Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal, Infant, and Child Health Outcomes Associated With the Special Supplemental Nutrition Program for Women, Infants, and Children

BACKGROUND The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is intended to improve maternal and child health outcomes. In 2009, the WIC food package changed to better align with national nutrition recommendations. PURPOSE To determine whether WIC participation was associated with improved maternal, neonatal-birth, and infant-child health outcomes or differences in outcomes by subgroups and WIC enrollment duration. DATA SOURCES Search (January 2009 to April 2022) included PubMed, Embase, CINAHL, ERIC, Scopus, PsycInfo, and the Cochrane Central Register of Controlled Trials. STUDY SELECTION Included studies had a comparator of WIC-eligible nonparticipants or comparison before and after the 2009 food package change. DATA EXTRACTION Paired team members independently screened articles for inclusion and evaluated risk of bias. DATA SYNTHESIS We identified 20 observational studies. We found: moderate strength of evidence (SOE) that maternal WIC participation during pregnancy is likely associated with lower risk for preterm birth, low birthweight infants, and infant mortality; low SOE that maternal WIC participation may be associated with a lower likelihood of inadequate gestational weight gain, as well as increased well-child visits and childhood immunizations; and low SOE that child WIC participation may be associated with increased childhood immunizations. We found low SOE for differences in some outcomes by race and ethnicity but insufficient evidence for differences by WIC enrollment duration. We found insufficient evidence related to maternal morbidity and mortality outcomes. LIMITATION Data are from observational studies with high potential for selection bias related to the choice to participate in WIC, and participation status was self-reported in most studies. CONCLUSION Participation in WIC was likely associated with improved birth outcomes and lower infant mortality, and also may be associated with increased child preventive service receipt. PRIMARY FUNDING SOURCE Agency for Healthcare Research and Quality. (PROSPERO: CRD42020222452).
Random Walk Sampling in Social Networks Involving Private Nodes

Analysis of social networks with limited data access is challenging for third parties. To address this challenge, a number of studies have developed algorithms that estimate properties of social networks via a simple random walk. However, most existing algorithms do not assume private nodes that do not publish their neighbors’ data when they are queried in empirical social networks. Here we propose a practical framework for estimating properties via random walk-based sampling in social networks involving private nodes. First, we develop a sampling algorithm by extending a simple random walk to the case of social networks involving private nodes. Then, we propose estimators with reduced biases induced by private nodes for the network size, average degree, and density of the node label. Our results show that the proposed estimators reduce biases induced by private nodes in the existing estimators by up to 92.6% on social network datasets involving private nodes.
Duopoly price competition with quality improvement spillover

When competing firms embark and explore a new market, two salient features are often observed. On the one hand, the firms need to improve their quality by accumulating more experience and climbing up the learning curve. On the other hand, their quality may jointly expand the brand awareness of all competing products. In this article, we study a two‐period duopoly price competition where firms can improve their quality based on the accumulated demand (learn‐by‐doing effect) and their potential market size is positively affected by both firms' quality levels (quality spillover effect). In addition, we investigate two pricing schemes, namely, committed pricing and dynamic pricing, and their impact on the equilibrium outcomes. Assuming the two firms are symmetric in every aspect, our main findings include the following. First, we establish the existence and uniqueness of the pure Nash equilibrium for the dynamic game under either pricing scheme, and show that firms always set a low price in the first period to leverage quality improvement. As the quality spillover effect gets stronger, firms tend to raise their first‐period price, leading to a lower individual quality improvement and a non‐monotonic impact on firms' profit. Moreover, we find that committed pricing scheme benefits the duopoly when the spillover effect is strong, otherwise dynamic pricing scheme brings more profits. Finally, we examine two asymmetric cases where the firms are different in certain attributes pertaining to their learning speed and the quality spillover strength. Our analysis shows that the findings in the symmetric case still hold qualitatively. Useful managerial insights are derived from these studies.
Comparing different computational approaches for detecting long-term vertical transmission in host-associated microbiota

