Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Work or Simulations? Voices of Millennial Digital Natives

Students nowadays grow up with electronic devices and are adept at navigating the virtual world. Practical activities may be more of a novelty for them than simulations. Using the topic of electric circuits as a context, we examined the ways in which Grade 11 students perceived and learned from practical work and simulations, respectively. In this quasi-experiment study, a group of 19 students used a free online simulations package “Circuit Construction Kit,” while another group of 17 students learnt through practical work. We administered a validated instrument to both groups and found that practical work and simulations supported students’ learning in similar ways. The interventions were then reversed so that all participants experienced both practical work and simulations. Finally, seven students from each group were selected for a group interview. Through the interviews, we identified features of simulations and practical work respectively that students believed contributed to their learning.
Late-Holocene palaeoenvironmental reconstruction from a lake in the Amazon Rainforest-Tropical Savanna (Cerrado) boundary in Brazil using a multi-proxy approach

As an ecotone, the region between the Amazon Rainforest and Tropical Savanna (Cerrado) biomes is, by definition, more susceptible to climate change. Therefore, understanding palaeoenvironmental dynamics is essential to address the future responses of such transition areas to climatic fluctuations. In this context, we present a new sediment record for the Late-Holocene retrieved from Barro-Preto, currently an oxbow lake located in an ecotone at the southern Brazilian Amazon border. Our multi-proxy data include carbon and nitrogen isotopes, as well as bulk TOC, chlorophyll derivatives, grain-size and microcharcoal analyses, all anchored on a radiocarbon-dated chronology. The sedimentary process recorded at the Barro-Preto Lake responded to both local and regional climate dynamics. It was influenced by river excursions associated to local responses to precipitation changes by the activation of the palaeochannel connecting the main-stem river and the Barro-Preto lake. This activation was evidenced by the presence of different colour lithology laminations accompanied by coarser sediments and also by climate conditions known to influence the Amazon region. Depositional processes linked to lake dynamics and different oxbow lake cycle stages were also important to explain the changes verified in the Barro-Preto record, endorsing the use of this lake formation for palaeoclimatic reconstructions. The record indicated a rising humidity trend, reflected by a progressive increase in lacustrine productivity, in accordance to other studies carried out in the Amazon region concerning the Late-Holocene, associated with a more southward displacement of the Intertropical Convergence Zone. Despite this rising humidity trend, dry episodic events during the Late-Holocene were evidenced by charcoal data, also coherent with regional Amazon studies, albeit exhibiting increased intensity, suggesting that the transitional nature of the environment might have influenced susceptibility to fires.
Lifestyle Medicine for Chronic Lower Back Pain: An Evidence-Based Approach

Lower back pain is a leading cause of work absence and activity limitations globally, with a 60% to 85% lifetime chance of occurrence. This article highlights the role that lifestyle medicine plays in managing lower back pain as a cost-effective intervention strategy. It is suggested that lifestyle medicine strategies, such as incorporating whole foods and a plant-based diet, sustainable physical activity and mind-body exercises, restorative sleep, stress resiliency, awareness and mitigation of substance abuse and addiction, and establishing meaningful social networks and self-care strategies, be a part of managing chronic lower back pain.
Task-Assisted GAN for Resolution Enhancement and Modality Translation in 1 Fluorescence Microscopy 2

13 We introduce a deep learning model for resolution enhancement and prediction of super-resolved biological structures, which 14 is based on a Generative Adversarial Network (GAN) assisted by a complementary segmentation task. It is applied to pre- 15 dict biological nanostructures from diﬀraction-limited images and to guide microscopists for quantitative ﬁxed- and live-cell 16 STimulated Emission Depletion (STED) microscopy. More speciﬁcally, we show that the use of a complementary segmen- 17 tation task improves the accuracy of the predicted nanostructures over state-of-the art resolution enhancement generative 18 approaches, allowing quantitative analysis of the sub-diﬀraction structures in the resulting generated images. 19 21 improve the of super-resolution microscopy and Super-resolution imaging was performed on a 4-color Abberior STED microscope (Abberior Instruments, using a 40 MHz pulsed 640 nm excitation laser, a ET685/70 (Chroma, ﬂuorescence ﬁlter, and a 775 nm pulsed (40 MHz) depletion laser. Scanning was conducted using a pixel dwell time of 5 µ s, a pixel size of 20 nm, and 8 line repetition sequence. STED is buﬀered 33 gently Imaging buﬀered After KCl Solutions were adjusted to Osmolality: 240 mOsm per kg and pH: 7.3.
Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. We sequenced the transcriptomes of 40,927 cells from the prenatal human hypothalamus spanning from 6 to 25 gestational weeks and 25,424 mature neurons in regions of the adult human hypothalamus, revealing a temporal trajectory from proliferative stem cell populations to mature neurons and glia. Developing hypothalamic neurons followed branching trajectories leading to 170 transcriptionally distinct neuronal subtypes in ten hypothalamic nuclei in the adult. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence from the same individuals, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. Cross-species comparisons to the mouse hypothalamus identified human-specific POMC populations expressing unique combinations of transcription factors and neuropeptides. These results provide the first comprehensive transcriptomic view of human hypothalamus development at cellular resolution. One-Sentence Summary Using single-cell genomics, we reconstructed the developmental lineages by which precursor populations give rise to 170 distinct neuronal subtypes in the human hypothalamus.
Genomic Epidemiology and Strain Taxonomy of Corynebacterium diphtheriae

