Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Role of Toxoplasma gondii ERK7 in the Maintenance and Stability of the Apical Complex

The conoid is an enigmatic, dynamic organelle positioned at the apical tip of the coccidian subgroup of the Apicomplexa, close to the apical polar ring (APR) from which the subpellicular microtubules (SPMTs) emerge and through which the secretory organelles (micronemes and rhoptries) reach the plasma membrane for exocytosis. In Toxoplasma gondii, the conoid protrudes concomitantly with microneme secretion, during egress, motility, and invasion. ABSTRACT Toxoplasma gondii extracellular signal-regulated kinase 7 (ERK7) is known to contribute to the integrity of the apical complex and to participate in the final step of conoid biogenesis. In the absence of ERK7, mature parasites lose their conoid complex and are unable to glide, invade, or egress from host cells. In contrast to a previous report, we show here that the depletion of ERK7 phenocopies the depletion of the apical cap protein AC9 or AC10. The absence of ERK7 leads to the loss of the apical polar ring (APR), the disorganization of the basket of subpellicular microtubules (SPMTs), and a severe impairment in microneme secretion. Ultrastructure expansion microscopy (U-ExM), coupled to N-hydroxysuccinimide ester (NHS-ester) staining on intracellular parasites, offers an unprecedented level of resolution and highlights the disorganization of the rhoptries as well as the dilated plasma membrane at the apical pole in the absence of ERK7. Comparative proteomics analysis of wild-type and ERK7-depleted parasites confirmed the disappearance of known apical complex proteins, including markers of the apical polar ring and a new apical cap named AC11. Concomitantly, the absence of ERK7 led to an accumulation of microneme proteins, resulting from the defect in the exocytosis of the organelles. AC9-depleted parasites were included as controls and exhibited an increase in inner membrane complex proteins, with two new proteins assigned to this compartment, namely, IMC33 and IMC34. IMPORTANCE The conoid is an enigmatic, dynamic organelle positioned at the apical tip of the coccidian subgroup of the Apicomplexa, close to the apical polar ring (APR) from which the subpellicular microtubules (SPMTs) emerge and through which the secretory organelles (micronemes and rhoptries) reach the plasma membrane for exocytosis. In Toxoplasma gondii, the conoid protrudes concomitantly with microneme secretion, during egress, motility, and invasion. The conditional depletion of the apical cap structural protein AC9 or AC10 leads to a disorganization of SPMTs as well as the loss of the APR and conoid, resulting in a microneme secretion defect and a block in motility, invasion, and egress. We show here that the depletion of the kinase ERK7 phenocopies AC9 and AC10 mutants. The combination of ultrastructure expansion microscopy and NHS-ester staining revealed that ERK7-depleted parasites exhibit a dilated apical plasma membrane and an altered positioning of the rhoptries, while electron microscopy images unambiguously highlight the loss of the APR.
Justice for All: Realities and Possibilities of Black English Learners in K–12 Schools

Background The number of students identified as English learners (ELs) in the U.S. has risen in the last two decades. Approximately 10% of the K–12 student population is identified as ELs. These students are not a monolithic group. They come from diverse ethnic groups, home languages, socioeconomic statuses, backgrounds, and experiences. Ultimately, schools that serve them are to provide equitable access to education. Included in that population are ELs who either become Black upon their arrival in the U.S. or have an additional identity that includes being Black in the U.S. In 2013, the United States Department of Education Office of English Language Acquisition (OELA) and the White House Initiative on Educational Excellence for African Americans (WHIEEAA) listed the top languages spoken by Black ELs as Spanish, French Creole, and French. Additionally, states with the highest concentration of Black ELs include Maine, Vermont, and Mississippi. Purpose This article explores the experiences of Black ELs, whether foreign born or native to the U.S., as they strive to achieve academic language necessary for becoming proficient in English. Recommendations for educators who serve Black ELs will be included since creating and sustaining a shared sense of responsibility for this traditionally marginalized student population is imperative. Conclusions/Recommendations Implications and recommendations for educators of Black English learners include a focus on critical pedagogy, an increase in educator awareness and intentional advocacy.
Dense and well-connected subgraph detection in dual networks

