Rapid Assessment of the Temporal Function and Phenotypic Reversibility of Neurodevelopmental Disorder Risk Genes in C. elegans

Hundreds of genes have been implicated in neurodevelopmental disorders. Previous studies have indicated that some phenotypes caused by decreased developmental function of select risk genes can be reversed by restoring gene function in adulthood. However, very few risk genes have been assessed for adult reversibility. We developed a strategy to rapidly assess the temporal requirements and phenotypic reversibility of neurodevelopmental disorder risk gene orthologs using a conditional protein degradation system and machine vision phenotypic profiling in Caenorhabditis elegans. Using this approach, we measured the effects of degrading and re-expressing orthologs of 3 neurodevelopmental risk genes EBF3, BRN3A, and DYNC1H1 across 30 morphological, locomotor, sensory, and learning phenotypes at multiple timepoints throughout development. We found some degree of phenotypic reversibility was possible for each gene studied. However, the temporal requirements of gene function and degree of phenotypic reversibility varied by gene and phenotype. The data reflects the dynamic nature of gene function and the importance of using multiple time windows of degradation and re-expression to understand the many roles a gene can play over developmental time. This work also demonstrates a strategy of using a high-throughput model system to investigate temporal requirements of gene function across a large number of phenotypes to rapidly prioritize neurodevelopmental disorder genes for re-expression studies in other organisms.

Studying the Innate Immune Response to Myocardial Infarction in a Highly Efficient Experimental Animal Model

The reduction in mortality following acute myocardial infarction (AMI) is an important achievement of modern medicine. Despite this progress, AMI remains the most common cause of heart failure (HF) and HF-related morbidity and mortality. The involvement of the innate immune response in different stages after AMI has at tracted important attention in recent years. With the increasing range of potential therapeutic compounds and delivery vectors, the need of highly efficient experimental AMI models is increasing, to support further advancement in this field. Here, we present a high-throughput model for the assessment of the innate immune response to AMI. The model is based on permanent surgical ligation of the left descending coronary artery (LAD) in mice, followed by complex flow-cytometry and histological analyses of immune cellular populations in blood and myocardium. We are presenting time-dependent qualitative and quantitative analysis results, demonstrating intense accumulation of Ly6Ghi neutrophils and Ly6Chi monocytes in the infarcted myocardium on days 1 and 3 post-AMI, followed by successive accumulation of reparatory Ly6CloMerTKhi macrophages, neovascularization and fibrosis development by day 7.

Materiality in the Seam Space: Sketches for a Transitional Port City Dome District

Biomass material volatility generates new opportunities for port-city relationships. Alternative energy markets require specialized port facilities to handle new bulk commodities like biomass. Wood pellets, a type of biomass, present warehousing challenges due to combustion danger. The industrial response to this risk has generated new storage forms for port regions. The return to bulk cargo reintroduces materiality as a focus for port city research, which had generally been regarded as a peripheral concern since the advent of the shipping container. The container had come to represent a borderless, ‘fast capitalism’ throughput model, but research on port ‘accidents’ has complicated this reductive globalization narrative. The programmatic dynamism of wood pellet dome structures suggests new spatially-porous possibilities for an interstitial border space at the port-city interface with material commonalities and hybrid potentials for resilient logistics and civic facilities. In contrast to container cargo unitization, the dome signifies the standardization of the coastal/riparian port environment. Dome structures can help ports plan for the complex challenges of cargo material behaviors and increasing extreme weather events. The article begins with wood pellet materiality to then explore programmatic possibilities that industrial construction technology generates. Conceptually, this joins the proposal of port as ‘seam space’ with port-city resilience planning and the porosity celebrated in recent urbanism literature. Scaling up from wood pellet materiality to an interstitial port-city district, the article contributes to calls for increased attention to materiality as a means to envision new urban agendas.

A Facile High-Throughput Model of Surface-Independent Staphylococcus aureus Biofilms by Spontaneous Aggregation