Corynebacterium diphtheriae is highly transmissible and can cause large diphtheria outbreaks where vaccination coverage is insufficient. Sporadic cases or small clusters are observed in high-vaccination settings. ABSTRACT Corynebacterium diphtheriae is highly transmissible and can cause large diphtheria outbreaks where vaccination coverage is insufficient. Sporadic cases or small clusters are observed in high-vaccination settings. The phylogeography and short timescale evolution of C. diphtheriae are not well understood, in part due to a lack of harmonized analytical approaches of genomic surveillance and strain tracking. We combined 1,305 genes with highly reproducible allele calls into a core genome multilocus sequence typing (cgMLST) scheme. We analyzed cgMLST gene diversity among 602 isolates from sporadic clinical cases, small clusters, or large outbreaks. We defined sublineages based on the phylogenetic structure within C. diphtheriae and strains based on the highest number of cgMLST mismatches within documented outbreaks. We performed time-scaled phylogenetic analyses of major sublineages. The cgMLST scheme showed high allele call rate in C. diphtheriae and the closely related species C. belfantii and C. rouxii. We demonstrate its utility to delineate epidemiological case clusters and outbreaks using a 25 mismatches threshold and reveal a number of cryptic transmission chains, most of which are geographically restricted to one or a few adjacent countries. Subcultures of the vaccine strain PW8 differed by up to 20 cgMLST mismatches. Phylogenetic analyses revealed a short-timescale evolutionary gain or loss of the diphtheria toxin and biovar-associated genes. We devised a genomic taxonomy of strains and deeper sublineages (defined using a 500-cgMLST-mismatch threshold), currently comprising 151 sublineages, only a few of which are geographically widespread based on current sampling. The cgMLST genotyping tool and nomenclature was made publicly accessible (https://bigsdb.pasteur.fr/diphtheria). Standardized genome-scale strain genotyping will help tracing transmission and geographic spread of C. diphtheriae. The unified genomic taxonomy of C. diphtheriae strains provides a common language for studies of ecology, evolution, and virulence heterogeneity among C. diphtheriae sublineages.
Multi-omics analysis identifies essential regulators of mitochondrial stress response in two wild-type C. elegans strains

The mitochondrial unfolded protein response (UPRmt) is a promising pharmacological target for aging and age-related diseases. However, the integrative analysis of the impact of UPRmt activation on different layers of signaling in animals with a different genetic background is lacking. In this study, we applied systems approaches to investigate the effect of UPRmt induced by administering doxycycline (Dox) on transcriptome, proteome, lipidome, and metabolome in two genetically divergent C. elegans strains. We found that Dox prolongs lifespan of both worm strains through pathways in both shared and strain-specific manners. From the integrated omics datasets, we observed a strong impact of Dox on mitochondrial functions, detected upregulated defense response and lipid metabolism, identified decreased triglycerides and lowered metabolome profiles in both strains. This conserved phenomic footprint has great translational value as it indicates that the beneficial effects of Dox-induced UPRmt on health and lifespan are consistent across different genetic backgrounds.
Neural Contextual Bandits without Regret