Dense subgraph discovery is a fundamental problem in graph mining with a wide range of applications \cite{gionis2015dense}. Despite a large number of applications ranging from computational neuroscience to social network analysis, that take as input a {\em dual} graph, namely a pair of graphs on the same set of nodes, dense subgraph discovery methods focus on a single graph input with few notable exceptions \cite{semertzidis2019finding,charikar2018finding,reinthal2016finding,jethava2015finding}. In this work, we focus the following problem: given a pair of graphs $G,H$ on the same set of nodes $V$, how do we find a subset of nodes $S \subseteq V$ that induces a well-connected subgraph in $G$ and a dense subgraph in $H$? Our formulation generalizes previous research on dual graphs \cite{Wu+15,WuZLFJZ16,Cui2018}, by enabling the {\em control} of the connectivity constraint on $G$. We propose a novel mathematical formulation based on $k$-edge connectivity, and prove that it is solvable exactly in polynomial time. We compare our method to state-of-the-art competitors; we find empirically that ranging the connectivity constraint enables the practitioner to obtain insightful information that is otherwise inaccessible. Finally, we show that our proposed mining tool can be used to better understand how users interact on Twitter, and connectivity aspects of human brain networks with and without Autism Spectrum Disorder (ASD).
Identification of nontarget-site mechanisms of glyphosate resistance in roots and pollen of Amaranthus and Ambrosia

Pollen germination in vitro is rapid and can be an effective technique to assess plants for expression of herbicide-resistance. A number of glyphosate-resistant (GR) common waterhemp (Amaranthus rudis Sauer) and Palmer amaranth (Amaranthus palmeri S. Wats.) populations are problematic throughout the US and current methods to identify resistant populations involve seed or seedling assays. The objectives of this research were to develop an in vitro pollen germination assay for common waterhemp and Palmer amaranth and assess the response to glyphosate in pollen of GR and glyphosate-susceptible (GS) common waterhemp and Palmer amaranth. GR in both Amaranthus species was not due to target-site mutations. Pollen germination media (PGM) containing sucrose, boric acid, calcium chloride, and agarose in deionized water buffered to a pH of 5.5 and at an incubation temperature of 32 C resulted in 25 to 30% pollen germination with pollen tube lengths of 80 to 200 μm for both species. Addition of glyphosate resulted in a similar response by GR and GS common waterhemp, with a 7 to 93% reduction of pollen tube length at concentrations from 0.0005 to 30 mM. Pollen tube growth in Palmer amaranth was more variable and somewhat insensitive to glyphosate, with ≤ 55% growth reduction for both GS and GR biotypes. Although * Authors: Graduate Research Assistant, Professor, Division of Plant Sciences, University of Missouri, 108 Waters Hall, Columbia, MO 65211. Corresponding author’s E-mail: tcs2m5@mail.missouri. edu 48 discrimination in pollen tube growth to glyphosate occurred in as little as 30 min after exposure, this rapid assay did not differentiate GR and GS Amaranthus. Nomenclature: glyphosate; common waterhemp, Amaranthus rudis Sauer; Palmer amaranth, Amaranthus palmeri S. Wats.
Reply from George A. Brooks

