Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Applications in Genetics and Molecular Biology

Abstract Approaches based upon sequence weights, to construct a position weight matrix ofnucleotides from aligned inputs, are popular but little effort has been expended to measure theirquality.We derive optimal sequence weights that minimize the sum of the variances of the estimatorsof base frequency parameters for sequences related by a phylogenetic tree. Using these we findthat approaches based upon sequence weights can perform very poorly in comparison toapproaches based upon a theoretically optimal maximum-likelihood method in the inference of theparameters of a position-weight matrix. Specifically, we find that among a collection of primatesequences, even an optimal sequences-weights approach is only 51% as efficient as the maximum-likelihood approach in inferences of base frequency parameters.We also show how to employ the variance estimators to obtain a greedy ordering of speciesfor sequencing. Application of this ordering for the weighted estimators to a primate collectionyields a curve with a long plateau that is not observed with maximum-likelihood estimators. Thisplateau indicates that the use of weighted estimators on these data seriously limits the utility ofobtaining the sequences of more than two or three additional species.
Multi-Tiered Strictly Local Functions

Tier-based Strictly Local functions, as they have so far been defined, are equipped with just a single tier. In light of this fact, they are currently incapable of modelling simultaneous phonological processes that would require different tiers. In this paper we consider whether and how we can allow a single function to operate over more than one tier. We conclude that multiple tiers can and should be permitted, but that the relationships between them must be restricted in some way to avoid overgeneration. The particular restriction that we propose comes in two parts. First, each input element is associated with a set of tiers that on their own can fully determine what the element is mapped to. Second, the set of tiers associated to a given input element must form a strict superset-subset hierarchy. In this way, we can track multiple, related sources of information when deciding how to process a particular input element. We demonstrate that doing so enables simple and intuitive analyses to otherwise challenging phonological phenomena.
A Conversation Analysis of Self-repair in Asian Englishes

Abstract The speakers of Asian Englishes in this study engage in self-repair when no corrections need to be made. The most common repair the interactants utilize is predominantly self-initiated self-completed repair. In addition, they employ their own characteristic signature phrase and each interlocutor’s respective signature phrase serves multiple functions at different times in their discourse, for example, as a repair initiator, a delaying device, or a pause filler. One salient and recurring feature in the utterances of the speakers of Asian Englishes in this study is self-repair with a hint of their native language background and mutual cultural knowledge. Obviously noticeable deviations in the areas of lexis, phonology, and morpho-syntax do not lead to repair or miscommunication. Despite the distinct “non-nativeness” of their utterances, the speakers of Asian Englishes in this study perform successful interaction, and no impediment to communication occurs.
The genus Lachnagrostis (Gramineae) in Australia

Jacobs, S.W. L (Royal Botanic Cardens, Sydney, NSW 2000, Australia) 2001 The genus Lachnagrostis (Gramineae) in Australia. Telopea 9(3): 439-44S. Lemma epidermal features were examined in species of Agrostis, Deycuxia and Calamagrostis. As a result the genus iMcImagrostis is recognised and new combinations provided for L. adamsonii, L. aequata, L. collicola, L. drummondiana, L. lacunis, L. limitanea, L. meionecles, L. punicea and L. robusta. A key distinguishing Lachnagrostis from the related genera Dicheiachne, Deyeuxia, Agrostis is given, along with a characterisation of the four genera. Synonymy is provided for the other species of Lachnagrostis recognised in Australia. It is concluded that both Deyeuxia and Calamagrostis are diverse and highly variable and little is to be gained by combining them at this stage though their current circumscriptions are probably suboptimal.
Diversity of Pectin Rhamnogalacturonan I Rhamnosyltransferases in Glycosyltransferase Family 106

Rhamnogalacturonan I (RG-I) comprises approximately one quarter of the pectin molecules in land plants, and the backbone of RG-I consists of a repeating sequence of [2)-α-L-Rha(1-4)-α-D-GalUA(1-] disaccharide. Four Arabidopsis thaliana genes encoding RG-I rhamnosyltransferases (AtRRT1 to AtRRT4), which synthesize the disaccharide repeats, have been identified in the glycosyltransferase family (GT106). However, the functional role of RG-I in plant cell walls and the evolutional history of RRTs remains to be clarified. Here, we characterized the sole ortholog of AtRRT1–AtRRT4 in liverwort, Marchantia polymorpha, namely, MpRRT1. MpRRT1 had RRT activity and genetically complemented the AtRRT1-deficient mutant phenotype in A. thaliana. However, the MpRRT1-deficient M. polymorpha mutants showed no prominent morphological changes and only an approximate 20% reduction in rhamnose content in the cell wall fraction compared to that in wild-type plants, suggesting the existence of other RRT gene(s) in the M. polymorpha genome. As expected, we detected RRT activities in other GT106 family proteins such as those encoded by MpRRT3 in M. polymorpha and FRB1/AtRRT8 in A. thaliana, the deficient mutant of which affects cell adhesion. Our results show that RRT genes are more redundant and diverse in GT106 than previously thought.
Opinion Formation on the Internet: The Influence of Personality, Network Structure, and Content on Sharing Messages Online

