Miracle-dz/UniFAR-FTUs huggingface dataset:
FLO1, FLO5 and FLO11 Flocculation Gene Expression Impacts Saccharomyces cerevisiae Attachment to Penicillium chrysogenum in a Co-immobilization Technique

A reoccurring flaw of most yeast immobilization systems that limits the potential of the technique is leakage of the cells from the matrix. Leakage may be due to weakly adherent cells, deterioration of the matrix, or to new growth and loss of non-adherent daughter cells. Yeast biocapsules are a spontaneous, cost effective system of immobilization whereby Saccharomyces cerevisiae cells are attached to the hyphae of Penicillium chrysogenum, creating hollow spheres that allow recovery and reutilization. This attachment is based on naturally occurring adherent properties of the yeast cell surface. We hypothesized that proteins associated with flocculation might play a role in adherence to fungal hyphae. To test this hypothesis, yeast strains with overexpressed and deleted flocculation genes (FLO1, FLO5, and FLO11) were evaluated for biocapsule formation to observe the impact of gene expression on biocapsule diameter, number, volume, dry mass, and percent immobilized versus non-immobilized cells. Overexpression of all three genes enhanced immobilization and resulted in larger diameter biocapsules. In particular, overexpression of FLO11 resulted in a five fold increase of absorbed cells versus the wild type isogenic strain. In addition, deletion of FLO1 and FLO11 significantly decreased the number of immobilized yeast cells compared to the wild type BY4742. These results confirm the role of natural adherent properties of yeast cells in attachment to fungal hyphae and offer the potential to create strongly adherent cells that will produce adherent progeny thereby reducing the potential for cell leakage from the matrix.
Integration between ROS Regulatory Systems and Other Signals in the Regulation of Various Types of Heat Responses in Plants

Because of their sessile lifestyle, plants cannot escape from heat stress and are forced to alter their cellular state to prevent damage. Plants, therefore, evolved complex mechanisms to adapt to irregular increases in temperature in the natural environment. In addition to the ability to adapt to an abrupt increase in temperature, plants possess strategies to reprogram their cellular state during pre-exposure to sublethal heat stress so that they are able to survive under subsequent severe heat stress. Such an acclimatory response to heat, i.e., acquired thermotolerance, might depend on the maintenance of heat memory and propagation of long-distance signaling. In addition, plants are able to tailor their specific cellular state to adapt to heat stress combined with other abiotic stresses. Many studies revealed significant roles of reactive oxygen species (ROS) regulatory systems in the regulation of these various heat responses in plants. However, the mode of coordination between ROS regulatory systems and other pathways is still largely unknown. In this review, we address how ROS regulatory systems are integrated with other signaling networks to control various types of heat responses in plants. In addition, differences and similarities in heat response signals between different growth stages are also addressed.
Bringing back simplicity and lightliness into neural image captioning

Neural Image Captioning (NIC) or neural caption generation has attracted a lot of attention over the last few years. Describing an image with a natural language has been an emerging challenge in both fields of computer vision and language processing. Therefore a lot of research has focused on driving this task forward with new creative ideas. So far, the goal has been to maximize scores on automated metric and to do so, one has to come up with a plurality of new modules and techniques. Once these add up, the models become complex and resource-hungry. In this paper, we take a small step backwards in order to study an architecture with interesting trade-off between performance and computational complexity. To do so, we tackle every component of a neural captioning model and propose one or more solution that lightens the model overall. Our ideas are inspired by two related tasks: Multimodal and Monomodal Neural Machine Translation.
The Influence of Online-Only Friends on the Substance Use of Young Adults with a History of Childhood Maltreatment

Abstract Background: Existing research shows that having offline friends that use substances increases risk for individual substance use in adolescence. However, there is little research examining the influence of online social networks on substance use in young adulthood. Objectives: This study examined the number of online-only Facebook friends (i.e. those with no in-person contact) that were substance users in relation to the participants’ alcohol and marijuana use in the past year. Methods: The sample (n = 114) were selected from participants enrolled in a longitudinal study on childhood maltreatment in an urban city within the United States. The mean age of the sample was 21.84 (SD = 1.43), 61% female. With participant permission, data were downloaded from Facebook to obtain the list of online friends. A self-report questionnaire recorded the substance use of each participant. Path analysis and logistic regression were used to examine (a) the number of online-only friends who drink as a predictor of participants’ problem drinking, (b) the number of online-only friends who smoke marijuana as a predictor of participants’ marijuana use, and (c) moderation by maltreatment status. Results: The results showed a significant effect of the number of online-only friends who smoke marijuana on the participant’s marijuana use. However, after including offline friends this significant effect was no longer present, but offline friends predicted individual marijuana use. Conclusions/Importance: These findings demonstrate that substance use in online social networks may be associated with individual use in young adulthood, but that offline influence is stronger.
Men and women with Down syndrome exhibit different kinematic (but not spatio‐temporal) gait patterns