Contextual bandits are a rich model for sequential decision making given side information, with important applications, e.g., in recommender systems. We propose novel algorithms for contextual bandits harnessing neural networks to approximate the unknown reward function. We resolve the open problem of proving sublinear regret bounds in this setting for general context sequences, considering both fully-connected and convolutional networks. To this end, we first analyze NTK-UCB, a kernelized bandit optimization algorithm employing the Neural Tangent Kernel (NTK), and bound its regret in terms of the NTK maximum information gain $\gamma_T$, a complexity parameter capturing the difficulty of learning. Our bounds on $\gamma_T$ for the NTK may be of independent interest. We then introduce our neural network based algorithm NN-UCB, and show that its regret closely tracks that of NTK-UCB. Under broad non-parametric assumptions about the reward function, our approach converges to the optimal policy at a $\tilde{\mathcal{O}}(T^{-1/2d})$ rate, where $d$ is the dimension of the context.
Reverse engineering models of software interfaces

Cyber-physical systems consist of many hardware and software components. Over the lifetime of these systems their components are often replaced or updated. To avoid integration problems, formal specifications of component interface behavior are crucial. Such a formal specification captures not only the set of provided operations but also the order of using them and the constraints on their timing behavior. Usually the order of operations are expressed in terms of a state machine. For new components such a formal specification can be derived from requirements. However, for legacy components such interface descriptions are usually not available. So they have to be reverse engineered from existing event logs and source code. This costs a lot of time and does not scale very well. To improve the efficiency of this process, we present a passive learning technique for interface models inspired by process mining techniques. The approach is based on representing causal relations between events present in an event log and their timing information as a timed-causal graph. The graph is further processed and eventually transformed into a state machine and a set of timing constraints. Compared to other approaches in literature which focus on the general problem of inferring state-based behavior, we exploit patterns of client-server interactions in event logs.
Optimization of Culture Condition for PHB Accumulating by Acidiphilium cryptum DX1-1

This experiment optimized the culture condition of Acidiphilium cryptum DX1-1,which was capable of producing poly-β-hydroxybutyrate(PHB). Glucose and KNO3 acted as the best carbon and nitrogen substrates for the production of PHB.The L16(43) orthogonal experiment improved the yield when 40 g/L glucose as carbon source,15 g/L KNO3 as nitrogen source and pH 3.0 were adopted.Then the cells growth curve and PHB production curve were measured under the optimum culture condition.It is found that the process of PHB accumulating was a growth-associated process.NMR analysis further supported that the product accumulated by that strain had the typical structure of PHB.The PHB production of strain DX1-1 increased to 19.75 g/L.
New Procedures of a Fractional Order Model of Novel Coronavirus (COVID-19) Outbreak via Wavelets Method

Coronaviruses are a group of RNA (ribonucleic acid) viruses with the capacity for rapid mutation and recombination. Coronaviruses are known to cause respiratory or intestinal infections in humans and animals. In this paper, a biologically compatible set of nonlinear fractional differential equations governing the outbreak of the novel coronavirus is suggested based on a model previously proposed in the literature. Then, this set is numerically solved utilizing two new methods employing sine–cosine and Bernoulli wavelets and their operational matrices. Moreover, the convergence of the solution is experimentally studied. Furthermore, the accuracy of the solution is proved via comparing the results with those obtained in previous research for the primary model. Furthermore, the computational costs are compared by measuring the CPU running time. Finally, the effects of the fractional orders on the outbreak of the COVID-19 are investigated.
Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)

Multilingual sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages, and with the help of Google Indic keyboard, people can express their sentiments (i.e., reviews) about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on natural language processing (NLP) using simple neural network (SNN), convolutional neural network(CNN), and long short-term memory (LSTM) neural network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4,000 samples of reviews in English, Hindi, and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.
The Association Between Particulate Air Pollution and Respiratory Mortality in Beijing Before, During, and After the 2008 Olympic and Paralympic Games