Long-term vertical transmissions of gut bacteria are thought to be frequent and functionally important in mammals. Several phylogenetic-based approaches have been proposed to detect, among species-rich microbiota, the bacteria that have been vertically transmitted during a host clade radiation. Applied to mammal microbiota, these methods have sometimes led to conflicting results; in addition, how they cope with the slow evolution of markers typically used to characterize bacterial microbiota remains unclear. Here, we use simulations to test the statistical performances of two widely-used global-fit approaches (ParaFit and PACo) and two event-based approaches (ALE and HOME). We find that these approaches have different strengths and weaknesses depending on the amount of variation in the bacterial DNA sequences and are therefore complementary. In particular, we show that ALE performs better when there is a lot of variation in the bacterial DNA sequences, whereas HOME performs better when there is not. Global-fit approaches (ParaFit and PACo) have higher type-I error rates (false positives) but have the advantage to be very fast to run. We apply these methods to the gut microbiota of primates and our results suggest that only a small fraction of their gut bacteria is vertically transmitted.
Effects of human-wildlife Conflicts on Local People’s Livelihoods and Wildlife Conservation in and around Alitash National Park, Northwest Ethiopia

Human-wildlife conflict becomes one of the fundamental aspects of wildlife management because it threatens both wildlife and human. People injured, abused, and killed wildlife in response to the perceived and/or actual damages from wildlife. Such negative interaction between humans and wildlife exists everywhere but more intense and costly in adjacent protected areas. This study aimed to assess the effects of human-wildlife conflicts on local people’s livelihood and wildlife conservation in and around Alitash National Park. The primary data were collected using multi-stage sampling techniques by employing a combination of social survey methods involving participatory techniques (focus group discussions and key informant interviews) and structured household interviewers. The survey questionnaire consisted of both open-ended and fixed-response questions designed to solicit information mainly for wildlife-caused damages and other costs to the local community along with human-induced wildlife decline and perceptions towards wildlife conservation. The results revealed that 59.6% of the respondents encountered at least a single type of damage. More than half (54%) of respondents encountered crop damage, of which 34.4% lost up to 25% of their crop fields whereas the amount of loss to the remaining (18.7%) was to 75% of the crop fields. The livestock loss during the last five years of the period prior to the survey administration was 287.89 tropical livestock units which were shared by 47.5% of the respondents. Wildlife attacks on humans were rare but eighteen attacks were caused by Panther leo, Crocuta crocuta and Panthera pardus were encountered when the victims cross the habitats of the species. The responses of the victims to the wildlife imposed the damage was negative and full of anger and resentment. This has been undermining conservation efforts and the socio-economic welfare of the local people around Alitash National Park. Providing alternative livelihood opportunities and creating a context-based conservation scheme along with continuous conservation education would help to reduce the negative effect of human-wildlife conflicts on both wildlife and people in the study area.
Exceeding expectations: the genomic basis of nitrogen utilization efficiency and integrated trait plasticity as avenues to improve nutrient stress tolerance in cultivated sunflower (Helianthus annuus L.)

Maintaining crop productivity is a challenge as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops’ nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation reduced RGR. Moreover, higher vigor (higher control RGR) was associated with a greater absolute decrease under stress. Given this trade-off, we focused on nutrient stress tolerance independent from vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide association analyses revealed regions associated with variation and plasticity in multiple traits, including two key regions with ostensibly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent from vigor and highlight specific traits and genomic regions that could play a role in enhancing tolerance. Highlight Genetic associations and trait correlations show that, in cultivated sunflower, selection for increased nitrogen utilization efficiency and plasticity in key traits is a promising avenue for increasing nutrient stress tolerance.
Parallel cognitive maps for short-term statistical and long-term semantic relationships in the hippocampal formation

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information, such as the transition probabilities between objects. However, objects can often be characterized in terms of different types of relations simultaneously, e.g. semantic similarities learned over the course of a lifetime as well as transitions experienced over a brief timeframe in an experimental setting. Here we ask how the hippocampal formation handles the embedding of stimuli in multiple relational structures that differ vastly in terms of their mode and timescale of acquisition: Does it integrate the different stimulus dimensions into one conjunctive map, or is each dimension represented in a parallel map? To this end, we reanalyzed functional magnetic resonance imaging (fMRI) data from Garvert et al. (2017) that had previously revealed an entorhinal map which coded for newly learnt statistical regularities. We used a triplet odd-one-out task to construct a semantic distance matrix for presented items and applied fMRI adaptation analysis to show that the degree of similarity of representations in bilateral hippocampus decreases as a function of semantic distance between presented objects. Importantly, while both maps localize to the hippocampal formation, this semantic map is anatomically distinct from the originally described entorhinal map. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps reflecting the embedding of objects in diverse relational structures.
Regularized Statistical Methods for Data of Grouped or Dynamic Nature