Langan and Navarro are thanked for providing commentary on our paper, ‘Lactate in contemporary biology: a phoenix risen’ (Brooks et al., 2022). We regard their thoughtful letter as a note in addition to our review on the lactate shuttle that is directly related to their provocative title: ‘How high can the lactate phoenix rise? ’ (Langan & Navarro, 2022). Clearly, we do not know, but it is imperative to find the apogee. Regarding the diverse signalling roles of lactate Langan and Navarro noted the role of lactate in activating the carotid body olfactory receptor (Olfr78) (Chang et al., 2015). Give the importance of breathing in physiology, ours was a significant omission making their contribution noteworthy. ‘How high’ indeed! Langan and Navarro distinguished between the roles of lactate and hypoxia in hypoxia-inducible factor (HIF) signalling. We touched on that also in our review, but for us to have emphasized the role of lactate over hypoxia in HIF signalling might have been viewed as over reaching. In our review we noted infancy of the field of histone lactylation on gene expression (Zhang et al., 2019). Already we see extensive evidence not only of histone, but protein lactylation (Leija et al., unpublished). Importantly, the role of lactate as a transcription factor was foreshadowed in an earlier report (Hashimoto et al., 2007). In our review we briefly touched on the role of lactate as fuelling the spiral mitochondrial reticulum in sperm tails. Most recently, Sharpley et al. commented extensively on the role of lactate in embryogenesis (Sharpley et al., 2020). In terms of a launchpad for studies of lactate’s role in biology, what could be more fundamental than the moment of conception and subsequent embryogenesis? In sum, we thank Langan and Navarro for extending the discussion on the biological roles of lactate, particularly as a signalling molecule. As well, others have made similar observations, for instance the role of lactate in glutamatergic signalling in the brain (Pellerin & Magistretti, 1994). One significant correction we have to offer is to their penultimate sentence and citation on lactate as the primary circulating energy source. The paper cited was confirmatory; there having been many previous primary sources and reviews on lactate as an energy source (e.g. Brooks, 1985; Brooks, 2002).
Phase transition in inhomogenous Erd\H{o}s-R\'enyi random graphs via tree counting

Consider the complete graph \(K_n\) on \(n\) vertices where each edge \(e\) is independently open with probability \(p_n(e)\) or closed otherwise. Here \(\frac{C-\alpha_n}{n} \leq p_n(e) \leq \frac{C+\alpha_n}{n}\) where \(C > 0\) is a constant not depending on~\(n\) or~\(e\) and \(0 \leq \alpha_n \longrightarrow 0\) as \(n \rightarrow \infty.\) The resulting random graph~\(G\) is inhomogenous and we use a tree counting argument to establish phase transition in \(G.\) We also obtain that the critical value for phase transition is one in the following sense. For \(C 1,\) with high probability, there is at least one giant component (containing at least \(\epsilon n\) vertices for some \(\epsilon > 0\)) and every component is either small or giant. For \(C > 8,\) with positive probability, the giant component is unique and every other component is small. As a consequence of our method, we directly obtain the fraction of vertices present in the giant component in the form of an infinite series.
On the different regimes of Stochastic Gradient Descent

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size [Formula: see text], and the step size or learning rate [Formula: see text]. For small [Formula: see text] and large [Formula: see text], SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the "temperature" [Formula: see text]. Yet this description is observed to break down for sufficiently large batches [Formula: see text], or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the [Formula: see text]-[Formula: see text] plane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch size [Formula: see text] separating regimes (i) and (ii) scale with the size [Formula: see text] of the training set, with an exponent that characterizes the hardness of the classification problem.
Rank-Modulation Codes for DNA Storage With Shotgun Sequencing

Synthesis of DNA molecules offers unprecedented advances in storage technology. Yet, the microscopic world in which these molecules reside induces error patterns that are fundamentally different from their digital counterparts. Hence, to maintain reliability in reading and writing, new coding schemes must be developed. In a reading technique called shotgun sequencing, a long DNA string is read in a sliding window fashion, and a profile vector is produced. It was recently suggested by Kiah et al. that such a vector can represent the permutation which is induced by its entries, and hence a rank-modulation scheme arises. Although this interpretation suggests high error tolerance, it is unclear which permutations are feasible and how to produce a DNA string whose profile vector induces a given permutation. In this paper, by observing some necessary conditions, an upper bound for the number of feasible permutations is given. Furthermore, a technique for deciding the feasibility of a permutation is devised. By using insights from this technique, an algorithm for producing a considerable number of feasible permutations is given, which applies to any alphabet size and any window length.
Analysis Of Deforestation Of Riverine Forest Of Sindh Using Remote Snsing And Analytic Hierarchy Process (AHP) Techniques