BACKGROUND Gait phenotypes are well documented in people with Down syndrome (pwDS), but sex-related differences are still unexplored. This study investigated the existence of possible differences in spatio-temporal and kinematic parameters of gait between men and women with DS using quantitative three-dimensional gait analysis. METHODS Gait patterns of 117 pwDS (53 F, 64 M) who underwent a computerised gait analysis from 2002 to 2017 were retrospectively analysed to obtain spatio-temporal gait parameters and kinematics in the sagittal plane at hip, knee and ankle joints, as well as foot progression. RESULTS Overall, when considered as a single group, the gait patterns found for pwDS confirmed the findings of previous studies. However, when analysed by sex, our data revealed that women with DS exhibit a larger hip flexion at late stance (42% to 54% of the gait cycle) and reduced knee flexion at the beginning of the swing phase (61% to 69% of the gait cycle). In contrast, men are characterised by larger foot extra-rotation angles through most of the stance phase (from 0% to 55% of the gait cycle) and at the end of the swing phase (92% to 99% of the gait cycle). No differences between men and women with DS were found concerning ankle dorsi- plantar-flexion or in all spatio-temporal parameters normalised by individuals' anthropometry, excluding cadence (higher in women). CONCLUSIONS The findings of the present study highlight the need to investigate gait dysfunctions in pwDS by taking their sex into consideration. Such an approach may be useful not only in gaining a better understanding of the pathophysiology of gait disturbances associated with DS but also in supporting a better orientation of rehabilitative treatments.
The use of an intensive physical exertion test as a final return to play measure in concussed athletes: a prospective cohort

ABSTRACT Objectives: To examine the utility of a novel physical exertion test developed by the Chicago Blackhawks medical staff as a final return to play (RTP) clearance test in youth and young adult athletes, and to determine the relationship between participant and test variables on RTP within asymptomatic athletes diagnosed with concussion. Methods: Once asymptomatic and following completion of all RTP steps, concussed athletes underwent the Gapski-Goodman Test (GGT) or modified GGT (mGGT) at partnered Complete Concussion Management Inc. (CCMI) clinics as part of RTP decision-making. Prospective data was collected electronically by trained CCMI clinicians utilizing the CCMI Concussion Database System. A review was conducted to examine data collected between January 2016 and February 2017. Participant and test variables were analyzed to determine relationships with pass/fail rate of the GGT/mGGT. Results: A total of 759 athletes performed the GGT/mGGT in the study period. Although all asymptomatic, 14.6% of concussed athletes failed the GGT/mGGT while attempting to achieve RTP clearance. Statistically significant relationships were found between failure of the test and symptom severity score on initial presentation and self-reported history of pre-morbid anxiety. When taken together, sex, age, and pre-morbid anxiety significantly predicted the length of time between injury and RTP clearance. Conclusion: The GGT may identify individuals who are not ready to RTP despite a self-reported asymptomatic status and completion of all RTP steps. These results illustrate that RTP clearance decisions based on self-reported asymptomatic status at rest may be inadequate. Instead, monitored, intensive, sport-specific, physical exertion testing should be utilized to inform clinical RTP decisions.
A reinforcement learning based approach to automated testing of Android applications

In recent years, researchers have actively proposed tools to automate testing for Android applications. Their techniques, however, still encounter major difficulties. First is the difficulty of achieving high code coverage because applications usually have a large number of possible combinations of operations and transitions, which makes testing all possible scenarios time-consuming and ineffective for large systems. Second is the difficulty of achieving a wide range of application functionalities, because some functionalities can only be reached through a specific sequence of events. Therefore they are tested less often in random testing. Facing these problems, we apply a reinforcement learning algorithm called Q-learning to take advantage of both random and model-based testing. A Q-learning agent interacts with the Android application, builds a behavioral model gradually and generates test cases based on the model. The agent explores the application in an optimal way that reveals as much functionalities of the application as possible. The exploration using Q-learning improves code coverage in comparison to random and model-based testing and is able to detect faults in applications under test.
What drives the motivation to self-protect on social networks? The role of privacy concerns and perceived threats