Today the majority of people uses online social networks not only to stay in contact with friends, but also to find information about relevant topics, or to spread information. While a lot of research has been conducted into opinion formation, only little is known about which factors influence whether a user of online social networks disseminates information or not. To answer this question, we created an agent-based model and simulated message spreading in social networks using a latent-process model. In our model, we varied four different content types, six different network types, and we varied between a model that includes a personality model for its agents and one that did not. We found that the network type has only a weak influence on the distribution of content, whereas the message type has a clear influence on how many users receive a message. Using a personality model helped achieved more realistic outcomes.
Integrating multimodal connectivity improves prediction of individual cognitive abilities

How white matter pathway integrity and neural co-activation patterns in the brain relate to complex cognitive functions remains a mystery in neuroscience. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how multimodal brain connectivity relates to cognition. Specifically, we evaluate whether integrating functional and structural connectivity improves prediction of individual crystallised and fluid abilities in 415 unrelated healthy young adults from the Human Connectome Project. Our primary results are two-fold. First, we demonstrate that integrating functional and structural information – at both a model input or output level – significantly outperforms functional or structural connectivity alone to predict individual verbal/language skills and fluid reasoning/executive function. Second, we show that distinct pairwise functional and structural connections are important for these predictions. In a secondary analysis, we find that structural connectivity derived from deterministic tractography is significantly better than structural connectivity derived from probabilistic tractography to predict individual cognitive abilities.
PchE Regulation of Escherichia coli O157:H7 Flagella, Controlling the Transition to Host Cell Attachment

Shiga toxins and intimate adhesion controlled by the locus of enterocyte effacement are major enterohemorrhagic Escherichia coli (EHEC) virulence factors. Curli fimbriae also contribute to cell adhesion and are essential biofilm components. The transcriptional regulator PchE represses the expression of curli and their adhesion to HEp-2 cells. Past studies indicate that pchE also represses additional adhesins that contribute to HEp-2 cell attachment. In this study, we tested for pchE regulation of several tissue adhesins and their regulators. Three adhesin-encoding genes (eae, lpfA1, fliC) and four master regulators (csgD, stpA, ler, flhDC) were controlled by pchE. pchE over-expression strongly up-regulated fliC but the marked flagella induction reduced the attachment of O157:H7 clinical isolate PA20 to HEp-2 cells, indicating that flagella were blocking cell attachments rather than functioning as an adhesin. Chemotaxis, motor, structural, and regulatory genes in the flagellar operons were all increased by pchE expression, as was PA20 motility. This study identifies new members in the pchE regulon and shows that pchE stimulates flagellar motility while repressing cell adhesion, likely to support EHEC movement to the intestinal surface early in infection. However, induced or inappropriate pchE-dependent flagellar expression could block cell attachments later during disease progression.
Tumor necrosis factor induces cholesterol accumulation in human arterial endothelial cells through LDL receptor surface redistribution

Tumor necrosis factor alpha (TNFα) and low density lipoprotein (LDL) are important modulators of the atherosclerotic process. Here, the effect of TNFα on confluent primary human aortic endothelial cell (pHAEC) LDL-derived lipids and trafficking were investigated. TNFα promoted up to 2 folds increase in cellular cholesterol and could induce a massive increase in the non-hydrolysable tracer, Dil, by over 200 folds. The lipid increase was associated with increased 125I-LDL surface binding. Further, Dil-LDL cellular association was blocked by excess unlabeled LDL, but not oxidized LDL (oxLDL). Moreover, TNFα-induced Dil and cholesterol increase were enhanced by the endosomal pH-raising agent, chloroquine. Internalization of Dil-LDL was reduced by the scavenger receptor B1 (SR-B1) antagonist, BLT-1, and the LDLR family antagonist, proprotein convertase subtilisin/kexin type 9 (PCSK9), but not receptor associated protein (R AP). Additionally, surface accessible LDLR was higher in TNFα-treated cells by about 30 folds, without a significant change in total LDLR. Correspondingly, specific LDLR antibody blocked Dil-LDL internalization to undetectable levels, and Dil-apoE3-VLDL by 94%, but had no effect on Dil-HDL3 internalization. TNFα did not increase Dil-HDL3 cellular association. Further, ACAT inhibitor reduced cholesteryl esters, but not the total cholesterol increase induced by TNFα. On pHAECs grown on transwell inserts, TNFα did not enhance apical (AP) to basolateral (BL) LDL cholesterol or Dil release. It is concluded that TNFα induces LDLR surface localization and that LDLR does not promote AP to BL LDL transport across pHAECs.
State-space optimal feedback control of optogenetically driven neural activity

Objective. The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse. Approach. Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch. Main results. This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications. Significance. To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.
Diagnostic accuracy of ultrasound in subscapularis tendon abnormalities and the importance of operator experience.

