors. Multivariate modeling confirmed similar findings. Medication-taking self-efficacy and employment status were associated with AET adherence above other related factors. Enhancing patients' confidence in their ability to take AET for breast cancer may represent an important intervention target to boost adherence.

Evaluating the Model Representation of Asian Summer Monsoon Upper Troposphere and Lower Stratosphere Transport and Composition Using Airborne In Situ Observations

AbstractChemistry Climate Models (CCMs) are essential tools for characterizing and predicting the role of atmospheric composition and chemistry in Earth's climate system. This study demonstrates the use of airborne in situ observations to diagnose the representation of chemical composition and transport by CCMs. Process‐based diagnostics using dynamical and chemical coordinates are presented which minimize the spatial and temporal sampling differences between airborne in situ measurements and CCM grid points. The chosen process is the chemical impact of the Asian summer monsoon (ASM), where deep convection serves as a rapid transport pathway for surface emissions to reach the upper troposphere and lower stratosphere (UTLS). We examine two CCM configurations for their representation of the ASM UTLS using a set of airborne observations from south Asia. The diagnostics reveal good model performance at representing tropospheric tracer distribution throughout the troposphere and lower stratosphere, and excellent representation of chemical aging in the lower stratosphere when chemical loss is dominated by photolysis. Identified model limitations include the use of zonally averaged mole fraction boundary conditions for species with sufficiently short tropospheric lifetimes, which may obscure enhanced regional emissions sources. Overall, the diagnostics underscore the skill of current‐generation models at representing pollution transport from the boundary layer to the stratosphere via the ASM mechanism, and demonstrate the strength of airborne in situ observations toward characterizing this representation.

Evaluating the quality of life impact of recurrent urinary tract infection: Validation and refinement of the Recurrent UTI Impact Questionnaire (RUTIIQ).

Recurrent urinary tract infection (rUTI) has significant negative consequences for a wide variety of quality of life (QoL) domains. Without adequate validation and assessment of the unique insights of people living with rUTI, clinical results cannot be fully understood. The Recurrent UTI Impact Questionnaire (RUTIIQ), a novel patient-reported outcome measure of rUTI psychosocial impact, has been robustly developed with extensive patient and clinician input to facilitate enhanced rUTI management and research. This study aimed to confirm the structural validity of the RUTIIQ, assessing its strength and bifactor model fit. A sample of 389 adults experiencing rUTI (96.9% female, aged 18-87 years) completed an online cross-sectional survey comprising a demographic questionnaire and the RUTIIQ. A bifactor graded response model was fitted to the data, optimizing the questionnaire structure based on item fit, discrimination capability, local dependence, and differential item functioning. The final RUTIIQ demonstrated excellent bifactor model fit (RMSEA = 0.054, CFI = 0.99, SRMSR = 0.052), and mean-square fit indices indicated that all included items were productive for measurement (MNSQ = 0.52-1.41). The final questionnaire comprised an 18-item general "rUTI QoL impact" factor, and five subfactor domains measuring "personal wellbeing" (three items), "social wellbeing" (four items), "work and activity interference" (four items), "patient satisfaction" (four items), and "sexual wellbeing" (three items). Together, the general factor and five subfactors explained 81.6% of the common model variance. All factor loadings were greater than 0.30 and communalities greater than 0.60, indicating good model fit and structural validity. The 18-item RUTIIQ is a robust, patient-tested questionnaire with excellent psychometric properties, which capably assesses the patient experience of rUTI-related impact to QoL and healthcare satisfaction. Facilitating standardized patient monitoring and improved shared decision-making, the RUTIIQ delivers the unique opportunity to improve patient-centered care.

Role of amorphous phase in II-I transition of iPB-1/iPP blends

Isotactic polybutene-1 (iPB-1) possesses excellent physical and mechanical properties, but its applications are limited owing to the slow II-I transition after melt processing. The concerned II-I transition mechanism is a long-lasting hot topic. After an isotactic polypropylene (iPP) was melt-blended into iPB-1, a good model system for II-I transition study to discuss on roles of amorphous phase was consequently obtained with amorphous phase tuned but form II crystalline structures almost kept fixed according to the affected crystallization and melting behaviors of iPB-1 in iPB-1/iPP blends. Both DSC and FTIR results revealed the enhanced II-I transition behaviors of iPB-1 in the blends and FTIR results especially confirmed form I direct crystallization from amorphous phase of iPB-1/iPP blends prior to form II consumption due to II-I transition. The earlier the direct form I crystallization, the faster II-I transition of iPB-1 in the iPB-1/iPP blends. It was proposed that form I direct crystallization of the iPB-1 chain segments which have ability to crystallize but remained in the amorphous phase lowered the energy barrier of transition from adjacent form II crystals to packed-mesophases due to the local volume contract resulting in a stretch to iPB-1 chains with parts contained in the adjacent form II crystals. Consequently, the II-I transition of adjacent form II crystals were triggered to start and it generated a ripple effect on the entire II-I transition process. The results are of great significance on understanding of iPB-1 II-I transition process and also of importance for iPB-1 industrial applications.