Social networks offer a wide range of services to their users resulting in a high degree of their involvement in social network users' everyday life. Users are however exposed to a variety of evolving cyberthreats that may be just as harmful as those in the material world even though they are sometimes hard to perceive as such in the cyberspace. To better understand the motivation of social network users to engage in self-protective behavior, we conducted an online survey among social network users (N = 227). The survey aimed to measure the role of perceived threat, privacy concerns and descriptive norms (also known as group norms) in user's intentions to adopt self-protective behavior. Analysis showed statistically significant correlations between mentioned constructs confirming most of our hypotheses. In addition to the well-known importance of perceived threat, results show that privacy concerns are also an important factor in explaining the intention of social network users to engage in self-protective behavior on social networks.
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations

Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks—averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.
Graph HyperNetworks for Neural Architecture Search

Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitively expensive as the search requires training thousands of different networks, while each can last for hours. In this work, we propose the Graph HyperNetwork (GHN) to amortize the search cost: given an architecture, it directly generates the weights by running inference on a graph neural network. GHNs model the topology of an architecture and therefore can predict network performance more accurately than regular hypernetworks and premature early stopping. To perform NAS, we randomly sample architectures and use the validation accuracy of networks with GHN generated weights as the surrogate search signal. GHNs are fast -- they can search nearly 10 times faster than other random search methods on CIFAR-10 and ImageNet. GHNs can be further extended to the anytime prediction setting, where they have found networks with better speed-accuracy tradeoff than the state-of-the-art manual designs.
Decision-making, knowledge, and psychosocial outcomes in pediatric siblings identified to donate hematopoietic stem cells

Abstract Purpose: To (a) describe the decision-making experience and psychosocial outcome of sibling hematopoietic stem cell (HSC) donors, and (b) to determine the feasibility of completing a prospective and longitudinal assessment of HSC sibling donors at a single institution. Design: A mixed-methods approach was utilized. Sample and methods: 12 potential siblings HSC donors aged 10–21 years completed various psychological measures and participated in semi-structured interviews at three time points in the donation experience: pre-donation, within 1 week after the harvest procedure, and six months post-donation. Caregivers also completed parent-proxy measures. Findings: Qualitative analysis indicated donors want to make their own decision about donation but may not be given the option or may feel that there is no choice given their limited awareness of alternative options. Donors felt well prepared for the donation procedure but demonstrated a poor understanding of possible recipient outcomes. A minority of donors endorsed emotional distress prior to and after donation; however, this was not linked to recipient health. Forty percent of donors felt that they had inadequate support following their donation. Small sample size restricted quantitative data analysis. Conclusions and implications: Utilizing a donor advocate offers opportunity to work with donors to encourage decision-making tied to ideals rather obligation, increase education about possible recipient outcomes, and offer support at key times, such as when a recipient dies. Future research should include prospective multi-site studies.
Incorporation of Guava Seeds Powder into a Commercial Diet Improves the Growth Performance, Nutritional and Quality Characteristics of Muscovy Duck’s Meat

Food wastes are valuable resources to be utilized for value-added products in animal feeding for producing meat that is more nutritious and reducing the cost of feeding materials. The effect of the guava seeds powder (GSP) incorporation into Muscovy duck’s diet on the growth performance (GP), physicochemical parameters, amino acids and fatty acids composition of duck meat was investigated. The GP and carcass traits (CT) of Muscovy birds were carried out. Chemical composition, physicochemical parameters, amino acids and fatty acids profile were determined in meat. Feed conversion ratio (FCR) and growth hormone (GH) were higher in GSP-diet fed ducks, significantly (P < 0.05). Non-significant enhancements in some carcass characteristics, chemical composition, and some physicochemical were observed. Contrariwise, essential amino acids (EAA), and non-essential amino acids (NEAA) were increased with increasing GSP levels by 4.5 and 8.8% in GD10, and 6.3 and 8.5% in GD20, respectively. The essential amino acids index (EAAI %) was 76.56% in GD10 and 79.30% in GD20 when compared with 73.24% in control duck meat (CD). Interestingly, the limiting AAs number was decreased from three in CD duck meat to one in duck meat fed with the GSP-diet at 20%. The majority of unsaturated fatty acids of GSP-diet duck meat was increased with increasing GSP content. Linolenic acid as a ɷ 3 FA was increased by 9.5 and 47.6% in CD10 and GD20, respectively. Incorporating GSP into duck’s diet tended to improve GP, some physicochemical, qualityrelated and nutritional characteristics. The improvements in amino acids and fatty acids profile because of GSP incorporation are promising. Inclusion of certain percentage of GSP in duck’s diets not only considered an economically feasible approach to reduce the costs of raising this bird, but it also conserved the physical, chemical and nutritional characteristics which encourage the revalorization of food-processing wastes.
Insights from social transmission of information in rodents