Regularized Statistical Methods for Data of Grouped or Dynamic Nature by Yun Li Co-Chairs: Naisyin Wang and Ji Zhu This dissertation consists of two parts. In the first part, one new convex regularized variable selection method is proposed for high-dimensional grouped data. Existing group variable selection methods via convex penalties, such as Yuan and Lin (2006) and Zhao et al. (2009), have the limitation of selecting variables in an “all-in-all-out” fashion and lack of selection flexibility within a group. In Chapter II, we propose a new group variable selection method via convex penalties that not only removes unimportant groups effectively, but also keeps the flexibility of selecting variables within an important group. Both the efficient numerical algorithm and high-dimensional theoretical estimation bounds are provided. Simulation results indicate that the proposed method works well in terms of both variable selection and prediction accuracy. In the second part of the dissertation, we develop the parameter estimation methods for the dynamic ordinary differential equations (ODEs). Ramsay et al. (2007) proposed a popular parameter cascading method that tries to strike a balance between the data and the ODE structure via a “loss + penalty” framework. In Chapter III, we investigate this method in detail and take an alternative view through variance evaluation on it. We found, through both theoretical evaluation and numerical x experiments, that the penalty term in Ramsay et al. (2007) could unnecessarily increase estimation variation. Consequently, we propose a simpler alternative structure for parameter cascading that achieves the minimum variation. We also provide theoretical explanations behind the observed phenomenon and report numerical findings on both simulations and one real dynamic data set. In Chapter IV, we consider the estimation problem with time-varying ODE parameters. This is often necessary when there are unknown sources of disturbances that lead to deviations from the standard constant-parameter ODE system. To keep the structure of the parameters simple, we propose a novel regularization method for estimating time-varying ODE parameters. Our numerical studies suggest that the proposed approach works better than competing methods. We also provide finitesample estimation error bounds under certain regularity conditions. The real-data applications of the proposed method lead to satisfactory and meaningful results.
Interpretable Prediction of mRNA Abundance from Promoter Sequence using Contextual Regression Models

While machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or hard to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression as well as motif combination grammars including several motif communities and distance constraints between cooperative motifs.
Lipotype acquisition during neural development in vivo is not recapitulated in stem cell-derived neurons

During development, different tissues acquire distinct lipotypes that are coupled to tissue function and homeostasis. In the brain, where complex membrane trafficking systems are required for neural function, specific glycerophospholipids, sphingolipids, and cholesterol are highly abundant, and defective lipid metabolism is associated with abnormal neural development and neurodegenerative disease. Notably, the production of tissue-specific lipotypes requires appropriate programming of the underlying lipid metabolic machinery, but when and how this occurs is unclear. To address this, we used high-resolution mass spectrometry-based (MSALL) lipidomics to perform a quantitative and comprehensive analysis of mouse brain development covering early embryonic and postnatal stages. We discovered a distinct bifurcation in the establishment of the neural lipotype, whereby the canonical brain lipid biomarkers 22:6-glycerophospholipids and 18:0-sphingolipids begin to be produced in utero, whereas cholesterol attains its characteristic high levels after birth. In contrast, when profiling rodent and human stem cell-derived neurons, we observed that these do not acquire a brain lipotype per se. However, upon probing the lipid metabolic wiring by supplementing brain lipid precursors, we found that the stem cell-derived neurons were partially able to establish a brain-like lipotype, demonstrating that the cells are partially metabolically committed. Altogether, our report provides an extensive lipidomic resource for brain development and highlights a potential challenge in using stem cell-derived neurons for mechanistic studies of lipid biochemistry, membrane biology and biophysics that can be mitigated by further optimizing in vitro differentiation protocols. Significance Statement We report an extensive time-resolved resource of lipid molecule abundances across mouse brain development, starting as early as 10 days post-fertilization. The resource reveals a bifurcation in the establishment of the neural lipotype where the canonical 22:6-glycerophospholipid and 18:0-sphingolipid biomarkers are attained in utero, whereas cholesterol is attained after birth. Furthermore, we uncover that the neural lipotype is not established in rodent and human stem cell-derived neurons in vitro.
Barotropic instability and asymmetric mixing in hurricanes with primary and secondary eyewalls