Parisi's Hypercube, Fock-Space Frustration, and Near-AdS_{2}/Near-CFT_{1} Holography.

We consider a model of Parisi where a single particle hops on an infinite-dimensional hypercube, under the influence of a uniform but disordered magnetic flux. We reinterpret the hypercube as the Fock-space graph of a many-body Hamiltonian and the flux as a frustration of the return amplitudes in Fock-space. We will identify the set of observables that have the same correlation functions as the double-scaled Sachdev-Ye-Kitaev (DS-SYK) model, and hence the hypercube model is an equally good quantum model for near-AdS_{2}/near-CFT_{1} (NAdS_{2}/NCFT_{1}) holography. Unlike the SYK model, the hypercube Hamiltonian is not p local. Instead, the SYK model can be understood as a Fock-space model with similar frustrations. Hence we propose this type of Fock-space frustration as the broader characterization for NAdS_{2}/NCFT_{1} microscopics, which encompasses the hypercube and the DS-SYK models as two specific examples. We then speculate on the possible origin of such frustrations.

Designing a Pattern of Recorded Experiences in the Organization in Facing and Dealing With the COVID-19 Crisis: A Case Study

Background: Empiricism is one of the important components of knowledge management. In empiricism, an attempt is made to document and publish the tacit knowledge of individuals and their teachings. The purpose of this study was to design a model for recording organizational experiences in facing and dealing with the COVID-19 crisis. Methods: The present study is an exploratory mixed study that was conducted to design a model for recording the experiences of the organization in facing and dealing with the COVID-19 crisis. In the first step, the main dimensions and concepts of the subject were formed by reviewing studies and consulting with experts and specialists. In the second step, the basic items of the model were developed. In the last step, psychometric properties, as well as validation, were examined. Results: In this model, first, critical occasions were identified. In the next step, the face and content validity of the instrument were determined both qualitatively and quantitatively (content validity ratio [CVR]=0.84 and content validity index [CVI]=0.76). Kendall’s coefficient of concordance was used to assess the unwavering quality of the instrument. In the present study, a coefficient of 0.90 indicates substantial agreement among the experts. Conclusion: Factors influencing experience recording and related questions can provide a good model for organizational empiricism. The results of this research can be used as a basis for managers’ planning in implementing experience documentation in the organization.

Individual sleep need is flexible and dynamically related to cognitive function.

Given that sleep deprivation studies consistently show that short sleep causes neurocognitive deficits, the effects of insufficient sleep on brain health and cognition are of great interest and concern. Here we argue that experimentally restricted sleep is not a good model for understanding the normal functions of sleep in naturalistic settings. Cross-disciplinary research suggests that human sleep is remarkably dependent on environmental conditions and social norms, thus escaping universally applicable rules. Sleep need varies over time and differs between individuals, showing a complex relationship with neurocognitive function. This aspect of sleep is rarely addressed in experimental work and is not reflected in expert recommendations about sleep duration. We recommend focusing on the role of individual and environmental factors to improve our understanding of the relationship between human sleep and cognition.

Population genomics of Agrotis segetum provide insights into the local adaptive evolution of agricultural pests

BackgroundThe adaptive mechanisms of agricultural pests are the key to understanding the evolution of the pests and to developing new control strategies. However, there are few studies on the genetic basis of adaptations of agricultural pests. The turnip moth, Agrotis segetum (Lepidoptera: Noctuidae) is an important underground pest that affects a wide range of host plants and has a strong capacity to adapt to new environments. It is thus a good model for studying the adaptive evolution of pest species.ResultsWe assembled a high-quality reference genome of A. segetum using PacBio reads. Then, we constructed a variation map of A. segetum by resequencing 98 individuals collected from six natural populations in China. The analysis of the population structure showed that all individuals were divided into four well-differentiated populations, corresponding to their geographical distribution. Selective sweep analysis and environmental association studies showed that candidate genes associated with local adaptation were functionally correlated with detoxification metabolism and glucose metabolism.ConclusionsOur study of A. segetum has provided insights into the genetic mechanisms of local adaptation and evolution; it has also produced genetic resources for developing new pest management strategies.