To improve ambient air quality during the 2008 Summer Olympic and Paralympic Games, the Chinese Government and Beijing’s municipal government implemented comprehensive emission control policies in Beijing and its neighboring regions before and during this period. The goal of this study was to investigate the association between particulate air pollution and cause-specific respiratory mortality before, during and after the period of the Olympic Games. Further, we wanted to assess whether changes in pollutant concentrations were linked to changes in respiratory mortality. We obtained daily data on mortality due to respiratory diseases (coded as J00-J99 according to the International Classification of Diseases and Related Health Problems 10th revision [ICD10]) and pneumonia (ICD10: J12–18), meteorology, particulate matter less than 10 µm or 2.5 μm in diameter (PM10, PM2.5) and particle number size distribution from official monitoring networks and sites located on the Peking University campus between May 20 and December 1, 2008. We assessed the effects of particulate air pollution on daily respiratory mortality using confounder-adjusted Quasi-Poisson regression models. Furthermore, we estimated air pollution effects for three periods—before (May 20 to July 20, 2008), during (August 1 to September 20, 2008) and after (October 1 to December 1, 2008)—by including interaction terms in the models. We found associations between different particle metrics and respiratory and pneumonia mortality, with more pronounced effects in smaller particle size ranges. For example, an interquartile range increase of 7,958 particles/cm3 in ultrafine particles (particles <100 nm in diameter) led to a 16.3% (95% confidence interval 4.3%; 26.5%) increase in respiratory mortality with a delay of seven days. When investigating the sub-periods, results indicate that a reduction in air pollution during the Olympics resulted in reduced (cause-specific) respiratory mortality. This reduction was especially pronounced for pneumonia mortality. The findings suggest that even a short-term reduction in pollution concentrations may lead to health benefits and that smaller particles in the ultrafine size range may be particularly important for respiratory health.
Endemism Patterns of Planthoppers (Fulgoroidea) in China

Studies on endemism are always of high interest in biogeography and contribute to better understanding of the evolution of species and making conservation plans. The present study aimed to investigate the endemism patterns of planthoppers in China by delimiting centers of endemism and areas of endemism. We collected 6,907 spatial distribution records for 860 endemic planthopper species from various resources. Centers of endemism were identified using weighted endemism values at 1° grid size. Parsimony analysis of endemicity and endemicity analysis were employed to detect areas of endemism at 1°, 1.5°, and 2° grid sizes. Six centers of endemism located in mountainous areas were identified: Taiwan Island, Hainan Island, eastern Yungui Plateau, Wuyi Mountains, western Qinling Mountains, and western Yunnan. We also delimited six areas of endemism, which were generally consistent with centers of endemism. Our findings demonstrated that mountainous areas have an essential role in facilitating the high level of endemism and formation of areas of endemism in planthoppers through the combined effects of complex topography, a long-term stable environment, and geological events. Dispersal ability and distribution of host plants also have important effects on the patterns of planthoppers’ endemism.
Soft Capsule Magnetic Millirobots for Region-Specific Drug Delivery in the Central Nervous System

Small soft robotic systems are being explored for myriad applications in medicine. Specifically, magnetically actuated microrobots capable of remote manipulation hold significant potential for the targeted delivery of therapeutics and biologicals. Much of previous efforts on microrobotics have been dedicated to locomotion in aqueous environments and hard surfaces. However, our human bodies are made of dense biological tissues, requiring researchers to develop new microrobotics that can locomote atop tissue surfaces. Tumbling microrobots are a sub-category of these devices capable of walking on surfaces guided by rotating magnetic fields. Using microrobots to deliver payloads to specific regions of sensitive tissues is a primary goal of medical microrobots. Central nervous system (CNS) tissues are a prime candidate given their delicate structure and highly region-specific function. Here we demonstrate surface walking of soft alginate capsules capable of moving on top of a rat cortex and mouse spinal cord ex vivo, demonstrating multi-location small molecule delivery to up to six different locations on each type of tissue with high spatial specificity. The softness of alginate gel prevents injuries that may arise from friction with CNS tissues during millirobot locomotion. Development of this technology may be useful in clinical and preclinical applications such as drug delivery, neural stimulation, and diagnostic imaging.
Wheat Blast: A Disease Spreading by Intercontinental Jumps and Its Management Strategies

Wheat blast (WB) caused by Magnaporthe oryzae pathotype Triticum (MoT) is an important fungal disease in tropical and subtropical wheat production regions. The disease was initially identified in Brazil in 1985, and it subsequently spread to some major wheat-producing areas of the country as well as several South American countries such as Bolivia, Paraguay, and Argentina. In recent years, WB has been introduced to Bangladesh and Zambia via international wheat trade, threatening wheat production in South Asia and Southern Africa with the possible further spreading in these two continents. Resistance source is mostly limited to 2NS carriers, which are being eroded by newly emerged MoT isolates, demonstrating an urgent need for identification and utilization of non-2NS resistance sources. Fungicides are also being heavily relied on to manage WB that resulted in increasing fungal resistance, which should be addressed by utilization of new fungicides or rotating different fungicides. Additionally, quarantine measures, cultural practices, non-fungicidal chemical treatment, disease forecasting, biocontrol etc., are also effective components of integrated WB management, which could be used in combination with varietal resistance and fungicides to obtain reasonable management of this disease.