The present research work focuses on mapping, monitoring, change detection, deforestation impacts and prioritization of deforestation factors.From our research it has been revealed that mapping area is of polygon shape and these polygons have been divided into four regions; region one comprises of two divisions Sukkur and Shikarpur (the total area of polygon is 199695 ha) official forest area of both divisions is (61116 ha). Region two comprises of two divisions Larkana and Dadu (the total area of polygon is 222808.79ha) official forest area of both divisions is (65124.7287 ha). Region three also comprises of two divisions Nawabshah and Hyderabad (the total area of polygon is 178266.7943ha), official forest area of both divisions is (68642.785 ha). Fourth region comprises of only Thatta division (the total area of polygon is 94226.6571ha) and official forest area is (51979.66 ha). Secondly our research revealed alarming changes in the whole region which have been continuously monitored from 1979 to 2010.The overall forest cover in 1979 was 28.86%, 1992, 23.80%, 1998, 9.59%, 2000, 6.44%, 2006, 5.47%, 2009, 3.89%, 2009-12, 3.71% and 2010, 2.96%; overall change observed in forest area is 25.07%. Similarly the study focused on regional temperature by continuous monitoring of riverine forest from 1992 to 2009, the average temperatures in 1992 were 39 °C, 1998, 43.38°C, 2000, 36.56°C, 2003, 42.42°C and 2009, 44.34°C.The variations of temperatures have influenced and impacted overall climate of the Indus basin, which has changed the riverine ecosystem.The satellite data indicates a significant rise in average temperatures to 4.15OC (1992- 2009). This variation in temperature has brought about associated factors such as deforestation, droughts, global warming and socioeconomic conditions. These changes in temperature and forest cover have a direct impact on depended factors such as Temperature Increase 7%, Reduction of Grazing area 22%, reduction in livestock 22%, Mass Migration 15%, Poverty 24%, Land Encroachment 8% and Bad law and order 7%, all these factors have been affected by deforestation in Indus basin. All these impacted factors have been assessed by Leopold interaction matrix. Finally this study makes prioritization of deforestation and attaches significant weightage to most of the affected factors which can be summarized in ascending order as under: 1. Droughts 2. Unauthorized cutting 3. Poverty 4. Unemployment 5. Low literacy ratio 6. Government utilization 7. Land encroachment 8. Bad law and order situation 9. Land use for cultivation. Likewise Analytic Hierarchy Process Model indicates that most affected divisions in terms of damage / loss could be placed in ascending order as follows: 1. Thatta 2. Shikarpur 3. Sukkur 4. Nawabshah 5. Dadu 6. Larkana 7. Hyderabad Division.
Myricitrin from Physalis pubescens L. leaves and frankincense decrease resistance of MCF-7 cells and ameliorate efficacy of epirubicin

Context: It was found that flavonoids and frankincense exert anti-cancer effect through their antioxidant and anti-inflammatory activities. Aims: To evaluate the cytotoxic effect against MCF-7 cells of flavonoids isolated from Physalis pubescens L. , frankincense ethanol extract and the combined therapy with epirubicin to reduce the resistance and the side effects. Methods: MTT assay against MCF-7 was carried out for rutin, quercitrin, myricitrin and frankincense. The compound or extract with the best anti-cancer effect was tested against WI-38 cells. 50% inhibitory concentration (IC50) of the different treatments and the combined therapy with epirubicin against MCF-7 was determined. Assessment the effect on expression of ABCB1, TGF-β1 and ATG7 genes was done by RT-qPCR. Results: Isolation and identification of myricitrin from leaves of Physalis pubescens L. was carried out for the first time. IC50 (µg/mL) regarding MCF-7 cells was of epirubicin (0.8 ± 0.052), rutin (350.16 ± 1.241), quercitrin (259.6 ± 1.45), myricitrin (114.0 ± 0.517), frankincense ethanol extract (86.8 ± 0.91), combined epirubicin + myricitrin (EM) (0.37 ± 0.087), combined epirubicin + frankincense (EF) (0.50 ± 0.1732). The IC50 µg/mL regarding WI-38 cells was of epirubicin (1.26 ± 0.0057), myricitrin (462.0 ± 1.062) and frankincense (299.5 ± 1.32). All assays were done at 48 h time interval. Myricitrin and EM reduced ABCB1 expression and upregulated expression of TGF-β1 and ATG7 genes. Frankincense and EF downregulated expression of ABCB1, TGF-β1 and ATG7 genes. Conclusions: Myricitrin and frankincense would be a promising adjuvant therapy to improve epirubicin anticancer activity with minimal adverse effect.
Cheating off your neighbors: Improving activity recognition through corroboration