Direct exposure to stimuli in their environment is not the only way that animals learn about important information. Individuals can infer fear from a social context through observation. Like humans, rats are very social animals, and may learn to infer information about their environment through their interactions with conspecifics. Here, we first review different models for social transmission of information in rodents. Second, we examine different modes of communication that are important to social learning. Then, we cover the different proximate factors that are thought to modulate the social transmission of information. Next, we identify social and environmental conditions that impact social learning, and finally, we conclude by revisiting social transmission through the lens of the Tinbergen framework.
Evidence That Parietal Lobe Fatty Acids May Be More Profoundly Affected in Moderate Alzheimer’s Disease (AD) Pathology Than in Severe AD Pathology

Brain is a lipid-rich tissue, and fatty acids (FAs) play a crucial role in brain function, including neuronal cell growth and development. This study used GC-MS to survey all detectable FAs in the human parietal cortex (Brodmann area 7). These FAs were accurately quantified in 27 cognitively normal age-matched controls, 16 cases of moderate Alzheimer’s disease (AD), 30 severe AD, and 14 dementia with Lewy bodies (DLB). A total of 24 FA species were identified. Multiple comparison procedures, using stepdown permutation tests, noted higher levels of 13 FAs but the majority of changes were in moderate AD and DLB, rather than severe AD. Subjects with moderate AD and DLB pathology exhibited significantly higher levels of a number of FAs (13 FAs and 12 FAs, respectively). These included nervonic, lignoceric, cis-13,16-docosadienoic, arachidonic, cis-11,14,17-eicosatrienoic, erucic, behenic, α-linolenic, stearic, oleic, cis-10-heptanoic, and palmitic acids. The similarities between moderate AD and DLB were quite striking—arachidic acid was the only FA which was higher in moderate AD than control, and was not similarly affected in DLB. Furthermore, there were no significant differences between moderate AD and DLB. The associations between each FA and a number of variables, including diagnosis, age, gender, Aβ plaque load, tau load, and frontal tissue pH, were also investigated. To conclude, the development of AD or DLB pathology affects brain FA composition but, intriguingly, moderate AD neuropathology impacts this to a much greater extent. Post-mortem delay is a potential confounding factor, but the findings here suggest that there could be a more dynamic metabolic response in the earlier stages of the disease pathology.
Functional Topography and Development of Inhibitory Reticulothalamic Barreloid Projections

The thalamic reticular nucleus (TRN) is the main source of inhibition to the somatosensory thalamus (ventrobasal nucleus, VB) in mice. However, the functional topography and development of these projections with respect to the VB barreloids has been largely unexplored. In this respect, to assist in the study of these projections, we have utilized a vesicular gamma-aminobutryic acid (GABA) transporter (VGAT)-Venus transgenic mouse line to develop a brain slice preparation that enables the rapid identification of inhibitory neurons and projections. We demonstrate the utility of our in vitro brain slice preparation for physiologically mapping inhibitory reticulothalamic (RT) topography, using laser-scanning photostimulation via glutamate uncaging. Furthermore, we utilized this slice preparation to compare the development of excitatory and inhibitory projections to VB. We found that excitatory projections to the barreloids, created by ascending projections from the brain stem, develop by postnatal day 2–3 (P2–P3). By contrast, inhibitory projections to the barreloids lag ~5 days behind excitatory projections to the barreloids, developing by P7–P8. We probed this lag in inhibitory projection development through early postnatal whisker lesions. We found that in whisker-lesioned animals, the development of inhibitory projections to the barreloids closed by P4, in register with that of the excitatory projections to the barreloids. Our findings demonstrate both developmental and topographic organizational features of the RT projection to the VB barreloids, whose mechanisms can now be further examined utilizing the VGAT-Venus mouse slice preparation that we have characterized.