Miracle-dz/UniFAR-FTUs huggingface dataset:
Allopatric divergence of cooperators limits cheating range and effects of a defector mutation

Studies of microbial social defectors that ‘cheat’ on cooperative genotypes generally focus on interactions with their cooperative parents, yet in nature defectors may meet diverse cooperators. Genotype-bygenotype interactions may constrain the ranges of cooperators upon which particular defectors can cheat, limiting the cheaters’ spread and potentially the overall equilibrium frequency of cheaters. The bacterium Myxococcus xanthus undergoes cooperative multicellular development upon starvation, but some developmental defectors can cheat on cooperators, outcompeting them within mixed groups. We show that a defector disrupted at the signaling gene csgA has a narrow cheating range among diverse natural cooperators owing to antagonisms not specifically targeted at defectors. More strikingly, lab-evolved cooperators only slightly differentiated from the defector have allopatrically evolved beyond its cheating range by accumulating fewer than 20 mutations when development was not directly under selection. Cooperators might diversify not only with respect to which defectors cheat on them, but also in the potential for a particular mutation to reduce expression of cooperative trait or generate a cheating phenotype. We tested this by constructing a new csgA mutation in several highly diverged cooperators. The mutation generated very different sporulation phenotypes – from a complete defect to no defect – indicating that genetic background effects can limit the set of genomes for which a given mutation creates a defector and potentiates cheating. Our results suggest that natural populations feature geographic mosaics of cooperators diversified in susceptibility to cheating by any given defector and in the social phenotypes generated by any given mutation in a cooperation gene.
Cholinergic forebrain activation improves cognition, boosts neurotrophin receptors, and lowers Aβ42 levels in the cerebral cortex of 5xFAD mice

The biological basis of Alzheimer’s dementia has been hypothesized in terms of basal forebrain cholinergic decline, and in terms reflecting the neuropathologies surrounding beta amyloid and tau. To shed light on the contributions of these different biological elements, we performed five month intermittent stimulation of the basal forebrain, which projects to the cortical mantle, in 5xFAD Alzheimer’s model mice and wild-type littermates, and subjected mice to behavioral testing and tissue analysis. The 5xFAD mice performed worse in water maze testing than their littermates. Stimulation improved both classes of mice, and removed performance differences between genotypes by the fifth testing day. Stimulated groups had two to four-fold increases in immunoblot measures of each of the neurotrophin receptors tropomyosin receptor kinase A and B. Stimulation also led to lower tissue Aβ42 and beta amyloid cleavage enzyme 1 in 5xFAD mice. Despite the lack of strong direct projections from the basal forebrain stimulation region to the hippocampus, the hippocampal tissues in stimulated mice had more nerve growth factor receptor, and lower levels of beta secretase for amyloid. These data support a causal relation between forebrain cholinergic pathways and cognitive decline dependent on Aβ42. Activation of cholinergic brain pathways improves neurotrophin pathways and reduces Aβ42 accumulation. The recruitment of both classes of neurotrophin receptors in the process suggests a serine protease cleavage intermediary between cholinergic response and neurotrophin activation.
Cell Chromatography: Biocompatible Chromatographic Separation and Interrogation of Microbial Cells

This is the first report of a method for separating microbial cells using chromatography, with full retention of cell viability. Differences in the surface chemistry of microbial cells provides a means of attracting cells to immobilized microbeads. ABSTRACT The isolation of pure, single colonies lies at the heart of experimental microbiology. However, a microbial colony typically contains around 1 million cells at all stages of the life cycle. Here, we describe a novel cell chromatography method that facilitates the capture, purification, and interrogation of microbial cell populations from both single and mixed cultures. The method described relies on, but is not limited to, differences in surface charge to separate bacterial strains. The method is fully biocompatible, leading to no significant loss of cell viability. The chromatographic capture of cells, combined with selective elution methods, facilitates a greater level of experimental control over the sample inputs required for downstream high-throughput and high-sensitivity analytical methods. The application of the method for interrogating the antibiotic resistance of bacterial strains and for the separation of bacteria from environmental samples is illustrated. IMPORTANCE This is the first report of a method for separating microbial cells using chromatography, with full retention of cell viability. Differences in the surface chemistry of microbial cells provides a means of attracting cells to immobilized microbeads. Some cells are attracted, and some are repelled. The differences in, for example, surface charge can be harnessed to capture, interrogate, and separate environmental samples, thus circumventing the need to use conventional bacterial plating methods. This method will greatly facilitate drug discovery and bioprospecting for novel microbial compounds.
Reconstitution of Kinetochore and Microtubule Dynamics Reveals a Role for a Kinesin-8 in Establishing End-on Attachments