Understanding the complexity of human activities solely through an individual's data can be challenging. However, in many situations, surrounding individuals are likely performing similar activities, while existing human activity recognition approaches focus almost exclusively on individual measurements and largely ignore the context of the activity. Consider two activities: attending a small group meeting and working at an office desk. From solely an individual's perspective, it can be difficult to differentiate between these activities as they may appear very similar, even though they are markedly different. Yet, by observing others nearby, it can be possible to distinguish between these activities. In this paper, we propose an approach to enhance the prediction accuracy of an individual's activities by incorporating insights from surrounding individuals. We have collected a real-world dataset from 20 participants with over 58 hours of data including activities such as attending lectures, having meetings, working in the office, and eating together. Compared to observing a single person in isolation, our proposed approach significantly improves accuracy. We regard this work as a first step in collaborative activity recognition, opening new possibilities for understanding human activity in group settings.
Deep Learning-Based Automatic Helmet and Number Plate Recognition

—In addition to wearing helmets, which reduce the risk of severe head and brain injuries by absorbing the force of a force or collision to the head, motorcycle riders must take additional precautions to protect their bodies. Riders and passengers who wear helmets have a considerably higher chance of surviving an accident than those who do not. Every motorcycle rider is required by law to wear a helmet at all times. However, a lot of biker’s disregard and operate their vehicle without any form of defense. The policeman tried to manually regulate the situation, but it was insufficient given the actual situation. Despite the recent requirement for helmets, many people continue to operate motor vehicles without them. Since helmets are the primary piece of safety gear used in developing nations, the number of fatalities has increased every year. Wearing a helmet is the greatest way to reduce fatalities and brain damage from motorcycle and cycling accidents. Motorcycle operators incur a high danger of suffering fatal head injuries if they don't wear helmets. In addition to the additional expenses that helmet-less motorcyclists inflict on hospitals, the cost of the incapacity caused by severe head injuries affects individuals, families, as well as their careers and social lives. In order to track which motorcycles are wearing helmets and which are not, we employ deep learning algorithms CNN and DenseNet Model to identify those that are. Further to achieve greater accuracy we compare both the deep learning algorithms. For the Number Plate detection YOLO is used.
Deep Graph Network for Multispectral Point Cloud Classification with Adaptive Multi-kernel Graph

As a multidimensional form of remote sensing data, multispectral point clouds provide the data basis for more refined land cover classification. But the challenge remains in modelling and representing the rich spatial-spectral information. With better performance on non-Euclidean data, graph neural networks have been used on multispectral point clouds. However, most of the current studies use a fixed graph structure, which means that the feature aggregation pattern is limited. Thus, this paper proposes a novel Multi-Kernel deep graph network with Initial residual and Identity mapping (MKII). The multi-kernel mechanism is used to obtain adaptive graph structure and the deep graph neural network is designed to better adapt to the complex aggregation patterns introduced by changing graph structure. Specifically, we measure the similarity between each point by multiple base kernels and form base graphs. Then the feature aggregation of different patterns is achieved by learning combinations of base graphs. And a deep graph neural network is used to implement the classification of multispectral point clouds, with initial residual and identity mapping introduced to overcome the over-smoothing of deep features. Experimental results demonstrate that the proposed MKII outperforms several state-of-the-art methods on a real multispectral point cloud.