ABSTRACT Many microbes in their natural habitats are found in biofilm ecosystems attached to surfaces and not as free-floating (planktonic) organisms. Furthermore, it is estimated that nearly 80% of human infections are associated with biofilms. Biofilms are traditionally defined as three-dimensional, structured microbial communities that are attached to a surface and encased in a matrix of exopolymeric material. While this view of biofilm largely arises from in vitro studies under static or flow conditions, in vivo observations have indicated that this view of biofilms is essentially true only for foreign-body infections on catheters or implants where biofilms are attached to the biomaterial. In mucosal infections such as chronic wounds or cystic fibrosis or joint infections, biofilms can be found unattached to a surface and as three-dimensional aggregates. In this work, we describe a high-throughput model of aggregate biofilms of methicillin-resistant Staphylococcus aureus (MRSA) using 96-well plate hanging-drop technology. We show that MRSA forms surface-independent biofilms, distinct from surface-attached biofilms, that are rich in exopolymeric proteins, polysaccharides, and extracellular DNA (eDNA), express biofilm-related genes, and exhibit heightened antibiotic resistance. We also show that the surface-independent biofilms of clinical isolates of MRSA from cystic fibrosis and central catheter-related infections demonstrate morphological differences. Overall, our results show that biofilms can form by spontaneous aggregation without attachment to a surface, and this new in vitro system can model surface-independent biofilms that may more closely mimic the corresponding physiological niche during infection. IMPORTANCE The canonical model of biofilm formation begins with the attachment and growth of microbial cells on a surface. While these in vitro models reasonably mimic biofilms formed on foreign bodies such as catheters and implants, this is not the case for biofilms formed in cystic fibrosis and chronic wound infections, which appear to present as aggregates not attached to a surface. The hanging-drop model of biofilms of methicillin-resistant Staphylococcus aureus (MRSA), the major causative organism of skin and soft tissue infections, shows that these biofilms display morphological and antibiotic response patterns that are distinct from those of their surface-attached counterparts, and biofilm growth is consistent with their in vivo location. The simplicity and throughput of this model enable adoption to investigate other single or polymicrobial biofilms in a physiologically relevant setting.

Clustering Optimization of LoRa Networks for Perturbed Ultra-Dense IoT Networks

Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network’s operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.

Comparison of Lead-Acid and Li-Ion Batteries Lifetime Prediction Models in Stand-Alone Photovoltaic Systems

Several models for estimating the lifetimes of lead-acid and Li-ion (LiFePO4) batteries are analyzed and applied to a photovoltaic (PV)-battery standalone system. This kind of system usually includes a battery bank sized for 2.5 autonomy days or more. The results obtained by each model in different locations with very different average temperatures are compared. Two different locations have been considered: the Pyrenees mountains in Spain and Tindouf in Argelia. Classical battery aging models (equivalent full cycles model and rainflow cycle count model) generally used by researchers and software tools are not adequate as they overestimate the battery life in all cases. For OPzS lead-acid batteries, an advanced weighted Ah-throughput model is necessary to correctly estimate its lifetime, obtaining a battery life of roughly 12 years for the Pyrenees and around 5 years for the case Tindouf. For Li-ion batteries, both the cycle and calendar aging must be considered, obtaining more than 20 years of battery life estimation for the Pyrenees and 13 years for Tindouf. In the cases studied, the lifetime of LiFePO4 batteries is around two times the OPzS lifetime. As nowadays the cost of LiFePO4 batteries is around two times the OPzS ones, Li-ion batteries can be competitive with OPzS batteries in PV-battery standalone systems.

Modeling and Performance Analysis of Opportunistic Link Selection for UAV Communication

In anticipation of wide implementation of 5G technologies, the scarcity of spectrum resources for the unmanned aerial vehicles (UAVs) communication remains one of the major challenges in arranging safe drone operations. Dynamic spectrum management among multiple UAVs as a tool that is able to address this issue, requires integrated solutions with considerations of heterogeneous link types and support of the multi-UAV operations. This paper proposes a synthesized resource allocation and opportunistic link selection (RA-OLS) scheme for the air-to-ground (A2G) UAV communication with dynamic link selections. The link opportunities using link hopping sequences (LHSs) are allocated in the GCSs for alleviating the internal collisions within the UAV network, offloading the on-board computations in the spectrum processing function, and avoiding the contention in the air. In this context, exclusive technical solutions are proposed to form the prototype system. A sub-optimal allocation method based on the greedy algorithm is presented for addressing the resource allocation problem. A mathematical model of the RA-OLS throughput with above propositions is formulated for the spectrum dense and scarce environments. An interference factor is introduced to measure the protection effects on the primary users. The proposed throughput model approximates the simulated communication under requirements of small errors in the spectrum dense environment and the spectrum scarce environment, where the sensitivity analysis is implemented. The proposed RA-OLS outperforms the static communication scheme in terms of the utilization rate by over 50% in case when multiple links are available. It also enables the collaborative communication when the spectral resources are in scarcity. The impacts from diverse parameters on the RA-OLS communication performance are analyzed.