OF DISSERTATION BAROTROPIC INSTABILITY AND ASYMMETRIC MIXING IN HURRICANES WITH PRIMARY AND SECONDARY EYEWALLS Asymmetric vorticity mixing in hurricanes is studied in the context of a nondivergent barotropic framework. The stability of primary and secondary eyewalls is considered through linear analyses and numerical experiments using a pseudospectral model. For the case of unstable primary eyewalls, which are idealized as annular rings of enhanced vorticity embedded in relatively weak vorticity, nonlinear rearrangement occurs in which most of the vorticity initially in the eyewall is transported inward. During this evolution, distinct mesovortices form, orbit the eye, and eventually merge and axisymmetrize near the vortex center. The end state of this type of evolution is an axisymmetric and monotonic vorticity field. Observational evidence of this process occuring in real hurricanes is introduced using aircraft flight-level data. For the case of hurricanes with secondary eyewalls, which are idealized as annular rings of enhanced vorticity with intense vortices at their center, two types of instabilities are discussed. With the first type (type 1), the instability is realized across the secondary eyewall. The nonlinear mixing associated with type 1 instability results in a broader and weaker secondary eyewall. The end state is axisymmetric and stable, iii but is not monotonic. Secondary wind maxima associated with secondary eyewalls are maintained during this type of mixing. In the second type (type 2), the instability is realized across the moat between the secondary eyewall and the central vortex. The nonlinear mixing associated with type 2 instability perturbs the central vortex and can result in the formation of a nearly steady tripole-like structure. The formation of a tripole offers an explanation for the observed formation and persistence of elliptical eyewalls in hurricanes. James P. Kossin Department of Atmospheric Science Colorado State University Fort Collins, Colorado 80523 Spring 2000
Efficient coding, channel capacity and the emergence of retinal mosaics

Among the most striking features of retinal organization is the grouping of its output neurons, the retinal ganglion cells (RGCs), into a diversity of functional types. Each of these types exhibits a mosaic-like organization of receptive fields (RFs) that tiles the retina and visual space. Previous work has shown that many features of RGC organization, including the existence of ON and OFF cell types, the structure of spatial RFs, and their relative arrangement, can be predicted on the basis of efficient coding theory. This theory posits that the nervous system is organized to maximize information in its encoding of stimuli while minimizing metabolic costs. Here, we use efficient coding theory to present a comprehensive account of mosaic organization in the case of natural videos as the retinal channel capacity—the number of simulated RGCs available for encoding—is varied. We show that mosaic density increases with channel capacity up to a series of critical points at which, surprisingly, new cell types emerge. Each successive cell type focuses on increasingly high temporal frequencies and integrates signals over large spatial areas. In addition, we show theoretically and in simulation that a transition from mosaic alignment to anti-alignment across pairs of cell types is observed with increasing output noise and decreasing input noise. Together, these results offer a unified perspective on the relationship between retinal mosaics, efficient coding, and channel capacity that can help to explain the stunning functional diversity of retinal cell types.
Two sides to every coin: reciprocal introgression line populations in Caenorhabditis elegans

Quantitative genetics seeks to understand the role of allelic variation in trait differences. Introgression lines (ILs) contain a single genetic locus introgressed into another genetic background, and are one of the most powerful quantitative trait locus (QTL) mapping designs. However, albeit useful for QTL discovery, this homogenous background confounds genetic interactions. Here, we created an IL population with N2 segments in a CB4856 background (ILCB4856), reciprocal to an N2 background with CB4856 introgressions population (ILN2). The ILCB4856 panel comprises a population of 145 strains with sequencing confirmed N2 introgressions in a CB4856 background. A core set of 87 strains covering the entire genome was selected. We present three experiments demonstrating the power of the reciprocal IL panels. First, we performed QTL mapping identifying new regions associated with lifespan. Second, the existence of opposite-effect loci regulating heat-stress survival is demonstrated. Third, by combining ILN2 and ILCB4856 strains, an interacting expression QTL was uncovered. In conclusion, the reciprocal IL panels are a unique and ready-to-use resource to identify, resolve, and refine complex trait architectures in C. elegans.