During mitosis, individual microtubules make attachments to chromosomes via a specialized protein complex called the kinetochore to faithfully segregate the chromosomes to daughter cells. Translocation of kinetochores on the lateral surface of the microtubule has been proposed to contribute to high fidelity chromosome capture and alignment at the mitotic midzone, but has been difficult to observe in vivo because of spatial and temporal constraints. To overcome these barriers, we used total internal reflection fluorescence (TIRF) microscopy to track the interactions between endogenously tagged tubulin, kinetochore proteins, and other microtubule-associated proteins in lysates from metaphase-arrested Saccharomyces cerevisiae. Using both TIRF microscopy and cryo-correlative light microscopy and electron tomography, we successfully reconstituted microtubule-bound, intact kinetochores. These kinetochores translocate on the lateral microtubule surface toward the microtubule plus end and transition to end-on attachment, whereupon microtubule depolymerization commences. The directional kinetochore movement is dependent on the highly processive kinesin-8, Kip3. We propose that Kip3 facilitates stable kinetochore attachment to microtubule plus ends through its abilities to move the kinetochore laterally on the surface of the microtubule and to regulate microtubule plus end dynamics.
SNP calling parameters have minimal impact on population structure and divergence time estimates for the rice blast fungus

Objectives Accurate single-nucleotide polymorphisms (SNP) calls are crucial for robust evolutionary and population genetic inferences in genomic analyses. Such inferences can reveal the time-scales and processes associated with the emergence and spread of pandemic plant pathogens, such as the rice blast fungus Magnaporthe oryzae (Syn. Pyricularia oryzae). However, the specificity and sensitivity of SNP calls depend on the filtering parameters applied to the data. Here, we used a benchmarking approach to evaluate the impact of SNP calling on different population genetic analyses of the rice blast fungus, namely genetic clustering, topology of phylogenetic reconstructions and estimation of evolutionary rates. Results To benchmark SNP calling parameters, we generated a gold standard set of validated SNPs by sequencing nine M. oryzae genomes with both Illumina short-reads and Oxford Nanopore Technologies (ONT). We used the gold standard set of SNPs to identify the SNP calling parameter configuration that maximizes sensitivity and specificity. We found that the choice of parameter configurations can substantially change the number of ascertained SNPs, preferentially affecting SNPs segregating at low population frequency. However, SNP calling parameter configurations did not significantly affect the clustering of isolates in clonal lineages, the monophyly of each clonal lineage, and the estimation of evolutionary rates. We leverage the evolutionary rates obtained from each SNP calling parameter configuration to generate divergence time estimates that take into account the uncertainty associated with both the estimation of evolutionary rates and SNP calling. Our analysis indicates that M. oryzae clonal lineage expansions took place ~300 years ago.
Mutation Rate Evolution Drives Immune Escape In Mismatch Repair-Deficient Cancer

Mutation rate optimisation drives evolution and immune evasion of bacteria and lentiviral strains, including HIV. Whether evolving cancer lineages similarly adapt mutation rates to increase tumour cell fitness is unknown. Here, by mapping the clonal topography of mismatch repair-deficient (MMRd) colorectal cancer, we show that genomic MMRd mutability co-evolves with neoantigen selection to drive intratumour diversification and immune escape. Mechanistically, we find that microsatellite instability modulates subclonal DNA repair by toggling two hypermutable mononucleotide homopolymer runs in the mismatch repair genes MSH6 and MSH3 (C8 and A8, respectively) through stochastic frameshift switching. Spontaneous mutation and reversion at these evolvability switches modulates subclonal mutation rate, mutation bias, and clonal HLA diversity during MMRd cancer evolution. Combined experimental and simulation studies demonstrate that subclonal immune selection favours incremental MMR mutations. MMRd cancers thus fuel intratumour heterogeneity by adapting subclonal mutation rate and mutation bias to immune selection, revealing a conserved co-evolutionary arms race between neoantigen selection and adaptive genomic mutability. Our work reveals layers of mutational complexity and microsatellite biology in MMRd cancer evolution previously hidden in bulk analyses.
Sex and estrous cycle affect experience-dependent plasticity in mouse primary visual cortex

Sex hormones can affect cellular physiology and modulate synaptic plasticity, but it is not always clear whether or how sex-dependent differences identified in vitro express themselves as functional dimorphisms in the brain. Historically, most experimental neuroscience has been conducted using only male animals and the literature is largely mute about whether including female mice in will introduce variability due to inherent sex differences or endogenous estrous cycles. Though this is beginning to change following an NIH directive that sex should be included as a factor in vertebrate research, the lack of information raises practical issues around how to design experimental controls and apply existing knowledge to more heterogeneous populations. Various lines of research suggest that visual processing can be affected by sex and estrous cycle stage. For these reasons, we performed a series of in vivo electrophysiological experiments to characterize baseline visual function and experience-dependent plasticity in the primary visual cortex (V1) of male and female mice. We find that sex and estrous stage have no statistically significant effect on baseline acuity measurements, but that both sex and estrous stage have can modulate two mechanistically distinct forms of experience dependent cortical plasticity. We also demonstrate that resulting variability can be largely controlled with appropriate normalizations. These findings suggest that V1 plasticity can be used for mechanistic studies focusing on how sex hormones effect experience dependent plasticity in the mammalian cortex.
Finite Element Simulation Analysis of Temperature Field of MEMS Gas Sensor Based on ANSYS Software