OBJECTIVE This study aimed to investigate the reasons behind the compliance, diagnostic success, and failure of ultrasound (US) examinations of two radiologists with reference to magnetic resonance imaging (MRI) in the abnormalities of subscapularis (SSC) tendon, including tendinosis. METHODS One less experienced radiologist (operator-1) and one senior radiologist, experienced in the musculoskeletal US (operator-2), performed the shoulder US on 78 patients (mean age: 53.18 ± 10.89 [22-73] years), who had undergone MRI for routine reasons except malignancy, within 1-4 weeks of MRI. Tendinopathy, partial (PT), or full-thickness (FT) tears were recorded in the subscapularis. The thickness of the anterior and lateral soft tissues in the shoulder girdle was measured by MRI. The inter-operator agreement on the US results, the US-MRI compliance for each operator, and the relationship between the US-MRI compliance and tissue thickness were investigated. RESULTS The inter-operator agreement on the US findings was moderate. The US-MRI compliance was fair for operator-1, but substantial for operator-2. The circumferential soft tissue thickness and US-MRI compliance were not correlated (p>0.05). The interobserver agreement in US was moderate (K: 0.415). The US-MRI compliance was fair for operator-1 (K: 0.344) and substantial for operator-2 (K: 0.616). The accuracy rates for the differentiation of normal tendon, tendinosis, PT, and FT were 59%, 75%, 72%, and 100%, respectively, for operator-1 and 87%, 83%, 85%, and 100%, respectively, for operator-2. However, the respective sensitivity of operator-1 was 46%, 19%, 44%, and 100%; and operator-2 was 91%, 67%, 82%, and 100%. The diagnostic performance of operator-1 was lower, except for FT. CONCLUSION The thickness of the surrounding tissue does not affect the US results. The differentiation between tendinosis and PT tear is difficult (more prominent in less experienced user); however, because this would not change the treatment choice, shoulder US is recommended after basic training in subscapularis pathologies. LEVEL OF EVIDENCE Level II, Diagnostic.
THE EFFECTS OF PERMANENT LOCAL EMIGRATION AND ENCOUNTER TECHNIQUE ON STOPOVER DURATION ESTIMATES AS REVEALED BY TELEMETRY AND MARK-RECAPTURE

Abstract ABSTRACT Studies of stopover durations of migrating birds using mark-recapture and resighting techniques are usually restricted to only a part of a stopover site. Therefore, estimates of stopover duration may be negatively biased if birds leave the trapping area permanently, but remain within the stopover site (permanent local emigration). We tested this possible effect by comparing stopover durations of Orphean Warblers (Sylvia hortensis) estimated from recapture and resighting data obtained from a trapping area covering only a part of a stopover site with durations estimated from relocation data of radio-marked individuals sampled over the entire stopover site. To test a possible effect of the different reencounter techniques on stopover duration estimates we compared estimates of stopover duration derived from the recapture and resighting data with those derived from the relocation data restricted to the same trapping area. Stopover duration estimated from relocation data obtained from the entire stopover site was twice as long as stopover duration estimated from recapture and resighting data. While similar estimates of stopover duration were obtained from relocation data when data from only the restricted trapping area were used, the proportion of transients was different between the two data sets. The different estimates of stopover duration were most likely not due to permanent local emigration, but rather to violations of essential model assumptions, such as equal catchability or that the probability of staying for another day is independent of the time a bird has already spent at the stopover site.
The Influence of Age on Firefighter Combat Challenge Performance and Exercise Training Habits.

Saari, AI, Renz, G, Davis, P, and Abel, MG. The influence of age on firefighter combat challenge performance and exercise training habits. J Strength Cond Res XX(X): 000-000, 2020-The primary purpose of this study was to compare older vs. younger physically trained structural firefighters' performance in an international firefighter physical ability competition (Scott Firefighter Combat Challenge) and their engagement in physical training. Secondarily, firefighters' perceived importance of fitness characteristics to perform occupational tasks was evaluated. Sixty-four trained male firefighters' completed a timed occupationally specific competition course and a survey to assess exercise habits and perceived importance of fitness characteristics to perform fireground tasks. Firefighters were stratified into younger (<37 years; n = 34) and older (≥37 years; n = 30) cohorts based on the sample's median age. Independent samples t-tests were used to analyze differences in outcome measures between younger and older cohorts. It took older firefighters 8.8% longer to complete the course compared with younger firefighters (p = 0.029). Both groups reported similar training frequencies of cardiovascular (CV) training (Younger: 4.1 ± 1.7 vs. Older: 3.6 ± 1.5 session·wk, p = 0.203) and strength training (Younger: 3.6 ± 1.2 vs. Older 3.2 ± 1.4 session·wk, p = 0.274). Furthermore, there was no difference in weekly training load for CV (p = 0.663) or strength training (p = 0.898) activities between the cohorts. On average, firefighters indicated that all fitness characteristics were at least somewhat important for occupational performance. Occupationally relevant competition performance among a sample of fit firefighters was lower in older firefighters compared with younger firefighters, despite similar self-reported training volume and intensity. Practitioners may expect age-related decreases in occupational performance despite performing similar amounts of physical training.
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks

Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative’s scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks.
Assessment of Alzheimer’s Disease Based on Texture Analysis of the Entorhinal Cortex

Alzheimer’s disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC’s of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.