MEMS gas sensor reflects the concentration of the target gas in the air through the change of the resistance of the surface sensitiuses. ANSYS software is used to simulate and analyze the structure of the traditional MEMS gas sensor in this paper. By setting different corrosion areas of Si backing, electrode pattern sizes, and the distance between pad pins and lines, the temperature field distribution diagram is simulated and analyzed, which provides a reference basis for the structural design and thermal design of MEMS gas sensors.
Compact-Size Wearable Button Antenna with Low SAR for IOT, Medical and GPS Applications

A compact size wearable button antenna has been designed and presented in this paper. The designed antenna covers widely used 2.4 GHz ISM band for internet of things (IOT) and medical applications. It can also be used for IRNSS-1, S-band (2484 MHz to 2500 MHz) GPS tracking application. The wearable antenna button is designed on a circular piece of low cost FR-4 epoxy substrate with radius 10 mm only. The operating frequency range of the antenna is 2.37 GHz to 2.6 GHz with 230 MHz bandwidth and 2.6 dB peak gain at 2.4 GHz. The usability of the antenna has been examined by on-cloth and on-body performance analyses. The average specific absorption rate (SAR) of the antenna with human body interaction is around 0.1 W/Kg satisfying the guideline of FCC.
Positive rate‐dependent action potential prolongation by modulating potassium ion channels

Pharmacological agents that prolong action potential duration (APD) to a larger extent at slow rates than at the fast excitation rates typical of ventricular tachycardia exhibit reverse rate dependence. Reverse rate dependence has been linked to the lack of efficacy of class III agents at preventing arrhythmias. In this report we show that, in computer models of the ventricular action potential, APD prolongation by accelerating phase 2 repolarization (by increasing IKs) and decelerating phase 3 repolarization (by blocking IKr and IK1) results in a robust positive rate dependence (i.e., larger APD prolongation at fast rates than at slow rates). In contrast, APD prolongation by blocking a specific potassium channel type results in reverse rate dependence or a marginal positive rate dependence. Interventions that result in a strong positive rate dependence tend to decrease the repolarization reserve because they require substantial IK1 block. However, limiting IK1 block to ∼50% results in a strong positive rate dependence with moderate decrease in repolarization reserve. In conclusion, the use of a combination of IKs activators and IKr and IK1 blockers could result in APD prolongation that potentially maximizes anti-arrhythmic effects (by maximizing APD prolongation at fast excitation rates) and minimizes pro-arrhythmic effects (by minimizing APD prolongation at slow excitation rates).
Analysis of Climbing Locomotion of Snake-Like Robot Based on Control Function

The control problem of the snake-like robot with orthogonal joints is discussed.A simple control law is prensented to make the snake-like robot implement the serpentine locomotion,traveling wave locomotion,lateral rolling locomotion and helical climbing locomotion,etc.The control parameters of the helical climbing funtion are analyzed and the optimization of the control parameters is studied.The control parameters are optimized through the particle swarm optimization(PSO),and then the fitting function is given between the control parameters and the helical radius,which provide a reference for the snake-like robot to implement different helical climbing locomotions.By using the Webots professional mobile robot simulation software,different locomotions are implemented and the feasibility of the control law and fitting function is verified.
Assessing methods for predicting green roof rainfall capture: A comparison between full-scale observations and four hydrologic models

To optimize the application of green roof technology, there is a need to quantify stormwater mitigation in advance of green roof construction. This study contributes toward meeting this need by assessing the utility of four hydrologic models for predicting green roof rainfall capture, including the: (1) curve number method, (2) characteristic runoff equation, (3) Hydrological Evaluation of Landfill Performance (HELP V3.9D) model, and (4) Storm Water Management Model (SWMM V5.1). Modeling results were compared to over twenty-four months of observed runoff data, collected between June 2011 and December 2013, from two full-scale green roofs in New York City (NYC). Both the curve number method and characteristic runoff equation had the highest Nash-Sutcliffe efficiency index (NSEI) between modeled and observed cumulative runoff depth per event (NSEI = 0.97) due to parameter calibration requirements, where error was mainly due to variations in green roof antecedent moisture conditions. The HELP model was originally intended for evaluation of a continuous landfill cover. As a result, HELP's inability to account for the non-vegetated areas on green roofs caused underestimation of runoff depth for most events (NSEI = 0.84). Alternatively, the SWMM model tended to overestimate event runoff depth (NSEI = 0.94), thought to be the result of its storage term parameterization. Model assessments point to the need for more robust parameter estimation methods, particularly for inputs that are statistical or difficult to measure directly, to improve pre-development accuracy of green roof performance models.
Thorough Investigation of Low Frequency Noise Mechanisms in AlGaN/GaN and Al2O3/GaN HEMTs

This paper investigates the AlGaN/GaN and Al2O3/GaN interface quality on GaN MIS-HEMT using low frequency noise (LFN) measurements. To address the issue of noise in the access resistance, we have also tested “non-gated” 2DEG devices. The LFN has a I/f- like behaviour caused by trapping/de-trapping processes which has been well described by carrier number fluctuations (CNF) and correlated mobility fluctuation (CMF) models. The border trap density extracted in optimised devices is better than previously reported GaN data and close to silicon CMOS results. Finally, a noise model for GaN-HEMT with recessed MIS gate including a 2DEG access resistance noise source is proposed.
A Survey on Exploring Deep Learning in Medical Image Processing

In this article we have analyzed the application of deep learning algorithm in the domain of medical image processing. Deep learning algorithms are very promising techniques and they are very successful in analyzing the data. As the amount and types of image generated in healthcare industry is growing very fast various machine learning algorithm are used to analyze the image and decide treatment of the patient. In this article we have tried to analyze the importance of deep learning algorithm in the domain of health care. Although different models are used convolutional neural network is most popular of those. It has to be customized according to the requirement of the problem. Transfer learning has made pretrained CNN computationally efficient in comparison to CNN which is trained from scratch. The use of transfer learning is also discussed in this article.
Demonstration of a Free-layer Developed With Atomistic Simulations Enabling BEOL Compatible VCMA-MRAM with a Coefficient ≥100fJ/Vm

For the first time in voltage control of magnetic anisotropy magnetic random-access memory (VCMA-MRAM), a large coefficient of ≥ 100 fJ/Vm has been demonstrated in fully integrated, BEOL compatible, magnetic tunnel junctions (MTJ). This was achieved while maintaining a high TMR (180%) and thermal stability factor ($\Delta > 40$). VCMA-induced switching is also explored, showing the benefits for low power and ultrafast memory applications. This was enabled thanks to the development of a VCMA-MRAM specific free-layer (FL) which incorporates the insertion of transition metals at the CoFeB|MgO interface. The design of this FL was driven by atomistic simulations using an imec developed, non-perfect epitaxial stack model.
Positive Technological Development for Young Children in the Context of Children's Mobile Apps

This dissertation examines the extent to which children's tablet software applications, commonly called apps, are designed appropriately to promote the optimal development of preschool children aged three to five. This study extends previous research, particularly the theoretical frameworks of developmentally appropriate practice and Bers' positive technological development. The researcher argues that, for children's mobile apps to be developmentally meaningful, they need to satisfy three conditions: (1) apps must be designed appropriately to accommodate the developmental stages and needs of young children; (2) content must be designed to promote young children's development in the areas of cognition, academic skills, social-­‐emotional skills, and physical development; and (3) digital interactions engage children in activities and behaviors that foster optimal developmental assets. The researcher devised three instruments to evaluate and examine the breadth, depth, and design quality of 100 children's apps from the Apple's App Store for iPad. Content analysis revealed that only a non-­‐ significant majority of apps (58%) were meaningfully designed for preschool children in terms of user interface, audio and visual design, and instructional support. The apps selected for this sample included games and learning activities, interactive eBooks, as well as creativity and utility apps. The content of these apps tended to cluster around school skills and they rarely engaged children in activities beyond academic drill-­‐and-­‐practice. These apps largely ignored the social, emotional, and physical aspects of children's development. Using numerous vignettes and examples as illustrations, the analysis highlights design techniques, 2 content offering, and technological features that could be productive toward
Fake News Detection using Hybrid of Deep Neural Network and Stacked LSTM

Fake News is one of the major concerns for the world and it could cause many problems like misleading people, the potential to influence people’s opinions, etc. Hence distinguishing fake news is very crucial. Fake news can be detected using artificial intelligence methodologies using a title, body of the news, and other attributes such as credibility of the authors, location, media such as images, audio, and video, etc. In this paper, a hybrid of deep neural networks and stacked LSTM (Long Short-Term Memory) is used to detect fake news. We used Glove 300d as a word embedding layer and implemented the concept of stacked LSTM. The model has been applied to two different datasets. The results are compared with previous models and have found with good performance in comparison to the selected previously existing models.